DiffSynth-Studio 2.0 major update

.github/workflows/publish.yaml

@@ -1,29 +0,0 @@
-name: release
-
-on:
-  push:
-    tags:
-      - 'v**'
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}-publish
-  cancel-in-progress: true
-
-jobs:
-  build-n-publish:
-    runs-on: ubuntu-20.04
-    #if: startsWith(github.event.ref, 'refs/tags')
-    steps:
-      - uses: actions/checkout@v2
-      - name: Set up Python 3.10
-        uses: actions/setup-python@v2
-        with:
-          python-version: '3.10'
-      - name: Install wheel
-        run: pip install wheel==0.44.0 && pip install -r requirements.txt
-      - name: Build DiffSynth
-        run: python setup.py sdist bdist_wheel
-      - name: Publish package to PyPI
-        run: |
-          pip install twine
-          twine upload dist/* --skip-existing -u __token__ -p ${{ secrets.PYPI_API_TOKEN }}

.gitignore

@@ -0,0 +1,175 @@
+/data
+/models
+/scripts
+/diffusers
+*.pkl
+*.safetensors
+*.pth
+*.ckpt
+*.pt
+*.bin
+*.DS_Store
+*.msc
+*.mv
+log*.txt
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

apps/gradio/DiffSynth_Studio.py

@@ -1,252 +0,0 @@
-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

@@ -1,390 +0,0 @@
-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/gradio/qwen_image_eligen.py

@@ -1,382 +0,0 @@
-import os
-import torch
-import numpy as np
-from PIL import Image, ImageDraw, ImageFont
-import random
-import json
-import gradio as gr
-from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
-from modelscope import dataset_snapshot_download, snapshot_download
-
-# pip install pydantic==2.10.6
-# pip install gradio==5.4.0
-
-snapshot_download("DiffSynth-Studio/Qwen-Image-EliGen", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen", allow_file_pattern="model.safetensors")
-
-dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/qwen-image/*")
-example_json = 'data/examples/eligen/qwen-image/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/qwen-image/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', encoding='utf-8') as f:
-        json.dump(sample, f, ensure_ascii=False, 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("wqy-zenhei.ttc", 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 = {
-    "max_num_painter_layers": 8,
-    "max_num_model_cache": 1,
-}
-
-model_dict = {}
-
-def load_model(model_type='qwen-image'):
-    global model_dict
-    model_key = f"{model_type}"
-    if model_key in model_dict:
-        return model_dict[model_key]
-    pipe = QwenImagePipeline.from_pretrained(
-        torch_dtype=torch.bfloat16,
-        device="cuda",
-        model_configs=[
-            ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
-            ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
-            ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
-        ],
-        tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
-    )
-    pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen/model.safetensors")
-    model_dict[model_key] = pipe
-    return pipe
-
-load_model('qwen-image')
-
-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('qwen-image')
-            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('qwen-image')
-                with gr.Accordion(label="Global prompt"):
-                    prompt = gr.Textbox(label="Global Prompt", lines=3)
-                    negative_prompt = gr.Textbox(label="Negative prompt", value="", 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=4.0, step=0.1, interactive=True, label="Classifier-free 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, visible=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=(1024, 1024),
-                                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):
-                                if canvas is None or canvas["background"] is None:
-                                    return np.ones((height, width, 3), dtype=np.uint8) * 255
-                                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, prompt, negative_prompt, cfg_scale, 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, prompt, negative_prompt, cfg_scale, 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)
-                        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 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_prompts = None if len(local_prompts) == 0 else local_prompts
-                        entity_masks = None if len(masks) == 0 or entity_prompts is None else masks
-                        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/qwen-image/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/qwen-image/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(share=False)

apps/streamlit/DiffSynth_Studio.py

@@ -1,15 +0,0 @@
-# Set web page format
-import streamlit as st
-st.set_page_config(layout="wide")
-# Disable virtual VRAM on windows system
-import torch
-torch.cuda.set_per_process_memory_fraction(0.999, 0)
-
-
-st.markdown("""
-# DiffSynth Studio
-
-[Source Code](https://github.com/Artiprocher/DiffSynth-Studio)
-
-Welcome to DiffSynth Studio.
-""")

apps/streamlit/pages/1_Image_Creator.py

@@ -1,362 +0,0 @@
-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, FluxImagePipeline
-from diffsynth.data.video import crop_and_resize
-
-
-config = {
-    "Stable Diffusion": {
-        "model_folder": "models/stable_diffusion",
-        "pipeline_class": SDImagePipeline,
-        "fixed_parameters": {}
-    },
-    "Stable Diffusion XL": {
-        "model_folder": "models/stable_diffusion_xl",
-        "pipeline_class": SDXLImagePipeline,
-        "fixed_parameters": {}
-    },
-    "Stable Diffusion 3": {
-        "model_folder": "models/stable_diffusion_3",
-        "pipeline_class": SD3ImagePipeline,
-        "fixed_parameters": {}
-    },
-    "Stable Diffusion XL Turbo": {
-        "model_folder": "models/stable_diffusion_xl_turbo",
-        "pipeline_class": SDXLImagePipeline,
-        "fixed_parameters": {
-            "negative_prompt": "",
-            "cfg_scale": 1.0,
-            "num_inference_steps": 1,
-            "height": 512,
-            "width": 512,
-        }
-    },
-    "Kolors": {
-        "model_folder": "models/kolors",
-        "pipeline_class": SDXLImagePipeline,
-        "fixed_parameters": {}
-    },
-    "HunyuanDiT": {
-        "model_folder": "models/HunyuanDiT",
-        "pipeline_class": HunyuanDiTImagePipeline,
-        "fixed_parameters": {
-            "height": 1024,
-            "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", "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 release_model():
-    if "model_manager" in st.session_state:
-        st.session_state["model_manager"].to("cpu")
-        del st.session_state["loaded_model_path"]
-        del st.session_state["model_manager"]
-        del st.session_state["pipeline"]
-        torch.cuda.empty_cache()
-
-
-def load_model(model_type, 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)
-    pipeline = config[model_type]["pipeline_class"].from_model_manager(model_manager)
-    st.session_state.loaded_model_path = model_path
-    st.session_state.model_manager = model_manager
-    st.session_state.pipeline = pipeline
-    return model_manager, pipeline
-
-
-def use_output_image_as_input(update=True):
-    # Search for input image
-    output_image_id = 0
-    selected_output_image = None
-    while True:
-        if f"use_output_as_input_{output_image_id}" not in st.session_state:
-            break
-        if st.session_state[f"use_output_as_input_{output_image_id}"]:
-            selected_output_image = st.session_state["output_images"][output_image_id]
-            break
-        output_image_id += 1
-    if update and selected_output_image is not None:
-        st.session_state["input_image"] = selected_output_image
-    return selected_output_image is not None
-
-
-def apply_stroke_to_image(stroke_image, image):
-    image = np.array(image.convert("RGB")).astype(np.float32)
-    height, width, _ = image.shape
-
-    stroke_image = np.array(Image.fromarray(stroke_image).resize((width, height))).astype(np.float32)
-    weight = stroke_image[:, :, -1:] / 255
-    stroke_image = stroke_image[:, :, :-1]
-
-    image = stroke_image * weight + image * (1 - weight)
-    image = np.clip(image, 0, 255).astype(np.uint8)
-    image = Image.fromarray(image)
-    return image
-
-
-@st.cache_data
-def image2bits(image):
-    image_byte = io.BytesIO()
-    image.save(image_byte, format="PNG")
-    image_byte = image_byte.getvalue()
-    return image_byte
-
-
-def show_output_image(image):
-    st.image(image, use_column_width="always")
-    st.button("Use it as input image", key=f"use_output_as_input_{image_id}")
-    st.download_button("Download", data=image2bits(image), file_name="image.png", mime="image/png", key=f"download_output_{image_id}")
-
-
-column_input, column_output = st.columns(2)
-with st.sidebar:
-    # Select a model
-    with st.expander("Model", expanded=True):
-        model_type = st.selectbox("Model type", [model_type_ for model_type_ in config])
-        fixed_parameters = config[model_type]["fixed_parameters"]
-        model_path_list = ["None"] + load_model_list(model_type)
-        model_path = st.selectbox("Model path", model_path_list)
-
-        # Load the model
-        if model_path == "None":
-            # No models are selected. Release VRAM.
-            st.markdown("No models are selected.")
-            release_model()
-        else:
-            # A model is selected.
-            model_path = os.path.join(config[model_type]["model_folder"], model_path)
-            if st.session_state.get("loaded_model_path", "") != model_path:
-                # The loaded model is not the selected model. Reload it.
-                st.markdown(f"Loading model at {model_path}.")
-                st.markdown("Please wait a moment...")
-                release_model()
-                model_manager, pipeline = load_model(model_type, model_path)
-                st.markdown("Done.")
-            else:
-                # The loaded model is not the selected model. Fetch it from `st.session_state`.
-                st.markdown(f"Loading model at {model_path}.")
-                st.markdown("Please wait a moment...")
-                model_manager, pipeline = st.session_state.model_manager, st.session_state.pipeline
-                st.markdown("Done.")
-
-    # Show parameters
-    with st.expander("Prompt", expanded=True):
-        prompt = st.text_area("Positive prompt")
-        if "negative_prompt" in fixed_parameters:
-            negative_prompt = fixed_parameters["negative_prompt"]
-        else:
-            negative_prompt = st.text_area("Negative prompt")
-        if "cfg_scale" in fixed_parameters:
-            cfg_scale = fixed_parameters["cfg_scale"]
-        else:
-            cfg_scale = st.slider("Classifier-free guidance scale", min_value=1.0, max_value=10.0, value=7.5)
-    with st.expander("Image", expanded=True):
-        if "num_inference_steps" in fixed_parameters:
-            num_inference_steps = fixed_parameters["num_inference_steps"]
-        else:
-            num_inference_steps = st.slider("Inference steps", min_value=1, max_value=100, value=20)
-        if "height" in fixed_parameters:
-            height = fixed_parameters["height"]
-        else:
-            height = st.select_slider("Height", options=[256, 512, 768, 1024, 2048], value=512)
-        if "width" in fixed_parameters:
-            width = fixed_parameters["width"]
-        else:
-            width = st.select_slider("Width", options=[256, 512, 768, 1024, 2048], value=512)
-        num_images = st.number_input("Number of images", value=2)
-        use_fixed_seed = st.checkbox("Use fixed seed", value=False)
-        if use_fixed_seed:
-            seed = st.number_input("Random seed", min_value=0, max_value=10**9, step=1, value=0)
-
-    # Other fixed parameters
-    denoising_strength = 1.0
-    repetition = 1
-
-
-# Show input image
-with column_input:
-    with st.expander("Input image (Optional)", expanded=True):
-        with st.container(border=True):
-            column_white_board, column_upload_image = st.columns([1, 2])
-            with column_white_board:
-                create_white_board = st.button("Create white board")
-                delete_input_image = st.button("Delete input image")
-            with column_upload_image:
-                upload_image = st.file_uploader("Upload image", type=["png", "jpg"], key="upload_image")
-
-        if upload_image is not None:
-            st.session_state["input_image"] = crop_and_resize(Image.open(upload_image), height, width)
-        elif create_white_board:
-            st.session_state["input_image"] = Image.fromarray(np.ones((height, width, 3), dtype=np.uint8) * 255)
-        else:
-            use_output_image_as_input()
-
-        if delete_input_image and "input_image" in st.session_state:
-            del st.session_state.input_image
-        if delete_input_image and "upload_image" in st.session_state:
-            del st.session_state.upload_image
-
-        input_image = st.session_state.get("input_image", None)
-        if input_image is not None:
-            with st.container(border=True):
-                column_drawing_mode, column_color_1, column_color_2 = st.columns([4, 1, 1])
-                with column_drawing_mode:
-                    drawing_mode = st.radio("Drawing tool", ["transform", "freedraw", "line", "rect"], horizontal=True, index=1)
-                with column_color_1:
-                    stroke_color = st.color_picker("Stroke color")
-                with column_color_2:
-                    fill_color = st.color_picker("Fill color")
-                stroke_width = st.slider("Stroke width", min_value=1, max_value=50, value=10)
-            with st.container(border=True):
-                denoising_strength = st.slider("Denoising strength", min_value=0.0, max_value=1.0, value=0.7)
-                repetition = st.slider("Repetition", min_value=1, max_value=8, value=1)
-            with st.container(border=True):
-                input_width, input_height = input_image.size
-                canvas_result = st_canvas(
-                    fill_color=fill_color,
-                    stroke_width=stroke_width,
-                    stroke_color=stroke_color,
-                    background_color="rgba(255, 255, 255, 0)",
-                    background_image=input_image,
-                    update_streamlit=True,
-                    height=int(512 / input_width * input_height),
-                    width=512,
-                    drawing_mode=drawing_mode,
-                    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")
-    auto_update = st.checkbox("Auto update", value=False)
-    num_image_columns = st.slider("Columns", min_value=1, max_value=8, value=2)
-    image_columns = st.columns(num_image_columns)
-
-    # Run
-    if (run_button or auto_update) and model_path != "None":
-
-        if input_image is not None:
-            input_image = input_image.resize((width, height))
-            if canvas_result.image_data is not None:
-                input_image = apply_stroke_to_image(canvas_result.image_data, input_image)
-
-        output_images = []
-        for image_id in range(num_images * repetition):
-            if use_fixed_seed:
-                torch.manual_seed(seed + image_id)
-            else:
-                torch.manual_seed(np.random.randint(0, 10**9))
-            if image_id >= num_images:
-                input_image = output_images[image_id - num_images]
-            with image_columns[image_id % num_image_columns]:
-                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,
-                    progress_bar_st=progress_bar_st
-                )
-                output_images.append(image)
-                progress_bar_st.progress(1.0)
-                show_output_image(image)
-                st.session_state["output_images"] = output_images
-
-    elif "output_images" in st.session_state:
-        for image_id in range(len(st.session_state.output_images)):
-            with image_columns[image_id % num_image_columns]:
-                image = st.session_state.output_images[image_id]
-                progress_bar = st.progress(1.0)
-                show_output_image(image)
-    if "upload_image" in st.session_state and use_output_image_as_input(update=False):
-        st.markdown("If you want to use an output image as input image, please delete the uploaded image manually.")

apps/streamlit/pages/2_Video_Creator.py

@@ -1,197 +0,0 @@
-import streamlit as st
-st.set_page_config(layout="wide")
-from diffsynth import SDVideoPipelineRunner
-import os
-import numpy as np
-
-
-def load_model_list(folder):
-    file_list = os.listdir(folder)
-    file_list = [i for i in file_list if i.endswith(".safetensors") or i.endswith(".pth") or i.endswith(".ckpt")]
-    file_list = sorted(file_list)
-    return file_list
-
-
-def match_processor_id(model_name, supported_processor_id_list):
-    sorted_processor_id = [i[1] for i in sorted([(-len(i), i) for i in supported_processor_id_list])]
-    for processor_id in sorted_processor_id:
-        if processor_id in model_name:
-            return supported_processor_id_list.index(processor_id) + 1
-    return 0
-
-
-config = {
-    "models": {
-        "model_list": [],
-        "textual_inversion_folder": "models/textual_inversion",
-        "device": "cuda",
-        "lora_alphas": [],
-        "controlnet_units": []
-    },
-    "data": {
-        "input_frames": None,
-        "controlnet_frames": [],
-        "output_folder": "output",
-        "fps": 60
-    },
-    "pipeline": {
-        "seed": 0,
-        "pipeline_inputs": {}
-    }
-}
-
-
-with st.expander("Model", expanded=True):
-    stable_diffusion_ckpt = st.selectbox("Stable Diffusion", ["None"] + load_model_list("models/stable_diffusion"))
-    if stable_diffusion_ckpt != "None":
-        config["models"]["model_list"].append(os.path.join("models/stable_diffusion", stable_diffusion_ckpt))
-    animatediff_ckpt = st.selectbox("AnimateDiff", ["None"] + load_model_list("models/AnimateDiff"))
-    if animatediff_ckpt != "None":
-        config["models"]["model_list"].append(os.path.join("models/AnimateDiff", animatediff_ckpt))
-    column_lora, column_lora_alpha = st.columns([2, 1])
-    with column_lora:
-        sd_lora_ckpt = st.selectbox("LoRA", ["None"] + load_model_list("models/lora"))
-    with column_lora_alpha:
-        lora_alpha = st.slider("LoRA Alpha", min_value=-4.0, max_value=4.0, value=1.0, step=0.1)
-    if sd_lora_ckpt != "None":
-        config["models"]["model_list"].append(os.path.join("models/lora", sd_lora_ckpt))
-        config["models"]["lora_alphas"].append(lora_alpha)
-
-
-with st.expander("Data", expanded=True):
-    with st.container(border=True):
-        input_video = st.text_input("Input Video File Path (e.g., data/your_video.mp4)", value="")
-        column_height, column_width, column_start_frame_index, column_end_frame_index = st.columns([2, 2, 1, 1])
-        with column_height:
-            height = st.select_slider("Height", options=[256, 512, 768, 1024, 1536, 2048], value=1024)
-        with column_width:
-            width = st.select_slider("Width", options=[256, 512, 768, 1024, 1536, 2048], value=1024)
-        with column_start_frame_index:
-            start_frame_id = st.number_input("Start Frame id", value=0)
-        with column_end_frame_index:
-            end_frame_id = st.number_input("End Frame id", value=16)
-        if input_video != "":
-            config["data"]["input_frames"] = {
-                "video_file": input_video,
-                "image_folder": None,
-                "height": height,
-                "width": width,
-                "start_frame_id": start_frame_id,
-                "end_frame_id": end_frame_id
-            }
-    with st.container(border=True):
-        output_video = st.text_input("Output Video File Path (e.g., data/a_folder_to_save_something)", value="output")
-        fps = st.number_input("FPS", value=60)
-        config["data"]["output_folder"] = output_video
-        config["data"]["fps"] = fps
-
-
-with st.expander("ControlNet Units", expanded=True):
-    supported_processor_id_list = ["canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "tile"]
-    controlnet_units = st.tabs(["ControlNet Unit 0", "ControlNet Unit 1", "ControlNet Unit 2"])
-    for controlnet_id in range(len(controlnet_units)):
-        with controlnet_units[controlnet_id]:
-            controlnet_ckpt = st.selectbox("ControlNet", ["None"] + load_model_list("models/ControlNet"),
-                                        key=f"controlnet_ckpt_{controlnet_id}")
-            processor_id = st.selectbox("Processor", ["None"] + supported_processor_id_list,
-                                        index=match_processor_id(controlnet_ckpt, supported_processor_id_list),
-                                        disabled=controlnet_ckpt == "None", key=f"processor_id_{controlnet_id}")
-            controlnet_scale = st.slider("Scale", min_value=0.0, max_value=1.0, step=0.01, value=0.5,
-                                        disabled=controlnet_ckpt == "None", key=f"controlnet_scale_{controlnet_id}")
-            use_input_video_as_controlnet_input = st.checkbox("Use input video as ControlNet input", value=True,
-                                                              disabled=controlnet_ckpt == "None",
-                                                              key=f"use_input_video_as_controlnet_input_{controlnet_id}")
-            if not use_input_video_as_controlnet_input:
-                controlnet_input_video = st.text_input("ControlNet Input Video File Path", value="",
-                                            disabled=controlnet_ckpt == "None", key=f"controlnet_input_video_{controlnet_id}")
-                column_height, column_width, column_start_frame_index, column_end_frame_index = st.columns([2, 2, 1, 1])
-                with column_height:
-                    height = st.select_slider("Height", options=[256, 512, 768, 1024, 1536, 2048], value=1024,
-                                              disabled=controlnet_ckpt == "None", key=f"controlnet_height_{controlnet_id}")
-                with column_width:
-                    width = st.select_slider("Width", options=[256, 512, 768, 1024, 1536, 2048], value=1024,
-                                              disabled=controlnet_ckpt == "None", key=f"controlnet_width_{controlnet_id}")
-                with column_start_frame_index:
-                    start_frame_id = st.number_input("Start Frame id", value=0,
-                                                     disabled=controlnet_ckpt == "None", key=f"controlnet_start_frame_id_{controlnet_id}")
-                with column_end_frame_index:
-                    end_frame_id = st.number_input("End Frame id", value=16,
-                                                   disabled=controlnet_ckpt == "None", key=f"controlnet_end_frame_id_{controlnet_id}")
-                if input_video != "":
-                    config["data"]["input_video"] = {
-                        "video_file": input_video,
-                        "image_folder": None,
-                        "height": height,
-                        "width": width,
-                        "start_frame_id": start_frame_id,
-                        "end_frame_id": end_frame_id
-                    }
-            if controlnet_ckpt != "None":
-                config["models"]["model_list"].append(os.path.join("models/ControlNet", controlnet_ckpt))
-                config["models"]["controlnet_units"].append({
-                    "processor_id": processor_id,
-                    "model_path": os.path.join("models/ControlNet", controlnet_ckpt),
-                    "scale": controlnet_scale,
-                })
-                if use_input_video_as_controlnet_input:
-                    config["data"]["controlnet_frames"].append(config["data"]["input_frames"])
-                else:
-                    config["data"]["controlnet_frames"].append({
-                        "video_file": input_video,
-                        "image_folder": None,
-                        "height": height,
-                        "width": width,
-                        "start_frame_id": start_frame_id,
-                        "end_frame_id": end_frame_id
-                    })
-
-
-with st.container(border=True):
-    with st.expander("Seed", expanded=True):
-        use_fixed_seed = st.checkbox("Use fixed seed", value=False)
-        if use_fixed_seed:
-            seed = st.number_input("Random seed", min_value=0, max_value=10**9, step=1, value=0)
-        else:
-            seed = np.random.randint(0, 10**9)
-    with st.expander("Textual Guidance", expanded=True):
-        prompt = st.text_area("Positive prompt")
-        negative_prompt = st.text_area("Negative prompt")
-        column_cfg_scale, column_clip_skip = st.columns(2)
-        with column_cfg_scale:
-            cfg_scale = st.slider("Classifier-free guidance scale", min_value=1.0, max_value=10.0, value=7.0)
-        with column_clip_skip:
-            clip_skip = st.slider("Clip Skip", min_value=1, max_value=4, value=1)
-    with st.expander("Denoising", expanded=True):
-        column_num_inference_steps, column_denoising_strength = st.columns(2)
-        with column_num_inference_steps:
-            num_inference_steps = st.slider("Inference steps", min_value=1, max_value=100, value=10)
-        with column_denoising_strength:
-            denoising_strength = st.slider("Denoising strength", min_value=0.0, max_value=1.0, value=1.0)
-    with st.expander("Efficiency", expanded=False):
-        animatediff_batch_size = st.slider("Animatediff batch size (sliding window size)", min_value=1, max_value=32, value=16, step=1)
-        animatediff_stride = st.slider("Animatediff stride",
-                                       min_value=1,
-                                       max_value=max(2, animatediff_batch_size),
-                                       value=max(1, animatediff_batch_size // 2),
-                                       step=1)
-        unet_batch_size = st.slider("UNet batch size", min_value=1, max_value=32, value=1, step=1)
-        controlnet_batch_size = st.slider("ControlNet batch size", min_value=1, max_value=32, value=1, step=1)
-        cross_frame_attention = st.checkbox("Enable Cross-Frame Attention", value=False)
-    config["pipeline"]["seed"] = seed
-    config["pipeline"]["pipeline_inputs"] = {
-        "prompt": prompt,
-        "negative_prompt": negative_prompt,
-        "cfg_scale": cfg_scale,
-        "clip_skip": clip_skip,
-        "denoising_strength": denoising_strength,
-        "num_inference_steps": num_inference_steps,
-        "animatediff_batch_size": animatediff_batch_size,
-        "animatediff_stride": animatediff_stride,
-        "unet_batch_size": unet_batch_size,
-        "controlnet_batch_size": controlnet_batch_size,
-        "cross_frame_attention": cross_frame_attention,
-    }
-
-run_button = st.button("☢️Run☢️", type="primary")
-if run_button:
-    SDVideoPipelineRunner(in_streamlit=True).run(config)

diffsynth/__init__.py

@@ -1,6 +1 @@
-from .data import *
-from .models import *
-from .prompters import *
-from .schedulers import *
-from .pipelines import *
-from .controlnets import *
+from .core import *

diffsynth/configs/__init__.py

@@ -0,0 +1,2 @@
+from .model_configs import MODEL_CONFIGS
+from .vram_management_module_maps import VRAM_MANAGEMENT_MODULE_MAPS

