wanx tiled encode

Stepvideo low VRAM support!

README.md

@@ -1,15 +1,26 @@
 # DiffSynth Studio
+[![PyPI](https://img.shields.io/pypi/v/DiffSynth)](https://pypi.org/project/DiffSynth/)
+[![license](https://img.shields.io/github/license/modelscope/DiffSynth-Studio.svg)](https://github.com/modelscope/DiffSynth-Studio/blob/master/LICENSE)
+[![open issues](https://isitmaintained.com/badge/open/modelscope/DiffSynth-Studio.svg)](https://github.com/modelscope/DiffSynth-Studio/issues)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/modelscope/DiffSynth-Studio.svg)](https://GitHub.com/modelscope/DiffSynth-Studio/pull/)
+[![GitHub latest commit](https://badgen.net/github/last-commit/modelscope/DiffSynth-Studio)](https://GitHub.com/modelscope/DiffSynth-Studio/commit/)
 
 <p align="center">
 <a href="https://trendshift.io/repositories/10946" target="_blank"><img src="https://trendshift.io/api/badge/repositories/10946" alt="modelscope%2FDiffSynth-Studio | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
 </p>
 
+Document: https://diffsynth-studio.readthedocs.io/zh-cn/latest/index.html
+
 ## Introduction
 
 DiffSynth Studio is a Diffusion engine. We have restructured architectures including Text Encoder, UNet, VAE, among others, maintaining compatibility with models from the open-source community while enhancing computational performance. We provide many interesting features. Enjoy the magic of Diffusion models!
 
 Until now, DiffSynth Studio has supported the following models:
 
+* [StepVideo](https://github.com/stepfun-ai/Step-Video-T2V)
+* [HunyuanVideo](https://github.com/Tencent/HunyuanVideo)
+* [CogVideoX](https://huggingface.co/THUDM/CogVideoX-5b)
+* [FLUX](https://huggingface.co/black-forest-labs/FLUX.1-dev)
 * [ExVideo](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1)
 * [Kolors](https://huggingface.co/Kwai-Kolors/Kolors)
 * [Stable Diffusion 3](https://huggingface.co/stabilityai/stable-diffusion-3-medium)
@@ -25,6 +36,38 @@ Until now, DiffSynth Studio has supported the following models:
 
 ## News
 
+- **February 17, 2024** We support [StepVideo](https://modelscope.cn/models/stepfun-ai/stepvideo-t2v/summary)! State-of-the-art video synthesis model! See [./examples/stepvideo](./examples/stepvideo/).
+
+- **December 31, 2024** We propose EliGen, a novel framework for precise entity-level controlled text-to-image generation, complemented by an inpainting fusion pipeline to extend its capabilities to image inpainting tasks. EliGen seamlessly integrates with existing community models, such as IP-Adapter and In-Context LoRA, enhancing its versatility. For more details, see [./examples/EntityControl](./examples/EntityControl/).
+  - Paper: [EliGen: Entity-Level Controlled Image Generation with Regional Attention](https://arxiv.org/abs/2501.01097)
+  - Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen)
+  - Online Demo: [ModelScope EliGen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/EliGen)
+  - Training Dataset: [EliGen Train Set](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet)
+
+- **December 19, 2024** We implement advanced VRAM management for HunyuanVideo, making it possible to generate videos at a resolution of 129x720x1280 using 24GB of VRAM, or at 129x512x384 resolution with just 6GB of VRAM. Please refer to [./examples/HunyuanVideo/](./examples/HunyuanVideo/) for more details.
+
+- **December 18, 2024** We propose ArtAug, an approach designed to improve text-to-image synthesis models through synthesis-understanding interactions. We have trained an ArtAug enhancement module for FLUX.1-dev in the format of LoRA. This model integrates the aesthetic understanding of Qwen2-VL-72B into FLUX.1-dev, leading to an improvement in the quality of generated images.
+  - Paper: https://arxiv.org/abs/2412.12888
+  - Examples: https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/ArtAug
+  - Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ArtAug-lora-FLUX.1dev-v1)
+  - Demo: [ModelScope](https://modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=7228&modelType=LoRA&sdVersion=FLUX_1&modelUrl=modelscope%3A%2F%2FDiffSynth-Studio%2FArtAug-lora-FLUX.1dev-v1%3Frevision%3Dv1.0), HuggingFace (Coming soon)
+
+- **October 25, 2024** We provide extensive FLUX ControlNet support. This project supports many different ControlNet models that can be freely combined, even if their structures differ. Additionally, ControlNet models are compatible with high-resolution refinement and partition control techniques, enabling very powerful controllable image generation. See [`./examples/ControlNet/`](./examples/ControlNet/).
+
+- **October 8, 2024.** We release the extended LoRA based on CogVideoX-5B and ExVideo. You can download this model from [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1) or [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1).
+
+- **August 22, 2024.** CogVideoX-5B is supported in this project. See [here](/examples/video_synthesis/). We provide several interesting features for this text-to-video model, including
+  - Text to video
+  - Video editing
+  - Self-upscaling
+  - Video interpolation
+
+- **August 22, 2024.** We have implemented an interesting painter that supports all text-to-image models. Now you can create stunning images using the painter, with assistance from AI!
+  - Use it in our [WebUI](#usage-in-webui).
+
+- **August 21, 2024.** FLUX is supported in DiffSynth-Studio.
+  - Enable CFG and highres-fix to improve visual quality. See [here](/examples/image_synthesis/README.md)
+  - LoRA, ControlNet, and additional models will be available soon.
 
 - **June 21, 2024.** 🔥🔥🔥 We propose ExVideo, a post-tuning technique aimed at enhancing the capability of video generation models. We have extended Stable Video Diffusion to achieve the generation of long videos up to 128 frames.
   - [Project Page](https://ecnu-cilab.github.io/ExVideoProjectPage/)
@@ -67,37 +110,64 @@ Until now, DiffSynth Studio has supported the following models:
 
 ## Installation
 
+Install from source code (recommended):
+
 ```
 git clone https://github.com/modelscope/DiffSynth-Studio.git
 cd DiffSynth-Studio
 pip install -e .
 ```
 
+Or install from pypi:
+
+```
+pip install diffsynth
+```
+
 ## Usage (in Python code)
 
 The Python examples are in [`examples`](./examples/). We provide an overview here.
 
-### Long Video Synthesis
+### Download Models
 
-We trained an extended video synthesis model, which can generate 128 frames. [`examples/ExVideo`](./examples/ExVideo/)
+Download the pre-set models. Model IDs can be found in [config file](/diffsynth/configs/model_config.py).
+
+```python
+from diffsynth import download_models
+
+download_models(["FLUX.1-dev", "Kolors"])
+```
+
+Download your own models.
+
+```python
+from diffsynth.models.downloader import download_from_huggingface, download_from_modelscope
+
+# From Modelscope (recommended)
+download_from_modelscope("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.fp16.bin", "models/kolors/Kolors/vae")
+# From Huggingface
+download_from_huggingface("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.fp16.safetensors", "models/kolors/Kolors/vae")
+```
+
+### Video Synthesis
+
+#### Text-to-video using CogVideoX-5B
+
+CogVideoX-5B is released by ZhiPu. We provide an improved pipeline, supporting text-to-video, video editing, self-upscaling and video interpolation. [`examples/video_synthesis`](./examples/video_synthesis/)
+
+The video on the left is generated using the original text-to-video pipeline, while the video on the right is the result after editing and frame interpolation.
+
+https://github.com/user-attachments/assets/26b044c1-4a60-44a4-842f-627ff289d006
+
+#### Long Video Synthesis
+
+We trained extended video synthesis models, which can generate 128 frames. [`examples/ExVideo`](./examples/ExVideo/)
 
 https://github.com/modelscope/DiffSynth-Studio/assets/35051019/d97f6aa9-8064-4b5b-9d49-ed6001bb9acc
 
-### Image Synthesis
+https://github.com/user-attachments/assets/321ee04b-8c17-479e-8a95-8cbcf21f8d7e
 
-Generate high-resolution images, by breaking the limitation of diffusion models! [`examples/image_synthesis`](./examples/image_synthesis/).
+#### Toon Shading
-
-LoRA fine-tuning is supported in [`examples/train`](./examples/train/).
-
-|Model|Example|
-|-|-|
-|Stable Diffusion|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/6fc84611-8da6-4a1f-8fee-9a34eba3b4a5)|
-|Stable Diffusion XL|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/67687748-e738-438c-aee5-96096f09ac90)|
-|Stable Diffusion 3|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/4df346db-6f91-420a-b4c1-26e205376098)|
-|Kolors|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/53ef6f41-da11-4701-8665-9f64392607bf)|
-|Hunyuan-DiT|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/60b022c8-df3f-4541-95ab-bf39f2fa8bb5)|
-
-### Toon Shading
 
 Render realistic videos in a flatten style and enable video editing features. [`examples/Diffutoon`](./examples/Diffutoon/)
 
@@ -105,16 +175,60 @@ https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/b54c05c5-d747-47
 
 https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/20528af5-5100-474a-8cdc-440b9efdd86c
 
-### Video Stylization
+#### Video Stylization
 
 Video stylization without video models. [`examples/diffsynth`](./examples/diffsynth/)
 
 https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-4481-b79f-0c3a7361a1ea
 
+### Image Synthesis
+
+Generate high-resolution images, by breaking the limitation of diffusion models! [`examples/image_synthesis`](./examples/image_synthesis/).
+
+LoRA fine-tuning is supported in [`examples/train`](./examples/train/).
+
+|FLUX|Stable Diffusion 3|
+|-|-|
+|![image_1024_cfg](https://github.com/user-attachments/assets/984561e9-553d-4952-9443-79ce144f379f)|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/4df346db-6f91-420a-b4c1-26e205376098)|
+
+|Kolors|Hunyuan-DiT|
+|-|-|
+|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/53ef6f41-da11-4701-8665-9f64392607bf)|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/60b022c8-df3f-4541-95ab-bf39f2fa8bb5)|
+
+|Stable Diffusion|Stable Diffusion XL|
+|-|-|
+|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/6fc84611-8da6-4a1f-8fee-9a34eba3b4a5)|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/67687748-e738-438c-aee5-96096f09ac90)|
+
 ## Usage (in WebUI)
 
+Create stunning images using the painter, with assistance from AI!
+
+https://github.com/user-attachments/assets/95265d21-cdd6-4125-a7cb-9fbcf6ceb7b0
+
+**This video is not rendered in real-time.**
+
+Before launching the WebUI, please download models to the folder `./models`. See [here](#download-models).
+
+* `Gradio` version
+
 ```
-python -m streamlit run DiffSynth_Studio.py
+pip install gradio
+```
+
+```
+python apps/gradio/DiffSynth_Studio.py
+```
+
+![20240822102002](https://github.com/user-attachments/assets/59613157-de51-4109-99b3-97cbffd88076)
+
+* `Streamlit` version
+
+```
+pip install streamlit streamlit-drawable-canvas
+```
+
+```
+python -m streamlit run apps/streamlit/DiffSynth_Studio.py
 ```
 
 https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/93085557-73f3-4eee-a205-9829591ef954

apps/gradio/DiffSynth_Studio.py

@@ -0,0 +1,252 @@
+import gradio as gr
+from diffsynth import ModelManager, SDImagePipeline, SDXLImagePipeline, SD3ImagePipeline, HunyuanDiTImagePipeline, FluxImagePipeline
+import os, torch
+from PIL import Image
+import numpy as np
+
+
+config = {
+    "model_config": {
+        "Stable Diffusion": {
+            "model_folder": "models/stable_diffusion",
+            "pipeline_class": SDImagePipeline,
+            "default_parameters": {
+                "cfg_scale": 7.0,
+                "height": 512,
+                "width": 512,
+            }
+        },
+        "Stable Diffusion XL": {
+            "model_folder": "models/stable_diffusion_xl",
+            "pipeline_class": SDXLImagePipeline,
+            "default_parameters": {
+                "cfg_scale": 7.0,
+            }
+        },
+        "Stable Diffusion 3": {
+            "model_folder": "models/stable_diffusion_3",
+            "pipeline_class": SD3ImagePipeline,
+            "default_parameters": {
+                "cfg_scale": 7.0,
+            }
+        },
+        "Stable Diffusion XL Turbo": {
+            "model_folder": "models/stable_diffusion_xl_turbo",
+            "pipeline_class": SDXLImagePipeline,
+            "default_parameters": {
+                "negative_prompt": "",
+                "cfg_scale": 1.0,
+                "num_inference_steps": 1,
+                "height": 512,
+                "width": 512,
+            }
+        },
+        "Kolors": {
+            "model_folder": "models/kolors",
+            "pipeline_class": SDXLImagePipeline,
+            "default_parameters": {
+                "cfg_scale": 7.0,
+            }
+        },
+        "HunyuanDiT": {
+            "model_folder": "models/HunyuanDiT",
+            "pipeline_class": HunyuanDiTImagePipeline,
+            "default_parameters": {
+                "cfg_scale": 7.0,
+            }
+        },
+        "FLUX": {
+            "model_folder": "models/FLUX",
+            "pipeline_class": FluxImagePipeline,
+            "default_parameters": {
+                "cfg_scale": 1.0,
+            }
+        }
+    },
+    "max_num_painter_layers": 8,
+    "max_num_model_cache": 1,
+}
+
+
+def load_model_list(model_type):
+    if model_type is None:
+        return []
+    folder = config["model_config"][model_type]["model_folder"]
+    file_list = [i for i in os.listdir(folder) if i.endswith(".safetensors")]
+    if model_type in ["HunyuanDiT", "Kolors", "FLUX"]:
+        file_list += [i for i in os.listdir(folder) if os.path.isdir(os.path.join(folder, i))]
+    file_list = sorted(file_list)
+    return file_list
+
+
+def load_model(model_type, model_path):
+    global model_dict
+    model_key = f"{model_type}:{model_path}"
+    if model_key in model_dict:
+        return model_dict[model_key]
+    model_path = os.path.join(config["model_config"][model_type]["model_folder"], model_path)
+    model_manager = ModelManager()
+    if model_type == "HunyuanDiT":
+        model_manager.load_models([
+            os.path.join(model_path, "clip_text_encoder/pytorch_model.bin"),
+            os.path.join(model_path, "mt5/pytorch_model.bin"),
+            os.path.join(model_path, "model/pytorch_model_ema.pt"),
+            os.path.join(model_path, "sdxl-vae-fp16-fix/diffusion_pytorch_model.bin"),
+        ])
+    elif model_type == "Kolors":
+        model_manager.load_models([
+            os.path.join(model_path, "text_encoder"),
+            os.path.join(model_path, "unet/diffusion_pytorch_model.safetensors"),
+            os.path.join(model_path, "vae/diffusion_pytorch_model.safetensors"),
+        ])
+    elif model_type == "FLUX":
+        model_manager.torch_dtype = torch.bfloat16
+        file_list = [
+            os.path.join(model_path, "text_encoder/model.safetensors"),
+            os.path.join(model_path, "text_encoder_2"),
+        ]
+        for file_name in os.listdir(model_path):
+            if file_name.endswith(".safetensors"):
+                file_list.append(os.path.join(model_path, file_name))
+        model_manager.load_models(file_list)
+    else:
+        model_manager.load_model(model_path)
+    pipe = config["model_config"][model_type]["pipeline_class"].from_model_manager(model_manager)
+    while len(model_dict) + 1 > config["max_num_model_cache"]:
+        key = next(iter(model_dict.keys()))
+        model_manager_to_release, _ = model_dict[key]
+        model_manager_to_release.to("cpu")
+        del model_dict[key]
+        torch.cuda.empty_cache()
+    model_dict[model_key] = model_manager, pipe
+    return model_manager, pipe
+
+
+model_dict = {}
+
+with gr.Blocks() as app:
+    gr.Markdown("# DiffSynth-Studio Painter")
+    with gr.Row():
+        with gr.Column(scale=382, min_width=100):
+
+            with gr.Accordion(label="Model"):
+                model_type = gr.Dropdown(choices=[i for i in config["model_config"]], label="Model type")
+                model_path = gr.Dropdown(choices=[], interactive=True, label="Model path")
+
+                @gr.on(inputs=model_type, outputs=model_path, triggers=model_type.change)
+                def model_type_to_model_path(model_type):
+                    return gr.Dropdown(choices=load_model_list(model_type))
+                
+            with gr.Accordion(label="Prompt"):
+                prompt = gr.Textbox(label="Prompt", lines=3)
+                negative_prompt = gr.Textbox(label="Negative prompt", lines=1)
+                cfg_scale = gr.Slider(minimum=1.0, maximum=10.0, value=7.0, step=0.1, interactive=True, label="Classifier-free guidance scale")
+                embedded_guidance = gr.Slider(minimum=0.0, maximum=10.0, value=0.0, step=0.1, interactive=True, label="Embedded guidance scale (only for FLUX)")
+            
+            with gr.Accordion(label="Image"):
+                num_inference_steps = gr.Slider(minimum=1, maximum=100, value=20, step=1, interactive=True, label="Inference steps")
+                height = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Height")
+                width = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Width")
+                with gr.Column():
+                    use_fixed_seed = gr.Checkbox(value=True, interactive=False, label="Use fixed seed")
+                    seed = gr.Number(minimum=0, maximum=10**9, value=0, interactive=True, label="Random seed", show_label=False)
+
+            @gr.on(
+                inputs=[model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width],
+                outputs=[prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width],
+                triggers=model_path.change
+            )
+            def model_path_to_default_params(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width):
+                load_model(model_type, model_path)
+                cfg_scale = config["model_config"][model_type]["default_parameters"].get("cfg_scale", cfg_scale)
+                embedded_guidance = config["model_config"][model_type]["default_parameters"].get("embedded_guidance", embedded_guidance)
+                num_inference_steps = config["model_config"][model_type]["default_parameters"].get("num_inference_steps", num_inference_steps)
+                height = config["model_config"][model_type]["default_parameters"].get("height", height)
+                width = config["model_config"][model_type]["default_parameters"].get("width", width)
+                return prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width
+                
+
+        with gr.Column(scale=618, min_width=100):
+            with gr.Accordion(label="Painter"):
+                enable_local_prompt_list = []
+                local_prompt_list = []
+                mask_scale_list = []
+                canvas_list = []
+                for painter_layer_id in range(config["max_num_painter_layers"]):
+                    with gr.Tab(label=f"Layer {painter_layer_id}"):
+                        enable_local_prompt = gr.Checkbox(label="Enable", value=False, key=f"enable_local_prompt_{painter_layer_id}")
+                        local_prompt = gr.Textbox(label="Local prompt", key=f"local_prompt_{painter_layer_id}")
+                        mask_scale = gr.Slider(minimum=0.0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Mask scale", key=f"mask_scale_{painter_layer_id}")
+                        canvas = gr.ImageEditor(canvas_size=(512, 1), sources=None, layers=False, interactive=True, image_mode="RGBA",
+                                                brush=gr.Brush(default_size=100, default_color="#000000", colors=["#000000"]),
+                                                label="Painter", key=f"canvas_{painter_layer_id}")
+                        @gr.on(inputs=[height, width, canvas], outputs=canvas, triggers=[height.change, width.change, canvas.clear, enable_local_prompt.change], show_progress="hidden")
+                        def resize_canvas(height, width, canvas):
+                            h, w = canvas["background"].shape[:2]
+                            if h != height or width != w:
+                                return np.ones((height, width, 3), dtype=np.uint8) * 255
+                            else:
+                                return canvas
+                        
+                        enable_local_prompt_list.append(enable_local_prompt)
+                        local_prompt_list.append(local_prompt)
+                        mask_scale_list.append(mask_scale)
+                        canvas_list.append(canvas)
+            with gr.Accordion(label="Results"):
+                run_button = gr.Button(value="Generate", variant="primary")
+                output_image = gr.Image(sources=None, show_label=False, interactive=False, type="pil")
+                with gr.Row():
+                    with gr.Column():
+                        output_to_painter_button = gr.Button(value="Set as painter's background")
+                    with gr.Column():
+                        output_to_input_button = gr.Button(value="Set as input image")
+                painter_background = gr.State(None)
+                input_background = gr.State(None)
+                @gr.on(
+                    inputs=[model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, seed] + enable_local_prompt_list + local_prompt_list + mask_scale_list + canvas_list,
+                    outputs=[output_image],
+                    triggers=run_button.click
+                )
+                def generate_image(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, seed, *args, progress=gr.Progress()):
+                    _, pipe = load_model(model_type, model_path)
+                    input_params = {
+                        "prompt": prompt,
+                        "negative_prompt": negative_prompt,
+                        "cfg_scale": cfg_scale,
+                        "num_inference_steps": num_inference_steps,
+                        "height": height,
+                        "width": width,
+                        "progress_bar_cmd": progress.tqdm,
+                    }
+                    if isinstance(pipe, FluxImagePipeline):
+                        input_params["embedded_guidance"] = embedded_guidance
+                    enable_local_prompt_list, local_prompt_list, mask_scale_list, canvas_list = (
+                        args[0 * config["max_num_painter_layers"]: 1 * config["max_num_painter_layers"]],
+                        args[1 * config["max_num_painter_layers"]: 2 * config["max_num_painter_layers"]],
+                        args[2 * config["max_num_painter_layers"]: 3 * config["max_num_painter_layers"]],
+                        args[3 * config["max_num_painter_layers"]: 4 * config["max_num_painter_layers"]]
+                    )
+                    local_prompts, masks, mask_scales = [], [], []
+                    for enable_local_prompt, local_prompt, mask_scale, canvas in zip(
+                        enable_local_prompt_list, local_prompt_list, mask_scale_list, canvas_list
+                    ):
+                        if enable_local_prompt:
+                            local_prompts.append(local_prompt)
+                            masks.append(Image.fromarray(canvas["layers"][0][:, :, -1]).convert("RGB"))
+                            mask_scales.append(mask_scale)
+                    input_params.update({
+                        "local_prompts": local_prompts,
+                        "masks": masks,
+                        "mask_scales": mask_scales,
+                    })
+                    torch.manual_seed(seed)
+                    image = pipe(**input_params)
+                    return image
+                
+                @gr.on(inputs=[output_image] + canvas_list, outputs=canvas_list, triggers=output_to_painter_button.click)
+                def send_output_to_painter_background(output_image, *canvas_list):
+                    for canvas in canvas_list:
+                        h, w = canvas["background"].shape[:2]
+                        canvas["background"] = output_image.resize((w, h))
+                    return tuple(canvas_list)
+app.launch()

apps/gradio/entity_level_control.py

@@ -0,0 +1,390 @@
+import os
+import torch
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+import random
+import json
+import gradio as gr
+from diffsynth import ModelManager, FluxImagePipeline, download_customized_models
+from modelscope import dataset_snapshot_download
+
+
+dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/entity_control/*")
+example_json = 'data/examples/eligen/entity_control/ui_examples.json'
+with open(example_json, 'r') as f:
+    examples = json.load(f)['examples']
+
+for idx in range(len(examples)):
+    example_id = examples[idx]['example_id']
+    entity_prompts = examples[idx]['local_prompt_list']
+    examples[idx]['mask_lists'] = [Image.open(f"data/examples/eligen/entity_control/example_{example_id}/{i}.png").convert('RGB') for i in range(len(entity_prompts))]
+
+def create_canvas_data(background, masks):
+    if background.shape[-1] == 3:
+        background = np.dstack([background, np.full(background.shape[:2], 255, dtype=np.uint8)])
+    layers = []
+    for mask in masks:
+        if mask is not None:
+            mask_single_channel = mask if mask.ndim == 2 else mask[..., 0]
+            layer = np.zeros((mask_single_channel.shape[0], mask_single_channel.shape[1], 4), dtype=np.uint8)
+            layer[..., -1] = mask_single_channel
+            layers.append(layer)
+        else:
+            layers.append(np.zeros_like(background))
+
+    composite = background.copy()
+    for layer in layers:
+        if layer.size > 0:
+            composite = np.where(layer[..., -1:] > 0, layer, composite)
+    return {
+        "background": background,
+        "layers": layers,
+        "composite": composite,
+    }
+
+def load_example(load_example_button):
+    example_idx = int(load_example_button.split()[-1]) - 1
+    example = examples[example_idx]
+    result = [
+        50,
+        example["global_prompt"],
+        example["negative_prompt"],
+        example["seed"],
+        *example["local_prompt_list"],
+    ]
+    num_entities = len(example["local_prompt_list"])
+    result += [""] * (config["max_num_painter_layers"] - num_entities)
+    masks = []
+    for mask in example["mask_lists"]:
+        mask_single_channel = np.array(mask.convert("L"))
+        masks.append(mask_single_channel)
+    for _ in range(config["max_num_painter_layers"] - len(masks)):
+        blank_mask = np.zeros_like(masks[0]) if masks else np.zeros((512, 512), dtype=np.uint8)
+        masks.append(blank_mask)
+    background = np.ones((masks[0].shape[0], masks[0].shape[1], 4), dtype=np.uint8) * 255
+    canvas_data_list = []
+    for mask in masks:
+        canvas_data = create_canvas_data(background, [mask])
+        canvas_data_list.append(canvas_data)
+    result.extend(canvas_data_list)
+    return result
+
+def save_mask_prompts(masks, mask_prompts, global_prompt, seed=0, random_dir='0000000'):
+    save_dir = os.path.join('workdirs/tmp_mask', random_dir)
+    print(f'save to {save_dir}')
+    os.makedirs(save_dir, exist_ok=True)
+    for i, mask in enumerate(masks):
+        save_path = os.path.join(save_dir, f'{i}.png')
+        mask.save(save_path)
+    sample = {
+        "global_prompt": global_prompt,
+        "mask_prompts": mask_prompts,
+        "seed": seed,
+    }
+    with open(os.path.join(save_dir, f"prompts.json"), 'w') as f:
+        json.dump(sample, f, indent=4)
+
+def visualize_masks(image, masks, mask_prompts, font_size=35, use_random_colors=False):
+    # Create a blank image for overlays
+    overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))
+    colors = [
+        (165, 238, 173, 80),
+        (76, 102, 221, 80),
+        (221, 160, 77, 80),
+        (204, 93, 71, 80),
+        (145, 187, 149, 80),
+        (134, 141, 172, 80),
+        (157, 137, 109, 80),
+        (153, 104, 95, 80),
+        (165, 238, 173, 80),
+        (76, 102, 221, 80),
+        (221, 160, 77, 80),
+        (204, 93, 71, 80),
+        (145, 187, 149, 80),
+        (134, 141, 172, 80),
+        (157, 137, 109, 80),
+        (153, 104, 95, 80),
+    ]
+    # Generate random colors for each mask
+    if use_random_colors:
+        colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))]
+    # Font settings
+    try:
+        font = ImageFont.truetype("arial", font_size)  # Adjust as needed
+    except IOError:
+        font = ImageFont.load_default(font_size)
+    # Overlay each mask onto the overlay image
+    for mask, mask_prompt, color in zip(masks, mask_prompts, colors):
+        if mask is None:
+            continue
+        # Convert mask to RGBA mode
+        mask_rgba = mask.convert('RGBA')
+        mask_data = mask_rgba.getdata()
+        new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data]
+        mask_rgba.putdata(new_data)
+        # Draw the mask prompt text on the mask
+        draw = ImageDraw.Draw(mask_rgba)
+        mask_bbox = mask.getbbox()  # Get the bounding box of the mask
+        if mask_bbox is None:
+            continue
+        text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10)  # Adjust text position based on mask position
+        draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font)
+        # Alpha composite the overlay with this mask
+        overlay = Image.alpha_composite(overlay, mask_rgba)
+    # Composite the overlay onto the original image
+    result = Image.alpha_composite(image.convert('RGBA'), overlay)
+    return result
+
+config = {
+    "model_config": {
+        "FLUX": {
+            "model_folder": "models/FLUX",
+            "pipeline_class": FluxImagePipeline,
+            "default_parameters": {
+                "cfg_scale": 3.0,
+                "embedded_guidance": 3.5,
+                "num_inference_steps": 30,
+            }
+        },
+    },
+    "max_num_painter_layers": 8,
+    "max_num_model_cache": 1,
+}
+
+model_dict = {}
+
+def load_model(model_type='FLUX', model_path='FLUX.1-dev'):
+    global model_dict
+    model_key = f"{model_type}:{model_path}"
+    if model_key in model_dict:
+        return model_dict[model_key]
+    model_path = os.path.join(config["model_config"][model_type]["model_folder"], model_path)
+    model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"])
+    model_manager.load_lora(
+        download_customized_models(
+            model_id="DiffSynth-Studio/Eligen",
+            origin_file_path="model_bf16.safetensors",
+            local_dir="models/lora/entity_control",
+        ),
+        lora_alpha=1,
+    )
+    pipe = config["model_config"][model_type]["pipeline_class"].from_model_manager(model_manager)
+    model_dict[model_key] = model_manager, pipe
+    return model_manager, pipe
+
+
+with gr.Blocks() as app:
+    gr.Markdown(
+        """## EliGen: Entity-Level Controllable Text-to-Image Model
+                1. On the left, input the **global prompt** for the overall image, such as "a person stands by the river."
+                2. On the right, input the **local prompt** for each entity, such as "person," and draw the corresponding mask in the **Entity Mask Painter**. Generally, solid rectangular masks yield better results.
+                3. Click the **Generate** button to create the image. By selecting different **random seeds**, you can generate diverse images.
+                4. **You can directly click the "Load Example" button on any sample at the bottom to load example inputs.**
+                """
+    )
+
+    loading_status = gr.Textbox(label="Loading Model...", value="Loading model... Please wait...", visible=True)
+    main_interface = gr.Column(visible=False)
+
+    def initialize_model():
+        try:
+            load_model()
+            return {
+                loading_status: gr.update(value="Model loaded successfully!", visible=False),
+                main_interface: gr.update(visible=True),
+            }
+        except Exception as e:
+            print(f'Failed to load model with error: {e}')
+            return {
+                loading_status: gr.update(value=f"Failed to load model: {str(e)}", visible=True),
+                main_interface: gr.update(visible=True),
+            }
+
+    app.load(initialize_model, inputs=None, outputs=[loading_status, main_interface])
+
+    with main_interface:
+        with gr.Row():
+            local_prompt_list = []
+            canvas_list = []
+            random_mask_dir = gr.State(f'{random.randint(0, 1000000):08d}')
+            with gr.Column(scale=382, min_width=100):
+                model_type = gr.State('FLUX')
+                model_path = gr.State('FLUX.1-dev')
+                with gr.Accordion(label="Global prompt"):
+                    prompt = gr.Textbox(label="Global Prompt", lines=3)
+                    negative_prompt = gr.Textbox(label="Negative prompt", value="worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw, blur,", lines=3)
+                with gr.Accordion(label="Inference Options", open=True):
+                    seed = gr.Number(minimum=0, maximum=10**9, value=42, interactive=True, label="Random seed", show_label=True)
+                    num_inference_steps = gr.Slider(minimum=1, maximum=100, value=30, step=1, interactive=True, label="Inference steps")
+                    cfg_scale = gr.Slider(minimum=2.0, maximum=10.0, value=3.0, step=0.1, interactive=True, label="Classifier-free guidance scale")
+                    embedded_guidance = gr.Slider(minimum=0.0, maximum=10.0, value=3.5, step=0.1, interactive=True, label="Embedded guidance scale")
+                    height = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Height")
+                    width = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Width")
+                with gr.Accordion(label="Inpaint Input Image", open=False):
+                    input_image = gr.Image(sources=None, show_label=False, interactive=True, type="pil")
+                    background_weight = gr.Slider(minimum=0.0, maximum=1000., value=0., step=1, interactive=False, label="background_weight", visible=False)
+
+                    with gr.Column():
+                        reset_input_button = gr.Button(value="Reset Inpaint Input")
+                        send_input_to_painter = gr.Button(value="Set as painter's background")
+                    @gr.on(inputs=[input_image], outputs=[input_image], triggers=reset_input_button.click)
+                    def reset_input_image(input_image):
+                        return None
+
+            with gr.Column(scale=618, min_width=100):
+                with gr.Accordion(label="Entity Painter"):
+                    for painter_layer_id in range(config["max_num_painter_layers"]):
+                        with gr.Tab(label=f"Entity {painter_layer_id}"):
+                            local_prompt = gr.Textbox(label="Local prompt", key=f"local_prompt_{painter_layer_id}")
+                            canvas = gr.ImageEditor(
+                                canvas_size=(512, 512),
+                                sources=None,
+                                layers=False,
+                                interactive=True,
+                                image_mode="RGBA",
+                                brush=gr.Brush(
+                                    default_size=50,
+                                    default_color="#000000",
+                                    colors=["#000000"],
+                                ),
+                                label="Entity Mask Painter",
+                                key=f"canvas_{painter_layer_id}",
+                                width=width,
+                                height=height,
+                            )
+                            @gr.on(inputs=[height, width, canvas], outputs=canvas, triggers=[height.change, width.change, canvas.clear], show_progress="hidden")
+                            def resize_canvas(height, width, canvas):
+                                h, w = canvas["background"].shape[:2]
+                                if h != height or width != w:
+                                    return np.ones((height, width, 3), dtype=np.uint8) * 255
+                                else:
+                                    return canvas
+                            local_prompt_list.append(local_prompt)
+                            canvas_list.append(canvas)
+                with gr.Accordion(label="Results"):
+                    run_button = gr.Button(value="Generate", variant="primary")
+                    output_image = gr.Image(sources=None, show_label=False, interactive=False, type="pil")
+                    with gr.Row():
+                        with gr.Column():
+                            output_to_painter_button = gr.Button(value="Set as painter's background")
+                        with gr.Column():
+                            return_with_mask = gr.Checkbox(value=False, interactive=True, label="show result with mask painting")
+                            output_to_input_button = gr.Button(value="Set as input image", visible=False, interactive=False)
+                    real_output = gr.State(None)
+                    mask_out = gr.State(None)
+
+                    @gr.on(
+                        inputs=[model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir] + local_prompt_list + canvas_list,
+                        outputs=[output_image, real_output, mask_out],
+                        triggers=run_button.click
+                    )
+                    def generate_image(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir, *args, progress=gr.Progress()):
+                        _, pipe = load_model(model_type, model_path)
+                        input_params = {
+                            "prompt": prompt,
+                            "negative_prompt": negative_prompt,
+                            "cfg_scale": cfg_scale,
+                            "num_inference_steps": num_inference_steps,
+                            "height": height,
+                            "width": width,
+                            "progress_bar_cmd": progress.tqdm,
+                        }
+                        if isinstance(pipe, FluxImagePipeline):
+                            input_params["embedded_guidance"] = embedded_guidance
+                        if input_image is not None:
+                            input_params["input_image"] = input_image.resize((width, height)).convert("RGB")
+                            input_params["enable_eligen_inpaint"] = True
+
+                        local_prompt_list, canvas_list = (
+                            args[0 * config["max_num_painter_layers"]: 1 * config["max_num_painter_layers"]],
+                            args[1 * config["max_num_painter_layers"]: 2 * config["max_num_painter_layers"]],
+                        )
+                        local_prompts, masks = [], []
+                        for local_prompt, canvas in zip(local_prompt_list, canvas_list):
+                            if isinstance(local_prompt, str) and len(local_prompt) > 0:
+                                local_prompts.append(local_prompt)
+                                masks.append(Image.fromarray(canvas["layers"][0][:, :, -1]).convert("RGB"))
+                        entity_masks = None if len(masks) == 0 else masks
+                        entity_prompts = None if len(local_prompts) == 0 else local_prompts
+                        input_params.update({
+                            "eligen_entity_prompts": entity_prompts,
+                            "eligen_entity_masks": entity_masks,
+                        })
+                        torch.manual_seed(seed)
+                        # save_mask_prompts(masks, local_prompts, prompt, seed, random_mask_dir)
+                        image = pipe(**input_params)
+                        masks = [mask.resize(image.size) for mask in masks]
+                        image_with_mask = visualize_masks(image, masks, local_prompts)
+
+                        real_output = gr.State(image)
+                        mask_out = gr.State(image_with_mask)
+
+                        if return_with_mask:
+                            return image_with_mask, real_output, mask_out
+                        return image, real_output, mask_out
+
+                    @gr.on(inputs=[input_image] + canvas_list, outputs=canvas_list, triggers=send_input_to_painter.click)
+                    def send_input_to_painter_background(input_image, *canvas_list):
+                        if input_image is None:
+                            return tuple(canvas_list)
+                        for canvas in canvas_list:
+                            h, w = canvas["background"].shape[:2]
+                            canvas["background"] = input_image.resize((w, h))
+                        return tuple(canvas_list)
+                    @gr.on(inputs=[real_output] + canvas_list, outputs=canvas_list, triggers=output_to_painter_button.click)
+                    def send_output_to_painter_background(real_output, *canvas_list):
+                        if real_output is None:
+                            return tuple(canvas_list)
+                        for canvas in canvas_list:
+                            h, w = canvas["background"].shape[:2]
+                            canvas["background"] = real_output.value.resize((w, h))
+                        return tuple(canvas_list)
+                    @gr.on(inputs=[return_with_mask, real_output, mask_out], outputs=[output_image], triggers=[return_with_mask.change], show_progress="hidden")
+                    def show_output(return_with_mask, real_output, mask_out):
+                        if return_with_mask:
+                            return mask_out.value
+                        else:
+                            return real_output.value
+                    @gr.on(inputs=[real_output], outputs=[input_image], triggers=output_to_input_button.click)
+                    def send_output_to_pipe_input(real_output):
+                        return real_output.value
+
+        with gr.Column():
+            gr.Markdown("## Examples")
+            for i in range(0, len(examples), 2):
+                with gr.Row():
+                    if i < len(examples):
+                        example = examples[i]
+                        with gr.Column():
+                            example_image = gr.Image(
+                                value=f"data/examples/eligen/entity_control/example_{example['example_id']}/example_image.png",
+                                label=example["description"],
+                                interactive=False,
+                                width=1024,
+                                height=512
+                            )
+                            load_example_button = gr.Button(value=f"Load Example {example['example_id']}")
+                            load_example_button.click(
+                                load_example,
+                                inputs=[load_example_button],
+                                outputs=[num_inference_steps, prompt, negative_prompt, seed] + local_prompt_list + canvas_list
+                            )
+
+                    if i + 1 < len(examples):
+                        example = examples[i + 1]
+                        with gr.Column():
+                            example_image = gr.Image(
+                                value=f"data/examples/eligen/entity_control/example_{example['example_id']}/example_image.png",
+                                label=example["description"],
+                                interactive=False,
+                                width=1024,
+                                height=512
+                            )
+                            load_example_button = gr.Button(value=f"Load Example {example['example_id']}")
+                            load_example_button.click(
+                                load_example,
+                                inputs=[load_example_button],
+                                outputs=[num_inference_steps, prompt, negative_prompt, seed] + local_prompt_list + canvas_list
+                            )
+app.config["show_progress"] = "hidden"
+app.launch()

apps/streamlit/pages/1_Image_Creator.py

@@ -1,11 +1,11 @@
-import torch, os, io
+import torch, os, io, json, time
 import numpy as np
 from PIL import Image
 import streamlit as st
 st.set_page_config(layout="wide")
 from streamlit_drawable_canvas import st_canvas
 from diffsynth.models import ModelManager
-from diffsynth.pipelines import SDImagePipeline, SDXLImagePipeline, SD3ImagePipeline, HunyuanDiTImagePipeline
+from diffsynth.pipelines import SDImagePipeline, SDXLImagePipeline, SD3ImagePipeline, HunyuanDiTImagePipeline, FluxImagePipeline
 from diffsynth.data.video import crop_and_resize
 
 
@@ -49,13 +49,20 @@ config = {
             "width": 1024,
         }
     },
+    "FLUX": {
+        "model_folder": "models/FLUX",
+        "pipeline_class": FluxImagePipeline,
+        "fixed_parameters": {
+            "cfg_scale": 1.0,
+        }
+    }
 }
 
 
 def load_model_list(model_type):
     folder = config[model_type]["model_folder"]
     file_list = [i for i in os.listdir(folder) if i.endswith(".safetensors")]
-    if model_type in ["HunyuanDiT", "Kolors"]:
+    if model_type in ["HunyuanDiT", "Kolors", "FLUX"]:
         file_list += [i for i in os.listdir(folder) if os.path.isdir(os.path.join(folder, i))]
     file_list = sorted(file_list)
     return file_list
@@ -85,6 +92,16 @@ def load_model(model_type, model_path):
             os.path.join(model_path, "unet/diffusion_pytorch_model.safetensors"),
             os.path.join(model_path, "vae/diffusion_pytorch_model.safetensors"),
         ])
+    elif model_type == "FLUX":
+        model_manager.torch_dtype = torch.bfloat16
+        file_list = [
+            os.path.join(model_path, "text_encoder/model.safetensors"),
+            os.path.join(model_path, "text_encoder_2"),
+        ]
+        for file_name in os.listdir(model_path):
+            if file_name.endswith(".safetensors"):
+                file_list.append(os.path.join(model_path, file_name))
+        model_manager.load_models(file_list)
     else:
         model_manager.load_model(model_path)
     pipeline = config[model_type]["pipeline_class"].from_model_manager(model_manager)
@@ -255,6 +272,48 @@ with column_input:
                     key="canvas"
                 )
 
+    num_painter_layer = st.number_input("Number of painter layers", min_value=0, max_value=10, step=1, value=0)
+    local_prompts, masks, mask_scales = [], [], []
+    white_board = Image.fromarray(np.ones((512, 512, 3), dtype=np.uint8) * 255)
+    painter_layers_json_data = []
+    for painter_tab_id in range(num_painter_layer):
+        with st.expander(f"Painter layer {painter_tab_id}", expanded=True):
+            enable_local_prompt = st.checkbox(f"Enable prompt {painter_tab_id}", value=True)
+            local_prompt = st.text_area(f"Prompt {painter_tab_id}")
+            mask_scale = st.slider(f"Mask scale {painter_tab_id}", min_value=0.0, max_value=3.0, value=1.0)
+            stroke_width = st.slider(f"Stroke width {painter_tab_id}", min_value=1, max_value=300, value=100)
+            canvas_result_local = st_canvas(
+                fill_color="#000000",
+                stroke_width=stroke_width,
+                stroke_color="#000000",
+                background_color="rgba(255, 255, 255, 0)",
+                background_image=white_board,
+                update_streamlit=True,
+                height=512,
+                width=512,
+                drawing_mode="freedraw",
+                key=f"canvas_{painter_tab_id}"
+            )
+            if canvas_result_local.json_data is not None:
+                painter_layers_json_data.append(canvas_result_local.json_data.copy())
+                painter_layers_json_data[-1]["prompt"] = local_prompt
+            if enable_local_prompt:
+                local_prompts.append(local_prompt)
+                if canvas_result_local.image_data is not None:
+                    mask = apply_stroke_to_image(canvas_result_local.image_data, white_board)
+                else:
+                    mask = white_board
+                mask = Image.fromarray(255 - np.array(mask))
+                masks.append(mask)
+                mask_scales.append(mask_scale)
+    save_painter_layers = st.button("Save painter layers")
+    if save_painter_layers:
+        os.makedirs("data/painter_layers", exist_ok=True)
+        json_file_path = f"data/painter_layers/{time.time_ns()}.json"
+        with open(json_file_path, "w") as f:
+            json.dump(painter_layers_json_data, f, indent=4)
+            st.markdown(f"Painter layers are saved in {json_file_path}.")
+
 
 with column_output:
     run_button = st.button("Generate image", type="primary")
@@ -282,6 +341,7 @@ with column_output:
                 progress_bar_st = st.progress(0.0)
                 image = pipeline(
                     prompt, negative_prompt=negative_prompt,
+                    local_prompts=local_prompts, masks=masks, mask_scales=mask_scales,
                     cfg_scale=cfg_scale, num_inference_steps=num_inference_steps,
                     height=height, width=width,
                     input_image=input_image, denoising_strength=denoising_strength,

diffsynth/configs/model_config.py

@@ -16,6 +16,7 @@ from ..models.sd3_vae_decoder import SD3VAEDecoder
 from ..models.sd3_vae_encoder import SD3VAEEncoder
 
 from ..models.sd_controlnet import SDControlNet
+from ..models.sdxl_controlnet import SDXLControlNetUnion
 
 from ..models.sd_motion import SDMotionModel
 from ..models.sdxl_motion import SDXLMotionModel
@@ -31,7 +32,29 @@ from ..models.sdxl_ipadapter import SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder
 from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder
 from ..models.hunyuan_dit import HunyuanDiT
 
+from ..models.flux_dit import FluxDiT
+from ..models.flux_text_encoder import FluxTextEncoder2
+from ..models.flux_vae import FluxVAEEncoder, FluxVAEDecoder
+from ..models.flux_controlnet import FluxControlNet
+from ..models.flux_ipadapter import FluxIpAdapter
 
+from ..models.cog_vae import CogVAEEncoder, CogVAEDecoder
+from ..models.cog_dit import CogDiT
+
+from ..models.omnigen import OmniGenTransformer
+
+from ..models.hunyuan_video_vae_decoder import HunyuanVideoVAEDecoder
+from ..models.hunyuan_video_vae_encoder import HunyuanVideoVAEEncoder
+
+from ..extensions.RIFE import IFNet
+from ..extensions.ESRGAN import RRDBNet
+
+from ..models.hunyuan_video_dit import HunyuanVideoDiT
+
+from ..models.stepvideo_vae import StepVideoVAE
+from ..models.stepvideo_dit import StepVideoModel
+
+from ..models.wanx_vae import WanXVideoVAE
 
 model_loader_configs = [
     # These configs are provided for detecting model type automatically.
@@ -60,13 +83,47 @@ model_loader_configs = [
     (None, "2a07abce74b4bdc696b76254ab474da6", ["svd_image_encoder", "svd_unet", "svd_vae_decoder", "svd_vae_encoder"], [SVDImageEncoder, SVDUNet, SVDVAEDecoder, SVDVAEEncoder], "civitai"),
     (None, "c96a285a6888465f87de22a984d049fb", ["sd_motion_modules"], [SDMotionModel], "civitai"),
     (None, "72907b92caed19bdb2adb89aa4063fe2", ["sdxl_motion_modules"], [SDXLMotionModel], "civitai"),
+    (None, "31d2d9614fba60511fc9bf2604aa01f7", ["sdxl_controlnet"], [SDXLControlNetUnion], "diffusers"),
+    (None, "94eefa3dac9cec93cb1ebaf1747d7b78", ["sd3_text_encoder_1"], [SD3TextEncoder1], "diffusers"),
+    (None, "1aafa3cc91716fb6b300cc1cd51b85a3", ["flux_vae_encoder", "flux_vae_decoder"], [FluxVAEEncoder, FluxVAEDecoder], "diffusers"),
+    (None, "21ea55f476dfc4fd135587abb59dfe5d", ["flux_vae_encoder", "flux_vae_decoder"], [FluxVAEEncoder, FluxVAEDecoder], "civitai"),
+    (None, "a29710fea6dddb0314663ee823598e50", ["flux_dit"], [FluxDiT], "civitai"),
+    (None, "57b02550baab820169365b3ee3afa2c9", ["flux_dit"], [FluxDiT], "civitai"),
+    (None, "3394f306c4cbf04334b712bf5aaed95f", ["flux_dit"], [FluxDiT], "civitai"),
+    (None, "023f054d918a84ccf503481fd1e3379e", ["flux_dit"], [FluxDiT], "civitai"),
+    (None, "280189ee084bca10f70907bf6ce1649d", ["cog_vae_encoder", "cog_vae_decoder"], [CogVAEEncoder, CogVAEDecoder], "diffusers"),
+    (None, "9b9313d104ac4df27991352fec013fd4", ["rife"], [IFNet], "civitai"),
+    (None, "6b7116078c4170bfbeaedc8fe71f6649", ["esrgan"], [RRDBNet], "civitai"),
+    (None, "61cbcbc7ac11f169c5949223efa960d1", ["omnigen_transformer"], [OmniGenTransformer], "diffusers"),
+    (None, "78d18b9101345ff695f312e7e62538c0", ["flux_controlnet"], [FluxControlNet], "diffusers"),
+    (None, "b001c89139b5f053c715fe772362dd2a", ["flux_controlnet"], [FluxControlNet], "diffusers"),
+    (None, "52357cb26250681367488a8954c271e8", ["flux_controlnet"], [FluxControlNet], "diffusers"),
+    (None, "0cfd1740758423a2a854d67c136d1e8c", ["flux_controlnet"], [FluxControlNet], "diffusers"),
+    (None, "4daaa66cc656a8fe369908693dad0a35", ["flux_ipadapter"], [FluxIpAdapter], "diffusers"),
+    (None, "51aed3d27d482fceb5e0739b03060e8f", ["sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"),
+    (None, "98cc34ccc5b54ae0e56bdea8688dcd5a", ["sd3_text_encoder_2"], [SD3TextEncoder2], "civitai"),
+    (None, "77ff18050dbc23f50382e45d51a779fe", ["sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"),
+    (None, "5da81baee73198a7c19e6d2fe8b5148e", ["sd3_text_encoder_1"], [SD3TextEncoder1], "diffusers"),
+    (None, "aeb82dce778a03dcb4d726cb03f3c43f", ["hunyuan_video_vae_decoder", "hunyuan_video_vae_encoder"], [HunyuanVideoVAEDecoder, HunyuanVideoVAEEncoder], "diffusers"),
+    (None, "b9588f02e78f5ccafc9d7c0294e46308", ["hunyuan_video_dit"], [HunyuanVideoDiT], "civitai"),
+    (None, "84ef4bd4757f60e906b54aa6a7815dc6", ["hunyuan_video_dit"], [HunyuanVideoDiT], "civitai"),
+    (None, "68beaf8429b7c11aa8ca05b1bd0058bd", ["stepvideo_vae"], [StepVideoVAE], "civitai"),
+    (None, "5c0216a2132b082c10cb7a0e0377e681", ["stepvideo_dit"], [StepVideoModel], "civitai"),
+    (None, "1378ea763357eea97acdef78e65d6d96", ["wanxvideo_vae"], [WanXVideoVAE], "civitai")
 ]
 huggingface_model_loader_configs = [
     # These configs are provided for detecting model type automatically.
-    # The format is (architecture_in_huggingface_config, huggingface_lib, model_name)
+    # The format is (architecture_in_huggingface_config, huggingface_lib, model_name, redirected_architecture)
-    ("ChatGLMModel", "diffsynth.models.kolors_text_encoder", "kolors_text_encoder"),
+    ("ChatGLMModel", "diffsynth.models.kolors_text_encoder", "kolors_text_encoder", None),
-    ("MarianMTModel", "transformers.models.marian.modeling_marian", "translator"),
+    ("MarianMTModel", "transformers.models.marian.modeling_marian", "translator", None),
-    ("BloomForCausalLM", "transformers.models.bloom.modeling_bloom", "beautiful_prompt"),
+    ("BloomForCausalLM", "transformers.models.bloom.modeling_bloom", "beautiful_prompt", None),
+    ("Qwen2ForCausalLM", "transformers.models.qwen2.modeling_qwen2", "qwen_prompt", None),
+    # ("LlamaForCausalLM", "transformers.models.llama.modeling_llama", "omost_prompt", None),
+    ("T5EncoderModel", "diffsynth.models.flux_text_encoder", "flux_text_encoder_2", "FluxTextEncoder2"),
+    ("CogVideoXTransformer3DModel", "diffsynth.models.cog_dit", "cog_dit", "CogDiT"),
+    ("SiglipModel", "transformers.models.siglip.modeling_siglip", "siglip_vision_model", "SiglipVisionModel"),
+    ("LlamaForCausalLM", "diffsynth.models.hunyuan_video_text_encoder", "hunyuan_video_text_encoder_2", "HunyuanVideoLLMEncoder"),
+    ("Step1Model", "diffsynth.models.stepvideo_text_encoder", "stepvideo_text_encoder_2", "STEP1TextEncoder"),
 ]
 patch_model_loader_configs = [
     # These configs are provided for detecting model type automatically.
@@ -87,6 +144,175 @@ preset_models_on_huggingface = {
     "ExVideo-SVD-128f-v1": [
         ("ECNU-CILab/ExVideo-SVD-128f-v1", "model.fp16.safetensors", "models/stable_video_diffusion"),
     ],
+    # Stable Diffusion
+    "StableDiffusion_v15": [
+        ("benjamin-paine/stable-diffusion-v1-5", "v1-5-pruned-emaonly.safetensors", "models/stable_diffusion"),
+    ],
+    "DreamShaper_8": [
+        ("Yntec/Dreamshaper8", "dreamshaper_8.safetensors", "models/stable_diffusion"),
+    ],
+    # Textual Inversion
+    "TextualInversion_VeryBadImageNegative_v1.3": [
+        ("gemasai/verybadimagenegative_v1.3", "verybadimagenegative_v1.3.pt", "models/textual_inversion"),
+    ],
+    # Stable Diffusion XL
+    "StableDiffusionXL_v1": [
+        ("stabilityai/stable-diffusion-xl-base-1.0", "sd_xl_base_1.0.safetensors", "models/stable_diffusion_xl"),
+    ],
+    "BluePencilXL_v200": [
+        ("frankjoshua/bluePencilXL_v200", "bluePencilXL_v200.safetensors", "models/stable_diffusion_xl"),
+    ],
+    "StableDiffusionXL_Turbo": [
+        ("stabilityai/sdxl-turbo", "sd_xl_turbo_1.0_fp16.safetensors", "models/stable_diffusion_xl_turbo"),
+    ],
+    # Stable Diffusion 3
+    "StableDiffusion3": [
+        ("stabilityai/stable-diffusion-3-medium", "sd3_medium_incl_clips_t5xxlfp16.safetensors", "models/stable_diffusion_3"),
+    ],
+    "StableDiffusion3_without_T5": [
+        ("stabilityai/stable-diffusion-3-medium", "sd3_medium_incl_clips.safetensors", "models/stable_diffusion_3"),
+    ],
+    # ControlNet
+    "ControlNet_v11f1p_sd15_depth": [
+        ("lllyasviel/ControlNet-v1-1", "control_v11f1p_sd15_depth.pth", "models/ControlNet"),
+        ("lllyasviel/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators")
+    ],
+    "ControlNet_v11p_sd15_softedge": [
+        ("lllyasviel/ControlNet-v1-1", "control_v11p_sd15_softedge.pth", "models/ControlNet"),
+        ("lllyasviel/Annotators", "ControlNetHED.pth", "models/Annotators")
+    ],
+    "ControlNet_v11f1e_sd15_tile": [
+        ("lllyasviel/ControlNet-v1-1", "control_v11f1e_sd15_tile.pth", "models/ControlNet")
+    ],
+    "ControlNet_v11p_sd15_lineart": [
+        ("lllyasviel/ControlNet-v1-1", "control_v11p_sd15_lineart.pth", "models/ControlNet"),
+        ("lllyasviel/Annotators", "sk_model.pth", "models/Annotators"),
+        ("lllyasviel/Annotators", "sk_model2.pth", "models/Annotators")
+    ],
+    "ControlNet_union_sdxl_promax": [
+        ("xinsir/controlnet-union-sdxl-1.0", "diffusion_pytorch_model_promax.safetensors", "models/ControlNet/controlnet_union"),
+        ("lllyasviel/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators")
+    ],
+    # AnimateDiff
+    "AnimateDiff_v2": [
+        ("guoyww/animatediff", "mm_sd_v15_v2.ckpt", "models/AnimateDiff"),
+    ],
+    "AnimateDiff_xl_beta": [
+        ("guoyww/animatediff", "mm_sdxl_v10_beta.ckpt", "models/AnimateDiff"),
+    ],
+
+    # Qwen Prompt
+    "QwenPrompt": [
+        ("Qwen/Qwen2-1.5B-Instruct", "config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+        ("Qwen/Qwen2-1.5B-Instruct", "generation_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+        ("Qwen/Qwen2-1.5B-Instruct", "model.safetensors", "models/QwenPrompt/qwen2-1.5b-instruct"),
+        ("Qwen/Qwen2-1.5B-Instruct", "special_tokens_map.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+        ("Qwen/Qwen2-1.5B-Instruct", "tokenizer.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+        ("Qwen/Qwen2-1.5B-Instruct", "tokenizer_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+        ("Qwen/Qwen2-1.5B-Instruct", "merges.txt", "models/QwenPrompt/qwen2-1.5b-instruct"),
+        ("Qwen/Qwen2-1.5B-Instruct", "vocab.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+    ],
+    # Beautiful Prompt
+    "BeautifulPrompt": [
+        ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
+        ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "generation_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
+        ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "model.safetensors", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
+        ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "special_tokens_map.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
+        ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "tokenizer.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
+        ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "tokenizer_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
+    ],
+    # Omost prompt
+    "OmostPrompt":[
+        ("lllyasviel/omost-llama-3-8b-4bits", "model-00001-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+        ("lllyasviel/omost-llama-3-8b-4bits", "model-00002-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+        ("lllyasviel/omost-llama-3-8b-4bits", "tokenizer.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+        ("lllyasviel/omost-llama-3-8b-4bits", "tokenizer_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),  
+        ("lllyasviel/omost-llama-3-8b-4bits", "config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+        ("lllyasviel/omost-llama-3-8b-4bits", "generation_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+        ("lllyasviel/omost-llama-3-8b-4bits", "model.safetensors.index.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+        ("lllyasviel/omost-llama-3-8b-4bits", "special_tokens_map.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+    ],
+    # Translator
+    "opus-mt-zh-en": [
+        ("Helsinki-NLP/opus-mt-zh-en", "config.json", "models/translator/opus-mt-zh-en"),
+        ("Helsinki-NLP/opus-mt-zh-en", "generation_config.json", "models/translator/opus-mt-zh-en"),
+        ("Helsinki-NLP/opus-mt-zh-en", "metadata.json", "models/translator/opus-mt-zh-en"),
+        ("Helsinki-NLP/opus-mt-zh-en", "pytorch_model.bin", "models/translator/opus-mt-zh-en"),
+        ("Helsinki-NLP/opus-mt-zh-en", "source.spm", "models/translator/opus-mt-zh-en"),
+        ("Helsinki-NLP/opus-mt-zh-en", "target.spm", "models/translator/opus-mt-zh-en"),
+        ("Helsinki-NLP/opus-mt-zh-en", "tokenizer_config.json", "models/translator/opus-mt-zh-en"),
+        ("Helsinki-NLP/opus-mt-zh-en", "vocab.json", "models/translator/opus-mt-zh-en"),
+    ],
+    # IP-Adapter
+    "IP-Adapter-SD": [
+        ("h94/IP-Adapter", "models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion/image_encoder"),
+        ("h94/IP-Adapter", "models/ip-adapter_sd15.bin", "models/IpAdapter/stable_diffusion"),
+    ],
+    "IP-Adapter-SDXL": [
+        ("h94/IP-Adapter", "sdxl_models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion_xl/image_encoder"),
+        ("h94/IP-Adapter", "sdxl_models/ip-adapter_sdxl.bin", "models/IpAdapter/stable_diffusion_xl"),
+    ],
+    "SDXL-vae-fp16-fix": [
+        ("madebyollin/sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors", "models/sdxl-vae-fp16-fix")
+    ],
+    # Kolors
+    "Kolors": [
+        ("Kwai-Kolors/Kolors", "text_encoder/config.json", "models/kolors/Kolors/text_encoder"),
+        ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model.bin.index.json", "models/kolors/Kolors/text_encoder"),
+        ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00001-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+        ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00002-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+        ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00003-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+        ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00004-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+        ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00005-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+        ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00006-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+        ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00007-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+        ("Kwai-Kolors/Kolors", "unet/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/unet"),
+        ("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/vae"),
+    ],
+    # FLUX
+    "FLUX.1-dev": [
+        ("black-forest-labs/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"),
+        ("black-forest-labs/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+        ("black-forest-labs/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+        ("black-forest-labs/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+        ("black-forest-labs/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+        ("black-forest-labs/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"),
+        ("black-forest-labs/FLUX.1-dev", "flux1-dev.safetensors", "models/FLUX/FLUX.1-dev"),
+    ],
+    "InstantX/FLUX.1-dev-IP-Adapter": {
+        "file_list": [
+            ("InstantX/FLUX.1-dev-IP-Adapter", "ip-adapter.bin", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter"),
+            ("google/siglip-so400m-patch14-384", "model.safetensors", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"),
+            ("google/siglip-so400m-patch14-384", "config.json", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"),
+        ],
+        "load_path": [
+            "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/ip-adapter.bin",
+            "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder",
+        ],
+    },
+    # RIFE
+    "RIFE": [
+        ("AlexWortega/RIFE", "flownet.pkl", "models/RIFE"),
+    ],
+    # CogVideo
+    "CogVideoX-5B": [
+        ("THUDM/CogVideoX-5b", "text_encoder/config.json", "models/CogVideo/CogVideoX-5b/text_encoder"),
+        ("THUDM/CogVideoX-5b", "text_encoder/model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/text_encoder"),
+        ("THUDM/CogVideoX-5b", "text_encoder/model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"),
+        ("THUDM/CogVideoX-5b", "text_encoder/model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"),
+        ("THUDM/CogVideoX-5b", "transformer/config.json", "models/CogVideo/CogVideoX-5b/transformer"),
+        ("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/transformer"),
+        ("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"),
+        ("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"),
+        ("THUDM/CogVideoX-5b", "vae/diffusion_pytorch_model.safetensors", "models/CogVideo/CogVideoX-5b/vae"),
+    ],
+    # Stable Diffusion 3.5
+    "StableDiffusion3.5-large": [
+        ("stabilityai/stable-diffusion-3.5-large", "sd3.5_large.safetensors", "models/stable_diffusion_3"),
+        ("stabilityai/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"),
+        ("stabilityai/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"),
+        ("stabilityai/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"),
+    ],
 }
 preset_models_on_modelscope = {
     # Hunyuan DiT
@@ -104,6 +330,9 @@ preset_models_on_modelscope = {
     "ExVideo-SVD-128f-v1": [
         ("ECNU-CILab/ExVideo-SVD-128f-v1", "model.fp16.safetensors", "models/stable_video_diffusion"),
     ],
+    "ExVideo-CogVideoX-LoRA-129f-v1": [
+        ("ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1", "ExVideo-CogVideoX-LoRA-129f-v1.safetensors", "models/lora"),
+    ],
     # Stable Diffusion
     "StableDiffusion_v15": [
         ("AI-ModelScope/stable-diffusion-v1-5", "v1-5-pruned-emaonly.safetensors", "models/stable_diffusion"),
@@ -131,6 +360,9 @@ preset_models_on_modelscope = {
     "StableDiffusionXL_Turbo": [
         ("AI-ModelScope/sdxl-turbo", "sd_xl_turbo_1.0_fp16.safetensors", "models/stable_diffusion_xl_turbo"),
     ],
+    "SDXL_lora_zyd232_ChineseInkStyle_SDXL_v1_0": [
+        ("sd_lora/zyd232_ChineseInkStyle_SDXL_v1_0", "zyd232_ChineseInkStyle_SDXL_v1_0.safetensors", "models/lora"),
+    ],
     # Stable Diffusion 3
     "StableDiffusion3": [
         ("AI-ModelScope/stable-diffusion-3-medium", "sd3_medium_incl_clips_t5xxlfp16.safetensors", "models/stable_diffusion_3"),
@@ -155,6 +387,28 @@ preset_models_on_modelscope = {
         ("sd_lora/Annotators", "sk_model.pth", "models/Annotators"),
         ("sd_lora/Annotators", "sk_model2.pth", "models/Annotators")
     ],
+    "ControlNet_union_sdxl_promax": [
+        ("AI-ModelScope/controlnet-union-sdxl-1.0", "diffusion_pytorch_model_promax.safetensors", "models/ControlNet/controlnet_union"),
+        ("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators")
+    ],
+    "Annotators:Depth": [
+        ("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators"),
+    ],
+    "Annotators:Softedge": [
+        ("sd_lora/Annotators", "ControlNetHED.pth", "models/Annotators"),
+    ],
+    "Annotators:Lineart": [
+        ("sd_lora/Annotators", "sk_model.pth", "models/Annotators"),
+        ("sd_lora/Annotators", "sk_model2.pth", "models/Annotators"),
+    ],
+    "Annotators:Normal": [
+        ("sd_lora/Annotators", "scannet.pt", "models/Annotators"),
+    ],
+    "Annotators:Openpose": [
+        ("sd_lora/Annotators", "body_pose_model.pth", "models/Annotators"),
+        ("sd_lora/Annotators", "facenet.pth", "models/Annotators"),
+        ("sd_lora/Annotators", "hand_pose_model.pth", "models/Annotators"),
+    ],
     # AnimateDiff
     "AnimateDiff_v2": [
         ("Shanghai_AI_Laboratory/animatediff", "mm_sd_v15_v2.ckpt", "models/AnimateDiff"),
@@ -166,8 +420,25 @@ preset_models_on_modelscope = {
     "RIFE": [
         ("Damo_XR_Lab/cv_rife_video-frame-interpolation", "flownet.pkl", "models/RIFE"),
     ],
+    # Qwen Prompt
+    "QwenPrompt": {
+        "file_list": [
+            ("qwen/Qwen2-1.5B-Instruct", "config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+            ("qwen/Qwen2-1.5B-Instruct", "generation_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+            ("qwen/Qwen2-1.5B-Instruct", "model.safetensors", "models/QwenPrompt/qwen2-1.5b-instruct"),
+            ("qwen/Qwen2-1.5B-Instruct", "special_tokens_map.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+            ("qwen/Qwen2-1.5B-Instruct", "tokenizer.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+            ("qwen/Qwen2-1.5B-Instruct", "tokenizer_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+            ("qwen/Qwen2-1.5B-Instruct", "merges.txt", "models/QwenPrompt/qwen2-1.5b-instruct"),
+            ("qwen/Qwen2-1.5B-Instruct", "vocab.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+        ],
+        "load_path": [
+            "models/QwenPrompt/qwen2-1.5b-instruct",
+        ],
+    },
     # Beautiful Prompt
-    "BeautifulPrompt": [
+    "BeautifulPrompt": {
+        "file_list": [
             ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
             ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "generation_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
             ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "model.safetensors", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
@@ -175,8 +446,29 @@ preset_models_on_modelscope = {
             ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "tokenizer.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
             ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "tokenizer_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
         ],
+        "load_path": [
+            "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd",
+        ],
+    },
+    # Omost prompt
+    "OmostPrompt": {
+        "file_list": [
+            ("Omost/omost-llama-3-8b-4bits", "model-00001-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+            ("Omost/omost-llama-3-8b-4bits", "model-00002-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+            ("Omost/omost-llama-3-8b-4bits", "tokenizer.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+            ("Omost/omost-llama-3-8b-4bits", "tokenizer_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),  
+            ("Omost/omost-llama-3-8b-4bits", "config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+            ("Omost/omost-llama-3-8b-4bits", "generation_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+            ("Omost/omost-llama-3-8b-4bits", "model.safetensors.index.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+            ("Omost/omost-llama-3-8b-4bits", "special_tokens_map.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+        ],
+        "load_path": [
+            "models/OmostPrompt/omost-llama-3-8b-4bits",
+        ],
+    },
     # Translator
-    "opus-mt-zh-en": [
+    "opus-mt-zh-en": {
+        "file_list": [
             ("moxying/opus-mt-zh-en", "config.json", "models/translator/opus-mt-zh-en"),
             ("moxying/opus-mt-zh-en", "generation_config.json", "models/translator/opus-mt-zh-en"),
             ("moxying/opus-mt-zh-en", "metadata.json", "models/translator/opus-mt-zh-en"),
@@ -186,6 +478,10 @@ preset_models_on_modelscope = {
             ("moxying/opus-mt-zh-en", "tokenizer_config.json", "models/translator/opus-mt-zh-en"),
             ("moxying/opus-mt-zh-en", "vocab.json", "models/translator/opus-mt-zh-en"),
         ],
+        "load_path": [
+            "models/translator/opus-mt-zh-en",
+        ],
+    },
     # IP-Adapter
     "IP-Adapter-SD": [
         ("AI-ModelScope/IP-Adapter", "models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion/image_encoder"),
@@ -196,7 +492,8 @@ preset_models_on_modelscope = {
         ("AI-ModelScope/IP-Adapter", "sdxl_models/ip-adapter_sdxl.bin", "models/IpAdapter/stable_diffusion_xl"),
     ],
     # Kolors
-    "Kolors": [
+    "Kolors": {
+        "file_list": [
             ("Kwai-Kolors/Kolors", "text_encoder/config.json", "models/kolors/Kolors/text_encoder"),
             ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model.bin.index.json", "models/kolors/Kolors/text_encoder"),
             ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00001-of-00007.bin", "models/kolors/Kolors/text_encoder"),
@@ -209,14 +506,190 @@ preset_models_on_modelscope = {
             ("Kwai-Kolors/Kolors", "unet/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/unet"),
             ("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/vae"),
         ],
+        "load_path": [
+            "models/kolors/Kolors/text_encoder",
+            "models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors",
+            "models/kolors/Kolors/vae/diffusion_pytorch_model.safetensors",
+        ],
+    },
     "SDXL-vae-fp16-fix": [
         ("AI-ModelScope/sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors", "models/sdxl-vae-fp16-fix")
     ],
+    # FLUX
+    "FLUX.1-dev": {
+        "file_list": [
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"),
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+            ("AI-ModelScope/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"),
+            ("AI-ModelScope/FLUX.1-dev", "flux1-dev.safetensors", "models/FLUX/FLUX.1-dev"),
+        ],
+        "load_path": [
+            "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
+            "models/FLUX/FLUX.1-dev/text_encoder_2",
+            "models/FLUX/FLUX.1-dev/ae.safetensors",
+            "models/FLUX/FLUX.1-dev/flux1-dev.safetensors"
+        ],
+    },
+    "FLUX.1-schnell": {
+        "file_list": [
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"),
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+            ("AI-ModelScope/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"),
+            ("AI-ModelScope/FLUX.1-schnell", "flux1-schnell.safetensors", "models/FLUX/FLUX.1-schnell"),
+        ],
+        "load_path": [
+            "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
+            "models/FLUX/FLUX.1-dev/text_encoder_2",
+            "models/FLUX/FLUX.1-dev/ae.safetensors",
+            "models/FLUX/FLUX.1-schnell/flux1-schnell.safetensors"
+        ],
+    },
+    "InstantX/FLUX.1-dev-Controlnet-Union-alpha": [
+        ("InstantX/FLUX.1-dev-Controlnet-Union-alpha", "diffusion_pytorch_model.safetensors", "models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha"),
+    ],
+    "jasperai/Flux.1-dev-Controlnet-Depth": [
+        ("jasperai/Flux.1-dev-Controlnet-Depth", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Depth"),
+    ],
+    "jasperai/Flux.1-dev-Controlnet-Surface-Normals": [
+        ("jasperai/Flux.1-dev-Controlnet-Surface-Normals", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Surface-Normals"),
+    ],
+    "jasperai/Flux.1-dev-Controlnet-Upscaler": [
+        ("jasperai/Flux.1-dev-Controlnet-Upscaler", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler"),
+    ],
+    "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha": [
+        ("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha", "diffusion_pytorch_model.safetensors", "models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha"),
+    ],
+    "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta": [
+        ("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", "diffusion_pytorch_model.safetensors", "models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta"),
+    ],
+    "Shakker-Labs/FLUX.1-dev-ControlNet-Depth": [
+        ("Shakker-Labs/FLUX.1-dev-ControlNet-Depth", "diffusion_pytorch_model.safetensors", "models/ControlNet/Shakker-Labs/FLUX.1-dev-ControlNet-Depth"),
+    ],
+    "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro": [
+        ("Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", "diffusion_pytorch_model.safetensors", "models/ControlNet/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro"),
+    ],
+    "InstantX/FLUX.1-dev-IP-Adapter": {
+        "file_list": [
+            ("InstantX/FLUX.1-dev-IP-Adapter", "ip-adapter.bin", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter"),
+            ("AI-ModelScope/siglip-so400m-patch14-384", "model.safetensors", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"),
+            ("AI-ModelScope/siglip-so400m-patch14-384", "config.json", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"),
+        ],
+        "load_path": [
+            "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/ip-adapter.bin",
+            "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder",
+        ],
+    },
+    # ESRGAN
+    "ESRGAN_x4": [
+        ("AI-ModelScope/Real-ESRGAN", "RealESRGAN_x4.pth", "models/ESRGAN"),
+    ],
+    # RIFE
+    "RIFE": [
+        ("AI-ModelScope/RIFE", "flownet.pkl", "models/RIFE"),
+    ],
+    # Omnigen
+    "OmniGen-v1": {
+        "file_list": [
+            ("BAAI/OmniGen-v1", "vae/diffusion_pytorch_model.safetensors", "models/OmniGen/OmniGen-v1/vae"),
+            ("BAAI/OmniGen-v1", "model.safetensors", "models/OmniGen/OmniGen-v1"),
+            ("BAAI/OmniGen-v1", "config.json", "models/OmniGen/OmniGen-v1"),
+            ("BAAI/OmniGen-v1", "special_tokens_map.json", "models/OmniGen/OmniGen-v1"),
+            ("BAAI/OmniGen-v1", "tokenizer_config.json", "models/OmniGen/OmniGen-v1"),
+            ("BAAI/OmniGen-v1", "tokenizer.json", "models/OmniGen/OmniGen-v1"),
+        ],
+        "load_path": [
+            "models/OmniGen/OmniGen-v1/vae/diffusion_pytorch_model.safetensors",
+            "models/OmniGen/OmniGen-v1/model.safetensors",
+        ]
+    },
+    # CogVideo
+    "CogVideoX-5B": {
+        "file_list": [
+            ("ZhipuAI/CogVideoX-5b", "text_encoder/config.json", "models/CogVideo/CogVideoX-5b/text_encoder"),
+            ("ZhipuAI/CogVideoX-5b", "text_encoder/model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/text_encoder"),
+            ("ZhipuAI/CogVideoX-5b", "text_encoder/model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"),
+            ("ZhipuAI/CogVideoX-5b", "text_encoder/model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"),
+            ("ZhipuAI/CogVideoX-5b", "transformer/config.json", "models/CogVideo/CogVideoX-5b/transformer"),
+            ("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/transformer"),
+            ("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"),
+            ("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"),
+            ("ZhipuAI/CogVideoX-5b", "vae/diffusion_pytorch_model.safetensors", "models/CogVideo/CogVideoX-5b/vae"),
+        ],
+        "load_path": [
+            "models/CogVideo/CogVideoX-5b/text_encoder",
+            "models/CogVideo/CogVideoX-5b/transformer",
+            "models/CogVideo/CogVideoX-5b/vae/diffusion_pytorch_model.safetensors",
+        ],
+    },
+    # Stable Diffusion 3.5
+    "StableDiffusion3.5-large": [
+        ("AI-ModelScope/stable-diffusion-3.5-large", "sd3.5_large.safetensors", "models/stable_diffusion_3"),
+        ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"),
+        ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"),
+        ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"),
+    ],
+    "StableDiffusion3.5-medium": [
+        ("AI-ModelScope/stable-diffusion-3.5-medium", "sd3.5_medium.safetensors", "models/stable_diffusion_3"),
+        ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"),
+        ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"),
+        ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"),
+    ],
+    "StableDiffusion3.5-large-turbo": [
+        ("AI-ModelScope/stable-diffusion-3.5-large-turbo", "sd3.5_large_turbo.safetensors", "models/stable_diffusion_3"),
+        ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"),
+        ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"),
+        ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"),
+    ],
+    "HunyuanVideo":{
+        "file_list": [
+            ("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideo/text_encoder"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00001-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00002-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00003-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00004-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "config.json", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model.safetensors.index.json", "models/HunyuanVideo/text_encoder_2"),
+            ("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/vae/pytorch_model.pt", "models/HunyuanVideo/vae"),
+            ("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt", "models/HunyuanVideo/transformers")
+        ],
+        "load_path": [
+            "models/HunyuanVideo/text_encoder/model.safetensors",
+            "models/HunyuanVideo/text_encoder_2",
+            "models/HunyuanVideo/vae/pytorch_model.pt",
+            "models/HunyuanVideo/transformers/mp_rank_00_model_states.pt"
+        ],
+    },
+    "HunyuanVideo-fp8":{
+        "file_list": [
+            ("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideo/text_encoder"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00001-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00002-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00003-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00004-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "config.json", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model.safetensors.index.json", "models/HunyuanVideo/text_encoder_2"),
+            ("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/vae/pytorch_model.pt", "models/HunyuanVideo/vae"),
+            ("DiffSynth-Studio/HunyuanVideo-safetensors", "model.fp8.safetensors", "models/HunyuanVideo/transformers")
+        ],
+        "load_path": [
+            "models/HunyuanVideo/text_encoder/model.safetensors",
+            "models/HunyuanVideo/text_encoder_2",
+            "models/HunyuanVideo/vae/pytorch_model.pt",
+            "models/HunyuanVideo/transformers/model.fp8.safetensors"
+        ],
+    },
 }
 Preset_model_id: TypeAlias = Literal[
     "HunyuanDiT",
     "stable-video-diffusion-img2vid-xt",
     "ExVideo-SVD-128f-v1",
+    "ExVideo-CogVideoX-LoRA-129f-v1",
     "StableDiffusion_v15",
     "DreamShaper_8",
     "AingDiffusion_v12",
@@ -240,4 +713,32 @@ Preset_model_id: TypeAlias = Literal[
     "StableDiffusion3_without_T5",
     "Kolors",
     "SDXL-vae-fp16-fix",
+    "ControlNet_union_sdxl_promax",
+    "FLUX.1-dev",
+    "FLUX.1-schnell",
+    "InstantX/FLUX.1-dev-Controlnet-Union-alpha",
+    "jasperai/Flux.1-dev-Controlnet-Depth",
+    "jasperai/Flux.1-dev-Controlnet-Surface-Normals",
+    "jasperai/Flux.1-dev-Controlnet-Upscaler",
+    "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha",
+    "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta",
+    "Shakker-Labs/FLUX.1-dev-ControlNet-Depth",
+    "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro",
+    "InstantX/FLUX.1-dev-IP-Adapter",
+    "SDXL_lora_zyd232_ChineseInkStyle_SDXL_v1_0",
+    "QwenPrompt",
+    "OmostPrompt",
+    "ESRGAN_x4",
+    "RIFE",
+    "OmniGen-v1",
+    "CogVideoX-5B",
+    "Annotators:Depth",
+    "Annotators:Softedge",
+    "Annotators:Lineart",
+    "Annotators:Normal",
+    "Annotators:Openpose",
+    "StableDiffusion3.5-large",
+    "StableDiffusion3.5-medium",
+    "HunyuanVideo",
+    "HunyuanVideo-fp8",
 ]

diffsynth/controlnets/__init__.py

@@ -1,2 +1,2 @@
-from .controlnet_unit import ControlNetConfigUnit, ControlNetUnit, MultiControlNetManager
+from .controlnet_unit import ControlNetConfigUnit, ControlNetUnit, MultiControlNetManager, FluxMultiControlNetManager
 from .processors import Annotator

diffsynth/controlnets/controlnet_unit.py

@@ -4,10 +4,11 @@ from .processors import Processor_id
 
 
 class ControlNetConfigUnit:
-    def __init__(self, processor_id: Processor_id, model_path, scale=1.0):
+    def __init__(self, processor_id: Processor_id, model_path, scale=1.0, skip_processor=False):
         self.processor_id = processor_id
         self.model_path = model_path
         self.scale = scale
+        self.skip_processor = skip_processor
 
 
 class ControlNetUnit:
@@ -23,6 +24,16 @@ class MultiControlNetManager:
         self.models = [unit.model for unit in controlnet_units]
         self.scales = [unit.scale for unit in controlnet_units]
 
+    def cpu(self):
+        for model in self.models:
+            model.cpu()
+
+    def to(self, device):
+        for model in self.models:
+            model.to(device)
+        for processor in self.processors:
+            processor.to(device)
+    
     def process_image(self, image, processor_id=None):
         if processor_id is None:
             processed_image = [processor(image) for processor in self.processors]
@@ -37,13 +48,14 @@ class MultiControlNetManager:
     def __call__(
         self,
         sample, timestep, encoder_hidden_states, conditionings,
-        tiled=False, tile_size=64, tile_stride=32
+        tiled=False, tile_size=64, tile_stride=32, **kwargs
     ):
         res_stack = None
-        for conditioning, model, scale in zip(conditionings, self.models, self.scales):
+        for processor, conditioning, model, scale in zip(self.processors, conditionings, self.models, self.scales):
             res_stack_ = model(
-                sample, timestep, encoder_hidden_states, conditioning,
+                sample, timestep, encoder_hidden_states, conditioning, **kwargs,
-                tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
+                tiled=tiled, tile_size=tile_size, tile_stride=tile_stride,
+                processor_id=processor.processor_id
             )
             res_stack_ = [res * scale for res in res_stack_]
             if res_stack is None:
@@ -51,3 +63,29 @@ class MultiControlNetManager:
             else:
                 res_stack = [i + j for i, j in zip(res_stack, res_stack_)]
         return res_stack
+
+
+class FluxMultiControlNetManager(MultiControlNetManager):
+    def __init__(self, controlnet_units=[]):
+        super().__init__(controlnet_units=controlnet_units)
+
+    def process_image(self, image, processor_id=None):
+        if processor_id is None:
+            processed_image = [processor(image) for processor in self.processors]
+        else:
+            processed_image = [self.processors[processor_id](image)]
+        return processed_image
+
+    def __call__(self, conditionings, **kwargs):
+        res_stack, single_res_stack = None, None
+        for processor, conditioning, model, scale in zip(self.processors, conditionings, self.models, self.scales):
+            res_stack_, single_res_stack_ = model(controlnet_conditioning=conditioning, processor_id=processor.processor_id, **kwargs)
+            res_stack_ = [res * scale for res in res_stack_]
+            single_res_stack_ = [res * scale for res in single_res_stack_]
+            if res_stack is None:
+                res_stack = res_stack_
+                single_res_stack = single_res_stack_
+            else:
+                res_stack = [i + j for i, j in zip(res_stack, res_stack_)]
+                single_res_stack = [i + j for i, j in zip(single_res_stack, single_res_stack_)]
+        return res_stack, single_res_stack

diffsynth/controlnets/processors.py

@@ -3,16 +3,17 @@ import warnings
 with warnings.catch_warnings():
     warnings.simplefilter("ignore")
     from controlnet_aux.processor import (
-        CannyDetector, MidasDetector, HEDdetector, LineartDetector, LineartAnimeDetector, OpenposeDetector
+        CannyDetector, MidasDetector, HEDdetector, LineartDetector, LineartAnimeDetector, OpenposeDetector, NormalBaeDetector
     )
 
 
 Processor_id: TypeAlias = Literal[
-    "canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "tile"
+    "canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "normal", "tile", "none", "inpaint"
 ]
 
 class Annotator:
-    def __init__(self, processor_id: Processor_id, model_path="models/Annotators", detect_resolution=None, device='cuda'):
+    def __init__(self, processor_id: Processor_id, model_path="models/Annotators", detect_resolution=None, device='cuda', skip_processor=False):
+        if not skip_processor:
             if processor_id == "canny":
                 self.processor = CannyDetector()
             elif processor_id == "depth":
@@ -25,15 +26,24 @@ class Annotator:
                 self.processor = LineartAnimeDetector.from_pretrained(model_path).to(device)
             elif processor_id == "openpose":
                 self.processor = OpenposeDetector.from_pretrained(model_path).to(device)
-        elif processor_id == "tile":
+            elif processor_id == "normal":
+                self.processor = NormalBaeDetector.from_pretrained(model_path).to(device)
+            elif processor_id == "tile" or processor_id == "none" or processor_id == "inpaint":
                 self.processor = None
             else:
                 raise ValueError(f"Unsupported processor_id: {processor_id}")
+        else:
+            self.processor = None
 
         self.processor_id = processor_id
         self.detect_resolution = detect_resolution
     
-    def __call__(self, image):
+    def to(self,device):
+        if hasattr(self.processor,"model") and hasattr(self.processor.model,"to"):
+
+            self.processor.model.to(device)
+
+    def __call__(self, image, mask=None):
         width, height = image.size
         if self.processor_id == "openpose":
             kwargs = {

diffsynth/data/simple_text_image.py

@@ -1,4 +1,4 @@
-import torch, os
+import torch, os, torchvision
 from torchvision import transforms
 import pandas as pd
 from PIL import Image
@@ -11,9 +11,10 @@ class TextImageDataset(torch.utils.data.Dataset):
         metadata = pd.read_csv(os.path.join(dataset_path, "train/metadata.csv"))
         self.path = [os.path.join(dataset_path, "train", file_name) for file_name in metadata["file_name"]]
         self.text = metadata["text"].to_list()
+        self.height = height
+        self.width = width
         self.image_processor = transforms.Compose(
             [
-                transforms.Resize(max(height, width), interpolation=transforms.InterpolationMode.BILINEAR),
                 transforms.CenterCrop((height, width)) if center_crop else transforms.RandomCrop((height, width)),
                 transforms.RandomHorizontalFlip() if random_flip else transforms.Lambda(lambda x: x),
                 transforms.ToTensor(),
@@ -27,6 +28,11 @@ class TextImageDataset(torch.utils.data.Dataset):
         data_id = (data_id + index) % len(self.path) # For fixed seed.
         text = self.text[data_id]
         image = Image.open(self.path[data_id]).convert("RGB")
+        target_height, target_width = self.height, self.width
+        width, height = image.size
+        scale = max(target_width / width, target_height / height)
+        shape = [round(height*scale),round(width*scale)]
+        image = torchvision.transforms.functional.resize(image,shape,interpolation=transforms.InterpolationMode.BILINEAR)
         image = self.image_processor(image)
         return {"text": text, "image": image}
 

diffsynth/data/video.py

@@ -135,8 +135,8 @@ class VideoData:
             frame.save(os.path.join(folder, f"{i}.png"))
 
 
-def save_video(frames, save_path, fps, quality=9):
+def save_video(frames, save_path, fps, quality=9, ffmpeg_params=None):
-    writer = imageio.get_writer(save_path, fps=fps, quality=quality)
+    writer = imageio.get_writer(save_path, fps=fps, quality=quality, ffmpeg_params=ffmpeg_params)
     for frame in tqdm(frames, desc="Saving video"):
         frame = np.array(frame)
         writer.append_data(frame)

diffsynth/extensions/ESRGAN/__init__.py

@@ -41,7 +41,7 @@ class RRDB(torch.nn.Module):
 
 class RRDBNet(torch.nn.Module):
 
-    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32):
+    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, **kwargs):
         super(RRDBNet, self).__init__()
         self.conv_first = torch.nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
         self.body = torch.torch.nn.Sequential(*[RRDB(num_feat=num_feat, num_grow_ch=num_grow_ch) for _ in range(num_block)])
@@ -66,6 +66,21 @@ class RRDBNet(torch.nn.Module):
         out = self.conv_last(self.lrelu(self.conv_hr(feat)))
         return out
     