diffsynth/configs/model_config.py

@@ -1,859 +0,0 @@
-from typing_extensions import Literal, TypeAlias
-
-from ..models.sd_text_encoder import SDTextEncoder
-from ..models.sd_unet import SDUNet
-from ..models.sd_vae_encoder import SDVAEEncoder
-from ..models.sd_vae_decoder import SDVAEDecoder
-
-from ..models.sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2
-from ..models.sdxl_unet import SDXLUNet
-from ..models.sdxl_vae_decoder import SDXLVAEDecoder
-from ..models.sdxl_vae_encoder import SDXLVAEEncoder
-
-from ..models.sd3_text_encoder import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3
-from ..models.sd3_dit import SD3DiT
-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
-
-from ..models.svd_image_encoder import SVDImageEncoder
-from ..models.svd_unet import SVDUNet
-from ..models.svd_vae_decoder import SVDVAEDecoder
-from ..models.svd_vae_encoder import SVDVAEEncoder
-
-from ..models.sd_ipadapter import SDIpAdapter, IpAdapterCLIPImageEmbedder
-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.flux_infiniteyou import InfiniteYouImageProjector
-
-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.wan_video_dit import WanModel
-from ..models.wan_video_dit_s2v import WanS2VModel
-from ..models.wan_video_text_encoder import WanTextEncoder
-from ..models.wan_video_image_encoder import WanImageEncoder
-from ..models.wan_video_vae import WanVideoVAE, WanVideoVAE38
-from ..models.wan_video_motion_controller import WanMotionControllerModel
-from ..models.wan_video_vace import VaceWanModel
-from ..models.wav2vec import WanS2VAudioEncoder
-from ..models.wan_video_animate_adapter import WanAnimateAdapter
-from ..models.wan_video_mot import MotWanModel
-
-from ..models.step1x_connector import Qwen2Connector
-
-from ..models.flux_value_control import SingleValueEncoder
-
-from ..lora.flux_lora import FluxLoraPatcher
-from ..models.flux_lora_encoder import FluxLoRAEncoder
-
-from ..models.nexus_gen_projector import NexusGenAdapter, NexusGenImageEmbeddingMerger
-from ..models.nexus_gen import NexusGenAutoregressiveModel
-
-from ..models.qwen_image_dit import QwenImageDiT
-from ..models.qwen_image_text_encoder import QwenImageTextEncoder
-from ..models.qwen_image_vae import QwenImageVAE
-from ..models.qwen_image_controlnet import QwenImageBlockWiseControlNet
-
-from ..models.longcat_video_dit import LongCatVideoTransformer3DModel
-
-model_loader_configs = [
-    # These configs are provided for detecting model type automatically.
-    # The format is (state_dict_keys_hash, state_dict_keys_hash_with_shape, model_names, model_classes, model_resource)
-    (None, "091b0e30e77c76626b3ba62acdf95343", ["sd_controlnet"], [SDControlNet], "civitai"),
-    (None, "4a6c8306a27d916dea81263c8c88f450", ["hunyuan_dit_clip_text_encoder"], [HunyuanDiTCLIPTextEncoder], "civitai"),
-    (None, "f4aec400fe394297961218c768004521", ["hunyuan_dit"], [HunyuanDiT], "civitai"),
-    (None, "9e6e58043a5a2e332803ed42f6ee7181", ["hunyuan_dit_t5_text_encoder"], [HunyuanDiTT5TextEncoder], "civitai"),
-    (None, "13115dd45a6e1c39860f91ab073b8a78", ["sdxl_vae_encoder", "sdxl_vae_decoder"], [SDXLVAEEncoder, SDXLVAEDecoder], "diffusers"),
-    (None, "d78aa6797382a6d455362358a3295ea9", ["sd_ipadapter_clip_image_encoder"], [IpAdapterCLIPImageEmbedder], "diffusers"),
-    (None, "e291636cc15e803186b47404262ef812", ["sd_ipadapter"], [SDIpAdapter], "civitai"),
-    (None, "399c81f2f8de8d1843d0127a00f3c224", ["sdxl_ipadapter_clip_image_encoder"], [IpAdapterXLCLIPImageEmbedder], "diffusers"),
-    (None, "a64eac9aa0db4b9602213bc0131281c7", ["sdxl_ipadapter"], [SDXLIpAdapter], "civitai"),
-    (None, "52817e4fdd89df154f02749ca6f692ac", ["sdxl_unet"], [SDXLUNet], "diffusers"),
-    (None, "03343c606f16d834d6411d0902b53636", ["sd_text_encoder", "sd_unet", "sd_vae_decoder", "sd_vae_encoder"], [SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder], "civitai"),
-    (None, "d4ba77a7ece070679b4a987f58f201e9", ["sd_text_encoder"], [SDTextEncoder], "civitai"),
-    (None, "d0c89e55c5a57cf3981def0cb1c9e65a", ["sd_vae_decoder", "sd_vae_encoder"], [SDVAEDecoder, SDVAEEncoder], "civitai"),
-    (None, "3926bf373b39a67eeafd7901478a47a7", ["sd_unet"], [SDUNet], "civitai"),
-    (None, "1e0c39ec176b9007c05f76d52b554a4d", ["sd3_text_encoder_1", "sd3_text_encoder_2", "sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3TextEncoder1, SD3TextEncoder2, SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"),
-    (None, "d9e0290829ba8d98e28e1a2b1407db4a", ["sd3_text_encoder_1", "sd3_text_encoder_2", "sd3_text_encoder_3", "sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3, SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"),
-    (None, "5072d0b24e406b49507abe861cf97691", ["sd3_text_encoder_3"], [SD3TextEncoder3], "civitai"),
-    (None, "4cf64a799d04260df438c6f33c9a047e", ["sdxl_text_encoder", "sdxl_text_encoder_2", "sdxl_unet", "sdxl_vae_decoder", "sdxl_vae_encoder"], [SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder], "civitai"),
-    (None, "d9b008a867c498ab12ad24042eff8e3f", ["sdxl_text_encoder", "sdxl_text_encoder_2", "sdxl_unet", "sdxl_vae_decoder", "sdxl_vae_encoder"], [SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder], "civitai"), # SDXL-Turbo
-    (None, "025bb7452e531a3853d951d77c63f032", ["sdxl_text_encoder", "sdxl_text_encoder_2"], [SDXLTextEncoder, SDXLTextEncoder2], "civitai"),
-    (None, "298997b403a4245c04102c9f36aac348", ["sdxl_unet"], [SDXLUNet], "civitai"),
-    (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, "d02f41c13549fa5093d3521f62a5570a", ["flux_dit"], [FluxDiT], "civitai"),
-    (None, "605c56eab23e9e2af863ad8f0813a25d", ["flux_dit"], [FluxDiT], "diffusers"),
-    (None, "0629116fce1472503a66992f96f3eb1a", ["flux_value_controller"], [SingleValueEncoder], "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, "7f9583eb8ba86642abb9a21a4b2c9e16", ["flux_controlnet"], [FluxControlNet], "diffusers"),
-    (None, "43ad5aaa27dd4ee01b832ed16773fa52", ["flux_controlnet"], [FluxControlNet], "diffusers"),
-    (None, "c07c0f04f5ff55e86b4e937c7a40d481", ["infiniteyou_image_projector"], [InfiniteYouImageProjector], "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, "9269f8db9040a9d860eaca435be61814", ["wan_video_dit"], [WanModel], "civitai"),
-    (None, "aafcfd9672c3a2456dc46e1cb6e52c70", ["wan_video_dit"], [WanModel], "civitai"),
-    (None, "6bfcfb3b342cb286ce886889d519a77e", ["wan_video_dit"], [WanModel], "civitai"),
-    (None, "6d6ccde6845b95ad9114ab993d917893", ["wan_video_dit"], [WanModel], "civitai"),
-    (None, "349723183fc063b2bfc10bb2835cf677", ["wan_video_dit"], [WanModel], "civitai"),
-    (None, "efa44cddf936c70abd0ea28b6cbe946c", ["wan_video_dit"], [WanModel], "civitai"),
-    (None, "3ef3b1f8e1dab83d5b71fd7b617f859f", ["wan_video_dit"], [WanModel], "civitai"),
-    (None, "70ddad9d3a133785da5ea371aae09504", ["wan_video_dit"], [WanModel], "civitai"),
-    (None, "26bde73488a92e64cc20b0a7485b9e5b", ["wan_video_dit"], [WanModel], "civitai"),
-    (None, "ac6a5aa74f4a0aab6f64eb9a72f19901", ["wan_video_dit"], [WanModel], "civitai"), 
-    (None, "b61c605c2adbd23124d152ed28e049ae", ["wan_video_dit"], [WanModel], "civitai"), 
-    (None, "1f5ab7703c6fc803fdded85ff040c316", ["wan_video_dit"], [WanModel], "civitai"),
-    (None, "5b013604280dd715f8457c6ed6d6a626", ["wan_video_dit"], [WanModel], "civitai"),
-    (None, "2267d489f0ceb9f21836532952852ee5", ["wan_video_dit"], [WanModel], "civitai"),
-    (None, "5ec04e02b42d2580483ad69f4e76346a", ["wan_video_dit"], [WanModel], "civitai"),
-    (None, "47dbeab5e560db3180adf51dc0232fb1", ["wan_video_dit"], [WanModel], "civitai"),
-    (None, "5f90e66a0672219f12d9a626c8c21f61", ["wan_video_dit", "wan_video_vap"], [WanModel,MotWanModel], "diffusers"),
-    (None, "a61453409b67cd3246cf0c3bebad47ba", ["wan_video_dit", "wan_video_vace"], [WanModel, VaceWanModel], "civitai"),
-    (None, "7a513e1f257a861512b1afd387a8ecd9", ["wan_video_dit", "wan_video_vace"], [WanModel, VaceWanModel], "civitai"),
-    (None, "cb104773c6c2cb6df4f9529ad5c60d0b", ["wan_video_dit"], [WanModel], "diffusers"),
-    (None, "966cffdcc52f9c46c391768b27637614", ["wan_video_dit"], [WanS2VModel], "civitai"),
-    (None, "8b27900f680d7251ce44e2dc8ae1ffef", ["wan_video_dit"], [LongCatVideoTransformer3DModel], "civitai"),
-    (None, "9c8818c2cbea55eca56c7b447df170da", ["wan_video_text_encoder"], [WanTextEncoder], "civitai"),
-    (None, "5941c53e207d62f20f9025686193c40b", ["wan_video_image_encoder"], [WanImageEncoder], "civitai"),
-    (None, "1378ea763357eea97acdef78e65d6d96", ["wan_video_vae"], [WanVideoVAE], "civitai"),
-    (None, "ccc42284ea13e1ad04693284c7a09be6", ["wan_video_vae"], [WanVideoVAE], "civitai"),
-    (None, "e1de6c02cdac79f8b739f4d3698cd216", ["wan_video_vae"], [WanVideoVAE38], "civitai"),
-    (None, "dbd5ec76bbf977983f972c151d545389", ["wan_video_motion_controller"], [WanMotionControllerModel], "civitai"),
-    (None, "d30fb9e02b1dbf4e509142f05cf7dd50", ["flux_dit", "step1x_connector"], [FluxDiT, Qwen2Connector], "civitai"),
-    (None, "30143afb2dea73d1ac580e0787628f8c", ["flux_lora_patcher"], [FluxLoraPatcher], "civitai"),
-    (None, "77c2e4dd2440269eb33bfaa0d004f6ab", ["flux_lora_encoder"], [FluxLoRAEncoder], "civitai"),
-    (None, "3e6c61b0f9471135fc9c6d6a98e98b6d", ["flux_dit", "nexus_gen_generation_adapter"], [FluxDiT, NexusGenAdapter], "civitai"),
-    (None, "63c969fd37cce769a90aa781fbff5f81", ["flux_dit", "nexus_gen_editing_adapter"], [FluxDiT, NexusGenImageEmbeddingMerger], "civitai"),
-    (None, "2bd19e845116e4f875a0a048e27fc219", ["nexus_gen_llm"], [NexusGenAutoregressiveModel], "civitai"),
-    (None, "0319a1cb19835fb510907dd3367c95ff", ["qwen_image_dit"], [QwenImageDiT], "civitai"),
-    (None, "8004730443f55db63092006dd9f7110e", ["qwen_image_text_encoder"], [QwenImageTextEncoder], "diffusers"),
-    (None, "ed4ea5824d55ec3107b09815e318123a", ["qwen_image_vae"], [QwenImageVAE], "diffusers"),
-    (None, "073bce9cf969e317e5662cd570c3e79c", ["qwen_image_blockwise_controlnet"], [QwenImageBlockWiseControlNet], "civitai"),
-    (None, "a9e54e480a628f0b956a688a81c33bab", ["qwen_image_blockwise_controlnet"], [QwenImageBlockWiseControlNet], "civitai"),
-    (None, "06be60f3a4526586d8431cd038a71486", ["wans2v_audio_encoder"], [WanS2VAudioEncoder], "civitai"),
-    (None, "31fa352acb8a1b1d33cd8764273d80a2", ["wan_video_dit", "wan_video_animate_adapter"], [WanModel, WanAnimateAdapter], "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, redirected_architecture)
-    ("ChatGLMModel", "diffsynth.models.kolors_text_encoder", "kolors_text_encoder", None),
-    ("MarianMTModel", "transformers.models.marian.modeling_marian", "translator", None),
-    ("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"),
-    ("LlavaForConditionalGeneration", "diffsynth.models.hunyuan_video_text_encoder", "hunyuan_video_text_encoder_2", "HunyuanVideoMLLMEncoder"),
-    ("Step1Model", "diffsynth.models.stepvideo_text_encoder", "stepvideo_text_encoder_2", "STEP1TextEncoder"),
-    ("Qwen2_5_VLForConditionalGeneration", "diffsynth.models.qwenvl", "qwenvl", "Qwen25VL_7b_Embedder"),
-]
-patch_model_loader_configs = [
-    # These configs are provided for detecting model type automatically.
-    # The format is (state_dict_keys_hash_with_shape, model_name, model_class, extra_kwargs)
-    ("9a4ab6869ac9b7d6e31f9854e397c867", ["svd_unet"], [SVDUNet], {"add_positional_conv": 128}),
-]
-
-preset_models_on_huggingface = {
-    "HunyuanDiT": [
-        ("Tencent-Hunyuan/HunyuanDiT", "t2i/clip_text_encoder/pytorch_model.bin", "models/HunyuanDiT/t2i/clip_text_encoder"),
-        ("Tencent-Hunyuan/HunyuanDiT", "t2i/mt5/pytorch_model.bin", "models/HunyuanDiT/t2i/mt5"),
-        ("Tencent-Hunyuan/HunyuanDiT", "t2i/model/pytorch_model_ema.pt", "models/HunyuanDiT/t2i/model"),
-        ("Tencent-Hunyuan/HunyuanDiT", "t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix"),
-    ],
-    "stable-video-diffusion-img2vid-xt": [
-        ("stabilityai/stable-video-diffusion-img2vid-xt", "svd_xt.safetensors", "models/stable_video_diffusion"),
-    ],
-    "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
-    "HunyuanDiT": [
-        ("modelscope/HunyuanDiT", "t2i/clip_text_encoder/pytorch_model.bin", "models/HunyuanDiT/t2i/clip_text_encoder"),
-        ("modelscope/HunyuanDiT", "t2i/mt5/pytorch_model.bin", "models/HunyuanDiT/t2i/mt5"),
-        ("modelscope/HunyuanDiT", "t2i/model/pytorch_model_ema.pt", "models/HunyuanDiT/t2i/model"),
-        ("modelscope/HunyuanDiT", "t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix"),
-    ],
-    # Stable Video Diffusion
-    "stable-video-diffusion-img2vid-xt": [
-        ("AI-ModelScope/stable-video-diffusion-img2vid-xt", "svd_xt.safetensors", "models/stable_video_diffusion"),
-    ],
-    # ExVideo
-    "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"),
-    ],
-    "DreamShaper_8": [
-        ("sd_lora/dreamshaper_8", "dreamshaper_8.safetensors", "models/stable_diffusion"),
-    ],
-    "AingDiffusion_v12": [
-        ("sd_lora/aingdiffusion_v12", "aingdiffusion_v12.safetensors", "models/stable_diffusion"),
-    ],
-    "Flat2DAnimerge_v45Sharp": [
-        ("sd_lora/Flat-2D-Animerge", "flat2DAnimerge_v45Sharp.safetensors", "models/stable_diffusion"),
-    ],
-    # Textual Inversion
-    "TextualInversion_VeryBadImageNegative_v1.3": [
-        ("sd_lora/verybadimagenegative_v1.3", "verybadimagenegative_v1.3.pt", "models/textual_inversion"),
-    ],
-    # Stable Diffusion XL
-    "StableDiffusionXL_v1": [
-        ("AI-ModelScope/stable-diffusion-xl-base-1.0", "sd_xl_base_1.0.safetensors", "models/stable_diffusion_xl"),
-    ],
-    "BluePencilXL_v200": [
-        ("sd_lora/bluePencilXL_v200", "bluePencilXL_v200.safetensors", "models/stable_diffusion_xl"),
-    ],
-    "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"),
-    ],
-    "StableDiffusion3_without_T5": [
-        ("AI-ModelScope/stable-diffusion-3-medium", "sd3_medium_incl_clips.safetensors", "models/stable_diffusion_3"),
-    ],
-    # ControlNet
-    "ControlNet_v11f1p_sd15_depth": [
-        ("AI-ModelScope/ControlNet-v1-1", "control_v11f1p_sd15_depth.pth", "models/ControlNet"),
-        ("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators")
-    ],
-    "ControlNet_v11p_sd15_softedge": [
-        ("AI-ModelScope/ControlNet-v1-1", "control_v11p_sd15_softedge.pth", "models/ControlNet"),
-        ("sd_lora/Annotators", "ControlNetHED.pth", "models/Annotators")
-    ],
-    "ControlNet_v11f1e_sd15_tile": [
-        ("AI-ModelScope/ControlNet-v1-1", "control_v11f1e_sd15_tile.pth", "models/ControlNet")
-    ],
-    "ControlNet_v11p_sd15_lineart": [
-        ("AI-ModelScope/ControlNet-v1-1", "control_v11p_sd15_lineart.pth", "models/ControlNet"),
-        ("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"),
-    ],
-    "AnimateDiff_xl_beta": [
-        ("Shanghai_AI_Laboratory/animatediff", "mm_sdxl_v10_beta.ckpt", "models/AnimateDiff"),
-    ],
-    # RIFE
-    "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": {
-        "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"),
-            ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "special_tokens_map.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
-            ("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": {
-        "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"),
-            ("moxying/opus-mt-zh-en", "pytorch_model.bin", "models/translator/opus-mt-zh-en"),
-            ("moxying/opus-mt-zh-en", "source.spm", "models/translator/opus-mt-zh-en"),
-            ("moxying/opus-mt-zh-en", "target.spm", "models/translator/opus-mt-zh-en"),
-            ("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"),
-        ("AI-ModelScope/IP-Adapter", "models/ip-adapter_sd15.bin", "models/IpAdapter/stable_diffusion"),
-    ],
-    "IP-Adapter-SDXL": [
-        ("AI-ModelScope/IP-Adapter", "sdxl_models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion_xl/image_encoder"),
-        ("AI-ModelScope/IP-Adapter", "sdxl_models/ip-adapter_sdxl.bin", "models/IpAdapter/stable_diffusion_xl"),
-    ],
-    # 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"),
-            ("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"),
-        ],
-        "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",
-        ],
-    },
-    "InfiniteYou":{
-        "file_list":[
-            ("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/InfuseNetModel/diffusion_pytorch_model-00001-of-00002.safetensors", "models/InfiniteYou/InfuseNetModel"),
-            ("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/InfuseNetModel/diffusion_pytorch_model-00002-of-00002.safetensors", "models/InfiniteYou/InfuseNetModel"),
-            ("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/image_proj_model.bin", "models/InfiniteYou"),
-            ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/1k3d68.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
-            ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/2d106det.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
-            ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/genderage.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
-            ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/glintr100.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
-            ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/scrfd_10g_bnkps.onnx", "models/InfiniteYou/insightface/models/antelopev2"),            
-        ],
-        "load_path":[
-            [
-                "models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00001-of-00002.safetensors",
-                "models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00002-of-00002.safetensors"
-            ],
-            "models/InfiniteYou/image_proj_model.bin",
-            ],
-    },
-    # 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"
-        ],
-    },
-    "HunyuanVideoI2V":{
-        "file_list": [
-            ("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideoI2V/text_encoder"),
-            ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00001-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"),
-            ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00002-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"),
-            ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00003-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"),
-            ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00004-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"),
-            ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "config.json", "models/HunyuanVideoI2V/text_encoder_2"),
-            ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model.safetensors.index.json", "models/HunyuanVideoI2V/text_encoder_2"),
-            ("AI-ModelScope/HunyuanVideo-I2V", "hunyuan-video-i2v-720p/vae/pytorch_model.pt", "models/HunyuanVideoI2V/vae"),
-            ("AI-ModelScope/HunyuanVideo-I2V", "hunyuan-video-i2v-720p/transformers/mp_rank_00_model_states.pt", "models/HunyuanVideoI2V/transformers")
-        ],
-        "load_path": [
-            "models/HunyuanVideoI2V/text_encoder/model.safetensors",
-            "models/HunyuanVideoI2V/text_encoder_2",
-            "models/HunyuanVideoI2V/vae/pytorch_model.pt",
-            "models/HunyuanVideoI2V/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",
-    "Flat2DAnimerge_v45Sharp",
-    "TextualInversion_VeryBadImageNegative_v1.3",
-    "StableDiffusionXL_v1",
-    "BluePencilXL_v200",
-    "StableDiffusionXL_Turbo",
-    "ControlNet_v11f1p_sd15_depth",
-    "ControlNet_v11p_sd15_softedge",
-    "ControlNet_v11f1e_sd15_tile",
-    "ControlNet_v11p_sd15_lineart",
-    "AnimateDiff_v2",
-    "AnimateDiff_xl_beta",
-    "RIFE",
-    "BeautifulPrompt",
-    "opus-mt-zh-en",
-    "IP-Adapter-SD",
-    "IP-Adapter-SDXL",
-    "StableDiffusion3",
-    "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",
-    "InfiniteYou",
-    "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",
-    "HunyuanVideoI2V",
-]

diffsynth/configs/model_configs.py

@@ -0,0 +1,486 @@
+qwen_image_series = [
+    {
+        # Example: ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors")
+        "model_hash": "0319a1cb19835fb510907dd3367c95ff",
+        "model_name": "qwen_image_dit",
+        "model_class": "diffsynth.models.qwen_image_dit.QwenImageDiT",
+    },
+    {
+        # Example: ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors")
+        "model_hash": "8004730443f55db63092006dd9f7110e",
+        "model_name": "qwen_image_text_encoder",
+        "model_class": "diffsynth.models.qwen_image_text_encoder.QwenImageTextEncoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.qwen_image_text_encoder.QwenImageTextEncoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors")
+        "model_hash": "ed4ea5824d55ec3107b09815e318123a",
+        "model_name": "qwen_image_vae",
+        "model_class": "diffsynth.models.qwen_image_vae.QwenImageVAE",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth", origin_file_pattern="model.safetensors")
+        "model_hash": "073bce9cf969e317e5662cd570c3e79c",
+        "model_name": "qwen_image_blockwise_controlnet",
+        "model_class": "diffsynth.models.qwen_image_controlnet.QwenImageBlockWiseControlNet",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors")
+        "model_hash": "a9e54e480a628f0b956a688a81c33bab",
+        "model_name": "qwen_image_blockwise_controlnet",
+        "model_class": "diffsynth.models.qwen_image_controlnet.QwenImageBlockWiseControlNet",
+        "extra_kwargs": {"additional_in_dim": 4},
+    },
+]
+
+wan_series = [
+    {
+        # Example: ModelConfig(model_id="krea/krea-realtime-video", origin_file_pattern="krea-realtime-video-14b.safetensors")
+        "model_hash": "5ec04e02b42d2580483ad69f4e76346a",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': False, 'patch_size': [1, 2, 2], 'in_dim': 16, 'dim': 5120, 'ffn_dim': 13824, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 40, 'num_layers': 40, 'eps': 1e-06},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.wan_video_dit.WanVideoDiTStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth")
+        "model_hash": "9c8818c2cbea55eca56c7b447df170da",
+        "model_name": "wan_video_text_encoder",
+        "model_class": "diffsynth.models.wan_video_text_encoder.WanTextEncoder",
+    },
+    {
+        # Example: ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth")
+        "model_hash": "ccc42284ea13e1ad04693284c7a09be6",
+        "model_name": "wan_video_vae",
+        "model_class": "diffsynth.models.wan_video_vae.WanVideoVAE",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.wan_video_vae.WanVideoVAEStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="meituan-longcat/LongCat-Video", origin_file_pattern="dit/diffusion_pytorch_model*.safetensors")
+        "model_hash": "8b27900f680d7251ce44e2dc8ae1ffef",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.longcat_video_dit.LongCatVideoTransformer3DModel",
+    },
+    {
+        # Example: ModelConfig(model_id="ByteDance/Video-As-Prompt-Wan2.1-14B", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors")
+        "model_hash": "5f90e66a0672219f12d9a626c8c21f61",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': True, 'patch_size': [1, 2, 2], 'in_dim': 36, 'dim': 5120, 'ffn_dim': 13824, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 40, 'num_layers': 40, 'eps': 1e-06},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.wan_video_dit.WanVideoDiTFromDiffusers"
+    },
+    {
+        # Example: ModelConfig(model_id="ByteDance/Video-As-Prompt-Wan2.1-14B", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors")
+        "model_hash": "5f90e66a0672219f12d9a626c8c21f61",
+        "model_name": "wan_video_vap",
+        "model_class": "diffsynth.models.wan_video_mot.MotWanModel",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.wan_video_mot.WanVideoMotStateDictConverter"
+    },
+    {
+        # Example: ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth")
+        "model_hash": "5941c53e207d62f20f9025686193c40b",
+        "model_name": "wan_video_image_encoder",
+        "model_class": "diffsynth.models.wan_video_image_encoder.WanImageEncoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.wan_video_image_encoder.WanImageEncoderStateDictConverter"
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1", origin_file_pattern="model.safetensors")
+        "model_hash": "dbd5ec76bbf977983f972c151d545389",
+        "model_name": "wan_video_motion_controller",
+        "model_class": "diffsynth.models.wan_video_motion_controller.WanMotionControllerModel",
+    },
+    {
+        # Example: ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "9269f8db9040a9d860eaca435be61814",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': False, 'patch_size': [1, 2, 2], 'in_dim': 16, 'dim': 1536, 'ffn_dim': 8960, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 12, 'num_layers': 30, 'eps': 1e-06}
+    },
+    {
+        # Example: ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "3ef3b1f8e1dab83d5b71fd7b617f859f",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': True, 'patch_size': [1, 2, 2], 'in_dim': 36, 'dim': 5120, 'ffn_dim': 13824, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 40, 'num_layers': 40, 'eps': 1e-06, 'has_image_pos_emb': True}
+    },
+    {
+        # Example: ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "349723183fc063b2bfc10bb2835cf677",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': True, 'patch_size': [1, 2, 2], 'in_dim': 48, 'dim': 1536, 'ffn_dim': 8960, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 12, 'num_layers': 30, 'eps': 1e-06}
+    },
+    {
+        # Example: ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "6d6ccde6845b95ad9114ab993d917893",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': True, 'patch_size': [1, 2, 2], 'in_dim': 36, 'dim': 1536, 'ffn_dim': 8960, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 12, 'num_layers': 30, 'eps': 1e-06}
+    },
+    {
+        # Example: ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "efa44cddf936c70abd0ea28b6cbe946c",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': True, 'patch_size': [1, 2, 2], 'in_dim': 48, 'dim': 5120, 'ffn_dim': 13824, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 40, 'num_layers': 40, 'eps': 1e-06}
+    },
+    {
+        # Example: ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "6bfcfb3b342cb286ce886889d519a77e",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': True, 'patch_size': [1, 2, 2], 'in_dim': 36, 'dim': 5120, 'ffn_dim': 13824, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 40, 'num_layers': 40, 'eps': 1e-06}
+    },
+    {
+        # Example: ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "ac6a5aa74f4a0aab6f64eb9a72f19901",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': True, 'patch_size': [1, 2, 2], 'in_dim': 32, 'dim': 1536, 'ffn_dim': 8960, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 12, 'num_layers': 30, 'eps': 1e-06, 'has_ref_conv': False, 'add_control_adapter': True, 'in_dim_control_adapter': 24}
+    },
+    {
+        # Example: ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "70ddad9d3a133785da5ea371aae09504",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': True, 'patch_size': [1, 2, 2], 'in_dim': 48, 'dim': 1536, 'ffn_dim': 8960, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 12, 'num_layers': 30, 'eps': 1e-06, 'has_ref_conv': True}
+    },
+    {
+        # Example: ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "b61c605c2adbd23124d152ed28e049ae",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': True, 'patch_size': [1, 2, 2], 'in_dim': 32, 'dim': 5120, 'ffn_dim': 13824, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 40, 'num_layers': 40, 'eps': 1e-06, 'has_ref_conv': False, 'add_control_adapter': True, 'in_dim_control_adapter': 24}
+    },
+    {
+        # Example: ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "26bde73488a92e64cc20b0a7485b9e5b",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': True, 'patch_size': [1, 2, 2], 'in_dim': 48, 'dim': 5120, 'ffn_dim': 13824, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 40, 'num_layers': 40, 'eps': 1e-06, 'has_ref_conv': True}
+    },
+    {
+        # Example: ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "aafcfd9672c3a2456dc46e1cb6e52c70",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': False, 'patch_size': [1, 2, 2], 'in_dim': 16, 'dim': 5120, 'ffn_dim': 13824, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 40, 'num_layers': 40, 'eps': 1e-06}
+    },
+    {
+        # Example: ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "a61453409b67cd3246cf0c3bebad47ba",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': False, 'patch_size': [1, 2, 2], 'in_dim': 16, 'dim': 1536, 'ffn_dim': 8960, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 12, 'num_layers': 30, 'eps': 1e-06},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.wan_video_dit.WanVideoDiTStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "a61453409b67cd3246cf0c3bebad47ba",
+        "model_name": "wan_video_vace",
+        "model_class": "diffsynth.models.wan_video_vace.VaceWanModel",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.wan_video_vace.VaceWanModelDictConverter"
+    },
+    {
+        # Example: ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "7a513e1f257a861512b1afd387a8ecd9",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': False, 'patch_size': [1, 2, 2], 'in_dim': 16, 'dim': 5120, 'ffn_dim': 13824, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 40, 'num_layers': 40, 'eps': 1e-06},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.wan_video_dit.WanVideoDiTStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "7a513e1f257a861512b1afd387a8ecd9",
+        "model_name": "wan_video_vace",
+        "model_class": "diffsynth.models.wan_video_vace.VaceWanModel",
+        "extra_kwargs": {'vace_layers': (0, 5, 10, 15, 20, 25, 30, 35), 'vace_in_dim': 96, 'patch_size': (1, 2, 2), 'has_image_input': False, 'dim': 5120, 'num_heads': 40, 'ffn_dim': 13824, 'eps': 1e-06},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.wan_video_vace.VaceWanModelDictConverter"
+    },
+    {
+        # Example: ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "31fa352acb8a1b1d33cd8764273d80a2",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': True, 'patch_size': [1, 2, 2], 'in_dim': 36, 'dim': 5120, 'ffn_dim': 13824, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 40, 'num_layers': 40, 'eps': 1e-06},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.wan_video_dit.WanVideoDiTStateDictConverter"
+    },
+    {
+        # Example: ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "31fa352acb8a1b1d33cd8764273d80a2",
+        "model_name": "wan_video_animate_adapter",
+        "model_class": "diffsynth.models.wan_video_animate_adapter.WanAnimateAdapter",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.wan_video_animate_adapter.WanAnimateAdapterStateDictConverter"
+    },
+    {
+        # Example: ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors")
+        "model_hash": "47dbeab5e560db3180adf51dc0232fb1",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': False, 'patch_size': [1, 2, 2], 'in_dim': 36, 'dim': 5120, 'ffn_dim': 13824, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 40, 'num_layers': 40, 'eps': 1e-06, 'has_ref_conv': False, 'add_control_adapter': True, 'in_dim_control_adapter': 24, 'require_clip_embedding': False}
+    },
+    {
+        # Example: ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors")
+        "model_hash": "2267d489f0ceb9f21836532952852ee5",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': False, 'patch_size': [1, 2, 2], 'in_dim': 52, 'dim': 5120, 'ffn_dim': 13824, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 40, 'num_layers': 40, 'eps': 1e-06, 'has_ref_conv': True, 'require_clip_embedding': False},
+    },
+    {
+        # Example: ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors")
+        "model_hash": "5b013604280dd715f8457c6ed6d6a626",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': False, 'patch_size': [1, 2, 2], 'in_dim': 36, 'dim': 5120, 'ffn_dim': 13824, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 16, 'num_heads': 40, 'num_layers': 40, 'eps': 1e-06, 'require_clip_embedding': False}
+    },
+    {
+        # Example: ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "966cffdcc52f9c46c391768b27637614",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit_s2v.WanS2VModel",
+        "extra_kwargs": {'dim': 5120, 'in_dim': 16, 'ffn_dim': 13824, 'out_dim': 16, 'text_dim': 4096, 'freq_dim': 256, 'eps': 1e-06, 'patch_size': (1, 2, 2), 'num_heads': 40, 'num_layers': 40, 'cond_dim': 16, 'audio_dim': 1024, 'num_audio_token': 4}
+    },
+    {
+        # Example: ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="diffusion_pytorch_model*.safetensors")
+        "model_hash": "1f5ab7703c6fc803fdded85ff040c316",
+        "model_name": "wan_video_dit",
+        "model_class": "diffsynth.models.wan_video_dit.WanModel",
+        "extra_kwargs": {'has_image_input': False, 'patch_size': [1, 2, 2], 'in_dim': 48, 'dim': 3072, 'ffn_dim': 14336, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 48, 'num_heads': 24, 'num_layers': 30, 'eps': 1e-06, 'seperated_timestep': True, 'require_clip_embedding': False, 'require_vae_embedding': False, 'fuse_vae_embedding_in_latents': True}
+    },
+    {
+        # Example: ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="Wan2.2_VAE.pth")
+        "model_hash": "e1de6c02cdac79f8b739f4d3698cd216",
+        "model_name": "wan_video_vae",
+        "model_class": "diffsynth.models.wan_video_vae.WanVideoVAE38",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.wan_video_vae.WanVideoVAEStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/model.safetensors")
+        "model_hash": "06be60f3a4526586d8431cd038a71486",
+        "model_name": "wans2v_audio_encoder",
+        "model_class": "diffsynth.models.wav2vec.WanS2VAudioEncoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.wans2v_audio_encoder.WanS2VAudioEncoderStateDictConverter",
+    },
+]
+
+flux_series = [
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors")
+        "model_hash": "a29710fea6dddb0314663ee823598e50",
+        "model_name": "flux_dit",
+        "model_class": "diffsynth.models.flux_dit.FluxDiT",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_dit.FluxDiTStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors")
+        "model_hash": "94eefa3dac9cec93cb1ebaf1747d7b78",
+        "model_name": "flux_text_encoder_clip",
+        "model_class": "diffsynth.models.flux_text_encoder_clip.FluxTextEncoderClip",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_text_encoder_clip.FluxTextEncoderClipStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/*.safetensors")
+        "model_hash": "22540b49eaedbc2f2784b2091a234c7c",
+        "model_name": "flux_text_encoder_t5",
+        "model_class": "diffsynth.models.flux_text_encoder_t5.FluxTextEncoderT5",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_text_encoder_t5.FluxTextEncoderT5StateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors")
+        "model_hash": "21ea55f476dfc4fd135587abb59dfe5d",
+        "model_name": "flux_vae_encoder",
+        "model_class": "diffsynth.models.flux_vae.FluxVAEEncoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_vae.FluxVAEEncoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors")
+        "model_hash": "21ea55f476dfc4fd135587abb59dfe5d",
+        "model_name": "flux_vae_decoder",
+        "model_class": "diffsynth.models.flux_vae.FluxVAEDecoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_vae.FluxVAEDecoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="ostris/Flex.2-preview", origin_file_pattern="Flex.2-preview.safetensors")
+        "model_hash": "d02f41c13549fa5093d3521f62a5570a",
+        "model_name": "flux_dit",
+        "model_class": "diffsynth.models.flux_dit.FluxDiT",
+        "extra_kwargs": {'input_dim': 196, 'num_blocks': 8},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_dit.FluxDiTStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/AttriCtrl-FLUX.1-Dev", origin_file_pattern="models/brightness.safetensors")
+        "model_hash": "0629116fce1472503a66992f96f3eb1a",
+        "model_name": "flux_value_controller",
+        "model_class": "diffsynth.models.flux_value_control.SingleValueEncoder",
+    },
+    {
+        # Example: ModelConfig(model_id="alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", origin_file_pattern="diffusion_pytorch_model.safetensors")
+        "model_hash": "52357cb26250681367488a8954c271e8",
+        "model_name": "flux_controlnet",
+        "model_class": "diffsynth.models.flux_controlnet.FluxControlNet",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_controlnet.FluxControlNetStateDictConverter",
+        "extra_kwargs": {"num_joint_blocks": 6, "num_single_blocks": 0, "additional_input_dim": 4},
+    },
+    {
+        # Example: ModelConfig(model_id="InstantX/FLUX.1-dev-Controlnet-Union-alpha", origin_file_pattern="diffusion_pytorch_model.safetensors")
+        "model_hash": "78d18b9101345ff695f312e7e62538c0",
+        "model_name": "flux_controlnet",
+        "model_class": "diffsynth.models.flux_controlnet.FluxControlNet",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_controlnet.FluxControlNetStateDictConverter",
+        "extra_kwargs": {"num_mode": 10, "mode_dict": {"canny": 0, "tile": 1, "depth": 2, "blur": 3, "pose": 4, "gray": 5, "lq": 6}},
+    },
+    {
+        # Example: ModelConfig(model_id="jasperai/Flux.1-dev-Controlnet-Upscaler", origin_file_pattern="diffusion_pytorch_model.safetensors")
+        "model_hash": "b001c89139b5f053c715fe772362dd2a",
+        "model_name": "flux_controlnet",
+        "model_class": "diffsynth.models.flux_controlnet.FluxControlNet",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_controlnet.FluxControlNetStateDictConverter",
+        "extra_kwargs": {"num_single_blocks": 0},
+    },
+    {
+        # Example: ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/image_proj_model.bin")
+        "model_hash": "c07c0f04f5ff55e86b4e937c7a40d481",
+        "model_name": "infiniteyou_image_projector",
+        "model_class": "diffsynth.models.flux_infiniteyou.InfiniteYouImageProjector",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_infiniteyou.FluxInfiniteYouImageProjectorStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/InfuseNetModel/*.safetensors")
+        "model_hash": "7f9583eb8ba86642abb9a21a4b2c9e16",
+        "model_name": "flux_controlnet",
+        "model_class": "diffsynth.models.flux_controlnet.FluxControlNet",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_controlnet.FluxControlNetStateDictConverter",
+        "extra_kwargs": {"num_joint_blocks": 4, "num_single_blocks": 10},
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev", origin_file_pattern="model.safetensors")
+        "model_hash": "77c2e4dd2440269eb33bfaa0d004f6ab",
+        "model_name": "flux_lora_encoder",
+        "model_class": "diffsynth.models.flux_lora_encoder.FluxLoRAEncoder",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev", origin_file_pattern="model.safetensors")
+        "model_hash": "30143afb2dea73d1ac580e0787628f8c",
+        "model_name": "flux_lora_patcher",
+        "model_class": "diffsynth.models.flux_lora_patcher.FluxLoraPatcher",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="model*.safetensors")
+        "model_hash": "2bd19e845116e4f875a0a048e27fc219",
+        "model_name": "nexus_gen_llm",
+        "model_class": "diffsynth.models.nexus_gen.NexusGenAutoregressiveModel",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.nexus_gen.NexusGenAutoregressiveModelStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="edit_decoder.bin")
+        "model_hash": "63c969fd37cce769a90aa781fbff5f81",
+        "model_name": "nexus_gen_editing_adapter",
+        "model_class": "diffsynth.models.nexus_gen_projector.NexusGenImageEmbeddingMerger",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.nexus_gen_projector.NexusGenMergerStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="edit_decoder.bin")
+        "model_hash": "63c969fd37cce769a90aa781fbff5f81",
+        "model_name": "flux_dit",
+        "model_class": "diffsynth.models.flux_dit.FluxDiT",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_dit.FluxDiTStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="generation_decoder.bin")
+        "model_hash": "3e6c61b0f9471135fc9c6d6a98e98b6d",
+        "model_name": "nexus_gen_generation_adapter",
+        "model_class": "diffsynth.models.nexus_gen_projector.NexusGenAdapter",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.nexus_gen_projector.NexusGenAdapterStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="generation_decoder.bin")
+        "model_hash": "3e6c61b0f9471135fc9c6d6a98e98b6d",
+        "model_name": "flux_dit",
+        "model_class": "diffsynth.models.flux_dit.FluxDiT",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_dit.FluxDiTStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="InstantX/FLUX.1-dev-IP-Adapter", origin_file_pattern="ip-adapter.bin")
+        "model_hash": "4daaa66cc656a8fe369908693dad0a35",
+        "model_name": "flux_ipadapter",
+        "model_class": "diffsynth.models.flux_ipadapter.FluxIpAdapter",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_ipadapter.FluxIpAdapterStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="google/siglip-so400m-patch14-384", origin_file_pattern="model.safetensors")
+        "model_hash": "04d8c1e20a1f1b25f7434f111992a33f",
+        "model_name": "siglip_vision_model",
+        "model_class": "diffsynth.models.flux_ipadapter.SiglipVisionModelSO400M",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_ipadapter.SiglipStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="stepfun-ai/Step1X-Edit", origin_file_pattern="step1x-edit-i1258.safetensors"),
+        "model_hash": "d30fb9e02b1dbf4e509142f05cf7dd50",
+        "model_name": "step1x_connector",
+        "model_class": "diffsynth.models.step1x_connector.Qwen2Connector",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.step1x_connector.Qwen2ConnectorStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="stepfun-ai/Step1X-Edit", origin_file_pattern="step1x-edit-i1258.safetensors"),
+        "model_hash": "d30fb9e02b1dbf4e509142f05cf7dd50",
+        "model_name": "flux_dit",
+        "model_class": "diffsynth.models.flux_dit.FluxDiT",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_dit.FluxDiTStateDictConverter",
+        "extra_kwargs": {"disable_guidance_embedder": True},
+    },
+]
+
+flux2_series = [
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.2-dev", origin_file_pattern="text_encoder/*.safetensors")
+        "model_hash": "28fca3d8e5bf2a2d1271748a773f6757",
+        "model_name": "flux2_text_encoder",
+        "model_class": "diffsynth.models.flux2_text_encoder.Flux2TextEncoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux2_text_encoder.Flux2TextEncoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.2-dev", origin_file_pattern="transformer/*.safetensors")
+        "model_hash": "d38e1d5c5aec3b0a11e79327ac6e3b0f",
+        "model_name": "flux2_dit",
+        "model_class": "diffsynth.models.flux2_dit.Flux2DiT",
+    },
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.2-dev", origin_file_pattern="vae/diffusion_pytorch_model.safetensors")
+        "model_hash": "c54288e3ee12ca215898840682337b95",
+        "model_name": "flux2_vae",
+        "model_class": "diffsynth.models.flux2_vae.Flux2VAE",
+    },
+]
+
+z_image_series = [
+    {
+        # Example: ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="transformer/*.safetensors")
+        "model_hash": "fc3a8a1247fe185ce116ccbe0e426c28",
+        "model_name": "z_image_dit",
+        "model_class": "diffsynth.models.z_image_dit.ZImageDiT",
+    },
+    {
+        # Example: ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="text_encoder/*.safetensors")
+        "model_hash": "0f050f62a88876fea6eae0a18dac5a2e",
+        "model_name": "z_image_text_encoder",
+        "model_class": "diffsynth.models.z_image_text_encoder.ZImageTextEncoder",
+    },
+    {
+        # Example: ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/vae/diffusion_pytorch_model.safetensors")
+        "model_hash": "1aafa3cc91716fb6b300cc1cd51b85a3",
+        "model_name": "flux_vae_encoder",
+        "model_class": "diffsynth.models.flux_vae.FluxVAEEncoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_vae.FluxVAEEncoderStateDictConverterDiffusers",
+        "extra_kwargs": {"use_conv_attention": False},
+    },
+    {
+        # Example: ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/vae/diffusion_pytorch_model.safetensors")
+        "model_hash": "1aafa3cc91716fb6b300cc1cd51b85a3",
+        "model_name": "flux_vae_decoder",
+        "model_class": "diffsynth.models.flux_vae.FluxVAEDecoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_vae.FluxVAEDecoderStateDictConverterDiffusers",
+        "extra_kwargs": {"use_conv_attention": False},
+    },
+]
+
+MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series + z_image_series

diffsynth/configs/vram_management_module_maps.py

@@ -0,0 +1,178 @@
+flux_general_vram_config = {
+    "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+    "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+    "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+    "torch.nn.GroupNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    "diffsynth.models.general_modules.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    "diffsynth.models.flux_lora_encoder.LoRALayerBlock": "diffsynth.core.vram.layers.AutoWrappedModule",
+    "diffsynth.models.flux_lora_patcher.LoraMerger": "diffsynth.core.vram.layers.AutoWrappedModule",
+}
+
+VRAM_MANAGEMENT_MODULE_MAPS = {
+    "diffsynth.models.qwen_image_dit.QwenImageDiT": {
+        "diffsynth.models.qwen_image_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+    },
+    "diffsynth.models.qwen_image_text_encoder.QwenImageTextEncoder": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.Qwen2_5_VLRotaryEmbedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.Qwen2RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.Qwen2_5_VisionPatchEmbed": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.Qwen2_5_VisionRotaryEmbedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.qwen_image_vae.QwenImageVAE": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Conv3d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.qwen_image_vae.QwenImageRMS_norm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.qwen_image_controlnet.BlockWiseControlBlock": {
+        "diffsynth.models.qwen_image_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+    },
+    "diffsynth.models.wan_video_animate_adapter.WanAnimateAdapter": {
+        "diffsynth.models.wan_video_animate_adapter.FaceEncoder": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_animate_adapter.EqualLinear": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_animate_adapter.ConvLayer": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_animate_adapter.FusedLeakyReLU": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_animate_adapter.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Conv1d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Conv3d": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.wan_video_dit_s2v.WanS2VModel": {
+        "diffsynth.models.wan_video_dit.Head": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_dit_s2v.WanS2VDiTBlock": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_dit_s2v.CausalAudioEncoder": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Conv3d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.wan_video_dit.WanModel": {
+        "diffsynth.models.wan_video_dit.MLP": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_dit.DiTBlock": "diffsynth.core.vram.layers.AutoWrappedNonRecurseModule",
+        "diffsynth.models.wan_video_dit.Head": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Conv3d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.wan_video_image_encoder.WanImageEncoder": {
+        "diffsynth.models.wan_video_image_encoder.VisionTransformer": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.wan_video_mot.MotWanModel": {
+        "diffsynth.models.wan_video_mot.MotWanAttentionBlock": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Conv3d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.wan_video_motion_controller.WanMotionControllerModel": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+    },
+    "diffsynth.models.wan_video_text_encoder.WanTextEncoder": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_text_encoder.T5RelativeEmbedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_text_encoder.T5LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.wan_video_vace.VaceWanModel": {
+        "diffsynth.models.wan_video_dit.DiTBlock": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Conv3d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.wan_video_vae.WanVideoVAE": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_vae.RMS_norm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_vae.CausalConv3d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_vae.Upsample": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.SiLU": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Dropout": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.wan_video_vae.WanVideoVAE38": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_vae.RMS_norm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_vae.CausalConv3d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_vae.Upsample": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.SiLU": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Dropout": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.wav2vec.WanS2VAudioEncoder": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Conv1d": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.longcat_video_dit.LongCatVideoTransformer3DModel": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Conv3d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.longcat_video_dit.RMSNorm_FP32": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.longcat_video_dit.LayerNorm_FP32": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.flux_dit.FluxDiT": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "diffsynth.models.flux_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.flux_text_encoder_clip.FluxTextEncoderClip": flux_general_vram_config,
+    "diffsynth.models.flux_vae.FluxVAEEncoder": flux_general_vram_config,
+    "diffsynth.models.flux_vae.FluxVAEDecoder": flux_general_vram_config,
+    "diffsynth.models.flux_controlnet.FluxControlNet": flux_general_vram_config,
+    "diffsynth.models.flux_infiniteyou.InfiniteYouImageProjector": flux_general_vram_config,
+    "diffsynth.models.flux_ipadapter.FluxIpAdapter": flux_general_vram_config,
+    "diffsynth.models.flux_lora_patcher.FluxLoraPatcher": flux_general_vram_config,
+    "diffsynth.models.step1x_connector.Qwen2Connector": flux_general_vram_config,
+    "diffsynth.models.flux_lora_encoder.FluxLoRAEncoder": flux_general_vram_config,
+    "diffsynth.models.flux_text_encoder_t5.FluxTextEncoderT5": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.t5.modeling_t5.T5LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.t5.modeling_t5.T5DenseActDense": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.t5.modeling_t5.T5DenseGatedActDense": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.flux_ipadapter.SiglipVisionModelSO400M": {
+        "transformers.models.siglip.modeling_siglip.SiglipVisionEmbeddings": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.siglip.modeling_siglip.SiglipEncoder": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.siglip.modeling_siglip.SiglipMultiheadAttentionPoolingHead": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.MultiheadAttention": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.flux2_dit.Flux2DiT": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.flux2_text_encoder.Flux2TextEncoder": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.mistral.modeling_mistral.MistralRMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.flux2_vae.Flux2VAE": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.GroupNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.z_image_text_encoder.ZImageTextEncoder": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "transformers.models.qwen3.modeling_qwen3.Qwen3RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.z_image_dit.ZImageDiT": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "diffsynth.models.z_image_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+}

diffsynth/controlnets/__init__.py

@@ -1,2 +0,0 @@
-from .controlnet_unit import ControlNetConfigUnit, ControlNetUnit, MultiControlNetManager, FluxMultiControlNetManager
-from .processors import Annotator

diffsynth/controlnets/controlnet_unit.py

@@ -1,91 +0,0 @@
-import torch
-import numpy as np
-from .processors import Processor_id
-
-
-class ControlNetConfigUnit:
-    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:
-    def __init__(self, processor, model, scale=1.0):
-        self.processor = processor
-        self.model = model
-        self.scale = scale
-
-
-class MultiControlNetManager:
-    def __init__(self, controlnet_units=[]):
-        self.processors = [unit.processor for unit in controlnet_units]
-        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]
-        else:
-            processed_image = [self.processors[processor_id](image)]
-        processed_image = torch.concat([
-            torch.Tensor(np.array(image_, dtype=np.float32) / 255).permute(2, 0, 1).unsqueeze(0)
-            for image_ in processed_image
-        ], dim=0)
-        return processed_image
-    
-    def __call__(
-        self,
-        sample, timestep, encoder_hidden_states, conditionings,
-        tiled=False, tile_size=64, tile_stride=32, **kwargs
-    ):
-        res_stack = None
-        for processor, conditioning, model, scale in zip(self.processors, conditionings, self.models, self.scales):
-            res_stack_ = model(
-                sample, timestep, encoder_hidden_states, conditioning, **kwargs,
-                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:
-                res_stack = res_stack_
-            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/core/__init__.py