+    @staticmethod
+    def state_dict_converter():
+        return RRDBNetStateDictConverter()
+    
+
+class RRDBNetStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict, {"upcast_to_float32": True}
+    
+    def from_civitai(self, state_dict):
+        return state_dict, {"upcast_to_float32": True}
+
 
 class ESRGAN(torch.nn.Module):
     def __init__(self, model):
@@ -73,12 +88,8 @@ class ESRGAN(torch.nn.Module):
         self.model = model
 
     @staticmethod
-    def from_pretrained(model_path):
+    def from_model_manager(model_manager):
-        model = RRDBNet()
+        return ESRGAN(model_manager.fetch_model("esrgan"))
-        state_dict = torch.load(model_path, map_location="cpu")["params_ema"]
-        model.load_state_dict(state_dict)
-        model.eval()
-        return ESRGAN(model)
 
     def process_image(self, image):
         image = torch.Tensor(np.array(image, dtype=np.float32) / 255).permute(2, 0, 1)
@@ -96,6 +107,12 @@ class ESRGAN(torch.nn.Module):
     
     @torch.no_grad()
     def upscale(self, images, batch_size=4, progress_bar=lambda x:x):
+        if not isinstance(images, list):
+            images = [images]
+            is_single_image = True
+        else:
+            is_single_image = False
+
         # Preprocess
         input_tensor = self.process_images(images)
 
@@ -115,4 +132,6 @@ class ESRGAN(torch.nn.Module):
 
         # To images
         output_images = self.decode_images(output_tensor)
+        if is_single_image:
+            output_images = output_images[0]
         return output_images

diffsynth/extensions/RIFE/__init__.py

@@ -58,7 +58,7 @@ class IFBlock(nn.Module):
 
 
 class IFNet(nn.Module):
-    def __init__(self):
+    def __init__(self, **kwargs):
         super(IFNet, self).__init__()
         self.block0 = IFBlock(7+4, c=90)
         self.block1 = IFBlock(7+4, c=90)
@@ -113,7 +113,7 @@ class IFNetStateDictConverter:
         return state_dict_
     
     def from_civitai(self, state_dict):
-        return self.from_diffusers(state_dict)
+        return self.from_diffusers(state_dict), {"upcast_to_float32": True}
 
 
 class RIFEInterpolater:
@@ -125,7 +125,7 @@ class RIFEInterpolater:
 
     @staticmethod
     def from_model_manager(model_manager):
-        return RIFEInterpolater(model_manager.RIFE, device=model_manager.device)
+        return RIFEInterpolater(model_manager.fetch_model("rife"), device=model_manager.device)
 
     def process_image(self, image):
         width, height = image.size
@@ -203,7 +203,7 @@ class RIFESmoother(RIFEInterpolater):
 
     @staticmethod
     def from_model_manager(model_manager):
-        return RIFESmoother(model_manager.RIFE, device=model_manager.device)
+        return RIFEInterpolater(model_manager.fetch_model("rife"), device=model_manager.device)
     
     def process_tensors(self, input_tensor, scale=1.0, batch_size=4):
         output_tensor = []

diffsynth/models/cog_dit.py

@@ -0,0 +1,408 @@
+import torch
+from einops import rearrange, repeat
+from .sd3_dit import TimestepEmbeddings
+from .attention import Attention
+from .utils import load_state_dict_from_folder
+from .tiler import TileWorker2Dto3D
+import numpy as np
+
+
+
+class CogPatchify(torch.nn.Module):
+    def __init__(self, dim_in, dim_out, patch_size) -> None:
+        super().__init__()
+        self.proj = torch.nn.Conv3d(dim_in, dim_out, kernel_size=(1, patch_size, patch_size), stride=(1, patch_size, patch_size))
+
+    def forward(self, hidden_states):
+        hidden_states = self.proj(hidden_states)
+        hidden_states = rearrange(hidden_states, "B C T H W -> B (T H W) C")
+        return hidden_states
+    
+
+
+class CogAdaLayerNorm(torch.nn.Module):
+    def __init__(self, dim, dim_cond, single=False):
+        super().__init__()
+        self.single = single
+        self.linear = torch.nn.Linear(dim_cond, dim * (2 if single else 6))
+        self.norm = torch.nn.LayerNorm(dim, elementwise_affine=True, eps=1e-5)
+
+
+    def forward(self, hidden_states, prompt_emb, emb):
+        emb = self.linear(torch.nn.functional.silu(emb))
+        if self.single:
+            shift, scale = emb.unsqueeze(1).chunk(2, dim=2)
+            hidden_states = self.norm(hidden_states) * (1 + scale) + shift
+            return hidden_states
+        else:
+            shift_a, scale_a, gate_a, shift_b, scale_b, gate_b = emb.unsqueeze(1).chunk(6, dim=2)
+            hidden_states = self.norm(hidden_states) * (1 + scale_a) + shift_a
+            prompt_emb = self.norm(prompt_emb) * (1 + scale_b) + shift_b
+            return hidden_states, prompt_emb, gate_a, gate_b
+
+
+
+class CogDiTBlock(torch.nn.Module):
+    def __init__(self, dim, dim_cond, num_heads):
+        super().__init__()
+        self.norm1 = CogAdaLayerNorm(dim, dim_cond)
+        self.attn1 = Attention(q_dim=dim, num_heads=48, head_dim=dim//num_heads, bias_q=True, bias_kv=True, bias_out=True)
+        self.norm_q = torch.nn.LayerNorm((dim//num_heads,), eps=1e-06, elementwise_affine=True)
+        self.norm_k = torch.nn.LayerNorm((dim//num_heads,), eps=1e-06, elementwise_affine=True)
+
+        self.norm2 = CogAdaLayerNorm(dim, dim_cond)
+        self.ff = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim*4),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(dim*4, dim)
+        )
+    
+
+    def apply_rotary_emb(self, x, freqs_cis):
+        cos, sin = freqs_cis  # [S, D]
+        cos = cos[None, None]
+        sin = sin[None, None]
+        cos, sin = cos.to(x.device), sin.to(x.device)
+        x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1)  # [B, S, H, D//2]
+        x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
+        out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)
+        return out
+    
+
+    def process_qkv(self, q, k, v, image_rotary_emb, text_seq_length):
+        q = self.norm_q(q)
+        k = self.norm_k(k)
+        q[:, :, text_seq_length:] = self.apply_rotary_emb(q[:, :, text_seq_length:], image_rotary_emb)
+        k[:, :, text_seq_length:] = self.apply_rotary_emb(k[:, :, text_seq_length:], image_rotary_emb)
+        return q, k, v
+        
+
+    def forward(self, hidden_states, prompt_emb, time_emb, image_rotary_emb):
+        # Attention
+        norm_hidden_states, norm_encoder_hidden_states, gate_a, gate_b = self.norm1(
+            hidden_states, prompt_emb, time_emb
+        )
+        attention_io = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
+        attention_io = self.attn1(
+            attention_io,
+            qkv_preprocessor=lambda q, k, v: self.process_qkv(q, k, v, image_rotary_emb, prompt_emb.shape[1])
+        )
+
+        hidden_states = hidden_states + gate_a * attention_io[:, prompt_emb.shape[1]:]
+        prompt_emb = prompt_emb + gate_b * attention_io[:, :prompt_emb.shape[1]]
+
+        # Feed forward
+        norm_hidden_states, norm_encoder_hidden_states, gate_a, gate_b = self.norm2(
+            hidden_states, prompt_emb, time_emb
+        )
+        ff_io = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
+        ff_io = self.ff(ff_io)
+
+        hidden_states = hidden_states + gate_a * ff_io[:, prompt_emb.shape[1]:]
+        prompt_emb = prompt_emb + gate_b * ff_io[:, :prompt_emb.shape[1]]
+
+        return hidden_states, prompt_emb
+
+
+
+class CogDiT(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.patchify = CogPatchify(16, 3072, 2)
+        self.time_embedder = TimestepEmbeddings(3072, 512)
+        self.context_embedder = torch.nn.Linear(4096, 3072)
+        self.blocks = torch.nn.ModuleList([CogDiTBlock(3072, 512, 48) for _ in range(42)])
+        self.norm_final = torch.nn.LayerNorm((3072,), eps=1e-05, elementwise_affine=True)
+        self.norm_out = CogAdaLayerNorm(3072, 512, single=True)
+        self.proj_out = torch.nn.Linear(3072, 64, bias=True)
+
+
+    def get_resize_crop_region_for_grid(self, src, tgt_width, tgt_height):
+        tw = tgt_width
+        th = tgt_height
+        h, w = src
+        r = h / w
+        if r > (th / tw):
+            resize_height = th
+            resize_width = int(round(th / h * w))
+        else:
+            resize_width = tw
+            resize_height = int(round(tw / w * h))
+
+        crop_top = int(round((th - resize_height) / 2.0))
+        crop_left = int(round((tw - resize_width) / 2.0))
+
+        return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
+    
+
+    def get_3d_rotary_pos_embed(
+        self, embed_dim, crops_coords, grid_size, temporal_size, theta: int = 10000, use_real: bool = True
+    ):
+        start, stop = crops_coords
+        grid_h = np.linspace(start[0], stop[0], grid_size[0], endpoint=False, dtype=np.float32)
+        grid_w = np.linspace(start[1], stop[1], grid_size[1], endpoint=False, dtype=np.float32)
+        grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32)
+
+        # Compute dimensions for each axis
+        dim_t = embed_dim // 4
+        dim_h = embed_dim // 8 * 3
+        dim_w = embed_dim // 8 * 3
+
+        # Temporal frequencies
+        freqs_t = 1.0 / (theta ** (torch.arange(0, dim_t, 2).float() / dim_t))
+        grid_t = torch.from_numpy(grid_t).float()
+        freqs_t = torch.einsum("n , f -> n f", grid_t, freqs_t)
+        freqs_t = freqs_t.repeat_interleave(2, dim=-1)
+
+        # Spatial frequencies for height and width
+        freqs_h = 1.0 / (theta ** (torch.arange(0, dim_h, 2).float() / dim_h))
+        freqs_w = 1.0 / (theta ** (torch.arange(0, dim_w, 2).float() / dim_w))
+        grid_h = torch.from_numpy(grid_h).float()
+        grid_w = torch.from_numpy(grid_w).float()
+        freqs_h = torch.einsum("n , f -> n f", grid_h, freqs_h)
+        freqs_w = torch.einsum("n , f -> n f", grid_w, freqs_w)
+        freqs_h = freqs_h.repeat_interleave(2, dim=-1)
+        freqs_w = freqs_w.repeat_interleave(2, dim=-1)
+
+        # Broadcast and concatenate tensors along specified dimension
+        def broadcast(tensors, dim=-1):
+            num_tensors = len(tensors)
+            shape_lens = {len(t.shape) for t in tensors}
+            assert len(shape_lens) == 1, "tensors must all have the same number of dimensions"
+            shape_len = list(shape_lens)[0]
+            dim = (dim + shape_len) if dim < 0 else dim
+            dims = list(zip(*(list(t.shape) for t in tensors)))
+            expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim]
+            assert all(
+                [*(len(set(t[1])) <= 2 for t in expandable_dims)]
+            ), "invalid dimensions for broadcastable concatenation"
+            max_dims = [(t[0], max(t[1])) for t in expandable_dims]
+            expanded_dims = [(t[0], (t[1],) * num_tensors) for t in max_dims]
+            expanded_dims.insert(dim, (dim, dims[dim]))
+            expandable_shapes = list(zip(*(t[1] for t in expanded_dims)))
+            tensors = [t[0].expand(*t[1]) for t in zip(tensors, expandable_shapes)]
+            return torch.cat(tensors, dim=dim)
+
+        freqs = broadcast((freqs_t[:, None, None, :], freqs_h[None, :, None, :], freqs_w[None, None, :, :]), dim=-1)
+
+        t, h, w, d = freqs.shape
+        freqs = freqs.view(t * h * w, d)
+
+        # Generate sine and cosine components
+        sin = freqs.sin()
+        cos = freqs.cos()
+
+        if use_real:
+            return cos, sin
+        else:
+            freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+            return freqs_cis
+    
+
+    def prepare_rotary_positional_embeddings(
+        self,
+        height: int,
+        width: int,
+        num_frames: int,
+        device: torch.device,
+    ):
+        grid_height = height // 2
+        grid_width = width // 2
+        base_size_width = 720 // (8 * 2)
+        base_size_height = 480 // (8 * 2)
+
+        grid_crops_coords = self.get_resize_crop_region_for_grid(
+            (grid_height, grid_width), base_size_width, base_size_height
+        )
+        freqs_cos, freqs_sin = self.get_3d_rotary_pos_embed(
+            embed_dim=64,
+            crops_coords=grid_crops_coords,
+            grid_size=(grid_height, grid_width),
+            temporal_size=num_frames,
+            use_real=True,
+        )
+
+        freqs_cos = freqs_cos.to(device=device)
+        freqs_sin = freqs_sin.to(device=device)
+        return freqs_cos, freqs_sin
+
+
+    def unpatchify(self, hidden_states, height, width):
+        hidden_states = rearrange(hidden_states, "B (T H W) (C P Q) -> B C T (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2)
+        return hidden_states
+    
+
+    def build_mask(self, T, H, W, dtype, device, is_bound):
+        t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W)
+        h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W)
+        w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W)
+        border_width = (H + W) // 4
+        pad = torch.ones_like(h) * border_width
+        mask = torch.stack([
+            pad if is_bound[0] else t + 1,
+            pad if is_bound[1] else T - t,
+            pad if is_bound[2] else h + 1,
+            pad if is_bound[3] else H - h,
+            pad if is_bound[4] else w + 1,
+            pad if is_bound[5] else W - w
+        ]).min(dim=0).values
+        mask = mask.clip(1, border_width)
+        mask = (mask / border_width).to(dtype=dtype, device=device)
+        mask = rearrange(mask, "T H W -> 1 1 T H W")
+        return mask
+    
+
+    def tiled_forward(self, hidden_states, timestep, prompt_emb, tile_size=(60, 90), tile_stride=(30, 45)):
+        B, C, T, H, W = hidden_states.shape
+        value = torch.zeros((B, C, T, H, W), dtype=hidden_states.dtype, device=hidden_states.device)
+        weight = torch.zeros((B, C, T, H, W), dtype=hidden_states.dtype, device=hidden_states.device)
+
+        # Split tasks
+        tasks = []
+        for h in range(0, H, tile_stride):
+            for w in range(0, W, tile_stride):
+                if (h-tile_stride >= 0 and h-tile_stride+tile_size >= H) or (w-tile_stride >= 0 and w-tile_stride+tile_size >= W):
+                    continue
+                h_, w_ = h + tile_size, w + tile_size
+                if h_ > H: h, h_ = max(H - tile_size, 0), H
+                if w_ > W: w, w_ = max(W - tile_size, 0), W
+                tasks.append((h, h_, w, w_))
+
+        # Run
+        for hl, hr, wl, wr in tasks:
+            mask = self.build_mask(
+                value.shape[2], (hr-hl), (wr-wl),
+                hidden_states.dtype, hidden_states.device,
+                is_bound=(True, True, hl==0, hr>=H, wl==0, wr>=W)
+            )
+            model_output = self.forward(hidden_states[:, :, :, hl:hr, wl:wr], timestep, prompt_emb)
+            value[:, :, :, hl:hr, wl:wr] += model_output * mask
+            weight[:, :, :, hl:hr, wl:wr] += mask
+        value = value / weight
+
+        return value
+
+
+    def forward(self, hidden_states, timestep, prompt_emb, image_rotary_emb=None, tiled=False, tile_size=90, tile_stride=30, use_gradient_checkpointing=False):
+        if tiled:
+            return TileWorker2Dto3D().tiled_forward(
+                forward_fn=lambda x: self.forward(x, timestep, prompt_emb),
+                model_input=hidden_states,
+                tile_size=tile_size, tile_stride=tile_stride,
+                tile_device=hidden_states.device, tile_dtype=hidden_states.dtype,
+                computation_device=self.context_embedder.weight.device, computation_dtype=self.context_embedder.weight.dtype
+            )
+        num_frames, height, width = hidden_states.shape[-3:]
+        if image_rotary_emb is None:
+            image_rotary_emb = self.prepare_rotary_positional_embeddings(height, width, num_frames, device=self.context_embedder.weight.device)
+        hidden_states = self.patchify(hidden_states)
+        time_emb = self.time_embedder(timestep, dtype=hidden_states.dtype)
+        prompt_emb = self.context_embedder(prompt_emb)
+
+        def create_custom_forward(module):
+            def custom_forward(*inputs):
+                return module(*inputs)
+            return custom_forward
+        
+        for block in self.blocks:
+            if self.training and use_gradient_checkpointing:
+                hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states, prompt_emb, time_emb, image_rotary_emb,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states, prompt_emb = block(hidden_states, prompt_emb, time_emb, image_rotary_emb)
+
+        hidden_states = torch.cat([prompt_emb, hidden_states], dim=1)
+        hidden_states = self.norm_final(hidden_states)
+        hidden_states = hidden_states[:, prompt_emb.shape[1]:]
+        hidden_states = self.norm_out(hidden_states, prompt_emb, time_emb)
+        hidden_states = self.proj_out(hidden_states)
+        hidden_states = self.unpatchify(hidden_states, height, width)
+
+        return hidden_states
+    
+
+    @staticmethod
+    def state_dict_converter():
+        return CogDiTStateDictConverter()
+    
+
+    @staticmethod
+    def from_pretrained(file_path, torch_dtype=torch.bfloat16):
+        model = CogDiT().to(torch_dtype)
+        state_dict = load_state_dict_from_folder(file_path, torch_dtype=torch_dtype)
+        state_dict = CogDiT.state_dict_converter().from_diffusers(state_dict)
+        model.load_state_dict(state_dict)
+        return model
+
+
+
+class CogDiTStateDictConverter:
+    def __init__(self):
+        pass
+
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "patch_embed.proj.weight": "patchify.proj.weight",
+            "patch_embed.proj.bias": "patchify.proj.bias",
+            "patch_embed.text_proj.weight": "context_embedder.weight",
+            "patch_embed.text_proj.bias": "context_embedder.bias",
+            "time_embedding.linear_1.weight": "time_embedder.timestep_embedder.0.weight",
+            "time_embedding.linear_1.bias": "time_embedder.timestep_embedder.0.bias",
+            "time_embedding.linear_2.weight": "time_embedder.timestep_embedder.2.weight",
+            "time_embedding.linear_2.bias": "time_embedder.timestep_embedder.2.bias",
+
+            "norm_final.weight": "norm_final.weight",
+            "norm_final.bias": "norm_final.bias",
+            "norm_out.linear.weight": "norm_out.linear.weight",
+            "norm_out.linear.bias": "norm_out.linear.bias",
+            "norm_out.norm.weight": "norm_out.norm.weight",
+            "norm_out.norm.bias": "norm_out.norm.bias",
+            "proj_out.weight": "proj_out.weight",
+            "proj_out.bias": "proj_out.bias",
+        }
+        suffix_dict = {
+            "norm1.linear.weight": "norm1.linear.weight",
+            "norm1.linear.bias": "norm1.linear.bias",
+            "norm1.norm.weight": "norm1.norm.weight",
+            "norm1.norm.bias": "norm1.norm.bias",
+            "attn1.norm_q.weight": "norm_q.weight",
+            "attn1.norm_q.bias": "norm_q.bias",
+            "attn1.norm_k.weight": "norm_k.weight",
+            "attn1.norm_k.bias": "norm_k.bias",
+            "attn1.to_q.weight": "attn1.to_q.weight",
+            "attn1.to_q.bias": "attn1.to_q.bias",
+            "attn1.to_k.weight": "attn1.to_k.weight",
+            "attn1.to_k.bias": "attn1.to_k.bias",
+            "attn1.to_v.weight": "attn1.to_v.weight",
+            "attn1.to_v.bias": "attn1.to_v.bias",
+            "attn1.to_out.0.weight": "attn1.to_out.weight",
+            "attn1.to_out.0.bias": "attn1.to_out.bias",
+            "norm2.linear.weight": "norm2.linear.weight",
+            "norm2.linear.bias": "norm2.linear.bias",
+            "norm2.norm.weight": "norm2.norm.weight",
+            "norm2.norm.bias": "norm2.norm.bias",
+            "ff.net.0.proj.weight": "ff.0.weight",
+            "ff.net.0.proj.bias": "ff.0.bias",
+            "ff.net.2.weight": "ff.2.weight",
+            "ff.net.2.bias": "ff.2.bias",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name in rename_dict:
+                if name == "patch_embed.proj.weight":
+                    param = param.unsqueeze(2)
+                state_dict_[rename_dict[name]] = param
+            else:
+                names = name.split(".")
+                if names[0] == "transformer_blocks":
+                    suffix = ".".join(names[2:])
+                    state_dict_[f"blocks.{names[1]}." + suffix_dict[suffix]] = param
+        return state_dict_
+    
+
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)

diffsynth/models/cog_vae.py

@@ -0,0 +1,518 @@
+import torch
+from einops import rearrange, repeat
+from .tiler import TileWorker2Dto3D
+
+
+
+class Downsample3D(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int = 3,
+        stride: int = 2,
+        padding: int = 0,
+        compress_time: bool = False,
+    ):
+        super().__init__()
+
+        self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
+        self.compress_time = compress_time
+
+    def forward(self, x: torch.Tensor, xq: torch.Tensor) -> torch.Tensor:
+        if self.compress_time:
+            batch_size, channels, frames, height, width = x.shape
+
+            # (batch_size, channels, frames, height, width) -> (batch_size, height, width, channels, frames) -> (batch_size * height * width, channels, frames)
+            x = x.permute(0, 3, 4, 1, 2).reshape(batch_size * height * width, channels, frames)
+
+            if x.shape[-1] % 2 == 1:
+                x_first, x_rest = x[..., 0], x[..., 1:]
+                if x_rest.shape[-1] > 0:
+                    # (batch_size * height * width, channels, frames - 1) -> (batch_size * height * width, channels, (frames - 1) // 2)
+                    x_rest = torch.nn.functional.avg_pool1d(x_rest, kernel_size=2, stride=2)
+
+                x = torch.cat([x_first[..., None], x_rest], dim=-1)
+                # (batch_size * height * width, channels, (frames // 2) + 1) -> (batch_size, height, width, channels, (frames // 2) + 1) -> (batch_size, channels, (frames // 2) + 1, height, width)
+                x = x.reshape(batch_size, height, width, channels, x.shape[-1]).permute(0, 3, 4, 1, 2)
+            else:
+                # (batch_size * height * width, channels, frames) -> (batch_size * height * width, channels, frames // 2)
+                x = torch.nn.functional.avg_pool1d(x, kernel_size=2, stride=2)
+                # (batch_size * height * width, channels, frames // 2) -> (batch_size, height, width, channels, frames // 2) -> (batch_size, channels, frames // 2, height, width)
+                x = x.reshape(batch_size, height, width, channels, x.shape[-1]).permute(0, 3, 4, 1, 2)
+
+        # Pad the tensor
+        pad = (0, 1, 0, 1)
+        x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+        batch_size, channels, frames, height, width = x.shape
+        # (batch_size, channels, frames, height, width) -> (batch_size, frames, channels, height, width) -> (batch_size * frames, channels, height, width)
+        x = x.permute(0, 2, 1, 3, 4).reshape(batch_size * frames, channels, height, width)
+        x = self.conv(x)
+        # (batch_size * frames, channels, height, width) -> (batch_size, frames, channels, height, width) -> (batch_size, channels, frames, height, width)
+        x = x.reshape(batch_size, frames, x.shape[1], x.shape[2], x.shape[3]).permute(0, 2, 1, 3, 4)
+        return x
+
+
+
+class Upsample3D(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int = 3,
+        stride: int = 1,
+        padding: int = 1,
+        compress_time: bool = False,
+    ) -> None:
+        super().__init__()
+        self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
+        self.compress_time = compress_time
+
+    def forward(self, inputs: torch.Tensor, xq: torch.Tensor) -> torch.Tensor:
+        if self.compress_time:
+            if inputs.shape[2] > 1 and inputs.shape[2] % 2 == 1:
+                # split first frame
+                x_first, x_rest = inputs[:, :, 0], inputs[:, :, 1:]
+
+                x_first = torch.nn.functional.interpolate(x_first, scale_factor=2.0)
+                x_rest = torch.nn.functional.interpolate(x_rest, scale_factor=2.0)
+                x_first = x_first[:, :, None, :, :]
+                inputs = torch.cat([x_first, x_rest], dim=2)
+            elif inputs.shape[2] > 1:
+                inputs = torch.nn.functional.interpolate(inputs, scale_factor=2.0)
+            else:
+                inputs = inputs.squeeze(2)
+                inputs = torch.nn.functional.interpolate(inputs, scale_factor=2.0)
+                inputs = inputs[:, :, None, :, :]
+        else:
+            # only interpolate 2D
+            b, c, t, h, w = inputs.shape
+            inputs = inputs.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
+            inputs = torch.nn.functional.interpolate(inputs, scale_factor=2.0)
+            inputs = inputs.reshape(b, t, c, *inputs.shape[2:]).permute(0, 2, 1, 3, 4)
+
+        b, c, t, h, w = inputs.shape
+        inputs = inputs.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
+        inputs = self.conv(inputs)
+        inputs = inputs.reshape(b, t, *inputs.shape[1:]).permute(0, 2, 1, 3, 4)
+
+        return inputs
+
+
+
+class CogVideoXSpatialNorm3D(torch.nn.Module):
+    def __init__(self, f_channels, zq_channels, groups):
+        super().__init__()
+        self.norm_layer = torch.nn.GroupNorm(num_channels=f_channels, num_groups=groups, eps=1e-6, affine=True)
+        self.conv_y = torch.nn.Conv3d(zq_channels, f_channels, kernel_size=1, stride=1)
+        self.conv_b = torch.nn.Conv3d(zq_channels, f_channels, kernel_size=1, stride=1)
+
+
+    def forward(self, f: torch.Tensor, zq: torch.Tensor) -> torch.Tensor:
+        if f.shape[2] > 1 and f.shape[2] % 2 == 1:
+            f_first, f_rest = f[:, :, :1], f[:, :, 1:]
+            f_first_size, f_rest_size = f_first.shape[-3:], f_rest.shape[-3:]
+            z_first, z_rest = zq[:, :, :1], zq[:, :, 1:]
+            z_first = torch.nn.functional.interpolate(z_first, size=f_first_size)
+            z_rest = torch.nn.functional.interpolate(z_rest, size=f_rest_size)
+            zq = torch.cat([z_first, z_rest], dim=2)
+        else:
+            zq = torch.nn.functional.interpolate(zq, size=f.shape[-3:])
+
+        norm_f = self.norm_layer(f)
+        new_f = norm_f * self.conv_y(zq) + self.conv_b(zq)
+        return new_f
+
+
+
+class Resnet3DBlock(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, spatial_norm_dim, groups, eps=1e-6, use_conv_shortcut=False):
+        super().__init__()
+        self.nonlinearity = torch.nn.SiLU()
+        if spatial_norm_dim is None:
+            self.norm1 = torch.nn.GroupNorm(num_channels=in_channels, num_groups=groups, eps=eps)
+            self.norm2 = torch.nn.GroupNorm(num_channels=out_channels, num_groups=groups, eps=eps)
+        else:
+            self.norm1 = CogVideoXSpatialNorm3D(in_channels, spatial_norm_dim, groups)
+            self.norm2 = CogVideoXSpatialNorm3D(out_channels, spatial_norm_dim, groups)
+
+        self.conv1 = CachedConv3d(in_channels, out_channels, kernel_size=3, padding=(0, 1, 1))
+
+        self.conv2 = CachedConv3d(out_channels, out_channels, kernel_size=3, padding=(0, 1, 1))
+
+        if in_channels != out_channels:
+            if use_conv_shortcut:
+                self.conv_shortcut = CachedConv3d(in_channels, out_channels, kernel_size=3, padding=(0, 1, 1))
+            else:
+                self.conv_shortcut = torch.nn.Conv3d(in_channels, out_channels, kernel_size=1)
+        else:
+            self.conv_shortcut = lambda x: x
+
+
+    def forward(self, hidden_states, zq):
+        residual = hidden_states
+
+        hidden_states = self.norm1(hidden_states, zq) if isinstance(self.norm1, CogVideoXSpatialNorm3D) else self.norm1(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.conv1(hidden_states)
+
+        hidden_states = self.norm2(hidden_states, zq) if isinstance(self.norm2, CogVideoXSpatialNorm3D) else self.norm2(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+
+        hidden_states = hidden_states + self.conv_shortcut(residual)
+
+        return hidden_states
+    
+
+
+class CachedConv3d(torch.nn.Conv3d):
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0):
+        super().__init__(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
+        self.cached_tensor = None
+
+
+    def clear_cache(self):
+        self.cached_tensor = None
+    
+
+    def forward(self, input: torch.Tensor, use_cache = True) -> torch.Tensor:
+        if use_cache:
+            if self.cached_tensor is None:
+                self.cached_tensor = torch.concat([input[:, :, :1]] * 2, dim=2)
+            input = torch.concat([self.cached_tensor, input], dim=2)
+            self.cached_tensor = input[:, :, -2:]
+        return super().forward(input)
+
+
+
+class CogVAEDecoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.7
+        self.conv_in = CachedConv3d(16, 512, kernel_size=3, stride=1, padding=(0, 1, 1))
+
+        self.blocks = torch.nn.ModuleList([
+            Resnet3DBlock(512, 512, 16, 32),
+            Resnet3DBlock(512, 512, 16, 32),
+            Resnet3DBlock(512, 512, 16, 32),
+            Resnet3DBlock(512, 512, 16, 32),
+            Resnet3DBlock(512, 512, 16, 32),
+            Resnet3DBlock(512, 512, 16, 32),
+            Upsample3D(512, 512, compress_time=True),
+            Resnet3DBlock(512, 256, 16, 32),
+            Resnet3DBlock(256, 256, 16, 32),
+            Resnet3DBlock(256, 256, 16, 32),
+            Resnet3DBlock(256, 256, 16, 32),
+            Upsample3D(256, 256, compress_time=True),
+            Resnet3DBlock(256, 256, 16, 32),
+            Resnet3DBlock(256, 256, 16, 32),
+            Resnet3DBlock(256, 256, 16, 32),
+            Resnet3DBlock(256, 256, 16, 32),
+            Upsample3D(256, 256, compress_time=False),
+            Resnet3DBlock(256, 128, 16, 32),
+            Resnet3DBlock(128, 128, 16, 32),
+            Resnet3DBlock(128, 128, 16, 32),
+            Resnet3DBlock(128, 128, 16, 32),
+        ])
+
+        self.norm_out = CogVideoXSpatialNorm3D(128, 16, 32)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = CachedConv3d(128, 3, kernel_size=3, stride=1, padding=(0, 1, 1))
+
+
+    def forward(self, sample):
+        sample = sample / self.scaling_factor
+        hidden_states = self.conv_in(sample)
+
+        for block in self.blocks:
+            hidden_states = block(hidden_states, sample)
+        
+        hidden_states = self.norm_out(hidden_states, sample)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+
+        return hidden_states
+    
+
+    def decode_video(self, sample, tiled=True, tile_size=(60, 90), tile_stride=(30, 45), progress_bar=lambda x:x):
+        if tiled:
+            B, C, T, H, W = sample.shape
+            return TileWorker2Dto3D().tiled_forward(
+                forward_fn=lambda x: self.decode_small_video(x),
+                model_input=sample,
+                tile_size=tile_size, tile_stride=tile_stride,
+                tile_device=sample.device, tile_dtype=sample.dtype,
+                computation_device=sample.device, computation_dtype=sample.dtype,
+                scales=(3/16, (T//2*8+T%2)/T, 8, 8),
+                progress_bar=progress_bar
+            )
+        else:
+            return self.decode_small_video(sample)
+    
+
+    def decode_small_video(self, sample):
+        B, C, T, H, W = sample.shape
+        computation_device = self.conv_in.weight.device
+        computation_dtype = self.conv_in.weight.dtype
+        value = []
+        for i in range(T//2):
+            tl = i*2 + T%2 - (T%2 and i==0)
+            tr = i*2 + 2 + T%2
+            model_input = sample[:, :, tl: tr, :, :].to(dtype=computation_dtype, device=computation_device)
+            model_output = self.forward(model_input).to(dtype=sample.dtype, device=sample.device)
+            value.append(model_output)
+        value = torch.concat(value, dim=2)
+        for name, module in self.named_modules():
+            if isinstance(module, CachedConv3d):
+                module.clear_cache()
+        return value
+    
+
+    @staticmethod
+    def state_dict_converter():
+        return CogVAEDecoderStateDictConverter()
+    
+
+
+class CogVAEEncoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.7
+        self.conv_in = CachedConv3d(3, 128, kernel_size=3, stride=1, padding=(0, 1, 1))
+
+        self.blocks = torch.nn.ModuleList([
+            Resnet3DBlock(128, 128, None, 32),
+            Resnet3DBlock(128, 128, None, 32),
+            Resnet3DBlock(128, 128, None, 32),
+            Downsample3D(128, 128, compress_time=True),
+            Resnet3DBlock(128, 256, None, 32),
+            Resnet3DBlock(256, 256, None, 32),
+            Resnet3DBlock(256, 256, None, 32),
+            Downsample3D(256, 256, compress_time=True),
+            Resnet3DBlock(256, 256, None, 32),
+            Resnet3DBlock(256, 256, None, 32),
+            Resnet3DBlock(256, 256, None, 32),
+            Downsample3D(256, 256, compress_time=False),
+            Resnet3DBlock(256, 512, None, 32),
+            Resnet3DBlock(512, 512, None, 32),
+            Resnet3DBlock(512, 512, None, 32),
+            Resnet3DBlock(512, 512, None, 32),
+            Resnet3DBlock(512, 512, None, 32),
+        ])
+
+        self.norm_out = torch.nn.GroupNorm(32, 512, eps=1e-06, affine=True)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = CachedConv3d(512, 32, kernel_size=3, stride=1, padding=(0, 1, 1))
+
+
+    def forward(self, sample):
+        hidden_states = self.conv_in(sample)
+
+        for block in self.blocks:
+            hidden_states = block(hidden_states, sample)
+        
+        hidden_states = self.norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)[:, :16]
+        hidden_states = hidden_states * self.scaling_factor
+
+        return hidden_states
+    
+
+    def encode_video(self, sample, tiled=True, tile_size=(60, 90), tile_stride=(30, 45), progress_bar=lambda x:x):
+        if tiled:
+            B, C, T, H, W = sample.shape
+            return TileWorker2Dto3D().tiled_forward(
+                forward_fn=lambda x: self.encode_small_video(x),
+                model_input=sample,
+                tile_size=(i * 8 for i in tile_size), tile_stride=(i * 8 for i in tile_stride),
+                tile_device=sample.device, tile_dtype=sample.dtype,
+                computation_device=sample.device, computation_dtype=sample.dtype,
+                scales=(16/3, (T//4+T%2)/T, 1/8, 1/8),
+                progress_bar=progress_bar
+            )
+        else:
+            return self.encode_small_video(sample)
+    
+
+    def encode_small_video(self, sample):
+        B, C, T, H, W = sample.shape
+        computation_device = self.conv_in.weight.device
+        computation_dtype = self.conv_in.weight.dtype
+        value = []
+        for i in range(T//8):
+            t = i*8 + T%2 - (T%2 and i==0)
+            t_ = i*8 + 8 + T%2
+            model_input = sample[:, :, t: t_, :, :].to(dtype=computation_dtype, device=computation_device)
+            model_output = self.forward(model_input).to(dtype=sample.dtype, device=sample.device)
+            value.append(model_output)
+        value = torch.concat(value, dim=2)
+        for name, module in self.named_modules():
+            if isinstance(module, CachedConv3d):
+                module.clear_cache()
+        return value
+    
+
+    @staticmethod
+    def state_dict_converter():
+        return CogVAEEncoderStateDictConverter()
+
+
+
+class CogVAEEncoderStateDictConverter:
+    def __init__(self):
+        pass
+
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "encoder.conv_in.conv.weight": "conv_in.weight",
+            "encoder.conv_in.conv.bias": "conv_in.bias",
+            "encoder.down_blocks.0.downsamplers.0.conv.weight": "blocks.3.conv.weight",
+            "encoder.down_blocks.0.downsamplers.0.conv.bias": "blocks.3.conv.bias",
+            "encoder.down_blocks.1.downsamplers.0.conv.weight": "blocks.7.conv.weight",
+            "encoder.down_blocks.1.downsamplers.0.conv.bias": "blocks.7.conv.bias",
+            "encoder.down_blocks.2.downsamplers.0.conv.weight": "blocks.11.conv.weight",
+            "encoder.down_blocks.2.downsamplers.0.conv.bias": "blocks.11.conv.bias",
+            "encoder.norm_out.weight": "norm_out.weight",
+            "encoder.norm_out.bias": "norm_out.bias",
+            "encoder.conv_out.conv.weight": "conv_out.weight",
+            "encoder.conv_out.conv.bias": "conv_out.bias",
+        }
+        prefix_dict = {
+            "encoder.down_blocks.0.resnets.0.": "blocks.0.",
+            "encoder.down_blocks.0.resnets.1.": "blocks.1.",
+            "encoder.down_blocks.0.resnets.2.": "blocks.2.",
+            "encoder.down_blocks.1.resnets.0.": "blocks.4.",
+            "encoder.down_blocks.1.resnets.1.": "blocks.5.",
+            "encoder.down_blocks.1.resnets.2.": "blocks.6.",
+            "encoder.down_blocks.2.resnets.0.": "blocks.8.",
+            "encoder.down_blocks.2.resnets.1.": "blocks.9.",
+            "encoder.down_blocks.2.resnets.2.": "blocks.10.",
+            "encoder.down_blocks.3.resnets.0.": "blocks.12.",
+            "encoder.down_blocks.3.resnets.1.": "blocks.13.",
+            "encoder.down_blocks.3.resnets.2.": "blocks.14.",
+            "encoder.mid_block.resnets.0.": "blocks.15.",
+            "encoder.mid_block.resnets.1.": "blocks.16.",
+        }
+        suffix_dict = {
+            "norm1.norm_layer.weight": "norm1.norm_layer.weight",
+            "norm1.norm_layer.bias": "norm1.norm_layer.bias",
+            "norm1.conv_y.conv.weight": "norm1.conv_y.weight",
+            "norm1.conv_y.conv.bias": "norm1.conv_y.bias",
+            "norm1.conv_b.conv.weight": "norm1.conv_b.weight",
+            "norm1.conv_b.conv.bias": "norm1.conv_b.bias",
+            "norm2.norm_layer.weight": "norm2.norm_layer.weight",
+            "norm2.norm_layer.bias": "norm2.norm_layer.bias",
+            "norm2.conv_y.conv.weight": "norm2.conv_y.weight",
+            "norm2.conv_y.conv.bias": "norm2.conv_y.bias",
+            "norm2.conv_b.conv.weight": "norm2.conv_b.weight",
+            "norm2.conv_b.conv.bias": "norm2.conv_b.bias",
+            "conv1.conv.weight": "conv1.weight",
+            "conv1.conv.bias": "conv1.bias",
+            "conv2.conv.weight": "conv2.weight",
+            "conv2.conv.bias": "conv2.bias",
+            "conv_shortcut.weight": "conv_shortcut.weight",
+            "conv_shortcut.bias": "conv_shortcut.bias",
+            "norm1.weight": "norm1.weight",
+            "norm1.bias": "norm1.bias",
+            "norm2.weight": "norm2.weight",
+            "norm2.bias": "norm2.bias",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name in rename_dict:
+                state_dict_[rename_dict[name]] = param
+            else:
+                for prefix in prefix_dict:
+                    if name.startswith(prefix):
+                        suffix = name[len(prefix):]
+                        state_dict_[prefix_dict[prefix] + suffix_dict[suffix]] = param
+        return state_dict_
+    
+
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
+
+
+
+class CogVAEDecoderStateDictConverter:
+    def __init__(self):
+        pass
+
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "decoder.conv_in.conv.weight": "conv_in.weight",
+            "decoder.conv_in.conv.bias": "conv_in.bias",
+            "decoder.up_blocks.0.upsamplers.0.conv.weight": "blocks.6.conv.weight",
+            "decoder.up_blocks.0.upsamplers.0.conv.bias": "blocks.6.conv.bias",
+            "decoder.up_blocks.1.upsamplers.0.conv.weight": "blocks.11.conv.weight",
+            "decoder.up_blocks.1.upsamplers.0.conv.bias": "blocks.11.conv.bias",
+            "decoder.up_blocks.2.upsamplers.0.conv.weight": "blocks.16.conv.weight",
+            "decoder.up_blocks.2.upsamplers.0.conv.bias": "blocks.16.conv.bias",
+            "decoder.norm_out.norm_layer.weight": "norm_out.norm_layer.weight",
+            "decoder.norm_out.norm_layer.bias": "norm_out.norm_layer.bias",
+            "decoder.norm_out.conv_y.conv.weight": "norm_out.conv_y.weight",
+            "decoder.norm_out.conv_y.conv.bias": "norm_out.conv_y.bias",
+            "decoder.norm_out.conv_b.conv.weight": "norm_out.conv_b.weight",
+            "decoder.norm_out.conv_b.conv.bias": "norm_out.conv_b.bias",
+            "decoder.conv_out.conv.weight": "conv_out.weight",
+            "decoder.conv_out.conv.bias": "conv_out.bias"
+        }
+        prefix_dict = {
+            "decoder.mid_block.resnets.0.": "blocks.0.",
+            "decoder.mid_block.resnets.1.": "blocks.1.",
+            "decoder.up_blocks.0.resnets.0.": "blocks.2.",
+            "decoder.up_blocks.0.resnets.1.": "blocks.3.",
+            "decoder.up_blocks.0.resnets.2.": "blocks.4.",
+            "decoder.up_blocks.0.resnets.3.": "blocks.5.",
+            "decoder.up_blocks.1.resnets.0.": "blocks.7.",
+            "decoder.up_blocks.1.resnets.1.": "blocks.8.",
+            "decoder.up_blocks.1.resnets.2.": "blocks.9.",
+            "decoder.up_blocks.1.resnets.3.": "blocks.10.",
+            "decoder.up_blocks.2.resnets.0.": "blocks.12.",
+            "decoder.up_blocks.2.resnets.1.": "blocks.13.",
+            "decoder.up_blocks.2.resnets.2.": "blocks.14.",
+            "decoder.up_blocks.2.resnets.3.": "blocks.15.",
+            "decoder.up_blocks.3.resnets.0.": "blocks.17.",
+            "decoder.up_blocks.3.resnets.1.": "blocks.18.",
+            "decoder.up_blocks.3.resnets.2.": "blocks.19.",
+            "decoder.up_blocks.3.resnets.3.": "blocks.20.",
+        }
+        suffix_dict = {
+            "norm1.norm_layer.weight": "norm1.norm_layer.weight",
+            "norm1.norm_layer.bias": "norm1.norm_layer.bias",
+            "norm1.conv_y.conv.weight": "norm1.conv_y.weight",
+            "norm1.conv_y.conv.bias": "norm1.conv_y.bias",
+            "norm1.conv_b.conv.weight": "norm1.conv_b.weight",
+            "norm1.conv_b.conv.bias": "norm1.conv_b.bias",
+            "norm2.norm_layer.weight": "norm2.norm_layer.weight",
+            "norm2.norm_layer.bias": "norm2.norm_layer.bias",
+            "norm2.conv_y.conv.weight": "norm2.conv_y.weight",
+            "norm2.conv_y.conv.bias": "norm2.conv_y.bias",
+            "norm2.conv_b.conv.weight": "norm2.conv_b.weight",
+            "norm2.conv_b.conv.bias": "norm2.conv_b.bias",
+            "conv1.conv.weight": "conv1.weight",
+            "conv1.conv.bias": "conv1.bias",
+            "conv2.conv.weight": "conv2.weight",
+            "conv2.conv.bias": "conv2.bias",
+            "conv_shortcut.weight": "conv_shortcut.weight",
+            "conv_shortcut.bias": "conv_shortcut.bias",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name in rename_dict:
+                state_dict_[rename_dict[name]] = param
+            else:
+                for prefix in prefix_dict:
+                    if name.startswith(prefix):
+                        suffix = name[len(prefix):]
+                        state_dict_[prefix_dict[prefix] + suffix_dict[suffix]] = param
+        return state_dict_
+    
+
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
+

diffsynth/models/downloader.py

@@ -8,11 +8,11 @@ from ..configs.model_config import preset_models_on_huggingface, preset_models_o
 
 def download_from_modelscope(model_id, origin_file_path, local_dir):
     os.makedirs(local_dir, exist_ok=True)
-    if os.path.basename(origin_file_path) in os.listdir(local_dir):
+    file_name = os.path.basename(origin_file_path)
-        print(f"    {os.path.basename(origin_file_path)} has been already in {local_dir}.")
+    if file_name in os.listdir(local_dir):
-        return
+        print(f"    {file_name} has been already in {local_dir}.")
     else:
-        print(f"    Start downloading {os.path.join(local_dir, os.path.basename(origin_file_path))}")
+        print(f"    Start downloading {os.path.join(local_dir, file_name)}")
         snapshot_download(model_id, allow_file_pattern=origin_file_path, local_dir=local_dir)
         downloaded_file_path = os.path.join(local_dir, origin_file_path)
         target_file_path = os.path.join(local_dir, os.path.split(origin_file_path)[-1])
@@ -23,12 +23,17 @@ def download_from_modelscope(model_id, origin_file_path, local_dir):
 
 def download_from_huggingface(model_id, origin_file_path, local_dir):
     os.makedirs(local_dir, exist_ok=True)
-    if os.path.basename(origin_file_path) in os.listdir(local_dir):
+    file_name = os.path.basename(origin_file_path)
-        print(f"    {os.path.basename(origin_file_path)} has been already in {local_dir}.")
+    if file_name in os.listdir(local_dir):
-        return
+        print(f"    {file_name} has been already in {local_dir}.")
     else:
-        print(f"    Start downloading {os.path.join(local_dir, os.path.basename(origin_file_path))}")
+        print(f"    Start downloading {os.path.join(local_dir, file_name)}")
         hf_hub_download(model_id, origin_file_path, local_dir=local_dir)
+        downloaded_file_path = os.path.join(local_dir, origin_file_path)
+        target_file_path = os.path.join(local_dir, file_name)
+        if downloaded_file_path != target_file_path:
+            shutil.move(downloaded_file_path, target_file_path)
+            shutil.rmtree(os.path.join(local_dir, origin_file_path.split("/")[0]))
 
 
 Preset_model_website: TypeAlias = Literal[
@@ -45,16 +50,14 @@ website_to_download_fn = {
 }
 
 
-def download_models(
+def download_customized_models(
-    model_id_list: List[Preset_model_id] = [],
+    model_id,
+    origin_file_path,
+    local_dir,
     downloading_priority: List[Preset_model_website] = ["ModelScope", "HuggingFace"],
 ):
-    print(f"Downloading models: {model_id_list}")
     downloaded_files = []
-    for model_id in model_id_list:
     for website in downloading_priority:
-            if model_id in website_to_preset_models[website]:
-                for model_id, origin_file_path, local_dir in website_to_preset_models[website][model_id]:
         # Check if the file is downloaded.
         file_to_download = os.path.join(local_dir, os.path.basename(origin_file_path))
         if file_to_download in downloaded_files:
@@ -64,3 +67,45 @@ def download_models(
         if os.path.basename(origin_file_path) in os.listdir(local_dir):
             downloaded_files.append(file_to_download)
     return downloaded_files
+
+
+def download_models(
+    model_id_list: List[Preset_model_id] = [],
+    downloading_priority: List[Preset_model_website] = ["ModelScope", "HuggingFace"],
+):
+    print(f"Downloading models: {model_id_list}")
+    downloaded_files = []
+    load_files = []
+
+    for model_id in model_id_list:
+        for website in downloading_priority:
+            if model_id in website_to_preset_models[website]:
+                
+                # Parse model metadata
+                model_metadata = website_to_preset_models[website][model_id]
+                if isinstance(model_metadata, list):
+                    file_data = model_metadata
+                else:
+                    file_data = model_metadata.get("file_list", [])
+
+                # Try downloading the model from this website.
+                model_files = []
+                for model_id, origin_file_path, local_dir in file_data:
+                    # Check if the file is downloaded.
+                    file_to_download = os.path.join(local_dir, os.path.basename(origin_file_path))
+                    if file_to_download in downloaded_files:
+                        continue
+                    # Download
+                    website_to_download_fn[website](model_id, origin_file_path, local_dir)
+                    if os.path.basename(origin_file_path) in os.listdir(local_dir):
+                        downloaded_files.append(file_to_download)
+                        model_files.append(file_to_download)
+                
+                # If the model is successfully downloaded, break.
+                if len(model_files) > 0:
+                    if isinstance(model_metadata, dict) and "load_path" in model_metadata:
+                        model_files = model_metadata["load_path"]
+                    load_files.extend(model_files)
+                    break
+                
+    return load_files

diffsynth/models/flux_controlnet.py

@@ -0,0 +1,327 @@
+import torch
+from einops import rearrange, repeat
+from .flux_dit import RoPEEmbedding, TimestepEmbeddings, FluxJointTransformerBlock, FluxSingleTransformerBlock, RMSNorm
+from .utils import hash_state_dict_keys, init_weights_on_device
+
+
+
+class FluxControlNet(torch.nn.Module):
+    def __init__(self, disable_guidance_embedder=False, num_joint_blocks=5, num_single_blocks=10, num_mode=0, mode_dict={}, additional_input_dim=0):
+        super().__init__()
+        self.pos_embedder = RoPEEmbedding(3072, 10000, [16, 56, 56])
+        self.time_embedder = TimestepEmbeddings(256, 3072)
+        self.guidance_embedder = None if disable_guidance_embedder else TimestepEmbeddings(256, 3072)
+        self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(768, 3072), torch.nn.SiLU(), torch.nn.Linear(3072, 3072))
+        self.context_embedder = torch.nn.Linear(4096, 3072)
+        self.x_embedder = torch.nn.Linear(64, 3072)
+
+        self.blocks = torch.nn.ModuleList([FluxJointTransformerBlock(3072, 24) for _ in range(num_joint_blocks)])
+        self.single_blocks = torch.nn.ModuleList([FluxSingleTransformerBlock(3072, 24) for _ in range(num_single_blocks)])
+
+        self.controlnet_blocks = torch.nn.ModuleList([torch.nn.Linear(3072, 3072) for _ in range(num_joint_blocks)])
+        self.controlnet_single_blocks = torch.nn.ModuleList([torch.nn.Linear(3072, 3072) for _ in range(num_single_blocks)])
+        
+        self.mode_dict = mode_dict
+        self.controlnet_mode_embedder = torch.nn.Embedding(num_mode, 3072) if len(mode_dict) > 0 else None
+        self.controlnet_x_embedder = torch.nn.Linear(64 + additional_input_dim, 3072)
+
+
+    def prepare_image_ids(self, latents):
+        batch_size, _, height, width = latents.shape
+        latent_image_ids = torch.zeros(height // 2, width // 2, 3)
+        latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
+        latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :]
+
+        latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
+
+        latent_image_ids = latent_image_ids[None, :].repeat(batch_size, 1, 1, 1)
+        latent_image_ids = latent_image_ids.reshape(
+            batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels
+        )
+        latent_image_ids = latent_image_ids.to(device=latents.device, dtype=latents.dtype)
+
+        return latent_image_ids
+    
+
+    def patchify(self, hidden_states):
+        hidden_states = rearrange(hidden_states, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2)
+        return hidden_states
+    
+
+    def align_res_stack_to_original_blocks(self, res_stack, num_blocks, hidden_states):
+        if len(res_stack) == 0:
+            return [torch.zeros_like(hidden_states)] * num_blocks
+        interval = (num_blocks + len(res_stack) - 1) // len(res_stack)
+        aligned_res_stack = [res_stack[block_id // interval] for block_id in range(num_blocks)]
+        return aligned_res_stack
+
+
+    def forward(
+        self,
+        hidden_states,
+        controlnet_conditioning,
+        timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None,
+        processor_id=None,
+        tiled=False, tile_size=128, tile_stride=64,
+        **kwargs
+    ):
+        if image_ids is None:
+            image_ids = self.prepare_image_ids(hidden_states)
+
+        conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb)
+        if self.guidance_embedder is not None:
+            guidance = guidance * 1000
+            conditioning = conditioning + self.guidance_embedder(guidance, hidden_states.dtype)
+        prompt_emb = self.context_embedder(prompt_emb)
+        if self.controlnet_mode_embedder is not None: # Different from FluxDiT
+            processor_id = torch.tensor([self.mode_dict[processor_id]], dtype=torch.int)
+            processor_id = repeat(processor_id, "D -> B D", B=1).to(text_ids.device)
+            prompt_emb = torch.concat([self.controlnet_mode_embedder(processor_id), prompt_emb], dim=1)
+            text_ids = torch.cat([text_ids[:, :1], text_ids], dim=1)
+        image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
+
+        hidden_states = self.patchify(hidden_states)
+        hidden_states = self.x_embedder(hidden_states)
+        controlnet_conditioning = self.patchify(controlnet_conditioning) # Different from FluxDiT
+        hidden_states = hidden_states + self.controlnet_x_embedder(controlnet_conditioning) # Different from FluxDiT
+
+        controlnet_res_stack = []
+        for block, controlnet_block in zip(self.blocks, self.controlnet_blocks):
+            hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb)
+            controlnet_res_stack.append(controlnet_block(hidden_states))
+
+        controlnet_single_res_stack = []
+        hidden_states = torch.cat([prompt_emb, hidden_states], dim=1)
+        for block, controlnet_block in zip(self.single_blocks, self.controlnet_single_blocks):
+            hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb)
+            controlnet_single_res_stack.append(controlnet_block(hidden_states[:, prompt_emb.shape[1]:]))
+
+        controlnet_res_stack = self.align_res_stack_to_original_blocks(controlnet_res_stack, 19, hidden_states[:, prompt_emb.shape[1]:])
+        controlnet_single_res_stack = self.align_res_stack_to_original_blocks(controlnet_single_res_stack, 38, hidden_states[:, prompt_emb.shape[1]:])
+
+        return controlnet_res_stack, controlnet_single_res_stack
+
+
+    @staticmethod
+    def state_dict_converter():
+        return FluxControlNetStateDictConverter()
+    
+    def quantize(self):
+        def cast_to(weight, dtype=None, device=None, copy=False):
+            if device is None or weight.device == device:
+                if not copy:
+                    if dtype is None or weight.dtype == dtype:
+                        return weight
+                return weight.to(dtype=dtype, copy=copy)
+
+            r = torch.empty_like(weight, dtype=dtype, device=device)
+            r.copy_(weight)
+            return r
+
+        def cast_weight(s, input=None, dtype=None, device=None):
+            if input is not None:
+                if dtype is None:
+                    dtype = input.dtype
+                if device is None:
+                    device = input.device
+            weight = cast_to(s.weight, dtype, device)
+            return weight
+
+        def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
+            if input is not None:
+                if dtype is None:
+                    dtype = input.dtype
+                if bias_dtype is None:
+                    bias_dtype = dtype
+                if device is None:
+                    device = input.device
+            bias = None
+            weight = cast_to(s.weight, dtype, device)
+            bias = cast_to(s.bias, bias_dtype, device)
+            return weight, bias
+
+        class quantized_layer:
+            class QLinear(torch.nn.Linear):
+                def __init__(self, *args, **kwargs):
+                    super().__init__(*args, **kwargs)
+                    
+                def forward(self,input,**kwargs):
+                    weight,bias= cast_bias_weight(self,input)
+                    return torch.nn.functional.linear(input,weight,bias)
+            
+            class QRMSNorm(torch.nn.Module):
+                def __init__(self, module):
+                    super().__init__()
+                    self.module = module
+                    
+                def forward(self,hidden_states,**kwargs):
+                    weight= cast_weight(self.module,hidden_states)
+                    input_dtype = hidden_states.dtype
+                    variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True)
+                    hidden_states = hidden_states * torch.rsqrt(variance + self.module.eps)
+                    hidden_states = hidden_states.to(input_dtype) * weight
+                    return hidden_states
+            
+            class QEmbedding(torch.nn.Embedding):
+                def __init__(self, *args, **kwargs):
+                    super().__init__(*args, **kwargs)
+                    
+                def forward(self,input,**kwargs):
+                    weight= cast_weight(self,input)
+                    return torch.nn.functional.embedding(
+                        input, weight, self.padding_idx, self.max_norm,
+                        self.norm_type, self.scale_grad_by_freq, self.sparse)
+            
+        def replace_layer(model):
+            for name, module in model.named_children():
+                if isinstance(module,quantized_layer.QRMSNorm):
+                    continue
+                if isinstance(module, torch.nn.Linear):
+                    with init_weights_on_device():
+                        new_layer = quantized_layer.QLinear(module.in_features,module.out_features)
+                    new_layer.weight = module.weight
+                    if module.bias is not None:
+                        new_layer.bias = module.bias
+                    setattr(model, name, new_layer)
+                elif isinstance(module, RMSNorm):
+                    if hasattr(module,"quantized"):
+                        continue
+                    module.quantized= True
+                    new_layer = quantized_layer.QRMSNorm(module)
+                    setattr(model, name, new_layer)
+                elif isinstance(module,torch.nn.Embedding):
+                    rows, cols = module.weight.shape
+                    new_layer = quantized_layer.QEmbedding(
+                        num_embeddings=rows,
+                        embedding_dim=cols,
+                        _weight=module.weight,
+                        # _freeze=module.freeze,
+                        padding_idx=module.padding_idx,
+                        max_norm=module.max_norm,
+                        norm_type=module.norm_type,
+                        scale_grad_by_freq=module.scale_grad_by_freq,
+                        sparse=module.sparse)
+                    setattr(model, name, new_layer)
+                else:
+                    replace_layer(module)
+
+        replace_layer(self)
+    
+
+
+class FluxControlNetStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        hash_value = hash_state_dict_keys(state_dict)
+        global_rename_dict = {
+            "context_embedder": "context_embedder",
+            "x_embedder": "x_embedder",
+            "time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0",
+            "time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2",
+            "time_text_embed.guidance_embedder.linear_1": "guidance_embedder.timestep_embedder.0",
+            "time_text_embed.guidance_embedder.linear_2": "guidance_embedder.timestep_embedder.2",
+            "time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0",
+            "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2",
+            "norm_out.linear": "final_norm_out.linear",
+            "proj_out": "final_proj_out",
+        }
+        rename_dict = {
+            "proj_out": "proj_out",
+            "norm1.linear": "norm1_a.linear",
+            "norm1_context.linear": "norm1_b.linear",
+            "attn.to_q": "attn.a_to_q",
+            "attn.to_k": "attn.a_to_k",
+            "attn.to_v": "attn.a_to_v",
+            "attn.to_out.0": "attn.a_to_out",
+            "attn.add_q_proj": "attn.b_to_q",
+            "attn.add_k_proj": "attn.b_to_k",
+            "attn.add_v_proj": "attn.b_to_v",
+            "attn.to_add_out": "attn.b_to_out",
+            "ff.net.0.proj": "ff_a.0",
+            "ff.net.2": "ff_a.2",
+            "ff_context.net.0.proj": "ff_b.0",
+            "ff_context.net.2": "ff_b.2",
+            "attn.norm_q": "attn.norm_q_a",
+            "attn.norm_k": "attn.norm_k_a",
+            "attn.norm_added_q": "attn.norm_q_b",
+            "attn.norm_added_k": "attn.norm_k_b",
+        }
+        rename_dict_single = {
+            "attn.to_q": "a_to_q",
+            "attn.to_k": "a_to_k",
+            "attn.to_v": "a_to_v",
+            "attn.norm_q": "norm_q_a",
+            "attn.norm_k": "norm_k_a",
+            "norm.linear": "norm.linear",
+            "proj_mlp": "proj_in_besides_attn",
+            "proj_out": "proj_out",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name.endswith(".weight") or name.endswith(".bias"):
+                suffix = ".weight" if name.endswith(".weight") else ".bias"
+                prefix = name[:-len(suffix)]
+                if prefix in global_rename_dict:
+                    state_dict_[global_rename_dict[prefix] + suffix] = param
+                elif prefix.startswith("transformer_blocks."):
+                    names = prefix.split(".")
+                    names[0] = "blocks"
+                    middle = ".".join(names[2:])
+                    if middle in rename_dict:
+                        name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]])
+                        state_dict_[name_] = param
+                elif prefix.startswith("single_transformer_blocks."):
+                    names = prefix.split(".")
+                    names[0] = "single_blocks"
+                    middle = ".".join(names[2:])
+                    if middle in rename_dict_single:
+                        name_ = ".".join(names[:2] + [rename_dict_single[middle]] + [suffix[1:]])
+                        state_dict_[name_] = param
+                    else:
+                        state_dict_[name] = param
+                else:
+                    state_dict_[name] = param
+        for name in list(state_dict_.keys()):
+            if ".proj_in_besides_attn." in name:
+                name_ = name.replace(".proj_in_besides_attn.", ".to_qkv_mlp.")
+                param = torch.concat([
+                    state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_q.")],
+                    state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_k.")],
+                    state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_v.")],
+                    state_dict_[name],
+                ], dim=0)
+                state_dict_[name_] = param
+                state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_q."))
+                state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_k."))
+                state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_v."))
+                state_dict_.pop(name)
+        for name in list(state_dict_.keys()):
+            for component in ["a", "b"]:
+                if f".{component}_to_q." in name:
+                    name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.")
+                    param = torch.concat([
+                        state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")],
+                        state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")],
+                        state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")],
+                    ], dim=0)
+                    state_dict_[name_] = param
+                    state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q."))
+                    state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k."))
+                    state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v."))
+        if hash_value == "78d18b9101345ff695f312e7e62538c0":
+            extra_kwargs = {"num_mode": 10, "mode_dict": {"canny": 0, "tile": 1, "depth": 2, "blur": 3, "pose": 4, "gray": 5, "lq": 6}}
+        elif hash_value == "b001c89139b5f053c715fe772362dd2a":
+            extra_kwargs = {"num_single_blocks": 0}
+        elif hash_value == "52357cb26250681367488a8954c271e8":
+            extra_kwargs = {"num_joint_blocks": 6, "num_single_blocks": 0, "additional_input_dim": 4}
+        elif hash_value == "0cfd1740758423a2a854d67c136d1e8c":
+            extra_kwargs = {"num_joint_blocks": 4, "num_single_blocks": 1}
+        else:
+            extra_kwargs = {}
+        return state_dict_, extra_kwargs
+    
+
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)

diffsynth/models/flux_dit.py

@@ -0,0 +1,739 @@
+import torch
+from .sd3_dit import TimestepEmbeddings, AdaLayerNorm, RMSNorm
+from einops import rearrange
+from .tiler import TileWorker
+from .utils import init_weights_on_device
+
+def interact_with_ipadapter(hidden_states, q, ip_k, ip_v, scale=1.0):
+    batch_size, num_tokens = hidden_states.shape[0:2]
+    ip_hidden_states = torch.nn.functional.scaled_dot_product_attention(q, ip_k, ip_v)
+    ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, num_tokens, -1)
+    hidden_states = hidden_states + scale * ip_hidden_states
+    return hidden_states
+
+
+class RoPEEmbedding(torch.nn.Module):
+    def __init__(self, dim, theta, axes_dim):
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        self.axes_dim = axes_dim
+
+
+    def rope(self, pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor:
+        assert dim % 2 == 0, "The dimension must be even."
+
+        scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
+        omega = 1.0 / (theta**scale)
+
+        batch_size, seq_length = pos.shape
+        out = torch.einsum("...n,d->...nd", pos, omega)
+        cos_out = torch.cos(out)
+        sin_out = torch.sin(out)
+
+        stacked_out = torch.stack([cos_out, -sin_out, sin_out, cos_out], dim=-1)
+        out = stacked_out.view(batch_size, -1, dim // 2, 2, 2)
+        return out.float()
+
+
+    def forward(self, ids):
+        n_axes = ids.shape[-1]
+        emb = torch.cat([self.rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], dim=-3)
+        return emb.unsqueeze(1)
+
+
+
+class FluxJointAttention(torch.nn.Module):
+    def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+        self.only_out_a = only_out_a
+
+        self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3)
+        self.b_to_qkv = torch.nn.Linear(dim_b, dim_b * 3)
+
+        self.norm_q_a = RMSNorm(head_dim, eps=1e-6)
+        self.norm_k_a = RMSNorm(head_dim, eps=1e-6)
+        self.norm_q_b = RMSNorm(head_dim, eps=1e-6)
+        self.norm_k_b = RMSNorm(head_dim, eps=1e-6)
+
+        self.a_to_out = torch.nn.Linear(dim_a, dim_a)
+        if not only_out_a:
+            self.b_to_out = torch.nn.Linear(dim_b, dim_b)
+
+
+    def apply_rope(self, xq, xk, freqs_cis):
+        xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+        xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+        xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+        xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+        return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
+
+    def forward(self, hidden_states_a, hidden_states_b, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
+        batch_size = hidden_states_a.shape[0]
+
+        # Part A
+        qkv_a = self.a_to_qkv(hidden_states_a)
+        qkv_a = qkv_a.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q_a, k_a, v_a = qkv_a.chunk(3, dim=1)
+        q_a, k_a = self.norm_q_a(q_a), self.norm_k_a(k_a)
+
+        # Part B
+        qkv_b = self.b_to_qkv(hidden_states_b)
+        qkv_b = qkv_b.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q_b, k_b, v_b = qkv_b.chunk(3, dim=1)
+        q_b, k_b = self.norm_q_b(q_b), self.norm_k_b(k_b)
+
+        q = torch.concat([q_b, q_a], dim=2)
+        k = torch.concat([k_b, k_a], dim=2)
+        v = torch.concat([v_b, v_a], dim=2)
+
+        q, k = self.apply_rope(q, k, image_rotary_emb)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+        hidden_states_b, hidden_states_a = hidden_states[:, :hidden_states_b.shape[1]], hidden_states[:, hidden_states_b.shape[1]:]
+        if ipadapter_kwargs_list is not None:
+            hidden_states_a = interact_with_ipadapter(hidden_states_a, q_a, **ipadapter_kwargs_list)
+        hidden_states_a = self.a_to_out(hidden_states_a)
+        if self.only_out_a:
+            return hidden_states_a
+        else:
+            hidden_states_b = self.b_to_out(hidden_states_b)
+            return hidden_states_a, hidden_states_b
+
+
+
+class FluxJointTransformerBlock(torch.nn.Module):
+    def __init__(self, dim, num_attention_heads):
+        super().__init__()
+        self.norm1_a = AdaLayerNorm(dim)
+        self.norm1_b = AdaLayerNorm(dim)
+
+        self.attn = FluxJointAttention(dim, dim, num_attention_heads, dim // num_attention_heads)
+
+        self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_a = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim*4),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(dim*4, dim)
+        )
+
+        self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_b = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim*4),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(dim*4, dim)
+        )
+
+
+    def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
+        norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb)
+        norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb)
+
+        # Attention
+        attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b, image_rotary_emb, attn_mask, ipadapter_kwargs_list)
+
+        # Part A
+        hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a
+        norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a
+        hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a)
+
+        # Part B
+        hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b
+        norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b
+        hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b)
+
+        return hidden_states_a, hidden_states_b
+
+
+
+class FluxSingleAttention(torch.nn.Module):
+    def __init__(self, dim_a, dim_b, num_heads, head_dim):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+
+        self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3)
+
+        self.norm_q_a = RMSNorm(head_dim, eps=1e-6)
+        self.norm_k_a = RMSNorm(head_dim, eps=1e-6)
+
+
+    def apply_rope(self, xq, xk, freqs_cis):
+        xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+        xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+        xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+        xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+        return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
+
+
+    def forward(self, hidden_states, image_rotary_emb):
+        batch_size = hidden_states.shape[0]
+
+        qkv_a = self.a_to_qkv(hidden_states)
+        qkv_a = qkv_a.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q_a, k_a, v = qkv_a.chunk(3, dim=1)
+        q_a, k_a = self.norm_q_a(q_a), self.norm_k_a(k_a)
+
+        q, k = self.apply_rope(q_a, k_a, image_rotary_emb)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+        return hidden_states
+
+
+
+class AdaLayerNormSingle(torch.nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.silu = torch.nn.SiLU()
+        self.linear = torch.nn.Linear(dim, 3 * dim, bias=True)
+        self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+
+    def forward(self, x, emb):
+        emb = self.linear(self.silu(emb))
+        shift_msa, scale_msa, gate_msa = emb.chunk(3, dim=1)
+        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
+        return x, gate_msa
+
+
+
+class FluxSingleTransformerBlock(torch.nn.Module):
+    def __init__(self, dim, num_attention_heads):
+        super().__init__()
+        self.num_heads = num_attention_heads
+        self.head_dim = dim // num_attention_heads
+        self.dim = dim
+
+        self.norm = AdaLayerNormSingle(dim)
+        self.to_qkv_mlp = torch.nn.Linear(dim, dim * (3 + 4))
+        self.norm_q_a = RMSNorm(self.head_dim, eps=1e-6)
+        self.norm_k_a = RMSNorm(self.head_dim, eps=1e-6)
+
+        self.proj_out = torch.nn.Linear(dim * 5, dim)
+
+
+    def apply_rope(self, xq, xk, freqs_cis):
+        xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+        xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+        xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+        xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+        return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
+
+
+    def process_attention(self, hidden_states, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
+        batch_size = hidden_states.shape[0]
+
+        qkv = hidden_states.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q, k, v = qkv.chunk(3, dim=1)
+        q, k = self.norm_q_a(q), self.norm_k_a(k)
+
+        q, k = self.apply_rope(q, k, image_rotary_emb)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+        if ipadapter_kwargs_list is not None:
+            hidden_states = interact_with_ipadapter(hidden_states, q, **ipadapter_kwargs_list)
+        return hidden_states
+
+
+    def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
+        residual = hidden_states_a
+        norm_hidden_states, gate = self.norm(hidden_states_a, emb=temb)
+        hidden_states_a = self.to_qkv_mlp(norm_hidden_states)
+        attn_output, mlp_hidden_states = hidden_states_a[:, :, :self.dim * 3], hidden_states_a[:, :, self.dim * 3:]
+
+        attn_output = self.process_attention(attn_output, image_rotary_emb, attn_mask, ipadapter_kwargs_list)
+        mlp_hidden_states = torch.nn.functional.gelu(mlp_hidden_states, approximate="tanh")
+
+        hidden_states_a = torch.cat([attn_output, mlp_hidden_states], dim=2)
+        hidden_states_a = gate.unsqueeze(1) * self.proj_out(hidden_states_a)
+        hidden_states_a = residual + hidden_states_a
+
+        return hidden_states_a, hidden_states_b
+
+
+
+class AdaLayerNormContinuous(torch.nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.silu = torch.nn.SiLU()
+        self.linear = torch.nn.Linear(dim, dim * 2, bias=True)
+        self.norm = torch.nn.LayerNorm(dim, eps=1e-6, elementwise_affine=False)
+
+    def forward(self, x, conditioning):
+        emb = self.linear(self.silu(conditioning))
+        scale, shift = torch.chunk(emb, 2, dim=1)
+        x = self.norm(x) * (1 + scale)[:, None] + shift[:, None]
+        return x
+
+
+
+class FluxDiT(torch.nn.Module):
+    def __init__(self, disable_guidance_embedder=False):
+        super().__init__()
+        self.pos_embedder = RoPEEmbedding(3072, 10000, [16, 56, 56])
+        self.time_embedder = TimestepEmbeddings(256, 3072)
+        self.guidance_embedder = None if disable_guidance_embedder else TimestepEmbeddings(256, 3072)
+        self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(768, 3072), torch.nn.SiLU(), torch.nn.Linear(3072, 3072))
+        self.context_embedder = torch.nn.Linear(4096, 3072)
+        self.x_embedder = torch.nn.Linear(64, 3072)
+
+        self.blocks = torch.nn.ModuleList([FluxJointTransformerBlock(3072, 24) for _ in range(19)])
+        self.single_blocks = torch.nn.ModuleList([FluxSingleTransformerBlock(3072, 24) for _ in range(38)])
+
+        self.final_norm_out = AdaLayerNormContinuous(3072)
+        self.final_proj_out = torch.nn.Linear(3072, 64)
+
+
+    def patchify(self, hidden_states):
+        hidden_states = rearrange(hidden_states, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2)
+        return hidden_states
+
+
+    def unpatchify(self, hidden_states, height, width):
+        hidden_states = rearrange(hidden_states, "B (H W) (C P Q) -> B C (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2)
+        return hidden_states
+
+
+    def prepare_image_ids(self, latents):
+        batch_size, _, height, width = latents.shape
+        latent_image_ids = torch.zeros(height // 2, width // 2, 3)
+        latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
+        latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :]
+
+        latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
+
+        latent_image_ids = latent_image_ids[None, :].repeat(batch_size, 1, 1, 1)
+        latent_image_ids = latent_image_ids.reshape(
+            batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels
+        )
+        latent_image_ids = latent_image_ids.to(device=latents.device, dtype=latents.dtype)
+
+        return latent_image_ids
+
+
+    def tiled_forward(
+        self,
+        hidden_states,
+        timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids,
+        tile_size=128, tile_stride=64,
+        **kwargs
+    ):
+        # Due to the global positional embedding, we cannot implement layer-wise tiled forward.
+        hidden_states = TileWorker().tiled_forward(
+            lambda x: self.forward(x, timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None),
+            hidden_states,
+            tile_size,
+            tile_stride,
+            tile_device=hidden_states.device,
+            tile_dtype=hidden_states.dtype
+        )
+        return hidden_states
+
+
+    def construct_mask(self, entity_masks, prompt_seq_len, image_seq_len):
+        N = len(entity_masks)
+        batch_size = entity_masks[0].shape[0]
+        total_seq_len = N * prompt_seq_len + image_seq_len
+        patched_masks = [self.patchify(entity_masks[i]) for i in range(N)]
+        attention_mask = torch.ones((batch_size, total_seq_len, total_seq_len), dtype=torch.bool).to(device=entity_masks[0].device)
+
+        image_start = N * prompt_seq_len
+        image_end = N * prompt_seq_len + image_seq_len
+        # prompt-image mask
+        for i in range(N):
+            prompt_start = i * prompt_seq_len
+            prompt_end = (i + 1) * prompt_seq_len
+            image_mask = torch.sum(patched_masks[i], dim=-1) > 0
+            image_mask = image_mask.unsqueeze(1).repeat(1, prompt_seq_len, 1)
+            # prompt update with image
+            attention_mask[:, prompt_start:prompt_end, image_start:image_end] = image_mask
+            # image update with prompt
+            attention_mask[:, image_start:image_end, prompt_start:prompt_end] = image_mask.transpose(1, 2)
+        # prompt-prompt mask
+        for i in range(N):
+            for j in range(N):
+                if i != j:
+                    prompt_start_i = i * prompt_seq_len
+                    prompt_end_i = (i + 1) * prompt_seq_len
+                    prompt_start_j = j * prompt_seq_len
+                    prompt_end_j = (j + 1) * prompt_seq_len
+                    attention_mask[:, prompt_start_i:prompt_end_i, prompt_start_j:prompt_end_j] = False
+
+        attention_mask = attention_mask.float()
+        attention_mask[attention_mask == 0] = float('-inf')
+        attention_mask[attention_mask == 1] = 0
+        return attention_mask
+
+
+    def process_entity_masks(self, hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids):
+        repeat_dim = hidden_states.shape[1]
+        max_masks = 0
+        attention_mask = None
+        prompt_embs = [prompt_emb]
+        if entity_masks is not None:
+            # entity_masks
+            batch_size, max_masks = entity_masks.shape[0], entity_masks.shape[1]
+            entity_masks = entity_masks.repeat(1, 1, repeat_dim, 1, 1)
+            entity_masks = [entity_masks[:, i, None].squeeze(1) for i in range(max_masks)]
+            # global mask
+            global_mask = torch.ones_like(entity_masks[0]).to(device=hidden_states.device, dtype=hidden_states.dtype)
+            entity_masks = entity_masks + [global_mask] # append global to last
+            # attention mask
+            attention_mask = self.construct_mask(entity_masks, prompt_emb.shape[1], hidden_states.shape[1])
+            attention_mask = attention_mask.to(device=hidden_states.device, dtype=hidden_states.dtype)
+            attention_mask = attention_mask.unsqueeze(1)
+            # embds: n_masks * b * seq * d
+            local_embs = [entity_prompt_emb[:, i, None].squeeze(1) for i in range(max_masks)]
+            prompt_embs = local_embs + prompt_embs # append global to last
+        prompt_embs = [self.context_embedder(prompt_emb) for prompt_emb in prompt_embs]
+        prompt_emb = torch.cat(prompt_embs, dim=1)
+
+        # positional embedding
+        text_ids = torch.cat([text_ids] * (max_masks + 1), dim=1)
+        image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
+        return prompt_emb, image_rotary_emb, attention_mask
+
+
+    def forward(
+        self,
+        hidden_states,
+        timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None,
+        tiled=False, tile_size=128, tile_stride=64, entity_prompt_emb=None, entity_masks=None,
+        use_gradient_checkpointing=False,
+        **kwargs
+    ):
+        if tiled:
+            return self.tiled_forward(
+                hidden_states,
+                timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids,
+                tile_size=tile_size, tile_stride=tile_stride,
+                **kwargs
+            )
+
+        if image_ids is None:
+            image_ids = self.prepare_image_ids(hidden_states)
+
+        conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb)
+        if self.guidance_embedder is not None:
+            guidance = guidance * 1000
+            conditioning = conditioning + self.guidance_embedder(guidance, hidden_states.dtype)
+
+        height, width = hidden_states.shape[-2:]
+        hidden_states = self.patchify(hidden_states)
+        hidden_states = self.x_embedder(hidden_states)
+
+        if entity_prompt_emb is not None and entity_masks is not None:
+            prompt_emb, image_rotary_emb, attention_mask = self.process_entity_masks(hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids)
+        else:
+            prompt_emb = self.context_embedder(prompt_emb)
+            image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
+            attention_mask = None
+
+        def create_custom_forward(module):
+            def custom_forward(*inputs):
+                return module(*inputs)
+            return custom_forward
+
+        for block in self.blocks:
+            if self.training and use_gradient_checkpointing:
+                hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask)
+
+        hidden_states = torch.cat([prompt_emb, hidden_states], dim=1)
+        for block in self.single_blocks:
+            if self.training and use_gradient_checkpointing:
+                hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask)
+        hidden_states = hidden_states[:, prompt_emb.shape[1]:]
+
+        hidden_states = self.final_norm_out(hidden_states, conditioning)
+        hidden_states = self.final_proj_out(hidden_states)
+        hidden_states = self.unpatchify(hidden_states, height, width)
+
+        return hidden_states
+
+
+    def quantize(self):
+        def cast_to(weight, dtype=None, device=None, copy=False):
+            if device is None or weight.device == device:
+                if not copy:
+                    if dtype is None or weight.dtype == dtype:
+                        return weight
+                return weight.to(dtype=dtype, copy=copy)
+
+            r = torch.empty_like(weight, dtype=dtype, device=device)
+            r.copy_(weight)
+            return r
+
+        def cast_weight(s, input=None, dtype=None, device=None):
+            if input is not None:
+                if dtype is None:
+                    dtype = input.dtype
+                if device is None:
+                    device = input.device
+            weight = cast_to(s.weight, dtype, device)
+            return weight
+
+        def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
+            if input is not None:
+                if dtype is None:
+                    dtype = input.dtype
+                if bias_dtype is None:
+                    bias_dtype = dtype
+                if device is None:
+                    device = input.device
+            bias = None
+            weight = cast_to(s.weight, dtype, device)
+            bias = cast_to(s.bias, bias_dtype, device)
+            return weight, bias
+
+        class quantized_layer:
+            class Linear(torch.nn.Linear):
+                def __init__(self, *args, **kwargs):
+                    super().__init__(*args, **kwargs)
+
+                def forward(self,input,**kwargs):
+                    weight,bias= cast_bias_weight(self,input)
+                    return torch.nn.functional.linear(input,weight,bias)
+
+            class RMSNorm(torch.nn.Module):
+                def __init__(self, module):
+                    super().__init__()
+                    self.module = module
+
+                def forward(self,hidden_states,**kwargs):
+                    weight= cast_weight(self.module,hidden_states)
+                    input_dtype = hidden_states.dtype
+                    variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True)
+                    hidden_states = hidden_states * torch.rsqrt(variance + self.module.eps)
+                    hidden_states = hidden_states.to(input_dtype) * weight
+                    return hidden_states
+
+        def replace_layer(model):
+            for name, module in model.named_children():
+                if isinstance(module, torch.nn.Linear):
+                    with init_weights_on_device():
+                        new_layer = quantized_layer.Linear(module.in_features,module.out_features)
+                    new_layer.weight = module.weight
+                    if module.bias is not None:
+                        new_layer.bias = module.bias
+                    # del module
+                    setattr(model, name, new_layer)
+                elif isinstance(module, RMSNorm):
+                    if hasattr(module,"quantized"):
+                        continue
+                    module.quantized= True
+                    new_layer = quantized_layer.RMSNorm(module)
+                    setattr(model, name, new_layer)
+                else:
+                    replace_layer(module)
+
+        replace_layer(self)
+
+
+    @staticmethod
+    def state_dict_converter():
+        return FluxDiTStateDictConverter()
+
+
+class FluxDiTStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        global_rename_dict = {
+            "context_embedder": "context_embedder",
+            "x_embedder": "x_embedder",
+            "time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0",
+            "time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2",
+            "time_text_embed.guidance_embedder.linear_1": "guidance_embedder.timestep_embedder.0",
+            "time_text_embed.guidance_embedder.linear_2": "guidance_embedder.timestep_embedder.2",
+            "time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0",
+            "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2",
+            "norm_out.linear": "final_norm_out.linear",
+            "proj_out": "final_proj_out",
+        }
+        rename_dict = {
+            "proj_out": "proj_out",
+            "norm1.linear": "norm1_a.linear",
+            "norm1_context.linear": "norm1_b.linear",
+            "attn.to_q": "attn.a_to_q",
+            "attn.to_k": "attn.a_to_k",
+            "attn.to_v": "attn.a_to_v",
+            "attn.to_out.0": "attn.a_to_out",
+            "attn.add_q_proj": "attn.b_to_q",
+            "attn.add_k_proj": "attn.b_to_k",
+            "attn.add_v_proj": "attn.b_to_v",
+            "attn.to_add_out": "attn.b_to_out",
+            "ff.net.0.proj": "ff_a.0",
+            "ff.net.2": "ff_a.2",
+            "ff_context.net.0.proj": "ff_b.0",
+            "ff_context.net.2": "ff_b.2",
+            "attn.norm_q": "attn.norm_q_a",
+            "attn.norm_k": "attn.norm_k_a",
+            "attn.norm_added_q": "attn.norm_q_b",
+            "attn.norm_added_k": "attn.norm_k_b",
+        }
+        rename_dict_single = {
+            "attn.to_q": "a_to_q",
+            "attn.to_k": "a_to_k",
+            "attn.to_v": "a_to_v",
+            "attn.norm_q": "norm_q_a",
+            "attn.norm_k": "norm_k_a",
+            "norm.linear": "norm.linear",
+            "proj_mlp": "proj_in_besides_attn",
+            "proj_out": "proj_out",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name.endswith(".weight") or name.endswith(".bias"):
+                suffix = ".weight" if name.endswith(".weight") else ".bias"
+                prefix = name[:-len(suffix)]
+                if prefix in global_rename_dict:
+                    state_dict_[global_rename_dict[prefix] + suffix] = param
+                elif prefix.startswith("transformer_blocks."):
+                    names = prefix.split(".")
+                    names[0] = "blocks"
+                    middle = ".".join(names[2:])
+                    if middle in rename_dict:
+                        name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]])
+                        state_dict_[name_] = param
+                elif prefix.startswith("single_transformer_blocks."):
+                    names = prefix.split(".")
+                    names[0] = "single_blocks"
+                    middle = ".".join(names[2:])
+                    if middle in rename_dict_single:
+                        name_ = ".".join(names[:2] + [rename_dict_single[middle]] + [suffix[1:]])
+                        state_dict_[name_] = param
+                    else:
+                        pass
+                else:
+                    pass
+        for name in list(state_dict_.keys()):
+            if ".proj_in_besides_attn." in name:
+                name_ = name.replace(".proj_in_besides_attn.", ".to_qkv_mlp.")
+                param = torch.concat([
+                    state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_q.")],
+                    state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_k.")],
+                    state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_v.")],
+                    state_dict_[name],
+                ], dim=0)
+                state_dict_[name_] = param
+                state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_q."))
+                state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_k."))
+                state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_v."))
+                state_dict_.pop(name)
+        for name in list(state_dict_.keys()):
+            for component in ["a", "b"]:
+                if f".{component}_to_q." in name:
+                    name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.")
+                    param = torch.concat([
+                        state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")],
+                        state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")],
+                        state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")],
+                    ], dim=0)
+                    state_dict_[name_] = param
+                    state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q."))
+                    state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k."))
+                    state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v."))
+        return state_dict_
+
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "time_in.in_layer.bias": "time_embedder.timestep_embedder.0.bias",
+            "time_in.in_layer.weight": "time_embedder.timestep_embedder.0.weight",
+            "time_in.out_layer.bias": "time_embedder.timestep_embedder.2.bias",
+            "time_in.out_layer.weight": "time_embedder.timestep_embedder.2.weight",
+            "txt_in.bias": "context_embedder.bias",
+            "txt_in.weight": "context_embedder.weight",
+            "vector_in.in_layer.bias": "pooled_text_embedder.0.bias",
+            "vector_in.in_layer.weight": "pooled_text_embedder.0.weight",
+            "vector_in.out_layer.bias": "pooled_text_embedder.2.bias",
+            "vector_in.out_layer.weight": "pooled_text_embedder.2.weight",
+            "final_layer.linear.bias": "final_proj_out.bias",
+            "final_layer.linear.weight": "final_proj_out.weight",
+            "guidance_in.in_layer.bias": "guidance_embedder.timestep_embedder.0.bias",
+            "guidance_in.in_layer.weight": "guidance_embedder.timestep_embedder.0.weight",
+            "guidance_in.out_layer.bias": "guidance_embedder.timestep_embedder.2.bias",
+            "guidance_in.out_layer.weight": "guidance_embedder.timestep_embedder.2.weight",
+            "img_in.bias": "x_embedder.bias",
+            "img_in.weight": "x_embedder.weight",
+            "final_layer.adaLN_modulation.1.weight": "final_norm_out.linear.weight",
+            "final_layer.adaLN_modulation.1.bias": "final_norm_out.linear.bias",
+        }
+        suffix_rename_dict = {
+            "img_attn.norm.key_norm.scale": "attn.norm_k_a.weight",
+            "img_attn.norm.query_norm.scale": "attn.norm_q_a.weight",
+            "img_attn.proj.bias": "attn.a_to_out.bias",
+            "img_attn.proj.weight": "attn.a_to_out.weight",
+            "img_attn.qkv.bias": "attn.a_to_qkv.bias",
+            "img_attn.qkv.weight": "attn.a_to_qkv.weight",
+            "img_mlp.0.bias": "ff_a.0.bias",
+            "img_mlp.0.weight": "ff_a.0.weight",
+            "img_mlp.2.bias": "ff_a.2.bias",
+            "img_mlp.2.weight": "ff_a.2.weight",
+            "img_mod.lin.bias": "norm1_a.linear.bias",
+            "img_mod.lin.weight": "norm1_a.linear.weight",
+            "txt_attn.norm.key_norm.scale": "attn.norm_k_b.weight",
+            "txt_attn.norm.query_norm.scale": "attn.norm_q_b.weight",
+            "txt_attn.proj.bias": "attn.b_to_out.bias",
+            "txt_attn.proj.weight": "attn.b_to_out.weight",
+            "txt_attn.qkv.bias": "attn.b_to_qkv.bias",
+            "txt_attn.qkv.weight": "attn.b_to_qkv.weight",
+            "txt_mlp.0.bias": "ff_b.0.bias",
+            "txt_mlp.0.weight": "ff_b.0.weight",
+            "txt_mlp.2.bias": "ff_b.2.bias",
+            "txt_mlp.2.weight": "ff_b.2.weight",
+            "txt_mod.lin.bias": "norm1_b.linear.bias",
+            "txt_mod.lin.weight": "norm1_b.linear.weight",
+
+            "linear1.bias": "to_qkv_mlp.bias",
+            "linear1.weight": "to_qkv_mlp.weight",
+            "linear2.bias": "proj_out.bias",
+            "linear2.weight": "proj_out.weight",
+            "modulation.lin.bias": "norm.linear.bias",
+            "modulation.lin.weight": "norm.linear.weight",
+            "norm.key_norm.scale": "norm_k_a.weight",
+            "norm.query_norm.scale": "norm_q_a.weight",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name.startswith("model.diffusion_model."):
+                name = name[len("model.diffusion_model."):]
+            names = name.split(".")
+            if name in rename_dict:
+                rename = rename_dict[name]
+                if name.startswith("final_layer.adaLN_modulation.1."):
+                    param = torch.concat([param[3072:], param[:3072]], dim=0)
+                state_dict_[rename] = param
+            elif names[0] == "double_blocks":
+                rename = f"blocks.{names[1]}." + suffix_rename_dict[".".join(names[2:])]
+                state_dict_[rename] = param
+            elif names[0] == "single_blocks":
+                if ".".join(names[2:]) in suffix_rename_dict:
+                    rename = f"single_blocks.{names[1]}." + suffix_rename_dict[".".join(names[2:])]
+                    state_dict_[rename] = param
+            else:
+                pass
+        if "guidance_embedder.timestep_embedder.0.weight" not in state_dict_:
+            return state_dict_, {"disable_guidance_embedder": True}
+        else:
+            return state_dict_