@@ -0,0 +1,5 @@
+from .attention import *
+from .data import *
+from .gradient import *
+from .loader import *
+from .vram import *

diffsynth/core/attention/__init__.py

@@ -0,0 +1 @@
+from .attention import attention_forward

diffsynth/core/attention/attention.py

@@ -0,0 +1,121 @@
+import torch, os
+from einops import rearrange
+
+
+try:
+    import flash_attn_interface
+    FLASH_ATTN_3_AVAILABLE = True
+except ModuleNotFoundError:
+    FLASH_ATTN_3_AVAILABLE = False
+
+try:
+    import flash_attn
+    FLASH_ATTN_2_AVAILABLE = True
+except ModuleNotFoundError:
+    FLASH_ATTN_2_AVAILABLE = False
+
+try:
+    from sageattention import sageattn
+    SAGE_ATTN_AVAILABLE = True
+except ModuleNotFoundError:
+    SAGE_ATTN_AVAILABLE = False
+
+try:
+    import xformers.ops as xops
+    XFORMERS_AVAILABLE = True
+except ModuleNotFoundError:
+    XFORMERS_AVAILABLE = False
+
+
+def initialize_attention_priority():
+    if os.environ.get('DIFFSYNTH_ATTENTION_IMPLEMENTATION') is not None:
+        return os.environ.get('DIFFSYNTH_ATTENTION_IMPLEMENTATION').lower()
+    elif FLASH_ATTN_3_AVAILABLE:
+        return "flash_attention_3"
+    elif FLASH_ATTN_2_AVAILABLE:
+        return "flash_attention_2"
+    elif SAGE_ATTN_AVAILABLE:
+        return "sage_attention"
+    elif XFORMERS_AVAILABLE:
+        return "xformers"
+    else:
+        return "torch"
+
+
+ATTENTION_IMPLEMENTATION = initialize_attention_priority()
+
+
+def rearrange_qkv(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_pattern="b n s d", k_pattern="b n s d", v_pattern="b n s d", required_in_pattern="b n s d", dims=None):
+    dims = {} if dims is None else dims
+    if q_pattern != required_in_pattern:
+        q = rearrange(q, f"{q_pattern} -> {required_in_pattern}", **dims)
+    if k_pattern != required_in_pattern:
+        k = rearrange(k, f"{k_pattern} -> {required_in_pattern}", **dims)
+    if v_pattern != required_in_pattern:
+        v = rearrange(v, f"{q_pattern} -> {required_in_pattern}", **dims)
+    return q, k, v
+
+
+def rearrange_out(out: torch.Tensor, out_pattern="b n s d", required_out_pattern="b n s d", dims=None):
+    dims = {} if dims is None else dims
+    if out_pattern != required_out_pattern:
+        out = rearrange(out, f"{required_out_pattern} -> {out_pattern}", **dims)
+    return out
+
+
+def torch_sdpa(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_pattern="b n s d", k_pattern="b n s d", v_pattern="b n s d", out_pattern="b n s d", dims=None, attn_mask=None, scale=None):
+    required_in_pattern, required_out_pattern= "b n s d", "b n s d"
+    q, k, v = rearrange_qkv(q, k, v, q_pattern, k_pattern, v_pattern, required_in_pattern, dims)
+    out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask, scale=scale)
+    out = rearrange_out(out, out_pattern, required_out_pattern, dims)
+    return out
+
+
+def flash_attention_3(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_pattern="b n s d", k_pattern="b n s d", v_pattern="b n s d", out_pattern="b n s d", dims=None, scale=None):
+    required_in_pattern, required_out_pattern= "b s n d", "b s n d"
+    q, k, v = rearrange_qkv(q, k, v, q_pattern, k_pattern, v_pattern, required_in_pattern, dims)
+    out = flash_attn_interface.flash_attn_func(q, k, v, softmax_scale=scale)
+    if isinstance(out, tuple):
+        out = out[0]
+    out = rearrange_out(out, out_pattern, required_out_pattern, dims)
+    return out
+
+
+def flash_attention_2(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_pattern="b n s d", k_pattern="b n s d", v_pattern="b n s d", out_pattern="b n s d", dims=None, scale=None):
+    required_in_pattern, required_out_pattern= "b s n d", "b s n d"
+    q, k, v = rearrange_qkv(q, k, v, q_pattern, k_pattern, v_pattern, required_in_pattern, dims)
+    out = flash_attn.flash_attn_func(q, k, v, softmax_scale=scale)
+    out = rearrange_out(out, out_pattern, required_out_pattern, dims)
+    return out
+
+
+def sage_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_pattern="b n s d", k_pattern="b n s d", v_pattern="b n s d", out_pattern="b n s d", dims=None, scale=None):
+    required_in_pattern, required_out_pattern= "b n s d", "b n s d"
+    q, k, v = rearrange_qkv(q, k, v, q_pattern, k_pattern, v_pattern, required_in_pattern, dims)
+    out = sageattn(q, k, v, sm_scale=scale)
+    out = rearrange_out(out, out_pattern, required_out_pattern, dims)
+    return out
+
+
+def xformers_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_pattern="b n s d", k_pattern="b n s d", v_pattern="b n s d", out_pattern="b n s d", dims=None, scale=None):
+    required_in_pattern, required_out_pattern= "b s n d", "b s n d"
+    q, k, v = rearrange_qkv(q, k, v, q_pattern, k_pattern, v_pattern, required_in_pattern, dims)
+    out = xops.memory_efficient_attention(q, k, v, scale=scale)
+    out = rearrange_out(out, out_pattern, required_out_pattern, dims)
+    return out
+
+
+def attention_forward(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_pattern="b n s d", k_pattern="b n s d", v_pattern="b n s d", out_pattern="b n s d", dims=None, attn_mask=None, scale=None, compatibility_mode=False):
+    if compatibility_mode or (attn_mask is not None):
+        return torch_sdpa(q, k, v, q_pattern, k_pattern, v_pattern, out_pattern, dims, attn_mask=attn_mask, scale=scale)
+    else:
+        if ATTENTION_IMPLEMENTATION == "flash_attention_3":
+            return flash_attention_3(q, k, v, q_pattern, k_pattern, v_pattern, out_pattern, dims, scale=scale)
+        elif ATTENTION_IMPLEMENTATION == "flash_attention_2":
+            return flash_attention_2(q, k, v, q_pattern, k_pattern, v_pattern, out_pattern, dims, scale=scale)
+        elif ATTENTION_IMPLEMENTATION == "sage_attention":
+            return sage_attention(q, k, v, q_pattern, k_pattern, v_pattern, out_pattern, dims, scale=scale)
+        elif ATTENTION_IMPLEMENTATION == "xformers":
+            return xformers_attention(q, k, v, q_pattern, k_pattern, v_pattern, out_pattern, dims, scale=scale)
+        else:
+            return torch_sdpa(q, k, v, q_pattern, k_pattern, v_pattern, out_pattern, dims, scale=scale)

diffsynth/core/data/__init__.py

@@ -0,0 +1 @@
+from .unified_dataset import UnifiedDataset

diffsynth/core/data/operators.py

@@ -1,9 +1,8 @@
-import torch, torchvision, imageio, os, json, pandas
+import torch, torchvision, imageio, os
 import imageio.v3 as iio
 from PIL import Image
 
 
-
 class DataProcessingPipeline:
     def __init__(self, operators=None):
         self.operators: list[DataProcessingOperator] = [] if operators is None else operators
@@ -19,7 +18,6 @@ class DataProcessingPipeline:
         return DataProcessingPipeline(self.operators + pipe.operators)
 
 
-
 class DataProcessingOperator:
     def __call__(self, data):
         raise NotImplementedError("DataProcessingOperator cannot be called directly.")
@@ -30,25 +28,21 @@ class DataProcessingOperator:
         return DataProcessingPipeline([self]).__rshift__(pipe)
 
 
-
 class DataProcessingOperatorRaw(DataProcessingOperator):
     def __call__(self, data):
         return data
 
 
-
 class ToInt(DataProcessingOperator):
     def __call__(self, data):
         return int(data)
 
 
-
 class ToFloat(DataProcessingOperator):
     def __call__(self, data):
         return float(data)
 
 
-
 class ToStr(DataProcessingOperator):
     def __init__(self, none_value=""):
         self.none_value = none_value
@@ -58,7 +52,6 @@ class ToStr(DataProcessingOperator):
         return str(data)
 
 
-
 class LoadImage(DataProcessingOperator):
     def __init__(self, convert_RGB=True):
         self.convert_RGB = convert_RGB
@@ -69,9 +62,8 @@ class LoadImage(DataProcessingOperator):
         return image
 
 
-
 class ImageCropAndResize(DataProcessingOperator):
-    def __init__(self, height, width, max_pixels, height_division_factor, width_division_factor):
+    def __init__(self, height=None, width=None, max_pixels=None, height_division_factor=1, width_division_factor=1):
         self.height = height
         self.width = width
         self.max_pixels = max_pixels
@@ -101,19 +93,16 @@ class ImageCropAndResize(DataProcessingOperator):
             height, width = self.height, self.width
         return height, width
     
-    
     def __call__(self, data: Image.Image):
         image = self.crop_and_resize(data, *self.get_height_width(data))
         return image
 
 
-
 class ToList(DataProcessingOperator):
     def __call__(self, data):
         return [data]
     
 
-
 class LoadVideo(DataProcessingOperator):
     def __init__(self, num_frames=81, time_division_factor=4, time_division_remainder=1, frame_processor=lambda x: x):
         self.num_frames = num_frames
@@ -143,7 +132,6 @@ class LoadVideo(DataProcessingOperator):
         return frames
 
 
-
 class SequencialProcess(DataProcessingOperator):
     def __init__(self, operator=lambda x: x):
         self.operator = operator
@@ -152,7 +140,6 @@ class SequencialProcess(DataProcessingOperator):
         return [self.operator(i) for i in data]
 
 
-
 class LoadGIF(DataProcessingOperator):
     def __init__(self, num_frames=81, time_division_factor=4, time_division_remainder=1, frame_processor=lambda x: x):
         self.num_frames = num_frames
@@ -181,7 +168,6 @@ class LoadGIF(DataProcessingOperator):
             if len(frames) >= num_frames:
                 break
         return frames
-    
 
 
 class RouteByExtensionName(DataProcessingOperator):
@@ -196,7 +182,6 @@ class RouteByExtensionName(DataProcessingOperator):
         raise ValueError(f"Unsupported file: {data}")
 
 
-
 class RouteByType(DataProcessingOperator):
     def __init__(self, operator_map):
         self.operator_map = operator_map
@@ -208,7 +193,6 @@ class RouteByType(DataProcessingOperator):
         raise ValueError(f"Unsupported data: {data}")
 
 
-
 class LoadTorchPickle(DataProcessingOperator):
     def __init__(self, map_location="cpu"):
         self.map_location = map_location
@@ -217,7 +201,6 @@ class LoadTorchPickle(DataProcessingOperator):
         return torch.load(data, map_location=self.map_location, weights_only=False)
 
 
-
 class ToAbsolutePath(DataProcessingOperator):
     def __init__(self, base_path=""):
         self.base_path = base_path
@@ -225,6 +208,7 @@ class ToAbsolutePath(DataProcessingOperator):
     def __call__(self, data):
         return os.path.join(self.base_path, data)
 
+
 class LoadAudio(DataProcessingOperator):
     def __init__(self, sr=16000):
         self.sr = sr
@@ -232,113 +216,3 @@ class LoadAudio(DataProcessingOperator):
         import librosa
         input_audio, sample_rate = librosa.load(data, sr=self.sr)
         return input_audio
-
-
-class UnifiedDataset(torch.utils.data.Dataset):
-    def __init__(
-        self,
-        base_path=None, metadata_path=None,
-        repeat=1,
-        data_file_keys=tuple(),
-        main_data_operator=lambda x: x,
-        special_operator_map=None,
-    ):
-        self.base_path = base_path
-        self.metadata_path = metadata_path
-        self.repeat = repeat
-        self.data_file_keys = data_file_keys
-        self.main_data_operator = main_data_operator
-        self.cached_data_operator = LoadTorchPickle()
-        self.special_operator_map = {} if special_operator_map is None else special_operator_map
-        self.data = []
-        self.cached_data = []
-        self.load_from_cache = metadata_path is None
-        self.load_metadata(metadata_path)
-    
-    @staticmethod
-    def default_image_operator(
-        base_path="",
-        max_pixels=1920*1080, height=None, width=None,
-        height_division_factor=16, width_division_factor=16,
-    ):
-        return RouteByType(operator_map=[
-            (str, ToAbsolutePath(base_path) >> LoadImage() >> ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor)),
-            (list, SequencialProcess(ToAbsolutePath(base_path) >> LoadImage() >> ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor))),
-        ])
-    
-    @staticmethod
-    def default_video_operator(
-        base_path="",
-        max_pixels=1920*1080, height=None, width=None,
-        height_division_factor=16, width_division_factor=16,
-        num_frames=81, time_division_factor=4, time_division_remainder=1,
-    ):
-        return RouteByType(operator_map=[
-            (str, ToAbsolutePath(base_path) >> RouteByExtensionName(operator_map=[
-                (("jpg", "jpeg", "png", "webp"), LoadImage() >> ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor) >> ToList()),
-                (("gif",), LoadGIF(
-                    num_frames, time_division_factor, time_division_remainder,
-                    frame_processor=ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor),
-                )),
-                (("mp4", "avi", "mov", "wmv", "mkv", "flv", "webm"), LoadVideo(
-                    num_frames, time_division_factor, time_division_remainder,
-                    frame_processor=ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor),
-                )),
-            ])),
-        ])
-        
-    def search_for_cached_data_files(self, path):
-        for file_name in os.listdir(path):
-            subpath = os.path.join(path, file_name)
-            if os.path.isdir(subpath):
-                self.search_for_cached_data_files(subpath)
-            elif subpath.endswith(".pth"):
-                self.cached_data.append(subpath)
-    
-    def load_metadata(self, metadata_path):
-        if metadata_path is None:
-            print("No metadata_path. Searching for cached data files.")
-            self.search_for_cached_data_files(self.base_path)
-            print(f"{len(self.cached_data)} cached data files found.")
-        elif metadata_path.endswith(".json"):
-            with open(metadata_path, "r") as f:
-                metadata = json.load(f)
-            self.data = metadata
-        elif metadata_path.endswith(".jsonl"):
-            metadata = []
-            with open(metadata_path, 'r') as f:
-                for line in f:
-                    metadata.append(json.loads(line.strip()))
-            self.data = metadata
-        else:
-            metadata = pandas.read_csv(metadata_path)
-            self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))]
-
-    def __getitem__(self, data_id):
-        if self.load_from_cache:
-            data = self.cached_data[data_id % len(self.cached_data)]
-            data = self.cached_data_operator(data)
-        else:
-            data = self.data[data_id % len(self.data)].copy()
-            for key in self.data_file_keys:
-                if key in data:
-                    if key in self.special_operator_map:
-                        data[key] = self.special_operator_map[key](data[key])
-                    elif key in self.data_file_keys:
-                        data[key] = self.main_data_operator(data[key])
-        return data
-
-    def __len__(self):
-        if self.load_from_cache:
-            return len(self.cached_data) * self.repeat
-        else:
-            return len(self.data) * self.repeat
-        
-    def check_data_equal(self, data1, data2):
-        # Debug only
-        if len(data1) != len(data2):
-            return False
-        for k in data1:
-            if data1[k] != data2[k]:
-                return False
-        return True

diffsynth/core/data/unified_dataset.py

@@ -0,0 +1,112 @@
+from .operators import *
+import torch, json, pandas
+
+
+class UnifiedDataset(torch.utils.data.Dataset):
+    def __init__(
+        self,
+        base_path=None, metadata_path=None,
+        repeat=1,
+        data_file_keys=tuple(),
+        main_data_operator=lambda x: x,
+        special_operator_map=None,
+    ):
+        self.base_path = base_path
+        self.metadata_path = metadata_path
+        self.repeat = repeat
+        self.data_file_keys = data_file_keys
+        self.main_data_operator = main_data_operator
+        self.cached_data_operator = LoadTorchPickle()
+        self.special_operator_map = {} if special_operator_map is None else special_operator_map
+        self.data = []
+        self.cached_data = []
+        self.load_from_cache = metadata_path is None
+        self.load_metadata(metadata_path)
+    
+    @staticmethod
+    def default_image_operator(
+        base_path="",
+        max_pixels=1920*1080, height=None, width=None,
+        height_division_factor=16, width_division_factor=16,
+    ):
+        return RouteByType(operator_map=[
+            (str, ToAbsolutePath(base_path) >> LoadImage() >> ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor)),
+            (list, SequencialProcess(ToAbsolutePath(base_path) >> LoadImage() >> ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor))),
+        ])
+    
+    @staticmethod
+    def default_video_operator(
+        base_path="",
+        max_pixels=1920*1080, height=None, width=None,
+        height_division_factor=16, width_division_factor=16,
+        num_frames=81, time_division_factor=4, time_division_remainder=1,
+    ):
+        return RouteByType(operator_map=[
+            (str, ToAbsolutePath(base_path) >> RouteByExtensionName(operator_map=[
+                (("jpg", "jpeg", "png", "webp"), LoadImage() >> ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor) >> ToList()),
+                (("gif",), LoadGIF(
+                    num_frames, time_division_factor, time_division_remainder,
+                    frame_processor=ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor),
+                )),
+                (("mp4", "avi", "mov", "wmv", "mkv", "flv", "webm"), LoadVideo(
+                    num_frames, time_division_factor, time_division_remainder,
+                    frame_processor=ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor),
+                )),
+            ])),
+        ])
+        
+    def search_for_cached_data_files(self, path):
+        for file_name in os.listdir(path):
+            subpath = os.path.join(path, file_name)
+            if os.path.isdir(subpath):
+                self.search_for_cached_data_files(subpath)
+            elif subpath.endswith(".pth"):
+                self.cached_data.append(subpath)
+    
+    def load_metadata(self, metadata_path):
+        if metadata_path is None:
+            print("No metadata_path. Searching for cached data files.")
+            self.search_for_cached_data_files(self.base_path)
+            print(f"{len(self.cached_data)} cached data files found.")
+        elif metadata_path.endswith(".json"):
+            with open(metadata_path, "r") as f:
+                metadata = json.load(f)
+            self.data = metadata
+        elif metadata_path.endswith(".jsonl"):
+            metadata = []
+            with open(metadata_path, 'r') as f:
+                for line in f:
+                    metadata.append(json.loads(line.strip()))
+            self.data = metadata
+        else:
+            metadata = pandas.read_csv(metadata_path)
+            self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))]
+
+    def __getitem__(self, data_id):
+        if self.load_from_cache:
+            data = self.cached_data[data_id % len(self.cached_data)]
+            data = self.cached_data_operator(data)
+        else:
+            data = self.data[data_id % len(self.data)].copy()
+            for key in self.data_file_keys:
+                if key in data:
+                    if key in self.special_operator_map:
+                        data[key] = self.special_operator_map[key](data[key])
+                    elif key in self.data_file_keys:
+                        data[key] = self.main_data_operator(data[key])
+        return data
+
+    def __len__(self):
+        if self.load_from_cache:
+            return len(self.cached_data) * self.repeat
+        else:
+            return len(self.data) * self.repeat
+        
+    def check_data_equal(self, data1, data2):
+        # Debug only
+        if len(data1) != len(data2):
+            return False
+        for k in data1:
+            if data1[k] != data2[k]:
+                return False
+        return True

diffsynth/core/gradient/__init__.py

@@ -0,0 +1 @@
+from .gradient_checkpoint import gradient_checkpoint_forward

diffsynth/core/loader/__init__.py

@@ -0,0 +1,3 @@
+from .file import load_state_dict, hash_state_dict_keys, hash_model_file
+from .model import load_model, load_model_with_disk_offload
+from .config import ModelConfig

diffsynth/core/loader/config.py

@@ -0,0 +1,117 @@
+import torch, glob, os
+from typing import Optional, Union
+from dataclasses import dataclass
+from modelscope import snapshot_download
+from huggingface_hub import snapshot_download as hf_snapshot_download
+from typing import Optional
+
+
+@dataclass
+class ModelConfig:
+    path: Union[str, list[str]] = None
+    model_id: str = None
+    origin_file_pattern: Union[str, list[str]] = None
+    download_source: str = None
+    local_model_path: str = None
+    skip_download: bool = None
+    offload_device: Optional[Union[str, torch.device]] = None
+    offload_dtype: Optional[torch.dtype] = None
+    onload_device: Optional[Union[str, torch.device]] = None
+    onload_dtype: Optional[torch.dtype] = None
+    preparing_device: Optional[Union[str, torch.device]] = None
+    preparing_dtype: Optional[torch.dtype] = None
+    computation_device: Optional[Union[str, torch.device]] = None
+    computation_dtype: Optional[torch.dtype] = None
+    clear_parameters: bool = False
+    
+    def check_input(self):
+        if self.path is None and self.model_id is None:
+            raise ValueError(f"""No valid model files. Please use `ModelConfig(path="xxx")` or `ModelConfig(model_id="xxx/yyy", origin_file_pattern="zzz")`. `skip_download=True` only supports the first one.""")
+    
+    def parse_original_file_pattern(self):
+        if self.origin_file_pattern is None or self.origin_file_pattern == "":
+            return "*"
+        elif self.origin_file_pattern.endswith("/"):
+            return self.origin_file_pattern + "*"
+        else:
+            return self.origin_file_pattern
+        
+    def parse_download_source(self):
+        if self.download_source is None:
+            if os.environ.get('DIFFSYNTH_DOWNLOAD_SOURCE') is not None:
+                return os.environ.get('DIFFSYNTH_DOWNLOAD_SOURCE')
+            else:
+                return "modelscope"
+        else:
+            return self.download_source
+        
+    def parse_skip_download(self):
+        if self.skip_download is None:
+            if os.environ.get('DIFFSYNTH_SKIP_DOWNLOAD') is not None:
+                if os.environ.get('DIFFSYNTH_SKIP_DOWNLOAD').lower() == "true":
+                    return True
+                elif os.environ.get('DIFFSYNTH_SKIP_DOWNLOAD').lower() == "false":
+                    return False
+            else:
+                return False
+        else:
+            return self.skip_download
+
+    def download(self):
+        origin_file_pattern = self.parse_original_file_pattern()
+        downloaded_files = glob.glob(origin_file_pattern, root_dir=os.path.join(self.local_model_path, self.model_id))
+        download_source = self.parse_download_source()
+        if download_source.lower() == "modelscope":
+            snapshot_download(
+                self.model_id,
+                local_dir=os.path.join(self.local_model_path, self.model_id),
+                allow_file_pattern=origin_file_pattern,
+                ignore_file_pattern=downloaded_files,
+                local_files_only=False
+            )
+        elif download_source.lower() == "huggingface":
+            hf_snapshot_download(
+                self.model_id,
+                local_dir=os.path.join(self.local_model_path, self.model_id),
+                allow_patterns=origin_file_pattern,
+                ignore_patterns=downloaded_files,
+                local_files_only=False
+            )
+        else:
+            raise ValueError("`download_source` should be `modelscope` or `huggingface`.")
+        
+    def require_downloading(self):
+        if self.path is not None:
+            return False
+        skip_download = self.parse_skip_download()
+        return not skip_download
+    
+    def reset_local_model_path(self):
+        if os.environ.get('DIFFSYNTH_MODEL_BASE_PATH') is not None:
+            self.local_model_path = os.environ.get('DIFFSYNTH_MODEL_BASE_PATH')
+        elif self.local_model_path is None:
+            self.local_model_path = "./models"
+
+    def download_if_necessary(self):
+        self.check_input()
+        self.reset_local_model_path()
+        if self.require_downloading():
+            self.download()
+            if self.origin_file_pattern is None or self.origin_file_pattern == "":
+                self.path = os.path.join(self.local_model_path, self.model_id)
+            else:
+                self.path = glob.glob(os.path.join(self.local_model_path, self.model_id, self.origin_file_pattern))
+        if isinstance(self.path, list) and len(self.path) == 1:
+            self.path = self.path[0]
+
+    def vram_config(self):
+        return {
+            "offload_device": self.offload_device,
+            "offload_dtype": self.offload_dtype,
+            "onload_device": self.onload_device,
+            "onload_dtype": self.onload_dtype,
+            "preparing_device": self.preparing_device,
+            "preparing_dtype": self.preparing_dtype,
+            "computation_device": self.computation_device,
+            "computation_dtype": self.computation_dtype,
+        }