diffsynth/models/flux_ipadapter.py

@@ -0,0 +1,94 @@
+from .svd_image_encoder import SVDImageEncoder
+from .sd3_dit import RMSNorm
+from transformers import CLIPImageProcessor
+import torch
+
+
+class MLPProjModel(torch.nn.Module):
+    def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4):
+        super().__init__()
+        
+        self.cross_attention_dim = cross_attention_dim
+        self.num_tokens = num_tokens
+        
+        self.proj = torch.nn.Sequential(
+            torch.nn.Linear(id_embeddings_dim, id_embeddings_dim*2),
+            torch.nn.GELU(),
+            torch.nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens),
+        )
+        self.norm = torch.nn.LayerNorm(cross_attention_dim)
+        
+    def forward(self, id_embeds):
+        x = self.proj(id_embeds)
+        x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
+        x = self.norm(x)
+        return x
+
+class IpAdapterModule(torch.nn.Module):
+    def __init__(self, num_attention_heads, attention_head_dim, input_dim):
+        super().__init__()
+        self.num_heads = num_attention_heads
+        self.head_dim = attention_head_dim
+        output_dim = num_attention_heads * attention_head_dim
+        self.to_k_ip = torch.nn.Linear(input_dim, output_dim, bias=False)
+        self.to_v_ip = torch.nn.Linear(input_dim, output_dim, bias=False)
+        self.norm_added_k = RMSNorm(attention_head_dim, eps=1e-5, elementwise_affine=False)
+        
+
+    def forward(self, hidden_states):
+        batch_size = hidden_states.shape[0]
+        # ip_k
+        ip_k = self.to_k_ip(hidden_states)
+        ip_k = ip_k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        ip_k = self.norm_added_k(ip_k)
+        # ip_v
+        ip_v = self.to_v_ip(hidden_states)
+        ip_v = ip_v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        return ip_k, ip_v
+
+
+class FluxIpAdapter(torch.nn.Module):
+    def __init__(self, num_attention_heads=24, attention_head_dim=128, cross_attention_dim=4096, num_tokens=128, num_blocks=57):
+        super().__init__()
+        self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(num_attention_heads, attention_head_dim, cross_attention_dim) for _ in range(num_blocks)])
+        self.image_proj = MLPProjModel(cross_attention_dim=cross_attention_dim, id_embeddings_dim=1152, num_tokens=num_tokens)
+        self.set_adapter()
+
+    def set_adapter(self):
+        self.call_block_id = {i:i for i in range(len(self.ipadapter_modules))}
+
+    def forward(self, hidden_states, scale=1.0):
+        hidden_states = self.image_proj(hidden_states)
+        hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1])
+        ip_kv_dict = {}
+        for block_id in self.call_block_id:
+            ipadapter_id = self.call_block_id[block_id]
+            ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states)
+            ip_kv_dict[block_id] = {
+                "ip_k": ip_k,
+                "ip_v": ip_v,
+                "scale": scale
+            }
+        return ip_kv_dict
+
+    @staticmethod
+    def state_dict_converter():
+        return FluxIpAdapterStateDictConverter()
+
+
+class FluxIpAdapterStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = {}
+        for name in state_dict["ip_adapter"]:
+            name_ = 'ipadapter_modules.' + name
+            state_dict_[name_] = state_dict["ip_adapter"][name]
+        for name in state_dict["image_proj"]:
+            name_ = "image_proj." + name
+            state_dict_[name_] = state_dict["image_proj"][name]
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)

diffsynth/models/flux_text_encoder.py

@@ -0,0 +1,32 @@
+import torch
+from transformers import T5EncoderModel, T5Config
+from .sd_text_encoder import SDTextEncoder
+
+
+
+class FluxTextEncoder2(T5EncoderModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.eval()
+
+    def forward(self, input_ids):
+        outputs = super().forward(input_ids=input_ids)
+        prompt_emb = outputs.last_hidden_state
+        return prompt_emb
+
+    @staticmethod
+    def state_dict_converter():
+        return FluxTextEncoder2StateDictConverter()
+
+
+
+class FluxTextEncoder2StateDictConverter():
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = state_dict
+        return state_dict_
+
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)

diffsynth/models/flux_vae.py

@@ -0,0 +1,303 @@
+from .sd3_vae_encoder import SD3VAEEncoder, SDVAEEncoderStateDictConverter
+from .sd3_vae_decoder import SD3VAEDecoder, SDVAEDecoderStateDictConverter
+
+
+class FluxVAEEncoder(SD3VAEEncoder):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.3611
+        self.shift_factor = 0.1159
+
+    @staticmethod
+    def state_dict_converter():
+        return FluxVAEEncoderStateDictConverter()
+
+
+class FluxVAEDecoder(SD3VAEDecoder):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.3611
+        self.shift_factor = 0.1159
+    
+    @staticmethod
+    def state_dict_converter():
+        return FluxVAEDecoderStateDictConverter()
+
+
+class FluxVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter):
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "encoder.conv_in.bias": "conv_in.bias",
+            "encoder.conv_in.weight": "conv_in.weight",
+            "encoder.conv_out.bias": "conv_out.bias",
+            "encoder.conv_out.weight": "conv_out.weight",
+            "encoder.down.0.block.0.conv1.bias": "blocks.0.conv1.bias",
+            "encoder.down.0.block.0.conv1.weight": "blocks.0.conv1.weight",
+            "encoder.down.0.block.0.conv2.bias": "blocks.0.conv2.bias",
+            "encoder.down.0.block.0.conv2.weight": "blocks.0.conv2.weight",
+            "encoder.down.0.block.0.norm1.bias": "blocks.0.norm1.bias",
+            "encoder.down.0.block.0.norm1.weight": "blocks.0.norm1.weight",
+            "encoder.down.0.block.0.norm2.bias": "blocks.0.norm2.bias",
+            "encoder.down.0.block.0.norm2.weight": "blocks.0.norm2.weight",
+            "encoder.down.0.block.1.conv1.bias": "blocks.1.conv1.bias",
+            "encoder.down.0.block.1.conv1.weight": "blocks.1.conv1.weight",
+            "encoder.down.0.block.1.conv2.bias": "blocks.1.conv2.bias",
+            "encoder.down.0.block.1.conv2.weight": "blocks.1.conv2.weight",
+            "encoder.down.0.block.1.norm1.bias": "blocks.1.norm1.bias",
+            "encoder.down.0.block.1.norm1.weight": "blocks.1.norm1.weight",
+            "encoder.down.0.block.1.norm2.bias": "blocks.1.norm2.bias",
+            "encoder.down.0.block.1.norm2.weight": "blocks.1.norm2.weight",
+            "encoder.down.0.downsample.conv.bias": "blocks.2.conv.bias",
+            "encoder.down.0.downsample.conv.weight": "blocks.2.conv.weight",
+            "encoder.down.1.block.0.conv1.bias": "blocks.3.conv1.bias",
+            "encoder.down.1.block.0.conv1.weight": "blocks.3.conv1.weight",
+            "encoder.down.1.block.0.conv2.bias": "blocks.3.conv2.bias",
+            "encoder.down.1.block.0.conv2.weight": "blocks.3.conv2.weight",
+            "encoder.down.1.block.0.nin_shortcut.bias": "blocks.3.conv_shortcut.bias",
+            "encoder.down.1.block.0.nin_shortcut.weight": "blocks.3.conv_shortcut.weight",
+            "encoder.down.1.block.0.norm1.bias": "blocks.3.norm1.bias",
+            "encoder.down.1.block.0.norm1.weight": "blocks.3.norm1.weight",
+            "encoder.down.1.block.0.norm2.bias": "blocks.3.norm2.bias",
+            "encoder.down.1.block.0.norm2.weight": "blocks.3.norm2.weight",
+            "encoder.down.1.block.1.conv1.bias": "blocks.4.conv1.bias",
+            "encoder.down.1.block.1.conv1.weight": "blocks.4.conv1.weight",
+            "encoder.down.1.block.1.conv2.bias": "blocks.4.conv2.bias",
+            "encoder.down.1.block.1.conv2.weight": "blocks.4.conv2.weight",
+            "encoder.down.1.block.1.norm1.bias": "blocks.4.norm1.bias",
+            "encoder.down.1.block.1.norm1.weight": "blocks.4.norm1.weight",
+            "encoder.down.1.block.1.norm2.bias": "blocks.4.norm2.bias",
+            "encoder.down.1.block.1.norm2.weight": "blocks.4.norm2.weight",
+            "encoder.down.1.downsample.conv.bias": "blocks.5.conv.bias",
+            "encoder.down.1.downsample.conv.weight": "blocks.5.conv.weight",
+            "encoder.down.2.block.0.conv1.bias": "blocks.6.conv1.bias",
+            "encoder.down.2.block.0.conv1.weight": "blocks.6.conv1.weight",
+            "encoder.down.2.block.0.conv2.bias": "blocks.6.conv2.bias",
+            "encoder.down.2.block.0.conv2.weight": "blocks.6.conv2.weight",
+            "encoder.down.2.block.0.nin_shortcut.bias": "blocks.6.conv_shortcut.bias",
+            "encoder.down.2.block.0.nin_shortcut.weight": "blocks.6.conv_shortcut.weight",
+            "encoder.down.2.block.0.norm1.bias": "blocks.6.norm1.bias",
+            "encoder.down.2.block.0.norm1.weight": "blocks.6.norm1.weight",
+            "encoder.down.2.block.0.norm2.bias": "blocks.6.norm2.bias",
+            "encoder.down.2.block.0.norm2.weight": "blocks.6.norm2.weight",
+            "encoder.down.2.block.1.conv1.bias": "blocks.7.conv1.bias",
+            "encoder.down.2.block.1.conv1.weight": "blocks.7.conv1.weight",
+            "encoder.down.2.block.1.conv2.bias": "blocks.7.conv2.bias",
+            "encoder.down.2.block.1.conv2.weight": "blocks.7.conv2.weight",
+            "encoder.down.2.block.1.norm1.bias": "blocks.7.norm1.bias",
+            "encoder.down.2.block.1.norm1.weight": "blocks.7.norm1.weight",
+            "encoder.down.2.block.1.norm2.bias": "blocks.7.norm2.bias",
+            "encoder.down.2.block.1.norm2.weight": "blocks.7.norm2.weight",
+            "encoder.down.2.downsample.conv.bias": "blocks.8.conv.bias",
+            "encoder.down.2.downsample.conv.weight": "blocks.8.conv.weight",
+            "encoder.down.3.block.0.conv1.bias": "blocks.9.conv1.bias",
+            "encoder.down.3.block.0.conv1.weight": "blocks.9.conv1.weight",
+            "encoder.down.3.block.0.conv2.bias": "blocks.9.conv2.bias",
+            "encoder.down.3.block.0.conv2.weight": "blocks.9.conv2.weight",
+            "encoder.down.3.block.0.norm1.bias": "blocks.9.norm1.bias",
+            "encoder.down.3.block.0.norm1.weight": "blocks.9.norm1.weight",
+            "encoder.down.3.block.0.norm2.bias": "blocks.9.norm2.bias",
+            "encoder.down.3.block.0.norm2.weight": "blocks.9.norm2.weight",
+            "encoder.down.3.block.1.conv1.bias": "blocks.10.conv1.bias",
+            "encoder.down.3.block.1.conv1.weight": "blocks.10.conv1.weight",
+            "encoder.down.3.block.1.conv2.bias": "blocks.10.conv2.bias",
+            "encoder.down.3.block.1.conv2.weight": "blocks.10.conv2.weight",
+            "encoder.down.3.block.1.norm1.bias": "blocks.10.norm1.bias",
+            "encoder.down.3.block.1.norm1.weight": "blocks.10.norm1.weight",
+            "encoder.down.3.block.1.norm2.bias": "blocks.10.norm2.bias",
+            "encoder.down.3.block.1.norm2.weight": "blocks.10.norm2.weight",
+            "encoder.mid.attn_1.k.bias": "blocks.12.transformer_blocks.0.to_k.bias",
+            "encoder.mid.attn_1.k.weight": "blocks.12.transformer_blocks.0.to_k.weight",
+            "encoder.mid.attn_1.norm.bias": "blocks.12.norm.bias",
+            "encoder.mid.attn_1.norm.weight": "blocks.12.norm.weight",
+            "encoder.mid.attn_1.proj_out.bias": "blocks.12.transformer_blocks.0.to_out.bias",
+            "encoder.mid.attn_1.proj_out.weight": "blocks.12.transformer_blocks.0.to_out.weight",
+            "encoder.mid.attn_1.q.bias": "blocks.12.transformer_blocks.0.to_q.bias",
+            "encoder.mid.attn_1.q.weight": "blocks.12.transformer_blocks.0.to_q.weight",
+            "encoder.mid.attn_1.v.bias": "blocks.12.transformer_blocks.0.to_v.bias",
+            "encoder.mid.attn_1.v.weight": "blocks.12.transformer_blocks.0.to_v.weight",
+            "encoder.mid.block_1.conv1.bias": "blocks.11.conv1.bias",
+            "encoder.mid.block_1.conv1.weight": "blocks.11.conv1.weight",
+            "encoder.mid.block_1.conv2.bias": "blocks.11.conv2.bias",
+            "encoder.mid.block_1.conv2.weight": "blocks.11.conv2.weight",
+            "encoder.mid.block_1.norm1.bias": "blocks.11.norm1.bias",
+            "encoder.mid.block_1.norm1.weight": "blocks.11.norm1.weight",
+            "encoder.mid.block_1.norm2.bias": "blocks.11.norm2.bias",
+            "encoder.mid.block_1.norm2.weight": "blocks.11.norm2.weight",
+            "encoder.mid.block_2.conv1.bias": "blocks.13.conv1.bias",
+            "encoder.mid.block_2.conv1.weight": "blocks.13.conv1.weight",
+            "encoder.mid.block_2.conv2.bias": "blocks.13.conv2.bias",
+            "encoder.mid.block_2.conv2.weight": "blocks.13.conv2.weight",
+            "encoder.mid.block_2.norm1.bias": "blocks.13.norm1.bias",
+            "encoder.mid.block_2.norm1.weight": "blocks.13.norm1.weight",
+            "encoder.mid.block_2.norm2.bias": "blocks.13.norm2.bias",
+            "encoder.mid.block_2.norm2.weight": "blocks.13.norm2.weight",
+            "encoder.norm_out.bias": "conv_norm_out.bias",
+            "encoder.norm_out.weight": "conv_norm_out.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if "transformer_blocks" in rename_dict[name]:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
+    
+
+
+class FluxVAEDecoderStateDictConverter(SDVAEDecoderStateDictConverter):
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "decoder.conv_in.bias": "conv_in.bias",
+            "decoder.conv_in.weight": "conv_in.weight",
+            "decoder.conv_out.bias": "conv_out.bias",
+            "decoder.conv_out.weight": "conv_out.weight",
+            "decoder.mid.attn_1.k.bias": "blocks.1.transformer_blocks.0.to_k.bias",
+            "decoder.mid.attn_1.k.weight": "blocks.1.transformer_blocks.0.to_k.weight",
+            "decoder.mid.attn_1.norm.bias": "blocks.1.norm.bias",
+            "decoder.mid.attn_1.norm.weight": "blocks.1.norm.weight",
+            "decoder.mid.attn_1.proj_out.bias": "blocks.1.transformer_blocks.0.to_out.bias",
+            "decoder.mid.attn_1.proj_out.weight": "blocks.1.transformer_blocks.0.to_out.weight",
+            "decoder.mid.attn_1.q.bias": "blocks.1.transformer_blocks.0.to_q.bias",
+            "decoder.mid.attn_1.q.weight": "blocks.1.transformer_blocks.0.to_q.weight",
+            "decoder.mid.attn_1.v.bias": "blocks.1.transformer_blocks.0.to_v.bias",
+            "decoder.mid.attn_1.v.weight": "blocks.1.transformer_blocks.0.to_v.weight",
+            "decoder.mid.block_1.conv1.bias": "blocks.0.conv1.bias",
+            "decoder.mid.block_1.conv1.weight": "blocks.0.conv1.weight",
+            "decoder.mid.block_1.conv2.bias": "blocks.0.conv2.bias",
+            "decoder.mid.block_1.conv2.weight": "blocks.0.conv2.weight",
+            "decoder.mid.block_1.norm1.bias": "blocks.0.norm1.bias",
+            "decoder.mid.block_1.norm1.weight": "blocks.0.norm1.weight",
+            "decoder.mid.block_1.norm2.bias": "blocks.0.norm2.bias",
+            "decoder.mid.block_1.norm2.weight": "blocks.0.norm2.weight",
+            "decoder.mid.block_2.conv1.bias": "blocks.2.conv1.bias",
+            "decoder.mid.block_2.conv1.weight": "blocks.2.conv1.weight",
+            "decoder.mid.block_2.conv2.bias": "blocks.2.conv2.bias",
+            "decoder.mid.block_2.conv2.weight": "blocks.2.conv2.weight",
+            "decoder.mid.block_2.norm1.bias": "blocks.2.norm1.bias",
+            "decoder.mid.block_2.norm1.weight": "blocks.2.norm1.weight",
+            "decoder.mid.block_2.norm2.bias": "blocks.2.norm2.bias",
+            "decoder.mid.block_2.norm2.weight": "blocks.2.norm2.weight",
+            "decoder.norm_out.bias": "conv_norm_out.bias",
+            "decoder.norm_out.weight": "conv_norm_out.weight",
+            "decoder.up.0.block.0.conv1.bias": "blocks.15.conv1.bias",
+            "decoder.up.0.block.0.conv1.weight": "blocks.15.conv1.weight",
+            "decoder.up.0.block.0.conv2.bias": "blocks.15.conv2.bias",
+            "decoder.up.0.block.0.conv2.weight": "blocks.15.conv2.weight",
+            "decoder.up.0.block.0.nin_shortcut.bias": "blocks.15.conv_shortcut.bias",
+            "decoder.up.0.block.0.nin_shortcut.weight": "blocks.15.conv_shortcut.weight",
+            "decoder.up.0.block.0.norm1.bias": "blocks.15.norm1.bias",
+            "decoder.up.0.block.0.norm1.weight": "blocks.15.norm1.weight",
+            "decoder.up.0.block.0.norm2.bias": "blocks.15.norm2.bias",
+            "decoder.up.0.block.0.norm2.weight": "blocks.15.norm2.weight",
+            "decoder.up.0.block.1.conv1.bias": "blocks.16.conv1.bias",
+            "decoder.up.0.block.1.conv1.weight": "blocks.16.conv1.weight",
+            "decoder.up.0.block.1.conv2.bias": "blocks.16.conv2.bias",
+            "decoder.up.0.block.1.conv2.weight": "blocks.16.conv2.weight",
+            "decoder.up.0.block.1.norm1.bias": "blocks.16.norm1.bias",
+            "decoder.up.0.block.1.norm1.weight": "blocks.16.norm1.weight",
+            "decoder.up.0.block.1.norm2.bias": "blocks.16.norm2.bias",
+            "decoder.up.0.block.1.norm2.weight": "blocks.16.norm2.weight",
+            "decoder.up.0.block.2.conv1.bias": "blocks.17.conv1.bias",
+            "decoder.up.0.block.2.conv1.weight": "blocks.17.conv1.weight",
+            "decoder.up.0.block.2.conv2.bias": "blocks.17.conv2.bias",
+            "decoder.up.0.block.2.conv2.weight": "blocks.17.conv2.weight",
+            "decoder.up.0.block.2.norm1.bias": "blocks.17.norm1.bias",
+            "decoder.up.0.block.2.norm1.weight": "blocks.17.norm1.weight",
+            "decoder.up.0.block.2.norm2.bias": "blocks.17.norm2.bias",
+            "decoder.up.0.block.2.norm2.weight": "blocks.17.norm2.weight",
+            "decoder.up.1.block.0.conv1.bias": "blocks.11.conv1.bias",
+            "decoder.up.1.block.0.conv1.weight": "blocks.11.conv1.weight",
+            "decoder.up.1.block.0.conv2.bias": "blocks.11.conv2.bias",
+            "decoder.up.1.block.0.conv2.weight": "blocks.11.conv2.weight",
+            "decoder.up.1.block.0.nin_shortcut.bias": "blocks.11.conv_shortcut.bias",
+            "decoder.up.1.block.0.nin_shortcut.weight": "blocks.11.conv_shortcut.weight",
+            "decoder.up.1.block.0.norm1.bias": "blocks.11.norm1.bias",
+            "decoder.up.1.block.0.norm1.weight": "blocks.11.norm1.weight",
+            "decoder.up.1.block.0.norm2.bias": "blocks.11.norm2.bias",
+            "decoder.up.1.block.0.norm2.weight": "blocks.11.norm2.weight",
+            "decoder.up.1.block.1.conv1.bias": "blocks.12.conv1.bias",
+            "decoder.up.1.block.1.conv1.weight": "blocks.12.conv1.weight",
+            "decoder.up.1.block.1.conv2.bias": "blocks.12.conv2.bias",
+            "decoder.up.1.block.1.conv2.weight": "blocks.12.conv2.weight",
+            "decoder.up.1.block.1.norm1.bias": "blocks.12.norm1.bias",
+            "decoder.up.1.block.1.norm1.weight": "blocks.12.norm1.weight",
+            "decoder.up.1.block.1.norm2.bias": "blocks.12.norm2.bias",
+            "decoder.up.1.block.1.norm2.weight": "blocks.12.norm2.weight",
+            "decoder.up.1.block.2.conv1.bias": "blocks.13.conv1.bias",
+            "decoder.up.1.block.2.conv1.weight": "blocks.13.conv1.weight",
+            "decoder.up.1.block.2.conv2.bias": "blocks.13.conv2.bias",
+            "decoder.up.1.block.2.conv2.weight": "blocks.13.conv2.weight",
+            "decoder.up.1.block.2.norm1.bias": "blocks.13.norm1.bias",
+            "decoder.up.1.block.2.norm1.weight": "blocks.13.norm1.weight",
+            "decoder.up.1.block.2.norm2.bias": "blocks.13.norm2.bias",
+            "decoder.up.1.block.2.norm2.weight": "blocks.13.norm2.weight",
+            "decoder.up.1.upsample.conv.bias": "blocks.14.conv.bias",
+            "decoder.up.1.upsample.conv.weight": "blocks.14.conv.weight",
+            "decoder.up.2.block.0.conv1.bias": "blocks.7.conv1.bias",
+            "decoder.up.2.block.0.conv1.weight": "blocks.7.conv1.weight",
+            "decoder.up.2.block.0.conv2.bias": "blocks.7.conv2.bias",
+            "decoder.up.2.block.0.conv2.weight": "blocks.7.conv2.weight",
+            "decoder.up.2.block.0.norm1.bias": "blocks.7.norm1.bias",
+            "decoder.up.2.block.0.norm1.weight": "blocks.7.norm1.weight",
+            "decoder.up.2.block.0.norm2.bias": "blocks.7.norm2.bias",
+            "decoder.up.2.block.0.norm2.weight": "blocks.7.norm2.weight",
+            "decoder.up.2.block.1.conv1.bias": "blocks.8.conv1.bias",
+            "decoder.up.2.block.1.conv1.weight": "blocks.8.conv1.weight",
+            "decoder.up.2.block.1.conv2.bias": "blocks.8.conv2.bias",
+            "decoder.up.2.block.1.conv2.weight": "blocks.8.conv2.weight",
+            "decoder.up.2.block.1.norm1.bias": "blocks.8.norm1.bias",
+            "decoder.up.2.block.1.norm1.weight": "blocks.8.norm1.weight",
+            "decoder.up.2.block.1.norm2.bias": "blocks.8.norm2.bias",
+            "decoder.up.2.block.1.norm2.weight": "blocks.8.norm2.weight",
+            "decoder.up.2.block.2.conv1.bias": "blocks.9.conv1.bias",
+            "decoder.up.2.block.2.conv1.weight": "blocks.9.conv1.weight",
+            "decoder.up.2.block.2.conv2.bias": "blocks.9.conv2.bias",
+            "decoder.up.2.block.2.conv2.weight": "blocks.9.conv2.weight",
+            "decoder.up.2.block.2.norm1.bias": "blocks.9.norm1.bias",
+            "decoder.up.2.block.2.norm1.weight": "blocks.9.norm1.weight",
+            "decoder.up.2.block.2.norm2.bias": "blocks.9.norm2.bias",
+            "decoder.up.2.block.2.norm2.weight": "blocks.9.norm2.weight",
+            "decoder.up.2.upsample.conv.bias": "blocks.10.conv.bias",
+            "decoder.up.2.upsample.conv.weight": "blocks.10.conv.weight",
+            "decoder.up.3.block.0.conv1.bias": "blocks.3.conv1.bias",
+            "decoder.up.3.block.0.conv1.weight": "blocks.3.conv1.weight",
+            "decoder.up.3.block.0.conv2.bias": "blocks.3.conv2.bias",
+            "decoder.up.3.block.0.conv2.weight": "blocks.3.conv2.weight",
+            "decoder.up.3.block.0.norm1.bias": "blocks.3.norm1.bias",
+            "decoder.up.3.block.0.norm1.weight": "blocks.3.norm1.weight",
+            "decoder.up.3.block.0.norm2.bias": "blocks.3.norm2.bias",
+            "decoder.up.3.block.0.norm2.weight": "blocks.3.norm2.weight",
+            "decoder.up.3.block.1.conv1.bias": "blocks.4.conv1.bias",
+            "decoder.up.3.block.1.conv1.weight": "blocks.4.conv1.weight",
+            "decoder.up.3.block.1.conv2.bias": "blocks.4.conv2.bias",
+            "decoder.up.3.block.1.conv2.weight": "blocks.4.conv2.weight",
+            "decoder.up.3.block.1.norm1.bias": "blocks.4.norm1.bias",
+            "decoder.up.3.block.1.norm1.weight": "blocks.4.norm1.weight",
+            "decoder.up.3.block.1.norm2.bias": "blocks.4.norm2.bias",
+            "decoder.up.3.block.1.norm2.weight": "blocks.4.norm2.weight",
+            "decoder.up.3.block.2.conv1.bias": "blocks.5.conv1.bias",
+            "decoder.up.3.block.2.conv1.weight": "blocks.5.conv1.weight",
+            "decoder.up.3.block.2.conv2.bias": "blocks.5.conv2.bias",
+            "decoder.up.3.block.2.conv2.weight": "blocks.5.conv2.weight",
+            "decoder.up.3.block.2.norm1.bias": "blocks.5.norm1.bias",
+            "decoder.up.3.block.2.norm1.weight": "blocks.5.norm1.weight",
+            "decoder.up.3.block.2.norm2.bias": "blocks.5.norm2.bias",
+            "decoder.up.3.block.2.norm2.weight": "blocks.5.norm2.weight",
+            "decoder.up.3.upsample.conv.bias": "blocks.6.conv.bias",
+            "decoder.up.3.upsample.conv.weight": "blocks.6.conv.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if "transformer_blocks" in rename_dict[name]:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        return state_dict_

diffsynth/models/hunyuan_video_dit.py

@@ -0,0 +1,885 @@
+import torch
+from .sd3_dit import TimestepEmbeddings, RMSNorm
+from .utils import init_weights_on_device
+from einops import rearrange, repeat
+from tqdm import tqdm
+from typing import Union, Tuple, List
+
+
+def HunyuanVideoRope(latents):
+    def _to_tuple(x, dim=2):
+        if isinstance(x, int):
+            return (x,) * dim
+        elif len(x) == dim:
+            return x
+        else:
+            raise ValueError(f"Expected length {dim} or int, but got {x}")
+
+
+    def get_meshgrid_nd(start, *args, dim=2):
+        """
+        Get n-D meshgrid with start, stop and num.
+
+        Args:
+            start (int or tuple): If len(args) == 0, start is num; If len(args) == 1, start is start, args[0] is stop,
+                step is 1; If len(args) == 2, start is start, args[0] is stop, args[1] is num. For n-dim, start/stop/num
+                should be int or n-tuple. If n-tuple is provided, the meshgrid will be stacked following the dim order in
+                n-tuples.
+            *args: See above.
+            dim (int): Dimension of the meshgrid. Defaults to 2.
+
+        Returns:
+            grid (np.ndarray): [dim, ...]
+        """
+        if len(args) == 0:
+            # start is grid_size
+            num = _to_tuple(start, dim=dim)
+            start = (0,) * dim
+            stop = num
+        elif len(args) == 1:
+            # start is start, args[0] is stop, step is 1
+            start = _to_tuple(start, dim=dim)
+            stop = _to_tuple(args[0], dim=dim)
+            num = [stop[i] - start[i] for i in range(dim)]
+        elif len(args) == 2:
+            # start is start, args[0] is stop, args[1] is num
+            start = _to_tuple(start, dim=dim)  # Left-Top       eg: 12,0
+            stop = _to_tuple(args[0], dim=dim)  # Right-Bottom   eg: 20,32
+            num = _to_tuple(args[1], dim=dim)  # Target Size    eg: 32,124
+        else:
+            raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}")
+
+        # PyTorch implement of np.linspace(start[i], stop[i], num[i], endpoint=False)
+        axis_grid = []
+        for i in range(dim):
+            a, b, n = start[i], stop[i], num[i]
+            g = torch.linspace(a, b, n + 1, dtype=torch.float32)[:n]
+            axis_grid.append(g)
+        grid = torch.meshgrid(*axis_grid, indexing="ij")  # dim x [W, H, D]
+        grid = torch.stack(grid, dim=0)  # [dim, W, H, D]
+
+        return grid
+
+
+    def get_1d_rotary_pos_embed(
+        dim: int,
+        pos: Union[torch.FloatTensor, int],
+        theta: float = 10000.0,
+        use_real: bool = False,
+        theta_rescale_factor: float = 1.0,
+        interpolation_factor: float = 1.0,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """
+        Precompute the frequency tensor for complex exponential (cis) with given dimensions.
+        (Note: `cis` means `cos + i * sin`, where i is the imaginary unit.)
+
+        This function calculates a frequency tensor with complex exponential using the given dimension 'dim'
+        and the end index 'end'. The 'theta' parameter scales the frequencies.
+        The returned tensor contains complex values in complex64 data type.
+
+        Args:
+            dim (int): Dimension of the frequency tensor.
+            pos (int or torch.FloatTensor): Position indices for the frequency tensor. [S] or scalar
+            theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0.
+            use_real (bool, optional): If True, return real part and imaginary part separately.
+                                    Otherwise, return complex numbers.
+            theta_rescale_factor (float, optional): Rescale factor for theta. Defaults to 1.0.
+
+        Returns:
+            freqs_cis: Precomputed frequency tensor with complex exponential. [S, D/2]
+            freqs_cos, freqs_sin: Precomputed frequency tensor with real and imaginary parts separately. [S, D]
+        """
+        if isinstance(pos, int):
+            pos = torch.arange(pos).float()
+
+        # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
+        # has some connection to NTK literature
+        if theta_rescale_factor != 1.0:
+            theta *= theta_rescale_factor ** (dim / (dim - 2))
+
+        freqs = 1.0 / (
+            theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)
+        )  # [D/2]
+        # assert interpolation_factor == 1.0, f"interpolation_factor: {interpolation_factor}"
+        freqs = torch.outer(pos * interpolation_factor, freqs)  # [S, D/2]
+        if use_real:
+            freqs_cos = freqs.cos().repeat_interleave(2, dim=1)  # [S, D]
+            freqs_sin = freqs.sin().repeat_interleave(2, dim=1)  # [S, D]
+            return freqs_cos, freqs_sin
+        else:
+            freqs_cis = torch.polar(
+                torch.ones_like(freqs), freqs
+            )  # complex64     # [S, D/2]
+            return freqs_cis
+
+
+    def get_nd_rotary_pos_embed(
+        rope_dim_list,
+        start,
+        *args,
+        theta=10000.0,
+        use_real=False,
+        theta_rescale_factor: Union[float, List[float]] = 1.0,
+        interpolation_factor: Union[float, List[float]] = 1.0,
+    ):
+        """
+        This is a n-d version of precompute_freqs_cis, which is a RoPE for tokens with n-d structure.
+
+        Args:
+            rope_dim_list (list of int): Dimension of each rope. len(rope_dim_list) should equal to n.
+                sum(rope_dim_list) should equal to head_dim of attention layer.
+            start (int | tuple of int | list of int): If len(args) == 0, start is num; If len(args) == 1, start is start,
+                args[0] is stop, step is 1; If len(args) == 2, start is start, args[0] is stop, args[1] is num.
+            *args: See above.
+            theta (float): Scaling factor for frequency computation. Defaults to 10000.0.
+            use_real (bool): If True, return real part and imaginary part separately. Otherwise, return complex numbers.
+                Some libraries such as TensorRT does not support complex64 data type. So it is useful to provide a real
+                part and an imaginary part separately.
+            theta_rescale_factor (float): Rescale factor for theta. Defaults to 1.0.
+
+        Returns:
+            pos_embed (torch.Tensor): [HW, D/2]
+        """
+
+        grid = get_meshgrid_nd(
+            start, *args, dim=len(rope_dim_list)
+        )  # [3, W, H, D] / [2, W, H]
+
+        if isinstance(theta_rescale_factor, int) or isinstance(theta_rescale_factor, float):
+            theta_rescale_factor = [theta_rescale_factor] * len(rope_dim_list)
+        elif isinstance(theta_rescale_factor, list) and len(theta_rescale_factor) == 1:
+            theta_rescale_factor = [theta_rescale_factor[0]] * len(rope_dim_list)
+        assert len(theta_rescale_factor) == len(
+            rope_dim_list
+        ), "len(theta_rescale_factor) should equal to len(rope_dim_list)"
+
+        if isinstance(interpolation_factor, int) or isinstance(interpolation_factor, float):
+            interpolation_factor = [interpolation_factor] * len(rope_dim_list)
+        elif isinstance(interpolation_factor, list) and len(interpolation_factor) == 1:
+            interpolation_factor = [interpolation_factor[0]] * len(rope_dim_list)
+        assert len(interpolation_factor) == len(
+            rope_dim_list
+        ), "len(interpolation_factor) should equal to len(rope_dim_list)"
+
+        # use 1/ndim of dimensions to encode grid_axis
+        embs = []
+        for i in range(len(rope_dim_list)):
+            emb = get_1d_rotary_pos_embed(
+                rope_dim_list[i],
+                grid[i].reshape(-1),
+                theta,
+                use_real=use_real,
+                theta_rescale_factor=theta_rescale_factor[i],
+                interpolation_factor=interpolation_factor[i],
+            )  # 2 x [WHD, rope_dim_list[i]]
+            embs.append(emb)
+
+        if use_real:
+            cos = torch.cat([emb[0] for emb in embs], dim=1)  # (WHD, D/2)
+            sin = torch.cat([emb[1] for emb in embs], dim=1)  # (WHD, D/2)
+            return cos, sin
+        else:
+            emb = torch.cat(embs, dim=1)  # (WHD, D/2)
+            return emb
+
+    freqs_cos, freqs_sin = get_nd_rotary_pos_embed(
+        [16, 56, 56],
+        [latents.shape[2], latents.shape[3] // 2, latents.shape[4] // 2],
+        theta=256,
+        use_real=True,
+        theta_rescale_factor=1,
+    )
+    return freqs_cos, freqs_sin
+
+
+class PatchEmbed(torch.nn.Module):
+    def __init__(self, patch_size=(1, 2, 2), in_channels=16, embed_dim=3072):
+        super().__init__()
+        self.proj = torch.nn.Conv3d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
+
+    def forward(self, x):
+        x = self.proj(x)
+        x = x.flatten(2).transpose(1, 2)
+        return x
+
+
+class IndividualTokenRefinerBlock(torch.nn.Module):
+    def __init__(self, hidden_size=3072, num_heads=24):
+        super().__init__()
+        self.num_heads = num_heads
+        self.norm1 = torch.nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6)
+        self.self_attn_qkv = torch.nn.Linear(hidden_size, hidden_size * 3)
+        self.self_attn_proj = torch.nn.Linear(hidden_size, hidden_size)
+
+        self.norm2 = torch.nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6)
+        self.mlp = torch.nn.Sequential(
+            torch.nn.Linear(hidden_size, hidden_size * 4),
+            torch.nn.SiLU(),
+            torch.nn.Linear(hidden_size * 4, hidden_size)
+        )
+        self.adaLN_modulation = torch.nn.Sequential(
+            torch.nn.SiLU(),
+            torch.nn.Linear(hidden_size, hidden_size * 2, device="cuda", dtype=torch.bfloat16),
+        )
+
+    def forward(self, x, c, attn_mask=None):
+        gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=1)
+
+        norm_x = self.norm1(x)
+        qkv = self.self_attn_qkv(norm_x)
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+
+        attn = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        attn = rearrange(attn, "B H L D -> B L (H D)")
+
+        x = x + self.self_attn_proj(attn) * gate_msa.unsqueeze(1)
+        x = x + self.mlp(self.norm2(x)) * gate_mlp.unsqueeze(1)
+
+        return x
+    
+
+class SingleTokenRefiner(torch.nn.Module):
+    def __init__(self, in_channels=4096, hidden_size=3072, depth=2):
+        super().__init__()
+        self.input_embedder = torch.nn.Linear(in_channels, hidden_size, bias=True)
+        self.t_embedder = TimestepEmbeddings(256, hidden_size, computation_device="cpu")
+        self.c_embedder = torch.nn.Sequential(
+            torch.nn.Linear(in_channels, hidden_size),
+            torch.nn.SiLU(),
+            torch.nn.Linear(hidden_size, hidden_size)
+        )
+        self.blocks = torch.nn.ModuleList([IndividualTokenRefinerBlock(hidden_size=hidden_size) for _ in range(depth)])
+
+    def forward(self, x, t, mask=None):
+        timestep_aware_representations = self.t_embedder(t, dtype=torch.float32)
+
+        mask_float = mask.float().unsqueeze(-1)
+        context_aware_representations = (x * mask_float).sum(dim=1) / mask_float.sum(dim=1)
+        context_aware_representations = self.c_embedder(context_aware_representations)
+        c = timestep_aware_representations + context_aware_representations
+
+        x = self.input_embedder(x)
+
+        mask = mask.to(device=x.device, dtype=torch.bool)
+        mask = repeat(mask, "B L -> B 1 D L", D=mask.shape[-1])
+        mask = mask & mask.transpose(2, 3)
+        mask[:, :, :, 0] = True
+
+        for block in self.blocks:
+            x = block(x, c, mask)
+
+        return x
+    
+
+class ModulateDiT(torch.nn.Module):
+    def __init__(self, hidden_size, factor=6):
+        super().__init__()
+        self.act = torch.nn.SiLU()
+        self.linear = torch.nn.Linear(hidden_size, factor * hidden_size)
+
+    def forward(self, x):
+        return self.linear(self.act(x))
+    
+
+def modulate(x, shift=None, scale=None):
+    if scale is None and shift is None:
+        return x
+    elif shift is None:
+        return x * (1 + scale.unsqueeze(1))
+    elif scale is None:
+        return x + shift.unsqueeze(1)
+    else:
+        return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+    
+
+def reshape_for_broadcast(
+    freqs_cis,
+    x: torch.Tensor,
+    head_first=False,
+):
+    ndim = x.ndim
+    assert 0 <= 1 < ndim
+
+    if isinstance(freqs_cis, tuple):
+        # freqs_cis: (cos, sin) in real space
+        if head_first:
+            assert freqs_cis[0].shape == (
+                x.shape[-2],
+                x.shape[-1],
+            ), f"freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}"
+            shape = [
+                d if i == ndim - 2 or i == ndim - 1 else 1
+                for i, d in enumerate(x.shape)
+            ]
+        else:
+            assert freqs_cis[0].shape == (
+                x.shape[1],
+                x.shape[-1],
+            ), f"freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}"
+            shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape)
+    else:
+        # freqs_cis: values in complex space
+        if head_first:
+            assert freqs_cis.shape == (
+                x.shape[-2],
+                x.shape[-1],
+            ), f"freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}"
+            shape = [
+                d if i == ndim - 2 or i == ndim - 1 else 1
+                for i, d in enumerate(x.shape)
+            ]
+        else:
+            assert freqs_cis.shape == (
+                x.shape[1],
+                x.shape[-1],
+            ), f"freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}"
+            shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return freqs_cis.view(*shape)
+
+
+def rotate_half(x):
+    x_real, x_imag = (
+        x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1)
+    )  # [B, S, H, D//2]
+    return torch.stack([-x_imag, x_real], dim=-1).flatten(3)
+    
+
+def apply_rotary_emb(
+    xq: torch.Tensor,
+    xk: torch.Tensor,
+    freqs_cis,
+    head_first: bool = False,
+):
+    xk_out = None
+    if isinstance(freqs_cis, tuple):
+        cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first)  # [S, D]
+        cos, sin = cos.to(xq.device), sin.to(xq.device)
+        # real * cos - imag * sin
+        # imag * cos + real * sin
+        xq_out = (xq.float() * cos + rotate_half(xq.float()) * sin).type_as(xq)
+        xk_out = (xk.float() * cos + rotate_half(xk.float()) * sin).type_as(xk)
+    else:
+        # view_as_complex will pack [..., D/2, 2](real) to [..., D/2](complex)
+        xq_ = torch.view_as_complex(
+            xq.float().reshape(*xq.shape[:-1], -1, 2)
+        )  # [B, S, H, D//2]
+        freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(
+            xq.device
+        )  # [S, D//2] --> [1, S, 1, D//2]
+        # (real, imag) * (cos, sin) = (real * cos - imag * sin, imag * cos + real * sin)
+        # view_as_real will expand [..., D/2](complex) to [..., D/2, 2](real)
+        xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq)
+        xk_ = torch.view_as_complex(
+            xk.float().reshape(*xk.shape[:-1], -1, 2)
+        )  # [B, S, H, D//2]
+        xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk)
+
+    return xq_out, xk_out
+
+
+def attention(q, k, v):
+    q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
+    x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+    x = x.transpose(1, 2).flatten(2, 3)
+    return x
+
+
+class MMDoubleStreamBlockComponent(torch.nn.Module):
+    def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4):
+        super().__init__()
+        self.heads_num = heads_num
+
+        self.mod = ModulateDiT(hidden_size)
+        self.norm1 = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+
+        self.to_qkv = torch.nn.Linear(hidden_size, hidden_size * 3)
+        self.norm_q = RMSNorm(dim=hidden_size // heads_num, eps=1e-6)
+        self.norm_k = RMSNorm(dim=hidden_size // heads_num, eps=1e-6)
+        self.to_out = torch.nn.Linear(hidden_size, hidden_size)
+
+        self.norm2 = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.ff = torch.nn.Sequential(
+            torch.nn.Linear(hidden_size, hidden_size * mlp_width_ratio),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(hidden_size * mlp_width_ratio, hidden_size)
+        )
+
+    def forward(self, hidden_states, conditioning, freqs_cis=None):
+        mod1_shift, mod1_scale, mod1_gate, mod2_shift, mod2_scale, mod2_gate = self.mod(conditioning).chunk(6, dim=-1)
+
+        norm_hidden_states = self.norm1(hidden_states)
+        norm_hidden_states = modulate(norm_hidden_states, shift=mod1_shift, scale=mod1_scale)
+        qkv = self.to_qkv(norm_hidden_states)
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num)
+
+        q = self.norm_q(q)
+        k = self.norm_k(k)
+
+        if freqs_cis is not None:
+            q, k = apply_rotary_emb(q, k, freqs_cis, head_first=False)
+
+        return (q, k, v), (mod1_gate, mod2_shift, mod2_scale, mod2_gate)
+    
+    def process_ff(self, hidden_states, attn_output, mod):
+        mod1_gate, mod2_shift, mod2_scale, mod2_gate = mod
+        hidden_states = hidden_states + self.to_out(attn_output) * mod1_gate.unsqueeze(1)
+        hidden_states = hidden_states + self.ff(modulate(self.norm2(hidden_states), shift=mod2_shift, scale=mod2_scale)) * mod2_gate.unsqueeze(1)
+        return hidden_states
+    
+
+class MMDoubleStreamBlock(torch.nn.Module):
+    def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4):
+        super().__init__()
+        self.component_a = MMDoubleStreamBlockComponent(hidden_size, heads_num, mlp_width_ratio)
+        self.component_b = MMDoubleStreamBlockComponent(hidden_size, heads_num, mlp_width_ratio)
+
+    def forward(self, hidden_states_a, hidden_states_b, conditioning, freqs_cis):
+        (q_a, k_a, v_a), mod_a = self.component_a(hidden_states_a, conditioning, freqs_cis)
+        (q_b, k_b, v_b), mod_b = self.component_b(hidden_states_b, conditioning, freqs_cis=None)
+
+        q_a, q_b = torch.concat([q_a, q_b[:, :71]], dim=1), q_b[:, 71:].contiguous()
+        k_a, k_b = torch.concat([k_a, k_b[:, :71]], dim=1), k_b[:, 71:].contiguous()
+        v_a, v_b = torch.concat([v_a, v_b[:, :71]], dim=1), v_b[:, 71:].contiguous()
+        attn_output_a = attention(q_a, k_a, v_a)
+        attn_output_b = attention(q_b, k_b, v_b)
+        attn_output_a, attn_output_b = attn_output_a[:, :-71].contiguous(), torch.concat([attn_output_a[:, -71:], attn_output_b], dim=1)
+
+        hidden_states_a = self.component_a.process_ff(hidden_states_a, attn_output_a, mod_a)
+        hidden_states_b = self.component_b.process_ff(hidden_states_b, attn_output_b, mod_b)
+        return hidden_states_a, hidden_states_b
+
+
+class MMSingleStreamBlockOriginal(torch.nn.Module):
+    def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.heads_num = heads_num
+        self.mlp_hidden_dim = hidden_size * mlp_width_ratio
+
+        self.linear1 = torch.nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
+        self.linear2 = torch.nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
+
+        self.q_norm = RMSNorm(dim=hidden_size // heads_num, eps=1e-6)
+        self.k_norm = RMSNorm(dim=hidden_size // heads_num, eps=1e-6)
+
+        self.pre_norm = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+
+        self.mlp_act = torch.nn.GELU(approximate="tanh")
+        self.modulation = ModulateDiT(hidden_size, factor=3)
+
+    def forward(self, x, vec, freqs_cis=None, txt_len=256):
+        mod_shift, mod_scale, mod_gate = self.modulation(vec).chunk(3, dim=-1)
+        x_mod = modulate(self.pre_norm(x), shift=mod_shift, scale=mod_scale)
+        qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num)
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        q_a, q_b = q[:, :-txt_len, :, :], q[:, -txt_len:, :, :]
+        k_a, k_b = k[:, :-txt_len, :, :], k[:, -txt_len:, :, :]
+        q_a, k_a = apply_rotary_emb(q_a, k_a, freqs_cis, head_first=False)
+        q = torch.cat((q_a, q_b), dim=1)
+        k = torch.cat((k_a, k_b), dim=1)
+
+        attn_output_a = attention(q[:, :-185].contiguous(), k[:, :-185].contiguous(), v[:, :-185].contiguous())
+        attn_output_b = attention(q[:, -185:].contiguous(), k[:, -185:].contiguous(), v[:, -185:].contiguous())
+        attn_output = torch.concat([attn_output_a, attn_output_b], dim=1)
+
+        output = self.linear2(torch.cat((attn_output, self.mlp_act(mlp)), 2))
+        return x + output * mod_gate.unsqueeze(1)
+    
+
+class MMSingleStreamBlock(torch.nn.Module):
+    def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4):
+        super().__init__()
+        self.heads_num = heads_num
+
+        self.mod = ModulateDiT(hidden_size, factor=3)
+        self.norm = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+
+        self.to_qkv = torch.nn.Linear(hidden_size, hidden_size * 3)
+        self.norm_q = RMSNorm(dim=hidden_size // heads_num, eps=1e-6)
+        self.norm_k = RMSNorm(dim=hidden_size // heads_num, eps=1e-6)
+        self.to_out = torch.nn.Linear(hidden_size, hidden_size)
+
+        self.ff = torch.nn.Sequential(
+            torch.nn.Linear(hidden_size, hidden_size * mlp_width_ratio),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(hidden_size * mlp_width_ratio, hidden_size, bias=False)
+        )
+
+    def forward(self, hidden_states, conditioning, freqs_cis=None, txt_len=256):
+        mod_shift, mod_scale, mod_gate = self.mod(conditioning).chunk(3, dim=-1)
+
+        norm_hidden_states = self.norm(hidden_states)
+        norm_hidden_states = modulate(norm_hidden_states, shift=mod_shift, scale=mod_scale)
+        qkv = self.to_qkv(norm_hidden_states)
+
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num)
+
+        q = self.norm_q(q)
+        k = self.norm_k(k)
+
+        q_a, q_b = q[:, :-txt_len, :, :], q[:, -txt_len:, :, :]
+        k_a, k_b = k[:, :-txt_len, :, :], k[:, -txt_len:, :, :]
+        q_a, k_a = apply_rotary_emb(q_a, k_a, freqs_cis, head_first=False)
+
+        q_a, q_b = torch.concat([q_a, q_b[:, :71]], dim=1), q_b[:, 71:].contiguous()
+        k_a, k_b = torch.concat([k_a, k_b[:, :71]], dim=1), k_b[:, 71:].contiguous()
+        v_a, v_b = v[:, :-185].contiguous(), v[:, -185:].contiguous()
+
+        attn_output_a = attention(q_a, k_a, v_a)
+        attn_output_b = attention(q_b, k_b, v_b)
+        attn_output = torch.concat([attn_output_a, attn_output_b], dim=1)
+
+        hidden_states = hidden_states + self.to_out(attn_output) * mod_gate.unsqueeze(1)
+        hidden_states = hidden_states + self.ff(norm_hidden_states) * mod_gate.unsqueeze(1)
+        return hidden_states
+
+
+class FinalLayer(torch.nn.Module):
+    def __init__(self, hidden_size=3072, patch_size=(1, 2, 2), out_channels=16):
+        super().__init__()
+
+        self.norm_final = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = torch.nn.Linear(hidden_size, patch_size[0] * patch_size[1] * patch_size[2] * out_channels)
+
+        self.adaLN_modulation = torch.nn.Sequential(torch.nn.SiLU(), torch.nn.Linear(hidden_size, 2 * hidden_size))
+
+    def forward(self, x, c):
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
+        x = modulate(self.norm_final(x), shift=shift, scale=scale)
+        x = self.linear(x)
+        return x
+
+
+class HunyuanVideoDiT(torch.nn.Module):
+    def __init__(self, in_channels=16, hidden_size=3072, text_dim=4096, num_double_blocks=20, num_single_blocks=40):
+        super().__init__()
+        self.img_in = PatchEmbed(in_channels=in_channels, embed_dim=hidden_size)
+        self.txt_in = SingleTokenRefiner(in_channels=text_dim, hidden_size=hidden_size)
+        self.time_in = TimestepEmbeddings(256, hidden_size, computation_device="cpu")
+        self.vector_in = torch.nn.Sequential(
+            torch.nn.Linear(768, hidden_size),
+            torch.nn.SiLU(),
+            torch.nn.Linear(hidden_size, hidden_size)
+        )
+        self.guidance_in = TimestepEmbeddings(256, hidden_size, computation_device="cpu")
+        self.double_blocks = torch.nn.ModuleList([MMDoubleStreamBlock(hidden_size) for _ in range(num_double_blocks)])
+        self.single_blocks = torch.nn.ModuleList([MMSingleStreamBlock(hidden_size) for _ in range(num_single_blocks)])
+        self.final_layer = FinalLayer(hidden_size)
+
+        # TODO: remove these parameters
+        self.dtype = torch.bfloat16
+        self.patch_size = [1, 2, 2]
+        self.hidden_size = 3072
+        self.heads_num = 24
+        self.rope_dim_list = [16, 56, 56]
+
+    def unpatchify(self, x, T, H, W):
+        x = rearrange(x, "B (T H W) (C pT pH pW) -> B C (T pT) (H pH) (W pW)", H=H, W=W, pT=1, pH=2, pW=2)
+        return x
+    
+    def enable_block_wise_offload(self, warm_device="cuda", cold_device="cpu"):
+        self.warm_device = warm_device
+        self.cold_device = cold_device
+        self.to(self.cold_device)
+
+    def load_models_to_device(self, loadmodel_names=[], device="cpu"):
+        for model_name in loadmodel_names:
+            model = getattr(self, model_name)
+            if model is not None:
+                model.to(device)
+        torch.cuda.empty_cache()
+
+    def prepare_freqs(self, latents):
+        return HunyuanVideoRope(latents)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        t: torch.Tensor,
+        prompt_emb: torch.Tensor = None,
+        text_mask: torch.Tensor = None,
+        pooled_prompt_emb: torch.Tensor = None,
+        freqs_cos: torch.Tensor = None,
+        freqs_sin: torch.Tensor = None,
+        guidance: torch.Tensor = None,
+        **kwargs
+    ):
+        B, C, T, H, W = x.shape
+
+        vec = self.time_in(t, dtype=torch.float32) + self.vector_in(pooled_prompt_emb) + self.guidance_in(guidance * 1000, dtype=torch.float32)
+        img = self.img_in(x)
+        txt = self.txt_in(prompt_emb, t, text_mask)
+        
+        for block in tqdm(self.double_blocks, desc="Double stream blocks"):
+            img, txt = block(img, txt, vec, (freqs_cos, freqs_sin))
+
+        x = torch.concat([img, txt], dim=1)
+        for block in tqdm(self.single_blocks, desc="Single stream blocks"):
+            x = block(x, vec, (freqs_cos, freqs_sin))
+
+        img = x[:, :-256]
+        img = self.final_layer(img, vec)
+        img = self.unpatchify(img, T=T//1, H=H//2, W=W//2)
+        return img
+    
+
+    def enable_auto_offload(self, dtype=torch.bfloat16, device="cuda"):
+        def cast_to(weight, dtype=None, device=None, copy=False):
+            if device is None or weight.device == device:
+                if not copy:
+                    if dtype is None or weight.dtype == dtype:
+                        return weight
+                return weight.to(dtype=dtype, copy=copy)
+
+            r = torch.empty_like(weight, dtype=dtype, device=device)
+            r.copy_(weight)
+            return r
+
+        def cast_weight(s, input=None, dtype=None, device=None):
+            if input is not None:
+                if dtype is None:
+                    dtype = input.dtype
+                if device is None:
+                    device = input.device
+            weight = cast_to(s.weight, dtype, device)
+            return weight
+
+        def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
+            if input is not None:
+                if dtype is None:
+                    dtype = input.dtype
+                if bias_dtype is None:
+                    bias_dtype = dtype
+                if device is None:
+                    device = input.device
+            weight = cast_to(s.weight, dtype, device)
+            bias = cast_to(s.bias, bias_dtype, device) if s.bias is not None else None
+            return weight, bias
+
+        class quantized_layer:
+            class Linear(torch.nn.Linear):
+                def __init__(self, *args, dtype=torch.bfloat16, device="cuda", **kwargs):
+                    super().__init__(*args, **kwargs)
+                    self.dtype = dtype
+                    self.device = device
+
+                def block_forward_(self, x, i, j, dtype, device):
+                    weight_ = cast_to(
+                        self.weight[j * self.block_size: (j + 1) * self.block_size, i * self.block_size: (i + 1) * self.block_size],
+                        dtype=dtype, device=device
+                    )
+                    if self.bias is None or i > 0:
+                        bias_ = None
+                    else:
+                        bias_ = cast_to(self.bias[j * self.block_size: (j + 1) * self.block_size], dtype=dtype, device=device)
+                    x_ = x[..., i * self.block_size: (i + 1) * self.block_size]
+                    y_ = torch.nn.functional.linear(x_, weight_, bias_)
+                    del x_, weight_, bias_
+                    torch.cuda.empty_cache()
+                    return y_
+                
+                def block_forward(self, x, **kwargs):
+                    # This feature can only reduce 2GB VRAM, so we disable it.
+                    y = torch.zeros(x.shape[:-1] + (self.out_features,), dtype=x.dtype, device=x.device)
+                    for i in range((self.in_features + self.block_size - 1) // self.block_size):
+                        for j in range((self.out_features + self.block_size - 1) // self.block_size):
+                            y[..., j * self.block_size: (j + 1) * self.block_size] += self.block_forward_(x, i, j, dtype=x.dtype, device=x.device)
+                    return y
+                    
+                def forward(self, x, **kwargs):
+                    weight, bias = cast_bias_weight(self, x, dtype=self.dtype, device=self.device)
+                    return torch.nn.functional.linear(x, weight, bias)
+
+            
+            class RMSNorm(torch.nn.Module):
+                def __init__(self, module, dtype=torch.bfloat16, device="cuda"):
+                    super().__init__()
+                    self.module = module
+                    self.dtype = dtype
+                    self.device = device
+                    
+                def forward(self, hidden_states, **kwargs):
+                    input_dtype = hidden_states.dtype
+                    variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True)
+                    hidden_states = hidden_states * torch.rsqrt(variance + self.module.eps)
+                    hidden_states = hidden_states.to(input_dtype)
+                    if self.module.weight is not None:
+                        weight = cast_weight(self.module, hidden_states, dtype=torch.bfloat16, device="cuda")
+                        hidden_states = hidden_states * weight
+                    return hidden_states
+                
+            class Conv3d(torch.nn.Conv3d):
+                def __init__(self, *args, dtype=torch.bfloat16, device="cuda", **kwargs):
+                    super().__init__(*args, **kwargs)
+                    self.dtype = dtype
+                    self.device = device
+                    
+                def forward(self, x):
+                    weight, bias = cast_bias_weight(self, x, dtype=self.dtype, device=self.device)
+                    return torch.nn.functional.conv3d(x, weight, bias, self.stride, self.padding, self.dilation, self.groups)
+                
+            class LayerNorm(torch.nn.LayerNorm):
+                def __init__(self, *args, dtype=torch.bfloat16, device="cuda", **kwargs):
+                    super().__init__(*args, **kwargs)
+                    self.dtype = dtype
+                    self.device = device
+                    
+                def forward(self, x):
+                    if self.weight is not None and self.bias is not None:
+                        weight, bias = cast_bias_weight(self, x, dtype=self.dtype, device=self.device)
+                        return torch.nn.functional.layer_norm(x, self.normalized_shape, weight, bias, self.eps)
+                    else:
+                        return torch.nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
+            
+        def replace_layer(model, dtype=torch.bfloat16, device="cuda"):
+            for name, module in model.named_children():
+                if isinstance(module, torch.nn.Linear):
+                    with init_weights_on_device():
+                        new_layer = quantized_layer.Linear(
+                            module.in_features, module.out_features, bias=module.bias is not None,
+                            dtype=dtype, device=device
+                        )
+                    new_layer.load_state_dict(module.state_dict(), assign=True)
+                    setattr(model, name, new_layer)
+                elif isinstance(module, torch.nn.Conv3d):
+                    with init_weights_on_device():
+                        new_layer = quantized_layer.Conv3d(
+                            module.in_channels, module.out_channels, kernel_size=module.kernel_size, stride=module.stride,
+                            dtype=dtype, device=device
+                        )
+                    new_layer.load_state_dict(module.state_dict(), assign=True)
+                    setattr(model, name, new_layer)
+                elif isinstance(module, RMSNorm):
+                    new_layer = quantized_layer.RMSNorm(
+                        module,
+                        dtype=dtype, device=device
+                    )
+                    setattr(model, name, new_layer)
+                elif isinstance(module, torch.nn.LayerNorm):
+                    with init_weights_on_device():
+                        new_layer = quantized_layer.LayerNorm(
+                            module.normalized_shape, elementwise_affine=module.elementwise_affine, eps=module.eps,
+                            dtype=dtype, device=device
+                        )
+                    new_layer.load_state_dict(module.state_dict(), assign=True)
+                    setattr(model, name, new_layer)
+                else:
+                    replace_layer(module, dtype=dtype, device=device)
+
+        replace_layer(self, dtype=dtype, device=device)
+
+    @staticmethod
+    def state_dict_converter():
+        return HunyuanVideoDiTStateDictConverter()
+
+
+
+class HunyuanVideoDiTStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        if "module" in state_dict:
+            state_dict = state_dict["module"]
+        direct_dict = {
+            "img_in.proj": "img_in.proj",
+            "time_in.mlp.0": "time_in.timestep_embedder.0",
+            "time_in.mlp.2": "time_in.timestep_embedder.2",
+            "vector_in.in_layer": "vector_in.0",
+            "vector_in.out_layer": "vector_in.2",
+            "guidance_in.mlp.0": "guidance_in.timestep_embedder.0",
+            "guidance_in.mlp.2": "guidance_in.timestep_embedder.2",
+            "txt_in.input_embedder": "txt_in.input_embedder",
+            "txt_in.t_embedder.mlp.0": "txt_in.t_embedder.timestep_embedder.0",
+            "txt_in.t_embedder.mlp.2": "txt_in.t_embedder.timestep_embedder.2",
+            "txt_in.c_embedder.linear_1": "txt_in.c_embedder.0",
+            "txt_in.c_embedder.linear_2": "txt_in.c_embedder.2",
+            "final_layer.linear": "final_layer.linear",
+            "final_layer.adaLN_modulation.1": "final_layer.adaLN_modulation.1",
+        }
+        txt_suffix_dict = {
+            "norm1": "norm1",
+            "self_attn_qkv": "self_attn_qkv",
+            "self_attn_proj": "self_attn_proj",
+            "norm2": "norm2",
+            "mlp.fc1": "mlp.0",
+            "mlp.fc2": "mlp.2",
+            "adaLN_modulation.1": "adaLN_modulation.1",
+        }
+        double_suffix_dict = {
+            "img_mod.linear": "component_a.mod.linear",
+            "img_attn_qkv": "component_a.to_qkv",
+            "img_attn_q_norm": "component_a.norm_q",
+            "img_attn_k_norm": "component_a.norm_k",
+            "img_attn_proj": "component_a.to_out",
+            "img_mlp.fc1": "component_a.ff.0",
+            "img_mlp.fc2": "component_a.ff.2",
+            "txt_mod.linear": "component_b.mod.linear",
+            "txt_attn_qkv": "component_b.to_qkv",
+            "txt_attn_q_norm": "component_b.norm_q",
+            "txt_attn_k_norm": "component_b.norm_k",
+            "txt_attn_proj": "component_b.to_out",
+            "txt_mlp.fc1": "component_b.ff.0",
+            "txt_mlp.fc2": "component_b.ff.2",
+        }
+        single_suffix_dict = {
+            "linear1": ["to_qkv", "ff.0"],
+            "linear2": ["to_out", "ff.2"],
+            "q_norm": "norm_q",
+            "k_norm": "norm_k",
+            "modulation.linear": "mod.linear",
+        }
+        # single_suffix_dict = {
+        #     "linear1": "linear1",
+        #     "linear2": "linear2",
+        #     "q_norm": "q_norm",
+        #     "k_norm": "k_norm",
+        #     "modulation.linear": "modulation.linear",
+        # }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            names = name.split(".")
+            direct_name = ".".join(names[:-1])
+            if direct_name in direct_dict:
+                name_ = direct_dict[direct_name] + "." + names[-1]
+                state_dict_[name_] = param
+            elif names[0] == "double_blocks":
+                prefix = ".".join(names[:2])
+                suffix = ".".join(names[2:-1])
+                name_ = prefix + "." + double_suffix_dict[suffix] + "." + names[-1]
+                state_dict_[name_] = param
+            elif names[0] == "single_blocks":
+                prefix = ".".join(names[:2])
+                suffix = ".".join(names[2:-1])
+                if isinstance(single_suffix_dict[suffix], list):
+                    if suffix == "linear1":
+                        name_a, name_b = single_suffix_dict[suffix]
+                        param_a, param_b = torch.split(param, (3072*3, 3072*4), dim=0)
+                        state_dict_[prefix + "." + name_a + "." + names[-1]] = param_a
+                        state_dict_[prefix + "." + name_b + "." + names[-1]] = param_b
+                    elif suffix == "linear2":
+                        if names[-1] == "weight":
+                            name_a, name_b = single_suffix_dict[suffix]
+                            param_a, param_b = torch.split(param, (3072*1, 3072*4), dim=-1)
+                            state_dict_[prefix + "." + name_a + "." + names[-1]] = param_a
+                            state_dict_[prefix + "." + name_b + "." + names[-1]] = param_b
+                        else:
+                            name_a, name_b = single_suffix_dict[suffix]
+                            state_dict_[prefix + "." + name_a + "." + names[-1]] = param
+                    else:
+                        pass
+                else:
+                    name_ = prefix + "." + single_suffix_dict[suffix] + "." + names[-1]
+                    state_dict_[name_] = param
+            elif names[0] == "txt_in":
+                prefix = ".".join(names[:4]).replace(".individual_token_refiner.", ".")
+                suffix = ".".join(names[4:-1])
+                name_ = prefix + "." + txt_suffix_dict[suffix] + "." + names[-1]
+                state_dict_[name_] = param
+            else:
+                pass
+        return state_dict_

diffsynth/models/hunyuan_video_text_encoder.py

@@ -0,0 +1,55 @@
+from transformers import LlamaModel, LlamaConfig, DynamicCache
+from copy import deepcopy
+import torch
+
+
+class HunyuanVideoLLMEncoder(LlamaModel):
+    def __init__(self, config: LlamaConfig):
+        super().__init__(config)
+        self.auto_offload = False
+
+
+    def enable_auto_offload(self, **kwargs):
+        self.auto_offload = True
+        
+
+    def forward(
+        self,
+        input_ids,
+        attention_mask,
+        hidden_state_skip_layer=2
+    ):
+        embed_tokens = deepcopy(self.embed_tokens).to(input_ids.device) if self.auto_offload else self.embed_tokens
+        inputs_embeds = embed_tokens(input_ids)
+
+        past_key_values = DynamicCache()
+
+        cache_position = torch.arange(0, inputs_embeds.shape[1], device=inputs_embeds.device)
+        position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, None, False)
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        rotary_emb = deepcopy(self.rotary_emb).to(input_ids.device) if self.auto_offload else self.rotary_emb
+        position_embeddings = rotary_emb(hidden_states, position_ids)
+
+        # decoder layers
+        for layer_id, decoder_layer in enumerate(self.layers):
+            if self.auto_offload:
+                decoder_layer = deepcopy(decoder_layer).to(hidden_states.device)
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                output_attentions=False,
+                use_cache=True,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings,
+            )
+            hidden_states = layer_outputs[0]
+            if layer_id + hidden_state_skip_layer + 1 >= len(self.layers):
+                break
+
+        return hidden_states

diffsynth/models/hunyuan_video_vae_decoder.py

@@ -0,0 +1,507 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+import numpy as np
+from tqdm import tqdm
+from einops import repeat
+
+
+class CausalConv3d(nn.Module):
+
+    def __init__(self, in_channel, out_channel, kernel_size, stride=1, dilation=1, pad_mode='replicate', **kwargs):
+        super().__init__()
+        self.pad_mode = pad_mode
+        self.time_causal_padding = (kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size - 1, 0
+                                   )  # W, H, T
+        self.conv = nn.Conv3d(in_channel, out_channel, kernel_size, stride=stride, dilation=dilation, **kwargs)
+
+    def forward(self, x):
+        x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)
+        return self.conv(x)
+
+
+class UpsampleCausal3D(nn.Module):
+
+    def __init__(self, channels, use_conv=False, out_channels=None, kernel_size=None, bias=True, upsample_factor=(2, 2, 2)):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.upsample_factor = upsample_factor
+        self.conv = None
+        if use_conv:
+            kernel_size = 3 if kernel_size is None else kernel_size
+            self.conv = CausalConv3d(self.channels, self.out_channels, kernel_size=kernel_size, bias=bias)
+
+    def forward(self, hidden_states):
+        # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
+        dtype = hidden_states.dtype
+        if dtype == torch.bfloat16:
+            hidden_states = hidden_states.to(torch.float32)
+
+        # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
+        if hidden_states.shape[0] >= 64:
+            hidden_states = hidden_states.contiguous()
+
+        # interpolate
+        B, C, T, H, W = hidden_states.shape
+        first_h, other_h = hidden_states.split((1, T - 1), dim=2)
+        if T > 1:
+            other_h = F.interpolate(other_h, scale_factor=self.upsample_factor, mode="nearest")
+        first_h = F.interpolate(first_h.squeeze(2), scale_factor=self.upsample_factor[1:], mode="nearest").unsqueeze(2)
+        hidden_states = torch.cat((first_h, other_h), dim=2) if T > 1 else first_h
+
+        # If the input is bfloat16, we cast back to bfloat16
+        if dtype == torch.bfloat16:
+            hidden_states = hidden_states.to(dtype)
+
+        if self.conv:
+            hidden_states = self.conv(hidden_states)
+
+        return hidden_states
+
+
+class ResnetBlockCausal3D(nn.Module):
+
+    def __init__(self, in_channels, out_channels=None, dropout=0.0, groups=32, eps=1e-6, conv_shortcut_bias=True):
+        super().__init__()
+        self.pre_norm = True
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+
+        self.norm1 = nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
+        self.conv1 = CausalConv3d(in_channels, out_channels, kernel_size=3, stride=1)
+
+        self.norm2 = nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True)
+        self.conv2 = CausalConv3d(out_channels, out_channels, kernel_size=3, stride=1)
+
+        self.dropout = nn.Dropout(dropout)
+        self.nonlinearity = nn.SiLU()
+
+        self.conv_shortcut = None
+        if in_channels != out_channels:
+            self.conv_shortcut = CausalConv3d(in_channels, out_channels, kernel_size=1, stride=1, bias=conv_shortcut_bias)
+
+    def forward(self, input_tensor):
+        hidden_states = input_tensor
+        # conv1
+        hidden_states = self.norm1(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.conv1(hidden_states)
+
+        # conv2
+        hidden_states = self.norm2(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+        # shortcut
+        if self.conv_shortcut is not None:
+            input_tensor = (self.conv_shortcut(input_tensor))
+        # shortcut and scale
+        output_tensor = input_tensor + hidden_states
+
+        return output_tensor
+
+
+def prepare_causal_attention_mask(n_frame, n_hw, dtype, device, batch_size=None):
+    seq_len = n_frame * n_hw
+    mask = torch.full((seq_len, seq_len), float("-inf"), dtype=dtype, device=device)
+    for i in range(seq_len):
+        i_frame = i // n_hw
+        mask[i, :(i_frame + 1) * n_hw] = 0
+    if batch_size is not None:
+        mask = mask.unsqueeze(0).expand(batch_size, -1, -1)
+    return mask
+
+
+class Attention(nn.Module):
+
+    def __init__(self,
+                 in_channels,
+                 num_heads,
+                 head_dim,
+                 num_groups=32,
+                 dropout=0.0,
+                 eps=1e-6,
+                 bias=True,
+                 residual_connection=True):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+        self.residual_connection = residual_connection
+        dim_inner = head_dim * num_heads
+        self.group_norm = nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=eps, affine=True)
+        self.to_q = nn.Linear(in_channels, dim_inner, bias=bias)
+        self.to_k = nn.Linear(in_channels, dim_inner, bias=bias)
+        self.to_v = nn.Linear(in_channels, dim_inner, bias=bias)
+        self.to_out = nn.Sequential(nn.Linear(dim_inner, in_channels, bias=bias), nn.Dropout(dropout))
+
+    def forward(self, input_tensor, attn_mask=None):
+        hidden_states = self.group_norm(input_tensor.transpose(1, 2)).transpose(1, 2)
+        batch_size = hidden_states.shape[0]
+
+        q = self.to_q(hidden_states)
+        k = self.to_k(hidden_states)
+        v = self.to_v(hidden_states)
+
+        q = q.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        k = k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        v = v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+
+        if attn_mask is not None:
+            attn_mask = attn_mask.view(batch_size, self.num_heads, -1, attn_mask.shape[-1])
+        hidden_states = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = self.to_out(hidden_states)
+        if self.residual_connection:
+            output_tensor = input_tensor + hidden_states
+        return output_tensor
+
+
+class UNetMidBlockCausal3D(nn.Module):
+
+    def __init__(self, in_channels, dropout=0.0, num_layers=1, eps=1e-6, num_groups=32, attention_head_dim=None):
+        super().__init__()
+        resnets = [
+            ResnetBlockCausal3D(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                dropout=dropout,
+                groups=num_groups,
+                eps=eps,
+            )
+        ]
+        attentions = []
+        attention_head_dim = attention_head_dim or in_channels
+
+        for _ in range(num_layers):
+            attentions.append(
+                Attention(
+                    in_channels,
+                    num_heads=in_channels // attention_head_dim,
+                    head_dim=attention_head_dim,
+                    num_groups=num_groups,
+                    dropout=dropout,
+                    eps=eps,
+                    bias=True,
+                    residual_connection=True,
+                ))
+
+            resnets.append(
+                ResnetBlockCausal3D(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    dropout=dropout,
+                    groups=num_groups,
+                    eps=eps,
+                ))
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+    def forward(self, hidden_states):
+        hidden_states = self.resnets[0](hidden_states)
+        for attn, resnet in zip(self.attentions, self.resnets[1:]):
+            B, C, T, H, W = hidden_states.shape
+            hidden_states = rearrange(hidden_states, "b c f h w -> b (f h w) c")
+            attn_mask = prepare_causal_attention_mask(T, H * W, hidden_states.dtype, hidden_states.device, batch_size=B)
+            hidden_states = attn(hidden_states, attn_mask=attn_mask)
+            hidden_states = rearrange(hidden_states, "b (f h w) c -> b c f h w", f=T, h=H, w=W)
+            hidden_states = resnet(hidden_states)
+
+        return hidden_states
+
+
+class UpDecoderBlockCausal3D(nn.Module):
+
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            dropout=0.0,
+            num_layers=1,
+            eps=1e-6,
+            num_groups=32,
+            add_upsample=True,
+            upsample_scale_factor=(2, 2, 2),
+    ):
+        super().__init__()
+        resnets = []
+        for i in range(num_layers):
+            cur_in_channel = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlockCausal3D(
+                    in_channels=cur_in_channel,
+                    out_channels=out_channels,
+                    groups=num_groups,
+                    dropout=dropout,
+                    eps=eps,
+                ))
+        self.resnets = nn.ModuleList(resnets)
+
+        self.upsamplers = None
+        if add_upsample:
+            self.upsamplers = nn.ModuleList([
+                UpsampleCausal3D(
+                    out_channels,
+                    use_conv=True,
+                    out_channels=out_channels,
+                    upsample_factor=upsample_scale_factor,
+                )
+            ])
+
+    def forward(self, hidden_states):
+        for resnet in self.resnets:
+            hidden_states = resnet(hidden_states)
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states)
+        return hidden_states
+
+
+class DecoderCausal3D(nn.Module):
+
+    def __init__(
+        self,
+        in_channels=16,
+        out_channels=3,
+        eps=1e-6,
+        dropout=0.0,
+        block_out_channels=[128, 256, 512, 512],
+        layers_per_block=2,
+        num_groups=32,
+        time_compression_ratio=4,
+        spatial_compression_ratio=8,
+        gradient_checkpointing=False,
+    ):
+        super().__init__()
+        self.layers_per_block = layers_per_block
+
+        self.conv_in = CausalConv3d(in_channels, block_out_channels[-1], kernel_size=3, stride=1)
+        self.up_blocks = nn.ModuleList([])
+
+        # mid
+        self.mid_block = UNetMidBlockCausal3D(
+            in_channels=block_out_channels[-1],
+            dropout=dropout,
+            eps=eps,
+            num_groups=num_groups,
+            attention_head_dim=block_out_channels[-1],
+        )
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        output_channel = reversed_block_out_channels[0]
+        for i in range(len(block_out_channels)):
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+            num_spatial_upsample_layers = int(np.log2(spatial_compression_ratio))
+            num_time_upsample_layers = int(np.log2(time_compression_ratio))
+
+            add_spatial_upsample = bool(i < num_spatial_upsample_layers)
+            add_time_upsample = bool(i >= len(block_out_channels) - 1 - num_time_upsample_layers and not is_final_block)
+
+            upsample_scale_factor_HW = (2, 2) if add_spatial_upsample else (1, 1)
+            upsample_scale_factor_T = (2,) if add_time_upsample else (1,)
+            upsample_scale_factor = tuple(upsample_scale_factor_T + upsample_scale_factor_HW)
+
+            up_block = UpDecoderBlockCausal3D(
+                in_channels=prev_output_channel,
+                out_channels=output_channel,
+                dropout=dropout,
+                num_layers=layers_per_block + 1,
+                eps=eps,
+                num_groups=num_groups,
+                add_upsample=bool(add_spatial_upsample or add_time_upsample),
+                upsample_scale_factor=upsample_scale_factor,
+            )
+
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        # out
+        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups, eps=eps)
+        self.conv_act = nn.SiLU()
+        self.conv_out = CausalConv3d(block_out_channels[0], out_channels, kernel_size=3)
+
+        self.gradient_checkpointing = gradient_checkpointing
+
+    def forward(self, hidden_states):
+        hidden_states = self.conv_in(hidden_states)
+        if self.training and self.gradient_checkpointing:
+
+            def create_custom_forward(module):
+
+                def custom_forward(*inputs):
+                    return module(*inputs)
+
+                return custom_forward
+
+            # middle
+            hidden_states = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(self.mid_block),
+                hidden_states,
+                use_reentrant=False,
+            )
+            # up
+            for up_block in self.up_blocks:
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(up_block),
+                    hidden_states,
+                    use_reentrant=False,
+                )
+        else:
+            # middle
+            hidden_states = self.mid_block(hidden_states)
+            # up
+            for up_block in self.up_blocks:
+                hidden_states = up_block(hidden_states)
+        # post-process
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+
+        return hidden_states
+
+
+class HunyuanVideoVAEDecoder(nn.Module):
+
+    def __init__(
+        self,
+        in_channels=16,
+        out_channels=3,
+        eps=1e-6,
+        dropout=0.0,
+        block_out_channels=[128, 256, 512, 512],
+        layers_per_block=2,
+        num_groups=32,
+        time_compression_ratio=4,
+        spatial_compression_ratio=8,
+        gradient_checkpointing=False,
+    ):
+        super().__init__()
+        self.decoder = DecoderCausal3D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            eps=eps,
+            dropout=dropout,
+            block_out_channels=block_out_channels,
+            layers_per_block=layers_per_block,
+            num_groups=num_groups,
+            time_compression_ratio=time_compression_ratio,
+            spatial_compression_ratio=spatial_compression_ratio,
+            gradient_checkpointing=gradient_checkpointing,
+        )
+        self.post_quant_conv = nn.Conv3d(in_channels, in_channels, kernel_size=1)
+        self.scaling_factor = 0.476986
+
+
+    def forward(self, latents):
+        latents = latents / self.scaling_factor
+        latents = self.post_quant_conv(latents)
+        dec = self.decoder(latents)
+        return dec
+    
+
+    def build_1d_mask(self, length, left_bound, right_bound, border_width):
+        x = torch.ones((length,))
+        if not left_bound:
+            x[:border_width] = (torch.arange(border_width) + 1) / border_width
+        if not right_bound:
+            x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,))
+        return x
+    
+
+    def build_mask(self, data, is_bound, border_width):
+        _, _, T, H, W = data.shape
+        t = self.build_1d_mask(T, is_bound[0], is_bound[1], border_width[0])
+        h = self.build_1d_mask(H, is_bound[2], is_bound[3], border_width[1])
+        w = self.build_1d_mask(W, is_bound[4], is_bound[5], border_width[2])
+
+        t = repeat(t, "T -> T H W", T=T, H=H, W=W)
+        h = repeat(h, "H -> T H W", T=T, H=H, W=W)
+        w = repeat(w, "W -> T H W", T=T, H=H, W=W)
+
+        mask = torch.stack([t, h, w]).min(dim=0).values
+        mask = rearrange(mask, "T H W -> 1 1 T H W")
+        return mask
+    
+
+    def tile_forward(self, hidden_states, tile_size, tile_stride):
+        B, C, T, H, W = hidden_states.shape
+        size_t, size_h, size_w = tile_size
+        stride_t, stride_h, stride_w = tile_stride
+
+        # Split tasks
+        tasks = []
+        for t in range(0, T, stride_t):
+            if (t-stride_t >= 0 and t-stride_t+size_t >= T): continue
+            for h in range(0, H, stride_h):
+                if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue
+                for w in range(0, W, stride_w):
+                    if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue
+                    t_, h_, w_ = t + size_t, h + size_h, w + size_w
+                    tasks.append((t, t_, h, h_, w, w_))
+
+        # Run
+        torch_dtype = self.post_quant_conv.weight.dtype
+        data_device = hidden_states.device
+        computation_device = self.post_quant_conv.weight.device
+
+        weight = torch.zeros((1, 1, (T - 1) * 4 + 1, H * 8, W * 8), dtype=torch_dtype, device=data_device)
+        values = torch.zeros((B, 3, (T - 1) * 4 + 1, H * 8, W * 8), dtype=torch_dtype, device=data_device)
+
+        for t, t_, h, h_, w, w_ in tqdm(tasks, desc="VAE decoding"):
+            hidden_states_batch = hidden_states[:, :, t:t_, h:h_, w:w_].to(computation_device)
+            hidden_states_batch = self.forward(hidden_states_batch).to(data_device)
+            if t > 0:
+                hidden_states_batch = hidden_states_batch[:, :, 1:]
+
+            mask = self.build_mask(
+                hidden_states_batch,
+                is_bound=(t==0, t_>=T, h==0, h_>=H, w==0, w_>=W),
+                border_width=((size_t - stride_t) * 4, (size_h - stride_h) * 8, (size_w - stride_w) * 8)
+            ).to(dtype=torch_dtype, device=data_device)
+
+            target_t = 0 if t==0 else t * 4 + 1
+            target_h = h * 8
+            target_w = w * 8
+            values[
+                :,
+                :,
+                target_t: target_t + hidden_states_batch.shape[2],
+                target_h: target_h + hidden_states_batch.shape[3],
+                target_w: target_w + hidden_states_batch.shape[4],
+            ] += hidden_states_batch * mask
+            weight[
+                :,
+                :,
+                target_t: target_t + hidden_states_batch.shape[2],
+                target_h: target_h + hidden_states_batch.shape[3],
+                target_w: target_w + hidden_states_batch.shape[4],
+            ] += mask
+        return values / weight
+
+
+    def decode_video(self, latents, tile_size=(17, 32, 32), tile_stride=(12, 24, 24)):
+        latents = latents.to(self.post_quant_conv.weight.dtype)
+        return self.tile_forward(latents, tile_size=tile_size, tile_stride=tile_stride)
+
+    @staticmethod
+    def state_dict_converter():
+        return HunyuanVideoVAEDecoderStateDictConverter()
+
+
+class HunyuanVideoVAEDecoderStateDictConverter:
+
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = {}
+        for name in state_dict:
+            if name.startswith('decoder.') or name.startswith('post_quant_conv.'):
+                state_dict_[name] = state_dict[name]
+        return state_dict_

diffsynth/models/hunyuan_video_vae_encoder.py

@@ -0,0 +1,307 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+import numpy as np
+from tqdm import tqdm
+from .hunyuan_video_vae_decoder import CausalConv3d, ResnetBlockCausal3D, UNetMidBlockCausal3D
+
+
+class DownsampleCausal3D(nn.Module):
+
+    def __init__(self, channels, out_channels, kernel_size=3, bias=True, stride=2):
+        super().__init__()
+        self.conv = CausalConv3d(channels, out_channels, kernel_size, stride=stride, bias=bias)
+
+    def forward(self, hidden_states):
+        hidden_states = self.conv(hidden_states)
+        return hidden_states
+
+
+class DownEncoderBlockCausal3D(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        dropout=0.0,
+        num_layers=1,
+        eps=1e-6,
+        num_groups=32,
+        add_downsample=True,
+        downsample_stride=2,
+    ):
+
+        super().__init__()
+        resnets = []
+        for i in range(num_layers):
+            cur_in_channel = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlockCausal3D(
+                    in_channels=cur_in_channel,
+                    out_channels=out_channels,
+                    groups=num_groups,
+                    dropout=dropout,
+                    eps=eps,
+                ))
+        self.resnets = nn.ModuleList(resnets)
+
+        self.downsamplers = None
+        if add_downsample:
+            self.downsamplers = nn.ModuleList([DownsampleCausal3D(
+                out_channels,
+                out_channels,
+                stride=downsample_stride,
+            )])
+
+    def forward(self, hidden_states):
+        for resnet in self.resnets:
+            hidden_states = resnet(hidden_states)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+        return hidden_states
+
+
+class EncoderCausal3D(nn.Module):
+
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 16,
+        eps=1e-6,
+        dropout=0.0,
+        block_out_channels=[128, 256, 512, 512],
+        layers_per_block=2,
+        num_groups=32,
+        time_compression_ratio: int = 4,
+        spatial_compression_ratio: int = 8,
+        gradient_checkpointing=False,
+    ):
+        super().__init__()
+        self.conv_in = CausalConv3d(in_channels, block_out_channels[0], kernel_size=3, stride=1)
+        self.down_blocks = nn.ModuleList([])
+
+        # down
+        output_channel = block_out_channels[0]
+        for i in range(len(block_out_channels)):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+            num_spatial_downsample_layers = int(np.log2(spatial_compression_ratio))
+            num_time_downsample_layers = int(np.log2(time_compression_ratio))
+
+            add_spatial_downsample = bool(i < num_spatial_downsample_layers)
+            add_time_downsample = bool(i >= (len(block_out_channels) - 1 - num_time_downsample_layers) and not is_final_block)
+
+            downsample_stride_HW = (2, 2) if add_spatial_downsample else (1, 1)
+            downsample_stride_T = (2,) if add_time_downsample else (1,)
+            downsample_stride = tuple(downsample_stride_T + downsample_stride_HW)
+            down_block = DownEncoderBlockCausal3D(
+                in_channels=input_channel,
+                out_channels=output_channel,
+                dropout=dropout,
+                num_layers=layers_per_block,
+                eps=eps,
+                num_groups=num_groups,
+                add_downsample=bool(add_spatial_downsample or add_time_downsample),
+                downsample_stride=downsample_stride,
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        self.mid_block = UNetMidBlockCausal3D(
+            in_channels=block_out_channels[-1],
+            dropout=dropout,
+            eps=eps,
+            num_groups=num_groups,
+            attention_head_dim=block_out_channels[-1],
+        )
+        # out
+        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=num_groups, eps=eps)
+        self.conv_act = nn.SiLU()
+        self.conv_out = CausalConv3d(block_out_channels[-1], 2 * out_channels, kernel_size=3)
+
+        self.gradient_checkpointing = gradient_checkpointing
+
+    def forward(self, hidden_states):
+        hidden_states = self.conv_in(hidden_states)
+        if self.training and self.gradient_checkpointing:
+
+            def create_custom_forward(module):
+
+                def custom_forward(*inputs):
+                    return module(*inputs)
+
+                return custom_forward
+
+            # down
+            for down_block in self.down_blocks:
+                torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(down_block),
+                    hidden_states,
+                    use_reentrant=False,
+                )
+            # middle
+            hidden_states = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(self.mid_block),
+                hidden_states,
+                use_reentrant=False,
+            )
+        else:
+            # down
+            for down_block in self.down_blocks:
+                hidden_states = down_block(hidden_states)
+            # middle
+            hidden_states = self.mid_block(hidden_states)
+        # post-process
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+
+        return hidden_states
+
+
+class HunyuanVideoVAEEncoder(nn.Module):
+
+    def __init__(
+        self,
+        in_channels=3,
+        out_channels=16,
+        eps=1e-6,
+        dropout=0.0,
+        block_out_channels=[128, 256, 512, 512],
+        layers_per_block=2,
+        num_groups=32,
+        time_compression_ratio=4,
+        spatial_compression_ratio=8,
+        gradient_checkpointing=False,
+    ):
+        super().__init__()
+        self.encoder = EncoderCausal3D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            eps=eps,
+            dropout=dropout,
+            block_out_channels=block_out_channels,
+            layers_per_block=layers_per_block,
+            num_groups=num_groups,
+            time_compression_ratio=time_compression_ratio,
+            spatial_compression_ratio=spatial_compression_ratio,
+            gradient_checkpointing=gradient_checkpointing,
+        )
+        self.quant_conv = nn.Conv3d(2 * out_channels, 2 * out_channels, kernel_size=1)
+        self.scaling_factor = 0.476986
+
+
+    def forward(self, images):
+        latents = self.encoder(images)
+        latents = self.quant_conv(latents)
+        latents = latents[:, :16]
+        latents = latents * self.scaling_factor
+        return latents
+    
+
+    def build_1d_mask(self, length, left_bound, right_bound, border_width):
+        x = torch.ones((length,))
+        if not left_bound:
+            x[:border_width] = (torch.arange(border_width) + 1) / border_width
+        if not right_bound:
+            x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,))
+        return x
+    
+
+    def build_mask(self, data, is_bound, border_width):
+        _, _, T, H, W = data.shape
+        t = self.build_1d_mask(T, is_bound[0], is_bound[1], border_width[0])
+        h = self.build_1d_mask(H, is_bound[2], is_bound[3], border_width[1])
+        w = self.build_1d_mask(W, is_bound[4], is_bound[5], border_width[2])
+
+        t = repeat(t, "T -> T H W", T=T, H=H, W=W)
+        h = repeat(h, "H -> T H W", T=T, H=H, W=W)
+        w = repeat(w, "W -> T H W", T=T, H=H, W=W)
+
+        mask = torch.stack([t, h, w]).min(dim=0).values
+        mask = rearrange(mask, "T H W -> 1 1 T H W")
+        return mask
+    
+
+    def tile_forward(self, hidden_states, tile_size, tile_stride):
+        B, C, T, H, W = hidden_states.shape
+        size_t, size_h, size_w = tile_size
+        stride_t, stride_h, stride_w = tile_stride
+
+        # Split tasks
+        tasks = []
+        for t in range(0, T, stride_t):
+            if (t-stride_t >= 0 and t-stride_t+size_t >= T): continue
+            for h in range(0, H, stride_h):
+                if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue
+                for w in range(0, W, stride_w):
+                    if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue
+                    t_, h_, w_ = t + size_t, h + size_h, w + size_w
+                    tasks.append((t, t_, h, h_, w, w_))
+
+        # Run
+        torch_dtype = self.quant_conv.weight.dtype
+        data_device = hidden_states.device
+        computation_device = self.quant_conv.weight.device
+
+        weight = torch.zeros((1, 1,  (T - 1) // 4 + 1, H // 8, W // 8), dtype=torch_dtype, device=data_device)
+        values = torch.zeros((B, 16, (T - 1) // 4 + 1, H // 8, W // 8), dtype=torch_dtype, device=data_device)
+
+        for t, t_, h, h_, w, w_ in tqdm(tasks, desc="VAE encoding"):
+            hidden_states_batch = hidden_states[:, :, t:t_, h:h_, w:w_].to(computation_device)
+            hidden_states_batch = self.forward(hidden_states_batch).to(data_device)
+            if t > 0:
+                hidden_states_batch = hidden_states_batch[:, :, 1:]
+
+            mask = self.build_mask(
+                hidden_states_batch,
+                is_bound=(t==0, t_>=T, h==0, h_>=H, w==0, w_>=W),
+                border_width=((size_t - stride_t) // 4, (size_h - stride_h) // 8, (size_w - stride_w) // 8)
+            ).to(dtype=torch_dtype, device=data_device)
+
+            target_t = 0 if t==0 else t // 4 + 1
+            target_h = h // 8
+            target_w = w // 8
+            values[
+                :,
+                :,
+                target_t: target_t + hidden_states_batch.shape[2],
+                target_h: target_h + hidden_states_batch.shape[3],
+                target_w: target_w + hidden_states_batch.shape[4],
+            ] += hidden_states_batch * mask
+            weight[
+                :,
+                :,
+                target_t: target_t + hidden_states_batch.shape[2],
+                target_h: target_h + hidden_states_batch.shape[3],
+                target_w: target_w + hidden_states_batch.shape[4],
+            ] += mask
+        return values / weight
+
+
+    def encode_video(self, latents, tile_size=(65, 256, 256), tile_stride=(48, 192, 192)):
+        latents = latents.to(self.quant_conv.weight.dtype)
+        return self.tile_forward(latents, tile_size=tile_size, tile_stride=tile_stride)
+
+
+    @staticmethod
+    def state_dict_converter():
+        return HunyuanVideoVAEEncoderStateDictConverter()
+
+
+class HunyuanVideoVAEEncoderStateDictConverter:
+
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = {}
+        for name in state_dict:
+            if name.startswith('encoder.') or name.startswith('quant_conv.'):
+                state_dict_[name] = state_dict[name]
+        return state_dict_

diffsynth/models/lora.py

@@ -4,7 +4,10 @@ from .sdxl_unet import SDXLUNet
 from .sd_text_encoder import SDTextEncoder
 from .sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2
 from .sd3_dit import SD3DiT
+from .flux_dit import FluxDiT
 from .hunyuan_dit import HunyuanDiT
+from .cog_dit import CogDiT
+from .hunyuan_video_dit import HunyuanVideoDiT
 
 
 
@@ -17,6 +20,13 @@ class LoRAFromCivitai:
 
 
     def convert_state_dict(self, state_dict, lora_prefix="lora_unet_", alpha=1.0):
+        for key in state_dict:
+            if ".lora_up" in key:
+                return self.convert_state_dict_up_down(state_dict, lora_prefix, alpha)
+        return self.convert_state_dict_AB(state_dict, lora_prefix, alpha)
+
+
+    def convert_state_dict_up_down(self, state_dict, lora_prefix="lora_unet_", alpha=1.0):
         renamed_lora_prefix = self.renamed_lora_prefix.get(lora_prefix, "")
         state_dict_ = {}
         for key in state_dict:
@@ -39,6 +49,29 @@ class LoRAFromCivitai:
         return state_dict_
     
 
+    def convert_state_dict_AB(self, state_dict, lora_prefix="", alpha=1.0, device="cuda", torch_dtype=torch.float16):
+        state_dict_ = {}
+        for key in state_dict:
+            if ".lora_B." not in key:
+                continue
+            if not key.startswith(lora_prefix):
+                continue
+            weight_up = state_dict[key].to(device=device, dtype=torch_dtype)
+            weight_down = state_dict[key.replace(".lora_B.", ".lora_A.")].to(device=device, dtype=torch_dtype)
+            if len(weight_up.shape) == 4:
+                weight_up = weight_up.squeeze(3).squeeze(2)
+                weight_down = weight_down.squeeze(3).squeeze(2)
+                lora_weight = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
+            else:
+                lora_weight = alpha * torch.mm(weight_up, weight_down)
+            keys = key.split(".")
+            keys.pop(keys.index("lora_B"))
+            target_name = ".".join(keys)
+            target_name = target_name[len(lora_prefix):]
+            state_dict_[target_name] = lora_weight.cpu()
+        return state_dict_
+    
+
     def load(self, model, state_dict_lora, lora_prefix, alpha=1.0, model_resource=None):
         state_dict_model = model.state_dict()
         state_dict_lora = self.convert_state_dict(state_dict_lora, lora_prefix=lora_prefix, alpha=alpha)
@@ -46,11 +79,19 @@ class LoRAFromCivitai:
             state_dict_lora = model.__class__.state_dict_converter().from_diffusers(state_dict_lora)
         elif model_resource == "civitai":
             state_dict_lora = model.__class__.state_dict_converter().from_civitai(state_dict_lora)
+        if isinstance(state_dict_lora, tuple):
+            state_dict_lora = state_dict_lora[0]
         if len(state_dict_lora) > 0:
             print(f"    {len(state_dict_lora)} tensors are updated.")
             for name in state_dict_lora:
+                fp8=False
+                if state_dict_model[name].dtype == torch.float8_e4m3fn:
+                    state_dict_model[name]= state_dict_model[name].to(state_dict_lora[name].dtype)
+                    fp8=True
                 state_dict_model[name] += state_dict_lora[name].to(
                     dtype=state_dict_model[name].dtype, device=state_dict_model[name].device)
+                if fp8:
+                    state_dict_model[name] = state_dict_model[name].to(torch.float8_e4m3fn)
             model.load_state_dict(state_dict_model)
     
 
@@ -65,6 +106,8 @@ class LoRAFromCivitai:
                     converter_fn = model.__class__.state_dict_converter().from_diffusers if model_resource == "diffusers" \
                         else model.__class__.state_dict_converter().from_civitai
                     state_dict_lora_ = converter_fn(state_dict_lora_)
+                    if isinstance(state_dict_lora_, tuple):
+                        state_dict_lora_ = state_dict_lora_[0]
                     if len(state_dict_lora_) == 0:
                         continue
                     for name in state_dict_lora_:
@@ -134,13 +177,39 @@ class SDXLLoRAFromCivitai(LoRAFromCivitai):
         }
         
 
+class FluxLoRAFromCivitai(LoRAFromCivitai):
+    def __init__(self):
+        super().__init__()
+        self.supported_model_classes = [FluxDiT, FluxDiT]
+        self.lora_prefix = ["lora_unet_", "transformer."]
+        self.renamed_lora_prefix = {}
+        self.special_keys = {
+            "single.blocks": "single_blocks",
+            "double.blocks": "double_blocks",
+            "img.attn": "img_attn",
+            "img.mlp": "img_mlp",
+            "img.mod": "img_mod",
+            "txt.attn": "txt_attn",
+            "txt.mlp": "txt_mlp",
+            "txt.mod": "txt_mod",
+        }
+
 
 class GeneralLoRAFromPeft:
     def __init__(self):
-        self.supported_model_classes = [SDUNet, SDXLUNet, SD3DiT, HunyuanDiT]
+        self.supported_model_classes = [SDUNet, SDXLUNet, SD3DiT, HunyuanDiT, FluxDiT, CogDiT]
 
 
-    def convert_state_dict(self, state_dict, alpha=1.0, device="cuda", torch_dtype=torch.float16):
+    def fetch_device_dtype_from_state_dict(self, state_dict):
+        device, torch_dtype = None, None
+        for name, param in state_dict.items():
+            device, torch_dtype = param.device, param.dtype
+            break
+        return device, torch_dtype
+
+
+    def convert_state_dict(self, state_dict, alpha=1.0, target_state_dict={}):
+        device, torch_dtype = self.fetch_device_dtype_from_state_dict(target_state_dict)
         state_dict_ = {}
         for key in state_dict:
             if ".lora_B." not in key:
@@ -154,25 +223,26 @@ class GeneralLoRAFromPeft:
             else:
                 lora_weight = alpha * torch.mm(weight_up, weight_down)
             keys = key.split(".")
+            if len(keys) > keys.index("lora_B") + 2:
                 keys.pop(keys.index("lora_B") + 1)
             keys.pop(keys.index("lora_B"))
             target_name = ".".join(keys)
+            if target_name not in target_state_dict:
+                return {}
             state_dict_[target_name] = lora_weight.cpu()
         return state_dict_
     
 
     def load(self, model, state_dict_lora, lora_prefix="", alpha=1.0, model_resource=""):
         state_dict_model = model.state_dict()
-        for name, param in state_dict_model.items():
+        state_dict_lora = self.convert_state_dict(state_dict_lora, alpha=alpha, target_state_dict=state_dict_model)
-            torch_dtype = param.dtype
-            device = param.device
-            break
-        state_dict_lora = self.convert_state_dict(state_dict_lora, alpha=alpha, device=device, torch_dtype=torch_dtype)
         if len(state_dict_lora) > 0:
             print(f"    {len(state_dict_lora)} tensors are updated.")
             for name in state_dict_lora:
                 state_dict_model[name] += state_dict_lora[name].to(
-                    dtype=state_dict_model[name].dtype, device=state_dict_model[name].device)
+                    dtype=state_dict_model[name].dtype,
+                    device=state_dict_model[name].device
+                )
             model.load_state_dict(state_dict_model)
     
 
@@ -182,14 +252,116 @@ class GeneralLoRAFromPeft:
                 continue
             state_dict_model = model.state_dict()
             try:
-                state_dict_lora_ = self.convert_state_dict(state_dict_lora, alpha=1.0)
+                state_dict_lora_ = self.convert_state_dict(state_dict_lora, alpha=1.0, target_state_dict=state_dict_model)
-                if len(state_dict_lora_) == 0:
+                if len(state_dict_lora_) > 0:
-                    continue
-                for name in state_dict_lora_:
-                    if name not in state_dict_model:
-                        break
-                else:
                     return "", ""
             except:
                 pass
         return None
+    
+
+class HunyuanVideoLoRAFromCivitai(LoRAFromCivitai):
+    def __init__(self):
+        super().__init__()
+        self.supported_model_classes = [HunyuanVideoDiT, HunyuanVideoDiT]
+        self.lora_prefix = ["diffusion_model.", "transformer."]
+        self.special_keys = {}
+    
+
+class FluxLoRAConverter:
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def align_to_opensource_format(state_dict, alpha=1.0):
+        prefix_rename_dict = {
+            "single_blocks": "lora_unet_single_blocks",
+            "blocks": "lora_unet_double_blocks",
+        }
+        middle_rename_dict = {
+            "norm.linear": "modulation_lin",
+            "to_qkv_mlp": "linear1",
+            "proj_out": "linear2",
+
+            "norm1_a.linear": "img_mod_lin",
+            "norm1_b.linear": "txt_mod_lin",
+            "attn.a_to_qkv": "img_attn_qkv",
+            "attn.b_to_qkv": "txt_attn_qkv",
+            "attn.a_to_out": "img_attn_proj",
+            "attn.b_to_out": "txt_attn_proj",
+            "ff_a.0": "img_mlp_0",
+            "ff_a.2": "img_mlp_2",
+            "ff_b.0": "txt_mlp_0",
+            "ff_b.2": "txt_mlp_2",
+        }
+        suffix_rename_dict = {
+            "lora_B.weight": "lora_up.weight",
+            "lora_A.weight": "lora_down.weight",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            names = name.split(".")
+            if names[-2] != "lora_A" and names[-2] != "lora_B":
+                names.pop(-2)
+            prefix = names[0]
+            middle = ".".join(names[2:-2])
+            suffix = ".".join(names[-2:])
+            block_id = names[1]
+            if middle not in middle_rename_dict:
+                continue
+            rename = prefix_rename_dict[prefix] + "_" + block_id + "_" + middle_rename_dict[middle] + "." + suffix_rename_dict[suffix]
+            state_dict_[rename] = param
+            if rename.endswith("lora_up.weight"):
+                state_dict_[rename.replace("lora_up.weight", "alpha")] = torch.tensor((alpha,))[0]
+        return state_dict_
+    
+    @staticmethod
+    def align_to_diffsynth_format(state_dict):
+        rename_dict = {
+            "lora_unet_double_blocks_blockid_img_mod_lin.lora_down.weight": "blocks.blockid.norm1_a.linear.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_img_mod_lin.lora_up.weight": "blocks.blockid.norm1_a.linear.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_txt_mod_lin.lora_down.weight": "blocks.blockid.norm1_b.linear.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_txt_mod_lin.lora_up.weight": "blocks.blockid.norm1_b.linear.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_img_attn_qkv.lora_down.weight": "blocks.blockid.attn.a_to_qkv.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_img_attn_qkv.lora_up.weight": "blocks.blockid.attn.a_to_qkv.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_txt_attn_qkv.lora_down.weight": "blocks.blockid.attn.b_to_qkv.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_txt_attn_qkv.lora_up.weight": "blocks.blockid.attn.b_to_qkv.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_img_attn_proj.lora_down.weight": "blocks.blockid.attn.a_to_out.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_img_attn_proj.lora_up.weight": "blocks.blockid.attn.a_to_out.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_txt_attn_proj.lora_down.weight": "blocks.blockid.attn.b_to_out.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_txt_attn_proj.lora_up.weight": "blocks.blockid.attn.b_to_out.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_img_mlp_0.lora_down.weight": "blocks.blockid.ff_a.0.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_img_mlp_0.lora_up.weight": "blocks.blockid.ff_a.0.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_img_mlp_2.lora_down.weight": "blocks.blockid.ff_a.2.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_img_mlp_2.lora_up.weight": "blocks.blockid.ff_a.2.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_txt_mlp_0.lora_down.weight": "blocks.blockid.ff_b.0.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_txt_mlp_0.lora_up.weight": "blocks.blockid.ff_b.0.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_txt_mlp_2.lora_down.weight": "blocks.blockid.ff_b.2.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_txt_mlp_2.lora_up.weight": "blocks.blockid.ff_b.2.lora_B.default.weight",
+            "lora_unet_single_blocks_blockid_modulation_lin.lora_down.weight": "single_blocks.blockid.norm.linear.lora_A.default.weight",
+            "lora_unet_single_blocks_blockid_modulation_lin.lora_up.weight": "single_blocks.blockid.norm.linear.lora_B.default.weight",
+            "lora_unet_single_blocks_blockid_linear1.lora_down.weight": "single_blocks.blockid.to_qkv_mlp.lora_A.default.weight",
+            "lora_unet_single_blocks_blockid_linear1.lora_up.weight": "single_blocks.blockid.to_qkv_mlp.lora_B.default.weight",
+            "lora_unet_single_blocks_blockid_linear2.lora_down.weight": "single_blocks.blockid.proj_out.lora_A.default.weight",
+            "lora_unet_single_blocks_blockid_linear2.lora_up.weight": "single_blocks.blockid.proj_out.lora_B.default.weight",
+        }
+        def guess_block_id(name):
+            names = name.split("_")
+            for i in names:
+                if i.isdigit():
+                    return i, name.replace(f"_{i}_", "_blockid_")
+            return None, None
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            block_id, source_name = guess_block_id(name)
+            if source_name in rename_dict:
+                target_name = rename_dict[source_name]
+                target_name = target_name.replace(".blockid.", f".{block_id}.")
+                state_dict_[target_name] = param
+            else:
+                state_dict_[name] = param
+        return state_dict_
+    
+
+def get_lora_loaders():
+    return [SDLoRAFromCivitai(), SDXLLoRAFromCivitai(), FluxLoRAFromCivitai(), HunyuanVideoLoRAFromCivitai(), GeneralLoRAFromPeft()]

diffsynth/models/model_manager.py

@@ -1,16 +1,13 @@
-import os, torch, hashlib, json, importlib
+import os, torch, json, importlib
-from safetensors import safe_open
-from torch import Tensor
-from typing_extensions import Literal, TypeAlias
 from typing import List
 
-from .downloader import download_models, Preset_model_id, Preset_model_website
+from .downloader import download_models, download_customized_models, Preset_model_id, Preset_model_website
 
 from .sd_text_encoder import SDTextEncoder
 from .sd_unet import SDUNet
 from .sd_vae_encoder import SDVAEEncoder
 from .sd_vae_decoder import SDVAEDecoder
-from .lora import SDLoRAFromCivitai, SDXLLoRAFromCivitai, GeneralLoRAFromPeft
+from .lora import get_lora_loaders
 
 from .sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2
 from .sdxl_unet import SDXLUNet
@@ -23,6 +20,7 @@ from .sd3_vae_decoder import SD3VAEDecoder
 from .sd3_vae_encoder import SD3VAEEncoder
 
 from .sd_controlnet import SDControlNet
+from .sdxl_controlnet import SDXLControlNetUnion
 
 from .sd_motion import SDMotionModel
 from .sdxl_motion import SDXLMotionModel
@@ -37,129 +35,22 @@ from .sdxl_ipadapter import SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder
 
 from .hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder
 from .hunyuan_dit import HunyuanDiT
+from .hunyuan_video_vae_decoder import HunyuanVideoVAEDecoder
+from .hunyuan_video_vae_encoder import HunyuanVideoVAEEncoder
+
+from .flux_dit import FluxDiT
+from .flux_text_encoder import FluxTextEncoder2
+from .flux_vae import FluxVAEEncoder, FluxVAEDecoder
+from .flux_ipadapter import FluxIpAdapter
+
+from .cog_vae import CogVAEEncoder, CogVAEDecoder
+from .cog_dit import CogDiT
+
+from ..extensions.RIFE import IFNet
+from ..extensions.ESRGAN import RRDBNet
 
 from ..configs.model_config import model_loader_configs, huggingface_model_loader_configs, patch_model_loader_configs
-
+from .utils import load_state_dict, init_weights_on_device, hash_state_dict_keys, split_state_dict_with_prefix
-
-
-def load_state_dict(file_path, torch_dtype=None):
-    if file_path.endswith(".safetensors"):
-        return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype)
-    else:
-        return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype)
-
-
-def load_state_dict_from_safetensors(file_path, torch_dtype=None):
-    state_dict = {}
-    with safe_open(file_path, framework="pt", device="cpu") as f:
-        for k in f.keys():
-            state_dict[k] = f.get_tensor(k)
-            if torch_dtype is not None:
-                state_dict[k] = state_dict[k].to(torch_dtype)
-    return state_dict
-
-
-def load_state_dict_from_bin(file_path, torch_dtype=None):
-    state_dict = torch.load(file_path, map_location="cpu")
-    if torch_dtype is not None:
-        for i in state_dict:
-            if isinstance(state_dict[i], torch.Tensor):
-                state_dict[i] = state_dict[i].to(torch_dtype)
-    return state_dict
-
-
-def search_for_embeddings(state_dict):
-    embeddings = []
-    for k in state_dict:
-        if isinstance(state_dict[k], torch.Tensor):
-            embeddings.append(state_dict[k])
-        elif isinstance(state_dict[k], dict):
-            embeddings += search_for_embeddings(state_dict[k])
-    return embeddings
-
-
-def search_parameter(param, state_dict):
-    for name, param_ in state_dict.items():
-        if param.numel() == param_.numel():
-            if param.shape == param_.shape:
-                if torch.dist(param, param_) < 1e-6:
-                    return name
-            else:
-                if torch.dist(param.flatten(), param_.flatten()) < 1e-6:
-                    return name
-    return None
-
-
-def build_rename_dict(source_state_dict, target_state_dict, split_qkv=False):
-    matched_keys = set()
-    with torch.no_grad():
-        for name in source_state_dict:
-            rename = search_parameter(source_state_dict[name], target_state_dict)
-            if rename is not None:
-                print(f'"{name}": "{rename}",')
-                matched_keys.add(rename)
-            elif split_qkv and len(source_state_dict[name].shape)>=1 and source_state_dict[name].shape[0]%3==0:
-                length = source_state_dict[name].shape[0] // 3
-                rename = []
-                for i in range(3):
-                    rename.append(search_parameter(source_state_dict[name][i*length: i*length+length], target_state_dict))
-                if None not in rename:
-                    print(f'"{name}": {rename},')
-                    for rename_ in rename:
-                        matched_keys.add(rename_)
-    for name in target_state_dict:
-        if name not in matched_keys:
-            print("Cannot find", name, target_state_dict[name].shape)
-
-
-def search_for_files(folder, extensions):
-    files = []
-    if os.path.isdir(folder):
-        for file in sorted(os.listdir(folder)):
-            files += search_for_files(os.path.join(folder, file), extensions)
-    elif os.path.isfile(folder):
-        for extension in extensions:
-            if folder.endswith(extension):
-                files.append(folder)
-                break
-    return files
-
-
-def convert_state_dict_keys_to_single_str(state_dict, with_shape=True):
-    keys = []
-    for key, value in state_dict.items():
-        if isinstance(key, str):
-            if isinstance(value, Tensor):
-                if with_shape:
-                    shape = "_".join(map(str, list(value.shape)))
-                    keys.append(key + ":" + shape)
-                keys.append(key)
-            elif isinstance(value, dict):
-                keys.append(key + "|" + convert_state_dict_keys_to_single_str(value, with_shape=with_shape))
-    keys.sort()
-    keys_str = ",".join(keys)
-    return keys_str
-
-
-def split_state_dict_with_prefix(state_dict):
-    keys = sorted([key for key in state_dict if isinstance(key, str)])
-    prefix_dict = {}
-    for key in  keys:
-        prefix = key if "." not in key else key.split(".")[0]
-        if prefix not in prefix_dict:
-            prefix_dict[prefix] = []
-        prefix_dict[prefix].append(key)
-    state_dicts = []
-    for prefix, keys in prefix_dict.items():
-        sub_state_dict = {key: state_dict[key] for key in keys}
-        state_dicts.append(sub_state_dict)
-    return state_dicts
-
-
-def hash_state_dict_keys(state_dict, with_shape=True):
-    keys_str = convert_state_dict_keys_to_single_str(state_dict, with_shape=with_shape)
-    keys_str = keys_str.encode(encoding="UTF-8")
-    return hashlib.md5(keys_str).hexdigest()
 
 
 def load_model_from_single_file(state_dict, model_names, model_classes, model_resource, torch_dtype, device):
@@ -177,8 +68,10 @@ def load_model_from_single_file(state_dict, model_names, model_classes, model_re
         else:
             model_state_dict, extra_kwargs = state_dict_results, {}
         torch_dtype = torch.float32 if extra_kwargs.get("upcast_to_float32", False) else torch_dtype
-        model = model_class(**extra_kwargs).to(dtype=torch_dtype, device=device)
+        with init_weights_on_device():
-        model.load_state_dict(model_state_dict)
+            model= model_class(**extra_kwargs)
+        model.load_state_dict(model_state_dict, assign=True)
+        model = model.to(dtype=torch_dtype, device=device)
         loaded_model_names.append(model_name)
         loaded_models.append(model)
     return loaded_model_names, loaded_models
@@ -187,10 +80,16 @@ def load_model_from_single_file(state_dict, model_names, model_classes, model_re
 def load_model_from_huggingface_folder(file_path, model_names, model_classes, torch_dtype, device):
     loaded_model_names, loaded_models = [], []
     for model_name, model_class in zip(model_names, model_classes):
+        if torch_dtype in [torch.float32, torch.float16, torch.bfloat16]:
             model = model_class.from_pretrained(file_path, torch_dtype=torch_dtype).eval()
+        else:
+            model = model_class.from_pretrained(file_path).eval().to(dtype=torch_dtype)
         if torch_dtype == torch.float16 and hasattr(model, "half"):
             model = model.half()
+        try:
             model = model.to(device=device)
+        except:
+            pass
         loaded_model_names.append(model_name)
         loaded_models.append(model)
     return loaded_model_names, loaded_models
@@ -259,7 +158,7 @@ class ModelDetectorFromSingleFile:
 
 
     def match(self, file_path="", state_dict={}):
-        if os.path.isdir(file_path):
+        if isinstance(file_path, str) and os.path.isdir(file_path):
             return False
         if len(state_dict) == 0:
             state_dict = load_state_dict(file_path)
@@ -301,7 +200,7 @@ class ModelDetectorFromSplitedSingleFile(ModelDetectorFromSingleFile):
 
 
     def match(self, file_path="", state_dict={}):
-        if os.path.isdir(file_path):
+        if isinstance(file_path, str) and os.path.isdir(file_path):
             return False
         if len(state_dict) == 0:
             state_dict = load_state_dict(file_path)
@@ -339,19 +238,19 @@ class ModelDetectorFromHuggingfaceFolder:
             self.add_model_metadata(*metadata)
 
 
-    def add_model_metadata(self, architecture, huggingface_lib, model_name):
+    def add_model_metadata(self, architecture, huggingface_lib, model_name, redirected_architecture):
-        self.architecture_dict[architecture] = (huggingface_lib, model_name)
+        self.architecture_dict[architecture] = (huggingface_lib, model_name, redirected_architecture)
 
 
     def match(self, file_path="", state_dict={}):
-        if os.path.isfile(file_path):
+        if not isinstance(file_path, str) or os.path.isfile(file_path):
             return False
         file_list = os.listdir(file_path)
         if "config.json" not in file_list:
             return False
         with open(os.path.join(file_path, "config.json"), "r") as f:
             config = json.load(f)
-        if "architectures" not in config:
+        if "architectures" not in config and "_class_name" not in config:
             return False
         return True
 
@@ -360,8 +259,11 @@ class ModelDetectorFromHuggingfaceFolder:
         with open(os.path.join(file_path, "config.json"), "r") as f:
             config = json.load(f)
         loaded_model_names, loaded_models = [], []
-        for architecture in config["architectures"]:
+        architectures = config["architectures"] if "architectures" in config else [config["_class_name"]]
-            huggingface_lib, model_name = self.architecture_dict[architecture]
+        for architecture in architectures:
+            huggingface_lib, model_name, redirected_architecture = self.architecture_dict[architecture]
+            if redirected_architecture is not None:
+                architecture = redirected_architecture
             model_class = importlib.import_module(huggingface_lib).__getattribute__(architecture)
             loaded_model_names_, loaded_models_ = load_model_from_huggingface_folder(file_path, [model_name], [model_class], torch_dtype, device)
             loaded_model_names += loaded_model_names_
@@ -382,7 +284,7 @@ class ModelDetectorFromPatchedSingleFile:
 
 
     def match(self, file_path="", state_dict={}):
-        if os.path.isdir(file_path):
+        if not isinstance(file_path, str) or os.path.isdir(file_path):
             return False
         if len(state_dict) == 0:
             state_dict = load_state_dict(file_path)
@@ -467,11 +369,15 @@ class ModelManager:
 
 
     def load_lora(self, file_path="", state_dict={}, lora_alpha=1.0):
+        if isinstance(file_path, list):
+            for file_path_ in file_path:
+                self.load_lora(file_path_, state_dict=state_dict, lora_alpha=lora_alpha)
+        else:
             print(f"Loading LoRA models from file: {file_path}")
             if len(state_dict) == 0:
                 state_dict = load_state_dict(file_path)
             for model_name, model, model_path in zip(self.model_name, self.model, self.model_path):
-            for lora in [SDLoRAFromCivitai(), SDXLLoRAFromCivitai(), GeneralLoRAFromPeft()]:
+                for lora in get_lora_loaders():
                     match_results = lora.match(model, state_dict)
                     if match_results is not None:
                         print(f"    Adding LoRA to {model_name} ({model_path}).")
@@ -480,9 +386,15 @@ class ModelManager:
                         break
 
 
-    def load_model(self, file_path, model_names=None):
+    def load_model(self, file_path, model_names=None, device=None, torch_dtype=None):
         print(f"Loading models from: {file_path}")
-        if os.path.isfile(file_path):
+        if device is None: device = self.device
+        if torch_dtype is None: torch_dtype = self.torch_dtype
+        if isinstance(file_path, list):
+            state_dict = {}
+            for path in file_path:
+                state_dict.update(load_state_dict(path))
+        elif os.path.isfile(file_path):
             state_dict = load_state_dict(file_path)
         else:
             state_dict = None
@@ -490,7 +402,7 @@ class ModelManager:
             if model_detector.match(file_path, state_dict):
                 model_names, models = model_detector.load(
                     file_path, state_dict,
-                    device=self.device, torch_dtype=self.torch_dtype,
+                    device=device, torch_dtype=torch_dtype,
                     allowed_model_names=model_names, model_manager=self
                 )
                 for model_name, model in zip(model_names, models):
@@ -503,9 +415,9 @@ class ModelManager:
             print(f"    We cannot detect the model type. No models are loaded.")
         
 
-    def load_models(self, file_path_list, model_names=None):
+    def load_models(self, file_path_list, model_names=None, device=None, torch_dtype=None):
         for file_path in file_path_list:
-            self.load_model(file_path, model_names)
+            self.load_model(file_path, model_names, device=device, torch_dtype=torch_dtype)
 
     
     def fetch_model(self, model_name, file_path=None, require_model_path=False):

diffsynth/models/omnigen.py

@@ -0,0 +1,803 @@
+# The code is revised from DiT
+import os
+import torch
+import torch.nn as nn
+import numpy as np
+import math
+from safetensors.torch import load_file
+from typing import List, Optional, Tuple, Union
+import torch.utils.checkpoint
+from huggingface_hub import snapshot_download
+from transformers.modeling_outputs import BaseModelOutputWithPast
+from transformers import Phi3Config, Phi3Model
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class Phi3Transformer(Phi3Model):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi3DecoderLayer`]
+    We only modified the attention mask
+    Args:
+        config: Phi3Config
+    """
+    def prefetch_layer(self, layer_idx: int, device: torch.device):
+        "Starts prefetching the next layer cache"
+        with torch.cuda.stream(self.prefetch_stream):
+            # Prefetch next layer tensors to GPU
+            for name, param in self.layers[layer_idx].named_parameters():
+                param.data = param.data.to(device, non_blocking=True)
+
+    def evict_previous_layer(self, layer_idx: int):
+        "Moves the previous layer cache to the CPU"
+        prev_layer_idx = layer_idx - 1
+        for name, param in self.layers[prev_layer_idx].named_parameters():
+            param.data = param.data.to("cpu", non_blocking=True)
+            
+    def get_offlaod_layer(self, layer_idx: int, device: torch.device):
+        # init stream
+        if not hasattr(self, "prefetch_stream"):
+            self.prefetch_stream = torch.cuda.Stream()
+
+        # delete previous layer
+        torch.cuda.current_stream().synchronize()
+        self.evict_previous_layer(layer_idx)
+        
+        # make sure the current layer is ready
+        torch.cuda.synchronize(self.prefetch_stream)
+
+        # load next layer
+        self.prefetch_layer((layer_idx + 1) % len(self.layers), device)
+        
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        offload_model: Optional[bool] = False,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # kept for BC (non `Cache` `past_key_values` inputs)
+        return_legacy_cache = False
+        if use_cache and not isinstance(past_key_values, Cache):
+            return_legacy_cache = True
+            if past_key_values is None:
+                past_key_values = DynamicCache()
+            else:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+                logger.warning_once(
+                    "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
+                    "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
+                    "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
+                )
+
+        # if inputs_embeds is None:
+        #     inputs_embeds = self.embed_tokens(input_ids)
+
+        # if cache_position is None:
+        #     past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+        #     cache_position = torch.arange(
+        #         past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+        #     )
+        # if position_ids is None:
+        #     position_ids = cache_position.unsqueeze(0)
+
+        if attention_mask is not None and attention_mask.dim() == 3:
+            dtype = inputs_embeds.dtype
+            min_dtype = torch.finfo(dtype).min
+            attention_mask = (1 - attention_mask) * min_dtype
+            attention_mask = attention_mask.unsqueeze(1).to(inputs_embeds.dtype)
+        else:
+            raise Exception("attention_mask parameter was unavailable or invalid")
+            # causal_mask = self._update_causal_mask(
+            #     attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+            # )
+
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        layer_idx = -1
+        for decoder_layer in self.layers:
+            layer_idx += 1
+
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                )
+            else:
+                if offload_model and not self.training:
+                    self.get_offlaod_layer(layer_idx, device=inputs_embeds.device)
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            print('************')
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+ 
+
+class TimestepEmbedder(nn.Module):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+    def __init__(self, hidden_size, frequency_embedding_size=256):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            nn.SiLU(),
+            nn.Linear(hidden_size, hidden_size, bias=True),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        """
+        Create sinusoidal timestep embeddings.
+        :param t: a 1-D Tensor of N indices, one per batch element.
+                          These may be fractional.
+        :param dim: the dimension of the output.
+        :param max_period: controls the minimum frequency of the embeddings.
+        :return: an (N, D) Tensor of positional embeddings.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+        ).to(device=t.device)
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+        return embedding
+
+    def forward(self, t, dtype=torch.float32):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of DiT.
+    """
+    def __init__(self, hidden_size, patch_size, out_channels):
+        super().__init__()
+        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(hidden_size, 2 * hidden_size, bias=True)
+        )
+
+    def forward(self, x, c):
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
+        x = modulate(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+
+def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=1):
+    """
+    grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or
+    [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+    """
+    if isinstance(grid_size, int):
+        grid_size = (grid_size, grid_size)
+
+    grid_h = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0] / base_size) / interpolation_scale
+    grid_w = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1] / base_size) / interpolation_scale
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)
+
+    grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    if cls_token and extra_tokens > 0:
+        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
+    return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+    assert embed_dim % 2 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
+
+    emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float64)
+    omega /= embed_dim / 2.
+    omega = 1. / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out) # (M, D/2)
+    emb_cos = np.cos(out) # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+
+class PatchEmbedMR(nn.Module):
+    """ 2D Image to Patch Embedding
+    """
+    def __init__(
+            self,
+            patch_size: int = 2,
+            in_chans: int = 4,
+            embed_dim: int = 768,
+            bias: bool = True,
+    ):
+        super().__init__()
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
+
+    def forward(self, x):
+        x = self.proj(x)
+        x = x.flatten(2).transpose(1, 2)  # NCHW -> NLC
+        return x
+
+
+class OmniGenOriginalModel(nn.Module):
+    """
+    Diffusion model with a Transformer backbone.
+    """
+    def __init__(
+        self,
+        transformer_config: Phi3Config,
+        patch_size=2,
+        in_channels=4,
+        pe_interpolation: float = 1.0,
+        pos_embed_max_size: int = 192,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = in_channels
+        self.patch_size = patch_size
+        self.pos_embed_max_size = pos_embed_max_size
+
+        hidden_size = transformer_config.hidden_size
+
+        self.x_embedder = PatchEmbedMR(patch_size, in_channels, hidden_size, bias=True)
+        self.input_x_embedder = PatchEmbedMR(patch_size, in_channels, hidden_size, bias=True)
+
+        self.time_token = TimestepEmbedder(hidden_size)
+        self.t_embedder = TimestepEmbedder(hidden_size)
+        
+        self.pe_interpolation = pe_interpolation
+        pos_embed = get_2d_sincos_pos_embed(hidden_size, pos_embed_max_size, interpolation_scale=self.pe_interpolation, base_size=64)
+        self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=True)
+
+        self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels)
+
+        self.initialize_weights()
+
+        self.llm = Phi3Transformer(config=transformer_config)
+        self.llm.config.use_cache = False
+    
+    @classmethod
+    def from_pretrained(cls, model_name):
+        if not os.path.exists(model_name):
+            cache_folder = os.getenv('HF_HUB_CACHE')
+            model_name = snapshot_download(repo_id=model_name,
+                                           cache_dir=cache_folder,
+                                           ignore_patterns=['flax_model.msgpack', 'rust_model.ot', 'tf_model.h5'])
+        config = Phi3Config.from_pretrained(model_name)
+        model = cls(config)
+        if os.path.exists(os.path.join(model_name, 'model.safetensors')):
+            print("Loading safetensors")
+            ckpt = load_file(os.path.join(model_name, 'model.safetensors'))
+        else:
+            ckpt = torch.load(os.path.join(model_name, 'model.pt'), map_location='cpu')
+        model.load_state_dict(ckpt)
+        return model
+
+    def initialize_weights(self):
+        assert not hasattr(self, "llama")
+
+        # Initialize transformer layers:
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+        self.apply(_basic_init)
+        
+        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
+        w = self.x_embedder.proj.weight.data
+        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
+        nn.init.constant_(self.x_embedder.proj.bias, 0)
+
+        w = self.input_x_embedder.proj.weight.data
+        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
+        nn.init.constant_(self.x_embedder.proj.bias, 0)
+
+
+        # Initialize timestep embedding MLP:
+        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
+        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
+        nn.init.normal_(self.time_token.mlp[0].weight, std=0.02)
+        nn.init.normal_(self.time_token.mlp[2].weight, std=0.02)
+
+        # Zero-out output layers:
+        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
+        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
+        nn.init.constant_(self.final_layer.linear.weight, 0)
+        nn.init.constant_(self.final_layer.linear.bias, 0)
+
+    def unpatchify(self, x, h, w):
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        c = self.out_channels
+
+        x = x.reshape(shape=(x.shape[0], h//self.patch_size, w//self.patch_size, self.patch_size, self.patch_size, c))
+        x = torch.einsum('nhwpqc->nchpwq', x)
+        imgs = x.reshape(shape=(x.shape[0], c, h, w))
+        return imgs
+
+
+    def cropped_pos_embed(self, height, width):
+        """Crops positional embeddings for SD3 compatibility."""
+        if self.pos_embed_max_size is None:
+            raise ValueError("`pos_embed_max_size` must be set for cropping.")
+
+        height = height // self.patch_size
+        width = width // self.patch_size
+        if height > self.pos_embed_max_size:
+            raise ValueError(
+                f"Height ({height}) cannot be greater than `pos_embed_max_size`: {self.pos_embed_max_size}."
+            )
+        if width > self.pos_embed_max_size:
+            raise ValueError(
+                f"Width ({width}) cannot be greater than `pos_embed_max_size`: {self.pos_embed_max_size}."
+            )
+
+        top = (self.pos_embed_max_size - height) // 2
+        left = (self.pos_embed_max_size - width) // 2
+        spatial_pos_embed = self.pos_embed.reshape(1, self.pos_embed_max_size, self.pos_embed_max_size, -1)
+        spatial_pos_embed = spatial_pos_embed[:, top : top + height, left : left + width, :]
+        # print(top, top + height, left, left + width, spatial_pos_embed.size())
+        spatial_pos_embed = spatial_pos_embed.reshape(1, -1, spatial_pos_embed.shape[-1])
+        return spatial_pos_embed
+
+
+    def patch_multiple_resolutions(self, latents, padding_latent=None, is_input_images:bool=False):
+        if isinstance(latents, list):
+            return_list = False
+            if padding_latent is None:
+                padding_latent = [None] * len(latents)
+                return_list = True
+            patched_latents, num_tokens, shapes = [], [], []
+            for latent, padding in zip(latents, padding_latent):
+                height, width = latent.shape[-2:]
+                if is_input_images:
+                    latent = self.input_x_embedder(latent)
+                else:
+                    latent = self.x_embedder(latent)
+                pos_embed = self.cropped_pos_embed(height, width)    
+                latent = latent + pos_embed
+                if padding is not None:
+                    latent = torch.cat([latent, padding], dim=-2)
+                patched_latents.append(latent)
+
+                num_tokens.append(pos_embed.size(1))
+                shapes.append([height, width])
+            if not return_list:
+                latents = torch.cat(patched_latents, dim=0)
+            else:
+                latents = patched_latents
+        else:
+            height, width = latents.shape[-2:]
+            if is_input_images:
+                latents = self.input_x_embedder(latents)
+            else:
+                latents = self.x_embedder(latents)
+            pos_embed = self.cropped_pos_embed(height, width)  
+            latents = latents + pos_embed
+            num_tokens = latents.size(1)
+            shapes = [height, width]
+        return latents, num_tokens, shapes
+
+    
+    def forward(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, padding_latent=None, past_key_values=None, return_past_key_values=True, offload_model:bool=False):
+        """
+        
+        """
+        input_is_list = isinstance(x, list)
+        x, num_tokens, shapes = self.patch_multiple_resolutions(x, padding_latent)
+        time_token = self.time_token(timestep, dtype=x[0].dtype).unsqueeze(1)   
+        
+        if input_img_latents is not None:
+            input_latents, _, _ = self.patch_multiple_resolutions(input_img_latents, is_input_images=True)
+        if input_ids is not None:
+            condition_embeds = self.llm.embed_tokens(input_ids).clone()
+            input_img_inx = 0
+            for b_inx in input_image_sizes.keys():
+                for start_inx, end_inx in input_image_sizes[b_inx]:
+                    condition_embeds[b_inx, start_inx: end_inx] = input_latents[input_img_inx]
+                    input_img_inx += 1
+            if input_img_latents is not None:
+                assert input_img_inx == len(input_latents) 
+
+            input_emb = torch.cat([condition_embeds, time_token, x], dim=1)
+        else:
+            input_emb = torch.cat([time_token, x], dim=1)
+        output = self.llm(inputs_embeds=input_emb, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, offload_model=offload_model)
+        output, past_key_values = output.last_hidden_state, output.past_key_values
+        if input_is_list:
+            image_embedding = output[:, -max(num_tokens):]
+            time_emb = self.t_embedder(timestep, dtype=x.dtype)
+            x = self.final_layer(image_embedding, time_emb)
+            latents = []
+            for i in range(x.size(0)):
+                latent = x[i:i+1, :num_tokens[i]]
+                latent = self.unpatchify(latent, shapes[i][0], shapes[i][1])
+                latents.append(latent)
+        else:
+            image_embedding = output[:, -num_tokens:]
+            time_emb = self.t_embedder(timestep, dtype=x.dtype)
+            x = self.final_layer(image_embedding, time_emb)
+            latents = self.unpatchify(x, shapes[0], shapes[1])
+
+        if return_past_key_values:
+            return latents, past_key_values
+        return latents
+
+    @torch.no_grad()
+    def forward_with_cfg(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, cfg_scale, use_img_cfg, img_cfg_scale, past_key_values, use_kv_cache, offload_model):      
+        self.llm.config.use_cache = use_kv_cache
+        model_out, past_key_values = self.forward(x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, past_key_values=past_key_values, return_past_key_values=True, offload_model=offload_model)
+        if use_img_cfg:
+            cond, uncond, img_cond = torch.split(model_out, len(model_out) // 3, dim=0)
+            cond = uncond + img_cfg_scale * (img_cond - uncond) + cfg_scale * (cond - img_cond)
+            model_out = [cond, cond, cond]
+        else:
+            cond, uncond = torch.split(model_out, len(model_out) // 2, dim=0)
+            cond = uncond + cfg_scale * (cond - uncond)
+            model_out = [cond, cond]
+        
+        return torch.cat(model_out, dim=0), past_key_values
+
+
+    @torch.no_grad()
+    def forward_with_separate_cfg(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, cfg_scale, use_img_cfg, img_cfg_scale, past_key_values, use_kv_cache, offload_model):
+        self.llm.config.use_cache = use_kv_cache
+        if past_key_values is None:
+            past_key_values = [None] * len(attention_mask)
+
+        x = torch.split(x, len(x) // len(attention_mask), dim=0)
+        timestep = timestep.to(x[0].dtype)
+        timestep = torch.split(timestep, len(timestep) // len(input_ids), dim=0)
+
+        model_out, pask_key_values = [], []
+        for i in range(len(input_ids)):
+            temp_out, temp_pask_key_values = self.forward(x[i], timestep[i], input_ids[i], input_img_latents[i], input_image_sizes[i], attention_mask[i], position_ids[i], past_key_values=past_key_values[i], return_past_key_values=True, offload_model=offload_model)
+            model_out.append(temp_out)
+            pask_key_values.append(temp_pask_key_values)
+
+        if len(model_out) == 3:
+            cond, uncond, img_cond = model_out
+            cond = uncond + img_cfg_scale * (img_cond - uncond) + cfg_scale * (cond - img_cond)
+            model_out = [cond, cond, cond]
+        elif len(model_out) == 2:
+            cond, uncond = model_out
+            cond = uncond + cfg_scale * (cond - uncond)
+            model_out = [cond, cond]
+        else:
+            return model_out[0]
+        
+        return torch.cat(model_out, dim=0), pask_key_values
+
+
+
+class OmniGenTransformer(OmniGenOriginalModel):
+    def __init__(self):
+        config = {
+            "_name_or_path": "Phi-3-vision-128k-instruct",
+            "architectures": [
+                "Phi3ForCausalLM"
+            ],
+            "attention_dropout": 0.0,
+            "bos_token_id": 1,
+            "eos_token_id": 2,
+            "hidden_act": "silu",
+            "hidden_size": 3072,
+            "initializer_range": 0.02,
+            "intermediate_size": 8192,
+            "max_position_embeddings": 131072,
+            "model_type": "phi3",
+            "num_attention_heads": 32,
+            "num_hidden_layers": 32,
+            "num_key_value_heads": 32,
+            "original_max_position_embeddings": 4096,
+            "rms_norm_eps": 1e-05,
+            "rope_scaling": {
+                "long_factor": [
+                1.0299999713897705,
+                1.0499999523162842,
+                1.0499999523162842,
+                1.0799999237060547,
+                1.2299998998641968,
+                1.2299998998641968,
+                1.2999999523162842,
+                1.4499999284744263,
+                1.5999999046325684,
+                1.6499998569488525,
+                1.8999998569488525,
+                2.859999895095825,
+                3.68999981880188,
+                5.419999599456787,
+                5.489999771118164,
+                5.489999771118164,
+                9.09000015258789,
+                11.579999923706055,
+                15.65999984741211,
+                15.769999504089355,
+                15.789999961853027,
+                18.360000610351562,
+                21.989999771118164,
+                23.079999923706055,
+                30.009998321533203,
+                32.35000228881836,
+                32.590003967285156,
+                35.56000518798828,
+                39.95000457763672,
+                53.840003967285156,
+                56.20000457763672,
+                57.95000457763672,
+                59.29000473022461,
+                59.77000427246094,
+                59.920005798339844,
+                61.190006256103516,
+                61.96000671386719,
+                62.50000762939453,
+                63.3700065612793,
+                63.48000717163086,
+                63.48000717163086,
+                63.66000747680664,
+                63.850006103515625,
+                64.08000946044922,
+                64.760009765625,
+                64.80001068115234,
+                64.81001281738281,
+                64.81001281738281
+                ],
+                "short_factor": [
+                1.05,
+                1.05,
+                1.05,
+                1.1,
+                1.1,
+                1.1,
+                1.2500000000000002,
+                1.2500000000000002,
+                1.4000000000000004,
+                1.4500000000000004,
+                1.5500000000000005,
+                1.8500000000000008,
+                1.9000000000000008,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.1000000000000005,
+                2.1000000000000005,
+                2.2,
+                2.3499999999999996,
+                2.3499999999999996,
+                2.3499999999999996,
+                2.3499999999999996,
+                2.3999999999999995,
+                2.3999999999999995,
+                2.6499999999999986,
+                2.6999999999999984,
+                2.8999999999999977,
+                2.9499999999999975,
+                3.049999999999997,
+                3.049999999999997,
+                3.049999999999997
+                ],
+                "type": "su"
+            },
+            "rope_theta": 10000.0,
+            "sliding_window": 131072,
+            "tie_word_embeddings": False,
+            "torch_dtype": "bfloat16",
+            "transformers_version": "4.38.1",
+            "use_cache": True,
+            "vocab_size": 32064,
+            "_attn_implementation": "sdpa"
+        }
+        config = Phi3Config(**config)
+        super().__init__(config)
+
+    
+    def forward(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, padding_latent=None, past_key_values=None, return_past_key_values=True, offload_model:bool=False):
+        input_is_list = isinstance(x, list)
+        x, num_tokens, shapes = self.patch_multiple_resolutions(x, padding_latent)
+        time_token = self.time_token(timestep, dtype=x[0].dtype).unsqueeze(1)   
+        
+        if input_img_latents is not None:
+            input_latents, _, _ = self.patch_multiple_resolutions(input_img_latents, is_input_images=True)
+        if input_ids is not None:
+            condition_embeds = self.llm.embed_tokens(input_ids).clone()
+            input_img_inx = 0
+            for b_inx in input_image_sizes.keys():
+                for start_inx, end_inx in input_image_sizes[b_inx]:
+                    condition_embeds[b_inx, start_inx: end_inx] = input_latents[input_img_inx]
+                    input_img_inx += 1
+            if input_img_latents is not None:
+                assert input_img_inx == len(input_latents) 
+
+            input_emb = torch.cat([condition_embeds, time_token, x], dim=1)
+        else:
+            input_emb = torch.cat([time_token, x], dim=1)
+        output = self.llm(inputs_embeds=input_emb, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, offload_model=offload_model)
+        output, past_key_values = output.last_hidden_state, output.past_key_values
+        if input_is_list:
+            image_embedding = output[:, -max(num_tokens):]
+            time_emb = self.t_embedder(timestep, dtype=x.dtype)
+            x = self.final_layer(image_embedding, time_emb)
+            latents = []
+            for i in range(x.size(0)):
+                latent = x[i:i+1, :num_tokens[i]]
+                latent = self.unpatchify(latent, shapes[i][0], shapes[i][1])
+                latents.append(latent)
+        else:
+            image_embedding = output[:, -num_tokens:]
+            time_emb = self.t_embedder(timestep, dtype=x.dtype)
+            x = self.final_layer(image_embedding, time_emb)
+            latents = self.unpatchify(x, shapes[0], shapes[1])
+
+        if return_past_key_values:
+            return latents, past_key_values
+        return latents
+    
+
+    @torch.no_grad()
+    def forward_with_separate_cfg(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, cfg_scale, use_img_cfg, img_cfg_scale, past_key_values, use_kv_cache, offload_model):
+        self.llm.config.use_cache = use_kv_cache
+        if past_key_values is None:
+            past_key_values = [None] * len(attention_mask)
+
+        x = torch.split(x, len(x) // len(attention_mask), dim=0)
+        timestep = timestep.to(x[0].dtype)
+        timestep = torch.split(timestep, len(timestep) // len(input_ids), dim=0)
+
+        model_out, pask_key_values = [], []
+        for i in range(len(input_ids)):
+            temp_out, temp_pask_key_values = self.forward(x[i], timestep[i], input_ids[i], input_img_latents[i], input_image_sizes[i], attention_mask[i], position_ids[i], past_key_values=past_key_values[i], return_past_key_values=True, offload_model=offload_model)
+            model_out.append(temp_out)
+            pask_key_values.append(temp_pask_key_values)
+
+        if len(model_out) == 3:
+            cond, uncond, img_cond = model_out
+            cond = uncond + img_cfg_scale * (img_cond - uncond) + cfg_scale * (cond - img_cond)
+            model_out = [cond, cond, cond]
+        elif len(model_out) == 2:
+            cond, uncond = model_out
+            cond = uncond + cfg_scale * (cond - uncond)
+            model_out = [cond, cond]
+        else:
+            return model_out[0]
+        
+        return torch.cat(model_out, dim=0), pask_key_values
+    
+
+    @staticmethod
+    def state_dict_converter():
+        return OmniGenTransformerStateDictConverter()
+
+
+
+class OmniGenTransformerStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict
+    
+    def from_civitai(self, state_dict):
+        return state_dict

diffsynth/models/sd3_dit.py

@@ -5,6 +5,26 @@ from .tiler import TileWorker
 
 
 
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim, eps, elementwise_affine=True):
+        super().__init__()
+        self.eps = eps
+        if elementwise_affine:
+            self.weight = torch.nn.Parameter(torch.ones((dim,)))
+        else:
+            self.weight = None
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
+        hidden_states = hidden_states.to(input_dtype)
+        if self.weight is not None:
+            hidden_states = hidden_states * self.weight
+        return hidden_states
+
+
+
 class PatchEmbed(torch.nn.Module):
     def __init__(self, patch_size=2, in_channels=16, embed_dim=1536, pos_embed_max_size=192):
         super().__init__()
@@ -12,7 +32,7 @@ class PatchEmbed(torch.nn.Module):
         self.patch_size = patch_size
 
         self.proj = torch.nn.Conv2d(in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size)
-        self.pos_embed = torch.nn.Parameter(torch.zeros(1, self.pos_embed_max_size, self.pos_embed_max_size, 1536))
+        self.pos_embed = torch.nn.Parameter(torch.zeros(1, self.pos_embed_max_size, self.pos_embed_max_size, embed_dim))
 
     def cropped_pos_embed(self, height, width):
         height = height // self.patch_size
@@ -32,9 +52,9 @@ class PatchEmbed(torch.nn.Module):
 
 
 class TimestepEmbeddings(torch.nn.Module):
-    def __init__(self, dim_in, dim_out):
+    def __init__(self, dim_in, dim_out, computation_device=None):
         super().__init__()
-        self.time_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0)
+        self.time_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0, computation_device=computation_device)
         self.timestep_embedder = torch.nn.Sequential(
             torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out)
         )
@@ -47,10 +67,11 @@ class TimestepEmbeddings(torch.nn.Module):
 
 
 class AdaLayerNorm(torch.nn.Module):
-    def __init__(self, dim, single=False):
+    def __init__(self, dim, single=False, dual=False):
         super().__init__()
         self.single = single
-        self.linear = torch.nn.Linear(dim, dim * (2 if single else 6))
+        self.dual = dual
+        self.linear = torch.nn.Linear(dim, dim * [[6, 2][single], 9][dual])
         self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
 
     def forward(self, x, emb):
@@ -59,6 +80,12 @@ class AdaLayerNorm(torch.nn.Module):
             scale, shift = emb.unsqueeze(1).chunk(2, dim=2)
             x = self.norm(x) * (1 + scale) + shift
             return x
+        elif self.dual:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp, shift_msa2, scale_msa2, gate_msa2 = emb.unsqueeze(1).chunk(9, dim=2)
+            norm_x = self.norm(x)
+            x = norm_x * (1 + scale_msa) + shift_msa
+            norm_x2 = norm_x * (1 + scale_msa2) + shift_msa2
+            return x, gate_msa, shift_mlp, scale_mlp, gate_mlp, norm_x2, gate_msa2
         else:
             shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.unsqueeze(1).chunk(6, dim=2)
             x = self.norm(x) * (1 + scale_msa) + shift_msa
@@ -67,7 +94,7 @@ class AdaLayerNorm(torch.nn.Module):
 
 
 class JointAttention(torch.nn.Module):
-    def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False):
+    def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False, use_rms_norm=False):
         super().__init__()
         self.num_heads = num_heads
         self.head_dim = head_dim
@@ -80,12 +107,38 @@ class JointAttention(torch.nn.Module):
         if not only_out_a:
             self.b_to_out = torch.nn.Linear(dim_b, dim_b)
 
+        if use_rms_norm:
+            self.norm_q_a = RMSNorm(head_dim, eps=1e-6)
+            self.norm_k_a = RMSNorm(head_dim, eps=1e-6)
+            self.norm_q_b = RMSNorm(head_dim, eps=1e-6)
+            self.norm_k_b = RMSNorm(head_dim, eps=1e-6)
+        else:
+            self.norm_q_a = None
+            self.norm_k_a = None
+            self.norm_q_b = None
+            self.norm_k_b = None
+
+
+    def process_qkv(self, hidden_states, to_qkv, norm_q, norm_k):
+        batch_size = hidden_states.shape[0]
+        qkv = to_qkv(hidden_states)
+        qkv = qkv.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q, k, v = qkv.chunk(3, dim=1)
+        if norm_q is not None:
+            q = norm_q(q)
+        if norm_k is not None:
+            k = norm_k(k)
+        return q, k, v
+
+
     def forward(self, hidden_states_a, hidden_states_b):
         batch_size = hidden_states_a.shape[0]
 
-        qkv = torch.concat([self.a_to_qkv(hidden_states_a), self.b_to_qkv(hidden_states_b)], dim=1)
+        qa, ka, va = self.process_qkv(hidden_states_a, self.a_to_qkv, self.norm_q_a, self.norm_k_a)
-        qkv = qkv.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        qb, kb, vb = self.process_qkv(hidden_states_b, self.b_to_qkv, self.norm_q_b, self.norm_k_b)
-        q, k, v = qkv.chunk(3, dim=1)
+        q = torch.concat([qa, qb], dim=2)
+        k = torch.concat([ka, kb], dim=2)
+        v = torch.concat([va, vb], dim=2)
 
         hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v)
         hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
@@ -100,13 +153,55 @@ class JointAttention(torch.nn.Module):
         
 
 
-class JointTransformerBlock(torch.nn.Module):
+class SingleAttention(torch.nn.Module):
-    def __init__(self, dim, num_attention_heads):
+    def __init__(self, dim_a, num_heads, head_dim, use_rms_norm=False):
         super().__init__()
-        self.norm1_a = AdaLayerNorm(dim)
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+
+        self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3)
+        self.a_to_out = torch.nn.Linear(dim_a, dim_a)
+
+        if use_rms_norm:
+            self.norm_q_a = RMSNorm(head_dim, eps=1e-6)
+            self.norm_k_a = RMSNorm(head_dim, eps=1e-6)
+        else:
+            self.norm_q_a = None
+            self.norm_k_a = None
+
+
+    def process_qkv(self, hidden_states, to_qkv, norm_q, norm_k):
+        batch_size = hidden_states.shape[0]
+        qkv = to_qkv(hidden_states)
+        qkv = qkv.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q, k, v = qkv.chunk(3, dim=1)
+        if norm_q is not None:
+            q = norm_q(q)
+        if norm_k is not None:
+            k = norm_k(k)
+        return q, k, v
+
+
+    def forward(self, hidden_states_a):
+        batch_size = hidden_states_a.shape[0]
+        q, k, v = self.process_qkv(hidden_states_a, self.a_to_qkv, self.norm_q_a, self.norm_k_a)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+        hidden_states = self.a_to_out(hidden_states)
+        return hidden_states
+        
+
+
+class DualTransformerBlock(torch.nn.Module):
+    def __init__(self, dim, num_attention_heads, use_rms_norm=False):
+        super().__init__()
+        self.norm1_a = AdaLayerNorm(dim, dual=True)
         self.norm1_b = AdaLayerNorm(dim)
 
-        self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads)
+        self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm)
+        self.attn2 = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm)
 
         self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
         self.ff_a = torch.nn.Sequential(
@@ -124,6 +219,56 @@ class JointTransformerBlock(torch.nn.Module):
 
 
     def forward(self, hidden_states_a, hidden_states_b, temb):
+        norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a, norm_hidden_states_a_2, gate_msa_a_2 = self.norm1_a(hidden_states_a, emb=temb)
+        norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb)
+
+        # Attention
+        attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b)
+
+        # Part A
+        hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a
+        hidden_states_a = hidden_states_a + gate_msa_a_2 * self.attn2(norm_hidden_states_a_2)
+        norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a
+        hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a)
+
+        # Part B
+        hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b
+        norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b
+        hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b)
+
+        return hidden_states_a, hidden_states_b
+
+
+
+class JointTransformerBlock(torch.nn.Module):
+    def __init__(self, dim, num_attention_heads, use_rms_norm=False, dual=False):
+        super().__init__()
+        self.norm1_a = AdaLayerNorm(dim, dual=dual)
+        self.norm1_b = AdaLayerNorm(dim)
+
+        self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm)
+        if dual:
+            self.attn2 = SingleAttention(dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm)
+
+        self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_a = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim*4),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(dim*4, dim)
+        )
+
+        self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_b = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim*4),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(dim*4, dim)
+        )
+
+
+    def forward(self, hidden_states_a, hidden_states_b, temb):
+        if self.norm1_a.dual:
+            norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a, norm_hidden_states_a_2, gate_msa_a_2 = self.norm1_a(hidden_states_a, emb=temb)
+        else:
             norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb)
         norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb)
 
@@ -132,6 +277,8 @@ class JointTransformerBlock(torch.nn.Module):
 
         # Part A
         hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a
+        if self.norm1_a.dual:
+            hidden_states_a = hidden_states_a + gate_msa_a_2 * self.attn2(norm_hidden_states_a_2)
         norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a
         hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a)
 
@@ -145,12 +292,12 @@ class JointTransformerBlock(torch.nn.Module):
 
 
 class JointTransformerFinalBlock(torch.nn.Module):
-    def __init__(self, dim, num_attention_heads):
+    def __init__(self, dim, num_attention_heads, use_rms_norm=False):
         super().__init__()
         self.norm1_a = AdaLayerNorm(dim)
         self.norm1_b = AdaLayerNorm(dim, single=True)
 
-        self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, only_out_a=True)
+        self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, only_out_a=True, use_rms_norm=use_rms_norm)
 
         self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
         self.ff_a = torch.nn.Sequential(
@@ -177,15 +324,17 @@ class JointTransformerFinalBlock(torch.nn.Module):
 
 
 class SD3DiT(torch.nn.Module):
-    def __init__(self):
+    def __init__(self, embed_dim=1536, num_layers=24, use_rms_norm=False, num_dual_blocks=0, pos_embed_max_size=192):
         super().__init__()
-        self.pos_embedder = PatchEmbed(patch_size=2, in_channels=16, embed_dim=1536, pos_embed_max_size=192)
+        self.pos_embedder = PatchEmbed(patch_size=2, in_channels=16, embed_dim=embed_dim, pos_embed_max_size=pos_embed_max_size)
-        self.time_embedder = TimestepEmbeddings(256, 1536)
+        self.time_embedder = TimestepEmbeddings(256, embed_dim)
-        self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(2048, 1536), torch.nn.SiLU(), torch.nn.Linear(1536, 1536))
+        self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(2048, embed_dim), torch.nn.SiLU(), torch.nn.Linear(embed_dim, embed_dim))
-        self.context_embedder = torch.nn.Linear(4096, 1536)
+        self.context_embedder = torch.nn.Linear(4096, embed_dim)
-        self.blocks = torch.nn.ModuleList([JointTransformerBlock(1536, 24) for _ in range(23)] + [JointTransformerFinalBlock(1536, 24)])
+        self.blocks = torch.nn.ModuleList([JointTransformerBlock(embed_dim, embed_dim//64, use_rms_norm=use_rms_norm, dual=True) for _ in range(num_dual_blocks)]
-        self.norm_out = AdaLayerNorm(1536, single=True)
+                                          + [JointTransformerBlock(embed_dim, embed_dim//64, use_rms_norm=use_rms_norm) for _ in range(num_layers-1-num_dual_blocks)]
-        self.proj_out = torch.nn.Linear(1536, 64)
+                                          + [JointTransformerFinalBlock(embed_dim, embed_dim//64, use_rms_norm=use_rms_norm)])
+        self.norm_out = AdaLayerNorm(embed_dim, single=True)
+        self.proj_out = torch.nn.Linear(embed_dim, 64)
 
     def tiled_forward(self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, tile_size=128, tile_stride=64):
         # Due to the global positional embedding, we cannot implement layer-wise tiled forward.
@@ -238,6 +387,24 @@ class SD3DiTStateDictConverter:
     def __init__(self):
         pass
 
+    def infer_architecture(self, state_dict):
+        embed_dim = state_dict["blocks.0.ff_a.0.weight"].shape[1]
+        num_layers = 100
+        while num_layers > 0 and f"blocks.{num_layers-1}.ff_a.0.bias" not in state_dict:
+            num_layers -= 1
+        use_rms_norm = "blocks.0.attn.norm_q_a.weight" in state_dict
+        num_dual_blocks = 0
+        while f"blocks.{num_dual_blocks}.attn2.a_to_out.bias" in state_dict:
+            num_dual_blocks += 1
+        pos_embed_max_size = state_dict["pos_embedder.pos_embed"].shape[1]
+        return {
+            "embed_dim": embed_dim,
+            "num_layers": num_layers,
+            "use_rms_norm": use_rms_norm,
+            "num_dual_blocks": num_dual_blocks,
+            "pos_embed_max_size": pos_embed_max_size
+        }
+
     def from_diffusers(self, state_dict):
         rename_dict = {
             "context_embedder": "context_embedder",
@@ -264,12 +431,17 @@ class SD3DiTStateDictConverter:
             "ff.net.2": "ff_a.2",
             "ff_context.net.0.proj": "ff_b.0",
             "ff_context.net.2": "ff_b.2",
+
+            "attn.norm_q": "attn.norm_q_a",
+            "attn.norm_k": "attn.norm_k_a",
+            "attn.norm_added_q": "attn.norm_q_b",
+            "attn.norm_added_k": "attn.norm_k_b",
         }
         state_dict_ = {}
         for name, param in state_dict.items():
             if name in rename_dict:
                 if name == "pos_embed.pos_embed":
-                    param = param.reshape((1, 192, 192, 1536))
+                    param = param.reshape((1, 192, 192, param.shape[-1]))
                 state_dict_[rename_dict[name]] = param
             elif name.endswith(".weight") or name.endswith(".bias"):
                 suffix = ".weight" if name.endswith(".weight") else ".bias"
@@ -283,7 +455,19 @@ class SD3DiTStateDictConverter:
                     if middle in rename_dict:
                         name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]])
                         state_dict_[name_] = param
-        return state_dict_
+        merged_keys = [name for name in state_dict_ if ".a_to_q." in name or ".b_to_q." in name]
+        for key in merged_keys:
+            param = torch.concat([
+                state_dict_[key.replace("to_q", "to_q")],
+                state_dict_[key.replace("to_q", "to_k")],
+                state_dict_[key.replace("to_q", "to_v")],
+            ], dim=0)
+            name = key.replace("to_q", "to_qkv")
+            state_dict_.pop(key.replace("to_q", "to_q"))
+            state_dict_.pop(key.replace("to_q", "to_k"))
+            state_dict_.pop(key.replace("to_q", "to_v"))
+            state_dict_[name] = param
+        return state_dict_, self.infer_architecture(state_dict_)
     