diffsynth/core/loader/file.py

@@ -0,0 +1,121 @@
+from safetensors import safe_open
+import torch, hashlib
+
+
+def load_state_dict(file_path, torch_dtype=None, device="cpu"):
+    if isinstance(file_path, list):
+        state_dict = {}
+        for file_path_ in file_path:
+            state_dict.update(load_state_dict(file_path_, torch_dtype, device))
+        return state_dict
+    if file_path.endswith(".safetensors"):
+        return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype, device=device)
+    else:
+        return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype, device=device)
+
+
+def load_state_dict_from_safetensors(file_path, torch_dtype=None, device="cpu"):
+    state_dict = {}
+    with safe_open(file_path, framework="pt", device=str(device)) 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, device="cpu"):
+    state_dict = torch.load(file_path, map_location=device, weights_only=True)
+    if len(state_dict) == 1:
+        if "state_dict" in state_dict:
+            state_dict = state_dict["state_dict"]
+        elif "module" in state_dict:
+            state_dict = state_dict["module"]
+        elif "model_state" in state_dict:
+            state_dict = state_dict["model_state"]
+    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 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 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_keys_dict(file_path):
+    if isinstance(file_path, list):
+        state_dict = {}
+        for file_path_ in file_path:
+            state_dict.update(load_keys_dict(file_path_))
+        return state_dict
+    if file_path.endswith(".safetensors"):
+        return load_keys_dict_from_safetensors(file_path)
+    else:
+        return load_keys_dict_from_bin(file_path)
+
+
+def load_keys_dict_from_safetensors(file_path):
+    keys_dict = {}
+    with safe_open(file_path, framework="pt", device="cpu") as f:
+        for k in f.keys():
+            keys_dict[k] = f.get_slice(k).get_shape()
+    return keys_dict
+
+
+def convert_state_dict_to_keys_dict(state_dict):
+    keys_dict = {}
+    for k, v in state_dict.items():
+        if isinstance(v, torch.Tensor):
+            keys_dict[k] = list(v.shape)
+        else:
+            keys_dict[k] = convert_state_dict_to_keys_dict(v)
+    return keys_dict
+
+
+def load_keys_dict_from_bin(file_path):
+    state_dict = load_state_dict_from_bin(file_path)
+    keys_dict = convert_state_dict_to_keys_dict(state_dict)
+    return keys_dict
+
+
+def convert_keys_dict_to_single_str(state_dict, with_shape=True):
+    keys = []
+    for key, value in state_dict.items():
+        if isinstance(key, str):
+            if isinstance(value, dict):
+                keys.append(key + "|" + convert_keys_dict_to_single_str(value, with_shape=with_shape))
+            else:
+                if with_shape:
+                    shape = "_".join(map(str, list(value)))
+                    keys.append(key + ":" + shape)
+                keys.append(key)
+    keys.sort()
+    keys_str = ",".join(keys)
+    return keys_str
+
+
+def hash_model_file(path, with_shape=True):
+    keys_dict = load_keys_dict(path)
+    keys_str = convert_keys_dict_to_single_str(keys_dict, with_shape=with_shape)
+    keys_str = keys_str.encode(encoding="UTF-8")
+    return hashlib.md5(keys_str).hexdigest()

diffsynth/core/loader/model.py

@@ -0,0 +1,79 @@
+from ..vram.initialization import skip_model_initialization
+from ..vram.disk_map import DiskMap
+from ..vram.layers import enable_vram_management
+from .file import load_state_dict
+import torch
+
+
+def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, device="cpu", state_dict_converter=None, use_disk_map=False, module_map=None, vram_config=None, vram_limit=None):
+    config = {} if config is None else config
+    # Why do we use `skip_model_initialization`?
+    # It skips the random initialization of model parameters,
+    # thereby speeding up model loading and avoiding excessive memory usage.
+    with skip_model_initialization():
+        model = model_class(**config)
+    # What is `module_map`?
+    # This is a module mapping table for VRAM management.
+    if module_map is not None:
+        devices = [vram_config["offload_device"], vram_config["onload_device"], vram_config["preparing_device"], vram_config["computation_device"]]
+        device = [d for d in devices if d != "disk"][0]
+        dtypes = [vram_config["offload_dtype"], vram_config["onload_dtype"], vram_config["preparing_dtype"], vram_config["computation_dtype"]]
+        dtype = [d for d in dtypes if d != "disk"][0]
+        if vram_config["offload_device"] != "disk":
+            state_dict = DiskMap(path, device, torch_dtype=dtype)
+            if state_dict_converter is not None:
+                state_dict = state_dict_converter(state_dict)
+            else:
+                state_dict = {i: state_dict[i] for i in state_dict}
+            model.load_state_dict(state_dict, assign=True)
+            model = enable_vram_management(model, module_map, vram_config=vram_config, disk_map=None, vram_limit=vram_limit)
+        else:
+            disk_map = DiskMap(path, device, state_dict_converter=state_dict_converter)
+            model = enable_vram_management(model, module_map, vram_config=vram_config, disk_map=disk_map, vram_limit=vram_limit)
+    else:
+        # Why do we use `DiskMap`?
+        # Sometimes a model file contains multiple models,
+        # and DiskMap can load only the parameters of a single model,
+        # avoiding the need to load all parameters in the file.
+        if use_disk_map:
+            state_dict = DiskMap(path, device, torch_dtype=torch_dtype)
+        else:
+            state_dict = load_state_dict(path, torch_dtype, device)
+        # Why do we use `state_dict_converter`?
+        # Some models are saved in complex formats,
+        # and we need to convert the state dict into the appropriate format.
+        if state_dict_converter is not None:
+            state_dict = state_dict_converter(state_dict)
+        else:
+            state_dict = {i: state_dict[i] for i in state_dict}
+        model.load_state_dict(state_dict, assign=True)
+        # Why do we call `to()`?
+        # Because some models override the behavior of `to()`,
+        # especially those from libraries like Transformers.
+        model = model.to(dtype=torch_dtype, device=device)
+    if hasattr(model, "eval"):
+        model = model.eval()
+    return model
+
+
+def load_model_with_disk_offload(model_class, path, config=None, torch_dtype=torch.bfloat16, device="cpu", state_dict_converter=None, module_map=None):
+    if isinstance(path, str):
+        path = [path]
+    config = {} if config is None else config
+    with skip_model_initialization():
+        model = model_class(**config)
+    if hasattr(model, "eval"):
+        model = model.eval()
+    disk_map = DiskMap(path, device, state_dict_converter=state_dict_converter)
+    vram_config = {
+        "offload_dtype": "disk",
+        "offload_device": "disk",
+        "onload_dtype": "disk",
+        "onload_device": "disk",
+        "preparing_dtype": torch.float8_e4m3fn,
+        "preparing_device": device,
+        "computation_dtype": torch_dtype,
+        "computation_device": device,
+    }
+    enable_vram_management(model, module_map, vram_config=vram_config, disk_map=disk_map, vram_limit=80)
+    return model

diffsynth/core/vram/__init__.py

@@ -0,0 +1,2 @@
+from .initialization import skip_model_initialization
+from .layers import *

diffsynth/core/vram/disk_map.py

@@ -0,0 +1,93 @@
+from safetensors import safe_open
+import torch, os
+
+
+class SafetensorsCompatibleTensor:
+    def __init__(self, tensor):
+        self.tensor = tensor
+    
+    def get_shape(self):
+        return list(self.tensor.shape)
+
+
+class SafetensorsCompatibleBinaryLoader:
+    def __init__(self, path, device):
+        print("Detected non-safetensors files, which may cause slower loading. It's recommended to convert it to a safetensors file.")
+        self.state_dict = torch.load(path, weights_only=True, map_location=device)
+        
+    def keys(self):
+        return self.state_dict.keys()
+    
+    def get_tensor(self, name):
+        return self.state_dict[name]
+    
+    def get_slice(self, name):
+        return SafetensorsCompatibleTensor(self.state_dict[name])
+
+
+class DiskMap:
+
+    def __init__(self, path, device, torch_dtype=None, state_dict_converter=None, buffer_size=10**9):
+        self.path = path if isinstance(path, list) else [path]
+        self.device = device
+        self.torch_dtype = torch_dtype
+        if os.environ.get('DIFFSYNTH_DISK_MAP_BUFFER_SIZE') is not None:
+            self.buffer_size = int(os.environ.get('DIFFSYNTH_DISK_MAP_BUFFER_SIZE'))
+        else:
+            self.buffer_size = buffer_size
+        self.files = []
+        self.flush_files()
+        self.name_map = {}
+        for file_id, file in enumerate(self.files):
+            for name in file.keys():
+                self.name_map[name] = file_id
+        self.rename_dict = self.fetch_rename_dict(state_dict_converter)
+        
+    def flush_files(self):
+        if len(self.files) == 0:
+            for path in self.path:
+                if path.endswith(".safetensors"):
+                    self.files.append(safe_open(path, framework="pt", device=str(self.device)))
+                else:
+                    self.files.append(SafetensorsCompatibleBinaryLoader(path, device=self.device))
+        else:
+            for i, path in enumerate(self.path):
+                if path.endswith(".safetensors"):
+                    self.files[i] = safe_open(path, framework="pt", device=str(self.device))
+        self.num_params = 0
+
+    def __getitem__(self, name):
+        if self.rename_dict is not None: name = self.rename_dict[name]
+        file_id = self.name_map[name]
+        param = self.files[file_id].get_tensor(name)
+        if self.torch_dtype is not None and isinstance(param, torch.Tensor):
+            param = param.to(self.torch_dtype)
+        if isinstance(param, torch.Tensor) and param.device == "cpu":
+            param = param.clone()
+        if isinstance(param, torch.Tensor):
+            self.num_params += param.numel()
+        if self.num_params > self.buffer_size:
+            self.flush_files()
+        return param
+
+    def fetch_rename_dict(self, state_dict_converter):
+        if state_dict_converter is None:
+            return None
+        state_dict = {}
+        for file in self.files:
+            for name in file.keys():
+                state_dict[name] = name
+        state_dict = state_dict_converter(state_dict)
+        return state_dict
+    
+    def __iter__(self):
+        if self.rename_dict is not None:
+            return self.rename_dict.__iter__()
+        else:
+            return self.name_map.__iter__()
+    
+    def __contains__(self, x):
+        if self.rename_dict is not None:
+            return x in self.rename_dict
+        else:
+            return x in self.name_map

diffsynth/core/vram/initialization.py

@@ -0,0 +1,21 @@
+import torch
+from contextlib import contextmanager
+
+
+@contextmanager
+def skip_model_initialization(device=torch.device("meta")):
+
+    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)
+
+    old_register_parameter = torch.nn.Module.register_parameter
+    torch.nn.Module.register_parameter = register_empty_parameter
+    try:
+        yield
+    finally:
+        torch.nn.Module.register_parameter = old_register_parameter

diffsynth/core/vram/layers.py

@@ -0,0 +1,475 @@
+import torch, copy
+from typing import Union
+from .initialization import skip_model_initialization
+from .disk_map import DiskMap
+
+
+class AutoTorchModule(torch.nn.Module):
+
+    def __init__(
+        self,
+        offload_dtype: torch.dtype = None,
+        offload_device: Union[str, torch.device] = None,
+        onload_dtype: torch.dtype = None,
+        onload_device: Union[str, torch.device] = None,
+        preparing_dtype: torch.dtype = None,
+        preparing_device: Union[str, torch.device] = None,
+        computation_dtype: torch.dtype = None,
+        computation_device: Union[str, torch.device] = None,
+        vram_limit: float = None,
+    ):
+        super().__init__()
+        self.set_dtype_and_device(
+            offload_dtype,
+            offload_device,
+            onload_dtype,
+            onload_device,
+            preparing_dtype,
+            preparing_device,
+            computation_dtype,
+            computation_device,
+            vram_limit,
+        )
+        self.state = 0
+        self.name = ""
+
+    def set_dtype_and_device(
+        self,
+        offload_dtype: torch.dtype = None,
+        offload_device: Union[str, torch.device] = None,
+        onload_dtype: torch.dtype = None,
+        onload_device: Union[str, torch.device] = None,
+        preparing_dtype: torch.dtype = None,
+        preparing_device: Union[str, torch.device] = None,
+        computation_dtype: torch.dtype = None,
+        computation_device: Union[str, torch.device] = None,
+        vram_limit: float = None,
+    ):
+        self.offload_dtype = offload_dtype or computation_dtype
+        self.offload_device = offload_device or computation_dtype
+        self.onload_dtype = onload_dtype or computation_dtype
+        self.onload_device = onload_device or computation_dtype
+        self.preparing_dtype = preparing_dtype or computation_dtype
+        self.preparing_device = preparing_device or computation_dtype
+        self.computation_dtype = computation_dtype
+        self.computation_device = computation_device
+        self.vram_limit = vram_limit
+
+    def cast_to(self, weight, dtype, device):
+        r = torch.empty_like(weight, dtype=dtype, device=device)
+        r.copy_(weight)
+        return r
+
+    def check_free_vram(self):
+        gpu_mem_state = torch.cuda.mem_get_info(self.computation_device)
+        used_memory = (gpu_mem_state[1] - gpu_mem_state[0]) / (1024**3)
+        return used_memory < self.vram_limit
+
+    def offload(self):
+        if self.state != 0:
+            self.to(dtype=self.offload_dtype, device=self.offload_device)
+            self.state = 0
+
+    def onload(self):
+        if self.state != 1:
+            self.to(dtype=self.onload_dtype, device=self.onload_device)
+            self.state = 1
+            
+    def param_name(self, name):
+        if self.name == "":
+            return name
+        else:
+            return self.name + "." + name
+
+
+class AutoWrappedModule(AutoTorchModule):
+
+    def __init__(
+        self,
+        module: torch.nn.Module,
+        offload_dtype: torch.dtype = None,
+        offload_device: Union[str, torch.device] = None,
+        onload_dtype: torch.dtype = None,
+        onload_device: Union[str, torch.device] = None,
+        preparing_dtype: torch.dtype = None,
+        preparing_device: Union[str, torch.device] = None,
+        computation_dtype: torch.dtype = None,
+        computation_device: Union[str, torch.device] = None,
+        vram_limit: float = None,
+        name: str = "",
+        disk_map: DiskMap = None,
+        **kwargs
+    ):
+        super().__init__(
+            offload_dtype,
+            offload_device,
+            onload_dtype,
+            onload_device,
+            preparing_dtype,
+            preparing_device,
+            computation_dtype,
+            computation_device,
+            vram_limit,
+        )
+        self.module = module
+        if offload_dtype == "disk":
+            self.name = name
+            self.disk_map = disk_map
+            self.required_params = [name for name, _ in self.module.named_parameters()]
+            self.disk_offload = True
+        else:
+            self.disk_offload = False
+            
+    def load_from_disk(self, torch_dtype, device, copy_module=False):
+        if copy_module:
+            module = copy.deepcopy(self.module)
+        else:
+            module = self.module
+        state_dict = {}
+        for name in self.required_params:
+            param = self.disk_map[self.param_name(name)]
+            param = param.to(dtype=torch_dtype, device=device)
+            state_dict[name] = param
+        module.load_state_dict(state_dict, assign=True)
+        module.to(dtype=torch_dtype, device=device)
+        return module
+    
+    def offload_to_disk(self, model: torch.nn.Module):
+        for buf in model.buffers():
+            # If there are some parameters are registed in buffers (not in state dict),
+            # We cannot offload the model.
+            for children in model.children():
+                self.offload_to_disk(children)
+            break
+        else:
+            model.to("meta")
+
+    def offload(self):
+        # offload / onload / preparing -> offload
+        if self.state != 0:
+            if self.disk_offload:
+                self.offload_to_disk(self.module)
+            else:
+                self.to(dtype=self.offload_dtype, device=self.offload_device)
+            self.state = 0
+
+    def onload(self):
+        # offload / onload / preparing -> onload
+        if self.state < 1:
+            if self.disk_offload and self.onload_device != "disk" and self.offload_device == "disk":
+                self.load_from_disk(self.onload_dtype, self.onload_device)
+            elif self.onload_device != "disk":
+                self.to(dtype=self.onload_dtype, device=self.onload_device)
+            self.state = 1
+            
+    def preparing(self):
+        # onload / preparing -> preparing
+        if self.state != 2:
+            if self.disk_offload and self.preparing_device != "disk" and self.onload_device == "disk":
+                self.load_from_disk(self.preparing_dtype, self.preparing_device)
+            elif self.preparing_device != "disk":
+                self.to(dtype=self.preparing_dtype, device=self.preparing_device)
+            self.state = 2
+
+    def cast_to(self, module, dtype, device):
+        return copy.deepcopy(module).to(dtype=dtype, device=device)
+            
+    def computation(self):
+        # onload / preparing -> computation (temporary)
+        if self.state == 2:
+            torch_dtype, device = self.preparing_dtype, self.preparing_device
+        else:
+            torch_dtype, device = self.onload_dtype, self.onload_device
+        if torch_dtype == self.computation_dtype and device == self.computation_device:
+            module = self.module
+        elif self.disk_offload and device == "disk":
+            module = self.load_from_disk(self.computation_dtype, self.computation_device, copy_module=True)
+        else:
+            module = self.cast_to(self.module, dtype=self.computation_dtype, device=self.computation_device)
+        return module
+
+    def forward(self, *args, **kwargs):
+        if self.state == 1 and (self.vram_limit is None or self.check_free_vram()):
+            self.preparing()
+        module = self.computation()
+        return module(*args, **kwargs)
+    
+    def __getattr__(self, name):
+        if name in self.__dict__ or name == "module":
+            return super().__getattr__(name)
+        else:
+            return getattr(self.module, name)
+
+
+class AutoWrappedNonRecurseModule(AutoWrappedModule):
+
+    def __init__(
+        self,
+        module: torch.nn.Module,
+        offload_dtype: torch.dtype = None,
+        offload_device: Union[str, torch.device] = None,
+        onload_dtype: torch.dtype = None,
+        onload_device: Union[str, torch.device] = None,
+        preparing_dtype: torch.dtype = None,
+        preparing_device: Union[str, torch.device] = None,
+        computation_dtype: torch.dtype = None,
+        computation_device: Union[str, torch.device] = None,
+        vram_limit: float = None,
+        name: str = "",
+        disk_map: DiskMap = None,
+        **kwargs
+    ):
+        super().__init__(
+            module,
+            offload_dtype,
+            offload_device,
+            onload_dtype,
+            onload_device,
+            preparing_dtype,
+            preparing_device,
+            computation_dtype,
+            computation_device,
+            vram_limit,
+            name,
+            disk_map,
+            **kwargs
+        )
+        if self.disk_offload:
+            self.required_params = [name for name, _ in self.module.named_parameters(recurse=False)]
+            
+    def load_from_disk(self, torch_dtype, device, copy_module=False):
+        if copy_module:
+            module = copy.deepcopy(self.module)
+        else:
+            module = self.module
+        state_dict = {}
+        for name in self.required_params:
+            param = self.disk_map[self.param_name(name)]
+            param = param.to(dtype=torch_dtype, device=device)
+            state_dict[name] = param
+        module.load_state_dict(state_dict, assign=True, strict=False)
+        return module
+    
+    def offload_to_disk(self, model: torch.nn.Module):
+        for name in self.required_params:
+            getattr(self, name).to("meta")
+    
+    def cast_to(self, module, dtype, device):
+        # Parameter casting is implemented in the model architecture.
+        return module
+    
+    def __getattr__(self, name):
+        if name in self.__dict__ or name == "module":
+            return super().__getattr__(name)
+        else:
+            return getattr(self.module, name)
+
+
+class AutoWrappedLinear(torch.nn.Linear, AutoTorchModule):
+    def __init__(
+        self,
+        module: torch.nn.Linear,
+        offload_dtype: torch.dtype = None,
+        offload_device: Union[str, torch.device] = None,
+        onload_dtype: torch.dtype = None,
+        onload_device: Union[str, torch.device] = None,
+        preparing_dtype: torch.dtype = None,
+        preparing_device: Union[str, torch.device] = None,
+        computation_dtype: torch.dtype = None,
+        computation_device: Union[str, torch.device] = None,
+        vram_limit: float = None,
+        name: str = "",
+        disk_map: DiskMap = None,
+        **kwargs
+    ):
+        with skip_model_initialization():
+            super().__init__(
+                in_features=module.in_features,
+                out_features=module.out_features,
+                bias=module.bias is not None,
+            )
+        self.set_dtype_and_device(
+            offload_dtype,
+            offload_device,
+            onload_dtype,
+            onload_device,
+            preparing_dtype,
+            preparing_device,
+            computation_dtype,
+            computation_device,
+            vram_limit,
+        )
+        self.weight = module.weight
+        self.bias = module.bias
+        self.state = 0
+        self.name = name
+        self.lora_A_weights = []
+        self.lora_B_weights = []
+        self.lora_merger = None
+        self.enable_fp8 = computation_dtype in [torch.float8_e4m3fn, torch.float8_e4m3fnuz]
+        
+        if offload_dtype == "disk":
+            self.disk_map = disk_map
+            self.disk_offload = True
+        else:
+            self.disk_offload = False
+    
+    def fp8_linear(
+        self,
+        input: torch.Tensor,
+        weight: torch.Tensor,
+        bias: torch.Tensor = None,
+    ) -> torch.Tensor:
+        device = input.device
+        origin_dtype = input.dtype
+        origin_shape = input.shape
+        input = input.reshape(-1, origin_shape[-1])
+
+        x_max = torch.max(torch.abs(input), dim=-1, keepdim=True).values
+        fp8_max = 448.0
+        # For float8_e4m3fnuz, the maximum representable value is half of that of e4m3fn.
+        # To avoid overflow and ensure numerical compatibility during FP8 computation,
+        # we scale down the input by 2.0 in advance.
+        # This scaling will be compensated later during the final result scaling.
+        if self.computation_dtype == torch.float8_e4m3fnuz:
+            fp8_max = fp8_max / 2.0
+        scale_a = torch.clamp(x_max / fp8_max, min=1.0).float().to(device=device)
+        scale_b = torch.ones((weight.shape[0], 1)).to(device=device)
+        input = input / (scale_a + 1e-8)
+        input = input.to(self.computation_dtype)
+        weight = weight.to(self.computation_dtype)
+        bias = bias.to(torch.bfloat16)
+
+        result = torch._scaled_mm(
+            input,
+            weight.T,
+            scale_a=scale_a,
+            scale_b=scale_b.T,
+            bias=bias,
+            out_dtype=origin_dtype,
+        )
+        new_shape = origin_shape[:-1] + result.shape[-1:]
+        result = result.reshape(new_shape)
+        return result
+            
+    def load_from_disk(self, torch_dtype, device, assign=True):
+        weight = self.disk_map[self.name + ".weight"].to(dtype=torch_dtype, device=device)
+        bias = None if self.bias is None else self.disk_map[self.name + ".bias"].to(dtype=torch_dtype, device=device)
+        if assign:
+            state_dict = {"weight": weight}
+            if bias is not None: state_dict["bias"] = bias
+            self.load_state_dict(state_dict, assign=True)
+        return weight, bias
+    
+    def offload(self):
+        # offload / onload / preparing -> offload
+        if self.state != 0:
+            if self.disk_offload:
+                self.to("meta")
+            else:
+                self.to(dtype=self.offload_dtype, device=self.offload_device)
+            self.state = 0
+
+    def onload(self):
+        # offload / onload / preparing -> onload
+        if self.state < 1:
+            if self.disk_offload and self.onload_device != "disk" and self.offload_device == "disk":
+                self.load_from_disk(self.onload_dtype, self.onload_device)
+            elif self.onload_device != "disk":
+                self.to(dtype=self.onload_dtype, device=self.onload_device)
+            self.state = 1
+            
+    def preparing(self):
+        # onload / preparing -> preparing
+        if self.state != 2:
+            if self.disk_offload and self.preparing_device != "disk" and self.onload_device == "disk":
+                self.load_from_disk(self.preparing_dtype, self.preparing_device)
+            elif self.preparing_device != "disk":
+                self.to(dtype=self.preparing_dtype, device=self.preparing_device)
+            self.state = 2
+            
+    def computation(self):
+        # onload / preparing -> computation (temporary)
+        if self.state == 2:
+            torch_dtype, device = self.preparing_dtype, self.preparing_device
+        else:
+            torch_dtype, device = self.onload_dtype, self.onload_device
+        if torch_dtype == self.computation_dtype and device == self.computation_device:
+            weight, bias = self.weight, self.bias
+        elif self.disk_offload and device == "disk":
+            weight, bias = self.load_from_disk(self.computation_dtype, self.computation_device, assign=False)
+        else:
+            weight = self.cast_to(self.weight, self.computation_dtype, self.computation_device)
+            bias = None if self.bias is None else self.cast_to(self.bias, self.computation_dtype, self.computation_device)
+        return weight, bias
+
+    def linear_forward(self, x, weight, bias):
+        if self.enable_fp8:
+            out = self.fp8_linear(x, weight, bias)
+        else:
+            out = torch.nn.functional.linear(x, weight, bias)
+        return out
+
+    def lora_forward(self, x, out):
+        if self.lora_merger is None:
+            for lora_A, lora_B in zip(self.lora_A_weights, self.lora_B_weights):
+                out = out + x @ lora_A.T @ lora_B.T
+        else:
+            lora_output = []
+            for lora_A, lora_B in zip(self.lora_A_weights, self.lora_B_weights):
+                lora_output.append(x @ lora_A.T @ lora_B.T)
+            lora_output = torch.stack(lora_output)
+            out = self.lora_merger(out, lora_output)
+        return out
+    
+    def forward(self, x, *args, **kwargs):
+        if self.state == 1 and (self.vram_limit is None or self.check_free_vram()):
+            self.preparing()
+        weight, bias = self.computation()
+        out = self.linear_forward(x, weight, bias)
+        if len(self.lora_A_weights) > 0:
+            out = self.lora_forward(x, out)
+        return out
+
+
+def enable_vram_management_recursively(model: torch.nn.Module, module_map: dict, vram_config: dict, vram_limit=None, name_prefix="", disk_map=None, **kwargs):
+    if isinstance(model, AutoWrappedNonRecurseModule):
+        model = model.module
+    for name, module in model.named_children():
+        layer_name = name if name_prefix == "" else name_prefix + "." + name
+        for source_module, target_module in module_map.items():
+            if isinstance(module, source_module):
+                module_ = target_module(module, **vram_config, vram_limit=vram_limit, name=layer_name, disk_map=disk_map, **kwargs)
+                if isinstance(module_, AutoWrappedNonRecurseModule):
+                    enable_vram_management_recursively(module_, module_map, vram_config, vram_limit=vram_limit, name_prefix=layer_name, disk_map=disk_map, **kwargs)
+                setattr(model, name, module_)
+                break
+        else:
+            enable_vram_management_recursively(module, module_map, vram_config, vram_limit=vram_limit, name_prefix=layer_name, disk_map=disk_map, **kwargs)
+
+
+def fill_vram_config(model, vram_config):
+    vram_config_ = vram_config.copy()
+    vram_config_["onload_dtype"] = vram_config["computation_dtype"]
+    vram_config_["onload_device"] = vram_config["computation_device"]
+    vram_config_["preparing_dtype"] = vram_config["computation_dtype"]
+    vram_config_["preparing_device"] = vram_config["computation_device"]
+    for k in vram_config:
+        if vram_config[k] != vram_config_[k]:
+            print(f"No fine-grained VRAM configuration is provided for {model.__class__.__name__}. [`onload`, `preparing`, `computation`] will be the same state. `vram_config` is set to {vram_config_}")
+            break
+    return vram_config_
+
+
+def enable_vram_management(model: torch.nn.Module, module_map: dict, vram_config: dict, vram_limit=None, disk_map=None, **kwargs):
+    for source_module, target_module in module_map.items():
+        # If no fine-grained VRAM configuration is provided, the entire model will be managed uniformly.
+        if isinstance(model, source_module):
+            vram_config = fill_vram_config(model, vram_config)
+            model = target_module(model, **vram_config, vram_limit=vram_limit, disk_map=disk_map, **kwargs)
+            break
+    else:
+        enable_vram_management_recursively(model, module_map, vram_config, vram_limit=vram_limit, disk_map=disk_map, **kwargs)
+    # `vram_management_enabled` is a flag that allows the pipeline to determine whether VRAM management is enabled.
+    model.vram_management_enabled = True
+    return model