     def from_civitai(self, state_dict):
         rename_dict = {
@@ -291,478 +475,7 @@ class SD3DiTStateDictConverter:
             "model.diffusion_model.context_embedder.weight": "context_embedder.weight",
             "model.diffusion_model.final_layer.linear.bias": "proj_out.bias",
             "model.diffusion_model.final_layer.linear.weight": "proj_out.weight",
-            "model.diffusion_model.joint_blocks.0.context_block.adaLN_modulation.1.bias": "blocks.0.norm1_b.linear.bias",
+
-            "model.diffusion_model.joint_blocks.0.context_block.adaLN_modulation.1.weight": "blocks.0.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.0.context_block.attn.proj.bias": "blocks.0.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.0.context_block.attn.proj.weight": "blocks.0.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.0.context_block.attn.qkv.bias": ['blocks.0.attn.b_to_q.bias', 'blocks.0.attn.b_to_k.bias', 'blocks.0.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.0.context_block.attn.qkv.weight": ['blocks.0.attn.b_to_q.weight', 'blocks.0.attn.b_to_k.weight', 'blocks.0.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.0.context_block.mlp.fc1.bias": "blocks.0.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.0.context_block.mlp.fc1.weight": "blocks.0.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.0.context_block.mlp.fc2.bias": "blocks.0.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.0.context_block.mlp.fc2.weight": "blocks.0.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.0.x_block.adaLN_modulation.1.bias": "blocks.0.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.0.x_block.adaLN_modulation.1.weight": "blocks.0.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.0.x_block.attn.proj.bias": "blocks.0.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.0.x_block.attn.proj.weight": "blocks.0.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.0.x_block.attn.qkv.bias": ['blocks.0.attn.a_to_q.bias', 'blocks.0.attn.a_to_k.bias', 'blocks.0.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.0.x_block.attn.qkv.weight": ['blocks.0.attn.a_to_q.weight', 'blocks.0.attn.a_to_k.weight', 'blocks.0.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.0.x_block.mlp.fc1.bias": "blocks.0.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.0.x_block.mlp.fc1.weight": "blocks.0.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.0.x_block.mlp.fc2.bias": "blocks.0.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.0.x_block.mlp.fc2.weight": "blocks.0.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.1.context_block.adaLN_modulation.1.bias": "blocks.1.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.1.context_block.adaLN_modulation.1.weight": "blocks.1.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.1.context_block.attn.proj.bias": "blocks.1.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.1.context_block.attn.proj.weight": "blocks.1.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.1.context_block.attn.qkv.bias": ['blocks.1.attn.b_to_q.bias', 'blocks.1.attn.b_to_k.bias', 'blocks.1.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.1.context_block.attn.qkv.weight": ['blocks.1.attn.b_to_q.weight', 'blocks.1.attn.b_to_k.weight', 'blocks.1.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.1.context_block.mlp.fc1.bias": "blocks.1.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.1.context_block.mlp.fc1.weight": "blocks.1.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.1.context_block.mlp.fc2.bias": "blocks.1.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.1.context_block.mlp.fc2.weight": "blocks.1.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.1.x_block.adaLN_modulation.1.bias": "blocks.1.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.1.x_block.adaLN_modulation.1.weight": "blocks.1.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.1.x_block.attn.proj.bias": "blocks.1.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.1.x_block.attn.proj.weight": "blocks.1.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.1.x_block.attn.qkv.bias": ['blocks.1.attn.a_to_q.bias', 'blocks.1.attn.a_to_k.bias', 'blocks.1.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.1.x_block.attn.qkv.weight": ['blocks.1.attn.a_to_q.weight', 'blocks.1.attn.a_to_k.weight', 'blocks.1.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.1.x_block.mlp.fc1.bias": "blocks.1.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.1.x_block.mlp.fc1.weight": "blocks.1.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.1.x_block.mlp.fc2.bias": "blocks.1.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.1.x_block.mlp.fc2.weight": "blocks.1.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.10.context_block.adaLN_modulation.1.bias": "blocks.10.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.10.context_block.adaLN_modulation.1.weight": "blocks.10.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.10.context_block.attn.proj.bias": "blocks.10.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.10.context_block.attn.proj.weight": "blocks.10.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.10.context_block.attn.qkv.bias": ['blocks.10.attn.b_to_q.bias', 'blocks.10.attn.b_to_k.bias', 'blocks.10.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.10.context_block.attn.qkv.weight": ['blocks.10.attn.b_to_q.weight', 'blocks.10.attn.b_to_k.weight', 'blocks.10.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.10.context_block.mlp.fc1.bias": "blocks.10.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.10.context_block.mlp.fc1.weight": "blocks.10.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.10.context_block.mlp.fc2.bias": "blocks.10.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.10.context_block.mlp.fc2.weight": "blocks.10.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.10.x_block.adaLN_modulation.1.bias": "blocks.10.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.10.x_block.adaLN_modulation.1.weight": "blocks.10.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.10.x_block.attn.proj.bias": "blocks.10.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.10.x_block.attn.proj.weight": "blocks.10.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.10.x_block.attn.qkv.bias": ['blocks.10.attn.a_to_q.bias', 'blocks.10.attn.a_to_k.bias', 'blocks.10.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.10.x_block.attn.qkv.weight": ['blocks.10.attn.a_to_q.weight', 'blocks.10.attn.a_to_k.weight', 'blocks.10.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.10.x_block.mlp.fc1.bias": "blocks.10.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.10.x_block.mlp.fc1.weight": "blocks.10.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.10.x_block.mlp.fc2.bias": "blocks.10.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.10.x_block.mlp.fc2.weight": "blocks.10.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.11.context_block.adaLN_modulation.1.bias": "blocks.11.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.11.context_block.adaLN_modulation.1.weight": "blocks.11.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.11.context_block.attn.proj.bias": "blocks.11.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.11.context_block.attn.proj.weight": "blocks.11.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.11.context_block.attn.qkv.bias": ['blocks.11.attn.b_to_q.bias', 'blocks.11.attn.b_to_k.bias', 'blocks.11.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.11.context_block.attn.qkv.weight": ['blocks.11.attn.b_to_q.weight', 'blocks.11.attn.b_to_k.weight', 'blocks.11.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.11.context_block.mlp.fc1.bias": "blocks.11.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.11.context_block.mlp.fc1.weight": "blocks.11.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.11.context_block.mlp.fc2.bias": "blocks.11.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.11.context_block.mlp.fc2.weight": "blocks.11.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.11.x_block.adaLN_modulation.1.bias": "blocks.11.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.11.x_block.adaLN_modulation.1.weight": "blocks.11.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.11.x_block.attn.proj.bias": "blocks.11.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.11.x_block.attn.proj.weight": "blocks.11.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.11.x_block.attn.qkv.bias": ['blocks.11.attn.a_to_q.bias', 'blocks.11.attn.a_to_k.bias', 'blocks.11.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.11.x_block.attn.qkv.weight": ['blocks.11.attn.a_to_q.weight', 'blocks.11.attn.a_to_k.weight', 'blocks.11.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.11.x_block.mlp.fc1.bias": "blocks.11.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.11.x_block.mlp.fc1.weight": "blocks.11.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.11.x_block.mlp.fc2.bias": "blocks.11.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.11.x_block.mlp.fc2.weight": "blocks.11.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.12.context_block.adaLN_modulation.1.bias": "blocks.12.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.12.context_block.adaLN_modulation.1.weight": "blocks.12.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.12.context_block.attn.proj.bias": "blocks.12.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.12.context_block.attn.proj.weight": "blocks.12.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.12.context_block.attn.qkv.bias": ['blocks.12.attn.b_to_q.bias', 'blocks.12.attn.b_to_k.bias', 'blocks.12.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.12.context_block.attn.qkv.weight": ['blocks.12.attn.b_to_q.weight', 'blocks.12.attn.b_to_k.weight', 'blocks.12.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.12.context_block.mlp.fc1.bias": "blocks.12.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.12.context_block.mlp.fc1.weight": "blocks.12.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.12.context_block.mlp.fc2.bias": "blocks.12.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.12.context_block.mlp.fc2.weight": "blocks.12.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.12.x_block.adaLN_modulation.1.bias": "blocks.12.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.12.x_block.adaLN_modulation.1.weight": "blocks.12.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.12.x_block.attn.proj.bias": "blocks.12.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.12.x_block.attn.proj.weight": "blocks.12.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.12.x_block.attn.qkv.bias": ['blocks.12.attn.a_to_q.bias', 'blocks.12.attn.a_to_k.bias', 'blocks.12.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.12.x_block.attn.qkv.weight": ['blocks.12.attn.a_to_q.weight', 'blocks.12.attn.a_to_k.weight', 'blocks.12.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.12.x_block.mlp.fc1.bias": "blocks.12.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.12.x_block.mlp.fc1.weight": "blocks.12.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.12.x_block.mlp.fc2.bias": "blocks.12.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.12.x_block.mlp.fc2.weight": "blocks.12.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.13.context_block.adaLN_modulation.1.bias": "blocks.13.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.13.context_block.adaLN_modulation.1.weight": "blocks.13.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.13.context_block.attn.proj.bias": "blocks.13.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.13.context_block.attn.proj.weight": "blocks.13.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.13.context_block.attn.qkv.bias": ['blocks.13.attn.b_to_q.bias', 'blocks.13.attn.b_to_k.bias', 'blocks.13.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.13.context_block.attn.qkv.weight": ['blocks.13.attn.b_to_q.weight', 'blocks.13.attn.b_to_k.weight', 'blocks.13.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.13.context_block.mlp.fc1.bias": "blocks.13.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.13.context_block.mlp.fc1.weight": "blocks.13.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.13.context_block.mlp.fc2.bias": "blocks.13.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.13.context_block.mlp.fc2.weight": "blocks.13.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.13.x_block.adaLN_modulation.1.bias": "blocks.13.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.13.x_block.adaLN_modulation.1.weight": "blocks.13.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.13.x_block.attn.proj.bias": "blocks.13.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.13.x_block.attn.proj.weight": "blocks.13.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.13.x_block.attn.qkv.bias": ['blocks.13.attn.a_to_q.bias', 'blocks.13.attn.a_to_k.bias', 'blocks.13.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.13.x_block.attn.qkv.weight": ['blocks.13.attn.a_to_q.weight', 'blocks.13.attn.a_to_k.weight', 'blocks.13.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.13.x_block.mlp.fc1.bias": "blocks.13.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.13.x_block.mlp.fc1.weight": "blocks.13.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.13.x_block.mlp.fc2.bias": "blocks.13.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.13.x_block.mlp.fc2.weight": "blocks.13.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.14.context_block.adaLN_modulation.1.bias": "blocks.14.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.14.context_block.adaLN_modulation.1.weight": "blocks.14.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.14.context_block.attn.proj.bias": "blocks.14.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.14.context_block.attn.proj.weight": "blocks.14.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.14.context_block.attn.qkv.bias": ['blocks.14.attn.b_to_q.bias', 'blocks.14.attn.b_to_k.bias', 'blocks.14.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.14.context_block.attn.qkv.weight": ['blocks.14.attn.b_to_q.weight', 'blocks.14.attn.b_to_k.weight', 'blocks.14.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.14.context_block.mlp.fc1.bias": "blocks.14.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.14.context_block.mlp.fc1.weight": "blocks.14.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.14.context_block.mlp.fc2.bias": "blocks.14.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.14.context_block.mlp.fc2.weight": "blocks.14.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.14.x_block.adaLN_modulation.1.bias": "blocks.14.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.14.x_block.adaLN_modulation.1.weight": "blocks.14.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.14.x_block.attn.proj.bias": "blocks.14.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.14.x_block.attn.proj.weight": "blocks.14.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.14.x_block.attn.qkv.bias": ['blocks.14.attn.a_to_q.bias', 'blocks.14.attn.a_to_k.bias', 'blocks.14.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.14.x_block.attn.qkv.weight": ['blocks.14.attn.a_to_q.weight', 'blocks.14.attn.a_to_k.weight', 'blocks.14.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.14.x_block.mlp.fc1.bias": "blocks.14.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.14.x_block.mlp.fc1.weight": "blocks.14.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.14.x_block.mlp.fc2.bias": "blocks.14.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.14.x_block.mlp.fc2.weight": "blocks.14.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.15.context_block.adaLN_modulation.1.bias": "blocks.15.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.15.context_block.adaLN_modulation.1.weight": "blocks.15.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.15.context_block.attn.proj.bias": "blocks.15.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.15.context_block.attn.proj.weight": "blocks.15.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.15.context_block.attn.qkv.bias": ['blocks.15.attn.b_to_q.bias', 'blocks.15.attn.b_to_k.bias', 'blocks.15.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.15.context_block.attn.qkv.weight": ['blocks.15.attn.b_to_q.weight', 'blocks.15.attn.b_to_k.weight', 'blocks.15.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.15.context_block.mlp.fc1.bias": "blocks.15.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.15.context_block.mlp.fc1.weight": "blocks.15.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.15.context_block.mlp.fc2.bias": "blocks.15.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.15.context_block.mlp.fc2.weight": "blocks.15.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.15.x_block.adaLN_modulation.1.bias": "blocks.15.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.15.x_block.adaLN_modulation.1.weight": "blocks.15.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.15.x_block.attn.proj.bias": "blocks.15.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.15.x_block.attn.proj.weight": "blocks.15.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.15.x_block.attn.qkv.bias": ['blocks.15.attn.a_to_q.bias', 'blocks.15.attn.a_to_k.bias', 'blocks.15.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.15.x_block.attn.qkv.weight": ['blocks.15.attn.a_to_q.weight', 'blocks.15.attn.a_to_k.weight', 'blocks.15.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.15.x_block.mlp.fc1.bias": "blocks.15.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.15.x_block.mlp.fc1.weight": "blocks.15.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.15.x_block.mlp.fc2.bias": "blocks.15.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.15.x_block.mlp.fc2.weight": "blocks.15.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.16.context_block.adaLN_modulation.1.bias": "blocks.16.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.16.context_block.adaLN_modulation.1.weight": "blocks.16.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.16.context_block.attn.proj.bias": "blocks.16.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.16.context_block.attn.proj.weight": "blocks.16.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.16.context_block.attn.qkv.bias": ['blocks.16.attn.b_to_q.bias', 'blocks.16.attn.b_to_k.bias', 'blocks.16.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.16.context_block.attn.qkv.weight": ['blocks.16.attn.b_to_q.weight', 'blocks.16.attn.b_to_k.weight', 'blocks.16.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.16.context_block.mlp.fc1.bias": "blocks.16.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.16.context_block.mlp.fc1.weight": "blocks.16.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.16.context_block.mlp.fc2.bias": "blocks.16.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.16.context_block.mlp.fc2.weight": "blocks.16.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.16.x_block.adaLN_modulation.1.bias": "blocks.16.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.16.x_block.adaLN_modulation.1.weight": "blocks.16.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.16.x_block.attn.proj.bias": "blocks.16.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.16.x_block.attn.proj.weight": "blocks.16.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.16.x_block.attn.qkv.bias": ['blocks.16.attn.a_to_q.bias', 'blocks.16.attn.a_to_k.bias', 'blocks.16.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.16.x_block.attn.qkv.weight": ['blocks.16.attn.a_to_q.weight', 'blocks.16.attn.a_to_k.weight', 'blocks.16.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.16.x_block.mlp.fc1.bias": "blocks.16.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.16.x_block.mlp.fc1.weight": "blocks.16.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.16.x_block.mlp.fc2.bias": "blocks.16.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.16.x_block.mlp.fc2.weight": "blocks.16.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.17.context_block.adaLN_modulation.1.bias": "blocks.17.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.17.context_block.adaLN_modulation.1.weight": "blocks.17.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.17.context_block.attn.proj.bias": "blocks.17.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.17.context_block.attn.proj.weight": "blocks.17.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.17.context_block.attn.qkv.bias": ['blocks.17.attn.b_to_q.bias', 'blocks.17.attn.b_to_k.bias', 'blocks.17.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.17.context_block.attn.qkv.weight": ['blocks.17.attn.b_to_q.weight', 'blocks.17.attn.b_to_k.weight', 'blocks.17.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.17.context_block.mlp.fc1.bias": "blocks.17.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.17.context_block.mlp.fc1.weight": "blocks.17.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.17.context_block.mlp.fc2.bias": "blocks.17.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.17.context_block.mlp.fc2.weight": "blocks.17.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.17.x_block.adaLN_modulation.1.bias": "blocks.17.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.17.x_block.adaLN_modulation.1.weight": "blocks.17.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.17.x_block.attn.proj.bias": "blocks.17.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.17.x_block.attn.proj.weight": "blocks.17.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.17.x_block.attn.qkv.bias": ['blocks.17.attn.a_to_q.bias', 'blocks.17.attn.a_to_k.bias', 'blocks.17.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.17.x_block.attn.qkv.weight": ['blocks.17.attn.a_to_q.weight', 'blocks.17.attn.a_to_k.weight', 'blocks.17.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.17.x_block.mlp.fc1.bias": "blocks.17.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.17.x_block.mlp.fc1.weight": "blocks.17.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.17.x_block.mlp.fc2.bias": "blocks.17.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.17.x_block.mlp.fc2.weight": "blocks.17.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.18.context_block.adaLN_modulation.1.bias": "blocks.18.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.18.context_block.adaLN_modulation.1.weight": "blocks.18.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.18.context_block.attn.proj.bias": "blocks.18.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.18.context_block.attn.proj.weight": "blocks.18.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.18.context_block.attn.qkv.bias": ['blocks.18.attn.b_to_q.bias', 'blocks.18.attn.b_to_k.bias', 'blocks.18.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.18.context_block.attn.qkv.weight": ['blocks.18.attn.b_to_q.weight', 'blocks.18.attn.b_to_k.weight', 'blocks.18.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.18.context_block.mlp.fc1.bias": "blocks.18.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.18.context_block.mlp.fc1.weight": "blocks.18.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.18.context_block.mlp.fc2.bias": "blocks.18.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.18.context_block.mlp.fc2.weight": "blocks.18.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.18.x_block.adaLN_modulation.1.bias": "blocks.18.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.18.x_block.adaLN_modulation.1.weight": "blocks.18.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.18.x_block.attn.proj.bias": "blocks.18.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.18.x_block.attn.proj.weight": "blocks.18.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.18.x_block.attn.qkv.bias": ['blocks.18.attn.a_to_q.bias', 'blocks.18.attn.a_to_k.bias', 'blocks.18.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.18.x_block.attn.qkv.weight": ['blocks.18.attn.a_to_q.weight', 'blocks.18.attn.a_to_k.weight', 'blocks.18.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.18.x_block.mlp.fc1.bias": "blocks.18.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.18.x_block.mlp.fc1.weight": "blocks.18.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.18.x_block.mlp.fc2.bias": "blocks.18.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.18.x_block.mlp.fc2.weight": "blocks.18.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.19.context_block.adaLN_modulation.1.bias": "blocks.19.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.19.context_block.adaLN_modulation.1.weight": "blocks.19.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.19.context_block.attn.proj.bias": "blocks.19.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.19.context_block.attn.proj.weight": "blocks.19.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.19.context_block.attn.qkv.bias": ['blocks.19.attn.b_to_q.bias', 'blocks.19.attn.b_to_k.bias', 'blocks.19.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.19.context_block.attn.qkv.weight": ['blocks.19.attn.b_to_q.weight', 'blocks.19.attn.b_to_k.weight', 'blocks.19.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.19.context_block.mlp.fc1.bias": "blocks.19.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.19.context_block.mlp.fc1.weight": "blocks.19.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.19.context_block.mlp.fc2.bias": "blocks.19.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.19.context_block.mlp.fc2.weight": "blocks.19.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.19.x_block.adaLN_modulation.1.bias": "blocks.19.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.19.x_block.adaLN_modulation.1.weight": "blocks.19.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.19.x_block.attn.proj.bias": "blocks.19.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.19.x_block.attn.proj.weight": "blocks.19.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.19.x_block.attn.qkv.bias": ['blocks.19.attn.a_to_q.bias', 'blocks.19.attn.a_to_k.bias', 'blocks.19.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.19.x_block.attn.qkv.weight": ['blocks.19.attn.a_to_q.weight', 'blocks.19.attn.a_to_k.weight', 'blocks.19.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.19.x_block.mlp.fc1.bias": "blocks.19.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.19.x_block.mlp.fc1.weight": "blocks.19.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.19.x_block.mlp.fc2.bias": "blocks.19.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.19.x_block.mlp.fc2.weight": "blocks.19.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.2.context_block.adaLN_modulation.1.bias": "blocks.2.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.2.context_block.adaLN_modulation.1.weight": "blocks.2.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.2.context_block.attn.proj.bias": "blocks.2.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.2.context_block.attn.proj.weight": "blocks.2.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.2.context_block.attn.qkv.bias": ['blocks.2.attn.b_to_q.bias', 'blocks.2.attn.b_to_k.bias', 'blocks.2.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.2.context_block.attn.qkv.weight": ['blocks.2.attn.b_to_q.weight', 'blocks.2.attn.b_to_k.weight', 'blocks.2.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.2.context_block.mlp.fc1.bias": "blocks.2.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.2.context_block.mlp.fc1.weight": "blocks.2.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.2.context_block.mlp.fc2.bias": "blocks.2.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.2.context_block.mlp.fc2.weight": "blocks.2.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.2.x_block.adaLN_modulation.1.bias": "blocks.2.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.2.x_block.adaLN_modulation.1.weight": "blocks.2.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.2.x_block.attn.proj.bias": "blocks.2.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.2.x_block.attn.proj.weight": "blocks.2.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.2.x_block.attn.qkv.bias": ['blocks.2.attn.a_to_q.bias', 'blocks.2.attn.a_to_k.bias', 'blocks.2.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.2.x_block.attn.qkv.weight": ['blocks.2.attn.a_to_q.weight', 'blocks.2.attn.a_to_k.weight', 'blocks.2.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.2.x_block.mlp.fc1.bias": "blocks.2.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.2.x_block.mlp.fc1.weight": "blocks.2.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.2.x_block.mlp.fc2.bias": "blocks.2.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.2.x_block.mlp.fc2.weight": "blocks.2.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.20.context_block.adaLN_modulation.1.bias": "blocks.20.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.20.context_block.adaLN_modulation.1.weight": "blocks.20.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.20.context_block.attn.proj.bias": "blocks.20.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.20.context_block.attn.proj.weight": "blocks.20.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.20.context_block.attn.qkv.bias": ['blocks.20.attn.b_to_q.bias', 'blocks.20.attn.b_to_k.bias', 'blocks.20.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.20.context_block.attn.qkv.weight": ['blocks.20.attn.b_to_q.weight', 'blocks.20.attn.b_to_k.weight', 'blocks.20.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.20.context_block.mlp.fc1.bias": "blocks.20.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.20.context_block.mlp.fc1.weight": "blocks.20.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.20.context_block.mlp.fc2.bias": "blocks.20.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.20.context_block.mlp.fc2.weight": "blocks.20.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.20.x_block.adaLN_modulation.1.bias": "blocks.20.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.20.x_block.adaLN_modulation.1.weight": "blocks.20.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.20.x_block.attn.proj.bias": "blocks.20.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.20.x_block.attn.proj.weight": "blocks.20.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.20.x_block.attn.qkv.bias": ['blocks.20.attn.a_to_q.bias', 'blocks.20.attn.a_to_k.bias', 'blocks.20.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.20.x_block.attn.qkv.weight": ['blocks.20.attn.a_to_q.weight', 'blocks.20.attn.a_to_k.weight', 'blocks.20.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.20.x_block.mlp.fc1.bias": "blocks.20.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.20.x_block.mlp.fc1.weight": "blocks.20.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.20.x_block.mlp.fc2.bias": "blocks.20.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.20.x_block.mlp.fc2.weight": "blocks.20.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.21.context_block.adaLN_modulation.1.bias": "blocks.21.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.21.context_block.adaLN_modulation.1.weight": "blocks.21.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.21.context_block.attn.proj.bias": "blocks.21.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.21.context_block.attn.proj.weight": "blocks.21.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.21.context_block.attn.qkv.bias": ['blocks.21.attn.b_to_q.bias', 'blocks.21.attn.b_to_k.bias', 'blocks.21.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.21.context_block.attn.qkv.weight": ['blocks.21.attn.b_to_q.weight', 'blocks.21.attn.b_to_k.weight', 'blocks.21.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.21.context_block.mlp.fc1.bias": "blocks.21.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.21.context_block.mlp.fc1.weight": "blocks.21.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.21.context_block.mlp.fc2.bias": "blocks.21.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.21.context_block.mlp.fc2.weight": "blocks.21.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.21.x_block.adaLN_modulation.1.bias": "blocks.21.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.21.x_block.adaLN_modulation.1.weight": "blocks.21.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.21.x_block.attn.proj.bias": "blocks.21.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.21.x_block.attn.proj.weight": "blocks.21.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.21.x_block.attn.qkv.bias": ['blocks.21.attn.a_to_q.bias', 'blocks.21.attn.a_to_k.bias', 'blocks.21.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.21.x_block.attn.qkv.weight": ['blocks.21.attn.a_to_q.weight', 'blocks.21.attn.a_to_k.weight', 'blocks.21.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.21.x_block.mlp.fc1.bias": "blocks.21.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.21.x_block.mlp.fc1.weight": "blocks.21.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.21.x_block.mlp.fc2.bias": "blocks.21.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.21.x_block.mlp.fc2.weight": "blocks.21.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.22.context_block.adaLN_modulation.1.bias": "blocks.22.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.22.context_block.adaLN_modulation.1.weight": "blocks.22.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.22.context_block.attn.proj.bias": "blocks.22.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.22.context_block.attn.proj.weight": "blocks.22.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.22.context_block.attn.qkv.bias": ['blocks.22.attn.b_to_q.bias', 'blocks.22.attn.b_to_k.bias', 'blocks.22.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.22.context_block.attn.qkv.weight": ['blocks.22.attn.b_to_q.weight', 'blocks.22.attn.b_to_k.weight', 'blocks.22.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.22.context_block.mlp.fc1.bias": "blocks.22.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.22.context_block.mlp.fc1.weight": "blocks.22.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.22.context_block.mlp.fc2.bias": "blocks.22.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.22.context_block.mlp.fc2.weight": "blocks.22.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.22.x_block.adaLN_modulation.1.bias": "blocks.22.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.22.x_block.adaLN_modulation.1.weight": "blocks.22.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.22.x_block.attn.proj.bias": "blocks.22.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.22.x_block.attn.proj.weight": "blocks.22.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.22.x_block.attn.qkv.bias": ['blocks.22.attn.a_to_q.bias', 'blocks.22.attn.a_to_k.bias', 'blocks.22.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.22.x_block.attn.qkv.weight": ['blocks.22.attn.a_to_q.weight', 'blocks.22.attn.a_to_k.weight', 'blocks.22.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.22.x_block.mlp.fc1.bias": "blocks.22.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.22.x_block.mlp.fc1.weight": "blocks.22.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.22.x_block.mlp.fc2.bias": "blocks.22.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.22.x_block.mlp.fc2.weight": "blocks.22.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.23.context_block.attn.qkv.bias": ['blocks.23.attn.b_to_q.bias', 'blocks.23.attn.b_to_k.bias', 'blocks.23.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.23.context_block.attn.qkv.weight": ['blocks.23.attn.b_to_q.weight', 'blocks.23.attn.b_to_k.weight', 'blocks.23.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.23.x_block.adaLN_modulation.1.bias": "blocks.23.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.23.x_block.adaLN_modulation.1.weight": "blocks.23.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.23.x_block.attn.proj.bias": "blocks.23.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.23.x_block.attn.proj.weight": "blocks.23.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.23.x_block.attn.qkv.bias": ['blocks.23.attn.a_to_q.bias', 'blocks.23.attn.a_to_k.bias', 'blocks.23.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.23.x_block.attn.qkv.weight": ['blocks.23.attn.a_to_q.weight', 'blocks.23.attn.a_to_k.weight', 'blocks.23.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.23.x_block.mlp.fc1.bias": "blocks.23.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.23.x_block.mlp.fc1.weight": "blocks.23.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.23.x_block.mlp.fc2.bias": "blocks.23.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.23.x_block.mlp.fc2.weight": "blocks.23.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.3.context_block.adaLN_modulation.1.bias": "blocks.3.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.3.context_block.adaLN_modulation.1.weight": "blocks.3.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.3.context_block.attn.proj.bias": "blocks.3.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.3.context_block.attn.proj.weight": "blocks.3.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.3.context_block.attn.qkv.bias": ['blocks.3.attn.b_to_q.bias', 'blocks.3.attn.b_to_k.bias', 'blocks.3.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.3.context_block.attn.qkv.weight": ['blocks.3.attn.b_to_q.weight', 'blocks.3.attn.b_to_k.weight', 'blocks.3.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.3.context_block.mlp.fc1.bias": "blocks.3.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.3.context_block.mlp.fc1.weight": "blocks.3.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.3.context_block.mlp.fc2.bias": "blocks.3.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.3.context_block.mlp.fc2.weight": "blocks.3.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.3.x_block.adaLN_modulation.1.bias": "blocks.3.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.3.x_block.adaLN_modulation.1.weight": "blocks.3.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.3.x_block.attn.proj.bias": "blocks.3.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.3.x_block.attn.proj.weight": "blocks.3.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.3.x_block.attn.qkv.bias": ['blocks.3.attn.a_to_q.bias', 'blocks.3.attn.a_to_k.bias', 'blocks.3.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.3.x_block.attn.qkv.weight": ['blocks.3.attn.a_to_q.weight', 'blocks.3.attn.a_to_k.weight', 'blocks.3.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.3.x_block.mlp.fc1.bias": "blocks.3.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.3.x_block.mlp.fc1.weight": "blocks.3.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.3.x_block.mlp.fc2.bias": "blocks.3.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.3.x_block.mlp.fc2.weight": "blocks.3.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.4.context_block.adaLN_modulation.1.bias": "blocks.4.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.4.context_block.adaLN_modulation.1.weight": "blocks.4.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.4.context_block.attn.proj.bias": "blocks.4.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.4.context_block.attn.proj.weight": "blocks.4.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.4.context_block.attn.qkv.bias": ['blocks.4.attn.b_to_q.bias', 'blocks.4.attn.b_to_k.bias', 'blocks.4.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.4.context_block.attn.qkv.weight": ['blocks.4.attn.b_to_q.weight', 'blocks.4.attn.b_to_k.weight', 'blocks.4.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.4.context_block.mlp.fc1.bias": "blocks.4.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.4.context_block.mlp.fc1.weight": "blocks.4.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.4.context_block.mlp.fc2.bias": "blocks.4.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.4.context_block.mlp.fc2.weight": "blocks.4.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.4.x_block.adaLN_modulation.1.bias": "blocks.4.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.4.x_block.adaLN_modulation.1.weight": "blocks.4.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.4.x_block.attn.proj.bias": "blocks.4.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.4.x_block.attn.proj.weight": "blocks.4.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.4.x_block.attn.qkv.bias": ['blocks.4.attn.a_to_q.bias', 'blocks.4.attn.a_to_k.bias', 'blocks.4.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.4.x_block.attn.qkv.weight": ['blocks.4.attn.a_to_q.weight', 'blocks.4.attn.a_to_k.weight', 'blocks.4.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.4.x_block.mlp.fc1.bias": "blocks.4.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.4.x_block.mlp.fc1.weight": "blocks.4.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.4.x_block.mlp.fc2.bias": "blocks.4.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.4.x_block.mlp.fc2.weight": "blocks.4.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.5.context_block.adaLN_modulation.1.bias": "blocks.5.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.5.context_block.adaLN_modulation.1.weight": "blocks.5.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.5.context_block.attn.proj.bias": "blocks.5.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.5.context_block.attn.proj.weight": "blocks.5.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.5.context_block.attn.qkv.bias": ['blocks.5.attn.b_to_q.bias', 'blocks.5.attn.b_to_k.bias', 'blocks.5.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.5.context_block.attn.qkv.weight": ['blocks.5.attn.b_to_q.weight', 'blocks.5.attn.b_to_k.weight', 'blocks.5.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.5.context_block.mlp.fc1.bias": "blocks.5.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.5.context_block.mlp.fc1.weight": "blocks.5.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.5.context_block.mlp.fc2.bias": "blocks.5.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.5.context_block.mlp.fc2.weight": "blocks.5.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.5.x_block.adaLN_modulation.1.bias": "blocks.5.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.5.x_block.adaLN_modulation.1.weight": "blocks.5.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.5.x_block.attn.proj.bias": "blocks.5.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.5.x_block.attn.proj.weight": "blocks.5.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.5.x_block.attn.qkv.bias": ['blocks.5.attn.a_to_q.bias', 'blocks.5.attn.a_to_k.bias', 'blocks.5.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.5.x_block.attn.qkv.weight": ['blocks.5.attn.a_to_q.weight', 'blocks.5.attn.a_to_k.weight', 'blocks.5.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.5.x_block.mlp.fc1.bias": "blocks.5.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.5.x_block.mlp.fc1.weight": "blocks.5.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.5.x_block.mlp.fc2.bias": "blocks.5.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.5.x_block.mlp.fc2.weight": "blocks.5.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.6.context_block.adaLN_modulation.1.bias": "blocks.6.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.6.context_block.adaLN_modulation.1.weight": "blocks.6.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.6.context_block.attn.proj.bias": "blocks.6.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.6.context_block.attn.proj.weight": "blocks.6.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.6.context_block.attn.qkv.bias": ['blocks.6.attn.b_to_q.bias', 'blocks.6.attn.b_to_k.bias', 'blocks.6.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.6.context_block.attn.qkv.weight": ['blocks.6.attn.b_to_q.weight', 'blocks.6.attn.b_to_k.weight', 'blocks.6.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.6.context_block.mlp.fc1.bias": "blocks.6.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.6.context_block.mlp.fc1.weight": "blocks.6.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.6.context_block.mlp.fc2.bias": "blocks.6.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.6.context_block.mlp.fc2.weight": "blocks.6.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.6.x_block.adaLN_modulation.1.bias": "blocks.6.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.6.x_block.adaLN_modulation.1.weight": "blocks.6.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.6.x_block.attn.proj.bias": "blocks.6.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.6.x_block.attn.proj.weight": "blocks.6.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.6.x_block.attn.qkv.bias": ['blocks.6.attn.a_to_q.bias', 'blocks.6.attn.a_to_k.bias', 'blocks.6.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.6.x_block.attn.qkv.weight": ['blocks.6.attn.a_to_q.weight', 'blocks.6.attn.a_to_k.weight', 'blocks.6.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.6.x_block.mlp.fc1.bias": "blocks.6.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.6.x_block.mlp.fc1.weight": "blocks.6.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.6.x_block.mlp.fc2.bias": "blocks.6.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.6.x_block.mlp.fc2.weight": "blocks.6.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.7.context_block.adaLN_modulation.1.bias": "blocks.7.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.7.context_block.adaLN_modulation.1.weight": "blocks.7.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.7.context_block.attn.proj.bias": "blocks.7.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.7.context_block.attn.proj.weight": "blocks.7.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.7.context_block.attn.qkv.bias": ['blocks.7.attn.b_to_q.bias', 'blocks.7.attn.b_to_k.bias', 'blocks.7.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.7.context_block.attn.qkv.weight": ['blocks.7.attn.b_to_q.weight', 'blocks.7.attn.b_to_k.weight', 'blocks.7.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.7.context_block.mlp.fc1.bias": "blocks.7.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.7.context_block.mlp.fc1.weight": "blocks.7.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.7.context_block.mlp.fc2.bias": "blocks.7.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.7.context_block.mlp.fc2.weight": "blocks.7.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.7.x_block.adaLN_modulation.1.bias": "blocks.7.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.7.x_block.adaLN_modulation.1.weight": "blocks.7.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.7.x_block.attn.proj.bias": "blocks.7.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.7.x_block.attn.proj.weight": "blocks.7.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.7.x_block.attn.qkv.bias": ['blocks.7.attn.a_to_q.bias', 'blocks.7.attn.a_to_k.bias', 'blocks.7.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.7.x_block.attn.qkv.weight": ['blocks.7.attn.a_to_q.weight', 'blocks.7.attn.a_to_k.weight', 'blocks.7.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.7.x_block.mlp.fc1.bias": "blocks.7.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.7.x_block.mlp.fc1.weight": "blocks.7.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.7.x_block.mlp.fc2.bias": "blocks.7.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.7.x_block.mlp.fc2.weight": "blocks.7.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.8.context_block.adaLN_modulation.1.bias": "blocks.8.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.8.context_block.adaLN_modulation.1.weight": "blocks.8.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.8.context_block.attn.proj.bias": "blocks.8.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.8.context_block.attn.proj.weight": "blocks.8.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.8.context_block.attn.qkv.bias": ['blocks.8.attn.b_to_q.bias', 'blocks.8.attn.b_to_k.bias', 'blocks.8.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.8.context_block.attn.qkv.weight": ['blocks.8.attn.b_to_q.weight', 'blocks.8.attn.b_to_k.weight', 'blocks.8.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.8.context_block.mlp.fc1.bias": "blocks.8.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.8.context_block.mlp.fc1.weight": "blocks.8.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.8.context_block.mlp.fc2.bias": "blocks.8.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.8.context_block.mlp.fc2.weight": "blocks.8.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.8.x_block.adaLN_modulation.1.bias": "blocks.8.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.8.x_block.adaLN_modulation.1.weight": "blocks.8.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.8.x_block.attn.proj.bias": "blocks.8.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.8.x_block.attn.proj.weight": "blocks.8.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.8.x_block.attn.qkv.bias": ['blocks.8.attn.a_to_q.bias', 'blocks.8.attn.a_to_k.bias', 'blocks.8.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.8.x_block.attn.qkv.weight": ['blocks.8.attn.a_to_q.weight', 'blocks.8.attn.a_to_k.weight', 'blocks.8.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.8.x_block.mlp.fc1.bias": "blocks.8.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.8.x_block.mlp.fc1.weight": "blocks.8.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.8.x_block.mlp.fc2.bias": "blocks.8.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.8.x_block.mlp.fc2.weight": "blocks.8.ff_a.2.weight",
-            "model.diffusion_model.joint_blocks.9.context_block.adaLN_modulation.1.bias": "blocks.9.norm1_b.linear.bias",
-            "model.diffusion_model.joint_blocks.9.context_block.adaLN_modulation.1.weight": "blocks.9.norm1_b.linear.weight",
-            "model.diffusion_model.joint_blocks.9.context_block.attn.proj.bias": "blocks.9.attn.b_to_out.bias",
-            "model.diffusion_model.joint_blocks.9.context_block.attn.proj.weight": "blocks.9.attn.b_to_out.weight",
-            "model.diffusion_model.joint_blocks.9.context_block.attn.qkv.bias": ['blocks.9.attn.b_to_q.bias', 'blocks.9.attn.b_to_k.bias', 'blocks.9.attn.b_to_v.bias'],
-            "model.diffusion_model.joint_blocks.9.context_block.attn.qkv.weight": ['blocks.9.attn.b_to_q.weight', 'blocks.9.attn.b_to_k.weight', 'blocks.9.attn.b_to_v.weight'],
-            "model.diffusion_model.joint_blocks.9.context_block.mlp.fc1.bias": "blocks.9.ff_b.0.bias",
-            "model.diffusion_model.joint_blocks.9.context_block.mlp.fc1.weight": "blocks.9.ff_b.0.weight",
-            "model.diffusion_model.joint_blocks.9.context_block.mlp.fc2.bias": "blocks.9.ff_b.2.bias",
-            "model.diffusion_model.joint_blocks.9.context_block.mlp.fc2.weight": "blocks.9.ff_b.2.weight",
-            "model.diffusion_model.joint_blocks.9.x_block.adaLN_modulation.1.bias": "blocks.9.norm1_a.linear.bias",
-            "model.diffusion_model.joint_blocks.9.x_block.adaLN_modulation.1.weight": "blocks.9.norm1_a.linear.weight",
-            "model.diffusion_model.joint_blocks.9.x_block.attn.proj.bias": "blocks.9.attn.a_to_out.bias",
-            "model.diffusion_model.joint_blocks.9.x_block.attn.proj.weight": "blocks.9.attn.a_to_out.weight",
-            "model.diffusion_model.joint_blocks.9.x_block.attn.qkv.bias": ['blocks.9.attn.a_to_q.bias', 'blocks.9.attn.a_to_k.bias', 'blocks.9.attn.a_to_v.bias'],
-            "model.diffusion_model.joint_blocks.9.x_block.attn.qkv.weight": ['blocks.9.attn.a_to_q.weight', 'blocks.9.attn.a_to_k.weight', 'blocks.9.attn.a_to_v.weight'],
-            "model.diffusion_model.joint_blocks.9.x_block.mlp.fc1.bias": "blocks.9.ff_a.0.bias",
-            "model.diffusion_model.joint_blocks.9.x_block.mlp.fc1.weight": "blocks.9.ff_a.0.weight",
-            "model.diffusion_model.joint_blocks.9.x_block.mlp.fc2.bias": "blocks.9.ff_a.2.bias",
-            "model.diffusion_model.joint_blocks.9.x_block.mlp.fc2.weight": "blocks.9.ff_a.2.weight",
             "model.diffusion_model.pos_embed": "pos_embedder.pos_embed",
             "model.diffusion_model.t_embedder.mlp.0.bias": "time_embedder.timestep_embedder.0.bias",
             "model.diffusion_model.t_embedder.mlp.0.weight": "time_embedder.timestep_embedder.0.weight",
@@ -780,19 +493,59 @@ class SD3DiTStateDictConverter:
             "model.diffusion_model.final_layer.adaLN_modulation.1.weight": "norm_out.linear.weight",
             "model.diffusion_model.final_layer.adaLN_modulation.1.bias": "norm_out.linear.bias",
         }
+        for i in range(40):
+            rename_dict.update({
+                f"model.diffusion_model.joint_blocks.{i}.context_block.adaLN_modulation.1.bias": f"blocks.{i}.norm1_b.linear.bias",
+                f"model.diffusion_model.joint_blocks.{i}.context_block.adaLN_modulation.1.weight": f"blocks.{i}.norm1_b.linear.weight",
+                f"model.diffusion_model.joint_blocks.{i}.context_block.attn.proj.bias": f"blocks.{i}.attn.b_to_out.bias",
+                f"model.diffusion_model.joint_blocks.{i}.context_block.attn.proj.weight": f"blocks.{i}.attn.b_to_out.weight",
+                f"model.diffusion_model.joint_blocks.{i}.context_block.attn.qkv.bias": [f'blocks.{i}.attn.b_to_q.bias', f'blocks.{i}.attn.b_to_k.bias', f'blocks.{i}.attn.b_to_v.bias'],
+                f"model.diffusion_model.joint_blocks.{i}.context_block.attn.qkv.weight": [f'blocks.{i}.attn.b_to_q.weight', f'blocks.{i}.attn.b_to_k.weight', f'blocks.{i}.attn.b_to_v.weight'],
+                f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc1.bias": f"blocks.{i}.ff_b.0.bias",
+                f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc1.weight": f"blocks.{i}.ff_b.0.weight",
+                f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc2.bias": f"blocks.{i}.ff_b.2.bias",
+                f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc2.weight": f"blocks.{i}.ff_b.2.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.adaLN_modulation.1.bias": f"blocks.{i}.norm1_a.linear.bias",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.adaLN_modulation.1.weight": f"blocks.{i}.norm1_a.linear.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn.proj.bias": f"blocks.{i}.attn.a_to_out.bias",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn.proj.weight": f"blocks.{i}.attn.a_to_out.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn.qkv.bias": [f'blocks.{i}.attn.a_to_q.bias', f'blocks.{i}.attn.a_to_k.bias', f'blocks.{i}.attn.a_to_v.bias'],
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn.qkv.weight": [f'blocks.{i}.attn.a_to_q.weight', f'blocks.{i}.attn.a_to_k.weight', f'blocks.{i}.attn.a_to_v.weight'],
+                f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc1.bias": f"blocks.{i}.ff_a.0.bias",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc1.weight": f"blocks.{i}.ff_a.0.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc2.bias": f"blocks.{i}.ff_a.2.bias",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc2.weight": f"blocks.{i}.ff_a.2.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn.ln_q.weight": f"blocks.{i}.attn.norm_q_a.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn.ln_k.weight": f"blocks.{i}.attn.norm_k_a.weight",
+                f"model.diffusion_model.joint_blocks.{i}.context_block.attn.ln_q.weight": f"blocks.{i}.attn.norm_q_b.weight",
+                f"model.diffusion_model.joint_blocks.{i}.context_block.attn.ln_k.weight": f"blocks.{i}.attn.norm_k_b.weight",
+
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.ln_q.weight": f"blocks.{i}.attn2.norm_q_a.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.ln_k.weight": f"blocks.{i}.attn2.norm_k_a.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.qkv.weight": f"blocks.{i}.attn2.a_to_qkv.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.qkv.bias": f"blocks.{i}.attn2.a_to_qkv.bias",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.proj.weight": f"blocks.{i}.attn2.a_to_out.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.proj.bias": f"blocks.{i}.attn2.a_to_out.bias",
+            })
         state_dict_ = {}
         for name in state_dict:
             if name in rename_dict:
                 param = state_dict[name]
-                if name.startswith("model.diffusion_model.joint_blocks.23.context_block.adaLN_modulation.1."):
+                if name == "model.diffusion_model.pos_embed":
-                    param = torch.concat([param[1536:], param[:1536]], axis=0)
+                    pos_embed_max_size = int(param.shape[1] ** 0.5 + 0.4)
-                elif name.startswith("model.diffusion_model.final_layer.adaLN_modulation.1."):
+                    param = param.reshape((1, pos_embed_max_size, pos_embed_max_size, param.shape[-1]))
-                    param = torch.concat([param[1536:], param[:1536]], axis=0)
-                elif name == "model.diffusion_model.pos_embed":
-                    param = param.reshape((1, 192, 192, 1536))
                 if isinstance(rename_dict[name], str):
                     state_dict_[rename_dict[name]] = param
                 else:
                     name_ = rename_dict[name][0].replace(".a_to_q.", ".a_to_qkv.").replace(".b_to_q.", ".b_to_qkv.")
                     state_dict_[name_] = param
-        return state_dict_
+        extra_kwargs = self.infer_architecture(state_dict_)
+        num_layers = extra_kwargs["num_layers"]
+        for name in [
+            f"blocks.{num_layers-1}.norm1_b.linear.weight", f"blocks.{num_layers-1}.norm1_b.linear.bias", "norm_out.linear.weight", "norm_out.linear.bias",
+        ]:
+            param = state_dict_[name]
+            dim = param.shape[0] // 2
+            param = torch.concat([param[dim:], param[:dim]], axis=0)
+            state_dict_[name] = param
+        return state_dict_, self.infer_architecture(state_dict_)

diffsynth/models/sd3_text_encoder.py

@@ -8,9 +8,12 @@ class SD3TextEncoder1(SDTextEncoder):
     def __init__(self, vocab_size=49408):
         super().__init__(vocab_size=vocab_size)
 
-    def forward(self, input_ids, clip_skip=2):
+    def forward(self, input_ids, clip_skip=2, extra_mask=None):
-        embeds = self.token_embedding(input_ids) + self.position_embeds
+        embeds = self.token_embedding(input_ids)
+        embeds = embeds + self.position_embeds.to(dtype=embeds.dtype, device=input_ids.device)
         attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype)
+        if extra_mask is not None:
+            attn_mask[:, extra_mask[0]==0] = float("-inf")
         for encoder_id, encoder in enumerate(self.encoders):
             embeds = encoder(embeds, attn_mask=attn_mask)
             if encoder_id + clip_skip == len(self.encoders):
@@ -322,6 +325,11 @@ class SD3TextEncoder1StateDictConverter:
                 if name == "text_encoders.clip_l.transformer.text_model.embeddings.position_embedding.weight":
                     param = param.reshape((1, param.shape[0], param.shape[1]))
                 state_dict_[rename_dict[name]] = param
+            elif ("text_encoders.clip_l.transformer." + name) in rename_dict:
+                param = state_dict[name]
+                if name == "text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict["text_encoders.clip_l.transformer." + name]] = param
         return state_dict_
     
 
@@ -860,6 +868,11 @@ class SD3TextEncoder2StateDictConverter(SDXLTextEncoder2StateDictConverter):
                 if name == "text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight":
                     param = param.reshape((1, param.shape[0], param.shape[1]))
                 state_dict_[rename_dict[name]] = param
+            elif ("text_encoders.clip_g.transformer." + name) in rename_dict:
+                param = state_dict[name]
+                if name == "text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict["text_encoders.clip_g.transformer." + name]] = param
         return state_dict_
 
 

diffsynth/models/sd_controlnet.py

@@ -97,6 +97,7 @@ class SDControlNet(torch.nn.Module):
         self,
         sample, timestep, encoder_hidden_states, conditioning,
         tiled=False, tile_size=64, tile_stride=32,
+        **kwargs
     ):
         # 1. time
         time_emb = self.time_proj(timestep).to(sample.dtype)

diffsynth/models/sdxl_controlnet.py

@@ -0,0 +1,318 @@
+import torch
+from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, DownSampler
+from .sdxl_unet import SDXLUNet
+from .tiler import TileWorker
+from .sd_controlnet import ControlNetConditioningLayer
+from collections import OrderedDict
+
+
+
+class QuickGELU(torch.nn.Module):
+
+    def forward(self, x: torch.Tensor):
+        return x * torch.sigmoid(1.702 * x)
+
+
+
+class ResidualAttentionBlock(torch.nn.Module):
+
+    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
+        super().__init__()
+
+        self.attn = torch.nn.MultiheadAttention(d_model, n_head)
+        self.ln_1 = torch.nn.LayerNorm(d_model)
+        self.mlp = torch.nn.Sequential(OrderedDict([
+            ("c_fc", torch.nn.Linear(d_model, d_model * 4)),
+            ("gelu", QuickGELU()),
+            ("c_proj", torch.nn.Linear(d_model * 4, d_model))
+        ]))
+        self.ln_2 = torch.nn.LayerNorm(d_model)
+        self.attn_mask = attn_mask
+
+    def attention(self, x: torch.Tensor):
+        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
+        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
+
+    def forward(self, x: torch.Tensor):
+        x = x + self.attention(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+
+class SDXLControlNetUnion(torch.nn.Module):
+    def __init__(self, global_pool=False):
+        super().__init__()
+        self.time_proj = Timesteps(320)
+        self.time_embedding = torch.nn.Sequential(
+            torch.nn.Linear(320, 1280),
+            torch.nn.SiLU(),
+            torch.nn.Linear(1280, 1280)
+        )
+        self.add_time_proj = Timesteps(256)
+        self.add_time_embedding = torch.nn.Sequential(
+            torch.nn.Linear(2816, 1280),
+            torch.nn.SiLU(),
+            torch.nn.Linear(1280, 1280)
+        )
+        self.control_type_proj = Timesteps(256)
+        self.control_type_embedding = torch.nn.Sequential(
+            torch.nn.Linear(256 * 8, 1280),
+            torch.nn.SiLU(),
+            torch.nn.Linear(1280, 1280)
+        )
+        self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1)
+
+        self.controlnet_conv_in = ControlNetConditioningLayer(channels=(3, 16, 32, 96, 256, 320))
+        self.controlnet_transformer = ResidualAttentionBlock(320, 8)
+        self.task_embedding = torch.nn.Parameter(torch.randn(8, 320))
+        self.spatial_ch_projs = torch.nn.Linear(320, 320)
+
+        self.blocks = torch.nn.ModuleList([
+            # DownBlock2D
+            ResnetBlock(320, 320, 1280),
+            PushBlock(),
+            ResnetBlock(320, 320, 1280),
+            PushBlock(),
+            DownSampler(320),
+            PushBlock(),
+            # CrossAttnDownBlock2D
+            ResnetBlock(320, 640, 1280),
+            AttentionBlock(10, 64, 640, 2, 2048),
+            PushBlock(),
+            ResnetBlock(640, 640, 1280),
+            AttentionBlock(10, 64, 640, 2, 2048),
+            PushBlock(),
+            DownSampler(640),
+            PushBlock(),
+            # CrossAttnDownBlock2D
+            ResnetBlock(640, 1280, 1280),
+            AttentionBlock(20, 64, 1280, 10, 2048),
+            PushBlock(),
+            ResnetBlock(1280, 1280, 1280),
+            AttentionBlock(20, 64, 1280, 10, 2048),
+            PushBlock(),
+            # UNetMidBlock2DCrossAttn
+            ResnetBlock(1280, 1280, 1280),
+            AttentionBlock(20, 64, 1280, 10, 2048),
+            ResnetBlock(1280, 1280, 1280),
+            PushBlock()
+        ])
+
+        self.controlnet_blocks = torch.nn.ModuleList([
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
+            torch.nn.Conv2d(640, 640, kernel_size=(1, 1)),
+            torch.nn.Conv2d(640, 640, kernel_size=(1, 1)),
+            torch.nn.Conv2d(640, 640, kernel_size=(1, 1)),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)),
+        ])
+
+        self.global_pool = global_pool
+
+        # 0 -- openpose
+        # 1 -- depth
+        # 2 -- hed/pidi/scribble/ted
+        # 3 -- canny/lineart/anime_lineart/mlsd
+        # 4 -- normal
+        # 5 -- segment
+        # 6 -- tile
+        # 7 -- repaint
+        self.task_id = {
+            "openpose": 0,
+            "depth": 1,
+            "softedge": 2,
+            "canny": 3,
+            "lineart": 3,
+            "lineart_anime": 3,
+            "tile": 6,
+            "inpaint": 7
+        }
+
+
+    def fuse_condition_to_input(self, hidden_states, task_id, conditioning):
+        controlnet_cond = self.controlnet_conv_in(conditioning)
+        feat_seq = torch.mean(controlnet_cond, dim=(2, 3))
+        feat_seq = feat_seq + self.task_embedding[task_id]
+        x = torch.stack([feat_seq, torch.mean(hidden_states, dim=(2, 3))], dim=1)
+        x = self.controlnet_transformer(x)
+
+        alpha = self.spatial_ch_projs(x[:,0]).unsqueeze(-1).unsqueeze(-1)
+        controlnet_cond_fuser = controlnet_cond + alpha
+
+        hidden_states = hidden_states + controlnet_cond_fuser
+        return hidden_states
+    
+
+    def forward(
+        self,
+        sample, timestep, encoder_hidden_states,
+        conditioning, processor_id, add_time_id, add_text_embeds,
+        tiled=False, tile_size=64, tile_stride=32,
+        unet:SDXLUNet=None,
+        **kwargs
+    ):
+        task_id = self.task_id[processor_id]
+
+        # 1. time
+        t_emb = self.time_proj(timestep).to(sample.dtype)
+        t_emb = self.time_embedding(t_emb)
+        
+        time_embeds = self.add_time_proj(add_time_id)
+        time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1))
+        add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1)
+        add_embeds = add_embeds.to(sample.dtype)
+        if unet is not None and unet.is_kolors:
+            add_embeds = unet.add_time_embedding(add_embeds)
+        else:
+            add_embeds = self.add_time_embedding(add_embeds)
+
+        control_type = torch.zeros((sample.shape[0], 8), dtype=sample.dtype, device=sample.device)
+        control_type[:, task_id] = 1
+        control_embeds = self.control_type_proj(control_type.flatten())
+        control_embeds = control_embeds.reshape((sample.shape[0], -1))
+        control_embeds = control_embeds.to(sample.dtype)
+        control_embeds = self.control_type_embedding(control_embeds)
+        time_emb = t_emb + add_embeds + control_embeds
+
+        # 2. pre-process
+        height, width = sample.shape[2], sample.shape[3]
+        hidden_states = self.conv_in(sample)
+        hidden_states = self.fuse_condition_to_input(hidden_states, task_id, conditioning)
+        text_emb = encoder_hidden_states
+        if unet is not None and unet.is_kolors:
+            text_emb = unet.text_intermediate_proj(text_emb)
+        res_stack = [hidden_states]
+
+        # 3. blocks
+        for i, block in enumerate(self.blocks):
+            if tiled and not isinstance(block, PushBlock):
+                _, _, inter_height, _ = hidden_states.shape
+                resize_scale = inter_height / height
+                hidden_states = TileWorker().tiled_forward(
+                    lambda x: block(x, time_emb, text_emb, res_stack)[0],
+                    hidden_states,
+                    int(tile_size * resize_scale),
+                    int(tile_stride * resize_scale),
+                    tile_device=hidden_states.device,
+                    tile_dtype=hidden_states.dtype
+                )
+            else:
+                hidden_states, _, _, _ = block(hidden_states, time_emb, text_emb, res_stack)
+
+        # 4. ControlNet blocks
+        controlnet_res_stack = [block(res) for block, res in zip(self.controlnet_blocks, res_stack)]
+
+        # pool
+        if self.global_pool:
+            controlnet_res_stack = [res.mean(dim=(2, 3), keepdim=True) for res in controlnet_res_stack]
+
+        return controlnet_res_stack
+
+    @staticmethod
+    def state_dict_converter():
+        return SDXLControlNetUnionStateDictConverter()
+
+
+
+class SDXLControlNetUnionStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        # architecture
+        block_types = [
+            "ResnetBlock", "PushBlock", "ResnetBlock", "PushBlock", "DownSampler", "PushBlock",
+            "ResnetBlock", "AttentionBlock", "PushBlock", "ResnetBlock", "AttentionBlock", "PushBlock", "DownSampler", "PushBlock",
+            "ResnetBlock", "AttentionBlock", "PushBlock", "ResnetBlock", "AttentionBlock", "PushBlock",
+            "ResnetBlock", "AttentionBlock", "ResnetBlock", "PushBlock"
+        ]
+
+        # controlnet_rename_dict
+        controlnet_rename_dict = {
+            "controlnet_cond_embedding.conv_in.weight": "controlnet_conv_in.blocks.0.weight",
+            "controlnet_cond_embedding.conv_in.bias": "controlnet_conv_in.blocks.0.bias",
+            "controlnet_cond_embedding.blocks.0.weight": "controlnet_conv_in.blocks.2.weight",
+            "controlnet_cond_embedding.blocks.0.bias": "controlnet_conv_in.blocks.2.bias",
+            "controlnet_cond_embedding.blocks.1.weight": "controlnet_conv_in.blocks.4.weight",
+            "controlnet_cond_embedding.blocks.1.bias": "controlnet_conv_in.blocks.4.bias",
+            "controlnet_cond_embedding.blocks.2.weight": "controlnet_conv_in.blocks.6.weight",
+            "controlnet_cond_embedding.blocks.2.bias": "controlnet_conv_in.blocks.6.bias",
+            "controlnet_cond_embedding.blocks.3.weight": "controlnet_conv_in.blocks.8.weight",
+            "controlnet_cond_embedding.blocks.3.bias": "controlnet_conv_in.blocks.8.bias",
+            "controlnet_cond_embedding.blocks.4.weight": "controlnet_conv_in.blocks.10.weight",
+            "controlnet_cond_embedding.blocks.4.bias": "controlnet_conv_in.blocks.10.bias",
+            "controlnet_cond_embedding.blocks.5.weight": "controlnet_conv_in.blocks.12.weight",
+            "controlnet_cond_embedding.blocks.5.bias": "controlnet_conv_in.blocks.12.bias",
+            "controlnet_cond_embedding.conv_out.weight": "controlnet_conv_in.blocks.14.weight",
+            "controlnet_cond_embedding.conv_out.bias": "controlnet_conv_in.blocks.14.bias",
+            "control_add_embedding.linear_1.weight": "control_type_embedding.0.weight",
+            "control_add_embedding.linear_1.bias": "control_type_embedding.0.bias",
+            "control_add_embedding.linear_2.weight": "control_type_embedding.2.weight",
+            "control_add_embedding.linear_2.bias": "control_type_embedding.2.bias",
+        }
+
+        # Rename each parameter
+        name_list = sorted([name for name in state_dict])
+        rename_dict = {}
+        block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1}
+        last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""}
+        for name in name_list:
+            names = name.split(".")
+            if names[0] in ["conv_in", "conv_norm_out", "conv_out", "task_embedding", "spatial_ch_projs"]:
+                pass
+            elif name in controlnet_rename_dict:
+                names = controlnet_rename_dict[name].split(".")
+            elif names[0] == "controlnet_down_blocks":
+                names[0] = "controlnet_blocks"
+            elif names[0] == "controlnet_mid_block":
+                names = ["controlnet_blocks", "9", names[-1]]
+            elif names[0] in ["time_embedding", "add_embedding"]:
+                if names[0] == "add_embedding":
+                    names[0] = "add_time_embedding"
+                names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]]
+            elif names[0] == "control_add_embedding":
+                names[0] = "control_type_embedding"
+            elif names[0] == "transformer_layes":
+                names[0] = "controlnet_transformer"
+                names.pop(1)
+            elif names[0] in ["down_blocks", "mid_block", "up_blocks"]:
+                if names[0] == "mid_block":
+                    names.insert(1, "0")
+                block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]]
+                block_type_with_id = ".".join(names[:4])
+                if block_type_with_id != last_block_type_with_id[block_type]:
+                    block_id[block_type] += 1
+                last_block_type_with_id[block_type] = block_type_with_id
+                while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
+                    block_id[block_type] += 1
+                block_type_with_id = ".".join(names[:4])
+                names = ["blocks", str(block_id[block_type])] + names[4:]
+                if "ff" in names:
+                    ff_index = names.index("ff")
+                    component = ".".join(names[ff_index:ff_index+3])
+                    component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component]
+                    names = names[:ff_index] + [component] + names[ff_index+3:]
+                if "to_out" in names:
+                    names.pop(names.index("to_out") + 1)
+            else:
+                print(name, state_dict[name].shape)
+                # raise ValueError(f"Unknown parameters: {name}")
+            rename_dict[name] = ".".join(names)
+
+        # Convert state_dict
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name not in rename_dict:
+                continue
+            if ".proj_in." in name or ".proj_out." in name:
+                param = param.squeeze()
+            state_dict_[rename_dict[name]] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)

diffsynth/models/sdxl_unet.py

@@ -83,6 +83,8 @@ class SDXLUNet(torch.nn.Module):
         self.conv_act = torch.nn.SiLU()
         self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1)
 
+        self.is_kolors = is_kolors
+
     def forward(
         self,
         sample, timestep, encoder_hidden_states, add_time_id, add_text_embeds,

diffsynth/models/stepvideo_dit.py

@@ -0,0 +1,940 @@
+# Copyright 2025 StepFun Inc. All Rights Reserved.
+# 
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+# ==============================================================================
+from typing import Dict, Optional, Tuple
+import torch, math
+from torch import nn
+from einops import rearrange, repeat
+from tqdm import tqdm
+
+
+class RMSNorm(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        elementwise_affine=True,
+        eps: float = 1e-6,
+        device=None,
+        dtype=None,
+    ):
+        """
+        Initialize the RMSNorm normalization layer.
+
+        Args:
+            dim (int): The dimension of the input tensor.
+            eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
+
+        Attributes:
+            eps (float): A small value added to the denominator for numerical stability.
+            weight (nn.Parameter): Learnable scaling parameter.
+
+        """
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.eps = eps
+        if elementwise_affine:
+            self.weight = nn.Parameter(torch.ones(dim, **factory_kwargs))
+
+    def _norm(self, x):
+        """
+        Apply the RMSNorm normalization to the input tensor.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+
+        Returns:
+            torch.Tensor: The normalized tensor.
+
+        """
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        """
+        Forward pass through the RMSNorm layer.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+
+        Returns:
+            torch.Tensor: The output tensor after applying RMSNorm.
+
+        """
+        output = self._norm(x.float()).type_as(x)
+        if hasattr(self, "weight"):
+            output = output * self.weight
+        return output
+    
+
+ACTIVATION_FUNCTIONS = {
+    "swish": nn.SiLU(),
+    "silu": nn.SiLU(),
+    "mish": nn.Mish(),
+    "gelu": nn.GELU(),
+    "relu": nn.ReLU(),
+}
+
+
+def get_activation(act_fn: str) -> nn.Module:
+    """Helper function to get activation function from string.
+
+    Args:
+        act_fn (str): Name of activation function.
+
+    Returns:
+        nn.Module: Activation function.
+    """
+
+    act_fn = act_fn.lower()
+    if act_fn in ACTIVATION_FUNCTIONS:
+        return ACTIVATION_FUNCTIONS[act_fn]
+    else:
+        raise ValueError(f"Unsupported activation function: {act_fn}")
+
+
+def get_timestep_embedding(
+    timesteps: torch.Tensor,
+    embedding_dim: int,
+    flip_sin_to_cos: bool = False,
+    downscale_freq_shift: float = 1,
+    scale: float = 1,
+    max_period: int = 10000,
+):
+    """
+    This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
+
+    :param timesteps: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the
+    embeddings. :return: an [N x dim] Tensor of positional embeddings.
+    """
+    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
+
+    half_dim = embedding_dim // 2
+    exponent = -math.log(max_period) * torch.arange(
+        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
+    )
+    exponent = exponent / (half_dim - downscale_freq_shift)
+
+    emb = torch.exp(exponent)
+    emb = timesteps[:, None].float() * emb[None, :]
+
+    # scale embeddings
+    emb = scale * emb
+
+    # concat sine and cosine embeddings
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
+
+    # flip sine and cosine embeddings
+    if flip_sin_to_cos:
+        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
+
+    # zero pad
+    if embedding_dim % 2 == 1:
+        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
+    return emb
+
+
+class Timesteps(nn.Module):
+    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float):
+        super().__init__()
+        self.num_channels = num_channels
+        self.flip_sin_to_cos = flip_sin_to_cos
+        self.downscale_freq_shift = downscale_freq_shift
+
+    def forward(self, timesteps):
+        t_emb = get_timestep_embedding(
+            timesteps,
+            self.num_channels,
+            flip_sin_to_cos=self.flip_sin_to_cos,
+            downscale_freq_shift=self.downscale_freq_shift,
+        )
+        return t_emb
+
+
+class TimestepEmbedding(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        time_embed_dim: int,
+        act_fn: str = "silu",
+        out_dim: int = None,
+        post_act_fn: Optional[str] = None,
+        cond_proj_dim=None,
+        sample_proj_bias=True
+    ):
+        super().__init__()
+        linear_cls = nn.Linear
+
+        self.linear_1 = linear_cls(
+                in_channels, 
+                time_embed_dim, 
+                bias=sample_proj_bias,
+            )
+
+        if cond_proj_dim is not None:
+            self.cond_proj = linear_cls(
+                    cond_proj_dim, 
+                    in_channels, 
+                    bias=False,
+                )
+        else:
+            self.cond_proj = None
+
+        self.act = get_activation(act_fn)
+
+        if out_dim is not None:
+            time_embed_dim_out = out_dim
+        else:
+            time_embed_dim_out = time_embed_dim
+            
+        self.linear_2 = linear_cls(
+                time_embed_dim, 
+                time_embed_dim_out, 
+                bias=sample_proj_bias, 
+            )
+
+        if post_act_fn is None:
+            self.post_act = None
+        else:
+            self.post_act = get_activation(post_act_fn)
+
+    def forward(self, sample, condition=None):
+        if condition is not None:
+            sample = sample + self.cond_proj(condition)
+        sample = self.linear_1(sample)
+
+        if self.act is not None:
+            sample = self.act(sample)
+
+        sample = self.linear_2(sample)
+
+        if self.post_act is not None:
+            sample = self.post_act(sample)
+        return sample
+
+
+class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module):
+    def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False):
+        super().__init__()
+
+        self.outdim = size_emb_dim
+        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
+        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+
+        self.use_additional_conditions = use_additional_conditions
+        if self.use_additional_conditions:
+            self.additional_condition_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
+            self.resolution_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim)
+            self.nframe_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+            self.fps_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+
+    def forward(self, timestep, resolution=None, nframe=None, fps=None):
+        hidden_dtype = timestep.dtype
+
+        timesteps_proj = self.time_proj(timestep)
+        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype))  # (N, D)
+
+        if self.use_additional_conditions:
+            batch_size = timestep.shape[0]
+            resolution_emb = self.additional_condition_proj(resolution.flatten()).to(hidden_dtype)
+            resolution_emb = self.resolution_embedder(resolution_emb).reshape(batch_size, -1)
+            nframe_emb = self.additional_condition_proj(nframe.flatten()).to(hidden_dtype)
+            nframe_emb = self.nframe_embedder(nframe_emb).reshape(batch_size, -1)
+            conditioning = timesteps_emb + resolution_emb + nframe_emb
+
+            if fps is not None:
+                fps_emb = self.additional_condition_proj(fps.flatten()).to(hidden_dtype)
+                fps_emb = self.fps_embedder(fps_emb).reshape(batch_size, -1)
+                conditioning = conditioning + fps_emb
+        else:
+            conditioning = timesteps_emb
+
+        return conditioning
+
+
+class AdaLayerNormSingle(nn.Module):
+    r"""
+        Norm layer adaptive layer norm single (adaLN-single).
+
+        As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3).
+
+        Parameters:
+            embedding_dim (`int`): The size of each embedding vector.
+            use_additional_conditions (`bool`): To use additional conditions for normalization or not.
+    """
+    def __init__(self, embedding_dim: int, use_additional_conditions: bool = False, time_step_rescale=1000):
+        super().__init__()
+
+        self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings(
+            embedding_dim, size_emb_dim=embedding_dim // 2, use_additional_conditions=use_additional_conditions
+        )
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True)
+
+        self.time_step_rescale = time_step_rescale  ## timestep usually in [0, 1], we rescale it to [0,1000] for stability
+
+    def forward(
+        self,
+        timestep: torch.Tensor,
+        added_cond_kwargs: Dict[str, torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        embedded_timestep = self.emb(timestep*self.time_step_rescale, **added_cond_kwargs)
+
+        out = self.linear(self.silu(embedded_timestep))
+
+        return out, embedded_timestep
+    
+
+class PixArtAlphaTextProjection(nn.Module):
+    """
+    Projects caption embeddings. Also handles dropout for classifier-free guidance.
+
+    Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
+    """
+
+    def __init__(self, in_features, hidden_size):
+        super().__init__()
+        self.linear_1 = nn.Linear(
+                in_features, 
+                hidden_size, 
+                bias=True, 
+            )        
+        self.act_1 = nn.GELU(approximate="tanh")
+        self.linear_2 = nn.Linear(
+                hidden_size, 
+                hidden_size, 
+                bias=True, 
+            )
+
+    def forward(self, caption):
+        hidden_states = self.linear_1(caption)
+        hidden_states = self.act_1(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+
+
+class Attention(nn.Module):
+    def __init__(self):
+        super().__init__()
+    
+    def attn_processor(self, attn_type):
+        if attn_type == 'torch':
+            return self.torch_attn_func
+        elif attn_type == 'parallel':
+            return self.parallel_attn_func
+        else:
+            raise Exception('Not supported attention type...')
+
+    def torch_attn_func(
+        self,
+        q,
+        k,
+        v,
+        attn_mask=None,
+        causal=False,
+        drop_rate=0.0,
+        **kwargs
+    ):
+
+        if attn_mask is not None and attn_mask.dtype != torch.bool:
+            attn_mask = attn_mask.to(q.dtype)
+            
+        if attn_mask is not None and attn_mask.ndim == 3:   ## no head
+            n_heads = q.shape[2]
+            attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1)
+        
+        q, k, v = map(lambda x: rearrange(x, 'b s h d -> b h s d'), (q, k, v))
+        if attn_mask is not None:
+            attn_mask = attn_mask.to(q.device)
+        x = torch.nn.functional.scaled_dot_product_attention(
+            q, k, v, attn_mask=attn_mask, dropout_p=drop_rate, is_causal=causal
+        )
+        x = rearrange(x, 'b h s d -> b s h d')
+        return x        
+
+
+class RoPE1D:
+    def __init__(self, freq=1e4, F0=1.0, scaling_factor=1.0):
+        self.base = freq
+        self.F0 = F0
+        self.scaling_factor = scaling_factor
+        self.cache = {}
+
+    def get_cos_sin(self, D, seq_len, device, dtype):
+        if (D, seq_len, device, dtype) not in self.cache:
+            inv_freq = 1.0 / (self.base ** (torch.arange(0, D, 2).float().to(device) / D))
+            t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype)
+            freqs = torch.einsum("i,j->ij", t, inv_freq).to(dtype)
+            freqs = torch.cat((freqs, freqs), dim=-1)
+            cos = freqs.cos()  # (Seq, Dim)
+            sin = freqs.sin()
+            self.cache[D, seq_len, device, dtype] = (cos, sin)
+        return self.cache[D, seq_len, device, dtype]
+
+    @staticmethod
+    def rotate_half(x):
+        x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
+        return torch.cat((-x2, x1), dim=-1)
+
+    def apply_rope1d(self, tokens, pos1d, cos, sin):
+        assert pos1d.ndim == 2
+        cos = torch.nn.functional.embedding(pos1d, cos)[:, :, None, :]
+        sin = torch.nn.functional.embedding(pos1d, sin)[:, :, None, :]
+        return (tokens * cos) + (self.rotate_half(tokens) * sin)
+
+    def __call__(self, tokens, positions):
+        """
+        input:
+            * tokens: batch_size x ntokens x nheads x dim
+            * positions: batch_size x ntokens (t position of each token)
+        output:
+            * tokens after appplying RoPE2D (batch_size x ntokens x nheads x dim)
+        """
+        D = tokens.size(3)
+        assert positions.ndim == 2  # Batch, Seq
+        cos, sin = self.get_cos_sin(D, int(positions.max()) + 1, tokens.device, tokens.dtype)
+        tokens = self.apply_rope1d(tokens, positions, cos, sin)
+        return tokens
+
+
+class RoPE3D(RoPE1D):
+    def __init__(self, freq=1e4, F0=1.0, scaling_factor=1.0):
+        super(RoPE3D, self).__init__(freq, F0, scaling_factor)
+        self.position_cache = {}
+
+    def get_mesh_3d(self, rope_positions, bsz):
+        f, h, w = rope_positions
+
+        if f"{f}-{h}-{w}" not in self.position_cache:
+            x = torch.arange(f, device='cpu')
+            y = torch.arange(h, device='cpu')
+            z = torch.arange(w, device='cpu')
+            self.position_cache[f"{f}-{h}-{w}"] = torch.cartesian_prod(x, y, z).view(1, f*h*w, 3).expand(bsz, -1, 3)
+        return self.position_cache[f"{f}-{h}-{w}"]
+     
+    def __call__(self, tokens, rope_positions, ch_split, parallel=False):
+        """
+        input:
+            * tokens: batch_size x ntokens x nheads x dim
+            * rope_positions: list of (f, h, w)
+        output:
+            * tokens after appplying RoPE2D (batch_size x ntokens x nheads x dim)
+        """
+        assert sum(ch_split) == tokens.size(-1); 
+
+        mesh_grid = self.get_mesh_3d(rope_positions, bsz=tokens.shape[0])
+        out = []
+        for i, (D, x) in enumerate(zip(ch_split, torch.split(tokens, ch_split, dim=-1))):
+            cos, sin = self.get_cos_sin(D, int(mesh_grid.max()) + 1, tokens.device, tokens.dtype)
+            
+            if parallel:
+                pass
+            else:
+                mesh = mesh_grid[:, :, i].clone()
+            x = self.apply_rope1d(x, mesh.to(tokens.device), cos, sin)
+            out.append(x)
+            
+        tokens = torch.cat(out, dim=-1)
+        return tokens
+
+
+class SelfAttention(Attention):
+    def __init__(self, hidden_dim, head_dim, bias=False, with_rope=True, with_qk_norm=True, attn_type='torch'):
+        super().__init__()
+        self.head_dim = head_dim
+        self.n_heads = hidden_dim // head_dim
+        
+        self.wqkv = nn.Linear(hidden_dim, hidden_dim*3, bias=bias)
+        self.wo = nn.Linear(hidden_dim, hidden_dim, bias=bias)
+        
+        self.with_rope = with_rope
+        self.with_qk_norm = with_qk_norm
+        if self.with_qk_norm:
+            self.q_norm = RMSNorm(head_dim, elementwise_affine=True)
+            self.k_norm = RMSNorm(head_dim, elementwise_affine=True)
+        
+        if self.with_rope:
+            self.rope_3d = RoPE3D(freq=1e4, F0=1.0, scaling_factor=1.0)
+            self.rope_ch_split = [64, 32, 32]
+        
+        self.core_attention = self.attn_processor(attn_type=attn_type)
+        self.parallel = attn_type=='parallel'
+        
+    def apply_rope3d(self, x, fhw_positions, rope_ch_split, parallel=True):
+        x = self.rope_3d(x, fhw_positions, rope_ch_split, parallel)
+        return x
+        
+    def forward(
+        self, 
+        x,
+        cu_seqlens=None,
+        max_seqlen=None,
+        rope_positions=None,
+        attn_mask=None
+    ):
+        xqkv = self.wqkv(x) 
+        xqkv = xqkv.view(*x.shape[:-1], self.n_heads, 3*self.head_dim)
+
+        xq, xk, xv = torch.split(xqkv, [self.head_dim]*3, dim=-1)  ## seq_len, n, dim
+    
+        if self.with_qk_norm:
+            xq = self.q_norm(xq)
+            xk = self.k_norm(xk)
+    
+        if self.with_rope:
+            xq = self.apply_rope3d(xq, rope_positions, self.rope_ch_split, parallel=self.parallel)
+            xk = self.apply_rope3d(xk, rope_positions, self.rope_ch_split, parallel=self.parallel)
+            
+        output = self.core_attention(
+                    xq,
+                    xk,
+                    xv,
+                    cu_seqlens=cu_seqlens,
+                    max_seqlen=max_seqlen,
+                    attn_mask=attn_mask
+                )
+        output = rearrange(output, 'b s h d -> b s (h d)')
+        output = self.wo(output)
+        
+        return output
+    
+    
+class CrossAttention(Attention):
+    def __init__(self, hidden_dim, head_dim, bias=False, with_qk_norm=True, attn_type='torch'):
+        super().__init__()
+        self.head_dim = head_dim
+        self.n_heads = hidden_dim // head_dim
+        
+        self.wq = nn.Linear(hidden_dim, hidden_dim, bias=bias)
+        self.wkv = nn.Linear(hidden_dim, hidden_dim*2, bias=bias)
+        self.wo = nn.Linear(hidden_dim, hidden_dim, bias=bias)
+        
+        self.with_qk_norm = with_qk_norm
+        if self.with_qk_norm:
+            self.q_norm = RMSNorm(head_dim, elementwise_affine=True)
+            self.k_norm = RMSNorm(head_dim, elementwise_affine=True)
+        
+        self.core_attention = self.attn_processor(attn_type=attn_type)
+
+    def forward(
+            self, 
+            x: torch.Tensor,
+            encoder_hidden_states: torch.Tensor,
+            attn_mask=None
+        ):
+        xq = self.wq(x) 
+        xq = xq.view(*xq.shape[:-1], self.n_heads, self.head_dim)
+        
+        xkv = self.wkv(encoder_hidden_states)
+        xkv = xkv.view(*xkv.shape[:-1], self.n_heads, 2*self.head_dim)
+
+        xk, xv = torch.split(xkv, [self.head_dim]*2, dim=-1)  ## seq_len, n, dim
+    
+        if self.with_qk_norm:
+            xq = self.q_norm(xq)
+            xk = self.k_norm(xk)
+
+        output = self.core_attention(
+                    xq,
+                    xk,
+                    xv,
+                    attn_mask=attn_mask
+                )
+        
+        output = rearrange(output, 'b s h d -> b s (h d)')
+        output = self.wo(output)
+        
+        return output
+
+    
+class GELU(nn.Module):
+    r"""
+    GELU activation function with tanh approximation support with `approximate="tanh"`.
+
+    Parameters:
+        dim_in (`int`): The number of channels in the input.
+        dim_out (`int`): The number of channels in the output.
+        approximate (`str`, *optional*, defaults to `"none"`): If `"tanh"`, use tanh approximation.
+        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
+    """
+
+    def __init__(self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out, bias=bias)
+        self.approximate = approximate
+
+    def gelu(self, gate: torch.Tensor) -> torch.Tensor:
+        return torch.nn.functional.gelu(gate, approximate=self.approximate)
+
+    def forward(self, hidden_states):
+        hidden_states = self.proj(hidden_states)
+        hidden_states = self.gelu(hidden_states)
+        return hidden_states
+    
+    
+class FeedForward(nn.Module):
+    def __init__(
+        self, 
+        dim: int,
+        inner_dim: Optional[int] = None,
+        dim_out: Optional[int] = None,
+        mult: int = 4,
+        bias: bool = False,
+    ):
+        super().__init__()
+        inner_dim = dim*mult if inner_dim is None else inner_dim
+        dim_out = dim if dim_out is None else dim_out
+        self.net = nn.ModuleList([
+            GELU(dim, inner_dim, approximate="tanh", bias=bias),
+            nn.Identity(),
+            nn.Linear(inner_dim, dim_out, bias=bias)
+        ])
+        
+        
+    def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor:
+        for module in self.net:
+            hidden_states = module(hidden_states)
+        return hidden_states
+    
+
+def modulate(x, scale, shift):
+    x = x * (1 + scale) + shift
+    return x
+
+
+def gate(x, gate):
+    x = gate * x
+    return x
+
+
+class StepVideoTransformerBlock(nn.Module):
+    r"""
+    A basic Transformer block.
+
+    Parameters:
+        dim (`int`): The number of channels in the input and output.
+        num_attention_heads (`int`): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`): The number of channels in each head.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        num_embeds_ada_norm (:
+            obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
+        attention_bias (:
+            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
+        only_cross_attention (`bool`, *optional*):
+            Whether to use only cross-attention layers. In this case two cross attention layers are used.
+        double_self_attention (`bool`, *optional*):
+            Whether to use two self-attention layers. In this case no cross attention layers are used.
+        upcast_attention (`bool`, *optional*):
+            Whether to upcast the attention computation to float32. This is useful for mixed precision training.
+        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
+            Whether to use learnable elementwise affine parameters for normalization.
+        norm_type (`str`, *optional*, defaults to `"layer_norm"`):
+            The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
+        final_dropout (`bool` *optional*, defaults to False):
+            Whether to apply a final dropout after the last feed-forward layer.
+        attention_type (`str`, *optional*, defaults to `"default"`):
+            The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`.
+        positional_embeddings (`str`, *optional*, defaults to `None`):
+            The type of positional embeddings to apply to.
+        num_positional_embeddings (`int`, *optional*, defaults to `None`):
+            The maximum number of positional embeddings to apply.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        attention_head_dim: int,
+        norm_eps: float = 1e-5,
+        ff_inner_dim: Optional[int] = None,
+        ff_bias: bool = False,
+        attention_type: str = 'parallel'
+    ):
+        super().__init__()
+        self.dim = dim
+        self.norm1 = nn.LayerNorm(dim, eps=norm_eps)
+        self.attn1 = SelfAttention(dim, attention_head_dim, bias=False, with_rope=True, with_qk_norm=True, attn_type=attention_type)
+        
+        self.norm2 = nn.LayerNorm(dim, eps=norm_eps)
+        self.attn2 = CrossAttention(dim, attention_head_dim, bias=False, with_qk_norm=True, attn_type='torch')
+
+        self.ff = FeedForward(dim=dim, inner_dim=ff_inner_dim, dim_out=dim, bias=ff_bias)
+
+        self.scale_shift_table = nn.Parameter(torch.randn(6, dim) /dim**0.5)
+
+    @torch.no_grad()
+    def forward(
+        self,
+        q: torch.Tensor,
+        kv: Optional[torch.Tensor] = None,
+        timestep: Optional[torch.LongTensor] =  None,
+        attn_mask = None,
+        rope_positions: list = None, 
+    ) -> torch.Tensor:
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+            torch.clone(chunk) for chunk in (self.scale_shift_table[None].to(dtype=q.dtype, device=q.device) + timestep.reshape(-1, 6, self.dim)).chunk(6, dim=1)
+        )
+        
+        scale_shift_q = modulate(self.norm1(q), scale_msa, shift_msa)
+
+        attn_q = self.attn1(
+            scale_shift_q,
+            rope_positions=rope_positions
+        )
+
+        q = gate(attn_q, gate_msa) + q
+        
+        attn_q = self.attn2(
+                q,
+                kv,
+                attn_mask
+            )
+
+        q = attn_q + q
+
+        scale_shift_q = modulate(self.norm2(q), scale_mlp, shift_mlp)
+
+        ff_output = self.ff(scale_shift_q)
+        
+        q = gate(ff_output, gate_mlp) + q
+        
+        return q
+    
+    
+class PatchEmbed(nn.Module):
+    """2D Image to Patch Embedding"""
+
+    def __init__(
+        self,
+        patch_size=64,
+        in_channels=3,
+        embed_dim=768,
+        layer_norm=False,
+        flatten=True,
+        bias=True,
+    ):
+        super().__init__()
+
+        self.flatten = flatten
+        self.layer_norm = layer_norm
+
+        self.proj = nn.Conv2d(
+            in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
+        )
+
+    def forward(self, latent):
+        latent = self.proj(latent).to(latent.dtype)   
+        if self.flatten:
+            latent = latent.flatten(2).transpose(1, 2)  # BCHW -> BNC
+        if self.layer_norm:
+            latent = self.norm(latent)
+
+        return latent
+
+
+class StepVideoModel(torch.nn.Module):
+    def __init__(
+        self,
+        num_attention_heads: int = 48,
+        attention_head_dim: int = 128,
+        in_channels: int = 64,
+        out_channels: Optional[int] = 64,
+        num_layers: int = 48,
+        dropout: float = 0.0,
+        patch_size: int = 1,
+        norm_type: str = "ada_norm_single",
+        norm_elementwise_affine: bool = False,
+        norm_eps: float = 1e-6,
+        use_additional_conditions: Optional[bool] = False,
+        caption_channels: Optional[int]|list|tuple = [6144, 1024],
+        attention_type: Optional[str] = "torch",
+    ):
+        super().__init__()
+
+        # Set some common variables used across the board.
+        self.inner_dim = num_attention_heads * attention_head_dim
+        self.out_channels = in_channels if out_channels is None else out_channels
+
+        self.use_additional_conditions = use_additional_conditions
+
+        self.pos_embed = PatchEmbed(
+            patch_size=patch_size,
+            in_channels=in_channels,
+            embed_dim=self.inner_dim,
+        )
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                StepVideoTransformerBlock(
+                    dim=self.inner_dim,
+                    attention_head_dim=attention_head_dim,
+                    attention_type=attention_type
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        # 3. Output blocks.
+        self.norm_out = nn.LayerNorm(self.inner_dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine)
+        self.scale_shift_table = nn.Parameter(torch.randn(2, self.inner_dim) / self.inner_dim**0.5)
+        self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels)
+        self.patch_size = patch_size
+
+        self.adaln_single = AdaLayerNormSingle(
+            self.inner_dim, use_additional_conditions=self.use_additional_conditions
+        )
+
+        if isinstance(caption_channels, int):
+            caption_channel = caption_channels
+        else:
+            caption_channel, clip_channel = caption_channels
+            self.clip_projection = nn.Linear(clip_channel, self.inner_dim) 
+
+        self.caption_norm = nn.LayerNorm(caption_channel,  eps=norm_eps, elementwise_affine=norm_elementwise_affine)
+        
+        self.caption_projection = PixArtAlphaTextProjection(
+            in_features=caption_channel, hidden_size=self.inner_dim
+        )
+        
+        self.parallel = attention_type=='parallel'
+
+    def patchfy(self, hidden_states):
+        hidden_states = rearrange(hidden_states, 'b f c h w -> (b f) c h w')
+        hidden_states = self.pos_embed(hidden_states)
+        return hidden_states
+
+    def prepare_attn_mask(self, encoder_attention_mask, encoder_hidden_states, q_seqlen):
+        kv_seqlens = encoder_attention_mask.sum(dim=1).int()
+        mask = torch.zeros([len(kv_seqlens), q_seqlen, max(kv_seqlens)], dtype=torch.bool, device=encoder_attention_mask.device)
+        encoder_hidden_states = encoder_hidden_states[:,: max(kv_seqlens)]
+        for i, kv_len in enumerate(kv_seqlens):
+            mask[i, :, :kv_len] = 1
+        return encoder_hidden_states, mask
+        
+        
+    def block_forward(
+        self,
+        hidden_states,
+        encoder_hidden_states=None,
+        timestep=None,
+        rope_positions=None,
+        attn_mask=None,
+        parallel=True
+    ):
+        for block in tqdm(self.transformer_blocks, desc="Transformer blocks"):
+            hidden_states = block(
+                hidden_states,
+                encoder_hidden_states,
+                timestep=timestep,
+                attn_mask=attn_mask,
+                rope_positions=rope_positions
+            )
+
+        return hidden_states
+        
+
+    @torch.inference_mode()
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_hidden_states_2: Optional[torch.Tensor] = None,
+        timestep: Optional[torch.LongTensor] = None,
+        added_cond_kwargs: Dict[str, torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        fps: torch.Tensor=None,
+        return_dict: bool = False,
+    ):
+        assert hidden_states.ndim==5; "hidden_states's shape should be (bsz, f, ch, h ,w)"
+
+        bsz, frame, _, height, width = hidden_states.shape
+        height, width = height // self.patch_size, width // self.patch_size
+                
+        hidden_states = self.patchfy(hidden_states) 
+        len_frame = hidden_states.shape[1]
+                
+        if self.use_additional_conditions:
+            added_cond_kwargs = {
+                "resolution": torch.tensor([(height, width)]*bsz, device=hidden_states.device, dtype=hidden_states.dtype),
+                "nframe": torch.tensor([frame]*bsz, device=hidden_states.device, dtype=hidden_states.dtype),
+                "fps": fps
+            }    
+        else:
+            added_cond_kwargs = {}
+        
+        timestep, embedded_timestep = self.adaln_single(
+            timestep, added_cond_kwargs=added_cond_kwargs
+        )
+
+        encoder_hidden_states = self.caption_projection(self.caption_norm(encoder_hidden_states))
+        
+        if encoder_hidden_states_2 is not None and hasattr(self, 'clip_projection'):
+            clip_embedding = self.clip_projection(encoder_hidden_states_2)
+            encoder_hidden_states = torch.cat([clip_embedding, encoder_hidden_states], dim=1)
+
+        hidden_states = rearrange(hidden_states, '(b f) l d->  b (f l) d', b=bsz, f=frame, l=len_frame).contiguous()
+        encoder_hidden_states, attn_mask = self.prepare_attn_mask(encoder_attention_mask, encoder_hidden_states, q_seqlen=frame*len_frame)
+        
+        hidden_states = self.block_forward(
+            hidden_states,
+            encoder_hidden_states,
+            timestep=timestep,
+            rope_positions=[frame, height, width],
+            attn_mask=attn_mask,
+            parallel=self.parallel
+        )
+        
+        hidden_states = rearrange(hidden_states, 'b (f l) d -> (b f) l d', b=bsz, f=frame, l=len_frame)
+        
+        embedded_timestep = repeat(embedded_timestep, 'b d -> (b f) d', f=frame).contiguous()
+        
+        shift, scale = (self.scale_shift_table[None].to(dtype=embedded_timestep.dtype, device=embedded_timestep.device) + embedded_timestep[:, None]).chunk(2, dim=1)
+        hidden_states = self.norm_out(hidden_states)
+        # Modulation
+        hidden_states = hidden_states * (1 + scale) + shift
+        hidden_states = self.proj_out(hidden_states)
+        
+        # unpatchify
+        hidden_states = hidden_states.reshape(
+            shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)
+        )
+        
+        hidden_states = rearrange(hidden_states, 'n h w p q c -> n c h p w q')
+        output = hidden_states.reshape(
+            shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)
+        )
+
+        output = rearrange(output, '(b f) c h w -> b f c h w', f=frame)
+
+        if return_dict:
+            return {'x': output}
+        return output
+    
+    @staticmethod
+    def state_dict_converter():
+        return StepVideoDiTStateDictConverter()
+
+
+class StepVideoDiTStateDictConverter:
+    def __init__(self):
+        super().__init__()
+
+    def from_diffusers(self, state_dict):
+        return state_dict
+    
+    def from_civitai(self, state_dict):
+        return state_dict
+
+    
+    

diffsynth/models/stepvideo_text_encoder.py

@@ -0,0 +1,553 @@
+# Copyright 2025 StepFun Inc. All Rights Reserved.
+# 
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+# ==============================================================================
+import os
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .stepvideo_dit import RMSNorm
+from safetensors.torch import load_file
+from transformers.modeling_utils import PretrainedConfig, PreTrainedModel
+from einops import rearrange
+import json
+from typing import List
+from functools import wraps
+import warnings
+
+
+
+class EmptyInitOnDevice(torch.overrides.TorchFunctionMode):
+    def __init__(self, device=None):
+        self.device = device
+
+    def __torch_function__(self, func, types, args=(), kwargs=None):
+        kwargs = kwargs or {}
+        if getattr(func, '__module__', None) == 'torch.nn.init':
+            if 'tensor' in kwargs:
+                return kwargs['tensor']
+            else:
+                return args[0]
+        if self.device is not None and func in torch.utils._device._device_constructors() and kwargs.get('device') is None:
+            kwargs['device'] = self.device
+        return func(*args, **kwargs)
+    
+
+def with_empty_init(func):
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        with EmptyInitOnDevice('cpu'):
+            return func(*args, **kwargs)
+    return wrapper
+
+
+
+class LLaMaEmbedding(nn.Module):
+    """Language model embeddings.
+
+    Arguments:
+        hidden_size: hidden size
+        vocab_size: vocabulary size
+        max_sequence_length: maximum size of sequence. This
+                             is used for positional embedding
+        embedding_dropout_prob: dropout probability for embeddings
+        init_method: weight initialization method
+        num_tokentypes: size of the token-type embeddings. 0 value
+                        will ignore this embedding
+    """
+
+    def __init__(self,
+                 cfg,
+                 ):
+        super().__init__()
+        self.hidden_size = cfg.hidden_size
+        self.params_dtype = cfg.params_dtype
+        self.fp32_residual_connection = cfg.fp32_residual_connection 
+        self.embedding_weights_in_fp32 = cfg.embedding_weights_in_fp32
+        self.word_embeddings = torch.nn.Embedding(
+            cfg.padded_vocab_size, self.hidden_size,
+        )
+        self.embedding_dropout = torch.nn.Dropout(cfg.hidden_dropout)
+
+    def forward(self, input_ids):
+        # Embeddings.
+        if self.embedding_weights_in_fp32:
+            self.word_embeddings = self.word_embeddings.to(torch.float32)
+        embeddings = self.word_embeddings(input_ids)
+        if self.embedding_weights_in_fp32:
+            embeddings = embeddings.to(self.params_dtype)
+            self.word_embeddings = self.word_embeddings.to(self.params_dtype)
+
+        # Data format change to avoid explicit tranposes : [b s h] --> [s b h].
+        embeddings = embeddings.transpose(0, 1).contiguous()
+
+        # If the input flag for fp32 residual connection is set, convert for float.
+        if self.fp32_residual_connection:
+            embeddings = embeddings.float()
+
+        # Dropout.
+        embeddings = self.embedding_dropout(embeddings)
+
+        return embeddings
+
+
+
+class StepChatTokenizer:
+    """Step Chat Tokenizer"""
+
+    def __init__(
+        self, model_file, name="StepChatTokenizer",
+        bot_token="<|BOT|>",  # Begin of Turn
+        eot_token="<|EOT|>",  # End of Turn
+        call_start_token="<|CALL_START|>",      # Call Start
+        call_end_token="<|CALL_END|>",          # Call End
+        think_start_token="<|THINK_START|>",    # Think Start
+        think_end_token="<|THINK_END|>",        # Think End
+        mask_start_token="<|MASK_1e69f|>",      # Mask start
+        mask_end_token="<|UNMASK_1e69f|>",      # Mask end
+    ):
+        import sentencepiece
+
+        self._tokenizer = sentencepiece.SentencePieceProcessor(model_file=model_file)
+
+        self._vocab = {}
+        self._inv_vocab = {}
+
+        self._special_tokens = {}
+        self._inv_special_tokens = {}
+
+        self._t5_tokens = []
+
+        for idx in range(self._tokenizer.get_piece_size()):
+            text = self._tokenizer.id_to_piece(idx)
+            self._inv_vocab[idx] = text
+            self._vocab[text] = idx
+
+            if self._tokenizer.is_control(idx) or self._tokenizer.is_unknown(idx):
+                self._special_tokens[text] = idx
+                self._inv_special_tokens[idx] = text
+
+        self._unk_id = self._tokenizer.unk_id()
+        self._bos_id = self._tokenizer.bos_id()
+        self._eos_id = self._tokenizer.eos_id()
+
+        for token in [
+            bot_token, eot_token, call_start_token, call_end_token,
+            think_start_token, think_end_token
+        ]:
+            assert token in self._vocab, f"Token '{token}' not found in tokenizer"
+            assert token in self._special_tokens, f"Token '{token}' is not a special token"
+
+        for token in [mask_start_token, mask_end_token]:
+            assert token in self._vocab, f"Token '{token}' not found in tokenizer"
+
+        self._bot_id = self._tokenizer.piece_to_id(bot_token)
+        self._eot_id = self._tokenizer.piece_to_id(eot_token)
+        self._call_start_id = self._tokenizer.piece_to_id(call_start_token)
+        self._call_end_id = self._tokenizer.piece_to_id(call_end_token)
+        self._think_start_id = self._tokenizer.piece_to_id(think_start_token)
+        self._think_end_id = self._tokenizer.piece_to_id(think_end_token)
+        self._mask_start_id = self._tokenizer.piece_to_id(mask_start_token)
+        self._mask_end_id = self._tokenizer.piece_to_id(mask_end_token)
+
+        self._underline_id = self._tokenizer.piece_to_id("\u2581")
+        
+    @property
+    def vocab(self):
+        return self._vocab
+
+    @property
+    def inv_vocab(self):
+        return self._inv_vocab
+
+    @property
+    def vocab_size(self):
+        return self._tokenizer.vocab_size()
+
+    def tokenize(self, text: str) -> List[int]:
+        return self._tokenizer.encode_as_ids(text)
+
+    def detokenize(self, token_ids: List[int]) -> str:
+        return self._tokenizer.decode_ids(token_ids)
+
+    
+class Tokens:
+    def __init__(self, input_ids, cu_input_ids, attention_mask, cu_seqlens, max_seq_len) -> None:
+        self.input_ids = input_ids
+        self.attention_mask = attention_mask
+        self.cu_input_ids = cu_input_ids
+        self.cu_seqlens = cu_seqlens
+        self.max_seq_len = max_seq_len
+    def to(self, device):
+        self.input_ids = self.input_ids.to(device)
+        self.attention_mask = self.attention_mask.to(device)
+        self.cu_input_ids = self.cu_input_ids.to(device)
+        self.cu_seqlens = self.cu_seqlens.to(device)
+        return self
+    
+class Wrapped_StepChatTokenizer(StepChatTokenizer):
+    def __call__(self, text, max_length=320, padding="max_length", truncation=True, return_tensors="pt"):
+        # [bos, ..., eos, pad, pad, ..., pad]
+        self.BOS = 1
+        self.EOS = 2
+        self.PAD = 2
+        out_tokens = []
+        attn_mask = []
+        if len(text) == 0:
+            part_tokens = [self.BOS] + [self.EOS]
+            valid_size = len(part_tokens)
+            if len(part_tokens) < max_length:
+                part_tokens += [self.PAD] * (max_length - valid_size)
+            out_tokens.append(part_tokens)
+            attn_mask.append([1]*valid_size+[0]*(max_length-valid_size))
+        else:
+            for part in text:
+                part_tokens = self.tokenize(part)
+                part_tokens = part_tokens[:(max_length - 2)] # leave 2 space for bos and eos
+                part_tokens = [self.BOS] + part_tokens + [self.EOS]
+                valid_size = len(part_tokens)
+                if len(part_tokens) < max_length:
+                    part_tokens += [self.PAD] * (max_length - valid_size)
+                out_tokens.append(part_tokens)
+                attn_mask.append([1]*valid_size+[0]*(max_length-valid_size))
+
+        out_tokens = torch.tensor(out_tokens, dtype=torch.long)
+        attn_mask = torch.tensor(attn_mask, dtype=torch.long)
+
+        # padding y based on tp size
+        padded_len = 0
+        padded_flag = True if padded_len > 0 else False
+        if padded_flag:
+            pad_tokens = torch.tensor([[self.PAD] * max_length], device=out_tokens.device)
+            pad_attn_mask = torch.tensor([[1]*padded_len+[0]*(max_length-padded_len)], device=attn_mask.device)
+            out_tokens = torch.cat([out_tokens, pad_tokens], dim=0)
+            attn_mask = torch.cat([attn_mask, pad_attn_mask], dim=0)
+        
+        # cu_seqlens
+        cu_out_tokens = out_tokens.masked_select(attn_mask != 0).unsqueeze(0)
+        seqlen = attn_mask.sum(dim=1).tolist()
+        cu_seqlens = torch.cumsum(torch.tensor([0]+seqlen), 0).to(device=out_tokens.device,dtype=torch.int32)
+        max_seq_len = max(seqlen)
+        return Tokens(out_tokens, cu_out_tokens, attn_mask, cu_seqlens, max_seq_len)
+
+
+
+def flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=True,
+                    return_attn_probs=False, tp_group_rank=0, tp_group_size=1):
+    softmax_scale = q.size(-1) ** (-0.5) if softmax_scale is None else softmax_scale
+    if hasattr(torch.ops.Optimus, "fwd"):
+        results = torch.ops.Optimus.fwd(q, k, v, None, dropout_p, softmax_scale, causal, return_attn_probs, None, tp_group_rank, tp_group_size)[0]
+    else:
+        warnings.warn("Cannot load `torch.ops.Optimus.fwd`. Using `torch.nn.functional.scaled_dot_product_attention` instead.")
+        results = torch.nn.functional.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), is_causal=True, scale=softmax_scale).transpose(1, 2)
+    return results
+
+
+class FlashSelfAttention(torch.nn.Module):
+    def __init__(
+        self,
+        attention_dropout=0.0,
+    ):
+        super().__init__()
+        self.dropout_p = attention_dropout
+
+
+    def forward(self, q, k, v, cu_seqlens=None, max_seq_len=None):
+        if cu_seqlens is None:
+            output = flash_attn_func(q, k, v, dropout_p=self.dropout_p)
+        else:
+            raise ValueError('cu_seqlens is not supported!')
+
+        return output
+
+
+    
+def safediv(n, d):
+    q, r = divmod(n, d)
+    assert r == 0
+    return q
+
+
+class MultiQueryAttention(nn.Module):
+    def __init__(self, cfg, layer_id=None):
+        super().__init__()
+
+        self.head_dim = cfg.hidden_size // cfg.num_attention_heads
+        self.max_seq_len = cfg.seq_length
+        self.use_flash_attention = cfg.use_flash_attn
+        assert self.use_flash_attention, 'FlashAttention is required!'
+
+        self.n_groups = cfg.num_attention_groups
+        self.tp_size = 1
+        self.n_local_heads = cfg.num_attention_heads
+        self.n_local_groups = self.n_groups
+
+        self.wqkv = nn.Linear(
+            cfg.hidden_size,
+            cfg.hidden_size + self.head_dim * 2 * self.n_groups,
+            bias=False,
+        )
+        self.wo = nn.Linear(
+            cfg.hidden_size,
+            cfg.hidden_size,
+            bias=False,
+        )
+
+        assert self.use_flash_attention, 'non-Flash attention not supported yet.'
+        self.core_attention = FlashSelfAttention(attention_dropout=cfg.attention_dropout)
+        
+        self.layer_id = layer_id
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask: Optional[torch.Tensor],
+        cu_seqlens: Optional[torch.Tensor],
+        max_seq_len: Optional[torch.Tensor],
+    ):
+        seqlen, bsz, dim = x.shape
+        xqkv = self.wqkv(x)
+
+        xq, xkv = torch.split(
+            xqkv,
+            (dim // self.tp_size,
+             self.head_dim*2*self.n_groups // self.tp_size
+            ),
+            dim=-1,
+        )
+
+        # gather on 1st dimention
+        xq = xq.view(seqlen, bsz, self.n_local_heads, self.head_dim)
+        xkv = xkv.view(seqlen, bsz, self.n_local_groups, 2 * self.head_dim)
+        xk, xv = xkv.chunk(2, -1)
+
+        # rotary embedding + flash attn
+        xq = rearrange(xq, "s b h d -> b s h d")
+        xk = rearrange(xk, "s b h d -> b s h d")
+        xv = rearrange(xv, "s b h d -> b s h d")
+
+        q_per_kv = self.n_local_heads // self.n_local_groups
+        if q_per_kv > 1:
+            b, s, h, d = xk.size()
+            if h == 1:
+                xk = xk.expand(b, s, q_per_kv, d)
+                xv = xv.expand(b, s, q_per_kv, d)
+            else:
+                ''' To cover the cases where h > 1, we have
+                    the following implementation, which is equivalent to:
+                        xk = xk.repeat_interleave(q_per_kv, dim=-2)
+                        xv = xv.repeat_interleave(q_per_kv, dim=-2)
+                    but can avoid calling aten::item() that involves cpu.
+                '''
+                idx = torch.arange(q_per_kv * h, device=xk.device).reshape(q_per_kv, -1).permute(1, 0).flatten()
+                xk = torch.index_select(xk.repeat(1, 1, q_per_kv, 1), 2, idx).contiguous()
+                xv = torch.index_select(xv.repeat(1, 1, q_per_kv, 1), 2, idx).contiguous()
+
+        if self.use_flash_attention:
+            output = self.core_attention(xq, xk, xv,
+                                      cu_seqlens=cu_seqlens,
+                                      max_seq_len=max_seq_len)
+            # reduce-scatter only support first dimention now
+            output = rearrange(output, "b s h d -> s b (h d)").contiguous()
+        else:
+            xq, xk, xv = [
+                rearrange(x, "b s ... -> s b ...").contiguous()
+                for x in (xq, xk, xv)
+            ]
+            output = self.core_attention(xq, xk, xv, mask)
+        output = self.wo(output)
+        return output
+
+
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        cfg,
+        dim: int,
+        hidden_dim: int,
+        layer_id: int,
+        multiple_of: int=256,
+    ):
+        super().__init__()
+
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+        def swiglu(x):
+            x = torch.chunk(x, 2, dim=-1)
+            return F.silu(x[0]) * x[1]
+        self.swiglu = swiglu
+            
+        self.w1 = nn.Linear(
+            dim,
+            2 * hidden_dim,
+            bias=False,
+        )
+        self.w2 = nn.Linear(
+            hidden_dim,
+            dim,
+            bias=False,
+        )
+
+    def forward(self, x):
+        x = self.swiglu(self.w1(x))
+        output = self.w2(x)
+        return output
+
+
+
+class TransformerBlock(nn.Module):
+    def __init__(
+        self, cfg, layer_id: int
+    ):
+        super().__init__()
+
+        self.n_heads = cfg.num_attention_heads
+        self.dim = cfg.hidden_size
+        self.head_dim = cfg.hidden_size // cfg.num_attention_heads
+        self.attention = MultiQueryAttention(
+            cfg,
+            layer_id=layer_id,
+        )
+
+        self.feed_forward = FeedForward(
+            cfg,
+            dim=cfg.hidden_size,
+            hidden_dim=cfg.ffn_hidden_size,
+            layer_id=layer_id,
+        )
+        self.layer_id = layer_id
+        self.attention_norm = RMSNorm(
+            cfg.hidden_size,
+            eps=cfg.layernorm_epsilon,
+        )
+        self.ffn_norm = RMSNorm(
+            cfg.hidden_size,
+            eps=cfg.layernorm_epsilon,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask: Optional[torch.Tensor],
+        cu_seqlens: Optional[torch.Tensor],
+        max_seq_len: Optional[torch.Tensor],
+    ):
+        residual = self.attention.forward(
+            self.attention_norm(x), mask,
+            cu_seqlens, max_seq_len
+        )
+        h = x + residual
+        ffn_res = self.feed_forward.forward(self.ffn_norm(h))
+        out = h + ffn_res
+        return out
+
+
+class Transformer(nn.Module):
+    def __init__(
+        self,
+        config,
+        max_seq_size=8192,
+    ):
+        super().__init__()
+        self.num_layers = config.num_layers
+        self.layers = self._build_layers(config)
+
+    def _build_layers(self, config):
+        layers = torch.nn.ModuleList()
+        for layer_id in range(self.num_layers):
+            layers.append(
+                TransformerBlock(
+                    config,
+                    layer_id=layer_id + 1 ,
+                )
+            )
+        return layers
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        cu_seqlens=None,
+        max_seq_len=None,
+    ):
+
+        if max_seq_len is not None and not isinstance(max_seq_len, torch.Tensor):
+            max_seq_len = torch.tensor(max_seq_len, dtype=torch.int32, device="cpu")
+
+        for lid, layer in enumerate(self.layers):
+            hidden_states = layer(
+                                    hidden_states,
+                                    attention_mask,
+                                    cu_seqlens,
+                                    max_seq_len,
+                                )
+        return hidden_states
+
+
+class Step1Model(PreTrainedModel):
+    config_class=PretrainedConfig
+    @with_empty_init
+    def __init__(
+        self,
+        config,
+    ):
+        super().__init__(config)
+        self.tok_embeddings = LLaMaEmbedding(config)
+        self.transformer = Transformer(config)
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+    ):
+
+        hidden_states = self.tok_embeddings(input_ids)
+
+        hidden_states = self.transformer(
+            hidden_states,
+            attention_mask,
+        )
+        return hidden_states
+    
+    
+
+class STEP1TextEncoder(torch.nn.Module):
+    def __init__(self, model_dir, max_length=320):
+        super(STEP1TextEncoder, self).__init__()
+        self.max_length = max_length
+        self.text_tokenizer = Wrapped_StepChatTokenizer(os.path.join(model_dir, 'step1_chat_tokenizer.model'))
+        text_encoder = Step1Model.from_pretrained(model_dir)
+        self.text_encoder = text_encoder.eval().to(torch.bfloat16)
+
+    @staticmethod
+    def from_pretrained(path, torch_dtype=torch.bfloat16):
+        model = STEP1TextEncoder(path).to(torch_dtype)
+        return model
+        
+    @torch.no_grad
+    def forward(self, prompts, with_mask=True, max_length=None, device="cuda"):
+        self.device = device
+        with torch.no_grad(), torch.amp.autocast(dtype=torch.bfloat16, device_type=device):
+            if type(prompts) is str:
+                prompts = [prompts]
+            
+            txt_tokens = self.text_tokenizer(
+                prompts, max_length=max_length or self.max_length, padding="max_length", truncation=True, return_tensors="pt"
+            )
+            y = self.text_encoder(
+                txt_tokens.input_ids.to(self.device), 
+                attention_mask=txt_tokens.attention_mask.to(self.device) if with_mask else None
+            )
+            y_mask = txt_tokens.attention_mask
+        return y.transpose(0,1), y_mask
+

diffsynth/models/svd_unet.py

@@ -44,6 +44,7 @@ def get_timestep_embedding(
     downscale_freq_shift: float = 1,
     scale: float = 1,
     max_period: int = 10000,
+    computation_device = None,
 ):
     """
     This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
@@ -57,11 +58,11 @@ def get_timestep_embedding(
 
     half_dim = embedding_dim // 2
     exponent = -math.log(max_period) * torch.arange(
-        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
+        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device if computation_device is None else computation_device
     )
     exponent = exponent / (half_dim - downscale_freq_shift)
 