diffsynth/data/__init__.py

@@ -1 +0,0 @@
-from .video import VideoData, save_video, save_frames, merge_video_audio, save_video_with_audio

diffsynth/data/simple_text_image.py

@@ -1,41 +0,0 @@
-import torch, os, torchvision
-from torchvision import transforms
-import pandas as pd
-from PIL import Image
-
-
-
-class TextImageDataset(torch.utils.data.Dataset):
-    def __init__(self, dataset_path, steps_per_epoch=10000, height=1024, width=1024, center_crop=True, random_flip=False):
-        self.steps_per_epoch = steps_per_epoch
-        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.CenterCrop((height, width)) if center_crop else transforms.RandomCrop((height, width)),
-                transforms.RandomHorizontalFlip() if random_flip else transforms.Lambda(lambda x: x),
-                transforms.ToTensor(),
-                transforms.Normalize([0.5], [0.5]),
-            ]
-        )
-
-
-    def __getitem__(self, index):
-        data_id = torch.randint(0, len(self.path), (1,))[0]
-        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}
-
-
-    def __len__(self):
-        return self.steps_per_epoch

diffsynth/diffusion/__init__.py

@@ -0,0 +1,6 @@
+from .flow_match import FlowMatchScheduler
+from .training_module import DiffusionTrainingModule
+from .logger import ModelLogger
+from .runner import launch_training_task, launch_data_process_task
+from .parsers import *
+from .loss import *

diffsynth/diffusion/base_pipeline.py

@@ -0,0 +1,439 @@
+from PIL import Image
+import torch
+import numpy as np
+from einops import repeat, reduce
+from typing import Union
+from ..core import AutoTorchModule, AutoWrappedLinear, load_state_dict, ModelConfig
+from ..utils.lora import GeneralLoRALoader
+from ..models.model_loader import ModelPool
+from ..utils.controlnet import ControlNetInput
+
+
+class PipelineUnit:
+    def __init__(
+        self,
+        seperate_cfg: bool = False,
+        take_over: bool = False,
+        input_params: tuple[str] = None,
+        output_params: tuple[str] = None,
+        input_params_posi: dict[str, str] = None,
+        input_params_nega: dict[str, str] = None,
+        onload_model_names: tuple[str] = None
+    ):
+        self.seperate_cfg = seperate_cfg
+        self.take_over = take_over
+        self.input_params = input_params
+        self.output_params = output_params
+        self.input_params_posi = input_params_posi
+        self.input_params_nega = input_params_nega
+        self.onload_model_names = onload_model_names
+
+    def fetch_input_params(self):
+        params = []
+        if self.input_params is not None:
+            for param in self.input_params:
+                params.append(param)
+        if self.input_params_posi is not None:
+            for _, param in self.input_params_posi.items():
+                params.append(param)
+        if self.input_params_nega is not None:
+            for _, param in self.input_params_nega.items():
+                params.append(param)
+        params = sorted(list(set(params)))
+        return params
+    
+    def fetch_output_params(self):
+        params = []
+        if self.output_params is not None:
+            for param in self.output_params:
+                params.append(param)
+        return params
+
+    def process(self, pipe, **kwargs) -> dict:
+        return {}
+    
+    def post_process(self, pipe, **kwargs) -> dict:
+        return {}
+
+
+class BasePipeline(torch.nn.Module):
+
+    def __init__(
+        self,
+        device="cuda", torch_dtype=torch.float16,
+        height_division_factor=64, width_division_factor=64,
+        time_division_factor=None, time_division_remainder=None,
+    ):
+        super().__init__()
+        # The device and torch_dtype is used for the storage of intermediate variables, not models.
+        self.device = device
+        self.torch_dtype = torch_dtype
+        # The following parameters are used for shape check.
+        self.height_division_factor = height_division_factor
+        self.width_division_factor = width_division_factor
+        self.time_division_factor = time_division_factor
+        self.time_division_remainder = time_division_remainder
+        # VRAM management
+        self.vram_management_enabled = False
+        # Pipeline Unit Runner
+        self.unit_runner = PipelineUnitRunner()
+        # LoRA Loader
+        self.lora_loader = GeneralLoRALoader
+        
+        
+    def to(self, *args, **kwargs):
+        device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(*args, **kwargs)
+        if device is not None:
+            self.device = device
+        if dtype is not None:
+            self.torch_dtype = dtype
+        super().to(*args, **kwargs)
+        return self
+
+
+    def check_resize_height_width(self, height, width, num_frames=None):
+        # Shape check
+        if height % self.height_division_factor != 0:
+            height = (height + self.height_division_factor - 1) // self.height_division_factor * self.height_division_factor
+            print(f"height % {self.height_division_factor} != 0. 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"width % {self.width_division_factor} != 0. We round it up to {width}.")
+        if num_frames is None:
+            return height, width
+        else:
+            if num_frames % self.time_division_factor != self.time_division_remainder:
+                num_frames = (num_frames + self.time_division_factor - 1) // self.time_division_factor * self.time_division_factor + self.time_division_remainder
+                print(f"num_frames % {self.time_division_factor} != {self.time_division_remainder}. We round it up to {num_frames}.")
+            return height, width, num_frames
+
+
+    def preprocess_image(self, image, torch_dtype=None, device=None, pattern="B C H W", min_value=-1, max_value=1):
+        # Transform a PIL.Image to torch.Tensor
+        image = torch.Tensor(np.array(image, dtype=np.float32))
+        image = image.to(dtype=torch_dtype or self.torch_dtype, device=device or self.device)
+        image = image * ((max_value - min_value) / 255) + min_value
+        image = repeat(image, f"H W C -> {pattern}", **({"B": 1} if "B" in pattern else {}))
+        return image
+
+
+    def preprocess_video(self, video, torch_dtype=None, device=None, pattern="B C T H W", min_value=-1, max_value=1):
+        # Transform a list of PIL.Image to torch.Tensor
+        video = [self.preprocess_image(image, torch_dtype=torch_dtype, device=device, min_value=min_value, max_value=max_value) for image in video]
+        video = torch.stack(video, dim=pattern.index("T") // 2)
+        return video
+
+
+    def vae_output_to_image(self, vae_output, pattern="B C H W", min_value=-1, max_value=1):
+        # Transform a torch.Tensor to PIL.Image
+        if pattern != "H W C":
+            vae_output = reduce(vae_output, f"{pattern} -> H W C", reduction="mean")
+        image = ((vae_output - min_value) * (255 / (max_value - min_value))).clip(0, 255)
+        image = image.to(device="cpu", dtype=torch.uint8)
+        image = Image.fromarray(image.numpy())
+        return image
+
+
+    def vae_output_to_video(self, vae_output, pattern="B C T H W", min_value=-1, max_value=1):
+        # Transform a torch.Tensor to list of PIL.Image
+        if pattern != "T H W C":
+            vae_output = reduce(vae_output, f"{pattern} -> T H W C", reduction="mean")
+        video = [self.vae_output_to_image(image, pattern="H W C", min_value=min_value, max_value=max_value) for image in vae_output]
+        return video
+
+
+    def load_models_to_device(self, model_names):
+        if self.vram_management_enabled:
+            # offload models
+            for name, model in self.named_children():
+                if name not in model_names:
+                    if hasattr(model, "vram_management_enabled") and model.vram_management_enabled:
+                        if hasattr(model, "offload"):
+                            model.offload()
+                        else:
+                            for module in model.modules():
+                                if hasattr(module, "offload"):
+                                    module.offload()
+            torch.cuda.empty_cache()
+            # onload models
+            for name, model in self.named_children():
+                if name in model_names:
+                    if hasattr(model, "vram_management_enabled") and model.vram_management_enabled:
+                        if hasattr(model, "onload"):
+                            model.onload()
+                        else:
+                            for module in model.modules():
+                                if hasattr(module, "onload"):
+                                    module.onload()
+
+
+    def generate_noise(self, shape, seed=None, rand_device="cpu", rand_torch_dtype=torch.float32, device=None, torch_dtype=None):
+        # Initialize Gaussian noise
+        generator = None if seed is None else torch.Generator(rand_device).manual_seed(seed)
+        noise = torch.randn(shape, generator=generator, device=rand_device, dtype=rand_torch_dtype)
+        noise = noise.to(dtype=torch_dtype or self.torch_dtype, device=device or self.device)
+        return noise
+
+        
+    def get_vram(self):
+        return torch.cuda.mem_get_info(self.device)[1] / (1024 ** 3)
+    
+    def get_module(self, model, name):
+        if "." in name:
+            name, suffix = name[:name.index(".")], name[name.index(".") + 1:]
+            if name.isdigit():
+                return self.get_module(model[int(name)], suffix)
+            else:
+                return self.get_module(getattr(model, name), suffix)
+        else:
+            return getattr(model, name)
+    
+    def freeze_except(self, model_names):
+        self.eval()
+        self.requires_grad_(False)
+        for name in model_names:
+            module = self.get_module(self, name)
+            if module is None:
+                print(f"No {name} models in the pipeline. We cannot enable training on the model. If this occurs during the data processing stage, it is normal.")
+                continue
+            module.train()
+            module.requires_grad_(True)
+                
+    
+    def blend_with_mask(self, base, addition, mask):
+        return base * (1 - mask) + addition * mask
+    
+    
+    def step(self, scheduler, latents, progress_id, noise_pred, input_latents=None, inpaint_mask=None, **kwargs):
+        timestep = scheduler.timesteps[progress_id]
+        if inpaint_mask is not None:
+            noise_pred_expected = scheduler.return_to_timestep(scheduler.timesteps[progress_id], latents, input_latents)
+            noise_pred = self.blend_with_mask(noise_pred_expected, noise_pred, inpaint_mask)
+        latents_next = scheduler.step(noise_pred, timestep, latents)
+        return latents_next
+    
+    
+    def split_pipeline_units(self, model_names: list[str]):
+        return PipelineUnitGraph().split_pipeline_units(self.units, model_names)
+    
+    
+    def flush_vram_management_device(self, device):
+        for module in self.modules():
+            if isinstance(module, AutoTorchModule):
+                module.offload_device = device
+                module.onload_device = device
+                module.preparing_device = device
+                module.computation_device = device
+                
+    
+    def load_lora(
+        self,
+        module: torch.nn.Module,
+        lora_config: Union[ModelConfig, str] = None,
+        alpha=1,
+        hotload=None,
+        state_dict=None,
+    ):
+        if state_dict is None:
+            if isinstance(lora_config, str):
+                lora = load_state_dict(lora_config, torch_dtype=self.torch_dtype, device=self.device)
+            else:
+                lora_config.download_if_necessary()
+                lora = load_state_dict(lora_config.path, torch_dtype=self.torch_dtype, device=self.device)
+        else:
+            lora = state_dict
+        lora_loader = self.lora_loader(torch_dtype=self.torch_dtype, device=self.device)
+        lora = lora_loader.convert_state_dict(lora)
+        if hotload is None:
+            hotload = hasattr(module, "vram_management_enabled") and getattr(module, "vram_management_enabled")
+        if hotload:
+            if not (hasattr(module, "vram_management_enabled") and getattr(module, "vram_management_enabled")):
+                raise ValueError("VRAM Management is not enabled. LoRA hotloading is not supported.")
+            updated_num = 0
+            for _, module in module.named_modules():
+                if isinstance(module, AutoWrappedLinear):
+                    name = module.name
+                    lora_a_name = f'{name}.lora_A.weight'
+                    lora_b_name = f'{name}.lora_B.weight'
+                    if lora_a_name in lora and lora_b_name in lora:
+                        updated_num += 1
+                        module.lora_A_weights.append(lora[lora_a_name] * alpha)
+                        module.lora_B_weights.append(lora[lora_b_name])
+            print(f"{updated_num} tensors are patched by LoRA. You can use `pipe.clear_lora()` to clear all LoRA layers.")
+        else:
+            lora_loader.fuse_lora_to_base_model(module, lora, alpha=alpha)
+            
+            
+    def clear_lora(self):
+        cleared_num = 0
+        for name, module in self.named_modules():
+            if isinstance(module, AutoWrappedLinear):
+                if hasattr(module, "lora_A_weights"):
+                    if len(module.lora_A_weights) > 0:
+                        cleared_num += 1
+                    module.lora_A_weights.clear()
+                if hasattr(module, "lora_B_weights"):
+                    module.lora_B_weights.clear()
+        print(f"{cleared_num} LoRA layers are cleared.")
+        
+    
+    def download_and_load_models(self, model_configs: list[ModelConfig] = [], vram_limit: float = None):
+        model_pool = ModelPool()
+        for model_config in model_configs:
+            model_config.download_if_necessary()
+            vram_config = model_config.vram_config()
+            vram_config["computation_dtype"] = vram_config["computation_dtype"] or self.torch_dtype
+            vram_config["computation_device"] = vram_config["computation_device"] or self.device
+            model_pool.auto_load_model(
+                model_config.path,
+                vram_config=vram_config,
+                vram_limit=vram_limit,
+                clear_parameters=model_config.clear_parameters,
+            )
+        return model_pool
+    
+    
+    def check_vram_management_state(self):
+        vram_management_enabled = False
+        for module in self.children():
+            if hasattr(module, "vram_management_enabled") and getattr(module, "vram_management_enabled"):
+                vram_management_enabled = True
+        return vram_management_enabled
+    
+    
+    def cfg_guided_model_fn(self, model_fn, cfg_scale, inputs_shared, inputs_posi, inputs_nega, **inputs_others):
+        noise_pred_posi = model_fn(**inputs_posi, **inputs_shared, **inputs_others)
+        if cfg_scale != 1.0:
+            noise_pred_nega = model_fn(**inputs_nega, **inputs_shared, **inputs_others)
+            noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
+        else:
+            noise_pred = noise_pred_posi
+        return noise_pred
+
+
+class PipelineUnitGraph:
+    def __init__(self):
+        pass
+    
+    def build_edges(self, units: list[PipelineUnit]):
+        # Establish dependencies between units
+        # to search for subsequent related computation units.
+        last_compute_unit_id = {}
+        edges = []
+        for unit_id, unit in enumerate(units):
+            for input_param in unit.fetch_input_params():
+                if input_param in last_compute_unit_id:
+                    edges.append((last_compute_unit_id[input_param], unit_id))
+            for output_param in unit.fetch_output_params():
+                last_compute_unit_id[output_param] = unit_id
+        return edges
+    
+    def build_chains(self, units: list[PipelineUnit]):
+        # Establish updating chains for each variable
+        # to track their computation process.
+        params = sum([unit.fetch_input_params() + unit.fetch_output_params() for unit in units], [])
+        params = sorted(list(set(params)))
+        chains = {param: [] for param in params}
+        for unit_id, unit in enumerate(units):
+            for param in unit.fetch_output_params():
+                chains[param].append(unit_id)
+        return chains
+    
+    def search_direct_unit_ids(self, units: list[PipelineUnit], model_names: list[str]):
+        # Search for units that directly participate in the model's computation.
+        related_unit_ids = []
+        for unit_id, unit in enumerate(units):
+            for model_name in model_names:
+                if unit.onload_model_names is not None and model_name in unit.onload_model_names:
+                    related_unit_ids.append(unit_id)
+                    break
+        return related_unit_ids
+    
+    def search_related_unit_ids(self, edges, start_unit_ids, direction="target"):
+        # Search for subsequent related computation units.
+        related_unit_ids = [unit_id for unit_id in start_unit_ids]
+        while True:
+            neighbors = []
+            for source, target in edges:
+                if direction == "target" and source in related_unit_ids and target not in related_unit_ids:
+                    neighbors.append(target)
+                elif direction == "source" and source not in related_unit_ids and target in related_unit_ids:
+                    neighbors.append(source)
+            neighbors = sorted(list(set(neighbors)))
+            if len(neighbors) == 0:
+                break
+            else:
+                related_unit_ids.extend(neighbors)
+        related_unit_ids = sorted(list(set(related_unit_ids)))
+        return related_unit_ids
+    
+    def search_updating_unit_ids(self, units: list[PipelineUnit], chains, related_unit_ids):
+        # If the input parameters of this subgraph are updated outside the subgraph,
+        # search for the units where these updates occur.
+        first_compute_unit_id = {}
+        for unit_id in related_unit_ids:
+            for param in units[unit_id].fetch_input_params():
+                if param not in first_compute_unit_id:
+                    first_compute_unit_id[param] = unit_id
+        updating_unit_ids = []
+        for param in first_compute_unit_id:
+            unit_id = first_compute_unit_id[param]
+            chain = chains[param]
+            if unit_id in chain and chain.index(unit_id) != len(chain) - 1:
+                for unit_id_ in chain[chain.index(unit_id) + 1:]:
+                    if unit_id_ not in related_unit_ids:
+                        updating_unit_ids.append(unit_id_)
+        related_unit_ids.extend(updating_unit_ids)
+        related_unit_ids = sorted(list(set(related_unit_ids)))
+        return related_unit_ids
+    
+    def split_pipeline_units(self, units: list[PipelineUnit], model_names: list[str]):
+        # Split the computation graph,
+        # separating all model-related computations.
+        related_unit_ids = self.search_direct_unit_ids(units, model_names)
+        edges = self.build_edges(units)
+        chains = self.build_chains(units)
+        while True:
+            num_related_unit_ids = len(related_unit_ids)
+            related_unit_ids = self.search_related_unit_ids(edges, related_unit_ids, "target")
+            related_unit_ids = self.search_updating_unit_ids(units, chains, related_unit_ids)
+            if len(related_unit_ids) == num_related_unit_ids:
+                break
+            else:
+                num_related_unit_ids = len(related_unit_ids)
+        related_units = [units[i] for i in related_unit_ids]
+        unrelated_units = [units[i] for i in range(len(units)) if i not in related_unit_ids]
+        return related_units, unrelated_units
+
+
+class PipelineUnitRunner:
+    def __init__(self):
+        pass
+
+    def __call__(self, unit: PipelineUnit, pipe: BasePipeline, inputs_shared: dict, inputs_posi: dict, inputs_nega: dict) -> tuple[dict, dict]:
+        if unit.take_over:
+            # Let the pipeline unit take over this function.
+            inputs_shared, inputs_posi, inputs_nega = unit.process(pipe, inputs_shared=inputs_shared, inputs_posi=inputs_posi, inputs_nega=inputs_nega)
+        elif unit.seperate_cfg:
+            # Positive side
+            processor_inputs = {name: inputs_posi.get(name_) for name, name_ in unit.input_params_posi.items()}
+            if unit.input_params is not None:
+                for name in unit.input_params:
+                    processor_inputs[name] = inputs_shared.get(name)
+            processor_outputs = unit.process(pipe, **processor_inputs)
+            inputs_posi.update(processor_outputs)
+            # Negative side
+            if inputs_shared["cfg_scale"] != 1:
+                processor_inputs = {name: inputs_nega.get(name_) for name, name_ in unit.input_params_nega.items()}
+                if unit.input_params is not None:
+                    for name in unit.input_params:
+                        processor_inputs[name] = inputs_shared.get(name)
+                processor_outputs = unit.process(pipe, **processor_inputs)
+                inputs_nega.update(processor_outputs)
+            else:
+                inputs_nega.update(processor_outputs)
+        else:
+            processor_inputs = {name: inputs_shared.get(name) for name in unit.input_params}
+            processor_outputs = unit.process(pipe, **processor_inputs)
+            inputs_shared.update(processor_outputs)
+        return inputs_shared, inputs_posi, inputs_nega

diffsynth/diffusion/flow_match.py

@@ -0,0 +1,179 @@
+import torch, math
+from typing_extensions import Literal
+
+
+class FlowMatchScheduler():
+
+    def __init__(self, template: Literal["FLUX.1", "Wan", "Qwen-Image", "FLUX.2", "Z-Image"] = "FLUX.1"):
+        self.set_timesteps_fn = {
+            "FLUX.1": FlowMatchScheduler.set_timesteps_flux,
+            "Wan": FlowMatchScheduler.set_timesteps_wan,
+            "Qwen-Image": FlowMatchScheduler.set_timesteps_qwen_image,
+            "FLUX.2": FlowMatchScheduler.set_timesteps_flux2,
+            "Z-Image": FlowMatchScheduler.set_timesteps_z_image,
+        }.get(template, FlowMatchScheduler.set_timesteps_flux)
+        self.num_train_timesteps = 1000
+
+    @staticmethod
+    def set_timesteps_flux(num_inference_steps=100, denoising_strength=1.0, shift=None):
+        sigma_min = 0.003/1.002
+        sigma_max = 1.0
+        shift = 3 if shift is None else shift
+        num_train_timesteps = 1000
+        sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
+        sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps)
+        sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
+        timesteps = sigmas * num_train_timesteps
+        return sigmas, timesteps
+    
+    @staticmethod
+    def set_timesteps_wan(num_inference_steps=100, denoising_strength=1.0, shift=None):
+        sigma_min = 0.0
+        sigma_max = 1.0
+        shift = 5 if shift is None else shift
+        num_train_timesteps = 1000
+        sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
+        sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
+        sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
+        timesteps = sigmas * num_train_timesteps
+        return sigmas, timesteps
+    
+    @staticmethod
+    def _calculate_shift_qwen_image(image_seq_len, base_seq_len=256, max_seq_len=8192, base_shift=0.5, max_shift=0.9):
+        m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+        b = base_shift - m * base_seq_len
+        mu = image_seq_len * m + b
+        return mu
+    
+    @staticmethod
+    def set_timesteps_qwen_image(num_inference_steps=100, denoising_strength=1.0, exponential_shift_mu=None, dynamic_shift_len=None):
+        sigma_min = 0.0
+        sigma_max = 1.0
+        num_train_timesteps = 1000
+        shift_terminal = 0.02
+        # Sigmas
+        sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
+        sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
+        # Mu
+        if exponential_shift_mu is not None:
+            mu = exponential_shift_mu
+        elif dynamic_shift_len is not None:
+            mu = FlowMatchScheduler._calculate_shift_qwen_image(dynamic_shift_len)
+        else:
+            mu = 0.8
+        sigmas = math.exp(mu) / (math.exp(mu) + (1 / sigmas - 1))
+        # Shift terminal
+        one_minus_z = 1 - sigmas
+        scale_factor = one_minus_z[-1] / (1 - shift_terminal)
+        sigmas = 1 - (one_minus_z / scale_factor)
+        # Timesteps
+        timesteps = sigmas * num_train_timesteps
+        return sigmas, timesteps
+    
+    @staticmethod
+    def compute_empirical_mu(image_seq_len, num_steps):
+        a1, b1 = 8.73809524e-05, 1.89833333
+        a2, b2 = 0.00016927, 0.45666666
+
+        if image_seq_len > 4300:
+            mu = a2 * image_seq_len + b2
+            return float(mu)
+
+        m_200 = a2 * image_seq_len + b2
+        m_10 = a1 * image_seq_len + b1
+
+        a = (m_200 - m_10) / 190.0
+        b = m_200 - 200.0 * a
+        mu = a * num_steps + b
+
+        return float(mu)
+    
+    @staticmethod
+    def set_timesteps_flux2(num_inference_steps=100, denoising_strength=1.0, dynamic_shift_len=1024//16*1024//16):
+        sigma_min = 1 / num_inference_steps
+        sigma_max = 1.0
+        num_train_timesteps = 1000
+        sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
+        sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps)
+        mu = FlowMatchScheduler.compute_empirical_mu(dynamic_shift_len, num_inference_steps)
+        sigmas = math.exp(mu) / (math.exp(mu) + (1 / sigmas - 1))
+        timesteps = sigmas * num_train_timesteps
+        return sigmas, timesteps
+
+    @staticmethod
+    def set_timesteps_z_image(num_inference_steps=100, denoising_strength=1.0, shift=None, target_timesteps=None):
+        sigma_min = 0.0
+        sigma_max = 1.0
+        shift = 3 if shift is None else shift
+        num_train_timesteps = 1000
+        sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
+        sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
+        sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
+        timesteps = sigmas * num_train_timesteps
+        if target_timesteps is not None:
+            target_timesteps = target_timesteps.to(dtype=timesteps.dtype, device=timesteps.device)
+            for timestep in target_timesteps:
+                timestep_id = torch.argmin((timesteps - timestep).abs())
+                timesteps[timestep_id] = timestep
+        return sigmas, timesteps
+    
+    def set_training_weight(self):
+        steps = 1000
+        x = self.timesteps
+        y = torch.exp(-2 * ((x - steps / 2) / steps) ** 2)
+        y_shifted = y - y.min()
+        bsmntw_weighing = y_shifted * (steps / y_shifted.sum())
+        if len(self.timesteps) != 1000:
+            # This is an empirical formula.
+            bsmntw_weighing = bsmntw_weighing * (len(self.timesteps) / steps)
+            bsmntw_weighing = bsmntw_weighing + bsmntw_weighing[1]
+        self.linear_timesteps_weights = bsmntw_weighing
+        
+    def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, training=False, **kwargs):
+        self.sigmas, self.timesteps = self.set_timesteps_fn(
+            num_inference_steps=num_inference_steps,
+            denoising_strength=denoising_strength,
+            **kwargs,
+        )
+        if training:
+            self.set_training_weight()
+            self.training = True
+        else:
+            self.training = False
+
+    def step(self, model_output, timestep, sample, to_final=False, **kwargs):
+        if isinstance(timestep, torch.Tensor):
+            timestep = timestep.cpu()
+        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
+        else:
+            sigma_ = self.sigmas[timestep_id + 1]
+        prev_sample = sample + model_output * (sigma_ - sigma)
+        return prev_sample
+    
+    def return_to_timestep(self, timestep, sample, sample_stablized):
+        if isinstance(timestep, torch.Tensor):
+            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):
+        if isinstance(timestep, torch.Tensor):
+            timestep = timestep.cpu()
+        timestep_id = torch.argmin((self.timesteps - timestep).abs())
+        sigma = self.sigmas[timestep_id]
+        sample = (1 - sigma) * original_samples + sigma * noise
+        return sample
+    
+    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/diffusion/logger.py

@@ -0,0 +1,43 @@
+import os, torch
+from accelerate import Accelerator
+
+
+class ModelLogger:
+    def __init__(self, output_path, remove_prefix_in_ckpt=None, state_dict_converter=lambda x:x):
+        self.output_path = output_path
+        self.remove_prefix_in_ckpt = remove_prefix_in_ckpt
+        self.state_dict_converter = state_dict_converter
+        self.num_steps = 0
+
+
+    def on_step_end(self, accelerator: Accelerator, model: torch.nn.Module, save_steps=None):
+        self.num_steps += 1
+        if save_steps is not None and self.num_steps % save_steps == 0:
+            self.save_model(accelerator, model, f"step-{self.num_steps}.safetensors")
+
+
+    def on_epoch_end(self, accelerator: Accelerator, model: torch.nn.Module, epoch_id):
+        accelerator.wait_for_everyone()
+        if accelerator.is_main_process:
+            state_dict = accelerator.get_state_dict(model)
+            state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt)
+            state_dict = self.state_dict_converter(state_dict)
+            os.makedirs(self.output_path, exist_ok=True)
+            path = os.path.join(self.output_path, f"epoch-{epoch_id}.safetensors")
+            accelerator.save(state_dict, path, safe_serialization=True)
+
+
+    def on_training_end(self, accelerator: Accelerator, model: torch.nn.Module, save_steps=None):
+        if save_steps is not None and self.num_steps % save_steps != 0:
+            self.save_model(accelerator, model, f"step-{self.num_steps}.safetensors")
+
+
+    def save_model(self, accelerator: Accelerator, model: torch.nn.Module, file_name):
+        accelerator.wait_for_everyone()
+        if accelerator.is_main_process:
+            state_dict = accelerator.get_state_dict(model)
+            state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt)
+            state_dict = self.state_dict_converter(state_dict)
+            os.makedirs(self.output_path, exist_ok=True)
+            path = os.path.join(self.output_path, file_name)
+            accelerator.save(state_dict, path, safe_serialization=True)

diffsynth/diffusion/loss.py

@@ -0,0 +1,119 @@
+from .base_pipeline import BasePipeline
+import torch
+
+
+def FlowMatchSFTLoss(pipe: BasePipeline, **inputs):
+    max_timestep_boundary = int(inputs.get("max_timestep_boundary", 1) * len(pipe.scheduler.timesteps))
+    min_timestep_boundary = int(inputs.get("min_timestep_boundary", 0) * len(pipe.scheduler.timesteps))
+
+    timestep_id = torch.randint(min_timestep_boundary, max_timestep_boundary, (1,))
+    timestep = pipe.scheduler.timesteps[timestep_id].to(dtype=pipe.torch_dtype, device=pipe.device)
+    
+    noise = torch.randn_like(inputs["input_latents"])
+    inputs["latents"] = pipe.scheduler.add_noise(inputs["input_latents"], noise, timestep)
+    training_target = pipe.scheduler.training_target(inputs["input_latents"], noise, timestep)
+    
+    models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
+    noise_pred = pipe.model_fn(**models, **inputs, timestep=timestep)
+    
+    loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float())
+    loss = loss * pipe.scheduler.training_weight(timestep)
+    return loss
+
+
+def DirectDistillLoss(pipe: BasePipeline, **inputs):
+    pipe.scheduler.set_timesteps(inputs["num_inference_steps"])
+    pipe.scheduler.training = True
+    models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
+    for progress_id, timestep in enumerate(pipe.scheduler.timesteps):
+        timestep = timestep.unsqueeze(0).to(dtype=pipe.torch_dtype, device=pipe.device)
+        noise_pred = pipe.model_fn(**models, **inputs, timestep=timestep, progress_id=progress_id)
+        inputs["latents"] = pipe.step(pipe.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs)
+    loss = torch.nn.functional.mse_loss(inputs["latents"].float(), inputs["input_latents"].float())
+    return loss
+
+
+class TrajectoryImitationLoss(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.initialized = False
+    
+    def initialize(self, device):
+        import lpips # TODO: remove it
+        self.loss_fn = lpips.LPIPS(net='alex').to(device)
+        self.initialized = True
+
+    def fetch_trajectory(self, pipe: BasePipeline, timesteps_student, inputs_shared, inputs_posi, inputs_nega, num_inference_steps, cfg_scale):
+        trajectory = [inputs_shared["latents"].clone()]
+
+        pipe.scheduler.set_timesteps(num_inference_steps, target_timesteps=timesteps_student)
+        models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
+        for progress_id, timestep in enumerate(pipe.scheduler.timesteps):
+            timestep = timestep.unsqueeze(0).to(dtype=pipe.torch_dtype, device=pipe.device)
+            noise_pred = pipe.cfg_guided_model_fn(
+                pipe.model_fn, cfg_scale,
+                inputs_shared, inputs_posi, inputs_nega,
+                **models, timestep=timestep, progress_id=progress_id
+            )
+            inputs_shared["latents"] = pipe.step(pipe.scheduler, progress_id=progress_id, noise_pred=noise_pred.detach(), **inputs_shared)
+
+            trajectory.append(inputs_shared["latents"].clone())
+        return pipe.scheduler.timesteps, trajectory
+    
+    def align_trajectory(self, pipe: BasePipeline, timesteps_teacher, trajectory_teacher, inputs_shared, inputs_posi, inputs_nega, num_inference_steps, cfg_scale):
+        loss = 0
+        pipe.scheduler.set_timesteps(num_inference_steps, training=True)
+        models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
+        for progress_id, timestep in enumerate(pipe.scheduler.timesteps):
+            timestep = timestep.unsqueeze(0).to(dtype=pipe.torch_dtype, device=pipe.device)
+
+            progress_id_teacher = torch.argmin((timesteps_teacher - timestep).abs())
+            inputs_shared["latents"] = trajectory_teacher[progress_id_teacher]
+
+            noise_pred = pipe.cfg_guided_model_fn(
+                pipe.model_fn, cfg_scale,
+                inputs_shared, inputs_posi, inputs_nega,
+                **models, timestep=timestep, progress_id=progress_id
+            )
+
+            sigma = pipe.scheduler.sigmas[progress_id]
+            sigma_ = 0 if progress_id + 1 >= len(pipe.scheduler.timesteps) else pipe.scheduler.sigmas[progress_id + 1]
+            if progress_id + 1 >= len(pipe.scheduler.timesteps):
+                latents_ = trajectory_teacher[-1]
+            else:
+                progress_id_teacher = torch.argmin((timesteps_teacher - pipe.scheduler.timesteps[progress_id + 1]).abs())
+                latents_ = trajectory_teacher[progress_id_teacher]
+            
+            target = (latents_ - inputs_shared["latents"]) / (sigma_ - sigma)
+            loss = loss + torch.nn.functional.mse_loss(noise_pred.float(), target.float()) * pipe.scheduler.training_weight(timestep)
+        return loss
+    
+    def compute_regularization(self, pipe: BasePipeline, trajectory_teacher, inputs_shared, inputs_posi, inputs_nega, num_inference_steps, cfg_scale):
+        inputs_shared["latents"] = trajectory_teacher[0]
+        pipe.scheduler.set_timesteps(num_inference_steps)
+        models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
+        for progress_id, timestep in enumerate(pipe.scheduler.timesteps):
+            timestep = timestep.unsqueeze(0).to(dtype=pipe.torch_dtype, device=pipe.device)
+            noise_pred = pipe.cfg_guided_model_fn(
+                pipe.model_fn, cfg_scale,
+                inputs_shared, inputs_posi, inputs_nega,
+                **models, timestep=timestep, progress_id=progress_id
+            )
+            inputs_shared["latents"] = pipe.step(pipe.scheduler, progress_id=progress_id, noise_pred=noise_pred.detach(), **inputs_shared)
+
+        image_pred = pipe.vae_decoder(inputs_shared["latents"])
+        image_real = pipe.vae_decoder(trajectory_teacher[-1])
+        loss = self.loss_fn(image_pred.float(), image_real.float())
+        return loss
+
+    def forward(self, pipe: BasePipeline, inputs_shared, inputs_posi, inputs_nega):
+        if not self.initialized:
+            self.initialize(pipe.device)
+        with torch.no_grad():
+            pipe.scheduler.set_timesteps(8)
+            timesteps_teacher, trajectory_teacher = self.fetch_trajectory(inputs_shared["teacher"], pipe.scheduler.timesteps, inputs_shared, inputs_posi, inputs_nega, 50, 2)
+            timesteps_teacher = timesteps_teacher.to(dtype=pipe.torch_dtype, device=pipe.device)
+        loss_1 = self.align_trajectory(pipe, timesteps_teacher, trajectory_teacher, inputs_shared, inputs_posi, inputs_nega, 8, 1)
+        loss_2 = self.compute_regularization(pipe, trajectory_teacher, inputs_shared, inputs_posi, inputs_nega, 8, 1)
+        loss = loss_1 + loss_2
+        return loss

diffsynth/diffusion/parsers.py

@@ -0,0 +1,70 @@
+import argparse
+
+
+def add_dataset_base_config(parser: argparse.ArgumentParser):
+    parser.add_argument("--dataset_base_path", type=str, default="", required=True, help="Base path of the dataset.")
+    parser.add_argument("--dataset_metadata_path", type=str, default=None, help="Path to the metadata file of the dataset.")
+    parser.add_argument("--dataset_repeat", type=int, default=1, help="Number of times to repeat the dataset per epoch.")
+    parser.add_argument("--dataset_num_workers", type=int, default=0, help="Number of workers for data loading.")
+    parser.add_argument("--data_file_keys", type=str, default="image,video", help="Data file keys in the metadata. Comma-separated.")
+    return parser
+
+def add_image_size_config(parser: argparse.ArgumentParser):
+    parser.add_argument("--height", type=int, default=None, help="Height of images. Leave `height` and `width` empty to enable dynamic resolution.")
+    parser.add_argument("--width", type=int, default=None, help="Width of images. Leave `height` and `width` empty to enable dynamic resolution.")
+    parser.add_argument("--max_pixels", type=int, default=1024*1024, help="Maximum number of pixels per frame, used for dynamic resolution.")
+    return parser
+
+def add_video_size_config(parser: argparse.ArgumentParser):
+    parser.add_argument("--height", type=int, default=None, help="Height of images. Leave `height` and `width` empty to enable dynamic resolution.")
+    parser.add_argument("--width", type=int, default=None, help="Width of images. Leave `height` and `width` empty to enable dynamic resolution.")
+    parser.add_argument("--max_pixels", type=int, default=1024*1024, help="Maximum number of pixels per frame, used for dynamic resolution.")
+    parser.add_argument("--num_frames", type=int, default=81, help="Number of frames per video. Frames are sampled from the video prefix.")
+    return parser
+
+def add_model_config(parser: argparse.ArgumentParser):
+    parser.add_argument("--model_paths", type=str, default=None, help="Paths to load models. In JSON format.")
+    parser.add_argument("--model_id_with_origin_paths", type=str, default=None, help="Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.")
+    parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.")
+    parser.add_argument("--fp8_models", default=None, help="Models with FP8 precision, comma-separated.")
+    parser.add_argument("--offload_models", default=None, help="Models with offload, comma-separated. Only used in splited training.")
+    return parser
+
+def add_training_config(parser: argparse.ArgumentParser):
+    parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate.")
+    parser.add_argument("--num_epochs", type=int, default=1, help="Number of epochs.")
+    parser.add_argument("--trainable_models", type=str, default=None, help="Models to train, e.g., dit, vae, text_encoder.")
+    parser.add_argument("--find_unused_parameters", default=False, action="store_true", help="Whether to find unused parameters in DDP.")
+    parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay.")
+    parser.add_argument("--task", type=str, default="sft", required=False, help="Task type.")
+    return parser
+
+def add_output_config(parser: argparse.ArgumentParser):
+    parser.add_argument("--output_path", type=str, default="./models", help="Output save path.")
+    parser.add_argument("--remove_prefix_in_ckpt", type=str, default="pipe.dit.", help="Remove prefix in ckpt.")
+    parser.add_argument("--save_steps", type=int, default=None, help="Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch.")
+    return parser
+
+def add_lora_config(parser: argparse.ArgumentParser):
+    parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.")
+    parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.")
+    parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.")
+    parser.add_argument("--lora_checkpoint", type=str, default=None, help="Path to the LoRA checkpoint. If provided, LoRA will be loaded from this checkpoint.")
+    parser.add_argument("--preset_lora_path", type=str, default=None, help="Path to the preset LoRA checkpoint. If provided, this LoRA will be fused to the base model.")
+    parser.add_argument("--preset_lora_model", type=str, default=None, help="Which model the preset LoRA is fused to.")
+    return parser
+
+def add_gradient_config(parser: argparse.ArgumentParser):
+    parser.add_argument("--use_gradient_checkpointing", default=False, action="store_true", help="Whether to use gradient checkpointing.")
+    parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.")
+    parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.")
+    return parser
+
+def add_general_config(parser: argparse.ArgumentParser):
+    parser = add_dataset_base_config(parser)
+    parser = add_model_config(parser)
+    parser = add_training_config(parser)
+    parser = add_output_config(parser)
+    parser = add_lora_config(parser)
+    parser = add_gradient_config(parser)
+    return parser

diffsynth/diffusion/runner.py

@@ -0,0 +1,71 @@
+import os, torch
+from tqdm import tqdm
+from accelerate import Accelerator
+from .training_module import DiffusionTrainingModule
+from .logger import ModelLogger
+
+
+def launch_training_task(
+    accelerator: Accelerator,
+    dataset: torch.utils.data.Dataset,
+    model: DiffusionTrainingModule,
+    model_logger: ModelLogger,
+    learning_rate: float = 1e-5,
+    weight_decay: float = 1e-2,
+    num_workers: int = 1,
+    save_steps: int = None,
+    num_epochs: int = 1,
+    args = None,
+):
+    if args is not None:
+        learning_rate = args.learning_rate
+        weight_decay = args.weight_decay
+        num_workers = args.dataset_num_workers
+        save_steps = args.save_steps
+        num_epochs = args.num_epochs
+    
+    optimizer = torch.optim.AdamW(model.trainable_modules(), lr=learning_rate, weight_decay=weight_decay)
+    scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer)
+    dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0], num_workers=num_workers)
+    
+    model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler)
+    
+    for epoch_id in range(num_epochs):
+        for data in tqdm(dataloader):
+            with accelerator.accumulate(model):
+                optimizer.zero_grad()
+                if dataset.load_from_cache:
+                    loss = model({}, inputs=data)
+                else:
+                    loss = model(data)
+                accelerator.backward(loss)
+                optimizer.step()
+                model_logger.on_step_end(accelerator, model, save_steps)
+                scheduler.step()
+        if save_steps is None:
+            model_logger.on_epoch_end(accelerator, model, epoch_id)
+    model_logger.on_training_end(accelerator, model, save_steps)
+
+
+def launch_data_process_task(
+    accelerator: Accelerator,
+    dataset: torch.utils.data.Dataset,
+    model: DiffusionTrainingModule,
+    model_logger: ModelLogger,
+    num_workers: int = 8,
+    args = None,
+):
+    if args is not None:
+        num_workers = args.dataset_num_workers
+        
+    dataloader = torch.utils.data.DataLoader(dataset, shuffle=False, collate_fn=lambda x: x[0], num_workers=num_workers)
+    model, dataloader = accelerator.prepare(model, dataloader)
+    
+    for data_id, data in enumerate(tqdm(dataloader)):
+        with accelerator.accumulate(model):
+            with torch.no_grad():
+                folder = os.path.join(model_logger.output_path, str(accelerator.process_index))
+                os.makedirs(folder, exist_ok=True)
+                save_path = os.path.join(model_logger.output_path, str(accelerator.process_index), f"{data_id}.pth")
+                data = model(data)
+                torch.save(data, save_path)

diffsynth/diffusion/training_module.py

@@ -0,0 +1,212 @@
+import torch, json
+from ..core import ModelConfig, load_state_dict
+from ..utils.controlnet import ControlNetInput
+from peft import LoraConfig, inject_adapter_in_model
+
+
+class DiffusionTrainingModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        
+        
+    def to(self, *args, **kwargs):
+        for name, model in self.named_children():
+            model.to(*args, **kwargs)
+        return self
+        
+        
+    def trainable_modules(self):
+        trainable_modules = filter(lambda p: p.requires_grad, self.parameters())
+        return trainable_modules
+    
+    
+    def trainable_param_names(self):
+        trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.named_parameters()))
+        trainable_param_names = set([named_param[0] for named_param in trainable_param_names])
+        return trainable_param_names
+    
+    
+    def add_lora_to_model(self, model, target_modules, lora_rank, lora_alpha=None, upcast_dtype=None):
+        if lora_alpha is None:
+            lora_alpha = lora_rank
+        if isinstance(target_modules, list) and len(target_modules) == 1:
+            target_modules = target_modules[0]
+        lora_config = LoraConfig(r=lora_rank, lora_alpha=lora_alpha, target_modules=target_modules)
+        model = inject_adapter_in_model(lora_config, model)
+        if upcast_dtype is not None:
+            for param in model.parameters():
+                if param.requires_grad:
+                    param.data = param.to(upcast_dtype)
+        return model
+
+
+    def mapping_lora_state_dict(self, state_dict):
+        new_state_dict = {}
+        for key, value in state_dict.items():
+            if "lora_A.weight" in key or "lora_B.weight" in key:
+                new_key = key.replace("lora_A.weight", "lora_A.default.weight").replace("lora_B.weight", "lora_B.default.weight")
+                new_state_dict[new_key] = value
+            elif "lora_A.default.weight" in key or "lora_B.default.weight" in key:
+                new_state_dict[key] = value
+        return new_state_dict
+
+
+    def export_trainable_state_dict(self, state_dict, remove_prefix=None):
+        trainable_param_names = self.trainable_param_names()
+        state_dict = {name: param for name, param in state_dict.items() if name in trainable_param_names}
+        if remove_prefix is not None:
+            state_dict_ = {}
+            for name, param in state_dict.items():
+                if name.startswith(remove_prefix):
+                    name = name[len(remove_prefix):]
+                state_dict_[name] = param
+            state_dict = state_dict_
+        return state_dict
+    
+    
+    def transfer_data_to_device(self, data, device, torch_float_dtype=None):
+        if data is None:
+            return data
+        elif isinstance(data, torch.Tensor):
+            data = data.to(device)
+            if torch_float_dtype is not None and data.dtype in [torch.float, torch.float16, torch.bfloat16]:
+                data = data.to(torch_float_dtype)
+            return data
+        elif isinstance(data, tuple):
+            data = tuple(self.transfer_data_to_device(x, device, torch_float_dtype) for x in data)
+            return data
+        elif isinstance(data, list):
+            data = list(self.transfer_data_to_device(x, device, torch_float_dtype) for x in data)
+            return data
+        elif isinstance(data, dict):
+            data = {i: self.transfer_data_to_device(data[i], device, torch_float_dtype) for i in data}
+            return data
+        else:
+            return data
+    
+    def parse_vram_config(self, fp8=False, offload=False, device="cpu"):
+        if fp8:
+            return {
+                "offload_dtype": torch.float8_e4m3fn,
+                "offload_device": device,
+                "onload_dtype": torch.float8_e4m3fn,
+                "onload_device": device,
+                "preparing_dtype": torch.float8_e4m3fn,
+                "preparing_device": device,
+                "computation_dtype": torch.bfloat16,
+                "computation_device": device,
+            }
+        elif offload:
+            return {
+                "offload_dtype": "disk",
+                "offload_device": "disk",
+                "onload_dtype": "disk",
+                "onload_device": "disk",
+                "preparing_dtype": torch.bfloat16,
+                "preparing_device": device,
+                "computation_dtype": torch.bfloat16,
+                "computation_device": device,
+                "clear_parameters": True,
+            }
+        else:
+            return {}
+    
+    def parse_model_configs(self, model_paths, model_id_with_origin_paths, fp8_models=None, offload_models=None, device="cpu"):
+        fp8_models = [] if fp8_models is None else fp8_models.split(",")
+        offload_models = [] if offload_models is None else offload_models.split(",")
+        model_configs = []
+        if model_paths is not None:
+            model_paths = json.loads(model_paths)
+            for path in model_paths:
+                vram_config = self.parse_vram_config(
+                    fp8=path in fp8_models,
+                    offload=path in offload_models,
+                    device=device
+                )
+                model_configs.append(ModelConfig(path=path, **vram_config))
+        if model_id_with_origin_paths is not None:
+            model_id_with_origin_paths = model_id_with_origin_paths.split(",")
+            for model_id_with_origin_path in model_id_with_origin_paths:
+                model_id, origin_file_pattern = model_id_with_origin_path.split(":")
+                vram_config = self.parse_vram_config(
+                    fp8=model_id_with_origin_path in fp8_models,
+                    offload=model_id_with_origin_path in offload_models,
+                    device=device
+                )
+                model_configs.append(ModelConfig(model_id=model_id, origin_file_pattern=origin_file_pattern, **vram_config))
+        return model_configs
+    
+    
+    def switch_pipe_to_training_mode(
+        self,
+        pipe,
+        trainable_models=None,
+        lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None,
+        preset_lora_path=None, preset_lora_model=None,
+        task="sft",
+    ):
+        # Scheduler
+        pipe.scheduler.set_timesteps(1000, training=True)
+        
+        # Freeze untrainable models
+        pipe.freeze_except([] if trainable_models is None else trainable_models.split(","))
+        
+        # Preset LoRA
+        if preset_lora_path is not None:
+            pipe.load_lora(getattr(pipe, preset_lora_model), preset_lora_path)
+        
+        # FP8
+        # FP8 relies on a model-specific memory management scheme.
+        # It is delegated to the subclass.
+        
+        # Add LoRA to the base models
+        if lora_base_model is not None and not task.endswith(":data_process"):
+            if (not hasattr(pipe, lora_base_model)) or getattr(pipe, lora_base_model) is None:
+                print(f"No {lora_base_model} models in the pipeline. We cannot patch LoRA on the model. If this occurs during the data processing stage, it is normal.")
+                return
+            model = self.add_lora_to_model(
+                getattr(pipe, lora_base_model),
+                target_modules=lora_target_modules.split(","),
+                lora_rank=lora_rank,
+                upcast_dtype=pipe.torch_dtype,
+            )
+            if lora_checkpoint is not None:
+                state_dict = load_state_dict(lora_checkpoint)
+                state_dict = self.mapping_lora_state_dict(state_dict)
+                load_result = model.load_state_dict(state_dict, strict=False)
+                print(f"LoRA checkpoint loaded: {lora_checkpoint}, total {len(state_dict)} keys")
+                if len(load_result[1]) > 0:
+                    print(f"Warning, LoRA key mismatch! Unexpected keys in LoRA checkpoint: {load_result[1]}")
+            setattr(pipe, lora_base_model, model)
+
+
+    def split_pipeline_units(self, task, pipe, trainable_models=None, lora_base_model=None):
+        models_require_backward = []
+        if trainable_models is not None:
+            models_require_backward += trainable_models.split(",")
+        if lora_base_model is not None:
+            models_require_backward += [lora_base_model]
+        if task.endswith(":data_process"):
+            _, pipe.units = pipe.split_pipeline_units(models_require_backward)
+        elif task.endswith(":train"):
+            pipe.units, _ = pipe.split_pipeline_units(models_require_backward)
+        return pipe
+    
+    def parse_extra_inputs(self, data, extra_inputs, inputs_shared):
+        controlnet_keys_map = (
+            ("blockwise_controlnet_", "blockwise_controlnet_inputs",),
+            ("controlnet_", "controlnet_inputs"),
+        )
+        controlnet_inputs = {}
+        for extra_input in extra_inputs:
+            for prefix, name in controlnet_keys_map:
+                if extra_input.startswith(prefix):
+                    if name not in controlnet_inputs:
+                        controlnet_inputs[name] = {}
+                    controlnet_inputs[name][extra_input.replace(prefix, "")] = data[extra_input]
+                    break
+            else:
+                inputs_shared[extra_input] = data[extra_input]
+        for name, params in controlnet_inputs.items():
+            inputs_shared[name] = [ControlNetInput(**params)]
+        return inputs_shared