-    emb = torch.exp(exponent)
+    emb = torch.exp(exponent).to(timesteps.device)
     emb = timesteps[:, None].float() * emb[None, :]
 
     # scale embeddings
@@ -81,11 +82,12 @@ def get_timestep_embedding(
 
 
 class TemporalTimesteps(torch.nn.Module):
-    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float):
+    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, computation_device = None):
         super().__init__()
         self.num_channels = num_channels
         self.flip_sin_to_cos = flip_sin_to_cos
         self.downscale_freq_shift = downscale_freq_shift
+        self.computation_device = computation_device
 
     def forward(self, timesteps):
         t_emb = get_timestep_embedding(
@@ -93,6 +95,7 @@ class TemporalTimesteps(torch.nn.Module):
             self.num_channels,
             flip_sin_to_cos=self.flip_sin_to_cos,
             downscale_freq_shift=self.downscale_freq_shift,
+            computation_device=self.computation_device,
         )
         return t_emb
     

diffsynth/models/tiler.py

@@ -104,3 +104,131 @@ class TileWorker:
         # Done!
         model_output = model_output.to(device=inference_device, dtype=inference_dtype)
         return model_output
+    
+
+
+class FastTileWorker:
+    def __init__(self):
+        pass
+
+
+    def build_mask(self, data, is_bound):
+        _, _, H, W = data.shape
+        h = repeat(torch.arange(H), "H -> H W", H=H, W=W)
+        w = repeat(torch.arange(W), "W -> H W", H=H, W=W)
+        border_width = (H + W) // 4
+        pad = torch.ones_like(h) * border_width
+        mask = torch.stack([
+            pad if is_bound[0] else h + 1,
+            pad if is_bound[1] else H - h,
+            pad if is_bound[2] else w + 1,
+            pad if is_bound[3] else W - w
+        ]).min(dim=0).values
+        mask = mask.clip(1, border_width)
+        mask = (mask / border_width).to(dtype=data.dtype, device=data.device)
+        mask = rearrange(mask, "H W -> 1 H W")
+        return mask
+
+
+    def tiled_forward(self, forward_fn, model_input, tile_size, tile_stride, tile_device="cpu", tile_dtype=torch.float32, border_width=None):
+        # Prepare
+        B, C, H, W = model_input.shape
+        border_width = int(tile_stride*0.5) if border_width is None else border_width
+        weight = torch.zeros((1, 1, H, W), dtype=tile_dtype, device=tile_device)
+        values = torch.zeros((B, C, H, W), dtype=tile_dtype, device=tile_device)
+
+        # Split tasks
+        tasks = []
+        for h in range(0, H, tile_stride):
+            for w in range(0, W, tile_stride):
+                if (h-tile_stride >= 0 and h-tile_stride+tile_size >= H) or (w-tile_stride >= 0 and w-tile_stride+tile_size >= W):
+                    continue
+                h_, w_ = h + tile_size, w + tile_size
+                if h_ > H: h, h_ = H - tile_size, H
+                if w_ > W: w, w_ = W - tile_size, W
+                tasks.append((h, h_, w, w_))
+        
+        # Run
+        for hl, hr, wl, wr in tasks:
+            # Forward
+            hidden_states_batch = forward_fn(hl, hr, wl, wr).to(dtype=tile_dtype, device=tile_device)
+
+            mask = self.build_mask(hidden_states_batch, is_bound=(hl==0, hr>=H, wl==0, wr>=W))
+            values[:, :, hl:hr, wl:wr] += hidden_states_batch * mask
+            weight[:, :, hl:hr, wl:wr] += mask
+        values /= weight
+        return values
+
+
+
+class TileWorker2Dto3D:
+    """
+    Process 3D tensors, but only enable TileWorker on 2D.
+    """
+    def __init__(self):
+        pass
+
+
+    def build_mask(self, T, H, W, dtype, device, is_bound, border_width):
+        t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W)
+        h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W)
+        w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W)
+        border_width = (H + W) // 4 if border_width is None else border_width
+        pad = torch.ones_like(h) * border_width
+        mask = torch.stack([
+            pad if is_bound[0] else t + 1,
+            pad if is_bound[1] else T - t,
+            pad if is_bound[2] else h + 1,
+            pad if is_bound[3] else H - h,
+            pad if is_bound[4] else w + 1,
+            pad if is_bound[5] else W - w
+        ]).min(dim=0).values
+        mask = mask.clip(1, border_width)
+        mask = (mask / border_width).to(dtype=dtype, device=device)
+        mask = rearrange(mask, "T H W -> 1 1 T H W")
+        return mask
+
+
+    def tiled_forward(
+        self,
+        forward_fn,
+        model_input,
+        tile_size, tile_stride,
+        tile_device="cpu", tile_dtype=torch.float32,
+        computation_device="cuda", computation_dtype=torch.float32,
+        border_width=None, scales=[1, 1, 1, 1],
+        progress_bar=lambda x:x
+    ):
+        B, C, T, H, W = model_input.shape
+        scale_C, scale_T, scale_H, scale_W = scales
+        tile_size_H, tile_size_W = tile_size
+        tile_stride_H, tile_stride_W = tile_stride
+
+        value = torch.zeros((B, int(C*scale_C), int(T*scale_T), int(H*scale_H), int(W*scale_W)), dtype=tile_dtype, device=tile_device)
+        weight = torch.zeros((1, 1, int(T*scale_T), int(H*scale_H), int(W*scale_W)), dtype=tile_dtype, device=tile_device)
+
+        # Split tasks
+        tasks = []
+        for h in range(0, H, tile_stride_H):
+            for w in range(0, W, tile_stride_W):
+                if (h-tile_stride_H >= 0 and h-tile_stride_H+tile_size_H >= H) or (w-tile_stride_W >= 0 and w-tile_stride_W+tile_size_W >= W):
+                    continue
+                h_, w_ = h + tile_size_H, w + tile_size_W
+                if h_ > H: h, h_ = max(H - tile_size_H, 0), H
+                if w_ > W: w, w_ = max(W - tile_size_W, 0), W
+                tasks.append((h, h_, w, w_))
+
+        # Run
+        for hl, hr, wl, wr in progress_bar(tasks):
+            mask = self.build_mask(
+                int(T*scale_T), int((hr-hl)*scale_H), int((wr-wl)*scale_W),
+                tile_dtype, tile_device,
+                is_bound=(True, True, hl==0, hr>=H, wl==0, wr>=W),
+                border_width=border_width
+            )
+            grid_input = model_input[:, :, :, hl:hr, wl:wr].to(dtype=computation_dtype, device=computation_device)
+            grid_output = forward_fn(grid_input).to(dtype=tile_dtype, device=tile_device)
+            value[:, :, :, int(hl*scale_H):int(hr*scale_H), int(wl*scale_W):int(wr*scale_W)] += grid_output * mask
+            weight[:, :, :, int(hl*scale_H):int(hr*scale_H), int(wl*scale_W):int(wr*scale_W)] += mask
+        value = value / weight
+        return value

diffsynth/models/utils.py

@@ -0,0 +1,182 @@
+import torch, os
+from safetensors import safe_open
+from contextlib import contextmanager
+import hashlib
+
+@contextmanager
+def init_weights_on_device(device = torch.device("meta"), include_buffers :bool = False):
+    
+    old_register_parameter = torch.nn.Module.register_parameter
+    if include_buffers:
+        old_register_buffer = torch.nn.Module.register_buffer
+    
+    def register_empty_parameter(module, name, param):
+        old_register_parameter(module, name, param)
+        if param is not None:
+            param_cls = type(module._parameters[name])
+            kwargs = module._parameters[name].__dict__
+            kwargs["requires_grad"] = param.requires_grad
+            module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs)
+
+    def register_empty_buffer(module, name, buffer, persistent=True):
+        old_register_buffer(module, name, buffer, persistent=persistent)
+        if buffer is not None:
+            module._buffers[name] = module._buffers[name].to(device)
+            
+    def patch_tensor_constructor(fn):
+        def wrapper(*args, **kwargs):
+            kwargs["device"] = device
+            return fn(*args, **kwargs)
+
+        return wrapper
+    
+    if include_buffers:
+        tensor_constructors_to_patch = {
+            torch_function_name: getattr(torch, torch_function_name)
+            for torch_function_name in ["empty", "zeros", "ones", "full"]
+        }
+    else:
+        tensor_constructors_to_patch = {}
+    
+    try:
+        torch.nn.Module.register_parameter = register_empty_parameter
+        if include_buffers:
+            torch.nn.Module.register_buffer = register_empty_buffer
+        for torch_function_name in tensor_constructors_to_patch.keys():
+            setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name)))
+        yield
+    finally:
+        torch.nn.Module.register_parameter = old_register_parameter
+        if include_buffers:
+            torch.nn.Module.register_buffer = old_register_buffer
+        for torch_function_name, old_torch_function in tensor_constructors_to_patch.items():
+            setattr(torch, torch_function_name, old_torch_function)
+
+def load_state_dict_from_folder(file_path, torch_dtype=None):
+    state_dict = {}
+    for file_name in os.listdir(file_path):
+        if "." in file_name and file_name.split(".")[-1] in [
+            "safetensors", "bin", "ckpt", "pth", "pt"
+        ]:
+            state_dict.update(load_state_dict(os.path.join(file_path, file_name), torch_dtype=torch_dtype))
+    return state_dict
+
+
+def load_state_dict(file_path, torch_dtype=None):
+    if file_path.endswith(".safetensors"):
+        return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype)
+    else:
+        return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype)
+
+
+def load_state_dict_from_safetensors(file_path, torch_dtype=None):
+    state_dict = {}
+    with safe_open(file_path, framework="pt", device="cpu") as f:
+        for k in f.keys():
+            state_dict[k] = f.get_tensor(k)
+            if torch_dtype is not None:
+                state_dict[k] = state_dict[k].to(torch_dtype)
+    return state_dict
+
+
+def load_state_dict_from_bin(file_path, torch_dtype=None):
+    state_dict = torch.load(file_path, map_location="cpu", weights_only=True)
+    if torch_dtype is not None:
+        for i in state_dict:
+            if isinstance(state_dict[i], torch.Tensor):
+                state_dict[i] = state_dict[i].to(torch_dtype)
+    return state_dict
+
+
+def search_for_embeddings(state_dict):
+    embeddings = []
+    for k in state_dict:
+        if isinstance(state_dict[k], torch.Tensor):
+            embeddings.append(state_dict[k])
+        elif isinstance(state_dict[k], dict):
+            embeddings += search_for_embeddings(state_dict[k])
+    return embeddings
+
+
+def search_parameter(param, state_dict):
+    for name, param_ in state_dict.items():
+        if param.numel() == param_.numel():
+            if param.shape == param_.shape:
+                if torch.dist(param, param_) < 1e-3:
+                    return name
+            else:
+                if torch.dist(param.flatten(), param_.flatten()) < 1e-3:
+                    return name
+    return None
+
+
+def build_rename_dict(source_state_dict, target_state_dict, split_qkv=False):
+    matched_keys = set()
+    with torch.no_grad():
+        for name in source_state_dict:
+            rename = search_parameter(source_state_dict[name], target_state_dict)
+            if rename is not None:
+                print(f'"{name}": "{rename}",')
+                matched_keys.add(rename)
+            elif split_qkv and len(source_state_dict[name].shape)>=1 and source_state_dict[name].shape[0]%3==0:
+                length = source_state_dict[name].shape[0] // 3
+                rename = []
+                for i in range(3):
+                    rename.append(search_parameter(source_state_dict[name][i*length: i*length+length], target_state_dict))
+                if None not in rename:
+                    print(f'"{name}": {rename},')
+                    for rename_ in rename:
+                        matched_keys.add(rename_)
+    for name in target_state_dict:
+        if name not in matched_keys:
+            print("Cannot find", name, target_state_dict[name].shape)
+
+
+def search_for_files(folder, extensions):
+    files = []
+    if os.path.isdir(folder):
+        for file in sorted(os.listdir(folder)):
+            files += search_for_files(os.path.join(folder, file), extensions)
+    elif os.path.isfile(folder):
+        for extension in extensions:
+            if folder.endswith(extension):
+                files.append(folder)
+                break
+    return files
+
+
+def convert_state_dict_keys_to_single_str(state_dict, with_shape=True):
+    keys = []
+    for key, value in state_dict.items():
+        if isinstance(key, str):
+            if isinstance(value, torch.Tensor):
+                if with_shape:
+                    shape = "_".join(map(str, list(value.shape)))
+                    keys.append(key + ":" + shape)
+                keys.append(key)
+            elif isinstance(value, dict):
+                keys.append(key + "|" + convert_state_dict_keys_to_single_str(value, with_shape=with_shape))
+    keys.sort()
+    keys_str = ",".join(keys)
+    return keys_str
+
+
+def split_state_dict_with_prefix(state_dict):
+    keys = sorted([key for key in state_dict if isinstance(key, str)])
+    prefix_dict = {}
+    for key in  keys:
+        prefix = key if "." not in key else key.split(".")[0]
+        if prefix not in prefix_dict:
+            prefix_dict[prefix] = []
+        prefix_dict[prefix].append(key)
+    state_dicts = []
+    for prefix, keys in prefix_dict.items():
+        sub_state_dict = {key: state_dict[key] for key in keys}
+        state_dicts.append(sub_state_dict)
+    return state_dicts
+
+
+def hash_state_dict_keys(state_dict, with_shape=True):
+    keys_str = convert_state_dict_keys_to_single_str(state_dict, with_shape=with_shape)
+    keys_str = keys_str.encode(encoding="UTF-8")
+    return hashlib.md5(keys_str).hexdigest()

diffsynth/models/wanx_text_encoder.py

@@ -0,0 +1,254 @@
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def fp16_clamp(x):
+    if x.dtype == torch.float16 and torch.isinf(x).any():
+        clamp = torch.finfo(x.dtype).max - 1000
+        x = torch.clamp(x, min=-clamp, max=clamp)
+    return x
+
+
+class GELU(nn.Module):
+
+    def forward(self, x):
+        return 0.5 * x * (1.0 + torch.tanh(
+            math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))
+
+
+class T5LayerNorm(nn.Module):
+
+    def __init__(self, dim, eps=1e-6):
+        super(T5LayerNorm, self).__init__()
+        self.dim = dim
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x):
+        x = x * torch.rsqrt(x.float().pow(2).mean(dim=-1, keepdim=True) +
+                            self.eps)
+        if self.weight.dtype in [torch.float16, torch.bfloat16]:
+            x = x.type_as(self.weight)
+        return self.weight * x
+
+
+class T5Attention(nn.Module):
+
+    def __init__(self, dim, dim_attn, num_heads, dropout=0.1):
+        assert dim_attn % num_heads == 0
+        super(T5Attention, self).__init__()
+        self.dim = dim
+        self.dim_attn = dim_attn
+        self.num_heads = num_heads
+        self.head_dim = dim_attn // num_heads
+
+        # layers
+        self.q = nn.Linear(dim, dim_attn, bias=False)
+        self.k = nn.Linear(dim, dim_attn, bias=False)
+        self.v = nn.Linear(dim, dim_attn, bias=False)
+        self.o = nn.Linear(dim_attn, dim, bias=False)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x, context=None, mask=None, pos_bias=None):
+        """
+        x:          [B, L1, C].
+        context:    [B, L2, C] or None.
+        mask:       [B, L2] or [B, L1, L2] or None.
+        """
+        # check inputs
+        context = x if context is None else context
+        b, n, c = x.size(0), self.num_heads, self.head_dim
+
+        # compute query, key, value
+        q = self.q(x).view(b, -1, n, c)
+        k = self.k(context).view(b, -1, n, c)
+        v = self.v(context).view(b, -1, n, c)
+
+        # attention bias
+        attn_bias = x.new_zeros(b, n, q.size(1), k.size(1))
+        if pos_bias is not None:
+            attn_bias += pos_bias
+        if mask is not None:
+            assert mask.ndim in [2, 3]
+            mask = mask.view(b, 1, 1,
+                             -1) if mask.ndim == 2 else mask.unsqueeze(1)
+            attn_bias.masked_fill_(mask == 0, torch.finfo(x.dtype).min)
+
+        # compute attention (T5 does not use scaling)
+        attn = torch.einsum('binc,bjnc->bnij', q, k) + attn_bias
+        attn = F.softmax(attn.float(), dim=-1).type_as(attn)
+        x = torch.einsum('bnij,bjnc->binc', attn, v)
+
+        # output
+        x = x.reshape(b, -1, n * c)
+        x = self.o(x)
+        x = self.dropout(x)
+        return x
+
+
+class T5FeedForward(nn.Module):
+
+    def __init__(self, dim, dim_ffn, dropout=0.1):
+        super(T5FeedForward, self).__init__()
+        self.dim = dim
+        self.dim_ffn = dim_ffn
+
+        # layers
+        self.gate = nn.Sequential(nn.Linear(dim, dim_ffn, bias=False), GELU())
+        self.fc1 = nn.Linear(dim, dim_ffn, bias=False)
+        self.fc2 = nn.Linear(dim_ffn, dim, bias=False)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        x = self.fc1(x) * self.gate(x)
+        x = self.dropout(x)
+        x = self.fc2(x)
+        x = self.dropout(x)
+        return x
+
+
+class T5SelfAttention(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 dim_attn,
+                 dim_ffn,
+                 num_heads,
+                 num_buckets,
+                 shared_pos=True,
+                 dropout=0.1):
+        super(T5SelfAttention, self).__init__()
+        self.dim = dim
+        self.dim_attn = dim_attn
+        self.dim_ffn = dim_ffn
+        self.num_heads = num_heads
+        self.num_buckets = num_buckets
+        self.shared_pos = shared_pos
+
+        # layers
+        self.norm1 = T5LayerNorm(dim)
+        self.attn = T5Attention(dim, dim_attn, num_heads, dropout)
+        self.norm2 = T5LayerNorm(dim)
+        self.ffn = T5FeedForward(dim, dim_ffn, dropout)
+        self.pos_embedding = None if shared_pos else T5RelativeEmbedding(
+            num_buckets, num_heads, bidirectional=True)
+
+    def forward(self, x, mask=None, pos_bias=None):
+        e = pos_bias if self.shared_pos else self.pos_embedding(
+            x.size(1), x.size(1))
+        x = fp16_clamp(x + self.attn(self.norm1(x), mask=mask, pos_bias=e))
+        x = fp16_clamp(x + self.ffn(self.norm2(x)))
+        return x
+
+
+class T5RelativeEmbedding(nn.Module):
+
+    def __init__(self, num_buckets, num_heads, bidirectional, max_dist=128):
+        super(T5RelativeEmbedding, self).__init__()
+        self.num_buckets = num_buckets
+        self.num_heads = num_heads
+        self.bidirectional = bidirectional
+        self.max_dist = max_dist
+
+        # layers
+        self.embedding = nn.Embedding(num_buckets, num_heads)
+
+    def forward(self, lq, lk):
+        device = self.embedding.weight.device
+        # rel_pos = torch.arange(lk).unsqueeze(0).to(device) - \
+        #     torch.arange(lq).unsqueeze(1).to(device)
+        rel_pos = torch.arange(lk, device=device).unsqueeze(0) - \
+            torch.arange(lq, device=device).unsqueeze(1)
+        rel_pos = self._relative_position_bucket(rel_pos)
+        rel_pos_embeds = self.embedding(rel_pos)
+        rel_pos_embeds = rel_pos_embeds.permute(2, 0, 1).unsqueeze(
+            0)  # [1, N, Lq, Lk]
+        return rel_pos_embeds.contiguous()
+
+    def _relative_position_bucket(self, rel_pos):
+        # preprocess
+        if self.bidirectional:
+            num_buckets = self.num_buckets // 2
+            rel_buckets = (rel_pos > 0).long() * num_buckets
+            rel_pos = torch.abs(rel_pos)
+        else:
+            num_buckets = self.num_buckets
+            rel_buckets = 0
+            rel_pos = -torch.min(rel_pos, torch.zeros_like(rel_pos))
+
+        # embeddings for small and large positions
+        max_exact = num_buckets // 2
+        rel_pos_large = max_exact + (torch.log(rel_pos.float() / max_exact) /
+                                     math.log(self.max_dist / max_exact) *
+                                     (num_buckets - max_exact)).long()
+        rel_pos_large = torch.min(
+            rel_pos_large, torch.full_like(rel_pos_large, num_buckets - 1))
+        rel_buckets += torch.where(rel_pos < max_exact, rel_pos, rel_pos_large)
+        return rel_buckets
+
+def init_weights(m):
+    if isinstance(m, T5LayerNorm):
+        nn.init.ones_(m.weight)
+    elif isinstance(m, T5FeedForward):
+        nn.init.normal_(m.gate[0].weight, std=m.dim**-0.5)
+        nn.init.normal_(m.fc1.weight, std=m.dim**-0.5)
+        nn.init.normal_(m.fc2.weight, std=m.dim_ffn**-0.5)
+    elif isinstance(m, T5Attention):
+        nn.init.normal_(m.q.weight, std=(m.dim * m.dim_attn)**-0.5)
+        nn.init.normal_(m.k.weight, std=m.dim**-0.5)
+        nn.init.normal_(m.v.weight, std=m.dim**-0.5)
+        nn.init.normal_(m.o.weight, std=(m.num_heads * m.dim_attn)**-0.5)
+    elif isinstance(m, T5RelativeEmbedding):
+        nn.init.normal_(
+            m.embedding.weight, std=(2 * m.num_buckets * m.num_heads)**-0.5)
+
+
+class WanXTextEncoder(torch.nn.Module):
+
+    def __init__(self,
+                 vocab=256384,
+                 dim=4096,
+                 dim_attn=4096,
+                 dim_ffn=10240,
+                 num_heads=64,
+                 num_layers=24,
+                 num_buckets=32,
+                 shared_pos=False,
+                 dropout=0.1):
+        super(WanXTextEncoder, self).__init__()
+        self.dim = dim
+        self.dim_attn = dim_attn
+        self.dim_ffn = dim_ffn
+        self.num_heads = num_heads
+        self.num_layers = num_layers
+        self.num_buckets = num_buckets
+        self.shared_pos = shared_pos
+
+        # layers
+        self.token_embedding = vocab if isinstance(vocab, nn.Embedding) \
+            else nn.Embedding(vocab, dim)
+        self.pos_embedding = T5RelativeEmbedding(
+            num_buckets, num_heads, bidirectional=True) if shared_pos else None
+        self.dropout = nn.Dropout(dropout)
+        self.blocks = nn.ModuleList([
+            T5SelfAttention(dim, dim_attn, dim_ffn, num_heads, num_buckets,
+                            shared_pos, dropout) for _ in range(num_layers)
+        ])
+        self.norm = T5LayerNorm(dim)
+
+        # initialize weights
+        self.apply(init_weights)
+
+    def forward(self, ids, mask=None):
+        x = self.token_embedding(ids)
+        x = self.dropout(x)
+        e = self.pos_embedding(x.size(1),
+                               x.size(1)) if self.shared_pos else None
+        for block in self.blocks:
+            x = block(x, mask, pos_bias=e)
+        x = self.norm(x)
+        x = self.dropout(x)
+        return x

diffsynth/models/wanx_vae.py

@@ -0,0 +1,794 @@
+from einops import rearrange, repeat
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+CACHE_T = 2
+
+def block_causal_mask(x, block_size):
+    # params
+    b, n, s, _, device = *x.size(), x.device
+    assert s % block_size == 0
+    num_blocks = s // block_size
+
+    # build mask
+    mask = torch.zeros(b, n, s, s, dtype=torch.bool, device=device)
+    for i in range(num_blocks):
+        mask[:, :,
+             i * block_size:(i + 1) * block_size, :(i + 1) * block_size] = 1
+    return mask
+
+
+class CausalConv3d(nn.Conv3d):
+    """
+    Causal 3d convolusion.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._padding = (self.padding[2], self.padding[2], self.padding[1],
+                         self.padding[1], 2 * self.padding[0], 0)
+        self.padding = (0, 0, 0)
+
+    def forward(self, x, cache_x=None):
+        padding = list(self._padding)
+        if cache_x is not None and self._padding[4] > 0:
+            cache_x = cache_x.to(x.device)
+            x = torch.cat([cache_x, x], dim=2)
+            padding[4] -= cache_x.shape[2]
+        x = F.pad(x, padding)
+
+        return super().forward(x)
+
+
+class RMS_norm(nn.Module):
+
+    def __init__(self, dim, channel_first=True, images=True, bias=False):
+        super().__init__()
+        broadcastable_dims = (1, 1, 1) if not images else (1, 1)
+        shape = (dim, *broadcastable_dims) if channel_first else (dim,)
+
+        self.channel_first = channel_first
+        self.scale = dim**0.5
+        self.gamma = nn.Parameter(torch.ones(shape))
+        self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0.
+
+    def forward(self, x):
+        return F.normalize(
+            x, dim=(1 if self.channel_first else
+                    -1)) * self.scale * self.gamma + self.bias
+
+
+class Upsample(nn.Upsample):
+
+    def forward(self, x):
+        """
+        Fix bfloat16 support for nearest neighbor interpolation.
+        """
+        return super().forward(x.float()).type_as(x)
+
+
+class Resample(nn.Module):
+
+    def __init__(self, dim, mode):
+        assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d',
+                        'downsample3d')
+        super().__init__()
+        self.dim = dim
+        self.mode = mode
+
+        # layers
+        if mode == 'upsample2d':
+            self.resample = nn.Sequential(
+                Upsample(scale_factor=(2., 2.), mode='nearest-exact'),
+                nn.Conv2d(dim, dim // 2, 3, padding=1))
+        elif mode == 'upsample3d':
+            self.resample = nn.Sequential(
+                Upsample(scale_factor=(2., 2.), mode='nearest-exact'),
+                nn.Conv2d(dim, dim // 2, 3, padding=1))
+            self.time_conv = CausalConv3d(dim,
+                                          dim * 2, (3, 1, 1),
+                                          padding=(1, 0, 0))
+
+        elif mode == 'downsample2d':
+            self.resample = nn.Sequential(
+                nn.ZeroPad2d((0, 1, 0, 1)),
+                nn.Conv2d(dim, dim, 3, stride=(2, 2)))
+        elif mode == 'downsample3d':
+            self.resample = nn.Sequential(
+                nn.ZeroPad2d((0, 1, 0, 1)),
+                nn.Conv2d(dim, dim, 3, stride=(2, 2)))
+            self.time_conv = CausalConv3d(dim,
+                                          dim, (3, 1, 1),
+                                          stride=(2, 1, 1),
+                                          padding=(0, 0, 0))
+
+        else:
+            self.resample = nn.Identity()
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        b, c, t, h, w = x.size()
+        if self.mode == 'upsample3d':
+            if feat_cache is not None:
+                idx = feat_idx[0]
+                if feat_cache[idx] is None:
+                    feat_cache[idx] = 'Rep'
+                    feat_idx[0] += 1
+                else:
+
+                    cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                    if cache_x.shape[2] < 2 and feat_cache[
+                            idx] is not None and feat_cache[idx] != 'Rep':
+                        # cache last frame of last two chunk
+                        cache_x = torch.cat([
+                            feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                                cache_x.device), cache_x
+                        ],
+                                            dim=2)
+                    if cache_x.shape[2] < 2 and feat_cache[
+                            idx] is not None and feat_cache[idx] == 'Rep':
+                        cache_x = torch.cat([
+                            torch.zeros_like(cache_x).to(cache_x.device),
+                            cache_x
+                        ],
+                                            dim=2)
+                    if feat_cache[idx] == 'Rep':
+                        x = self.time_conv(x)
+                    else:
+                        x = self.time_conv(x, feat_cache[idx])
+                    feat_cache[idx] = cache_x
+                    feat_idx[0] += 1
+
+                    x = x.reshape(b, 2, c, t, h, w)
+                    x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]),
+                                    3)
+                    x = x.reshape(b, c, t * 2, h, w)
+        t = x.shape[2]
+        x = rearrange(x, 'b c t h w -> (b t) c h w')
+        x = self.resample(x)
+        x = rearrange(x, '(b t) c h w -> b c t h w', t=t)
+
+        if self.mode == 'downsample3d':
+            if feat_cache is not None:
+                idx = feat_idx[0]
+                if feat_cache[idx] is None:
+                    feat_cache[idx] = x.clone()
+                    feat_idx[0] += 1
+                else:
+                    cache_x = x[:, :, -1:, :, :].clone()
+                    x = self.time_conv(
+                        torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
+                    feat_cache[idx] = cache_x
+                    feat_idx[0] += 1
+        return x
+
+    def init_weight(self, conv):
+        conv_weight = conv.weight
+        nn.init.zeros_(conv_weight)
+        c1, c2, t, h, w = conv_weight.size()
+        one_matrix = torch.eye(c1, c2)
+        init_matrix = one_matrix
+        nn.init.zeros_(conv_weight)
+        conv_weight.data[:, :, 1, 0, 0] = init_matrix
+        conv.weight.data.copy_(conv_weight)
+        nn.init.zeros_(conv.bias.data)
+
+    def init_weight2(self, conv):
+        conv_weight = conv.weight.data
+        nn.init.zeros_(conv_weight)
+        c1, c2, t, h, w = conv_weight.size()
+        init_matrix = torch.eye(c1 // 2, c2)
+        conv_weight[:c1 // 2, :, -1, 0, 0] = init_matrix
+        conv_weight[c1 // 2:, :, -1, 0, 0] = init_matrix
+        conv.weight.data.copy_(conv_weight)
+        nn.init.zeros_(conv.bias.data)
+
+
+class ResidualBlock(nn.Module):
+
+    def __init__(self, in_dim, out_dim, dropout=0.0):
+        super().__init__()
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+        # layers
+        self.residual = nn.Sequential(
+            RMS_norm(in_dim, images=False), nn.SiLU(),
+            CausalConv3d(in_dim, out_dim, 3, padding=1),
+            RMS_norm(out_dim, images=False), nn.SiLU(), nn.Dropout(dropout),
+            CausalConv3d(out_dim, out_dim, 3, padding=1))
+        self.shortcut = CausalConv3d(in_dim, out_dim, 1) \
+            if in_dim != out_dim else nn.Identity()
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        h = self.shortcut(x)
+        for layer in self.residual:
+            if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    # cache last frame of last two chunk
+                    cache_x = torch.cat([
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device), cache_x
+                    ],
+                                        dim=2)
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+        return x + h
+
+
+class AttentionBlock(nn.Module):
+    """
+    Causal self-attention with a single head.
+    """
+
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+        # layers
+        self.norm = RMS_norm(dim)
+        self.to_qkv = nn.Conv2d(dim, dim * 3, 1)
+        self.proj = nn.Conv2d(dim, dim, 1)
+
+        # zero out the last layer params
+        nn.init.zeros_(self.proj.weight)
+
+    def forward(self, x):
+        identity = x
+        b, c, t, h, w = x.size()
+        x = rearrange(x, 'b c t h w -> (b t) c h w')
+        x = self.norm(x)
+        # compute query, key, value
+        q, k, v = self.to_qkv(x).reshape(b * t, 1, c * 3, -1).permute(
+            0, 1, 3, 2).contiguous().chunk(3, dim=-1)
+
+        # apply attention
+        x = F.scaled_dot_product_attention(
+            q,
+            k,
+            v,
+            #attn_mask=block_causal_mask(q, block_size=h * w)
+        )
+        x = x.squeeze(1).permute(0, 2, 1).reshape(b * t, c, h, w)
+
+        # output
+        x = self.proj(x)
+        x = rearrange(x, '(b t) c h w-> b c t h w', t=t)
+        return x + identity
+
+
+class Encoder3d(nn.Module):
+
+    def __init__(self,
+                 dim=128,
+                 z_dim=4,
+                 dim_mult=[1, 2, 4, 4],
+                 num_res_blocks=2,
+                 attn_scales=[],
+                 temperal_downsample=[True, True, False],
+                 dropout=0.0):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_downsample = temperal_downsample
+
+        # dimensions
+        dims = [dim * u for u in [1] + dim_mult]
+        scale = 1.0
+
+        # init block
+        self.conv1 = CausalConv3d(3, dims[0], 3, padding=1)
+
+        # downsample blocks
+        downsamples = []
+        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
+            # residual (+attention) blocks
+            for _ in range(num_res_blocks):
+                downsamples.append(ResidualBlock(in_dim, out_dim, dropout))
+                if scale in attn_scales:
+                    downsamples.append(AttentionBlock(out_dim))
+                in_dim = out_dim
+
+            # downsample block
+            if i != len(dim_mult) - 1:
+                mode = 'downsample3d' if temperal_downsample[
+                    i] else 'downsample2d'
+                downsamples.append(Resample(out_dim, mode=mode))
+                scale /= 2.0
+        self.downsamples = nn.Sequential(*downsamples)
+
+        # middle blocks
+        self.middle = nn.Sequential(ResidualBlock(out_dim, out_dim, dropout),
+                                    AttentionBlock(out_dim),
+                                    ResidualBlock(out_dim, out_dim, dropout))
+
+        # output blocks
+        self.head = nn.Sequential(RMS_norm(out_dim, images=False), nn.SiLU(),
+                                  CausalConv3d(out_dim, z_dim, 3, padding=1))
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                # cache last frame of last two chunk
+                cache_x = torch.cat([
+                    feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                        cache_x.device), cache_x
+                ],
+                                    dim=2)
+            x = self.conv1(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv1(x)
+
+        ## downsamples
+        for layer in self.downsamples:
+            if feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## middle
+        for layer in self.middle:
+            if isinstance(layer, ResidualBlock) and feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## head
+        for layer in self.head:
+            if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    # cache last frame of last two chunk
+                    cache_x = torch.cat([
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device), cache_x
+                    ],
+                                        dim=2)
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+        return x
+
+
+class Decoder3d(nn.Module):
+
+    def __init__(self,
+                 dim=128,
+                 z_dim=4,
+                 dim_mult=[1, 2, 4, 4],
+                 num_res_blocks=2,
+                 attn_scales=[],
+                 temperal_upsample=[False, True, True],
+                 dropout=0.0):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_upsample = temperal_upsample
+
+        # dimensions
+        dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
+        scale = 1.0 / 2**(len(dim_mult) - 2)
+
+        # init block
+        self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1)
+
+        # middle blocks
+        self.middle = nn.Sequential(ResidualBlock(dims[0], dims[0], dropout),
+                                    AttentionBlock(dims[0]),
+                                    ResidualBlock(dims[0], dims[0], dropout))
+
+        # upsample blocks
+        upsamples = []
+        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
+            # residual (+attention) blocks
+            if i == 1 or i == 2 or i == 3:
+                in_dim = in_dim // 2
+            for _ in range(num_res_blocks + 1):
+                upsamples.append(ResidualBlock(in_dim, out_dim, dropout))
+                if scale in attn_scales:
+                    upsamples.append(AttentionBlock(out_dim))
+                in_dim = out_dim
+
+            # upsample block
+            if i != len(dim_mult) - 1:
+                mode = 'upsample3d' if temperal_upsample[i] else 'upsample2d'
+                upsamples.append(Resample(out_dim, mode=mode))
+                scale *= 2.0
+        self.upsamples = nn.Sequential(*upsamples)
+
+        # output blocks
+        self.head = nn.Sequential(RMS_norm(out_dim, images=False), nn.SiLU(),
+                                  CausalConv3d(out_dim, 3, 3, padding=1))
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        ## conv1
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                # cache last frame of last two chunk
+                cache_x = torch.cat([
+                    feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                        cache_x.device), cache_x
+                ],
+                                    dim=2)
+            x = self.conv1(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv1(x)
+
+        ## middle
+        for layer in self.middle:
+            if isinstance(layer, ResidualBlock) and feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## upsamples
+        for layer in self.upsamples:
+            if feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## head
+        for layer in self.head:
+            if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    # cache last frame of last two chunk
+                    cache_x = torch.cat([
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device), cache_x
+                    ],
+                                        dim=2)
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+        return x
+
+
+def count_conv3d(model):
+    count = 0
+    for m in model.modules():
+        if isinstance(m, CausalConv3d):
+            count += 1
+    return count
+
+
+class VideoVAE_(nn.Module):
+
+    def __init__(self,
+                 dim=96,
+                 z_dim=16,
+                 dim_mult=[1, 2, 4, 4],
+                 num_res_blocks=2,
+                 attn_scales=[],
+                 temperal_downsample=[False, True, True],
+                 dropout=0.0):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_downsample = temperal_downsample
+        self.temperal_upsample = temperal_downsample[::-1]
+
+        # modules
+        self.encoder = Encoder3d(dim, z_dim * 2, dim_mult, num_res_blocks,
+                                 attn_scales, self.temperal_downsample, dropout)
+        self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1)
+        self.conv2 = CausalConv3d(z_dim, z_dim, 1)
+        self.decoder = Decoder3d(dim, z_dim, dim_mult, num_res_blocks,
+                                 attn_scales, self.temperal_upsample, dropout)
+
+    def forward(self, x):
+        mu, log_var = self.encode(x)
+        z = self.reparameterize(mu, log_var)
+        x_recon = self.decode(z)
+        return x_recon, mu, log_var
+
+    def encode(self, x, scale):
+        self.clear_cache()
+        ## cache
+        t = x.shape[2]
+        iter_ = 1 + (t - 1) // 4
+
+        for i in range(iter_):
+            self._enc_conv_idx = [0]
+            if i == 0:
+                out = self.encoder(x[:, :, :1, :, :],
+                                   feat_cache=self._enc_feat_map,
+                                   feat_idx=self._enc_conv_idx)
+            else:
+                out_ = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
+                                    feat_cache=self._enc_feat_map,
+                                    feat_idx=self._enc_conv_idx)
+                out = torch.cat([out, out_], 2)
+        mu, log_var = self.conv1(out).chunk(2, dim=1)
+        if isinstance(scale[0], torch.Tensor):
+            scale = [s.to(dtype=mu.dtype, device=mu.device) for s in scale]
+            mu = (mu - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view(
+                1, self.z_dim, 1, 1, 1)
+        else:
+            scale = scale.to(dtype=mu.dtype, device=mu.device)
+            mu = (mu - scale[0]) * scale[1]
+        return mu
+
+    def decode(self, z, scale):
+        self.clear_cache()
+        # z: [b,c,t,h,w]
+        if isinstance(scale[0], torch.Tensor):
+            scale = [s.to(dtype=z.dtype, device=z.device) for s in scale]
+            z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view(
+                1, self.z_dim, 1, 1, 1)
+        else:
+            scale = scale.to(dtype=z.dtype, device=z.device)
+            z = z / scale[1] + scale[0]
+        iter_ = z.shape[2]
+        x = self.conv2(z)
+        for i in range(iter_):
+            self._conv_idx = [0]
+            if i == 0:
+                out = self.decoder(x[:, :, i:i + 1, :, :],
+                                   feat_cache=self._feat_map,
+                                   feat_idx=self._conv_idx)
+            else:
+                out_ = self.decoder(x[:, :, i:i + 1, :, :],
+                                    feat_cache=self._feat_map,
+                                    feat_idx=self._conv_idx)
+                out = torch.cat([out, out_], 2) # may add tensor offload
+        return out
+
+    def reparameterize(self, mu, log_var):
+        std = torch.exp(0.5 * log_var)
+        eps = torch.randn_like(std)
+        return eps * std + mu
+
+    def sample(self, imgs, deterministic=False):
+        mu, log_var = self.encode(imgs)
+        if deterministic:
+            return mu
+        std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0))
+        return mu + std * torch.randn_like(std)
+
+    def clear_cache(self):
+        self._conv_num = count_conv3d(self.decoder)
+        self._conv_idx = [0]
+        self._feat_map = [None] * self._conv_num
+        # cache encode
+        self._enc_conv_num = count_conv3d(self.encoder)
+        self._enc_conv_idx = [0]
+        self._enc_feat_map = [None] * self._enc_conv_num
+
+
+class WanXVideoVAE(nn.Module):
+
+    def __init__(self, z_dim=16):
+        super().__init__()
+
+        mean = [
+            -0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508,
+            0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921
+        ]
+        std = [
+            2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743,
+            3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160
+        ]
+        self.mean = torch.tensor(mean)
+        self.std = torch.tensor(std)
+        self.scale = [self.mean, 1.0 / self.std]
+
+        # init model
+        self.model = VideoVAE_(z_dim=z_dim).eval().requires_grad_(False)
+        self.upsampling_factor = 8
+
+
+    def build_1d_mask(self, length, left_bound, right_bound, border_width):
+        x = torch.ones((length,))
+        if not left_bound:
+            x[:border_width] = (torch.arange(border_width) + 1) / border_width
+        if not right_bound:
+            x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,))
+        return x
+
+
+    def build_mask(self, data, is_bound, border_width):
+        _, _, _, H, W = data.shape
+        h = self.build_1d_mask(H, is_bound[0], is_bound[1], border_width[0])
+        w = self.build_1d_mask(W, is_bound[2], is_bound[3], border_width[1])
+
+        h = repeat(h, "H -> H W", H=H, W=W)
+        w = repeat(w, "W -> H W", H=H, W=W)
+
+        mask = torch.stack([h, w]).min(dim=0).values
+        mask = rearrange(mask, "H W -> 1 1 1 H W")
+        return mask
+
+
+    def tiled_decode(self, hidden_states, device, tile_size, tile_stride):
+        _, _, T, H, W = hidden_states.shape
+        size_h, size_w = tile_size
+        stride_h, stride_w = tile_stride
+
+        # Split tasks
+        tasks = []
+        for h in range(0, H, stride_h):
+            if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue
+            for w in range(0, W, stride_w):
+                if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue
+                h_, w_ = h + size_h, w + size_w
+                tasks.append((h, h_, w, w_))
+
+        data_device = "cpu"
+        computation_device = device
+
+        out_T = T * 4 - 3
+        weight = torch.zeros((1, 1, out_T, H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device)
+        values = torch.zeros((1, 3, out_T, H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device)
+
+        for h, h_, w, w_ in tqdm(tasks, desc="VAE decoding"):
+            hidden_states_batch = hidden_states[:, :, :, h:h_, w:w_].to(computation_device)
+            hidden_states_batch = self.model.decode(hidden_states_batch, self.scale).to(data_device)
+
+            mask = self.build_mask(
+                hidden_states_batch,
+                is_bound=(h==0, h_>=H, w==0, w_>=W),
+                border_width=((size_h - stride_h) * self.upsampling_factor, (size_w - stride_w) * self.upsampling_factor)
+            ).to(dtype=hidden_states.dtype, device=data_device)
+
+            target_h = h * self.upsampling_factor
+            target_w = w * self.upsampling_factor
+            values[
+                :,
+                :,
+                :,
+                target_h:target_h + hidden_states_batch.shape[3],
+                target_w:target_w + hidden_states_batch.shape[4],
+            ] += hidden_states_batch * mask
+            weight[
+                :,
+                :,
+                :,
+                target_h: target_h + hidden_states_batch.shape[3],
+                target_w: target_w + hidden_states_batch.shape[4],
+            ] += mask
+        values = values / weight
+        values = values.float().clamp_(-1, 1)
+        return values
+
+
+    def tiled_encode(self, video, device, tile_size, tile_stride):
+        _, _, T, H, W = video.shape
+        size_h, size_w = tile_size
+        stride_h, stride_w = tile_stride
+
+        # Split tasks
+        tasks = []
+        for h in range(0, H, stride_h):
+            if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue
+            for w in range(0, W, stride_w):
+                if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue
+                h_, w_ = h + size_h, w + size_w
+                tasks.append((h, h_, w, w_))
+
+        data_device = "cpu"
+        computation_device = device
+
+        out_T = (T + 3) // 4
+        weight = torch.zeros((1, 1, out_T, H // self.upsampling_factor, W // self.upsampling_factor), dtype=video.dtype, device=data_device)
+        values = torch.zeros((1, 16, out_T, H // self.upsampling_factor, W // self.upsampling_factor), dtype=video.dtype, device=data_device)
+
+        for h, h_, w, w_ in tqdm(tasks, desc="VAE encoding"):
+            hidden_states_batch = video[:, :, :, h:h_, w:w_].to(computation_device)
+            hidden_states_batch = self.model.encode(hidden_states_batch, self.scale).to(data_device)
+
+            mask = self.build_mask(
+                hidden_states_batch,
+                is_bound=(h==0, h_>=H, w==0, w_>=W),
+                border_width=((size_h - stride_h) // self.upsampling_factor, (size_w - stride_w) // self.upsampling_factor)
+            ).to(dtype=video.dtype, device=data_device)
+
+            target_h = h // self.upsampling_factor
+            target_w = w // self.upsampling_factor
+            values[
+                :,
+                :,
+                :,
+                target_h:target_h + hidden_states_batch.shape[3],
+                target_w:target_w + hidden_states_batch.shape[4],
+            ] += hidden_states_batch * mask
+            weight[
+                :,
+                :,
+                :,
+                target_h: target_h + hidden_states_batch.shape[3],
+                target_w: target_w + hidden_states_batch.shape[4],
+            ] += mask
+        values = values / weight
+        values = values.float()
+        return values
+
+
+    def single_encode(self, video, device):
+        video = video.to(device)
+        x = self.model.encode(video, self.scale)
+        return x.float()
+
+
+    def single_decode(self, hidden_state, device):
+        hidden_state = hidden_state.to(device)
+        video = self.model.decode(hidden_state, self.scale)
+        return video.float().clamp_(-1, 1)
+
+
+    def encode(self, videos, device, tiled=False, tile_size=(272, 272), tile_stride=(144, 128)):
+
+        videos = [video.to("cpu") for video in videos]
+        hidden_states = []
+        for video in videos:
+            video = video.unsqueeze(0)
+            if tiled:
+                assert tile_size[0] % self.upsampling_factor == 0 and tile_size[1] % self.upsampling_factor == 0, f"tile_size must be devisible by {self.upsampling_factor}"
+                hidden_state = self.tiled_encode(video, device, tile_size, tile_stride)
+            else:
+                hidden_state = self.single_encode(video, device)
+            hidden_state = hidden_state.squeeze(0)
+            hidden_states.append(hidden_state)
+        return hidden_states
+
+
+    def decode(self, hidden_states, device, tiled=False, tile_size=(34, 34), tile_stride=(18, 16)):
+        hidden_states = [hidden_state.to("cpu") for hidden_state in hidden_states]
+        videos = []
+        for hidden_state in hidden_states:
+            hidden_state = hidden_state.unsqueeze(0)
+            if tiled:
+                video = self.tiled_decode(hidden_state, device, tile_size, tile_stride)
+            else:
+                video = self.single_decode(hidden_state, device)
+            video = video.squeeze(0)
+            videos.append(video)
+        return videos
+
+
+    @staticmethod
+    def state_dict_converter():
+        return WanXVideoVAEStateDictConverter()
+
+
+class WanXVideoVAEStateDictConverter:
+
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        state_dict_ = {}
+        for name in state_dict['model_state']:
+            state_dict_['model.' + name] = state_dict['model_state'][name]
+        return state_dict_

diffsynth/pipelines/__init__.py

@@ -5,5 +5,10 @@ from .sdxl_video import SDXLVideoPipeline
 from .sd3_image import SD3ImagePipeline
 from .hunyuan_image import HunyuanDiTImagePipeline
 from .svd_video import SVDVideoPipeline
+from .flux_image import FluxImagePipeline
+from .cog_video import CogVideoPipeline
+from .omnigen_image import OmnigenImagePipeline
 from .pipeline_runner import SDVideoPipelineRunner
+from .hunyuan_video import HunyuanVideoPipeline
+from .step_video import StepVideoPipeline
 KolorsImagePipeline = SDXLImagePipeline

diffsynth/pipelines/base.py

@@ -1,15 +1,30 @@
 import torch
 import numpy as np
 from PIL import Image
+from torchvision.transforms import GaussianBlur
 
 
 
 class BasePipeline(torch.nn.Module):
 
-    def __init__(self, device="cuda", torch_dtype=torch.float16):
+    def __init__(self, device="cuda", torch_dtype=torch.float16, height_division_factor=64, width_division_factor=64):
         super().__init__()
         self.device = device
         self.torch_dtype = torch_dtype
+        self.height_division_factor = height_division_factor
+        self.width_division_factor = width_division_factor
+        self.cpu_offload = False
+        self.model_names = []
+
+
+    def check_resize_height_width(self, height, width):
+        if height % self.height_division_factor != 0:
+            height = (height + self.height_division_factor - 1) // self.height_division_factor * self.height_division_factor
+            print(f"The height cannot be evenly divided by {self.height_division_factor}. We round it up to {height}.")
+        if width % self.width_division_factor != 0:
+            width = (width + self.width_division_factor - 1) // self.width_division_factor * self.width_division_factor
+            print(f"The width cannot be evenly divided by {self.width_division_factor}. We round it up to {width}.")
+        return height, width
 
 
     def preprocess_image(self, image):
@@ -22,7 +37,7 @@ class BasePipeline(torch.nn.Module):
     
 
     def vae_output_to_image(self, vae_output):
-        image = vae_output[0].cpu().permute(1, 2, 0).numpy()
+        image = vae_output[0].cpu().float().permute(1, 2, 0).numpy()
         image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8"))
         return image
     
@@ -32,3 +47,81 @@ class BasePipeline(torch.nn.Module):
         video = [Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8")) for image in video]
         return video
 
+    
+    def merge_latents(self, value, latents, masks, scales, blur_kernel_size=33, blur_sigma=10.0):
+        if len(latents) > 0:
+            blur = GaussianBlur(kernel_size=blur_kernel_size, sigma=blur_sigma)
+            height, width = value.shape[-2:]
+            weight = torch.ones_like(value)
+            for latent, mask, scale in zip(latents, masks, scales):
+                mask = self.preprocess_image(mask.resize((width, height))).mean(dim=1, keepdim=True) > 0
+                mask = mask.repeat(1, latent.shape[1], 1, 1).to(dtype=latent.dtype, device=latent.device)
+                mask = blur(mask)
+                value += latent * mask * scale
+                weight += mask * scale
+            value /= weight
+        return value
+
+
+    def control_noise_via_local_prompts(self, prompt_emb_global, prompt_emb_locals, masks, mask_scales, inference_callback, special_kwargs=None, special_local_kwargs_list=None):
+        if special_kwargs is None:
+            noise_pred_global = inference_callback(prompt_emb_global)
+        else:
+            noise_pred_global = inference_callback(prompt_emb_global, special_kwargs)
+        if special_local_kwargs_list is None:
+            noise_pred_locals = [inference_callback(prompt_emb_local) for prompt_emb_local in prompt_emb_locals]
+        else:
+            noise_pred_locals = [inference_callback(prompt_emb_local, special_kwargs) for prompt_emb_local, special_kwargs in zip(prompt_emb_locals, special_local_kwargs_list)]
+        noise_pred = self.merge_latents(noise_pred_global, noise_pred_locals, masks, mask_scales)
+        return noise_pred
+    
+
+    def extend_prompt(self, prompt, local_prompts, masks, mask_scales):
+        local_prompts = local_prompts or []
+        masks = masks or []
+        mask_scales = mask_scales or []
+        extended_prompt_dict = self.prompter.extend_prompt(prompt)
+        prompt = extended_prompt_dict.get("prompt", prompt)
+        local_prompts += extended_prompt_dict.get("prompts", [])
+        masks += extended_prompt_dict.get("masks", [])
+        mask_scales += [100.0] * len(extended_prompt_dict.get("masks", []))
+        return prompt, local_prompts, masks, mask_scales
+    
+    
+    def enable_cpu_offload(self):
+        self.cpu_offload = True
+
+
+    def load_models_to_device(self, loadmodel_names=[]):
+        # only load models to device if cpu_offload is enabled
+        if not self.cpu_offload:
+            return
+        # offload the unneeded models to cpu
+        for model_name in self.model_names:
+            if model_name not in loadmodel_names:
+                model = getattr(self, model_name)
+                if model is not None:
+                    if hasattr(model, "vram_management_enabled") and model.vram_management_enabled:
+                        for module in model.modules():
+                            if hasattr(module, "offload"):
+                                module.offload()
+                    else:
+                        model.cpu()
+        # load the needed models to device
+        for model_name in loadmodel_names:
+            model = getattr(self, model_name)
+            if model is not None:
+                if hasattr(model, "vram_management_enabled") and model.vram_management_enabled:
+                    for module in model.modules():
+                        if hasattr(module, "onload"):
+                            module.onload()
+                else:
+                    model.to(self.device)
+        # fresh the cuda cache
+        torch.cuda.empty_cache()
+
+    
+    def generate_noise(self, shape, seed=None, device="cpu", dtype=torch.float16):
+        generator = None if seed is None else torch.Generator(device).manual_seed(seed)
+        noise = torch.randn(shape, generator=generator, device=device, dtype=dtype)
+        return noise

diffsynth/pipelines/cog_video.py

@@ -0,0 +1,135 @@
+from ..models import ModelManager, FluxTextEncoder2, CogDiT, CogVAEEncoder, CogVAEDecoder
+from ..prompters import CogPrompter
+from ..schedulers import EnhancedDDIMScheduler
+from .base import BasePipeline
+import torch
+from tqdm import tqdm
+from PIL import Image
+import numpy as np
+from einops import rearrange
+
+
+
+class CogVideoPipeline(BasePipeline):
+
+    def __init__(self, device="cuda", torch_dtype=torch.float16):
+        super().__init__(device=device, torch_dtype=torch_dtype, height_division_factor=16, width_division_factor=16)
+        self.scheduler = EnhancedDDIMScheduler(rescale_zero_terminal_snr=True, prediction_type="v_prediction")
+        self.prompter = CogPrompter()
+        # models
+        self.text_encoder: FluxTextEncoder2 = None
+        self.dit: CogDiT = None
+        self.vae_encoder: CogVAEEncoder = None
+        self.vae_decoder: CogVAEDecoder = None
+    
+
+    def fetch_models(self, model_manager: ModelManager, prompt_refiner_classes=[]):
+        self.text_encoder = model_manager.fetch_model("flux_text_encoder_2")
+        self.dit = model_manager.fetch_model("cog_dit")
+        self.vae_encoder = model_manager.fetch_model("cog_vae_encoder")
+        self.vae_decoder = model_manager.fetch_model("cog_vae_decoder")
+        self.prompter.fetch_models(self.text_encoder)
+        self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes)
+
+
+    @staticmethod
+    def from_model_manager(model_manager: ModelManager, prompt_refiner_classes=[]):
+        pipe = CogVideoPipeline(
+            device=model_manager.device,
+            torch_dtype=model_manager.torch_dtype
+        )
+        pipe.fetch_models(model_manager, prompt_refiner_classes)
+        return pipe
+    
+
+    def tensor2video(self, frames):
+        frames = rearrange(frames, "C T H W -> T H W C")
+        frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8)
+        frames = [Image.fromarray(frame) for frame in frames]
+        return frames
+    
+
+    def encode_prompt(self, prompt, positive=True):
+        prompt_emb = self.prompter.encode_prompt(prompt, device=self.device, positive=positive)
+        return {"prompt_emb": prompt_emb}
+    
+
+    def prepare_extra_input(self, latents):
+        return {"image_rotary_emb": self.dit.prepare_rotary_positional_embeddings(latents.shape[3], latents.shape[4], latents.shape[2], device=self.device)}
+
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt,
+        negative_prompt="",
+        input_video=None,
+        cfg_scale=7.0,
+        denoising_strength=1.0,
+        num_frames=49,
+        height=480,
+        width=720,
+        num_inference_steps=20,
+        tiled=False,
+        tile_size=(60, 90),
+        tile_stride=(30, 45),
+        seed=None,
+        progress_bar_cmd=tqdm,
+        progress_bar_st=None,
+    ):
+        height, width = self.check_resize_height_width(height, width)
+        
+        # Tiler parameters
+        tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}
+
+        # Prepare scheduler
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength)
+
+        # Prepare latent tensors
+        noise = self.generate_noise((1, 16, num_frames // 4 + 1, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype)
+        
+        if denoising_strength == 1.0:
+            latents = noise.clone()
+        else:
+            input_video = self.preprocess_images(input_video)
+            input_video = torch.stack(input_video, dim=2)
+            latents = self.vae_encoder.encode_video(input_video, **tiler_kwargs, progress_bar=progress_bar_cmd).to(dtype=self.torch_dtype)
+            latents = self.scheduler.add_noise(latents, noise, self.scheduler.timesteps[0])
+        if not tiled: latents = latents.to(self.device)
+
+        # Encode prompt
+        prompt_emb_posi = self.encode_prompt(prompt, positive=True)
+        if cfg_scale != 1.0:
+            prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False)
+
+        # Extra input
+        extra_input = self.prepare_extra_input(latents)
+
+        # Denoise
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            timestep = timestep.unsqueeze(0).to(self.device)
+
+            # Classifier-free guidance
+            noise_pred_posi = self.dit(
+                latents, timestep=timestep, **prompt_emb_posi, **tiler_kwargs, **extra_input
+            )
+            if cfg_scale != 1.0:
+                noise_pred_nega = self.dit(
+                    latents, timestep=timestep, **prompt_emb_nega, **tiler_kwargs, **extra_input
+                )
+                noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
+            else:
+                noise_pred = noise_pred_posi
+
+            # DDIM
+            latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents)
+            
+            # Update progress bar
+            if progress_bar_st is not None:
+                progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
+
+        # Decode image
+        video = self.vae_decoder.decode_video(latents.to("cpu"), **tiler_kwargs, progress_bar=progress_bar_cmd)
+        video = self.tensor2video(video[0])
+
+        return video

diffsynth/pipelines/dancer.py

@@ -136,6 +136,40 @@ def lets_dance_xl(
     device = "cuda",
     vram_limit_level = 0,
 ):
+    # 0. Text embedding alignment (only for video processing)
+    if encoder_hidden_states.shape[0] != sample.shape[0]:
+        encoder_hidden_states = encoder_hidden_states.repeat(sample.shape[0], 1, 1, 1)
+    if add_text_embeds.shape[0] != sample.shape[0]:
+        add_text_embeds = add_text_embeds.repeat(sample.shape[0], 1)
+    
+    # 1. ControlNet
+    controlnet_insert_block_id = 22
+    if controlnet is not None and controlnet_frames is not None:
+        res_stacks = []
+        # process controlnet frames with batch
+        for batch_id in range(0, sample.shape[0], controlnet_batch_size):
+            batch_id_ = min(batch_id + controlnet_batch_size, sample.shape[0])
+            res_stack = controlnet(
+                sample[batch_id: batch_id_],
+                timestep,
+                encoder_hidden_states[batch_id: batch_id_],
+                controlnet_frames[:, batch_id: batch_id_],
+                add_time_id=add_time_id,
+                add_text_embeds=add_text_embeds,
+                tiled=tiled, tile_size=tile_size, tile_stride=tile_stride,
+                unet=unet, # for Kolors, some modules in ControlNets will be replaced.
+            )
+            if vram_limit_level >= 1:
+                res_stack = [res.cpu() for res in res_stack]
+            res_stacks.append(res_stack)
+        # concat the residual
+        additional_res_stack = []
+        for i in range(len(res_stacks[0])):
+            res = torch.concat([res_stack[i] for res_stack in res_stacks], dim=0)
+            additional_res_stack.append(res)
+    else:
+        additional_res_stack = None
+
     # 2. time
     t_emb = unet.time_proj(timestep).to(sample.dtype)
     t_emb = unet.time_embedding(t_emb)
@@ -156,11 +190,31 @@ def lets_dance_xl(
 
     # 4. blocks
     for block_id, block in enumerate(unet.blocks):
-        hidden_states, time_emb, text_emb, res_stack = block(
+        # 4.1 UNet
-            hidden_states, time_emb, text_emb, res_stack,
+        if isinstance(block, PushBlock):
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+            if vram_limit_level>=1:
+                res_stack[-1] = res_stack[-1].cpu()
+        elif isinstance(block, PopBlock):
+            if vram_limit_level>=1:
+                res_stack[-1] = res_stack[-1].to(device)
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+        else:
+            hidden_states_input = hidden_states
+            hidden_states_output = []
+            for batch_id in range(0, sample.shape[0], unet_batch_size):
+                batch_id_ = min(batch_id + unet_batch_size, sample.shape[0])
+                hidden_states, _, _, _ = block(
+                    hidden_states_input[batch_id: batch_id_],
+                    time_emb[batch_id: batch_id_],
+                    text_emb[batch_id: batch_id_],
+                    res_stack,
+                    cross_frame_attention=cross_frame_attention,
+                    ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, {}),
                     tiled=tiled, tile_size=tile_size, tile_stride=tile_stride,
-            ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, {})
                 )
+                hidden_states_output.append(hidden_states)
+            hidden_states = torch.concat(hidden_states_output, dim=0)
         # 4.2 AnimateDiff
         if motion_modules is not None:
             if block_id in motion_modules.call_block_id:
@@ -169,6 +223,10 @@ def lets_dance_xl(
                     hidden_states, time_emb, text_emb, res_stack,
                     batch_size=1
                 )
+        # 4.3 ControlNet
+        if block_id == controlnet_insert_block_id and additional_res_stack is not None:
+            hidden_states += additional_res_stack.pop().to(device)
+            res_stack = [res + additional_res for res, additional_res in zip(res_stack, additional_res_stack)]
 
     # 5. output
     hidden_states = unet.conv_norm_out(hidden_states)

diffsynth/pipelines/flux_image.py

@@ -0,0 +1,646 @@
+from ..models import ModelManager, FluxDiT, SD3TextEncoder1, FluxTextEncoder2, FluxVAEDecoder, FluxVAEEncoder, FluxIpAdapter
+from ..controlnets import FluxMultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator
+from ..prompters import FluxPrompter
+from ..schedulers import FlowMatchScheduler
+from .base import BasePipeline
+from typing import List
+import torch
+from tqdm import tqdm
+import numpy as np
+from PIL import Image
+from ..models.tiler import FastTileWorker
+from transformers import SiglipVisionModel
+from copy import deepcopy
+from transformers.models.t5.modeling_t5 import T5LayerNorm, T5DenseActDense, T5DenseGatedActDense
+from ..models.flux_dit import RMSNorm
+from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear
+
+
+class FluxImagePipeline(BasePipeline):
+
+    def __init__(self, device="cuda", torch_dtype=torch.float16):
+        super().__init__(device=device, torch_dtype=torch_dtype, height_division_factor=16, width_division_factor=16)
+        self.scheduler = FlowMatchScheduler()
+        self.prompter = FluxPrompter()
+        # models
+        self.text_encoder_1: SD3TextEncoder1 = None
+        self.text_encoder_2: FluxTextEncoder2 = None
+        self.dit: FluxDiT = None
+        self.vae_decoder: FluxVAEDecoder = None
+        self.vae_encoder: FluxVAEEncoder = None
+        self.controlnet: FluxMultiControlNetManager = None
+        self.ipadapter: FluxIpAdapter = None
+        self.ipadapter_image_encoder: SiglipVisionModel = None
+        self.model_names = ['text_encoder_1', 'text_encoder_2', 'dit', 'vae_decoder', 'vae_encoder', 'controlnet', 'ipadapter', 'ipadapter_image_encoder']
+
+
+    def enable_vram_management(self, num_persistent_param_in_dit=None):
+        dtype = next(iter(self.text_encoder_1.parameters())).dtype
+        enable_vram_management(
+            self.text_encoder_1,
+            module_map = {
+                torch.nn.Linear: AutoWrappedLinear,
+                torch.nn.Embedding: AutoWrappedModule,
+                torch.nn.LayerNorm: AutoWrappedModule,
+            },
+            module_config = dict(
+                offload_dtype=dtype,
+                offload_device="cpu",
+                onload_dtype=dtype,
+                onload_device="cpu",
+                computation_dtype=self.torch_dtype,
+                computation_device=self.device,
+            ),
+        )
+        dtype = next(iter(self.text_encoder_2.parameters())).dtype
+        enable_vram_management(
+            self.text_encoder_2,
+            module_map = {
+                torch.nn.Linear: AutoWrappedLinear,
+                torch.nn.Embedding: AutoWrappedModule,
+                T5LayerNorm: AutoWrappedModule,
+                T5DenseActDense: AutoWrappedModule,
+                T5DenseGatedActDense: AutoWrappedModule,
+            },
+            module_config = dict(
+                offload_dtype=dtype,
+                offload_device="cpu",
+                onload_dtype=dtype,
+                onload_device="cpu",
+                computation_dtype=self.torch_dtype,
+                computation_device=self.device,
+            ),
+        )
+        dtype = next(iter(self.dit.parameters())).dtype
+        enable_vram_management(
+            self.dit,
+            module_map = {
+                RMSNorm: AutoWrappedModule,
+                torch.nn.Linear: AutoWrappedLinear,
+            },
+            module_config = dict(
+                offload_dtype=dtype,
+                offload_device="cpu",
+                onload_dtype=dtype,
+                onload_device="cuda",
+                computation_dtype=self.torch_dtype,
+                computation_device=self.device,
+            ),
+            max_num_param=num_persistent_param_in_dit,
+            overflow_module_config = dict(
+                offload_dtype=dtype,
+                offload_device="cpu",
+                onload_dtype=dtype,
+                onload_device="cpu",
+                computation_dtype=self.torch_dtype,
+                computation_device=self.device,
+            ),
+        )
+        dtype = next(iter(self.vae_decoder.parameters())).dtype
+        enable_vram_management(
+            self.vae_decoder,
+            module_map = {
+                torch.nn.Linear: AutoWrappedLinear,
+                torch.nn.Conv2d: AutoWrappedModule,
+                torch.nn.GroupNorm: AutoWrappedModule,
+            },
+            module_config = dict(
+                offload_dtype=dtype,
+                offload_device="cpu",
+                onload_dtype=dtype,
+                onload_device="cpu",
+                computation_dtype=self.torch_dtype,
+                computation_device=self.device,
+            ),
+        )
+        dtype = next(iter(self.vae_encoder.parameters())).dtype
+        enable_vram_management(
+            self.vae_encoder,
+            module_map = {
+                torch.nn.Linear: AutoWrappedLinear,
+                torch.nn.Conv2d: AutoWrappedModule,
+                torch.nn.GroupNorm: AutoWrappedModule,
+            },
+            module_config = dict(
+                offload_dtype=dtype,
+                offload_device="cpu",
+                onload_dtype=dtype,
+                onload_device="cpu",
+                computation_dtype=self.torch_dtype,
+                computation_device=self.device,
+            ),
+        )
+        self.enable_cpu_offload()
+
+
+    def denoising_model(self):
+        return self.dit
+
+
+    def fetch_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[], prompt_extender_classes=[]):
+        self.text_encoder_1 = model_manager.fetch_model("sd3_text_encoder_1")
+        self.text_encoder_2 = model_manager.fetch_model("flux_text_encoder_2")
+        self.dit = model_manager.fetch_model("flux_dit")
+        self.vae_decoder = model_manager.fetch_model("flux_vae_decoder")
+        self.vae_encoder = model_manager.fetch_model("flux_vae_encoder")
+        self.prompter.fetch_models(self.text_encoder_1, self.text_encoder_2)
+        self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes)
+        self.prompter.load_prompt_extenders(model_manager, prompt_extender_classes)
+
+        # ControlNets
+        controlnet_units = []
+        for config in controlnet_config_units:
+            controlnet_unit = ControlNetUnit(
+                Annotator(config.processor_id, device=self.device, skip_processor=config.skip_processor),
+                model_manager.fetch_model("flux_controlnet", config.model_path),
+                config.scale
+            )
+            controlnet_units.append(controlnet_unit)
+        self.controlnet = FluxMultiControlNetManager(controlnet_units)
+
+        # IP-Adapters
+        self.ipadapter = model_manager.fetch_model("flux_ipadapter")
+        self.ipadapter_image_encoder = model_manager.fetch_model("siglip_vision_model")
+
+
+    @staticmethod
+    def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[], prompt_extender_classes=[], device=None, torch_dtype=None):
+        pipe = FluxImagePipeline(
+            device=model_manager.device if device is None else device,
+            torch_dtype=model_manager.torch_dtype if torch_dtype is None else torch_dtype,
+        )
+        pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes, prompt_extender_classes)
+        return pipe
+    
+
+    def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32):
+        latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+        return latents
+    
+
+    def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32):
+        image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+        image = self.vae_output_to_image(image)
+        return image
+    
+
+    def encode_prompt(self, prompt, positive=True, t5_sequence_length=512):
+        prompt_emb, pooled_prompt_emb, text_ids = self.prompter.encode_prompt(
+            prompt, device=self.device, positive=positive, t5_sequence_length=t5_sequence_length
+        )
+        return {"prompt_emb": prompt_emb, "pooled_prompt_emb": pooled_prompt_emb, "text_ids": text_ids}
+    
+
+    def prepare_extra_input(self, latents=None, guidance=1.0):
+        latent_image_ids = self.dit.prepare_image_ids(latents)
+        guidance = torch.Tensor([guidance] * latents.shape[0]).to(device=latents.device, dtype=latents.dtype)
+        return {"image_ids": latent_image_ids, "guidance": guidance}
+    
+
+    def apply_controlnet_mask_on_latents(self, latents, mask):
+        mask = (self.preprocess_image(mask) + 1) / 2
+        mask = mask.mean(dim=1, keepdim=True)
+        mask = mask.to(dtype=self.torch_dtype, device=self.device)
+        mask = 1 - torch.nn.functional.interpolate(mask, size=latents.shape[-2:])
+        latents = torch.concat([latents, mask], dim=1)
+        return latents
+    
+
+    def apply_controlnet_mask_on_image(self, image, mask):
+        mask = mask.resize(image.size)
+        mask = self.preprocess_image(mask).mean(dim=[0, 1])
+        image = np.array(image)
+        image[mask > 0] = 0
+        image = Image.fromarray(image)
+        return image
+    
+
+    def prepare_controlnet_input(self, controlnet_image, controlnet_inpaint_mask, tiler_kwargs):
+        if isinstance(controlnet_image, Image.Image):
+            controlnet_image = [controlnet_image] * len(self.controlnet.processors)
+
+        controlnet_frames = []
+        for i in range(len(self.controlnet.processors)):
+            # image annotator
+            image = self.controlnet.process_image(controlnet_image[i], processor_id=i)[0]
+            if controlnet_inpaint_mask is not None and self.controlnet.processors[i].processor_id == "inpaint":
+                image = self.apply_controlnet_mask_on_image(image, controlnet_inpaint_mask)
+
+            # image to tensor
+            image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype)
+
+            # vae encoder
+            image = self.encode_image(image, **tiler_kwargs)
+            if controlnet_inpaint_mask is not None and self.controlnet.processors[i].processor_id == "inpaint":
+                image = self.apply_controlnet_mask_on_latents(image, controlnet_inpaint_mask)
+            
+            # store it
+            controlnet_frames.append(image)
+        return controlnet_frames
+
+
+    def prepare_ipadapter_inputs(self, images, height=384, width=384):
+        images = [image.convert("RGB").resize((width, height), resample=3) for image in images]
+        images = [self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) for image in images]
+        return torch.cat(images, dim=0)
+
+
+    def inpaint_fusion(self, latents, inpaint_latents, pred_noise, fg_mask, bg_mask, progress_id, background_weight=0.):
+        # inpaint noise
+        inpaint_noise = (latents - inpaint_latents) / self.scheduler.sigmas[progress_id]
+        # merge noise
+        weight = torch.ones_like(inpaint_noise)
+        inpaint_noise[fg_mask] = pred_noise[fg_mask]
+        inpaint_noise[bg_mask] += pred_noise[bg_mask] * background_weight
+        weight[bg_mask] += background_weight
+        inpaint_noise /= weight
+        return inpaint_noise
+
+
+    def preprocess_masks(self, masks, height, width, dim):
+        out_masks = []
+        for mask in masks:
+            mask = self.preprocess_image(mask.resize((width, height), resample=Image.NEAREST)).mean(dim=1, keepdim=True) > 0
+            mask = mask.repeat(1, dim, 1, 1).to(device=self.device, dtype=self.torch_dtype)
+            out_masks.append(mask)
+        return out_masks
+
+
+    def prepare_entity_inputs(self, entity_prompts, entity_masks, width, height, t5_sequence_length=512, enable_eligen_inpaint=False):
+        fg_mask, bg_mask = None, None
+        if enable_eligen_inpaint:
+            masks_ = deepcopy(entity_masks)
+            fg_masks = torch.cat([self.preprocess_image(mask.resize((width//8, height//8))).mean(dim=1, keepdim=True) for mask in masks_])
+            fg_masks = (fg_masks > 0).float()
+            fg_mask = fg_masks.sum(dim=0, keepdim=True).repeat(1, 16, 1, 1) > 0
+            bg_mask = ~fg_mask
+        entity_masks = self.preprocess_masks(entity_masks, height//8, width//8, 1)
+        entity_masks = torch.cat(entity_masks, dim=0).unsqueeze(0) # b, n_mask, c, h, w
+        entity_prompts = self.encode_prompt(entity_prompts, t5_sequence_length=t5_sequence_length)['prompt_emb'].unsqueeze(0)
+        return entity_prompts, entity_masks, fg_mask, bg_mask
+
+
+    def prepare_latents(self, input_image, height, width, seed, tiled, tile_size, tile_stride):
+        if input_image is not None:
+            self.load_models_to_device(['vae_encoder'])
+            image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype)
+            input_latents = self.encode_image(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+            noise = self.generate_noise((1, 16, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype)
+            latents = self.scheduler.add_noise(input_latents, noise, timestep=self.scheduler.timesteps[0])
+        else:
+            latents = self.generate_noise((1, 16, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype)
+            input_latents = None
+        return latents, input_latents
+
+
+    def prepare_ipadapter(self, ipadapter_images, ipadapter_scale):
+        if ipadapter_images is not None:
+            self.load_models_to_device(['ipadapter_image_encoder'])
+            ipadapter_images = self.prepare_ipadapter_inputs(ipadapter_images)
+            ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images).pooler_output
+            self.load_models_to_device(['ipadapter'])
+            ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)}
+            ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))}
+        else:
+            ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": {}}, {"ipadapter_kwargs_list": {}}
+        return ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega
+
+
+    def prepare_controlnet(self, controlnet_image, masks, controlnet_inpaint_mask, tiler_kwargs, enable_controlnet_on_negative):
+        if controlnet_image is not None:
+            self.load_models_to_device(['vae_encoder'])
+            controlnet_kwargs_posi = {"controlnet_frames": self.prepare_controlnet_input(controlnet_image, controlnet_inpaint_mask, tiler_kwargs)}
+            if len(masks) > 0 and controlnet_inpaint_mask is not None:
+                print("The controlnet_inpaint_mask will be overridden by masks.")
+                local_controlnet_kwargs = [{"controlnet_frames": self.prepare_controlnet_input(controlnet_image, mask, tiler_kwargs)} for mask in masks]
+            else:
+                local_controlnet_kwargs = None
+        else:
+            controlnet_kwargs_posi, local_controlnet_kwargs = {"controlnet_frames": None}, [{}] * len(masks)
+        controlnet_kwargs_nega = controlnet_kwargs_posi if enable_controlnet_on_negative else {}
+        return controlnet_kwargs_posi, controlnet_kwargs_nega, local_controlnet_kwargs
+
+
+    def prepare_eligen(self, prompt_emb_nega, eligen_entity_prompts, eligen_entity_masks, width, height, t5_sequence_length, enable_eligen_inpaint, enable_eligen_on_negative, cfg_scale):
+        if eligen_entity_masks is not None:
+            entity_prompt_emb_posi, entity_masks_posi, fg_mask, bg_mask = self.prepare_entity_inputs(eligen_entity_prompts, eligen_entity_masks, width, height, t5_sequence_length, enable_eligen_inpaint)
+            if enable_eligen_on_negative and cfg_scale != 1.0:
+                entity_prompt_emb_nega = prompt_emb_nega['prompt_emb'].unsqueeze(1).repeat(1, entity_masks_posi.shape[1], 1, 1)
+                entity_masks_nega = entity_masks_posi
+            else:
+                entity_prompt_emb_nega, entity_masks_nega = None, None
+        else:
+            entity_prompt_emb_posi, entity_masks_posi, entity_prompt_emb_nega, entity_masks_nega = None, None, None, None
+            fg_mask, bg_mask = None, None
+        eligen_kwargs_posi = {"entity_prompt_emb": entity_prompt_emb_posi, "entity_masks": entity_masks_posi}
+        eligen_kwargs_nega = {"entity_prompt_emb": entity_prompt_emb_nega, "entity_masks": entity_masks_nega}
+        return eligen_kwargs_posi, eligen_kwargs_nega, fg_mask, bg_mask
+
+
+    def prepare_prompts(self, prompt, local_prompts, masks, mask_scales, t5_sequence_length, negative_prompt, cfg_scale):
+        # Extend prompt
+        self.load_models_to_device(['text_encoder_1', 'text_encoder_2'])
+        prompt, local_prompts, masks, mask_scales = self.extend_prompt(prompt, local_prompts, masks, mask_scales)
+
+        # Encode prompts
+        prompt_emb_posi = self.encode_prompt(prompt, t5_sequence_length=t5_sequence_length)
+        prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False, t5_sequence_length=t5_sequence_length) if cfg_scale != 1.0 else None
+        prompt_emb_locals = [self.encode_prompt(prompt_local, t5_sequence_length=t5_sequence_length) for prompt_local in local_prompts]
+        return prompt_emb_posi, prompt_emb_nega, prompt_emb_locals
+
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        # Prompt
+        prompt,
+        negative_prompt="",
+        cfg_scale=1.0,
+        embedded_guidance=3.5,
+        t5_sequence_length=512,
+        # Image
+        input_image=None,
+        denoising_strength=1.0,
+        height=1024,
+        width=1024,
+        seed=None,
+        # Steps
+        num_inference_steps=30,
+        # local prompts
+        local_prompts=(),
+        masks=(),
+        mask_scales=(),
+        # ControlNet
+        controlnet_image=None,
+        controlnet_inpaint_mask=None,
+        enable_controlnet_on_negative=False,
+        # IP-Adapter
+        ipadapter_images=None,
+        ipadapter_scale=1.0,
+        # EliGen
+        eligen_entity_prompts=None,
+        eligen_entity_masks=None,
+        enable_eligen_on_negative=False,
+        enable_eligen_inpaint=False,
+        # TeaCache
+        tea_cache_l1_thresh=None,
+        # Tile
+        tiled=False,
+        tile_size=128,
+        tile_stride=64,
+        # Progress bar
+        progress_bar_cmd=tqdm,
+        progress_bar_st=None,
+    ):
+        height, width = self.check_resize_height_width(height, width)
+
+        # Tiler parameters
+        tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}
+
+        # Prepare scheduler
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
+
+        # Prepare latent tensors
+        latents, input_latents = self.prepare_latents(input_image, height, width, seed, tiled, tile_size, tile_stride)
+
+        # Prompt
+        prompt_emb_posi, prompt_emb_nega, prompt_emb_locals = self.prepare_prompts(prompt, local_prompts, masks, mask_scales, t5_sequence_length, negative_prompt, cfg_scale)
+
+        # Extra input
+        extra_input = self.prepare_extra_input(latents, guidance=embedded_guidance)
+
+        # Entity control
+        eligen_kwargs_posi, eligen_kwargs_nega, fg_mask, bg_mask = self.prepare_eligen(prompt_emb_nega, eligen_entity_prompts, eligen_entity_masks, width, height, t5_sequence_length, enable_eligen_inpaint, enable_eligen_on_negative, cfg_scale)
+
+        # IP-Adapter
+        ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = self.prepare_ipadapter(ipadapter_images, ipadapter_scale)
+
+        # ControlNets
+        controlnet_kwargs_posi, controlnet_kwargs_nega, local_controlnet_kwargs = self.prepare_controlnet(controlnet_image, masks, controlnet_inpaint_mask, tiler_kwargs, enable_controlnet_on_negative)
+
+        # TeaCache
+        tea_cache_kwargs = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh) if tea_cache_l1_thresh is not None else None}
+
+        # Denoise
+        self.load_models_to_device(['dit', 'controlnet'])
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            timestep = timestep.unsqueeze(0).to(self.device)
+
+            # Positive side
+            inference_callback = lambda prompt_emb_posi, controlnet_kwargs: lets_dance_flux(
+                dit=self.dit, controlnet=self.controlnet,
+                hidden_states=latents, timestep=timestep,
+                **prompt_emb_posi, **tiler_kwargs, **extra_input, **controlnet_kwargs, **ipadapter_kwargs_list_posi, **eligen_kwargs_posi, **tea_cache_kwargs,
+            )
+            noise_pred_posi = self.control_noise_via_local_prompts(
+                prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback,
+                special_kwargs=controlnet_kwargs_posi, special_local_kwargs_list=local_controlnet_kwargs
+            )
+
+            # Inpaint
+            if enable_eligen_inpaint:
+                noise_pred_posi = self.inpaint_fusion(latents, input_latents, noise_pred_posi, fg_mask, bg_mask, progress_id)
+            
+            # Classifier-free guidance
+            if cfg_scale != 1.0:
+                # Negative side
+                noise_pred_nega = lets_dance_flux(
+                    dit=self.dit, controlnet=self.controlnet,
+                    hidden_states=latents, timestep=timestep,
+                    **prompt_emb_nega, **tiler_kwargs, **extra_input, **controlnet_kwargs_nega, **ipadapter_kwargs_list_nega, **eligen_kwargs_nega,
+                )
+                noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
+            else:
+                noise_pred = noise_pred_posi
+
+            # Iterate
+            latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents)
+
+            # UI
+            if progress_bar_st is not None:
+                progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
+        
+        # Decode image
+        self.load_models_to_device(['vae_decoder'])
+        image = self.decode_image(latents, **tiler_kwargs)
+
+        # Offload all models
+        self.load_models_to_device([])
+        return image
+
+
+class TeaCache:
+    def __init__(self, num_inference_steps, rel_l1_thresh):
+        self.num_inference_steps = num_inference_steps
+        self.step = 0
+        self.accumulated_rel_l1_distance = 0
+        self.previous_modulated_input = None
+        self.rel_l1_thresh = rel_l1_thresh
+        self.previous_residual = None
+        self.previous_hidden_states = None
+
+    def check(self, dit: FluxDiT, hidden_states, conditioning):
+        inp = hidden_states.clone()
+        temb_ = conditioning.clone()
+        modulated_inp, _, _, _, _ = dit.blocks[0].norm1_a(inp, emb=temb_)
+        if self.step == 0 or self.step == self.num_inference_steps - 1:
+            should_calc = True
+            self.accumulated_rel_l1_distance = 0
+        else: 
+            coefficients = [4.98651651e+02, -2.83781631e+02,  5.58554382e+01, -3.82021401e+00, 2.64230861e-01]
+            rescale_func = np.poly1d(coefficients)
+            self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item())
+            if self.accumulated_rel_l1_distance < self.rel_l1_thresh:
+                should_calc = False
+            else:
+                should_calc = True
+                self.accumulated_rel_l1_distance = 0
+        self.previous_modulated_input = modulated_inp 
+        self.step += 1
+        if self.step == self.num_inference_steps:
+            self.step = 0
+        if should_calc:
+            self.previous_hidden_states = hidden_states.clone()
+        return not should_calc
+    
+    def store(self, hidden_states):
+        self.previous_residual = hidden_states - self.previous_hidden_states
+        self.previous_hidden_states = None
+
+    def update(self, hidden_states):
+        hidden_states = hidden_states + self.previous_residual
+        return hidden_states
+
+
+def lets_dance_flux(
+    dit: FluxDiT,
+    controlnet: FluxMultiControlNetManager = None,
+    hidden_states=None,
+    timestep=None,
+    prompt_emb=None,
+    pooled_prompt_emb=None,
+    guidance=None,
+    text_ids=None,
+    image_ids=None,
+    controlnet_frames=None,
+    tiled=False,
+    tile_size=128,
+    tile_stride=64,
+    entity_prompt_emb=None,
+    entity_masks=None,
+    ipadapter_kwargs_list={},
+    tea_cache: TeaCache = None,
+    **kwargs
+):
+    if tiled:
+        def flux_forward_fn(hl, hr, wl, wr):
+            tiled_controlnet_frames = [f[:, :, hl: hr, wl: wr] for f in controlnet_frames] if controlnet_frames is not None else None
+            return lets_dance_flux(
+                dit=dit,
+                controlnet=controlnet,
+                hidden_states=hidden_states[:, :, hl: hr, wl: wr],
+                timestep=timestep,
+                prompt_emb=prompt_emb,
+                pooled_prompt_emb=pooled_prompt_emb,
+                guidance=guidance,
+                text_ids=text_ids,
+                image_ids=None,
+                controlnet_frames=tiled_controlnet_frames,
+                tiled=False,
+                **kwargs
+            )
+        return FastTileWorker().tiled_forward(
+            flux_forward_fn,
+            hidden_states,
+            tile_size=tile_size,
+            tile_stride=tile_stride,
+            tile_device=hidden_states.device,
+            tile_dtype=hidden_states.dtype
+        )
+
+
+    # ControlNet
+    if controlnet is not None and controlnet_frames is not None:
+        controlnet_extra_kwargs = {
+            "hidden_states": hidden_states,
+            "timestep": timestep,
+            "prompt_emb": prompt_emb,
+            "pooled_prompt_emb": pooled_prompt_emb,
+            "guidance": guidance,
+            "text_ids": text_ids,
+            "image_ids": image_ids,
+            "tiled": tiled,
+            "tile_size": tile_size,
+            "tile_stride": tile_stride,
+        }
+        controlnet_res_stack, controlnet_single_res_stack = controlnet(
+            controlnet_frames, **controlnet_extra_kwargs
+        )
+
+    if image_ids is None:
+        image_ids = dit.prepare_image_ids(hidden_states)
+    
+    conditioning = dit.time_embedder(timestep, hidden_states.dtype) + dit.pooled_text_embedder(pooled_prompt_emb)
+    if dit.guidance_embedder is not None:
+        guidance = guidance * 1000
+        conditioning = conditioning + dit.guidance_embedder(guidance, hidden_states.dtype)
+
+    height, width = hidden_states.shape[-2:]
+    hidden_states = dit.patchify(hidden_states)
+    hidden_states = dit.x_embedder(hidden_states)
+
+    if entity_prompt_emb is not None and entity_masks is not None:
+        prompt_emb, image_rotary_emb, attention_mask = dit.process_entity_masks(hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids)
+    else:
+        prompt_emb = dit.context_embedder(prompt_emb)
+        image_rotary_emb = dit.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
+        attention_mask = None
+
+    # TeaCache
+    if tea_cache is not None:
+        tea_cache_update = tea_cache.check(dit, hidden_states, conditioning)
+    else:
+        tea_cache_update = False
+
+    if tea_cache_update:
+        hidden_states = tea_cache.update(hidden_states)
+    else:
+        # Joint Blocks
+        for block_id, block in enumerate(dit.blocks):
+            hidden_states, prompt_emb = block(
+                hidden_states,
+                prompt_emb,
+                conditioning,
+                image_rotary_emb,
+                attention_mask,
+                ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, None)
+            )
+            # ControlNet
+            if controlnet is not None and controlnet_frames is not None:
+                hidden_states = hidden_states + controlnet_res_stack[block_id]
+
+        # Single Blocks
+        hidden_states = torch.cat([prompt_emb, hidden_states], dim=1)
+        num_joint_blocks = len(dit.blocks)
+        for block_id, block in enumerate(dit.single_blocks):
+            hidden_states, prompt_emb = block(
+                hidden_states,
+                prompt_emb,
+                conditioning,
+                image_rotary_emb,
+                attention_mask,
+                ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id + num_joint_blocks, None)
+            )
+            # ControlNet
+            if controlnet is not None and controlnet_frames is not None:
+                hidden_states[:, prompt_emb.shape[1]:] = hidden_states[:, prompt_emb.shape[1]:] + controlnet_single_res_stack[block_id]
+        hidden_states = hidden_states[:, prompt_emb.shape[1]:]
+
+        if tea_cache is not None:
+            tea_cache.store(hidden_states)
+
+    hidden_states = dit.final_norm_out(hidden_states, conditioning)
+    hidden_states = dit.final_proj_out(hidden_states)
+    hidden_states = dit.unpatchify(hidden_states, height, width)
+
+    return hidden_states

diffsynth/pipelines/hunyuan_image.py

@@ -125,7 +125,7 @@ class ImageSizeManager:
 class HunyuanDiTImagePipeline(BasePipeline):
 
     def __init__(self, device="cuda", torch_dtype=torch.float16):
-        super().__init__(device=device, torch_dtype=torch_dtype)
+        super().__init__(device=device, torch_dtype=torch_dtype, height_division_factor=16, width_division_factor=16)
         self.scheduler = EnhancedDDIMScheduler(prediction_type="v_prediction", beta_start=0.00085, beta_end=0.03)
         self.prompter = HunyuanDiTPrompter()
         self.image_size_manager = ImageSizeManager()
@@ -135,6 +135,7 @@ class HunyuanDiTImagePipeline(BasePipeline):
         self.dit: HunyuanDiT = None
         self.vae_decoder: SDXLVAEDecoder = None
         self.vae_encoder: SDXLVAEEncoder = None
+        self.model_names = ['text_encoder', 'text_encoder_t5', 'dit', 'vae_decoder', 'vae_encoder']
 
 
     def denoising_model(self):
@@ -153,9 +154,9 @@ class HunyuanDiTImagePipeline(BasePipeline):
 
 
     @staticmethod
-    def from_model_manager(model_manager: ModelManager, prompt_refiner_classes=[]):
+    def from_model_manager(model_manager: ModelManager, prompt_refiner_classes=[], device=None):
         pipe = HunyuanDiTImagePipeline(
-            device=model_manager.device,
+            device=model_manager.device if device is None else device,
             torch_dtype=model_manager.torch_dtype,
         )
         pipe.fetch_models(model_manager, prompt_refiner_classes)
@@ -209,6 +210,9 @@ class HunyuanDiTImagePipeline(BasePipeline):
     def __call__(
         self,
         prompt,
+        local_prompts=[],
+        masks=[],
+        mask_scales=[],
         negative_prompt="",
         cfg_scale=7.5,
         clip_skip=1,
@@ -222,15 +226,19 @@ class HunyuanDiTImagePipeline(BasePipeline):
         tiled=False,
         tile_size=64,
         tile_stride=32,
+        seed=None,
         progress_bar_cmd=tqdm,
         progress_bar_st=None,
     ):
+        height, width = self.check_resize_height_width(height, width)
+        
         # Prepare scheduler
         self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
 
         # Prepare latent tensors
-        noise = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype)
+        noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype)
         if input_image is not None:
+            self.load_models_to_device(['vae_encoder'])
             image = self.preprocess_image(input_image).to(device=self.device, dtype=torch.float32)
             latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(self.torch_dtype)
             latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
@@ -238,21 +246,24 @@ class HunyuanDiTImagePipeline(BasePipeline):
             latents = noise.clone()
 
         # Encode prompts
+        self.load_models_to_device(['text_encoder', 'text_encoder_t5'])
         prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True)
         if cfg_scale != 1.0:
             prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True)
+        prompt_emb_locals = [self.encode_prompt(prompt_local, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True) for prompt_local in local_prompts]
 
         # Prepare positional id
         extra_input = self.prepare_extra_input(latents, tiled, tile_size)
 
         # Denoise
+        self.load_models_to_device(['dit'])
         for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
             timestep = torch.tensor([timestep]).to(dtype=self.torch_dtype, device=self.device)
 
             # Positive side
-            noise_pred_posi = self.dit(
+            inference_callback = lambda prompt_emb_posi: self.dit(latents, timestep=timestep, **prompt_emb_posi, **extra_input)
-                latents, timestep=timestep, **prompt_emb_posi, **extra_input,
+            noise_pred_posi = self.control_noise_via_local_prompts(prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback)
-            )
+            
             if cfg_scale != 1.0:
                 # Negative side
                 noise_pred_nega = self.dit(
@@ -269,6 +280,9 @@ class HunyuanDiTImagePipeline(BasePipeline):
                 progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
         
         # Decode image
+        self.load_models_to_device(['vae_decoder'])
         image = self.decode_image(latents.to(torch.float32), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
 
+        # Offload all models
+        self.load_models_to_device([])
         return image

diffsynth/pipelines/hunyuan_video.py

@@ -0,0 +1,265 @@
+from ..models import ModelManager, SD3TextEncoder1, HunyuanVideoVAEDecoder, HunyuanVideoVAEEncoder
+from ..models.hunyuan_video_dit import HunyuanVideoDiT
+from ..models.hunyuan_video_text_encoder import HunyuanVideoLLMEncoder
+from ..schedulers.flow_match import FlowMatchScheduler
+from .base import BasePipeline
+from ..prompters import HunyuanVideoPrompter
+import torch
+from einops import rearrange
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+
+
+
+class HunyuanVideoPipeline(BasePipeline):
+
+    def __init__(self, device="cuda", torch_dtype=torch.float16):
+        super().__init__(device=device, torch_dtype=torch_dtype)
+        self.scheduler = FlowMatchScheduler(shift=7.0, sigma_min=0.0, extra_one_step=True)
+        self.prompter = HunyuanVideoPrompter()
+        self.text_encoder_1: SD3TextEncoder1 = None
+        self.text_encoder_2: HunyuanVideoLLMEncoder = None
+        self.dit: HunyuanVideoDiT = None
+        self.vae_decoder: HunyuanVideoVAEDecoder = None
+        self.vae_encoder: HunyuanVideoVAEEncoder = None
+        self.model_names = ['text_encoder_1', 'text_encoder_2', 'dit', 'vae_decoder', 'vae_encoder']
+        self.vram_management = False
+
+
+    def enable_vram_management(self):
+        self.vram_management = True
+        self.enable_cpu_offload()
+        self.text_encoder_2.enable_auto_offload(dtype=self.torch_dtype, device=self.device)
+        self.dit.enable_auto_offload(dtype=self.torch_dtype, device=self.device)
+
+
+    def fetch_models(self, model_manager: ModelManager):
+        self.text_encoder_1 = model_manager.fetch_model("sd3_text_encoder_1")
+        self.text_encoder_2 = model_manager.fetch_model("hunyuan_video_text_encoder_2")
+        self.dit = model_manager.fetch_model("hunyuan_video_dit")
+        self.vae_decoder = model_manager.fetch_model("hunyuan_video_vae_decoder")
+        self.vae_encoder = model_manager.fetch_model("hunyuan_video_vae_encoder")
+        self.prompter.fetch_models(self.text_encoder_1, self.text_encoder_2)
+
+
+    @staticmethod
+    def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None, enable_vram_management=True):
+        if device is None: device = model_manager.device
+        if torch_dtype is None: torch_dtype = model_manager.torch_dtype
+        pipe = HunyuanVideoPipeline(device=device, torch_dtype=torch_dtype)
+        pipe.fetch_models(model_manager)
+        if enable_vram_management:
+            pipe.enable_vram_management()
+        return pipe
+
+
+    def encode_prompt(self, prompt, positive=True, clip_sequence_length=77, llm_sequence_length=256):
+        prompt_emb, pooled_prompt_emb, text_mask = self.prompter.encode_prompt(
+            prompt, device=self.device, positive=positive, clip_sequence_length=clip_sequence_length, llm_sequence_length=llm_sequence_length
+        )
+        return {"prompt_emb": prompt_emb, "pooled_prompt_emb": pooled_prompt_emb, "text_mask": text_mask}
+
+
+    def prepare_extra_input(self, latents=None, guidance=1.0):
+        freqs_cos, freqs_sin = self.dit.prepare_freqs(latents)
+        guidance = torch.Tensor([guidance] * latents.shape[0]).to(device=latents.device, dtype=latents.dtype)
+        return {"freqs_cos": freqs_cos, "freqs_sin": freqs_sin, "guidance": guidance}
+
+
+    def tensor2video(self, frames):
+        frames = rearrange(frames, "C T H W -> T H W C")
+        frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8)
+        frames = [Image.fromarray(frame) for frame in frames]
+        return frames
+
+
+    def encode_video(self, frames, tile_size=(17, 30, 30), tile_stride=(12, 20, 20)):
+        tile_size = ((tile_size[0] - 1) * 4 + 1, tile_size[1] * 8, tile_size[2] * 8)
+        tile_stride = (tile_stride[0] * 4, tile_stride[1] * 8, tile_stride[2] * 8)
+        latents = self.vae_encoder.encode_video(frames, tile_size=tile_size, tile_stride=tile_stride)
+        return latents
+
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt,
+        negative_prompt="",
+        input_video=None,
+        denoising_strength=1.0,
+        seed=None,
+        rand_device=None,
+        height=720,
+        width=1280,
+        num_frames=129,
+        embedded_guidance=6.0,
+        cfg_scale=1.0,
+        num_inference_steps=30,
+        tea_cache_l1_thresh=None,
+        tile_size=(17, 30, 30),
+        tile_stride=(12, 20, 20),
+        step_processor=None,
+        progress_bar_cmd=lambda x: x,
+        progress_bar_st=None,
+    ):
+        # Tiler parameters
+        tiler_kwargs = {"tile_size": tile_size, "tile_stride": tile_stride}
+        
+        # Scheduler
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
+
+        # Initialize noise
+        rand_device = self.device if rand_device is None else rand_device
+        noise = self.generate_noise((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), seed=seed, device=rand_device, dtype=self.torch_dtype).to(self.device)
+        if input_video is not None:
+            self.load_models_to_device(['vae_encoder'])
+            input_video = self.preprocess_images(input_video)
+            input_video = torch.stack(input_video, dim=2)
+            latents = self.encode_video(input_video, **tiler_kwargs).to(dtype=self.torch_dtype, device=self.device)
+            latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
+        else:
+            latents = noise
+        
+        # Encode prompts
+        self.load_models_to_device(["text_encoder_1"] if self.vram_management else ["text_encoder_1", "text_encoder_2"])
+        prompt_emb_posi = self.encode_prompt(prompt, positive=True)
+        if cfg_scale != 1.0:
+            prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False)
+
+        # Extra input
+        extra_input = self.prepare_extra_input(latents, guidance=embedded_guidance)
+
+        # TeaCache
+        tea_cache_kwargs = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh) if tea_cache_l1_thresh is not None else None}
+
+        # Denoise
+        self.load_models_to_device([] if self.vram_management else ["dit"])
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            timestep = timestep.unsqueeze(0).to(self.device)
+            print(f"Step {progress_id + 1} / {len(self.scheduler.timesteps)}")
+
+            # Inference
+            with torch.autocast(device_type=self.device, dtype=self.torch_dtype):
+                noise_pred_posi = lets_dance_hunyuan_video(self.dit, latents, timestep, **prompt_emb_posi, **extra_input, **tea_cache_kwargs)
+                if cfg_scale != 1.0:
+                    noise_pred_nega = lets_dance_hunyuan_video(self.dit, latents, timestep, **prompt_emb_nega, **extra_input)
+                    noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
+                else:
+                    noise_pred = noise_pred_posi
+
+            # (Experimental feature, may be removed in the future)
+            if step_processor is not None:
+                self.load_models_to_device(['vae_decoder'])
+                rendered_frames = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents, to_final=True)
+                rendered_frames = self.vae_decoder.decode_video(rendered_frames, **tiler_kwargs)
+                rendered_frames = self.tensor2video(rendered_frames[0])
+                rendered_frames = step_processor(rendered_frames, original_frames=input_video)
+                self.load_models_to_device(['vae_encoder'])
+                rendered_frames = self.preprocess_images(rendered_frames)
+                rendered_frames = torch.stack(rendered_frames, dim=2)
+                target_latents = self.encode_video(rendered_frames).to(dtype=self.torch_dtype, device=self.device)
+                noise_pred = self.scheduler.return_to_timestep(self.scheduler.timesteps[progress_id], latents, target_latents)
+                self.load_models_to_device([] if self.vram_management else ["dit"])
+
+            # Scheduler
+            latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents)
+
+        # Decode
+        self.load_models_to_device(['vae_decoder'])
+        frames = self.vae_decoder.decode_video(latents, **tiler_kwargs)
+        self.load_models_to_device([])
+        frames = self.tensor2video(frames[0])
+
+        return frames
+
+
+
+class TeaCache:
+    def __init__(self, num_inference_steps, rel_l1_thresh):
+        self.num_inference_steps = num_inference_steps
+        self.step = 0
+        self.accumulated_rel_l1_distance = 0
+        self.previous_modulated_input = None
+        self.rel_l1_thresh = rel_l1_thresh
+        self.previous_residual = None
+        self.previous_hidden_states = None
+
+    def check(self, dit: HunyuanVideoDiT, img, vec):
+        img_ = img.clone()
+        vec_ = vec.clone()
+        img_mod1_shift, img_mod1_scale, _, _, _, _ = dit.double_blocks[0].component_a.mod(vec_).chunk(6, dim=-1)
+        normed_inp = dit.double_blocks[0].component_a.norm1(img_)
+        modulated_inp = normed_inp * (1 + img_mod1_scale.unsqueeze(1)) + img_mod1_shift.unsqueeze(1)
+        if self.step == 0 or self.step == self.num_inference_steps - 1:
+            should_calc = True
+            self.accumulated_rel_l1_distance = 0
+        else: 
+            coefficients = [7.33226126e+02, -4.01131952e+02,  6.75869174e+01, -3.14987800e+00, 9.61237896e-02]
+            rescale_func = np.poly1d(coefficients)
+            self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item())
+            if self.accumulated_rel_l1_distance < self.rel_l1_thresh:
+                should_calc = False
+            else:
+                should_calc = True
+                self.accumulated_rel_l1_distance = 0
+        self.previous_modulated_input = modulated_inp 
+        self.step += 1
+        if self.step == self.num_inference_steps:
+            self.step = 0
+        if should_calc:
+            self.previous_hidden_states = img.clone()
+        return not should_calc
+    
+    def store(self, hidden_states):
+        self.previous_residual = hidden_states - self.previous_hidden_states
+        self.previous_hidden_states = None
+
+    def update(self, hidden_states):
+        hidden_states = hidden_states + self.previous_residual
+        return hidden_states
+
+
+
+def lets_dance_hunyuan_video(
+    dit: HunyuanVideoDiT,
+    x: torch.Tensor,
+    t: torch.Tensor,
+    prompt_emb: torch.Tensor = None,
+    text_mask: torch.Tensor = None,
+    pooled_prompt_emb: torch.Tensor = None,
+    freqs_cos: torch.Tensor = None,
+    freqs_sin: torch.Tensor = None,
+    guidance: torch.Tensor = None,
+    tea_cache: TeaCache = None,
+    **kwargs
+):
+    B, C, T, H, W = x.shape
+
+    vec = dit.time_in(t, dtype=torch.float32) + dit.vector_in(pooled_prompt_emb) + dit.guidance_in(guidance * 1000, dtype=torch.float32)
+    img = dit.img_in(x)
+    txt = dit.txt_in(prompt_emb, t, text_mask)
+
+    # TeaCache
+    if tea_cache is not None:
+        tea_cache_update = tea_cache.check(dit, img, vec)
+    else:
+        tea_cache_update = False
+
+    if tea_cache_update:
+        print("TeaCache skip forward.")
+        img = tea_cache.update(img)
+    else:
+        for block in tqdm(dit.double_blocks, desc="Double stream blocks"):
+            img, txt = block(img, txt, vec, (freqs_cos, freqs_sin))
+        
+        x = torch.concat([img, txt], dim=1)
+        for block in tqdm(dit.single_blocks, desc="Single stream blocks"):
+            x = block(x, vec, (freqs_cos, freqs_sin))
+        img = x[:, :-256]
+
+        if tea_cache is not None:
+            tea_cache.store(img)
+    img = dit.final_layer(img, vec)
+    img = dit.unpatchify(img, T=T//1, H=H//2, W=W//2)
+    return img

diffsynth/pipelines/omnigen_image.py

@@ -0,0 +1,289 @@
+from ..models.omnigen import OmniGenTransformer
+from ..models.sdxl_vae_encoder import SDXLVAEEncoder
+from ..models.sdxl_vae_decoder import SDXLVAEDecoder
+from ..models.model_manager import ModelManager
+from ..prompters.omnigen_prompter import OmniGenPrompter
+from ..schedulers import FlowMatchScheduler
+from .base import BasePipeline
+from typing import Optional, Dict, Any, Tuple, List
+from transformers.cache_utils import DynamicCache
+import torch, os
+from tqdm import tqdm
+
+
+
+class OmniGenCache(DynamicCache):
+    def __init__(self, 
+                    num_tokens_for_img: int, offload_kv_cache: bool=False) -> None:
+        if not torch.cuda.is_available():
+            print("No avaliable GPU, offload_kv_cache wiil be set to False, which will result in large memory usage and time cost when input multiple images!!!")
+            offload_kv_cache = False
+            raise RuntimeError("OffloadedCache can only be used with a GPU")
+        super().__init__()
+        self.original_device = []
+        self.prefetch_stream = torch.cuda.Stream()
+        self.num_tokens_for_img = num_tokens_for_img
+        self.offload_kv_cache = offload_kv_cache
+
+    def prefetch_layer(self, layer_idx: int):
+        "Starts prefetching the next layer cache"
+        if layer_idx < len(self):
+            with torch.cuda.stream(self.prefetch_stream):
+                # Prefetch next layer tensors to GPU
+                device = self.original_device[layer_idx]
+                self.key_cache[layer_idx] = self.key_cache[layer_idx].to(device, non_blocking=True)
+                self.value_cache[layer_idx] = self.value_cache[layer_idx].to(device, non_blocking=True)
+
+    
+    def evict_previous_layer(self, layer_idx: int):
+        "Moves the previous layer cache to the CPU"
+        if len(self) > 2:
+            # We do it on the default stream so it occurs after all earlier computations on these tensors are done
+            if layer_idx == 0: 
+                prev_layer_idx = -1
+            else:
+                prev_layer_idx = (layer_idx - 1) % len(self)
+            self.key_cache[prev_layer_idx] = self.key_cache[prev_layer_idx].to("cpu", non_blocking=True)
+            self.value_cache[prev_layer_idx] = self.value_cache[prev_layer_idx].to("cpu", non_blocking=True)
+
+
+    def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]:
+        "Gets the cache for this layer to the device. Prefetches the next and evicts the previous layer."
+        if layer_idx < len(self):
+            if self.offload_kv_cache:
+                # Evict the previous layer if necessary
+                torch.cuda.current_stream().synchronize()
+                self.evict_previous_layer(layer_idx)
+                # Load current layer cache to its original device if not already there
+                original_device = self.original_device[layer_idx]
+                # self.prefetch_stream.synchronize(original_device)
+                torch.cuda.synchronize(self.prefetch_stream)
+                key_tensor = self.key_cache[layer_idx]
+                value_tensor = self.value_cache[layer_idx]
+                
+                # Prefetch the next layer
+                self.prefetch_layer((layer_idx + 1) % len(self))
+            else:
+                key_tensor = self.key_cache[layer_idx]
+                value_tensor = self.value_cache[layer_idx]
+            return (key_tensor, value_tensor)
+        else:
+            raise KeyError(f"Cache only has {len(self)} layers, attempted to access layer with index {layer_idx}")
+        
+       
+    def update(
+        self,
+        key_states: torch.Tensor, 
+        value_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
+        Parameters:
+            key_states (`torch.Tensor`):
+                The new key states to cache.
+            value_states (`torch.Tensor`):
+                The new value states to cache.
+            layer_idx (`int`):
+                The index of the layer to cache the states for.
+            cache_kwargs (`Dict[str, Any]`, `optional`):
+                Additional arguments for the cache subclass. No additional arguments are used in `OffloadedCache`.
+        Return:
+            A tuple containing the updated key and value states.
+        """
+        # Update the cache
+        if len(self.key_cache) < layer_idx:
+            raise ValueError("OffloadedCache does not support model usage where layers are skipped. Use DynamicCache.")
+        elif len(self.key_cache) == layer_idx:
+            # only cache the states for condition tokens
+            key_states = key_states[..., :-(self.num_tokens_for_img+1), :]
+            value_states = value_states[..., :-(self.num_tokens_for_img+1), :]
+
+             # Update the number of seen tokens
+            if layer_idx == 0:
+                self._seen_tokens += key_states.shape[-2]
+                
+            self.key_cache.append(key_states)
+            self.value_cache.append(value_states)
+            self.original_device.append(key_states.device)
+            if self.offload_kv_cache:
+                self.evict_previous_layer(layer_idx)
+            return self.key_cache[layer_idx], self.value_cache[layer_idx]
+        else:
+            # only cache the states for condition tokens
+            key_tensor, value_tensor = self[layer_idx]
+            k = torch.cat([key_tensor, key_states], dim=-2)
+            v = torch.cat([value_tensor, value_states], dim=-2)
+            return k, v
+
+
+
+class OmnigenImagePipeline(BasePipeline):
+
+    def __init__(self, device="cuda", torch_dtype=torch.float16):
+        super().__init__(device=device, torch_dtype=torch_dtype)
+        self.scheduler = FlowMatchScheduler(num_train_timesteps=1, shift=1, inverse_timesteps=True, sigma_min=0, sigma_max=1)
+        # models
+        self.vae_decoder: SDXLVAEDecoder = None
+        self.vae_encoder: SDXLVAEEncoder = None
+        self.transformer: OmniGenTransformer = None
+        self.prompter: OmniGenPrompter = None
+        self.model_names = ['transformer', 'vae_decoder', 'vae_encoder']
+
+
+    def denoising_model(self):
+        return self.transformer
+
+
+    def fetch_models(self, model_manager: ModelManager, prompt_refiner_classes=[]):
+        # Main models
+        self.transformer, model_path = model_manager.fetch_model("omnigen_transformer", require_model_path=True)
+        self.vae_decoder = model_manager.fetch_model("sdxl_vae_decoder")
+        self.vae_encoder = model_manager.fetch_model("sdxl_vae_encoder")
+        self.prompter = OmniGenPrompter.from_pretrained(os.path.dirname(model_path))
+
+
+    @staticmethod
+    def from_model_manager(model_manager: ModelManager, prompt_refiner_classes=[], device=None):
+        pipe = OmnigenImagePipeline(
+            device=model_manager.device if device is None else device,
+            torch_dtype=model_manager.torch_dtype,
+        )
+        pipe.fetch_models(model_manager, prompt_refiner_classes=[])
+        return pipe
+    
+
+    def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32):
+        latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+        return latents
+
+    
+    def encode_images(self, images, tiled=False, tile_size=64, tile_stride=32):
+        latents = [self.encode_image(image.to(device=self.device), tiled, tile_size, tile_stride).to(self.torch_dtype) for image in images]
+        return latents
+    
+
+    def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32):
+        image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+        image = self.vae_output_to_image(image)
+        return image
+    
+
+    def encode_prompt(self, prompt, clip_skip=1, positive=True):
+        prompt_emb = self.prompter.encode_prompt(prompt, clip_skip=clip_skip, device=self.device, positive=positive)
+        return {"encoder_hidden_states": prompt_emb}
+    
+
+    def prepare_extra_input(self, latents=None):
+        return {}
+    
+
+    def crop_position_ids_for_cache(self, position_ids, num_tokens_for_img):
+        if isinstance(position_ids, list):
+            for i in range(len(position_ids)):
+                position_ids[i] = position_ids[i][:, -(num_tokens_for_img+1):]
+        else:
+            position_ids = position_ids[:, -(num_tokens_for_img+1):]
+        return position_ids
+    
+    
+    def crop_attention_mask_for_cache(self, attention_mask, num_tokens_for_img):
+        if isinstance(attention_mask, list):
+            return [x[..., -(num_tokens_for_img+1):, :] for x in attention_mask]
+        return attention_mask[..., -(num_tokens_for_img+1):, :]
+    
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt,
+        reference_images=[],
+        cfg_scale=2.0,
+        image_cfg_scale=2.0,
+        use_kv_cache=True,
+        offload_kv_cache=True,
+        input_image=None,
+        denoising_strength=1.0,
+        height=1024,
+        width=1024,
+        num_inference_steps=20,
+        tiled=False,
+        tile_size=64,
+        tile_stride=32,
+        seed=None,
+        progress_bar_cmd=tqdm,
+        progress_bar_st=None,
+    ):
+        height, width = self.check_resize_height_width(height, width)
+        
+        # Tiler parameters
+        tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}
+
+        # Prepare scheduler
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
+
+        # Prepare latent tensors
+        if input_image is not None:
+            self.load_models_to_device(['vae_encoder'])
+            image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype)
+            latents = self.encode_image(image, **tiler_kwargs)
+            noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype)
+            latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
+        else:
+            latents = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype)
+        latents = latents.repeat(3, 1, 1, 1)
+
+        # Encode prompts
+        input_data = self.prompter(prompt, reference_images, height=height, width=width, use_img_cfg=True, separate_cfg_input=True, use_input_image_size_as_output=False)
+
+        # Encode images
+        reference_latents = [self.encode_images(images, **tiler_kwargs) for images in input_data['input_pixel_values']]
+        
+        # Pack all parameters
+        model_kwargs = dict(input_ids=[input_ids.to(self.device) for input_ids in input_data['input_ids']], 
+            input_img_latents=reference_latents, 
+            input_image_sizes=input_data['input_image_sizes'], 
+            attention_mask=[attention_mask.to(self.device) for attention_mask in input_data["attention_mask"]], 
+            position_ids=[position_ids.to(self.device) for position_ids in input_data["position_ids"]], 
+            cfg_scale=cfg_scale,
+            img_cfg_scale=image_cfg_scale,
+            use_img_cfg=True,
+            use_kv_cache=use_kv_cache,
+            offload_model=False,
+        )
+        
+        # Denoise
+        self.load_models_to_device(['transformer'])
+        cache = [OmniGenCache(latents.size(-1)*latents.size(-2) // 4, offload_kv_cache) for _ in range(len(model_kwargs['input_ids']))] if use_kv_cache else None
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            timestep = timestep.unsqueeze(0).repeat(latents.shape[0]).to(self.device)
+
+            # Forward
+            noise_pred, cache = self.transformer.forward_with_separate_cfg(latents, timestep, past_key_values=cache, **model_kwargs)
+
+            # Scheduler
+            latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents)
+
+            # Update KV cache
+            if progress_id == 0 and use_kv_cache:
+                num_tokens_for_img = latents.size(-1)*latents.size(-2) // 4
+                if isinstance(cache, list):
+                    model_kwargs['input_ids'] = [None] * len(cache)
+                else:
+                    model_kwargs['input_ids'] = None
+                model_kwargs['position_ids'] = self.crop_position_ids_for_cache(model_kwargs['position_ids'], num_tokens_for_img)
+                model_kwargs['attention_mask'] = self.crop_attention_mask_for_cache(model_kwargs['attention_mask'], num_tokens_for_img)
+
+            # UI
+            if progress_bar_st is not None:
+                progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
+        
+        # Decode image
+        del cache
+        self.load_models_to_device(['vae_decoder'])
+        image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+
+        # offload all models
+        self.load_models_to_device([])
+        return image

diffsynth/pipelines/sd3_image.py

@@ -10,7 +10,7 @@ from tqdm import tqdm
 class SD3ImagePipeline(BasePipeline):
 
     def __init__(self, device="cuda", torch_dtype=torch.float16):
-        super().__init__(device=device, torch_dtype=torch_dtype)
+        super().__init__(device=device, torch_dtype=torch_dtype, height_division_factor=16, width_division_factor=16)
         self.scheduler = FlowMatchScheduler()
         self.prompter = SD3Prompter()
         # models
@@ -20,6 +20,7 @@ class SD3ImagePipeline(BasePipeline):
         self.dit: SD3DiT = None
         self.vae_decoder: SD3VAEDecoder = None
         self.vae_encoder: SD3VAEEncoder = None
+        self.model_names = ['text_encoder_1', 'text_encoder_2', 'text_encoder_3', 'dit', 'vae_decoder', 'vae_encoder']
 
 
     def denoising_model(self):
@@ -29,7 +30,6 @@ class SD3ImagePipeline(BasePipeline):
     def fetch_models(self, model_manager: ModelManager, prompt_refiner_classes=[]):
         self.text_encoder_1 = model_manager.fetch_model("sd3_text_encoder_1")
         self.text_encoder_2 = model_manager.fetch_model("sd3_text_encoder_2")
-        if "sd3_text_encoder_3" in model_manager.model:
         self.text_encoder_3 = model_manager.fetch_model("sd3_text_encoder_3")
         self.dit = model_manager.fetch_model("sd3_dit")
         self.vae_decoder = model_manager.fetch_model("sd3_vae_decoder")
@@ -39,9 +39,9 @@ class SD3ImagePipeline(BasePipeline):
 
 
     @staticmethod
-    def from_model_manager(model_manager: ModelManager, prompt_refiner_classes=[]):
+    def from_model_manager(model_manager: ModelManager, prompt_refiner_classes=[], device=None):
         pipe = SD3ImagePipeline(
-            device=model_manager.device,
+            device=model_manager.device if device is None else device,
             torch_dtype=model_manager.torch_dtype,
         )
         pipe.fetch_models(model_manager, prompt_refiner_classes)
@@ -59,9 +59,9 @@ class SD3ImagePipeline(BasePipeline):
         return image
     
 
-    def encode_prompt(self, prompt, positive=True):
+    def encode_prompt(self, prompt, positive=True, t5_sequence_length=77):
         prompt_emb, pooled_prompt_emb = self.prompter.encode_prompt(
-            prompt, device=self.device, positive=positive
+            prompt, device=self.device, positive=positive, t5_sequence_length=t5_sequence_length
         )
         return {"prompt_emb": prompt_emb, "pooled_prompt_emb": pooled_prompt_emb}
     
@@ -74,6 +74,9 @@ class SD3ImagePipeline(BasePipeline):
     def __call__(
         self,
         prompt,
+        local_prompts=[],
+        masks=[],
+        mask_scales=[],
         negative_prompt="",
         cfg_scale=7.5,
         input_image=None,
@@ -81,12 +84,16 @@ class SD3ImagePipeline(BasePipeline):
         height=1024,
         width=1024,
         num_inference_steps=20,
+        t5_sequence_length=77,
         tiled=False,
         tile_size=128,
         tile_stride=64,
+        seed=None,
         progress_bar_cmd=tqdm,
         progress_bar_st=None,
     ):
+        height, width = self.check_resize_height_width(height, width)
+        
         # Tiler parameters
         tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}
 
@@ -95,25 +102,30 @@ class SD3ImagePipeline(BasePipeline):
 
         # Prepare latent tensors
         if input_image is not None:
+            self.load_models_to_device(['vae_encoder'])
             image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype)
             latents = self.encode_image(image, **tiler_kwargs)
-            noise = torch.randn((1, 16, height//8, width//8), device=self.device, dtype=self.torch_dtype)
+            noise = self.generate_noise((1, 16, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype)
             latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
         else:
-            latents = torch.randn((1, 16, height//8, width//8), device=self.device, dtype=self.torch_dtype)
+            latents = self.generate_noise((1, 16, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype)
 
         # Encode prompts
-        prompt_emb_posi = self.encode_prompt(prompt, positive=True)
+        self.load_models_to_device(['text_encoder_1', 'text_encoder_2', 'text_encoder_3'])
-        prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False)
+        prompt_emb_posi = self.encode_prompt(prompt, positive=True, t5_sequence_length=t5_sequence_length)
+        prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False, t5_sequence_length=t5_sequence_length)
+        prompt_emb_locals = [self.encode_prompt(prompt_local, t5_sequence_length=t5_sequence_length) for prompt_local in local_prompts]
 
         # Denoise
+        self.load_models_to_device(['dit'])
         for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
             timestep = timestep.unsqueeze(0).to(self.device)
 
             # Classifier-free guidance
-            noise_pred_posi = self.dit(
+            inference_callback = lambda prompt_emb_posi: self.dit(
                 latents, timestep=timestep, **prompt_emb_posi, **tiler_kwargs,
             )
+            noise_pred_posi = self.control_noise_via_local_prompts(prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback)
             noise_pred_nega = self.dit(
                 latents, timestep=timestep, **prompt_emb_nega, **tiler_kwargs,
             )
@@ -127,6 +139,9 @@ class SD3ImagePipeline(BasePipeline):
                 progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
         
         # Decode image
+        self.load_models_to_device(['vae_decoder'])
         image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
 
+        # offload all models
+        self.load_models_to_device([])
         return image

diffsynth/pipelines/sd_image.py

@@ -25,6 +25,7 @@ class SDImagePipeline(BasePipeline):
         self.controlnet: MultiControlNetManager = None
         self.ipadapter_image_encoder: IpAdapterCLIPImageEmbedder = None
         self.ipadapter: SDIpAdapter = None
+        self.model_names = ['text_encoder', 'unet', 'vae_decoder', 'vae_encoder', 'controlnet', 'ipadapter_image_encoder', 'ipadapter']
 
 
     def denoising_model(self):
@@ -57,9 +58,9 @@ class SDImagePipeline(BasePipeline):
 
 
     @staticmethod
-    def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]):
+    def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[], device=None):
         pipe = SDImagePipeline(
-            device=model_manager.device,
+            device=model_manager.device if device is None else device,
             torch_dtype=model_manager.torch_dtype,
         )
         pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes=[])
@@ -90,6 +91,9 @@ class SDImagePipeline(BasePipeline):
     def __call__(
         self,
         prompt,
+        local_prompts=[],
+        masks=[],
+        mask_scales=[],
         negative_prompt="",
         cfg_scale=7.5,
         clip_skip=1,
@@ -104,9 +108,12 @@ class SDImagePipeline(BasePipeline):
         tiled=False,
         tile_size=64,
         tile_stride=32,
+        seed=None,
         progress_bar_cmd=tqdm,
         progress_bar_st=None,
     ):
+        height, width = self.check_resize_height_width(height, width)
+        
         # Tiler parameters
         tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}
 
@@ -115,20 +122,25 @@ class SDImagePipeline(BasePipeline):
 
         # Prepare latent tensors
         if input_image is not None:
+            self.load_models_to_device(['vae_encoder'])
             image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype)
             latents = self.encode_image(image, **tiler_kwargs)
-            noise = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype)
+            noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype)
             latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
         else:
-            latents = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype)
+            latents = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype)
 
         # Encode prompts
+        self.load_models_to_device(['text_encoder'])
         prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, positive=True)
         prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, positive=False)
+        prompt_emb_locals = [self.encode_prompt(prompt_local, clip_skip=clip_skip, positive=True) for prompt_local in local_prompts]
 
         # IP-Adapter
         if ipadapter_images is not None:
+            self.load_models_to_device(['ipadapter_image_encoder'])
             ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images)
+            self.load_models_to_device(['ipadapter'])
             ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)}
             ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))}
         else:
@@ -136,6 +148,7 @@ class SDImagePipeline(BasePipeline):
 
         # Prepare ControlNets
         if controlnet_image is not None:
+            self.load_models_to_device(['controlnet'])
             controlnet_image = self.controlnet.process_image(controlnet_image).to(device=self.device, dtype=self.torch_dtype)
             controlnet_image = controlnet_image.unsqueeze(1)
             controlnet_kwargs = {"controlnet_frames": controlnet_image}
@@ -143,16 +156,18 @@ class SDImagePipeline(BasePipeline):
             controlnet_kwargs = {"controlnet_frames": None}
         
         # Denoise
+        self.load_models_to_device(['controlnet', 'unet'])
         for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
             timestep = timestep.unsqueeze(0).to(self.device)
 
             # Classifier-free guidance
-            noise_pred_posi = lets_dance(
+            inference_callback = lambda prompt_emb_posi: lets_dance(
                 self.unet, motion_modules=None, controlnet=self.controlnet,
                 sample=latents, timestep=timestep, 
                 **prompt_emb_posi, **controlnet_kwargs, **tiler_kwargs, **ipadapter_kwargs_list_posi,
                 device=self.device,
             )
+            noise_pred_posi = self.control_noise_via_local_prompts(prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback)
             noise_pred_nega = lets_dance(
                 self.unet, motion_modules=None, controlnet=self.controlnet,
                 sample=latents, timestep=timestep, **prompt_emb_nega, **controlnet_kwargs, **tiler_kwargs, **ipadapter_kwargs_list_nega,
@@ -168,6 +183,9 @@ class SDImagePipeline(BasePipeline):
                 progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
         
         # Decode image
+        self.load_models_to_device(['vae_decoder'])
         image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
 
+        # offload all models
+        self.load_models_to_device([])
         return image

diffsynth/pipelines/sd_video.py

@@ -166,9 +166,12 @@ class SDVideoPipeline(SDImagePipeline):
         tiled=False,
         tile_size=64,
         tile_stride=32,
+        seed=None,
         progress_bar_cmd=tqdm,
         progress_bar_st=None,
     ):
+        height, width = self.check_resize_height_width(height, width)
+        
         # Tiler parameters, batch size ...
         tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}
         other_kwargs = {
@@ -182,9 +185,9 @@ class SDVideoPipeline(SDImagePipeline):
 
         # Prepare latent tensors
         if self.motion_modules is None:
-            noise = torch.randn((1, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype).repeat(num_frames, 1, 1, 1)
+            noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype).repeat(num_frames, 1, 1, 1)
         else:
-            noise = torch.randn((num_frames, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype)
+            noise = self.generate_noise((num_frames, 4, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype)
         if input_frames is None or denoising_strength == 1.0:
             latents = noise
         else:

diffsynth/pipelines/sdxl_image.py

@@ -9,6 +9,7 @@ from .dancer import lets_dance_xl
 from typing import List
 import torch
 from tqdm import tqdm
+from einops import repeat
 
 
 
@@ -25,9 +26,10 @@ class SDXLImagePipeline(BasePipeline):
         self.unet: SDXLUNet = None
         self.vae_decoder: SDXLVAEDecoder = None
         self.vae_encoder: SDXLVAEEncoder = None
-        # self.controlnet: MultiControlNetManager = None (TODO)
+        self.controlnet: MultiControlNetManager = None
         self.ipadapter_image_encoder: IpAdapterXLCLIPImageEmbedder = None
         self.ipadapter: SDXLIpAdapter = None
+        self.model_names = ['text_encoder', 'text_encoder_2', 'text_encoder_kolors', 'unet', 'vae_decoder', 'vae_encoder', 'controlnet', 'ipadapter_image_encoder', 'ipadapter']
 
 
     def denoising_model(self):
@@ -43,7 +45,16 @@ class SDXLImagePipeline(BasePipeline):
         self.vae_decoder = model_manager.fetch_model("sdxl_vae_decoder")
         self.vae_encoder = model_manager.fetch_model("sdxl_vae_encoder")
 
-        # ControlNets (TODO)
+        # ControlNets
+        controlnet_units = []
+        for config in controlnet_config_units:
+            controlnet_unit = ControlNetUnit(
+                Annotator(config.processor_id, device=self.device),
+                model_manager.fetch_model("sdxl_controlnet", config.model_path),
+                config.scale
+            )
+            controlnet_units.append(controlnet_unit)
+        self.controlnet = MultiControlNetManager(controlnet_units)
 
         # IP-Adapters
         self.ipadapter = model_manager.fetch_model("sdxl_ipadapter")
@@ -61,9 +72,9 @@ class SDXLImagePipeline(BasePipeline):
 
 
     @staticmethod
-    def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]):
+    def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[], device=None):
         pipe = SDXLImagePipeline(
-            device=model_manager.device,
+            device=model_manager.device if device is None else device,
             torch_dtype=model_manager.torch_dtype,
         )
         pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes)
@@ -93,13 +104,17 @@ class SDXLImagePipeline(BasePipeline):
 
     def prepare_extra_input(self, latents=None):
         height, width = latents.shape[2] * 8, latents.shape[3] * 8
-        return {"add_time_id": torch.tensor([height, width, 0, 0, height, width], device=self.device)}
+        add_time_id = torch.tensor([height, width, 0, 0, height, width], device=self.device).repeat(latents.shape[0])
+        return {"add_time_id": add_time_id}
     
 
     @torch.no_grad()
     def __call__(
         self,
         prompt,
+        local_prompts=[],
+        masks=[],
+        mask_scales=[],
         negative_prompt="",
         cfg_scale=7.5,
         clip_skip=1,
@@ -116,9 +131,12 @@ class SDXLImagePipeline(BasePipeline):
         tiled=False,
         tile_size=64,
         tile_stride=32,
+        seed=None,
         progress_bar_cmd=tqdm,
         progress_bar_st=None,
     ):
+        height, width = self.check_resize_height_width(height, width)
+        
         # Tiler parameters
         tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}
 
@@ -127,16 +145,19 @@ class SDXLImagePipeline(BasePipeline):
 
         # Prepare latent tensors
         if input_image is not None:
+            self.load_models_to_device(['vae_encoder'])
             image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype)
             latents = self.encode_image(image, **tiler_kwargs)
-            noise = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype)
+            noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype)
             latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
         else:
-            latents = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype)
+            latents = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype)
 
         # Encode prompts
+        self.load_models_to_device(['text_encoder', 'text_encoder_2', 'text_encoder_kolors'])
         prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True)
         prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=False)
+        prompt_emb_locals = [self.encode_prompt(prompt_local, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True) for prompt_local in local_prompts]
 
         # IP-Adapter
         if ipadapter_images is not None:
@@ -144,32 +165,43 @@ class SDXLImagePipeline(BasePipeline):
                 self.ipadapter.set_less_adapter()
             else:
                 self.ipadapter.set_full_adapter()
+            self.load_models_to_device(['ipadapter_image_encoder'])
             ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images)
+            self.load_models_to_device(['ipadapter'])
             ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)}
             ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))}
         else:
             ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": {}}, {"ipadapter_kwargs_list": {}}
 
-        # Prepare ControlNets (TODO)
+        # Prepare ControlNets
+        if controlnet_image is not None:
+            self.load_models_to_device(['controlnet'])
+            controlnet_image = self.controlnet.process_image(controlnet_image).to(device=self.device, dtype=self.torch_dtype)
+            controlnet_image = controlnet_image.unsqueeze(1)
+            controlnet_kwargs = {"controlnet_frames": controlnet_image}
+        else:
             controlnet_kwargs = {"controlnet_frames": None}
 
         # Prepare extra input
         extra_input = self.prepare_extra_input(latents)
         
         # Denoise
+        self.load_models_to_device(['controlnet', 'unet'])
         for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
             timestep = timestep.unsqueeze(0).to(self.device)
 
             # Classifier-free guidance
-            noise_pred_posi = lets_dance_xl(
+            inference_callback = lambda prompt_emb_posi: lets_dance_xl(
-                self.unet, motion_modules=None, controlnet=None,
+                self.unet, motion_modules=None, controlnet=self.controlnet,
                 sample=latents, timestep=timestep, **extra_input,
                 **prompt_emb_posi, **controlnet_kwargs, **tiler_kwargs, **ipadapter_kwargs_list_posi,
                 device=self.device,
             )
+            noise_pred_posi = self.control_noise_via_local_prompts(prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback)
+
             if cfg_scale != 1.0:
                 noise_pred_nega = lets_dance_xl(
-                    self.unet, motion_modules=None, controlnet=None,
+                    self.unet, motion_modules=None, controlnet=self.controlnet,
                     sample=latents, timestep=timestep, **extra_input,
                     **prompt_emb_nega, **controlnet_kwargs, **tiler_kwargs, **ipadapter_kwargs_list_nega,
                     device=self.device,
@@ -186,6 +218,9 @@ class SDXLImagePipeline(BasePipeline):
                 progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
         
         # Decode image
+        self.load_models_to_device(['vae_decoder'])
         image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
 
+        # offload all models
+        self.load_models_to_device([])
         return image

diffsynth/pipelines/sdxl_video.py

@@ -120,9 +120,12 @@ class SDXLVideoPipeline(SDXLImagePipeline):
         tiled=False,
         tile_size=64,
         tile_stride=32,
+        seed=None,
         progress_bar_cmd=tqdm,
         progress_bar_st=None,
     ):
+        height, width = self.check_resize_height_width(height, width)
+        
         # Tiler parameters, batch size ...
         tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}
 
@@ -131,9 +134,9 @@ class SDXLVideoPipeline(SDXLImagePipeline):
 
         # Prepare latent tensors
         if self.motion_modules is None:
-            noise = torch.randn((1, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype).repeat(num_frames, 1, 1, 1)
+            noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype).repeat(num_frames, 1, 1, 1)
         else:
-            noise = torch.randn((num_frames, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype)
+            noise = self.generate_noise((num_frames, 4, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype)
         if input_frames is None or denoising_strength == 1.0:
             latents = noise
         else:

diffsynth/pipelines/step_video.py

@@ -0,0 +1,209 @@
+from ..models import ModelManager
+from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder
+from ..models.stepvideo_text_encoder import STEP1TextEncoder
+from ..models.stepvideo_dit import StepVideoModel
+from ..models.stepvideo_vae import StepVideoVAE
+from ..schedulers.flow_match import FlowMatchScheduler
+from .base import BasePipeline
+from ..prompters import StepVideoPrompter
+import torch
+from einops import rearrange
+import numpy as np
+from PIL import Image
+from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear
+from transformers.models.bert.modeling_bert import BertEmbeddings
+from ..models.stepvideo_dit import RMSNorm
+from ..models.stepvideo_vae import CausalConv, CausalConvAfterNorm, Upsample2D, BaseGroupNorm
+
+
+
+class StepVideoPipeline(BasePipeline):
+
+    def __init__(self, device="cuda", torch_dtype=torch.float16):
+        super().__init__(device=device, torch_dtype=torch_dtype)
+        self.scheduler = FlowMatchScheduler(sigma_min=0.0, extra_one_step=True, shift=13.0, reverse_sigmas=True, num_train_timesteps=1)
+        self.prompter = StepVideoPrompter()
+        self.text_encoder_1: HunyuanDiTCLIPTextEncoder = None
+        self.text_encoder_2: STEP1TextEncoder = None
+        self.dit: StepVideoModel = None
+        self.vae: StepVideoVAE = None
+        self.model_names = ['text_encoder_1', 'text_encoder_2', 'dit', 'vae']
+
+
+    def enable_vram_management(self, num_persistent_param_in_dit=None):
+        dtype = next(iter(self.text_encoder_1.parameters())).dtype
+        enable_vram_management(
+            self.text_encoder_1,
+            module_map = {
+                torch.nn.Linear: AutoWrappedLinear,
+                BertEmbeddings: AutoWrappedModule,
+                torch.nn.LayerNorm: AutoWrappedModule,
+            },
+            module_config = dict(
+                offload_dtype=dtype,
+                offload_device="cpu",
+                onload_dtype=dtype,
+                onload_device="cpu",
+                computation_dtype=torch.float32,
+                computation_device=self.device,
+            ),
+        )
+        dtype = next(iter(self.text_encoder_2.parameters())).dtype
+        enable_vram_management(
+            self.text_encoder_2,
+            module_map = {
+                torch.nn.Linear: AutoWrappedLinear,
+                RMSNorm: AutoWrappedModule,
+                torch.nn.Embedding: AutoWrappedModule,
+            },
+            module_config = dict(
+                offload_dtype=dtype,
+                offload_device="cpu",
+                onload_dtype=dtype,
+                onload_device="cpu",
+                computation_dtype=self.torch_dtype,
+                computation_device=self.device,
+            ),
+        )
+        dtype = next(iter(self.dit.parameters())).dtype
+        enable_vram_management(
+            self.dit,
+            module_map = {
+                torch.nn.Linear: AutoWrappedLinear,
+                torch.nn.Conv2d: AutoWrappedModule,
+                torch.nn.LayerNorm: AutoWrappedModule,
+                RMSNorm: AutoWrappedModule,
+            },
+            module_config = dict(
+                offload_dtype=dtype,
+                offload_device="cpu",
+                onload_dtype=dtype,
+                onload_device=self.device,
+                computation_dtype=self.torch_dtype,
+                computation_device=self.device,
+            ),
+            max_num_param=num_persistent_param_in_dit,
+            overflow_module_config = dict(
+                offload_dtype=dtype,
+                offload_device="cpu",
+                onload_dtype=dtype,
+                onload_device="cpu",
+                computation_dtype=self.torch_dtype,
+                computation_device=self.device,
+            ),
+        )
+        dtype = next(iter(self.vae.parameters())).dtype
+        enable_vram_management(
+            self.vae,
+            module_map = {
+                torch.nn.Linear: AutoWrappedLinear,
+                torch.nn.Conv3d: AutoWrappedModule,
+                CausalConv: AutoWrappedModule,
+                CausalConvAfterNorm: AutoWrappedModule,
+                Upsample2D: AutoWrappedModule,
+                BaseGroupNorm: AutoWrappedModule,
+            },
+            module_config = dict(
+                offload_dtype=dtype,
+                offload_device="cpu",
+                onload_dtype=dtype,
+                onload_device="cpu",
+                computation_dtype=self.torch_dtype,
+                computation_device=self.device,
+            ),
+        )
+        self.enable_cpu_offload()
+
+
+    def fetch_models(self, model_manager: ModelManager):
+        self.text_encoder_1 = model_manager.fetch_model("hunyuan_dit_clip_text_encoder")
+        self.text_encoder_2 = model_manager.fetch_model("stepvideo_text_encoder_2")
+        self.dit = model_manager.fetch_model("stepvideo_dit")
+        self.vae = model_manager.fetch_model("stepvideo_vae")
+        self.prompter.fetch_models(self.text_encoder_1, self.text_encoder_2)
+
+
+    @staticmethod
+    def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None):
+        if device is None: device = model_manager.device
+        if torch_dtype is None: torch_dtype = model_manager.torch_dtype
+        pipe = StepVideoPipeline(device=device, torch_dtype=torch_dtype)
+        pipe.fetch_models(model_manager)
+        return pipe
+
+
+    def encode_prompt(self, prompt, positive=True):
+        clip_embeds, llm_embeds, llm_mask = self.prompter.encode_prompt(prompt, device=self.device, positive=positive)
+        clip_embeds = clip_embeds.to(dtype=self.torch_dtype, device=self.device)
+        llm_embeds = llm_embeds.to(dtype=self.torch_dtype, device=self.device)
+        llm_mask = llm_mask.to(dtype=self.torch_dtype, device=self.device)
+        return {"encoder_hidden_states_2": clip_embeds, "encoder_hidden_states": llm_embeds, "encoder_attention_mask": llm_mask}
+
+
+    def tensor2video(self, frames):
+        frames = rearrange(frames, "C T H W -> T H W C")
+        frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8)
+        frames = [Image.fromarray(frame) for frame in frames]
+        return frames
+
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt,
+        negative_prompt="",
+        input_video=None,
+        denoising_strength=1.0,
+        seed=None,
+        rand_device="cpu",
+        height=544,
+        width=992,
+        num_frames=204,
+        cfg_scale=9.0,
+        num_inference_steps=30,
+        tiled=True,
+        tile_size=(34, 34),
+        tile_stride=(16, 16),
+        smooth_scale=0.6,
+        progress_bar_cmd=lambda x: x,
+        progress_bar_st=None,
+    ):
+        # Tiler parameters
+        tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}
+
+        # Scheduler
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
+
+        # Initialize noise
+        latents = self.generate_noise((1, max(num_frames//17*3, 1), 64, height//16, width//16), seed=seed, device=rand_device, dtype=self.torch_dtype).to(self.device)
+        
+        # Encode prompts
+        self.load_models_to_device(["text_encoder_1", "text_encoder_2"])
+        prompt_emb_posi = self.encode_prompt(prompt, positive=True)
+        if cfg_scale != 1.0:
+            prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False)
+
+        # Denoise
+        self.load_models_to_device(["dit"])
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
+            print(f"Step {progress_id + 1} / {len(self.scheduler.timesteps)}")
+
+            # Inference
+            noise_pred_posi = self.dit(latents, timestep=timestep, **prompt_emb_posi)
+            if cfg_scale != 1.0:
+                noise_pred_nega = self.dit(latents, timestep=timestep, **prompt_emb_nega)
+                noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
+            else:
+                noise_pred = noise_pred_posi
+
+            # Scheduler
+            latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents)
+
+        # Decode
+        self.load_models_to_device(['vae'])
+        frames = self.vae.decode(latents, device=self.device, smooth_scale=smooth_scale, **tiler_kwargs)
+        self.load_models_to_device([])
+        frames = self.tensor2video(frames[0])
+
+        return frames

diffsynth/pipelines/svd_video.py

@@ -49,9 +49,9 @@ class SVDVideoPipeline(BasePipeline):
         return image_emb
     
 
-    def encode_image_with_vae(self, image, noise_aug_strength):
+    def encode_image_with_vae(self, image, noise_aug_strength, seed=None):
         image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype)
-        noise = torch.randn(image.shape, device="cpu", dtype=self.torch_dtype).to(self.device)
+        noise = self.generate_noise(image.shape, seed=seed, device=self.device, dtype=self.torch_dtype)
         image = image + noise_aug_strength * noise
         image_emb = self.vae_encoder(image) / self.vae_encoder.scaling_factor
         return image_emb
@@ -126,14 +126,17 @@ class SVDVideoPipeline(BasePipeline):
         num_inference_steps=20,
         post_normalize=True,
         contrast_enhance_scale=1.2,
+        seed=None,
         progress_bar_cmd=tqdm,
         progress_bar_st=None,
     ):
+        height, width = self.check_resize_height_width(height, width)
+        
         # Prepare scheduler
         self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength)
 
         # Prepare latent tensors
-        noise = torch.randn((num_frames, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype).to(self.device)
+        noise = self.generate_noise((num_frames, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype)
         if denoising_strength == 1.0:
             latents = noise.clone()
         else:
@@ -147,7 +150,7 @@ class SVDVideoPipeline(BasePipeline):
         # Encode image
         image_emb_clip_posi = self.encode_image_with_clip(input_image)
         image_emb_clip_nega = torch.zeros_like(image_emb_clip_posi)
-        image_emb_vae_posi = repeat(self.encode_image_with_vae(input_image, noise_aug_strength), "B C H W -> (B T) C H W", T=num_frames)
+        image_emb_vae_posi = repeat(self.encode_image_with_vae(input_image, noise_aug_strength, seed=seed), "B C H W -> (B T) C H W", T=num_frames)
         image_emb_vae_nega = torch.zeros_like(image_emb_vae_posi)
 
         # Prepare classifier-free guidance

diffsynth/prompters/__init__.py

@@ -1,6 +1,12 @@
-from .prompt_refiners import Translator, BeautifulPrompt
+from .prompt_refiners import Translator, BeautifulPrompt, QwenPrompt
 from .sd_prompter import SDPrompter
 from .sdxl_prompter import SDXLPrompter
 from .sd3_prompter import SD3Prompter
 from .hunyuan_dit_prompter import HunyuanDiTPrompter
 from .kolors_prompter import KolorsPrompter
+from .flux_prompter import FluxPrompter
+from .omost import OmostPromter
+from .cog_prompter import CogPrompter
+from .hunyuan_video_prompter import HunyuanVideoPrompter
+from .stepvideo_prompter import StepVideoPrompter
+from .wanx_prompter import WanXPrompter

diffsynth/prompters/base_prompter.py

@@ -37,15 +37,21 @@ def tokenize_long_prompt(tokenizer, prompt, max_length=None):
 
 
 class BasePrompter:
-    def __init__(self, refiners=[]):
+    def __init__(self):
-        self.refiners = refiners
+        self.refiners = []
+        self.extenders = []
 
 
-    def load_prompt_refiners(self, model_nameger: ModelManager, refiner_classes=[]):
+    def load_prompt_refiners(self, model_manager: ModelManager, refiner_classes=[]):
         for refiner_class in refiner_classes:
-            refiner = refiner_class.from_model_manager(model_nameger)
+            refiner = refiner_class.from_model_manager(model_manager)
             self.refiners.append(refiner)
     
+    def load_prompt_extenders(self,model_manager:ModelManager,extender_classes=[]):
+        for extender_class in extender_classes:
+            extender = extender_class.from_model_manager(model_manager)
+            self.extenders.append(extender)
+
 
     @torch.no_grad()
     def process_prompt(self, prompt, positive=True):
@@ -55,3 +61,10 @@ class BasePrompter:
             for refiner in self.refiners:
                 prompt = refiner(prompt, positive=positive)
         return prompt
+
+    @torch.no_grad()
+    def extend_prompt(self, prompt:str, positive=True):
+        extended_prompt = dict(prompt=prompt)
+        for extender in self.extenders:
+            extended_prompt = extender(extended_prompt)
+        return extended_prompt

diffsynth/prompters/cog_prompter.py

@@ -0,0 +1,46 @@
+from .base_prompter import BasePrompter
+from ..models.flux_text_encoder import FluxTextEncoder2
+from transformers import T5TokenizerFast
+import os
+
+
+class CogPrompter(BasePrompter):
+    def __init__(
+        self,
+        tokenizer_path=None
+    ):
+        if tokenizer_path is None:
+            base_path = os.path.dirname(os.path.dirname(__file__))
+            tokenizer_path = os.path.join(base_path, "tokenizer_configs/cog/tokenizer")
+        super().__init__()
+        self.tokenizer = T5TokenizerFast.from_pretrained(tokenizer_path)
+        self.text_encoder: FluxTextEncoder2 = None
+
+
+    def fetch_models(self, text_encoder: FluxTextEncoder2 = None):
+        self.text_encoder = text_encoder
+
+
+    def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_length, device):
+        input_ids = tokenizer(
+            prompt,
+            return_tensors="pt",
+            padding="max_length",
+            max_length=max_length,
+            truncation=True,
+        ).input_ids.to(device)
+        prompt_emb = text_encoder(input_ids)
+        prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1))
+
+        return prompt_emb
+    
+
+    def encode_prompt(
+        self,
+        prompt,
+        positive=True,
+        device="cuda"
+    ):
+        prompt = self.process_prompt(prompt, positive=positive)
+        prompt_emb = self.encode_prompt_using_t5(prompt, self.text_encoder, self.tokenizer, 226, device)
+        return prompt_emb

diffsynth/prompters/flux_prompter.py

@@ -0,0 +1,74 @@
+from .base_prompter import BasePrompter
+from ..models.flux_text_encoder import FluxTextEncoder2
+from ..models.sd3_text_encoder import SD3TextEncoder1
+from transformers import CLIPTokenizer, T5TokenizerFast
+import os, torch
+
+
+class FluxPrompter(BasePrompter):
+    def __init__(
+        self,
+        tokenizer_1_path=None,
+        tokenizer_2_path=None
+    ):
+        if tokenizer_1_path is None:
+            base_path = os.path.dirname(os.path.dirname(__file__))
+            tokenizer_1_path = os.path.join(base_path, "tokenizer_configs/flux/tokenizer_1")
+        if tokenizer_2_path is None:
+            base_path = os.path.dirname(os.path.dirname(__file__))
+            tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/flux/tokenizer_2")
+        super().__init__()
+        self.tokenizer_1 = CLIPTokenizer.from_pretrained(tokenizer_1_path)
+        self.tokenizer_2 = T5TokenizerFast.from_pretrained(tokenizer_2_path)
+        self.text_encoder_1: SD3TextEncoder1 = None
+        self.text_encoder_2: FluxTextEncoder2 = None
+
+
+    def fetch_models(self, text_encoder_1: SD3TextEncoder1 = None, text_encoder_2: FluxTextEncoder2 = None):
+        self.text_encoder_1 = text_encoder_1
+        self.text_encoder_2 = text_encoder_2
+
+
+    def encode_prompt_using_clip(self, prompt, text_encoder, tokenizer, max_length, device):
+        input_ids = tokenizer(
+            prompt,
+            return_tensors="pt",
+            padding="max_length",
+            max_length=max_length,
+            truncation=True
+        ).input_ids.to(device)
+        pooled_prompt_emb, _ = text_encoder(input_ids)
+        return pooled_prompt_emb
+    
+
+    def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_length, device):
+        input_ids = tokenizer(
+            prompt,
+            return_tensors="pt",
+            padding="max_length",
+            max_length=max_length,
+            truncation=True,
+        ).input_ids.to(device)
+        prompt_emb = text_encoder(input_ids)
+        return prompt_emb
+    
+
+    def encode_prompt(
+        self,
+        prompt,
+        positive=True,
+        device="cuda",
+        t5_sequence_length=512,
+    ):
+        prompt = self.process_prompt(prompt, positive=positive)
+        
+        # CLIP
+        pooled_prompt_emb = self.encode_prompt_using_clip(prompt, self.text_encoder_1, self.tokenizer_1, 77, device)
+        
+        # T5
+        prompt_emb = self.encode_prompt_using_t5(prompt, self.text_encoder_2, self.tokenizer_2, t5_sequence_length, device)
+
+        # text_ids
+        text_ids = torch.zeros(prompt_emb.shape[0], prompt_emb.shape[1], 3).to(device=device, dtype=prompt_emb.dtype)
+
+        return prompt_emb, pooled_prompt_emb, text_ids

diffsynth/prompters/hunyuan_video_prompter.py

@@ -0,0 +1,143 @@
+from .base_prompter import BasePrompter
+from ..models.sd3_text_encoder import SD3TextEncoder1
+from ..models.hunyuan_video_text_encoder import HunyuanVideoLLMEncoder
+from transformers import CLIPTokenizer, LlamaTokenizerFast
+import os, torch
+
+PROMPT_TEMPLATE_ENCODE = (
+    "<|start_header_id|>system<|end_header_id|>\n\nDescribe the image by detailing the color, shape, size, texture, "
+    "quantity, text, spatial relationships of the objects and background:<|eot_id|>"
+    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>")
+
+PROMPT_TEMPLATE_ENCODE_VIDEO = (
+    "<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: "
+    "1. The main content and theme of the video."
+    "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects."
+    "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects."
+    "4. background environment, light, style and atmosphere."
+    "5. camera angles, movements, and transitions used in the video:<|eot_id|>"
+    "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>")
+
+PROMPT_TEMPLATE = {
+    "dit-llm-encode": {
+        "template": PROMPT_TEMPLATE_ENCODE,
+        "crop_start": 36,
+    },
+    "dit-llm-encode-video": {
+        "template": PROMPT_TEMPLATE_ENCODE_VIDEO,
+        "crop_start": 95,
+    },
+}
+
+NEGATIVE_PROMPT = "Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion"
+
+
+class HunyuanVideoPrompter(BasePrompter):
+
+    def __init__(
+        self,
+        tokenizer_1_path=None,
+        tokenizer_2_path=None,
+    ):
+        if tokenizer_1_path is None:
+            base_path = os.path.dirname(os.path.dirname(__file__))
+            tokenizer_1_path = os.path.join(
+                base_path, "tokenizer_configs/hunyuan_video/tokenizer_1")
+        if tokenizer_2_path is None:
+            base_path = os.path.dirname(os.path.dirname(__file__))
+            tokenizer_2_path = os.path.join(
+                base_path, "tokenizer_configs/hunyuan_video/tokenizer_2")
+        super().__init__()
+        self.tokenizer_1 = CLIPTokenizer.from_pretrained(tokenizer_1_path)
+        self.tokenizer_2 = LlamaTokenizerFast.from_pretrained(tokenizer_2_path, padding_side='right')
+        self.text_encoder_1: SD3TextEncoder1 = None
+        self.text_encoder_2: HunyuanVideoLLMEncoder = None
+
+        self.prompt_template = PROMPT_TEMPLATE['dit-llm-encode']
+        self.prompt_template_video = PROMPT_TEMPLATE['dit-llm-encode-video']
+
+    def fetch_models(self, text_encoder_1: SD3TextEncoder1 = None, text_encoder_2: HunyuanVideoLLMEncoder = None):
+        self.text_encoder_1 = text_encoder_1
+        self.text_encoder_2 = text_encoder_2
+
+    def apply_text_to_template(self, text, template):
+        assert isinstance(template, str)
+        if isinstance(text, list):
+            return [self.apply_text_to_template(text_) for text_ in text]
+        elif isinstance(text, str):
+            # Will send string to tokenizer. Used for llm
+            return template.format(text)
+        else:
+            raise TypeError(f"Unsupported prompt type: {type(text)}")
+
+    def encode_prompt_using_clip(self, prompt, max_length, device):
+        tokenized_result = self.tokenizer_1(
+            prompt,
+            return_tensors="pt",
+            padding="max_length",
+            max_length=max_length,
+            truncation=True,
+            return_attention_mask=True
+        )
+        input_ids = tokenized_result.input_ids.to(device)
+        attention_mask = tokenized_result.attention_mask.to(device)
+        return self.text_encoder_1(input_ids=input_ids, extra_mask=attention_mask)[0]
+
+    def encode_prompt_using_llm(self,
+                                prompt,
+                                max_length,
+                                device,
+                                crop_start,
+                                hidden_state_skip_layer=2,
+                                use_attention_mask=True):
+        max_length += crop_start
+        inputs = self.tokenizer_2(prompt,
+                                  return_tensors="pt",
+                                  padding="max_length",
+                                  max_length=max_length,
+                                  truncation=True)
+        input_ids = inputs.input_ids.to(device)
+        attention_mask = inputs.attention_mask.to(device)
+        last_hidden_state = self.text_encoder_2(input_ids, attention_mask, hidden_state_skip_layer)
+
+        # crop out
+        if crop_start > 0:
+            last_hidden_state = last_hidden_state[:, crop_start:]
+            attention_mask = (attention_mask[:, crop_start:] if use_attention_mask else None)
+
+        return last_hidden_state, attention_mask
+
+    def encode_prompt(self,
+                      prompt,
+                      positive=True,
+                      device="cuda",
+                      clip_sequence_length=77,
+                      llm_sequence_length=256,
+                      data_type='video',
+                      use_template=True,
+                      hidden_state_skip_layer=2,
+                      use_attention_mask=True):
+
+        prompt = self.process_prompt(prompt, positive=positive)
+
+        # apply template
+        if use_template:
+            template = self.prompt_template_video if data_type == 'video' else self.prompt_template
+            prompt_formated = self.apply_text_to_template(prompt, template['template'])
+        else:
+            prompt_formated = prompt
+        # Text encoder
+        if data_type == 'video':
+            crop_start = self.prompt_template_video.get("crop_start", 0)
+        else:
+            crop_start = self.prompt_template.get("crop_start", 0)
+
+        # CLIP
+        pooled_prompt_emb = self.encode_prompt_using_clip(prompt, clip_sequence_length, device)
+
+        # LLM
+        prompt_emb, attention_mask = self.encode_prompt_using_llm(
+            prompt_formated, llm_sequence_length, device, crop_start,
+            hidden_state_skip_layer, use_attention_mask)
+
+        return prompt_emb, pooled_prompt_emb, attention_mask

diffsynth/prompters/kolors_prompter.py

@@ -245,6 +245,7 @@ class ChatGLMTokenizer(PreTrainedTokenizer):
             padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
             pad_to_multiple_of: Optional[int] = None,
             return_attention_mask: Optional[bool] = None,
+            padding_side: Optional[str] = None,
     ) -> dict:
         """
         Pad encoded inputs (on left/right and up to predefined length or max length in the batch)

diffsynth/prompters/omnigen_prompter.py

@@ -0,0 +1,356 @@
+import os
+import re
+from typing import Dict, List
+
+import torch
+from PIL import Image
+from torchvision import transforms
+from transformers import AutoTokenizer
+from huggingface_hub import snapshot_download
+import numpy as np
+
+
+
+def crop_arr(pil_image, max_image_size):
+    while min(*pil_image.size) >= 2 * max_image_size:
+        pil_image = pil_image.resize(
+            tuple(x // 2 for x in pil_image.size), resample=Image.BOX
+        )
+
+    if max(*pil_image.size) > max_image_size:
+        scale = max_image_size / max(*pil_image.size)
+        pil_image = pil_image.resize(
+            tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
+        )
+    
+    if min(*pil_image.size) < 16:
+        scale = 16 / min(*pil_image.size)
+        pil_image = pil_image.resize(
+            tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
+        )
+    
+    arr = np.array(pil_image)
+    crop_y1 = (arr.shape[0] % 16) // 2
+    crop_y2 = arr.shape[0] % 16 - crop_y1
+
+    crop_x1 = (arr.shape[1] % 16) // 2
+    crop_x2 = arr.shape[1] % 16 - crop_x1
+
+    arr = arr[crop_y1:arr.shape[0]-crop_y2, crop_x1:arr.shape[1]-crop_x2]    
+    return Image.fromarray(arr)
+
+
+
+class OmniGenPrompter:
+    def __init__(self, 
+                text_tokenizer, 
+                max_image_size: int=1024):
+        self.text_tokenizer = text_tokenizer
+        self.max_image_size = max_image_size
+
+        self.image_transform = transforms.Compose([
+            transforms.Lambda(lambda pil_image: crop_arr(pil_image, max_image_size)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
+        ])
+
+        self.collator = OmniGenCollator()
+        self.separate_collator = OmniGenSeparateCollator()
+
+    @classmethod
+    def from_pretrained(cls, model_name):
+        if not os.path.exists(model_name):
+            cache_folder = os.getenv('HF_HUB_CACHE')
+            model_name = snapshot_download(repo_id=model_name,
+                                           cache_dir=cache_folder,
+                                           allow_patterns="*.json")
+        text_tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+        return cls(text_tokenizer)
+
+
+    def process_image(self, image):
+        return self.image_transform(image)
+    
+    def process_multi_modal_prompt(self, text, input_images):
+        text = self.add_prefix_instruction(text)
+        if input_images is None or len(input_images) == 0:
+            model_inputs = self.text_tokenizer(text)
+            return {"input_ids": model_inputs.input_ids, "pixel_values": None, "image_sizes": None}
+
+        pattern = r"<\|image_\d+\|>"
+        prompt_chunks = [self.text_tokenizer(chunk).input_ids for chunk in re.split(pattern, text)] 
+
+        for i in range(1, len(prompt_chunks)):
+            if prompt_chunks[i][0] == 1:
+                prompt_chunks[i] = prompt_chunks[i][1:]
+
+        image_tags = re.findall(pattern, text) 
+        image_ids = [int(s.split("|")[1].split("_")[-1]) for s in image_tags]
+
+        unique_image_ids = sorted(list(set(image_ids)))
+        assert unique_image_ids == list(range(1, len(unique_image_ids)+1)), f"image_ids must start from 1, and must be continuous int, e.g. [1, 2, 3], cannot be {unique_image_ids}"
+        # total images must be the same as the number of image tags
+        assert len(unique_image_ids) == len(input_images), f"total images must be the same as the number of image tags, got {len(unique_image_ids)} image tags and {len(input_images)} images"
+        
+        input_images = [input_images[x-1] for x in image_ids]
+
+        all_input_ids = []
+        img_inx = []
+        idx = 0
+        for i in range(len(prompt_chunks)):
+            all_input_ids.extend(prompt_chunks[i])
+            if i != len(prompt_chunks) -1:
+                start_inx = len(all_input_ids)
+                size = input_images[i].size(-2) *  input_images[i].size(-1) // 16 // 16
+                img_inx.append([start_inx, start_inx+size])
+                all_input_ids.extend([0]*size)
+
+        return {"input_ids": all_input_ids, "pixel_values": input_images, "image_sizes": img_inx}
+
+
+    def add_prefix_instruction(self, prompt):
+        user_prompt = '<|user|>\n'
+        generation_prompt = 'Generate an image according to the following instructions\n'
+        assistant_prompt = '<|assistant|>\n<|diffusion|>'
+        prompt_suffix = "<|end|>\n"
+        prompt = f"{user_prompt}{generation_prompt}{prompt}{prompt_suffix}{assistant_prompt}"
+        return prompt
+
+
+    def __call__(self, 
+                instructions: List[str], 
+                input_images: List[List[str]] = None,
+                height: int = 1024,
+                width: int = 1024,
+                negative_prompt: str = "low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers.",
+                use_img_cfg: bool = True,
+                separate_cfg_input: bool = False,
+                use_input_image_size_as_output: bool=False,
+                ) -> Dict:
+
+        if input_images is None:
+            use_img_cfg = False
+        if isinstance(instructions, str):
+            instructions = [instructions]
+            input_images = [input_images]
+        
+        input_data = []
+        for i in range(len(instructions)):
+            cur_instruction = instructions[i]
+            cur_input_images = None if input_images is None else input_images[i]
+            if cur_input_images is not None and len(cur_input_images) > 0:
+                cur_input_images = [self.process_image(x) for x in cur_input_images]
+            else:
+                cur_input_images = None
+                assert "<img><|image_1|></img>" not in cur_instruction
+            
+            mllm_input = self.process_multi_modal_prompt(cur_instruction, cur_input_images)
+
+        
+            neg_mllm_input, img_cfg_mllm_input = None, None
+            neg_mllm_input = self.process_multi_modal_prompt(negative_prompt, None)
+            if use_img_cfg:
+                if cur_input_images is not None and len(cur_input_images) >= 1:
+                    img_cfg_prompt = [f"<img><|image_{i+1}|></img>" for i in range(len(cur_input_images))]
+                    img_cfg_mllm_input = self.process_multi_modal_prompt(" ".join(img_cfg_prompt), cur_input_images)
+                else:
+                    img_cfg_mllm_input = neg_mllm_input
+
+            if use_input_image_size_as_output:
+                input_data.append((mllm_input, neg_mllm_input, img_cfg_mllm_input, [mllm_input['pixel_values'][0].size(-2), mllm_input['pixel_values'][0].size(-1)]))
+            else:
+                input_data.append((mllm_input, neg_mllm_input, img_cfg_mllm_input, [height, width]))
+
+        if separate_cfg_input:
+            return self.separate_collator(input_data)
+        return self.collator(input_data)
+
+
+
+
+class OmniGenCollator:
+    def __init__(self, pad_token_id=2, hidden_size=3072):
+        self.pad_token_id = pad_token_id
+        self.hidden_size = hidden_size
+    
+    def create_position(self, attention_mask, num_tokens_for_output_images):
+        position_ids = []
+        text_length = attention_mask.size(-1)
+        img_length = max(num_tokens_for_output_images)  
+        for mask in attention_mask:
+            temp_l = torch.sum(mask)
+            temp_position = [0]*(text_length-temp_l) + [i for i in range(temp_l+img_length+1)] # we add a time embedding into the sequence, so add one more token
+            position_ids.append(temp_position)
+        return torch.LongTensor(position_ids)
+    
+    def create_mask(self, attention_mask, num_tokens_for_output_images):
+        extended_mask = []
+        padding_images = []
+        text_length = attention_mask.size(-1)
+        img_length = max(num_tokens_for_output_images)
+        seq_len = text_length + img_length + 1 # we add a time embedding into the sequence, so add one more token
+        inx = 0
+        for mask in attention_mask:
+            temp_l = torch.sum(mask)
+            pad_l = text_length - temp_l
+
+            temp_mask = torch.tril(torch.ones(size=(temp_l+1, temp_l+1)))
+
+            image_mask = torch.zeros(size=(temp_l+1, img_length))
+            temp_mask = torch.cat([temp_mask, image_mask], dim=-1)
+
+            image_mask = torch.ones(size=(img_length, temp_l+img_length+1))
+            temp_mask = torch.cat([temp_mask, image_mask], dim=0)
+
+            if pad_l > 0:
+                pad_mask = torch.zeros(size=(temp_l+1+img_length, pad_l))
+                temp_mask = torch.cat([pad_mask, temp_mask], dim=-1)
+
+                pad_mask = torch.ones(size=(pad_l, seq_len))
+                temp_mask = torch.cat([pad_mask, temp_mask], dim=0)
+
+            true_img_length = num_tokens_for_output_images[inx]
+            pad_img_length = img_length - true_img_length
+            if pad_img_length > 0:
+                temp_mask[:, -pad_img_length:] = 0
+                temp_padding_imgs = torch.zeros(size=(1, pad_img_length, self.hidden_size))
+            else:
+                temp_padding_imgs = None
+            
+            extended_mask.append(temp_mask.unsqueeze(0))
+            padding_images.append(temp_padding_imgs)
+            inx += 1
+        return torch.cat(extended_mask, dim=0), padding_images
+    
+    def adjust_attention_for_input_images(self, attention_mask, image_sizes):
+        for b_inx in image_sizes.keys():
+            for start_inx, end_inx in image_sizes[b_inx]:
+                attention_mask[b_inx][start_inx:end_inx, start_inx:end_inx] = 1
+
+        return attention_mask
+    
+    def pad_input_ids(self, input_ids, image_sizes):
+        max_l = max([len(x) for x in input_ids])
+        padded_ids = []
+        attention_mask = []
+        new_image_sizes = []
+
+        for i in range(len(input_ids)):
+            temp_ids = input_ids[i]
+            temp_l = len(temp_ids)
+            pad_l = max_l - temp_l
+            if pad_l == 0:
+                attention_mask.append([1]*max_l)
+                padded_ids.append(temp_ids)
+            else:
+                attention_mask.append([0]*pad_l+[1]*temp_l)
+                padded_ids.append([self.pad_token_id]*pad_l+temp_ids)
+            
+            if i in image_sizes:
+                new_inx = []
+                for old_inx in image_sizes[i]:
+                    new_inx.append([x+pad_l for x in old_inx])
+                image_sizes[i] = new_inx
+
+        return torch.LongTensor(padded_ids), torch.LongTensor(attention_mask), image_sizes
+
+
+    def process_mllm_input(self, mllm_inputs, target_img_size):
+        num_tokens_for_output_images = []
+        for img_size in target_img_size:
+            num_tokens_for_output_images.append(img_size[0]*img_size[1]//16//16)
+
+        pixel_values, image_sizes = [], {}
+        b_inx = 0
+        for x in mllm_inputs:
+            if x['pixel_values'] is not None:
+                pixel_values.extend(x['pixel_values'])
+                for size in x['image_sizes']:
+                    if b_inx not in image_sizes:
+                        image_sizes[b_inx] = [size]
+                    else:
+                        image_sizes[b_inx].append(size)
+            b_inx += 1     
+        pixel_values = [x.unsqueeze(0) for x in pixel_values]
+
+        
+        input_ids = [x['input_ids'] for x in mllm_inputs]
+        padded_input_ids, attention_mask, image_sizes = self.pad_input_ids(input_ids, image_sizes)
+        position_ids = self.create_position(attention_mask, num_tokens_for_output_images)
+        attention_mask, padding_images = self.create_mask(attention_mask, num_tokens_for_output_images)
+        attention_mask = self.adjust_attention_for_input_images(attention_mask, image_sizes)
+
+        return padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes
+    
+    
+    def __call__(self, features):
+        mllm_inputs = [f[0] for f in features]
+        cfg_mllm_inputs = [f[1] for f in features]
+        img_cfg_mllm_input = [f[2] for f in features]
+        target_img_size = [f[3] for f in features]
+
+        
+        if img_cfg_mllm_input[0] is not None:
+            mllm_inputs = mllm_inputs + cfg_mllm_inputs + img_cfg_mllm_input
+            target_img_size = target_img_size + target_img_size + target_img_size
+        else:
+            mllm_inputs = mllm_inputs + cfg_mllm_inputs
+            target_img_size = target_img_size + target_img_size
+
+
+        all_padded_input_ids, all_position_ids, all_attention_mask, all_padding_images, all_pixel_values, all_image_sizes = self.process_mllm_input(mllm_inputs, target_img_size)
+
+        data = {"input_ids": all_padded_input_ids,
+        "attention_mask": all_attention_mask,
+        "position_ids": all_position_ids,
+        "input_pixel_values": all_pixel_values,
+        "input_image_sizes": all_image_sizes,
+        "padding_images": all_padding_images,
+        }
+        return data
+
+
+class OmniGenSeparateCollator(OmniGenCollator):
+    def __call__(self, features):
+        mllm_inputs = [f[0] for f in features]
+        cfg_mllm_inputs = [f[1] for f in features]
+        img_cfg_mllm_input = [f[2] for f in features]
+        target_img_size = [f[3] for f in features]
+        
+        all_padded_input_ids, all_attention_mask, all_position_ids, all_pixel_values, all_image_sizes, all_padding_images = [], [], [], [], [], []
+
+
+        padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes = self.process_mllm_input(mllm_inputs, target_img_size)
+        all_padded_input_ids.append(padded_input_ids)
+        all_attention_mask.append(attention_mask)
+        all_position_ids.append(position_ids)
+        all_pixel_values.append(pixel_values)
+        all_image_sizes.append(image_sizes)
+        all_padding_images.append(padding_images)
+
+        if cfg_mllm_inputs[0] is not None:
+            padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes = self.process_mllm_input(cfg_mllm_inputs, target_img_size)
+            all_padded_input_ids.append(padded_input_ids)
+            all_attention_mask.append(attention_mask)
+            all_position_ids.append(position_ids)
+            all_pixel_values.append(pixel_values)
+            all_image_sizes.append(image_sizes)
+            all_padding_images.append(padding_images)
+        if img_cfg_mllm_input[0] is not None:
+            padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes = self.process_mllm_input(img_cfg_mllm_input, target_img_size)
+            all_padded_input_ids.append(padded_input_ids)
+            all_attention_mask.append(attention_mask)
+            all_position_ids.append(position_ids)
+            all_pixel_values.append(pixel_values)
+            all_image_sizes.append(image_sizes)
+            all_padding_images.append(padding_images)
+
+        data = {"input_ids": all_padded_input_ids,
+        "attention_mask": all_attention_mask,
+        "position_ids": all_position_ids,
+        "input_pixel_values": all_pixel_values,
+        "input_image_sizes": all_image_sizes,
+        "padding_images": all_padding_images,
+        }
+        return data

diffsynth/prompters/omost.py

@@ -0,0 +1,323 @@
+from transformers import AutoTokenizer, TextIteratorStreamer
+import difflib
+import torch
+import numpy as np
+import re
+from ..models.model_manager import ModelManager
+from PIL import Image
+
+valid_colors = {  # r, g, b
+    'aliceblue': (240, 248, 255), 'antiquewhite': (250, 235, 215), 'aqua': (0, 255, 255),
+    'aquamarine': (127, 255, 212), 'azure': (240, 255, 255), 'beige': (245, 245, 220),
+    'bisque': (255, 228, 196), 'black': (0, 0, 0), 'blanchedalmond': (255, 235, 205), 'blue': (0, 0, 255),
+    'blueviolet': (138, 43, 226), 'brown': (165, 42, 42), 'burlywood': (222, 184, 135),
+    'cadetblue': (95, 158, 160), 'chartreuse': (127, 255, 0), 'chocolate': (210, 105, 30),
+    'coral': (255, 127, 80), 'cornflowerblue': (100, 149, 237), 'cornsilk': (255, 248, 220),
+    'crimson': (220, 20, 60), 'cyan': (0, 255, 255), 'darkblue': (0, 0, 139), 'darkcyan': (0, 139, 139),
+    'darkgoldenrod': (184, 134, 11), 'darkgray': (169, 169, 169), 'darkgrey': (169, 169, 169),
+    'darkgreen': (0, 100, 0), 'darkkhaki': (189, 183, 107), 'darkmagenta': (139, 0, 139),
+    'darkolivegreen': (85, 107, 47), 'darkorange': (255, 140, 0), 'darkorchid': (153, 50, 204),
+    'darkred': (139, 0, 0), 'darksalmon': (233, 150, 122), 'darkseagreen': (143, 188, 143),
+    'darkslateblue': (72, 61, 139), 'darkslategray': (47, 79, 79), 'darkslategrey': (47, 79, 79),
+    'darkturquoise': (0, 206, 209), 'darkviolet': (148, 0, 211), 'deeppink': (255, 20, 147),
+    'deepskyblue': (0, 191, 255), 'dimgray': (105, 105, 105), 'dimgrey': (105, 105, 105),
+    'dodgerblue': (30, 144, 255), 'firebrick': (178, 34, 34), 'floralwhite': (255, 250, 240),
+    'forestgreen': (34, 139, 34), 'fuchsia': (255, 0, 255), 'gainsboro': (220, 220, 220),
+    'ghostwhite': (248, 248, 255), 'gold': (255, 215, 0), 'goldenrod': (218, 165, 32),
+    'gray': (128, 128, 128), 'grey': (128, 128, 128), 'green': (0, 128, 0), 'greenyellow': (173, 255, 47),
+    'honeydew': (240, 255, 240), 'hotpink': (255, 105, 180), 'indianred': (205, 92, 92),
+    'indigo': (75, 0, 130), 'ivory': (255, 255, 240), 'khaki': (240, 230, 140), 'lavender': (230, 230, 250),
+    'lavenderblush': (255, 240, 245), 'lawngreen': (124, 252, 0), 'lemonchiffon': (255, 250, 205),
+    'lightblue': (173, 216, 230), 'lightcoral': (240, 128, 128), 'lightcyan': (224, 255, 255),
+    'lightgoldenrodyellow': (250, 250, 210), 'lightgray': (211, 211, 211), 'lightgrey': (211, 211, 211),
+    'lightgreen': (144, 238, 144), 'lightpink': (255, 182, 193), 'lightsalmon': (255, 160, 122),
+    'lightseagreen': (32, 178, 170), 'lightskyblue': (135, 206, 250), 'lightslategray': (119, 136, 153),
+    'lightslategrey': (119, 136, 153), 'lightsteelblue': (176, 196, 222), 'lightyellow': (255, 255, 224),
+    'lime': (0, 255, 0), 'limegreen': (50, 205, 50), 'linen': (250, 240, 230), 'magenta': (255, 0, 255),
+    'maroon': (128, 0, 0), 'mediumaquamarine': (102, 205, 170), 'mediumblue': (0, 0, 205),
+    'mediumorchid': (186, 85, 211), 'mediumpurple': (147, 112, 219), 'mediumseagreen': (60, 179, 113),
+    'mediumslateblue': (123, 104, 238), 'mediumspringgreen': (0, 250, 154),
+    'mediumturquoise': (72, 209, 204), 'mediumvioletred': (199, 21, 133), 'midnightblue': (25, 25, 112),
+    'mintcream': (245, 255, 250), 'mistyrose': (255, 228, 225), 'moccasin': (255, 228, 181),
+    'navajowhite': (255, 222, 173), 'navy': (0, 0, 128), 'navyblue': (0, 0, 128),
+    'oldlace': (253, 245, 230), 'olive': (128, 128, 0), 'olivedrab': (107, 142, 35),
+    'orange': (255, 165, 0), 'orangered': (255, 69, 0), 'orchid': (218, 112, 214),
+    'palegoldenrod': (238, 232, 170), 'palegreen': (152, 251, 152), 'paleturquoise': (175, 238, 238),
+    'palevioletred': (219, 112, 147), 'papayawhip': (255, 239, 213), 'peachpuff': (255, 218, 185),
+    'peru': (205, 133, 63), 'pink': (255, 192, 203), 'plum': (221, 160, 221), 'powderblue': (176, 224, 230),
+    'purple': (128, 0, 128), 'rebeccapurple': (102, 51, 153), 'red': (255, 0, 0),
+    'rosybrown': (188, 143, 143), 'royalblue': (65, 105, 225), 'saddlebrown': (139, 69, 19),
+    'salmon': (250, 128, 114), 'sandybrown': (244, 164, 96), 'seagreen': (46, 139, 87),
+    'seashell': (255, 245, 238), 'sienna': (160, 82, 45), 'silver': (192, 192, 192),
+    'skyblue': (135, 206, 235), 'slateblue': (106, 90, 205), 'slategray': (112, 128, 144),
+    'slategrey': (112, 128, 144), 'snow': (255, 250, 250), 'springgreen': (0, 255, 127),
+    'steelblue': (70, 130, 180), 'tan': (210, 180, 140), 'teal': (0, 128, 128), 'thistle': (216, 191, 216),
+    'tomato': (255, 99, 71), 'turquoise': (64, 224, 208), 'violet': (238, 130, 238),
+    'wheat': (245, 222, 179), 'white': (255, 255, 255), 'whitesmoke': (245, 245, 245),
+    'yellow': (255, 255, 0), 'yellowgreen': (154, 205, 50)
+}
+
+valid_locations = {  # x, y in 90*90
+    'in the center': (45, 45),
+    'on the left': (15, 45),
+    'on the right': (75, 45),
+    'on the top': (45, 15),
+    'on the bottom': (45, 75),
+    'on the top-left': (15, 15),
+    'on the top-right': (75, 15),
+    'on the bottom-left': (15, 75),
+    'on the bottom-right': (75, 75)
+}
+
+valid_offsets = {  # x, y in 90*90
+    'no offset': (0, 0),
+    'slightly to the left': (-10, 0),
+    'slightly to the right': (10, 0),
+    'slightly to the upper': (0, -10),
+    'slightly to the lower': (0, 10),
+    'slightly to the upper-left': (-10, -10),
+    'slightly to the upper-right': (10, -10),
+    'slightly to the lower-left': (-10, 10),
+    'slightly to the lower-right': (10, 10)}
+
+valid_areas = {  # w, h in 90*90
+    "a small square area": (50, 50),
+    "a small vertical area": (40, 60),
+    "a small horizontal area": (60, 40),
+    "a medium-sized square area": (60, 60),
+    "a medium-sized vertical area": (50, 80),
+    "a medium-sized horizontal area": (80, 50),
+    "a large square area": (70, 70),
+    "a large vertical area": (60, 90),
+    "a large horizontal area": (90, 60)
+}
+
+def safe_str(x):
+    return x.strip(',. ') + '.'
+
+def closest_name(input_str, options):
+    input_str = input_str.lower()
+
+    closest_match = difflib.get_close_matches(input_str, list(options.keys()), n=1, cutoff=0.5)
+    assert isinstance(closest_match, list) and len(closest_match) > 0, f'The value [{input_str}] is not valid!'
+    result = closest_match[0]
+
+    if result != input_str:
+        print(f'Automatically corrected [{input_str}] -> [{result}].')
+
+    return result
+
+class Canvas:
+    @staticmethod
+    def from_bot_response(response: str):
+        
+        matched = re.search(r'```python\n(.*?)\n```', response, re.DOTALL)
+        assert matched, 'Response does not contain codes!'
+        code_content = matched.group(1)
+        assert 'canvas = Canvas()' in code_content, 'Code block must include valid canvas var!'
+        local_vars = {'Canvas': Canvas}
+        exec(code_content, {}, local_vars)
+        canvas = local_vars.get('canvas', None)
+        assert isinstance(canvas, Canvas), 'Code block must produce valid canvas var!'
+        return canvas
+
+    def __init__(self):
+        self.components = []
+        self.color = None
+        self.record_tags = True
+        self.prefixes = []
+        self.suffixes = []
+        return
+
+    def set_global_description(self, description: str, detailed_descriptions: list, tags: str,
+                               HTML_web_color_name: str):
+        assert isinstance(description, str), 'Global description is not valid!'
+        assert isinstance(detailed_descriptions, list) and all(isinstance(item, str) for item in detailed_descriptions), \
+            'Global detailed_descriptions is not valid!'
+        assert isinstance(tags, str), 'Global tags is not valid!'
+
+        HTML_web_color_name = closest_name(HTML_web_color_name, valid_colors)
+        self.color = np.array([[valid_colors[HTML_web_color_name]]], dtype=np.uint8)
+
+        self.prefixes = [description]
+        self.suffixes = detailed_descriptions
+
+        if self.record_tags:
+            self.suffixes = self.suffixes + [tags]
+
+        self.prefixes = [safe_str(x) for x in self.prefixes]
+        self.suffixes = [safe_str(x) for x in self.suffixes]
+
+        return
+
+    def add_local_description(self, location: str, offset: str, area: str, distance_to_viewer: float, description: str,
+                              detailed_descriptions: list, tags: str, atmosphere: str, style: str,
+                              quality_meta: str, HTML_web_color_name: str):
+        assert isinstance(description, str), 'Local description is wrong!'
+        assert isinstance(distance_to_viewer, (int, float)) and distance_to_viewer > 0, \
+            f'The distance_to_viewer for [{description}] is not positive float number!'
+        assert isinstance(detailed_descriptions, list) and all(isinstance(item, str) for item in detailed_descriptions), \
+            f'The detailed_descriptions for [{description}] is not valid!'
+        assert isinstance(tags, str), f'The tags for [{description}] is not valid!'
+        assert isinstance(atmosphere, str), f'The atmosphere for [{description}] is not valid!'
+        assert isinstance(style, str), f'The style for [{description}] is not valid!'
+        assert isinstance(quality_meta, str), f'The quality_meta for [{description}] is not valid!'
+
+        location = closest_name(location, valid_locations)
+        offset = closest_name(offset, valid_offsets)
+        area = closest_name(area, valid_areas)
+        HTML_web_color_name = closest_name(HTML_web_color_name, valid_colors)
+
+        xb, yb = valid_locations[location]
+        xo, yo = valid_offsets[offset]
+        w, h = valid_areas[area]
+        rect = (yb + yo - h // 2, yb + yo + h // 2, xb + xo - w // 2, xb + xo + w // 2)
+        rect = [max(0, min(90, i)) for i in rect]
+        color = np.array([[valid_colors[HTML_web_color_name]]], dtype=np.uint8)
+
+        prefixes = self.prefixes + [description]
+        suffixes = detailed_descriptions
+
+        if self.record_tags:
+            suffixes = suffixes + [tags, atmosphere, style, quality_meta]
+
+        prefixes = [safe_str(x) for x in prefixes]
+        suffixes = [safe_str(x) for x in suffixes]
+
+        self.components.append(dict(
+            rect=rect,
+            distance_to_viewer=distance_to_viewer,
+            color=color,
+            prefixes=prefixes,
+            suffixes=suffixes,
+            location=location,
+        ))
+
+        return
+
+    def process(self):
+        # sort components
+        self.components = sorted(self.components, key=lambda x: x['distance_to_viewer'], reverse=True)
+
+        # compute initial latent
+        # print(self.color)
+        initial_latent = np.zeros(shape=(90, 90, 3), dtype=np.float32) + self.color
+
+        for component in self.components:
+            a, b, c, d = component['rect']
+            initial_latent[a:b, c:d] = 0.7 * component['color'] + 0.3 * initial_latent[a:b, c:d]
+
+        initial_latent = initial_latent.clip(0, 255).astype(np.uint8)
+
+        # compute conditions
+
+        bag_of_conditions = [
+            dict(mask=np.ones(shape=(90, 90), dtype=np.float32), prefixes=self.prefixes, suffixes=self.suffixes,location= "full")
+        ]
+
+        for i, component in enumerate(self.components):
+            a, b, c, d = component['rect']
+            m = np.zeros(shape=(90, 90), dtype=np.float32)
+            m[a:b, c:d] = 1.0
+            bag_of_conditions.append(dict(
+                mask = m,
+                prefixes = component['prefixes'],
+                suffixes = component['suffixes'],
+                location = component['location'],
+            ))
+            
+        return dict(
+            initial_latent = initial_latent,
+            bag_of_conditions = bag_of_conditions,
+        )
+
+
+class OmostPromter(torch.nn.Module):
+    
+    def __init__(self,model = None,tokenizer = None, template = "",device="cpu"):
+        super().__init__()
+        self.model=model
+        self.tokenizer = tokenizer
+        self.device = device
+        if template == "":
+            template = r'''You are a helpful AI assistant to compose images using the below python class `Canvas`:
+            ```python
+            class Canvas:
+                def set_global_description(self, description: str, detailed_descriptions: list[str], tags: str, HTML_web_color_name: str):
+                    pass
+
+                def add_local_description(self, location: str, offset: str, area: str, distance_to_viewer: float, description: str, detailed_descriptions: list[str], tags: str, atmosphere: str, style: str, quality_meta: str, HTML_web_color_name: str):
+                    assert location in ["in the center", "on the left", "on the right", "on the top", "on the bottom", "on the top-left", "on the top-right", "on the bottom-left", "on the bottom-right"]
+                    assert offset in ["no offset", "slightly to the left", "slightly to the right", "slightly to the upper", "slightly to the lower", "slightly to the upper-left", "slightly to the upper-right", "slightly to the lower-left", "slightly to the lower-right"]
+                    assert area in ["a small square area", "a small vertical area", "a small horizontal area", "a medium-sized square area", "a medium-sized vertical area", "a medium-sized horizontal area", "a large square area", "a large vertical area", "a large horizontal area"]
+                    assert distance_to_viewer > 0
+                    pass
+            ```'''
+        self.template = template
+        
+    @staticmethod
+    def from_model_manager(model_manager: ModelManager):
+        model, model_path = model_manager.fetch_model("omost_prompt", require_model_path=True)
+        tokenizer = AutoTokenizer.from_pretrained(model_path)
+        omost = OmostPromter(
+            model=  model,
+            tokenizer = tokenizer,
+            device = model_manager.device
+        )
+        return omost
+    
+    
+    def __call__(self,prompt_dict:dict):
+        raw_prompt=prompt_dict["prompt"]
+        conversation = [{"role": "system", "content": self.template}]
+        conversation.append({"role": "user", "content": raw_prompt})
+        
+        input_ids = self.tokenizer.apply_chat_template(conversation, return_tensors="pt", add_generation_prompt=True).to(self.device)
+        streamer = TextIteratorStreamer(self.tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.bfloat16, device=self.device)
+        
+        generate_kwargs = dict(
+            input_ids = input_ids,
+            streamer = streamer,
+            # stopping_criteria=stopping_criteria,
+            # max_new_tokens=max_new_tokens,
+            do_sample = True,
+            attention_mask = attention_mask,
+            pad_token_id = self.tokenizer.eos_token_id,
+            # temperature=temperature,
+            # top_p=top_p,
+        )
+        self.model.generate(**generate_kwargs)
+        outputs = []
+        for text in streamer:
+            outputs.append(text)
+        llm_outputs = "".join(outputs)
+        
+        canvas = Canvas.from_bot_response(llm_outputs)
+        canvas_output = canvas.process()
+        
+        prompts = [" ".join(_["prefixes"]+_["suffixes"][:2]) for _ in canvas_output["bag_of_conditions"]]
+        canvas_output["prompt"] = prompts[0]
+        canvas_output["prompts"] = prompts[1:]
+        
+        raw_masks = [_["mask"] for _ in canvas_output["bag_of_conditions"]]
+        masks=[]
+        for mask in raw_masks:
+            mask[mask>0.5]=255
+            mask = np.stack([mask] * 3, axis=-1).astype("uint8")
+            masks.append(Image.fromarray(mask))
+            
+        canvas_output["masks"] = masks
+        prompt_dict.update(canvas_output)
+        print(f"Your prompt is extended by Omost:\n")
+        cnt = 0
+        for component,pmt in zip(canvas_output["bag_of_conditions"],prompts):
+            loc = component["location"]
+            cnt += 1
+            print(f"Component {cnt} - Location : {loc}\nPrompt:{pmt}\n")
+        
+        return prompt_dict
+    
+    
+        
+    

diffsynth/prompters/prompt_refiners.py

@@ -1,8 +1,7 @@
 from transformers import AutoTokenizer
 from ..models.model_manager import ModelManager
 import torch
-
+from .omost import OmostPromter
-
 
 class BeautifulPrompt(torch.nn.Module):
     def __init__(self, tokenizer_path=None, model=None, template=""):
@@ -13,8 +12,8 @@ class BeautifulPrompt(torch.nn.Module):
 
 
     @staticmethod
-    def from_model_manager(model_nameger: ModelManager):
+    def from_model_manager(model_manager: ModelManager):
-        model, model_path = model_nameger.fetch_model("beautiful_prompt", require_model_path=True)
+        model, model_path = model_manager.fetch_model("beautiful_prompt", require_model_path=True)
         template = 'Instruction: Give a simple description of the image to generate a drawing prompt.\nInput: {raw_prompt}\nOutput:'
         if model_path.endswith("v2"):
             template = """Converts a simple image description into a prompt. \
@@ -55,6 +54,60 @@ but make sure there is a correlation between the input and output.\n\
 
 
 
+class QwenPrompt(torch.nn.Module):
+    # This class leverages the open-source Qwen model to translate Chinese prompts into English, 
+    #    with an integrated optimization mechanism for enhanced translation quality.
+    def __init__(self, tokenizer_path=None, model=None, system_prompt=""):
+        super().__init__()
+        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+        self.model = model
+        self.system_prompt = system_prompt
+
+
+    @staticmethod
+    def from_model_manager(model_nameger: ModelManager):
+        model, model_path = model_nameger.fetch_model("qwen_prompt", require_model_path=True)
+        system_prompt = """You are an English image describer. Here are some example image styles:\n\n1. Extreme close-up: Clear focus on a single object with a blurred background, highlighted under natural sunlight.\n2. Vintage: A photograph of a historical scene, using techniques such as Daguerreotype or cyanotype.\n3. Anime: A stylized cartoon image, emphasizing hyper-realistic portraits and luminous brushwork.\n4. Candid: A natural, unposed shot capturing spontaneous moments, often with cinematic qualities.\n5. Landscape: A photorealistic image of natural scenery, such as a sunrise over the sea.\n6. Design: Colorful and detailed illustrations, often in the style of 2D game art or botanical illustrations.\n7. Urban: An ultrarealistic scene in a modern setting, possibly a cityscape viewed from indoors.\n\nYour task is to translate a given Chinese image description into a concise and precise English description. Ensure that the imagery is vivid and descriptive, and include stylistic elements to enrich the description.\nPlease note the following points:\n\n1. Capture the essence and mood of the Chinese description without including direct phrases or words from the examples provided.\n2. You should add appropriate words to make the images described in the prompt more aesthetically pleasing. If the Chinese description does not specify a style, you need to add some stylistic descriptions based on the essence of the Chinese text.\n3. The generated English description should not exceed 200 words.\n\n"""
+        qwen_prompt = QwenPrompt(
+            tokenizer_path=model_path,
+            model=model,
+            system_prompt=system_prompt
+        )
+        return qwen_prompt
+
+
+    def __call__(self, raw_prompt, positive=True, **kwargs):
+        if positive:
+            messages = [{
+                'role': 'system',
+                'content': self.system_prompt
+            }, {
+                'role': 'user',
+                'content': raw_prompt
+            }]
+            text = self.tokenizer.apply_chat_template(
+                messages,
+                tokenize=False,
+                add_generation_prompt=True
+            )
+            model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device)
+
+            generated_ids = self.model.generate(
+                model_inputs.input_ids,
+                max_new_tokens=512
+            )
+            generated_ids = [
+                output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+            ]
+
+            prompt = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+            print(f"Your prompt is refined by Qwen: {prompt}")
+            return prompt
+        else:
+            return raw_prompt
+
+
+
 class Translator(torch.nn.Module):
     def __init__(self, tokenizer_path=None, model=None):
         super().__init__()
@@ -63,8 +116,8 @@ class Translator(torch.nn.Module):
 
 
     @staticmethod
-    def from_model_manager(model_nameger: ModelManager):
+    def from_model_manager(model_manager: ModelManager):
-        model, model_path = model_nameger.fetch_model("translator", require_model_path=True)
+        model, model_path = model_manager.fetch_model("translator", require_model_path=True)
         translator = Translator(tokenizer_path=model_path, model=model)
         return translator
     

diffsynth/prompters/sd3_prompter.py

@@ -67,7 +67,8 @@ class SD3Prompter(BasePrompter):
         self,
         prompt,
         positive=True,
-        device="cuda"
+        device="cuda",
+        t5_sequence_length=77,
     ):
         prompt = self.process_prompt(prompt, positive=positive)
         
@@ -77,9 +78,9 @@ class SD3Prompter(BasePrompter):
 
         # T5
         if self.text_encoder_3 is None:
-            prompt_emb_3 = torch.zeros((prompt_emb_1.shape[0], 256, 4096), dtype=prompt_emb_1.dtype, device=device)
+            prompt_emb_3 = torch.zeros((prompt_emb_1.shape[0], t5_sequence_length, 4096), dtype=prompt_emb_1.dtype, device=device)
         else:
-            prompt_emb_3 = self.encode_prompt_using_t5(prompt, self.text_encoder_3, self.tokenizer_3, 256, device)
+            prompt_emb_3 = self.encode_prompt_using_t5(prompt, self.text_encoder_3, self.tokenizer_3, t5_sequence_length, device)
             prompt_emb_3 = prompt_emb_3.to(prompt_emb_1.dtype) # float32 -> float16
 
         # Merge

diffsynth/prompters/sd_prompter.py

@@ -1,5 +1,5 @@
 from .base_prompter import BasePrompter, tokenize_long_prompt
-from ..models.model_manager import ModelManager, load_state_dict, search_for_embeddings
+from ..models.utils import load_state_dict, search_for_embeddings
 from ..models import SDTextEncoder
 from transformers import CLIPTokenizer
 import torch, os

diffsynth/prompters/stepvideo_prompter.py

@@ -0,0 +1,56 @@
+from .base_prompter import BasePrompter
+from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder
+from ..models.stepvideo_text_encoder import STEP1TextEncoder
+from transformers import BertTokenizer
+import os, torch
+
+
+class StepVideoPrompter(BasePrompter):
+
+    def __init__(
+        self,
+        tokenizer_1_path=None,
+    ):
+        if tokenizer_1_path is None:
+            base_path = os.path.dirname(os.path.dirname(__file__))
+            tokenizer_1_path = os.path.join(
+                base_path, "tokenizer_configs/hunyuan_dit/tokenizer")
+        super().__init__()
+        self.tokenizer_1 = BertTokenizer.from_pretrained(tokenizer_1_path)
+
+    def fetch_models(self, text_encoder_1: HunyuanDiTCLIPTextEncoder = None, text_encoder_2: STEP1TextEncoder = None):
+        self.text_encoder_1 = text_encoder_1
+        self.text_encoder_2 = text_encoder_2
+
+    def encode_prompt_using_clip(self, prompt, max_length, device):
+        text_inputs = self.tokenizer_1(
+            prompt,
+            padding="max_length",
+            max_length=max_length,
+            truncation=True,
+            return_attention_mask=True,
+            return_tensors="pt",
+        )
+        prompt_embeds = self.text_encoder_1(
+            text_inputs.input_ids.to(device),
+            attention_mask=text_inputs.attention_mask.to(device),
+        )
+        return prompt_embeds
+
+    def encode_prompt_using_llm(self, prompt, max_length, device):
+        y, y_mask = self.text_encoder_2(prompt, max_length=max_length, device=device)
+        return y, y_mask
+
+    def encode_prompt(self,
+                      prompt,
+                      positive=True,
+                      device="cuda"):
+
+        prompt = self.process_prompt(prompt, positive=positive)
+
+        clip_embeds = self.encode_prompt_using_clip(prompt, max_length=77, device=device)
+        llm_embeds, llm_mask = self.encode_prompt_using_llm(prompt, max_length=320, device=device)
+
+        llm_mask = torch.nn.functional.pad(llm_mask, (clip_embeds.shape[1], 0), value=1)
+
+        return clip_embeds, llm_embeds, llm_mask

diffsynth/prompters/wanx_prompter.py

@@ -0,0 +1,103 @@
+from .base_prompter import BasePrompter
+from ..models.wanx_text_encoder import WanXTextEncoder
+from transformers import AutoTokenizer
+import os, torch
+import ftfy
+import html
+import string
+
+import regex as re
+
+
+def basic_clean(text):
+    text = ftfy.fix_text(text)
+    text = html.unescape(html.unescape(text))
+    return text.strip()
+
+def whitespace_clean(text):
+    text = re.sub(r'\s+', ' ', text)
+    text = text.strip()
+    return text
+
+def canonicalize(text, keep_punctuation_exact_string=None):
+    text = text.replace('_', ' ')
+    if keep_punctuation_exact_string:
+        text = keep_punctuation_exact_string.join(
+            part.translate(str.maketrans('', '', string.punctuation))
+            for part in text.split(keep_punctuation_exact_string))
+    else:
+        text = text.translate(str.maketrans('', '', string.punctuation))
+    text = text.lower()
+    text = re.sub(r'\s+', ' ', text)
+    return text.strip()
+
+class HuggingfaceTokenizer:
+
+    def __init__(self, name, seq_len=None, clean=None, **kwargs):
+        assert clean in (None, 'whitespace', 'lower', 'canonicalize')
+        self.name = name
+        self.seq_len = seq_len
+        self.clean = clean
+
+        # init tokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(name, **kwargs)
+        self.vocab_size = self.tokenizer.vocab_size
+
+    def __call__(self, sequence, **kwargs):
+        return_mask = kwargs.pop('return_mask', False)
+
+        # arguments
+        _kwargs = {'return_tensors': 'pt'}
+        if self.seq_len is not None:
+            _kwargs.update({
+                'padding': 'max_length',
+                'truncation': True,
+                'max_length': self.seq_len
+            })
+        _kwargs.update(**kwargs)
+
+        # tokenization
+        if isinstance(sequence, str):
+            sequence = [sequence]
+        if self.clean:
+            sequence = [self._clean(u) for u in sequence]
+        ids = self.tokenizer(sequence, **_kwargs)
+
+        # output
+        if return_mask:
+            return ids.input_ids, ids.attention_mask
+        else:
+            return ids.input_ids
+
+    def _clean(self, text):
+        if self.clean == 'whitespace':
+            text = whitespace_clean(basic_clean(text))
+        elif self.clean == 'lower':
+            text = whitespace_clean(basic_clean(text)).lower()
+        elif self.clean == 'canonicalize':
+            text = canonicalize(basic_clean(text))
+        return text
+
+class WanXPrompter(BasePrompter):
+
+    def __init__(self, tokenizer_path=None, text_len=512):
+        if tokenizer_path is None:
+            base_path = os.path.dirname(os.path.dirname(__file__))
+            tokenizer_path = os.path.join(
+                base_path, "tokenizer_configs/hunyuan_dit/tokenizer")
+        super().__init__()
+        self.tokenizer = HuggingfaceTokenizer(name=tokenizer_path, seq_len=text_len, clean='whitespace')
+        self.text_encoder = None
+        
+    def fetch_models(self, text_encoder: WanXTextEncoder = None):
+        self.text_encoder = text_encoder
+
+    def encode_prompt(self, prompt, device="cuda"):
+        ids, mask = self.tokenizer(prompt, return_mask=True, add_special_tokens=True)
+        ids = ids.to(device)
+        mask = mask.to(device)
+        seq_lens = mask.gt(0).sum(dim=1).long()
+        prompt_emb = self.text_encoder(ids, mask)
+        prompt_emb = [u[:v] for u, v in zip(prompt_emb, seq_lens)]
+        return prompt_emb
+

diffsynth/schedulers/continuous_ode.py

@@ -10,7 +10,7 @@ class ContinuousODEScheduler():
         self.set_timesteps(num_inference_steps)
 
 
-    def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0):
+    def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, **kwargs):
         ramp = torch.linspace(1-denoising_strength, 1, num_inference_steps)
         min_inv_rho = torch.pow(torch.tensor((self.sigma_min,)), (1 / self.rho))
         max_inv_rho = torch.pow(torch.tensor((self.sigma_max,)), (1 / self.rho))

diffsynth/schedulers/ddim.py

@@ -3,7 +3,7 @@ import torch, math
 
 class EnhancedDDIMScheduler():
 
-    def __init__(self, num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", prediction_type="epsilon"):
+    def __init__(self, num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", prediction_type="epsilon", rescale_zero_terminal_snr=False):
         self.num_train_timesteps = num_train_timesteps
         if beta_schedule == "scaled_linear":
             betas = torch.square(torch.linspace(math.sqrt(beta_start), math.sqrt(beta_end), num_train_timesteps, dtype=torch.float32))
@@ -11,12 +11,34 @@ class EnhancedDDIMScheduler():
             betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         else:
             raise NotImplementedError(f"{beta_schedule} is not implemented")
-        self.alphas_cumprod = torch.cumprod(1.0 - betas, dim=0).tolist()
+        self.alphas_cumprod = torch.cumprod(1.0 - betas, dim=0)
+        if rescale_zero_terminal_snr:
+            self.alphas_cumprod = self.rescale_zero_terminal_snr(self.alphas_cumprod)
+        self.alphas_cumprod = self.alphas_cumprod.tolist()
         self.set_timesteps(10)
         self.prediction_type = prediction_type
 
 
-    def set_timesteps(self, num_inference_steps, denoising_strength=1.0):
+    def rescale_zero_terminal_snr(self, alphas_cumprod):
+        alphas_bar_sqrt = alphas_cumprod.sqrt()
+
+        # Store old values.
+        alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
+        alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
+
+        # Shift so the last timestep is zero.
+        alphas_bar_sqrt -= alphas_bar_sqrt_T
+
+        # Scale so the first timestep is back to the old value.
+        alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
+
+        # Convert alphas_bar_sqrt to betas
+        alphas_bar = alphas_bar_sqrt.square()  # Revert sqrt
+
+        return alphas_bar
+
+
+    def set_timesteps(self, num_inference_steps, denoising_strength=1.0, **kwargs):
         # The timesteps are aligned to 999...0, which is different from other implementations,
         # but I think this implementation is more reasonable in theory.
         max_timestep = max(round(self.num_train_timesteps * denoising_strength) - 1, 0)
@@ -77,3 +99,7 @@ class EnhancedDDIMScheduler():
             sqrt_one_minus_alpha_prod = math.sqrt(1 - self.alphas_cumprod[int(timestep.flatten().tolist()[0])])
             target = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
             return target
+        
+    
+    def training_weight(self, timestep):
+        return 1.0

diffsynth/schedulers/flow_match.py

@@ -4,19 +4,35 @@ import torch
 
 class FlowMatchScheduler():
 
-    def __init__(self, num_inference_steps=100, num_train_timesteps=1000, shift=3.0, sigma_max=1.0, sigma_min=0.003/1.002):
+    def __init__(self, num_inference_steps=100, num_train_timesteps=1000, shift=3.0, sigma_max=1.0, sigma_min=0.003/1.002, inverse_timesteps=False, extra_one_step=False, reverse_sigmas=False):
         self.num_train_timesteps = num_train_timesteps
         self.shift = shift
         self.sigma_max = sigma_max
         self.sigma_min = sigma_min
+        self.inverse_timesteps = inverse_timesteps
+        self.extra_one_step = extra_one_step
+        self.reverse_sigmas = reverse_sigmas
         self.set_timesteps(num_inference_steps)
 
 
-    def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0):
+    def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, training=False):
         sigma_start = self.sigma_min + (self.sigma_max - self.sigma_min) * denoising_strength
+        if self.extra_one_step:
+            self.sigmas = torch.linspace(sigma_start, self.sigma_min, num_inference_steps + 1)[:-1]
+        else:
             self.sigmas = torch.linspace(sigma_start, self.sigma_min, num_inference_steps)
+        if self.inverse_timesteps:
+            self.sigmas = torch.flip(self.sigmas, dims=[0])
         self.sigmas = self.shift * self.sigmas / (1 + (self.shift - 1) * self.sigmas)
+        if self.reverse_sigmas:
+            self.sigmas = 1 - self.sigmas
         self.timesteps = self.sigmas * self.num_train_timesteps
+        if training:
+            x = self.timesteps
+            y = torch.exp(-2 * ((x - num_inference_steps / 2) / num_inference_steps) ** 2)
+            y_shifted = y - y.min()
+            bsmntw_weighing = y_shifted * (num_inference_steps / y_shifted.sum())
+            self.linear_timesteps_weights = bsmntw_weighing
 
 
     def step(self, model_output, timestep, sample, to_final=False):
@@ -25,7 +41,7 @@ class FlowMatchScheduler():
         timestep_id = torch.argmin((self.timesteps - timestep).abs())
         sigma = self.sigmas[timestep_id]
         if to_final or timestep_id + 1 >= len(self.timesteps):
-            sigma_ = 0
+            sigma_ = 1 if (self.inverse_timesteps or self.reverse_sigmas) else 0
         else:
             sigma_ = self.sigmas[timestep_id + 1]
         prev_sample = sample + model_output * (sigma_ - sigma)
@@ -33,8 +49,12 @@ class FlowMatchScheduler():
     
 
     def return_to_timestep(self, timestep, sample, sample_stablized):
-        # This scheduler doesn't support this function.
+        if isinstance(timestep, torch.Tensor):
-        pass
+            timestep = timestep.cpu()
+        timestep_id = torch.argmin((self.timesteps - timestep).abs())
+        sigma = self.sigmas[timestep_id]
+        model_output = (sample - sample_stablized) / sigma
+        return model_output
     
     
     def add_noise(self, original_samples, noise, timestep):
@@ -49,3 +69,9 @@ class FlowMatchScheduler():
     def training_target(self, sample, noise, timestep):
         target = noise - sample
         return target
+    
+
+    def training_weight(self, timestep):
+        timestep_id = torch.argmin((self.timesteps - timestep.to(self.timesteps.device)).abs())
+        weights = self.linear_timesteps_weights[timestep_id]
+        return weights

diffsynth/tokenizer_configs/cog/tokenizer/added_tokens.json

@@ -0,0 +1,102 @@
+{
+  "<extra_id_0>": 32099,
+  "<extra_id_10>": 32089,
+  "<extra_id_11>": 32088,
+  "<extra_id_12>": 32087,
+  "<extra_id_13>": 32086,
+  "<extra_id_14>": 32085,
+  "<extra_id_15>": 32084,
+  "<extra_id_16>": 32083,
+  "<extra_id_17>": 32082,
+  "<extra_id_18>": 32081,
+  "<extra_id_19>": 32080,
+  "<extra_id_1>": 32098,
+  "<extra_id_20>": 32079,
+  "<extra_id_21>": 32078,
+  "<extra_id_22>": 32077,
+  "<extra_id_23>": 32076,
+  "<extra_id_24>": 32075,
+  "<extra_id_25>": 32074,
+  "<extra_id_26>": 32073,
+  "<extra_id_27>": 32072,
+  "<extra_id_28>": 32071,
+  "<extra_id_29>": 32070,
+  "<extra_id_2>": 32097,
+  "<extra_id_30>": 32069,
+  "<extra_id_31>": 32068,
+  "<extra_id_32>": 32067,
+  "<extra_id_33>": 32066,
+  "<extra_id_34>": 32065,
+  "<extra_id_35>": 32064,
+  "<extra_id_36>": 32063,
+  "<extra_id_37>": 32062,
+  "<extra_id_38>": 32061,
+  "<extra_id_39>": 32060,
+  "<extra_id_3>": 32096,
+  "<extra_id_40>": 32059,
+  "<extra_id_41>": 32058,
+  "<extra_id_42>": 32057,
+  "<extra_id_43>": 32056,
+  "<extra_id_44>": 32055,
+  "<extra_id_45>": 32054,
+  "<extra_id_46>": 32053,
+  "<extra_id_47>": 32052,
+  "<extra_id_48>": 32051,
+  "<extra_id_49>": 32050,
+  "<extra_id_4>": 32095,
+  "<extra_id_50>": 32049,
+  "<extra_id_51>": 32048,
+  "<extra_id_52>": 32047,
+  "<extra_id_53>": 32046,
+  "<extra_id_54>": 32045,
+  "<extra_id_55>": 32044,
+  "<extra_id_56>": 32043,
+  "<extra_id_57>": 32042,
+  "<extra_id_58>": 32041,
+  "<extra_id_59>": 32040,
+  "<extra_id_5>": 32094,
+  "<extra_id_60>": 32039,
+  "<extra_id_61>": 32038,
+  "<extra_id_62>": 32037,
+  "<extra_id_63>": 32036,
+  "<extra_id_64>": 32035,
+  "<extra_id_65>": 32034,
+  "<extra_id_66>": 32033,
+  "<extra_id_67>": 32032,
+  "<extra_id_68>": 32031,
+  "<extra_id_69>": 32030,
+  "<extra_id_6>": 32093,
+  "<extra_id_70>": 32029,
+  "<extra_id_71>": 32028,
+  "<extra_id_72>": 32027,
+  "<extra_id_73>": 32026,
+  "<extra_id_74>": 32025,
+  "<extra_id_75>": 32024,
+  "<extra_id_76>": 32023,
+  "<extra_id_77>": 32022,
+  "<extra_id_78>": 32021,
+  "<extra_id_79>": 32020,
+  "<extra_id_7>": 32092,
+  "<extra_id_80>": 32019,
+  "<extra_id_81>": 32018,
+  "<extra_id_82>": 32017,
+  "<extra_id_83>": 32016,
+  "<extra_id_84>": 32015,
+  "<extra_id_85>": 32014,
+  "<extra_id_86>": 32013,
+  "<extra_id_87>": 32012,
+  "<extra_id_88>": 32011,
+  "<extra_id_89>": 32010,
+  "<extra_id_8>": 32091,
+  "<extra_id_90>": 32009,
+  "<extra_id_91>": 32008,
+  "<extra_id_92>": 32007,
+  "<extra_id_93>": 32006,
+  "<extra_id_94>": 32005,
+  "<extra_id_95>": 32004,
+  "<extra_id_96>": 32003,
+  "<extra_id_97>": 32002,
+  "<extra_id_98>": 32001,
+  "<extra_id_99>": 32000,
+  "<extra_id_9>": 32090
+}

diffsynth/tokenizer_configs/cog/tokenizer/special_tokens_map.json

@@ -0,0 +1,125 @@
+{
+  "additional_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
+    "<extra_id_5>",
+    "<extra_id_6>",
+    "<extra_id_7>",
+    "<extra_id_8>",
+    "<extra_id_9>",
+    "<extra_id_10>",
+    "<extra_id_11>",
+    "<extra_id_12>",
+    "<extra_id_13>",
+    "<extra_id_14>",
+    "<extra_id_15>",
+    "<extra_id_16>",
+    "<extra_id_17>",
+    "<extra_id_18>",
+    "<extra_id_19>",
+    "<extra_id_20>",
+    "<extra_id_21>",
+    "<extra_id_22>",
+    "<extra_id_23>",
+    "<extra_id_24>",
+    "<extra_id_25>",
+    "<extra_id_26>",
+    "<extra_id_27>",
+    "<extra_id_28>",
+    "<extra_id_29>",
+    "<extra_id_30>",
+    "<extra_id_31>",
+    "<extra_id_32>",
+    "<extra_id_33>",
+    "<extra_id_34>",
+    "<extra_id_35>",
+    "<extra_id_36>",
+    "<extra_id_37>",
+    "<extra_id_38>",
+    "<extra_id_39>",
+    "<extra_id_40>",
+    "<extra_id_41>",
+    "<extra_id_42>",
+    "<extra_id_43>",
+    "<extra_id_44>",
+    "<extra_id_45>",
+    "<extra_id_46>",
+    "<extra_id_47>",
+    "<extra_id_48>",
+    "<extra_id_49>",
+    "<extra_id_50>",
+    "<extra_id_51>",
+    "<extra_id_52>",
+    "<extra_id_53>",
+    "<extra_id_54>",
+    "<extra_id_55>",
+    "<extra_id_56>",
+    "<extra_id_57>",
+    "<extra_id_58>",
+    "<extra_id_59>",
+    "<extra_id_60>",
+    "<extra_id_61>",
+    "<extra_id_62>",
+    "<extra_id_63>",
+    "<extra_id_64>",
+    "<extra_id_65>",
+    "<extra_id_66>",
+    "<extra_id_67>",
+    "<extra_id_68>",
+    "<extra_id_69>",
+    "<extra_id_70>",
+    "<extra_id_71>",
+    "<extra_id_72>",
+    "<extra_id_73>",
+    "<extra_id_74>",
+    "<extra_id_75>",
+    "<extra_id_76>",
+    "<extra_id_77>",
+    "<extra_id_78>",
+    "<extra_id_79>",
+    "<extra_id_80>",
+    "<extra_id_81>",
+    "<extra_id_82>",
+    "<extra_id_83>",
+    "<extra_id_84>",
+    "<extra_id_85>",
+    "<extra_id_86>",
+    "<extra_id_87>",
+    "<extra_id_88>",
+    "<extra_id_89>",
+    "<extra_id_90>",
+    "<extra_id_91>",
+    "<extra_id_92>",
+    "<extra_id_93>",
+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

diffsynth/tokenizer_configs/cog/tokenizer/tokenizer_config.json

@@ -0,0 +1,940 @@
+{
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<extra_id_99>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32001": {
+      "content": "<extra_id_98>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32002": {
+      "content": "<extra_id_97>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32003": {
+      "content": "<extra_id_96>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32004": {
+      "content": "<extra_id_95>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32005": {
+      "content": "<extra_id_94>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32006": {
+      "content": "<extra_id_93>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32007": {
+      "content": "<extra_id_92>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32008": {
+      "content": "<extra_id_91>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32009": {
+      "content": "<extra_id_90>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32010": {
+      "content": "<extra_id_89>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32011": {
+      "content": "<extra_id_88>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32012": {
+      "content": "<extra_id_87>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32013": {
+      "content": "<extra_id_86>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32014": {
+      "content": "<extra_id_85>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32015": {
+      "content": "<extra_id_84>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32016": {
+      "content": "<extra_id_83>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32017": {
+      "content": "<extra_id_82>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32018": {
+      "content": "<extra_id_81>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32019": {
+      "content": "<extra_id_80>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32020": {
+      "content": "<extra_id_79>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32021": {
+      "content": "<extra_id_78>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32022": {
+      "content": "<extra_id_77>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32023": {
+      "content": "<extra_id_76>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32024": {
+      "content": "<extra_id_75>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32025": {
+      "content": "<extra_id_74>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32026": {
+      "content": "<extra_id_73>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32027": {
+      "content": "<extra_id_72>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32028": {
+      "content": "<extra_id_71>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32029": {
+      "content": "<extra_id_70>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32030": {
+      "content": "<extra_id_69>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32031": {
+      "content": "<extra_id_68>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32032": {
+      "content": "<extra_id_67>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32033": {
+      "content": "<extra_id_66>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32034": {
+      "content": "<extra_id_65>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32035": {
+      "content": "<extra_id_64>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32036": {
+      "content": "<extra_id_63>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32037": {
+      "content": "<extra_id_62>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32038": {
+      "content": "<extra_id_61>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32039": {
+      "content": "<extra_id_60>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32040": {
+      "content": "<extra_id_59>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32041": {
+      "content": "<extra_id_58>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32042": {
+      "content": "<extra_id_57>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32043": {
+      "content": "<extra_id_56>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32044": {
+      "content": "<extra_id_55>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32045": {
+      "content": "<extra_id_54>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32046": {
+      "content": "<extra_id_53>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32047": {
+      "content": "<extra_id_52>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32048": {
+      "content": "<extra_id_51>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32049": {
+      "content": "<extra_id_50>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32050": {
+      "content": "<extra_id_49>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32051": {
+      "content": "<extra_id_48>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32052": {
+      "content": "<extra_id_47>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32053": {
+      "content": "<extra_id_46>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32054": {
+      "content": "<extra_id_45>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32055": {
+      "content": "<extra_id_44>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32056": {
+      "content": "<extra_id_43>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32057": {
+      "content": "<extra_id_42>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32058": {
+      "content": "<extra_id_41>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32059": {
+      "content": "<extra_id_40>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32060": {
+      "content": "<extra_id_39>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32061": {
+      "content": "<extra_id_38>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32062": {
+      "content": "<extra_id_37>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32063": {
+      "content": "<extra_id_36>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32064": {
+      "content": "<extra_id_35>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32065": {
+      "content": "<extra_id_34>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32066": {
+      "content": "<extra_id_33>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32067": {
+      "content": "<extra_id_32>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32068": {
+      "content": "<extra_id_31>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32069": {
+      "content": "<extra_id_30>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32070": {
+      "content": "<extra_id_29>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32071": {
+      "content": "<extra_id_28>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32072": {
+      "content": "<extra_id_27>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32073": {
+      "content": "<extra_id_26>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32074": {
+      "content": "<extra_id_25>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32075": {
+      "content": "<extra_id_24>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32076": {
+      "content": "<extra_id_23>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32077": {
+      "content": "<extra_id_22>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32078": {
+      "content": "<extra_id_21>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32079": {
+      "content": "<extra_id_20>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32080": {
+      "content": "<extra_id_19>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32081": {
+      "content": "<extra_id_18>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32082": {
+      "content": "<extra_id_17>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32083": {
+      "content": "<extra_id_16>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32084": {
+      "content": "<extra_id_15>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32085": {
+      "content": "<extra_id_14>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32086": {
+      "content": "<extra_id_13>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32087": {
+      "content": "<extra_id_12>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32088": {
+      "content": "<extra_id_11>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32089": {
+      "content": "<extra_id_10>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32090": {
+      "content": "<extra_id_9>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32091": {
+      "content": "<extra_id_8>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32092": {
+      "content": "<extra_id_7>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32093": {
+      "content": "<extra_id_6>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32094": {
+      "content": "<extra_id_5>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32095": {
+      "content": "<extra_id_4>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32096": {
+      "content": "<extra_id_3>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32097": {
+      "content": "<extra_id_2>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32098": {
+      "content": "<extra_id_1>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32099": {
+      "content": "<extra_id_0>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
+    "<extra_id_5>",
+    "<extra_id_6>",
+    "<extra_id_7>",
+    "<extra_id_8>",
+    "<extra_id_9>",
+    "<extra_id_10>",
+    "<extra_id_11>",
+    "<extra_id_12>",
+    "<extra_id_13>",
+    "<extra_id_14>",
+    "<extra_id_15>",
+    "<extra_id_16>",
+    "<extra_id_17>",
+    "<extra_id_18>",
+    "<extra_id_19>",
+    "<extra_id_20>",
+    "<extra_id_21>",
+    "<extra_id_22>",
+    "<extra_id_23>",
+    "<extra_id_24>",
+    "<extra_id_25>",
+    "<extra_id_26>",
+    "<extra_id_27>",
+    "<extra_id_28>",
+    "<extra_id_29>",
+    "<extra_id_30>",
+    "<extra_id_31>",
+    "<extra_id_32>",
+    "<extra_id_33>",
+    "<extra_id_34>",
+    "<extra_id_35>",
+    "<extra_id_36>",
+    "<extra_id_37>",
+    "<extra_id_38>",
+    "<extra_id_39>",
+    "<extra_id_40>",
+    "<extra_id_41>",
+    "<extra_id_42>",
+    "<extra_id_43>",
+    "<extra_id_44>",
+    "<extra_id_45>",
+    "<extra_id_46>",
+    "<extra_id_47>",
+    "<extra_id_48>",
+    "<extra_id_49>",
+    "<extra_id_50>",
+    "<extra_id_51>",
+    "<extra_id_52>",
+    "<extra_id_53>",
+    "<extra_id_54>",
+    "<extra_id_55>",
+    "<extra_id_56>",
+    "<extra_id_57>",
+    "<extra_id_58>",
+    "<extra_id_59>",
+    "<extra_id_60>",
+    "<extra_id_61>",
+    "<extra_id_62>",
+    "<extra_id_63>",
+    "<extra_id_64>",
+    "<extra_id_65>",
+    "<extra_id_66>",
+    "<extra_id_67>",
+    "<extra_id_68>",
+    "<extra_id_69>",
+    "<extra_id_70>",
+    "<extra_id_71>",
+    "<extra_id_72>",
+    "<extra_id_73>",
+    "<extra_id_74>",
+    "<extra_id_75>",
+    "<extra_id_76>",
+    "<extra_id_77>",
+    "<extra_id_78>",
+    "<extra_id_79>",
+    "<extra_id_80>",
+    "<extra_id_81>",
+    "<extra_id_82>",
+    "<extra_id_83>",
+    "<extra_id_84>",
+    "<extra_id_85>",
+    "<extra_id_86>",
+    "<extra_id_87>",
+    "<extra_id_88>",
+    "<extra_id_89>",
+    "<extra_id_90>",
+    "<extra_id_91>",
+    "<extra_id_92>",
+    "<extra_id_93>",
+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "</s>",
+  "extra_ids": 100,
+  "legacy": true,
+  "model_max_length": 226,
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>"
+}

diffsynth/tokenizer_configs/flux/tokenizer_1/special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

diffsynth/tokenizer_configs/flux/tokenizer_1/tokenizer_config.json

@@ -0,0 +1,30 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "49406": {
+      "content": "<|startoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "49407": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 77,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": "<|endoftext|>"
+}

diffsynth/tokenizer_configs/flux/tokenizer_2/special_tokens_map.json

@@ -0,0 +1,125 @@
+{
+  "additional_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
+    "<extra_id_5>",
+    "<extra_id_6>",
+    "<extra_id_7>",
+    "<extra_id_8>",
+    "<extra_id_9>",
+    "<extra_id_10>",
+    "<extra_id_11>",
+    "<extra_id_12>",
+    "<extra_id_13>",
+    "<extra_id_14>",
+    "<extra_id_15>",
+    "<extra_id_16>",
+    "<extra_id_17>",
+    "<extra_id_18>",
+    "<extra_id_19>",
+    "<extra_id_20>",
+    "<extra_id_21>",
+    "<extra_id_22>",
+    "<extra_id_23>",
+    "<extra_id_24>",
+    "<extra_id_25>",
+    "<extra_id_26>",
+    "<extra_id_27>",
+    "<extra_id_28>",
+    "<extra_id_29>",
+    "<extra_id_30>",
+    "<extra_id_31>",
+    "<extra_id_32>",
+    "<extra_id_33>",
+    "<extra_id_34>",
+    "<extra_id_35>",
+    "<extra_id_36>",
+    "<extra_id_37>",
+    "<extra_id_38>",
+    "<extra_id_39>",
+    "<extra_id_40>",
+    "<extra_id_41>",
+    "<extra_id_42>",
+    "<extra_id_43>",
+    "<extra_id_44>",
+    "<extra_id_45>",
+    "<extra_id_46>",
+    "<extra_id_47>",
+    "<extra_id_48>",
+    "<extra_id_49>",
+    "<extra_id_50>",
+    "<extra_id_51>",
+    "<extra_id_52>",
+    "<extra_id_53>",
+    "<extra_id_54>",
+    "<extra_id_55>",
+    "<extra_id_56>",
+    "<extra_id_57>",
+    "<extra_id_58>",
+    "<extra_id_59>",
+    "<extra_id_60>",
+    "<extra_id_61>",
+    "<extra_id_62>",
+    "<extra_id_63>",
+    "<extra_id_64>",
+    "<extra_id_65>",
+    "<extra_id_66>",
+    "<extra_id_67>",
+    "<extra_id_68>",
+    "<extra_id_69>",
+    "<extra_id_70>",
+    "<extra_id_71>",
+    "<extra_id_72>",
+    "<extra_id_73>",
+    "<extra_id_74>",
+    "<extra_id_75>",
+    "<extra_id_76>",
+    "<extra_id_77>",
+    "<extra_id_78>",
+    "<extra_id_79>",
+    "<extra_id_80>",
+    "<extra_id_81>",
+    "<extra_id_82>",
+    "<extra_id_83>",
+    "<extra_id_84>",
+    "<extra_id_85>",
+    "<extra_id_86>",
+    "<extra_id_87>",
+    "<extra_id_88>",
+    "<extra_id_89>",
+    "<extra_id_90>",
+    "<extra_id_91>",
+    "<extra_id_92>",
+    "<extra_id_93>",
+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

diffsynth/tokenizer_configs/flux/tokenizer_2/tokenizer_config.json

@@ -0,0 +1,940 @@
+{
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<extra_id_99>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32001": {
+      "content": "<extra_id_98>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32002": {
+      "content": "<extra_id_97>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32003": {
+      "content": "<extra_id_96>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32004": {
+      "content": "<extra_id_95>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32005": {
+      "content": "<extra_id_94>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32006": {
+      "content": "<extra_id_93>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32007": {
+      "content": "<extra_id_92>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32008": {
+      "content": "<extra_id_91>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32009": {
+      "content": "<extra_id_90>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32010": {
+      "content": "<extra_id_89>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32011": {
+      "content": "<extra_id_88>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32012": {
+      "content": "<extra_id_87>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32013": {
+      "content": "<extra_id_86>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32014": {
+      "content": "<extra_id_85>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32015": {
+      "content": "<extra_id_84>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32016": {
+      "content": "<extra_id_83>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32017": {
+      "content": "<extra_id_82>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32018": {
+      "content": "<extra_id_81>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32019": {
+      "content": "<extra_id_80>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32020": {
+      "content": "<extra_id_79>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32021": {
+      "content": "<extra_id_78>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32022": {
+      "content": "<extra_id_77>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32023": {
+      "content": "<extra_id_76>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32024": {
+      "content": "<extra_id_75>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32025": {
+      "content": "<extra_id_74>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32026": {
+      "content": "<extra_id_73>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32027": {
+      "content": "<extra_id_72>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32028": {
+      "content": "<extra_id_71>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32029": {
+      "content": "<extra_id_70>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32030": {
+      "content": "<extra_id_69>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32031": {
+      "content": "<extra_id_68>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32032": {
+      "content": "<extra_id_67>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32033": {
+      "content": "<extra_id_66>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32034": {
+      "content": "<extra_id_65>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32035": {
+      "content": "<extra_id_64>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32036": {
+      "content": "<extra_id_63>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32037": {
+      "content": "<extra_id_62>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32038": {
+      "content": "<extra_id_61>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32039": {
+      "content": "<extra_id_60>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32040": {
+      "content": "<extra_id_59>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32041": {
+      "content": "<extra_id_58>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32042": {
+      "content": "<extra_id_57>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32043": {
+      "content": "<extra_id_56>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32044": {
+      "content": "<extra_id_55>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32045": {
+      "content": "<extra_id_54>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32046": {
+      "content": "<extra_id_53>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32047": {
+      "content": "<extra_id_52>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32048": {
+      "content": "<extra_id_51>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32049": {
+      "content": "<extra_id_50>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32050": {
+      "content": "<extra_id_49>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32051": {
+      "content": "<extra_id_48>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32052": {
+      "content": "<extra_id_47>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32053": {
+      "content": "<extra_id_46>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32054": {
+      "content": "<extra_id_45>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32055": {
+      "content": "<extra_id_44>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32056": {
+      "content": "<extra_id_43>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32057": {
+      "content": "<extra_id_42>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32058": {
+      "content": "<extra_id_41>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32059": {
+      "content": "<extra_id_40>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32060": {
+      "content": "<extra_id_39>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32061": {
+      "content": "<extra_id_38>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32062": {
+      "content": "<extra_id_37>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32063": {
+      "content": "<extra_id_36>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32064": {
+      "content": "<extra_id_35>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32065": {
+      "content": "<extra_id_34>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32066": {
+      "content": "<extra_id_33>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32067": {
+      "content": "<extra_id_32>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32068": {
+      "content": "<extra_id_31>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32069": {
+      "content": "<extra_id_30>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32070": {
+      "content": "<extra_id_29>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32071": {
+      "content": "<extra_id_28>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32072": {
+      "content": "<extra_id_27>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32073": {
+      "content": "<extra_id_26>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32074": {
+      "content": "<extra_id_25>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32075": {
+      "content": "<extra_id_24>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32076": {
+      "content": "<extra_id_23>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32077": {
+      "content": "<extra_id_22>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32078": {
+      "content": "<extra_id_21>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32079": {
+      "content": "<extra_id_20>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32080": {
+      "content": "<extra_id_19>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32081": {
+      "content": "<extra_id_18>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32082": {
+      "content": "<extra_id_17>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32083": {
+      "content": "<extra_id_16>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32084": {
+      "content": "<extra_id_15>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32085": {
+      "content": "<extra_id_14>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32086": {
+      "content": "<extra_id_13>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32087": {
+      "content": "<extra_id_12>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32088": {
+      "content": "<extra_id_11>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32089": {
+      "content": "<extra_id_10>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32090": {
+      "content": "<extra_id_9>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32091": {
+      "content": "<extra_id_8>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32092": {
+      "content": "<extra_id_7>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32093": {
+      "content": "<extra_id_6>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32094": {
+      "content": "<extra_id_5>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32095": {
+      "content": "<extra_id_4>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32096": {
+      "content": "<extra_id_3>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32097": {
+      "content": "<extra_id_2>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32098": {
+      "content": "<extra_id_1>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32099": {
+      "content": "<extra_id_0>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
+    "<extra_id_5>",
+    "<extra_id_6>",
+    "<extra_id_7>",
+    "<extra_id_8>",
+    "<extra_id_9>",
+    "<extra_id_10>",
+    "<extra_id_11>",
+    "<extra_id_12>",
+    "<extra_id_13>",
+    "<extra_id_14>",
+    "<extra_id_15>",
+    "<extra_id_16>",
+    "<extra_id_17>",
+    "<extra_id_18>",
+    "<extra_id_19>",
+    "<extra_id_20>",
+    "<extra_id_21>",
+    "<extra_id_22>",
+    "<extra_id_23>",
+    "<extra_id_24>",
+    "<extra_id_25>",
+    "<extra_id_26>",
+    "<extra_id_27>",
+    "<extra_id_28>",
+    "<extra_id_29>",
+    "<extra_id_30>",
+    "<extra_id_31>",
+    "<extra_id_32>",
+    "<extra_id_33>",
+    "<extra_id_34>",
+    "<extra_id_35>",
+    "<extra_id_36>",
+    "<extra_id_37>",
+    "<extra_id_38>",
+    "<extra_id_39>",
+    "<extra_id_40>",
+    "<extra_id_41>",
+    "<extra_id_42>",
+    "<extra_id_43>",
+    "<extra_id_44>",
+    "<extra_id_45>",
+    "<extra_id_46>",
+    "<extra_id_47>",
+    "<extra_id_48>",
+    "<extra_id_49>",
+    "<extra_id_50>",
+    "<extra_id_51>",
+    "<extra_id_52>",
+    "<extra_id_53>",
+    "<extra_id_54>",
+    "<extra_id_55>",
+    "<extra_id_56>",
+    "<extra_id_57>",
+    "<extra_id_58>",
+    "<extra_id_59>",
+    "<extra_id_60>",
+    "<extra_id_61>",
+    "<extra_id_62>",
+    "<extra_id_63>",
+    "<extra_id_64>",
+    "<extra_id_65>",
+    "<extra_id_66>",
+    "<extra_id_67>",
+    "<extra_id_68>",
+    "<extra_id_69>",
+    "<extra_id_70>",
+    "<extra_id_71>",
+    "<extra_id_72>",
+    "<extra_id_73>",
+    "<extra_id_74>",
+    "<extra_id_75>",
+    "<extra_id_76>",
+    "<extra_id_77>",
+    "<extra_id_78>",
+    "<extra_id_79>",
+    "<extra_id_80>",
+    "<extra_id_81>",
+    "<extra_id_82>",
+    "<extra_id_83>",
+    "<extra_id_84>",
+    "<extra_id_85>",
+    "<extra_id_86>",
+    "<extra_id_87>",
+    "<extra_id_88>",
+    "<extra_id_89>",
+    "<extra_id_90>",
+    "<extra_id_91>",
+    "<extra_id_92>",
+    "<extra_id_93>",
+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "</s>",
+  "extra_ids": 100,
+  "legacy": true,
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>"
+}

diffsynth/tokenizer_configs/hunyuan_video/tokenizer_1/special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

diffsynth/tokenizer_configs/hunyuan_video/tokenizer_1/tokenizer_config.json

@@ -0,0 +1,30 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "49406": {
+      "content": "<|startoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "49407": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 77,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": "<|endoftext|>"
+}

diffsynth/tokenizer_configs/hunyuan_video/tokenizer_2/special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

diffsynth/trainers/text_to_image.py

@@ -3,6 +3,7 @@ from peft import LoraConfig, inject_adapter_in_model
 import torch, os
 from ..data.simple_text_image import TextImageDataset
 from modelscope.hub.api import HubApi
+from ..models.utils import load_state_dict
 
 
 
@@ -11,11 +12,14 @@ class LightningModelForT2ILoRA(pl.LightningModule):
         self,
         learning_rate=1e-4,
         use_gradient_checkpointing=True,
+        state_dict_converter=None,
     ):
         super().__init__()
         # Set parameters
         self.learning_rate = learning_rate
         self.use_gradient_checkpointing = use_gradient_checkpointing
+        self.state_dict_converter = state_dict_converter
+        self.lora_alpha = None
 
 
     def load_models(self):
@@ -30,12 +34,16 @@ class LightningModelForT2ILoRA(pl.LightningModule):
         self.pipe.denoising_model().train()
 
     
-    def add_lora_to_model(self, model, lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out"):
+    def add_lora_to_model(self, model, lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian", pretrained_lora_path=None, state_dict_converter=None):
         # Add LoRA to UNet
+        self.lora_alpha = lora_alpha
+        if init_lora_weights == "kaiming":
+            init_lora_weights = True
+            
         lora_config = LoraConfig(
             r=lora_rank,
             lora_alpha=lora_alpha,
-            init_lora_weights="gaussian",
+            init_lora_weights=init_lora_weights,
             target_modules=lora_target_modules.split(","),
         )
         model = inject_adapter_in_model(lora_config, model)
@@ -44,6 +52,17 @@ class LightningModelForT2ILoRA(pl.LightningModule):
             if param.requires_grad:
                 param.data = param.to(torch.float32)
 
+        # Lora pretrained lora weights
+        if pretrained_lora_path is not None:
+            state_dict = load_state_dict(pretrained_lora_path)
+            if state_dict_converter is not None:
+                state_dict = state_dict_converter(state_dict)
+            missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
+            all_keys = [i for i, _ in model.named_parameters()]
+            num_updated_keys = len(all_keys) - len(missing_keys)
+            num_unexpected_keys = len(unexpected_keys)
+            print(f"{num_updated_keys} parameters are loaded from {pretrained_lora_path}. {num_unexpected_keys} parameters are unexpected.")
+
 
     def training_step(self, batch, batch_idx):
         # Data
@@ -52,6 +71,9 @@ class LightningModelForT2ILoRA(pl.LightningModule):
         # Prepare input parameters
         self.pipe.device = self.device
         prompt_emb = self.pipe.encode_prompt(text, positive=True)
+        if "latents" in batch:
+            latents = batch["latents"].to(dtype=self.pipe.torch_dtype, device=self.device)
+        else:
             latents = self.pipe.vae_encoder(image.to(dtype=self.pipe.torch_dtype, device=self.device))
         noise = torch.randn_like(latents)
         timestep_id = torch.randint(0, self.pipe.scheduler.num_train_timesteps, (1,))
@@ -65,7 +87,8 @@ class LightningModelForT2ILoRA(pl.LightningModule):
             noisy_latents, timestep=timestep, **prompt_emb, **extra_input,
             use_gradient_checkpointing=self.use_gradient_checkpointing
         )
-        loss = torch.nn.functional.mse_loss(noise_pred, training_target)
+        loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float())
+        loss = loss * self.pipe.scheduler.training_weight(timestep)
 
         # Record log
         self.log("train_loss", loss, prog_bar=True)
@@ -83,9 +106,13 @@ class LightningModelForT2ILoRA(pl.LightningModule):
         trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.pipe.denoising_model().named_parameters()))
         trainable_param_names = set([named_param[0] for named_param in trainable_param_names])
         state_dict = self.pipe.denoising_model().state_dict()
+        lora_state_dict = {}
         for name, param in state_dict.items():
             if name in trainable_param_names:
-                checkpoint[name] = param
+                lora_state_dict[name] = param
+        if self.state_dict_converter is not None:
+            lora_state_dict = self.state_dict_converter(lora_state_dict, alpha=self.lora_alpha)
+        checkpoint.update(lora_state_dict)
 
 
 
@@ -152,7 +179,7 @@ def add_general_parsers(parser):
         "--precision",
         type=str,
         default="16-mixed",
-        choices=["32", "16", "16-mixed"],
+        choices=["32", "16", "16-mixed", "bf16"],
         help="Training precision",
     )
     parser.add_argument(
@@ -173,6 +200,13 @@ def add_general_parsers(parser):
         default=4.0,
         help="The weight of the LoRA update matrices.",
     )
+    parser.add_argument(
+        "--init_lora_weights",
+        type=str,
+        default="kaiming",
+        choices=["gaussian", "kaiming"],
+        help="The initializing method of LoRA weight.",
+    )
     parser.add_argument(
         "--use_gradient_checkpointing",
         default=False,
@@ -210,6 +244,12 @@ def add_general_parsers(parser):
         default=None,
         help="Access key on ModelScope (https://www.modelscope.cn/). Required if you want to upload the model to ModelScope.",
     )
+    parser.add_argument(
+        "--pretrained_lora_path",
+        type=str,
+        default=None,
+        help="Pretrained LoRA path. Required if the training is resumed.",
+    )
     return parser
 
 

diffsynth/vram_management/__init__.py

@@ -0,0 +1 @@
+from .layers import *

diffsynth/vram_management/layers.py

@@ -0,0 +1,95 @@
+import torch, copy
+from ..models.utils import init_weights_on_device
+
+
+def cast_to(weight, dtype, device):
+    r = torch.empty_like(weight, dtype=dtype, device=device)
+    r.copy_(weight)
+    return r
+
+
+class AutoWrappedModule(torch.nn.Module):
+    def __init__(self, module: torch.nn.Module, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device):
+        super().__init__()
+        self.module = module.to(dtype=offload_dtype, device=offload_device)
+        self.offload_dtype = offload_dtype
+        self.offload_device = offload_device
+        self.onload_dtype = onload_dtype
+        self.onload_device = onload_device
+        self.computation_dtype = computation_dtype
+        self.computation_device = computation_device
+        self.state = 0
+
+    def offload(self):
+        if self.state == 1 and (self.offload_dtype != self.onload_dtype or self.offload_device != self.onload_device):
+            self.module.to(dtype=self.offload_dtype, device=self.offload_device)
+            self.state = 0
+
+    def onload(self):
+        if self.state == 0 and (self.offload_dtype != self.onload_dtype or self.offload_device != self.onload_device):
+            self.module.to(dtype=self.onload_dtype, device=self.onload_device)
+            self.state = 1
+
+    def forward(self, *args, **kwargs):
+        if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device:
+            module = self.module
+        else:
+            module = copy.deepcopy(self.module).to(dtype=self.computation_dtype, device=self.computation_device)
+        return module(*args, **kwargs)
+    
+
+class AutoWrappedLinear(torch.nn.Linear):
+    def __init__(self, module: torch.nn.Linear, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device):
+        with init_weights_on_device(device=torch.device("meta")):
+            super().__init__(in_features=module.in_features, out_features=module.out_features, bias=module.bias is not None, dtype=offload_dtype, device=offload_device)
+        self.weight = module.weight
+        self.bias = module.bias
+        self.offload_dtype = offload_dtype
+        self.offload_device = offload_device
+        self.onload_dtype = onload_dtype
+        self.onload_device = onload_device
+        self.computation_dtype = computation_dtype
+        self.computation_device = computation_device
+        self.state = 0
+
+    def offload(self):
+        if self.state == 1 and (self.offload_dtype != self.onload_dtype or self.offload_device != self.onload_device):
+            self.to(dtype=self.offload_dtype, device=self.offload_device)
+            self.state = 0
+
+    def onload(self):
+        if self.state == 0 and (self.offload_dtype != self.onload_dtype or self.offload_device != self.onload_device):
+            self.to(dtype=self.onload_dtype, device=self.onload_device)
+            self.state = 1
+
+    def forward(self, x, *args, **kwargs):
+        if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device:
+            weight, bias = self.weight, self.bias
+        else:
+            weight = cast_to(self.weight, self.computation_dtype, self.computation_device)
+            bias = None if self.bias is None else cast_to(self.bias, self.computation_dtype, self.computation_device)
+        return torch.nn.functional.linear(x, weight, bias)
+
+
+def enable_vram_management_recursively(model: torch.nn.Module, module_map: dict, module_config: dict, max_num_param=None, overflow_module_config: dict = None, total_num_param=0):
+    for name, module in model.named_children():
+        for source_module, target_module in module_map.items():
+            if isinstance(module, source_module):
+                num_param = sum(p.numel() for p in module.parameters())
+                if max_num_param is not None and total_num_param + num_param > max_num_param:
+                    module_config_ = overflow_module_config
+                else:
+                    module_config_ = module_config
+                module_ = target_module(module, **module_config_)
+                setattr(model, name, module_)
+                total_num_param += num_param
+                break
+        else:
+            total_num_param = enable_vram_management_recursively(module, module_map, module_config, max_num_param, overflow_module_config, total_num_param)
+    return total_num_param
+
+
+def enable_vram_management(model: torch.nn.Module, module_map: dict, module_config: dict, max_num_param=None, overflow_module_config: dict = None):
+    enable_vram_management_recursively(model, module_map, module_config, max_num_param, overflow_module_config, total_num_param=0)
+    model.vram_management_enabled = True
+

environment.yml

@@ -1,21 +0,0 @@
-name: DiffSynthStudio
-channels:
-  - pytorch
-  - nvidia
-  - defaults
-dependencies:
-  - python=3.9.16
-  - pip=23.0.1
-  - cudatoolkit
-  - pytorch
-  - cupy
-  - pip:
-    - transformers
-    - controlnet-aux==0.0.7
-    - streamlit
-    - streamlit-drawable-canvas
-    - imageio
-    - imageio[ffmpeg]
-    - safetensors
-    - einops
-    - sentencepiece

examples/ArtAug/README.md

@@ -0,0 +1,43 @@
+# FLUX Aesthetics Enhancement LoRA
+
+## Introduction
+
+This is a LoRA model trained for FLUX.1-dev, which enhances the aesthetic quality of images generated by the model. The improvements include, but are not limited to: rich details, beautiful lighting and shadows, aesthetic composition, and clear visuals. This model does not require any trigger words.
+
+* Paper: https://arxiv.org/abs/2412.12888
+* Github: https://github.com/modelscope/DiffSynth-Studio
+* Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ArtAug-lora-FLUX.1dev-v1)
+* Demo: [ModelScope](https://modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=7228&modelType=LoRA&sdVersion=FLUX_1&modelUrl=modelscope%3A%2F%2FDiffSynth-Studio%2FArtAug-lora-FLUX.1dev-v1%3Frevision%3Dv1.0), HuggingFace (Coming soon)
+
+## Methodology
+
+![workflow](https://github.com/user-attachments/assets/cee969af-d49f-4480-911c-bedc1c095f9b)
+
+The ArtAug project is inspired by reasoning approaches like GPT-o1, which rely on model interaction and self-correction. We developed a framework aimed at enhancing the capabilities of image generation models through interaction with image understanding models. The training process of ArtAug consists of the following steps:
+
+1. **Synthesis-Understanding Interaction**: After generating an image using the image generation model, we employ a multimodal large language model (Qwen2-VL-72B) to analyze the image content and provide suggestions for modifications, which then lead to the regeneration of a higher quality image.
+   
+2. **Data Generation and Filtering**: Interactive generation involves long inference times and sometimes produce poor image content. Therefore, we generate a large batch of image pairs offline, filter them, and use them for subsequent training.
+
+3. **Differential Training**: We apply differential training techniques to train a LoRA model, enabling it to learn the differences between images before and after enhancement, rather than directly training on the dataset of enhanced images.
+
+4. **Iterative Enhancement**: The trained LoRA model is fused into the base model, and the entire process is repeated multiple times with the fused model until the interaction algorithm no longer provides significant enhancements. The LoRA models produced in each iteration are combined to produce this final model.
+
+This model integrates the aesthetic understanding of Qwen2-VL-72B into FLUX.1[dev], leading to an improvement in the quality of generated images.
+
+## Usage
+
+Please see [./artaug_flux.py](./artaug_flux.py) for more details.
+
+Since this model is encapsulated in the universal FLUX LoRA format, it can be loaded by most LoRA loaders, allowing you to integrate this LoRA model into your own workflow.
+
+## Examples
+
+|FLUX.1-dev|FLUX.1-dev + ArtAug LoRA|
+|-|-|
+|![image_1_base](https://github.com/user-attachments/assets/e1d5c505-b423-45fe-be01-25c2758f5417)|![image_1_enhance](https://github.com/user-attachments/assets/335908e3-d0bd-41c2-9d99-d10528a2d719)|
+|![image_2_base](https://github.com/user-attachments/assets/7f38e8d4-3c62-492e-bd96-be60f0855037)|![image_2_enhance](https://github.com/user-attachments/assets/ae3a1daf-7a7c-44fd-bdbc-1d2a83bc3de3)|
+|![image_3_base](https://github.com/user-attachments/assets/e2ae4879-9202-45d6-9df7-fbcbd2093d19)|![image_3_enhance](https://github.com/user-attachments/assets/4df6e5b9-65de-408b-88c6-51db39aad801)|
+|![image_4_base](https://github.com/user-attachments/assets/dbc65387-60df-4a18-b1bb-45eaa5be5c1d)|![image_4_enhance](https://github.com/user-attachments/assets/fc19860d-3e28-468b-b013-8745255ac6db)|
+|![image_5_base](https://github.com/user-attachments/assets/bb65c1ba-c0c6-4d3b-b3ef-bdbbb5f03a48)|![image_5_enhance](https://github.com/user-attachments/assets/03570c62-9a0b-428f-8c86-6e01c1421202)|
+|![image_6_base](https://github.com/user-attachments/assets/18e9a4e7-2afd-4ca9-bc49-7736042c25dc)|![image_6_enhance](https://github.com/user-attachments/assets/aa73571f-098a-4e65-9eda-b9729ba379cd)|

examples/ArtAug/artaug_flux.py

@@ -0,0 +1,14 @@
+import torch
+from diffsynth import ModelManager, FluxImagePipeline, download_customized_models
+
+lora_path = download_customized_models(
+    model_id="DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1",
+    origin_file_path="merged_lora.safetensors",
+    local_dir="models/lora"
+)[0]
+model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"])
+model_manager.load_lora(lora_path, lora_alpha=1.0)
+pipe = FluxImagePipeline.from_model_manager(model_manager)
+
+image = pipe(prompt="a house", seed=0)
+image.save("image_artaug.jpg")

examples/ControlNet/README.md

@@ -0,0 +1,91 @@
+# ControlNet
+
+We provide extensive ControlNet support. Taking the FLUX model as an example, we support many different ControlNet models that can be freely combined, even if their structures differ. Additionally, ControlNet models are compatible with high-resolution refinement and partition control techniques, enabling very powerful controllable image generation.
+
+These examples are in [`flux_controlnet.py`](./flux_controlnet.py).
+
+## Canny/Depth/Normal: Structure Control
+
+Structural control is the most fundamental capability of the ControlNet model. By using Canny to extract edge information, or by utilizing depth maps and normal maps, we can extract the structure of an image, which can then serve as control information during the image generation process.
+
+Model link: https://modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha
+
+For example, if we generate an image of a cat and use a model like InstantX/FLUX.1-dev-Controlnet-Union-alpha that supports multiple control conditions, we can simultaneously enable both Canny and Depth controls to transform the environment into a twilight setting.
+
+|![image_5](https://github.com/user-attachments/assets/19d2abc4-36ae-4163-a8da-df5732d1a737)|![image_6](https://github.com/user-attachments/assets/28378271-3782-484c-bd51-3d3311dd85c6)|
+|-|-|
+
+The control strength of ControlNet for structure can be adjusted. For example, in the case below, when we move the girl from summer to winter, we can appropriately lower the control strength of ControlNet so that the model will adapt to the content of the image and change her into warm clothes.
+
+|![image_7](https://github.com/user-attachments/assets/a7b8555b-bfd9-4e92-aa77-16bca81b07e3)|![image_8](https://github.com/user-attachments/assets/a1bab36b-6cce-4f29-8233-4cb824b524a8)|
+|-|-|
+
+## Upscaler/Tile/Blur: High-Resolution Image Synthesis
+
+There are many ControlNet models that support high definition, such as:
+
+Model link: https://modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler, https://modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha, https://modelscope.cn/models/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro
+
+These models can transform blurry, noisy low-quality images into clear ones. In DiffSynth-Studio, the native high-resolution patch processing technology supported by the framework can overcome the resolution limitations of the models, enabling image generation at resolutions of 2048 or even higher, significantly enhancing the capabilities of these models. In the example below, we can see that in the high-definition image enlarged to 2048 resolution, the cat's fur is rendered in exquisite detail, and the skin texture of the characters is delicate and realistic.
+
+|![image_1](https://github.com/user-attachments/assets/9038158a-118c-4ad7-ab01-22865f6a06fc)|![image_2](https://github.com/user-attachments/assets/88583a33-cd74-4cb9-8fd4-c6e14c0ada0c)|
+|-|-|
+
+|![image_3](https://github.com/user-attachments/assets/13061ecf-bb57-448a-82c6-7e4655c9cd85)|![image_4](https://github.com/user-attachments/assets/0b7ae80f-de58-4d1d-a49c-ad17e7631bdc)|
+|-|-|
+
+## Inpaint: Image Restoration
+
+The Inpaint ControlNet model can repaint specific areas in an image. For example, we can put sunglasses on a cat.
+
+Model link: https://modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta
+
+|![image_9](https://github.com/user-attachments/assets/babddad0-2d67-4624-b77a-c953250ebdab)|![mask_9](https://github.com/user-attachments/assets/d5bc2878-1817-457a-bdfa-200f955233d3)|![image_10](https://github.com/user-attachments/assets/e3197f2c-190b-4522-83ab-a2e0451b39f6)|
+|-|-|-|
+
+However, we noticed that the head movements of the cat have changed. If we want to preserve the original structural features, we can use the Canny, Depth, and Normal models. DiffSynth-Studio provides seamless support for ControlNet of different structures. By using a Normal ControlNet, we can ensure that the structure of the image remains unchanged during local redrawing.
+
+Model link: https://modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Surface-Normals
+
+|![image_11](https://github.com/user-attachments/assets/c028e6fc-5125-4cba-b35a-b6211c2e6600)|![mask_11](https://github.com/user-attachments/assets/1928ee9a-7594-4c6e-9c71-5bd0b043d8f4)|![image_12](https://github.com/user-attachments/assets/97b3b9e1-f821-405e-971b-9e1c31a209aa)|
+|-|-|-|
+
+## MultiControlNet+MultiDiffusion: Fine-Grained Control
+
+DiffSynth-Studio not only supports the simultaneous activation of multiple ControlNet structures, but also allows for the partitioned control of content within an image using different prompts. Additionally, it supports the chunk processing of ultra-high-resolution large images, enabling us to achieve extremely detailed high-level control. Next, we will showcase the creative process behind a beautiful image.
+
+First, use the prompt "a beautiful Asian woman and a cat on a bed. The woman wears a dress" to generate a cat and a young girl.
+
+![image_13](https://github.com/user-attachments/assets/8da006e4-0e68-4fa5-b407-31ef5dbe8e5a)
+
+Then, enable Inpaint ControlNet and Canny ControlNet.
+
+Model link: https://modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta, https://modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha
+
+We control the image using two component.
+
+|Prompt: an orange cat, highly detailed|Prompt: a girl wearing a red camisole|
+|-|-|
+|![mask_13_1](https://github.com/user-attachments/assets/188530a0-913c-48db-a7f1-62f0384bfdc3)|![mask_13_2](https://github.com/user-attachments/assets/99c4d0d5-8cc3-47a0-8e56-ceb37db4dfdc)|
+
+Generate!
+
+![image_14](https://github.com/user-attachments/assets/f5b9d3dd-a690-4597-91a8-a019c6fc2523)
+
+The background is a bit blurry, so we use deblurring LoRA for image-to-image generation.
+
+Model link: https://modelscope.cn/models/LiblibAI/FLUX.1-dev-LoRA-AntiBlur
+
+![image_15](https://github.com/user-attachments/assets/32ed2667-2260-4d80-aaa9-4435d6920a2a)
+
+The entire image is much clearer now. Next, let's use the high-definition model to increase the resolution to 4096*4096!
+
+Model link: https://modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler
+
+![image_17](https://github.com/user-attachments/assets/1a688a12-1544-4973-8aca-aa3a23cb34c1)
+
+Zoom in to see details.
+
+![image_17_cropped](https://github.com/user-attachments/assets/461a1fbc-9ffa-4da5-80fd-e1af9667c804)
+
+Enjoy!