diffsynth/extensions/ESRGAN/__init__.py

@@ -1,137 +0,0 @@
-import torch
-from einops import repeat
-from PIL import Image
-import numpy as np
-
-
-class ResidualDenseBlock(torch.nn.Module):
-
-    def __init__(self, num_feat=64, num_grow_ch=32):
-        super(ResidualDenseBlock, self).__init__()
-        self.conv1 = torch.nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
-        self.conv2 = torch.nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv3 = torch.nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv4 = torch.nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv5 = torch.nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
-        self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x):
-        x1 = self.lrelu(self.conv1(x))
-        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
-        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
-        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        return x5 * 0.2 + x
-
-
-class RRDB(torch.nn.Module):
-
-    def __init__(self, num_feat, num_grow_ch=32):
-        super(RRDB, self).__init__()
-        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
-
-    def forward(self, x):
-        out = self.rdb1(x)
-        out = self.rdb2(out)
-        out = self.rdb3(out)
-        return out * 0.2 + x
-
-
-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, **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)])
-        self.conv_body = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        # upsample
-        self.conv_up1 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_up2 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_hr = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_last = torch.nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-        self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x):
-        feat = x
-        feat = self.conv_first(feat)
-        body_feat = self.conv_body(self.body(feat))
-        feat = feat + body_feat
-        # upsample
-        feat = repeat(feat, "B C H W -> B C (H 2) (W 2)")
-        feat = self.lrelu(self.conv_up1(feat))
-        feat = repeat(feat, "B C H W -> B C (H 2) (W 2)")
-        feat = self.lrelu(self.conv_up2(feat))
-        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):
-        super().__init__()
-        self.model = model
-
-    @staticmethod
-    def from_model_manager(model_manager):
-        return ESRGAN(model_manager.fetch_model("esrgan"))
-
-    def process_image(self, image):
-        image = torch.Tensor(np.array(image, dtype=np.float32) / 255).permute(2, 0, 1)
-        return image
-    
-    def process_images(self, images):
-        images = [self.process_image(image) for image in images]
-        images = torch.stack(images)
-        return images
-    
-    def decode_images(self, images):
-        images = (images.permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8)
-        images = [Image.fromarray(image) for image in images]
-        return images
-    
-    @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)
-
-        # Interpolate
-        output_tensor = []
-        for batch_id in progress_bar(range(0, input_tensor.shape[0], batch_size)):
-            batch_id_ = min(batch_id + batch_size, input_tensor.shape[0])
-            batch_input_tensor = input_tensor[batch_id: batch_id_]
-            batch_input_tensor = batch_input_tensor.to(
-                device=self.model.conv_first.weight.device,
-                dtype=self.model.conv_first.weight.dtype)
-            batch_output_tensor = self.model(batch_input_tensor)
-            output_tensor.append(batch_output_tensor.cpu())
-        
-        # Output
-        output_tensor = torch.concat(output_tensor, dim=0)
-
-        # To images
-        output_images = self.decode_images(output_tensor)
-        if is_single_image:
-            output_images = output_images[0]
-        return output_images

diffsynth/extensions/FastBlend/__init__.py

@@ -1,63 +0,0 @@
-from .runners.fast import TableManager, PyramidPatchMatcher
-from PIL import Image
-import numpy as np
-import cupy as cp
-
-
-class FastBlendSmoother:
-    def __init__(self):
-        self.batch_size = 8
-        self.window_size = 64
-        self.ebsynth_config = {
-            "minimum_patch_size": 5,
-            "threads_per_block": 8,
-            "num_iter": 5,
-            "gpu_id": 0,
-            "guide_weight": 10.0,
-            "initialize": "identity",
-            "tracking_window_size": 0,
-        }
-
-    @staticmethod
-    def from_model_manager(model_manager):
-        # TODO: fetch GPU ID from model_manager
-        return FastBlendSmoother()
-
-    def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config):
-        frames_guide = [np.array(frame) for frame in frames_guide]
-        frames_style = [np.array(frame) for frame in frames_style]
-        table_manager = TableManager()
-        patch_match_engine = PyramidPatchMatcher(
-            image_height=frames_style[0].shape[0],
-            image_width=frames_style[0].shape[1],
-            channel=3,
-            **ebsynth_config
-        )
-        # left part
-        table_l = table_manager.build_remapping_table(frames_guide, frames_style, patch_match_engine, batch_size, desc="FastBlend Step 1/4")
-        table_l = table_manager.remapping_table_to_blending_table(table_l)
-        table_l = table_manager.process_window_sum(frames_guide, table_l, patch_match_engine, window_size, batch_size, desc="FastBlend Step 2/4")
-        # right part
-        table_r = table_manager.build_remapping_table(frames_guide[::-1], frames_style[::-1], patch_match_engine, batch_size, desc="FastBlend Step 3/4")
-        table_r = table_manager.remapping_table_to_blending_table(table_r)
-        table_r = table_manager.process_window_sum(frames_guide[::-1], table_r, patch_match_engine, window_size, batch_size, desc="FastBlend Step 4/4")[::-1]
-        # merge
-        frames = []
-        for (frame_l, weight_l), frame_m, (frame_r, weight_r) in zip(table_l, frames_style, table_r):
-            weight_m = -1
-            weight = weight_l + weight_m + weight_r
-            frame = frame_l * (weight_l / weight) + frame_m * (weight_m / weight) + frame_r * (weight_r / weight)
-            frames.append(frame)
-        frames = [Image.fromarray(frame.clip(0, 255).astype("uint8")) for frame in frames]
-        return frames
-    
-    def __call__(self, rendered_frames, original_frames=None, **kwargs):
-        frames = self.run(
-            original_frames, rendered_frames,
-            self.batch_size, self.window_size, self.ebsynth_config
-        )
-        mempool = cp.get_default_memory_pool()
-        pinned_mempool = cp.get_default_pinned_memory_pool()
-        mempool.free_all_blocks()
-        pinned_mempool.free_all_blocks()
-        return frames

diffsynth/extensions/FastBlend/api.py

@@ -1,397 +0,0 @@
-from .runners import AccurateModeRunner, FastModeRunner, BalancedModeRunner, InterpolationModeRunner, InterpolationModeSingleFrameRunner
-from .data import VideoData, get_video_fps, save_video, search_for_images
-import os
-import gradio as gr
-
-
-def check_input_for_blending(video_guide, video_guide_folder, video_style, video_style_folder):
-    frames_guide = VideoData(video_guide, video_guide_folder)
-    frames_style = VideoData(video_style, video_style_folder)
-    message = ""
-    if len(frames_guide) < len(frames_style):
-        message += f"The number of frames mismatches. Only the first {len(frames_guide)} frames of style video will be used.\n"
-        frames_style.set_length(len(frames_guide))
-    elif len(frames_guide) > len(frames_style):
-        message += f"The number of frames mismatches. Only the first {len(frames_style)} frames of guide video will be used.\n"
-        frames_guide.set_length(len(frames_style))
-    height_guide, width_guide = frames_guide.shape()
-    height_style, width_style = frames_style.shape()
-    if height_guide != height_style or width_guide != width_style:
-        message += f"The shape of frames mismatches. The frames in style video will be resized to (height: {height_guide}, width: {width_guide})\n"
-        frames_style.set_shape(height_guide, width_guide)
-    return frames_guide, frames_style, message
-
-
-def smooth_video(
-    video_guide,
-    video_guide_folder,
-    video_style,
-    video_style_folder,
-    mode,
-    window_size,
-    batch_size,
-    tracking_window_size,
-    output_path,
-    fps,
-    minimum_patch_size,
-    num_iter,
-    guide_weight,
-    initialize,
-    progress = None,
-):
-    # input
-    frames_guide, frames_style, message = check_input_for_blending(video_guide, video_guide_folder, video_style, video_style_folder)
-    if len(message) > 0:
-        print(message)
-    # output
-    if output_path == "":
-        if video_style is None:
-            output_path = os.path.join(video_style_folder, "output")
-        else:
-            output_path = os.path.join(os.path.split(video_style)[0], "output")
-        os.makedirs(output_path, exist_ok=True)
-        print("No valid output_path. Your video will be saved here:", output_path)
-    elif not os.path.exists(output_path):
-        os.makedirs(output_path, exist_ok=True)
-        print("Your video will be saved here:", output_path)
-    frames_path = os.path.join(output_path, "frames")
-    video_path = os.path.join(output_path, "video.mp4")
-    os.makedirs(frames_path, exist_ok=True)
-    # process
-    if mode == "Fast" or mode == "Balanced":
-        tracking_window_size = 0
-    ebsynth_config = {
-        "minimum_patch_size": minimum_patch_size,
-        "threads_per_block": 8,
-        "num_iter": num_iter,
-        "gpu_id": 0,
-        "guide_weight": guide_weight,
-        "initialize": initialize,
-        "tracking_window_size": tracking_window_size,
-    }
-    if mode == "Fast":
-        FastModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path)
-    elif mode == "Balanced":
-        BalancedModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path)
-    elif mode == "Accurate":
-        AccurateModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path)
-    # output
-    try:
-        fps = int(fps)
-    except:
-        fps = get_video_fps(video_style) if video_style is not None else 30
-    print("Fps:", fps)
-    print("Saving video...")
-    video_path = save_video(frames_path, video_path, num_frames=len(frames_style), fps=fps)
-    print("Success!")
-    print("Your frames are here:", frames_path)
-    print("Your video is here:", video_path)
-    return output_path, fps, video_path
-
-
-class KeyFrameMatcher:
-    def __init__(self):
-        pass
-
-    def extract_number_from_filename(self, file_name):
-        result = []
-        number = -1
-        for i in file_name:
-            if ord(i)>=ord("0") and ord(i)<=ord("9"):
-                if number == -1:
-                    number = 0
-                number = number*10 + ord(i) - ord("0")
-            else:
-                if number != -1:
-                    result.append(number)
-                    number = -1
-        if number != -1:
-            result.append(number)
-        result = tuple(result)
-        return result
-
-    def extract_number_from_filenames(self, file_names):
-        numbers = [self.extract_number_from_filename(file_name) for file_name in file_names]
-        min_length = min(len(i) for i in numbers)
-        for i in range(min_length-1, -1, -1):
-            if len(set(number[i] for number in numbers))==len(file_names):
-                return [number[i] for number in numbers]
-        return list(range(len(file_names)))
-
-    def match_using_filename(self, file_names_a, file_names_b):
-        file_names_b_set = set(file_names_b)
-        matched_file_name = []
-        for file_name in file_names_a:
-            if file_name not in file_names_b_set:
-                matched_file_name.append(None)
-            else:
-                matched_file_name.append(file_name)
-        return matched_file_name
-
-    def match_using_numbers(self, file_names_a, file_names_b):
-        numbers_a = self.extract_number_from_filenames(file_names_a)
-        numbers_b = self.extract_number_from_filenames(file_names_b)
-        numbers_b_dict = {number: file_name for number, file_name in zip(numbers_b, file_names_b)}
-        matched_file_name = []
-        for number in numbers_a:
-            if number in numbers_b_dict:
-                matched_file_name.append(numbers_b_dict[number])
-            else:
-                matched_file_name.append(None)
-        return matched_file_name
-
-    def match_filenames(self, file_names_a, file_names_b):
-        matched_file_name = self.match_using_filename(file_names_a, file_names_b)
-        if sum([i is not None for i in matched_file_name]) > 0:
-            return matched_file_name
-        matched_file_name = self.match_using_numbers(file_names_a, file_names_b)
-        return matched_file_name
-
-
-def detect_frames(frames_path, keyframes_path):
-    if not os.path.exists(frames_path) and not os.path.exists(keyframes_path):
-        return "Please input the directory of guide video and rendered frames"
-    elif not os.path.exists(frames_path):
-        return "Please input the directory of guide video"
-    elif not os.path.exists(keyframes_path):
-        return "Please input the directory of rendered frames"
-    frames = [os.path.split(i)[-1] for i in search_for_images(frames_path)]
-    keyframes = [os.path.split(i)[-1] for i in search_for_images(keyframes_path)]
-    if len(frames)==0:
-        return f"No images detected in {frames_path}"
-    if len(keyframes)==0:
-        return f"No images detected in {keyframes_path}"
-    matched_keyframes = KeyFrameMatcher().match_filenames(frames, keyframes)
-    max_filename_length = max([len(i) for i in frames])
-    if sum([i is not None for i in matched_keyframes])==0:
-        message = ""
-        for frame, matched_keyframe in zip(frames, matched_keyframes):
-            message += frame + " " * (max_filename_length - len(frame) + 1)
-            message += "--> No matched keyframes\n"
-    else:
-        message = ""
-        for frame, matched_keyframe in zip(frames, matched_keyframes):
-            message += frame + " " * (max_filename_length - len(frame) + 1)
-            if matched_keyframe is None:
-                message += "--> [to be rendered]\n"
-            else:
-                message += f"--> {matched_keyframe}\n"
-    return message
-
-
-def check_input_for_interpolating(frames_path, keyframes_path):
-    # search for images
-    frames = [os.path.split(i)[-1] for i in search_for_images(frames_path)]
-    keyframes = [os.path.split(i)[-1] for i in search_for_images(keyframes_path)]
-    # match frames
-    matched_keyframes = KeyFrameMatcher().match_filenames(frames, keyframes)
-    file_list = [file_name for file_name in matched_keyframes if file_name is not None]
-    index_style = [i for i, file_name in enumerate(matched_keyframes) if file_name is not None]
-    frames_guide = VideoData(None, frames_path)
-    frames_style = VideoData(None, keyframes_path, file_list=file_list)
-    # match shape
-    message = ""
-    height_guide, width_guide = frames_guide.shape()
-    height_style, width_style = frames_style.shape()
-    if height_guide != height_style or width_guide != width_style:
-        message += f"The shape of frames mismatches. The rendered keyframes will be resized to (height: {height_guide}, width: {width_guide})\n"
-        frames_style.set_shape(height_guide, width_guide)
-    return frames_guide, frames_style, index_style, message
-
-
-def interpolate_video(
-    frames_path,
-    keyframes_path,
-    output_path,
-    fps,
-    batch_size,
-    tracking_window_size,
-    minimum_patch_size,
-    num_iter,
-    guide_weight,
-    initialize,
-    progress = None,
-):
-    # input
-    frames_guide, frames_style, index_style, message = check_input_for_interpolating(frames_path, keyframes_path)
-    if len(message) > 0:
-        print(message)
-    # output
-    if output_path == "":
-        output_path = os.path.join(keyframes_path, "output")
-        os.makedirs(output_path, exist_ok=True)
-        print("No valid output_path. Your video will be saved here:", output_path)
-    elif not os.path.exists(output_path):
-        os.makedirs(output_path, exist_ok=True)
-        print("Your video will be saved here:", output_path)
-    output_frames_path = os.path.join(output_path, "frames")
-    output_video_path = os.path.join(output_path, "video.mp4")
-    os.makedirs(output_frames_path, exist_ok=True)
-    # process
-    ebsynth_config = {
-        "minimum_patch_size": minimum_patch_size,
-        "threads_per_block": 8,
-        "num_iter": num_iter,
-        "gpu_id": 0,
-        "guide_weight": guide_weight,
-        "initialize": initialize,
-        "tracking_window_size": tracking_window_size
-    }
-    if len(index_style)==1:
-        InterpolationModeSingleFrameRunner().run(frames_guide, frames_style, index_style, batch_size=batch_size, ebsynth_config=ebsynth_config, save_path=output_frames_path)
-    else:
-        InterpolationModeRunner().run(frames_guide, frames_style, index_style, batch_size=batch_size, ebsynth_config=ebsynth_config, save_path=output_frames_path)
-    try:
-        fps = int(fps)
-    except:
-        fps = 30
-    print("Fps:", fps)
-    print("Saving video...")
-    video_path = save_video(output_frames_path, output_video_path, num_frames=len(frames_guide), fps=fps)
-    print("Success!")
-    print("Your frames are here:", output_frames_path)
-    print("Your video is here:", video_path)
-    return output_path, fps, video_path
-
-
-def on_ui_tabs():
-    with gr.Blocks(analytics_enabled=False) as ui_component:
-        with gr.Tab("Blend"):
-            gr.Markdown("""
-# Blend
-
-Given a guide video and a style video, this algorithm will make the style video fluent according to the motion features of the guide video. Click [here](https://github.com/Artiprocher/sd-webui-fastblend/assets/35051019/208d902d-6aba-48d7-b7d5-cd120ebd306d) to see the example. Note that this extension doesn't support long videos. Please use short videos (e.g., several seconds). The algorithm is mainly designed for 512*512 resolution. Please use a larger `Minimum patch size` for higher resolution.
-            """)
-            with gr.Row():
-                with gr.Column():
-                    with gr.Tab("Guide video"):
-                        video_guide = gr.Video(label="Guide video")
-                    with gr.Tab("Guide video (images format)"):
-                        video_guide_folder = gr.Textbox(label="Guide video (images format)", value="")
-                with gr.Column():
-                    with gr.Tab("Style video"):
-                        video_style = gr.Video(label="Style video")
-                    with gr.Tab("Style video (images format)"):
-                        video_style_folder = gr.Textbox(label="Style video (images format)", value="")
-                with gr.Column():
-                    output_path = gr.Textbox(label="Output directory", value="", placeholder="Leave empty to use the directory of style video")
-                    fps = gr.Textbox(label="Fps", value="", placeholder="Leave empty to use the default fps")
-                    video_output = gr.Video(label="Output video", interactive=False, show_share_button=True)
-            btn = gr.Button(value="Blend")
-            with gr.Row():
-                with gr.Column():
-                    gr.Markdown("# Settings")
-                    mode = gr.Radio(["Fast", "Balanced", "Accurate"], label="Inference mode", value="Fast", interactive=True)
-                    window_size = gr.Slider(label="Sliding window size", value=15, minimum=1, maximum=1000, step=1, interactive=True)
-                    batch_size = gr.Slider(label="Batch size", value=8, minimum=1, maximum=128, step=1, interactive=True)
-                    tracking_window_size = gr.Slider(label="Tracking window size (only for accurate mode)", value=0, minimum=0, maximum=10, step=1, interactive=True)
-                    gr.Markdown("## Advanced Settings")
-                    minimum_patch_size = gr.Slider(label="Minimum patch size (odd number)", value=5, minimum=5, maximum=99, step=2, interactive=True)
-                    num_iter = gr.Slider(label="Number of iterations", value=5, minimum=1, maximum=10, step=1, interactive=True)
-                    guide_weight = gr.Slider(label="Guide weight", value=10.0, minimum=0.0, maximum=100.0, step=0.1, interactive=True)
-                    initialize = gr.Radio(["identity", "random"], label="NNF initialization", value="identity", interactive=True)
-                with gr.Column():
-                    gr.Markdown("""
-# Reference
-
-* Output directory: the directory to save the video.
-* Inference mode
-
-|Mode|Time|Memory|Quality|Frame by frame output|Description|
-|-|-|-|-|-|-|
-|Fast|■|■■■|■■|No|Blend the frames using a tree-like data structure, which requires much RAM but is fast.|
-|Balanced|■■|■|■■|Yes|Blend the frames naively.|
-|Accurate|■■■|■|■■■|Yes|Blend the frames and align them together for higher video quality. When [batch size] >= [sliding window size] * 2 + 1, the performance is the best.|
-
-* Sliding window size: our algorithm will blend the frames in a sliding windows. If the size is n, each frame will be blended with the last n frames and the next n frames. A large sliding window can make the video fluent but sometimes smoggy.
-* Batch size: a larger batch size makes the program faster but requires more VRAM.
-* Tracking window size (only for accurate mode): The size of window in which our algorithm tracks moving objects. Empirically, 1 is enough.
-* Advanced settings
-    * Minimum patch size (odd number): the minimum patch size used for patch matching. (Default: 5)
-    * Number of iterations: the number of iterations of patch matching. (Default: 5)
-    * Guide weight: a parameter that determines how much motion feature applied to the style video. (Default: 10)
-    * NNF initialization: how to initialize the NNF (Nearest Neighbor Field). (Default: identity)
-                    """)
-            btn.click(
-                smooth_video,
-                inputs=[
-                    video_guide,
-                    video_guide_folder,
-                    video_style,
-                    video_style_folder,
-                    mode,
-                    window_size,
-                    batch_size,
-                    tracking_window_size,
-                    output_path,
-                    fps,
-                    minimum_patch_size,
-                    num_iter,
-                    guide_weight,
-                    initialize
-                ],
-                outputs=[output_path, fps, video_output]
-            )
-        with gr.Tab("Interpolate"):
-            gr.Markdown("""
-# Interpolate
-
-Given a guide video and some rendered keyframes, this algorithm will render the remaining frames. Click [here](https://github.com/Artiprocher/sd-webui-fastblend/assets/35051019/3490c5b4-8f67-478f-86de-f9adc2ace16a) to see the example. The algorithm is experimental and is only tested for 512*512 resolution.
-            """)
-            with gr.Row():
-                with gr.Column():
-                    with gr.Row():
-                        with gr.Column():
-                            video_guide_folder_ = gr.Textbox(label="Guide video (images format)", value="")
-                        with gr.Column():
-                            rendered_keyframes_ = gr.Textbox(label="Rendered keyframes (images format)", value="")
-                    with gr.Row():
-                        detected_frames = gr.Textbox(label="Detected frames", value="Please input the directory of guide video and rendered frames", lines=9, max_lines=9, interactive=False)
-                    video_guide_folder_.change(detect_frames, inputs=[video_guide_folder_, rendered_keyframes_], outputs=detected_frames)
-                    rendered_keyframes_.change(detect_frames, inputs=[video_guide_folder_, rendered_keyframes_], outputs=detected_frames)
-                with gr.Column():
-                    output_path_ = gr.Textbox(label="Output directory", value="", placeholder="Leave empty to use the directory of rendered keyframes")
-                    fps_ = gr.Textbox(label="Fps", value="", placeholder="Leave empty to use the default fps")
-                    video_output_ = gr.Video(label="Output video", interactive=False, show_share_button=True)
-            btn_ = gr.Button(value="Interpolate")
-            with gr.Row():
-                with gr.Column():
-                    gr.Markdown("# Settings")
-                    batch_size_ = gr.Slider(label="Batch size", value=8, minimum=1, maximum=128, step=1, interactive=True)
-                    tracking_window_size_ = gr.Slider(label="Tracking window size", value=0, minimum=0, maximum=10, step=1, interactive=True)
-                    gr.Markdown("## Advanced Settings")
-                    minimum_patch_size_ = gr.Slider(label="Minimum patch size (odd number, larger is better)", value=15, minimum=5, maximum=99, step=2, interactive=True)
-                    num_iter_ = gr.Slider(label="Number of iterations", value=5, minimum=1, maximum=10, step=1, interactive=True)
-                    guide_weight_ = gr.Slider(label="Guide weight", value=10.0, minimum=0.0, maximum=100.0, step=0.1, interactive=True)
-                    initialize_ = gr.Radio(["identity", "random"], label="NNF initialization", value="identity", interactive=True)
-                with gr.Column():
-                    gr.Markdown("""
-# Reference
-
-* Output directory: the directory to save the video.
-* Batch size: a larger batch size makes the program faster but requires more VRAM.
-* Tracking window size (only for accurate mode): The size of window in which our algorithm tracks moving objects. Empirically, 1 is enough.
-* Advanced settings
-    * Minimum patch size (odd number): the minimum patch size used for patch matching. **This parameter should be larger than that in blending. (Default: 15)**
-    * Number of iterations: the number of iterations of patch matching. (Default: 5)
-    * Guide weight: a parameter that determines how much motion feature applied to the style video. (Default: 10)
-    * NNF initialization: how to initialize the NNF (Nearest Neighbor Field). (Default: identity)
-                    """)
-            btn_.click(
-                interpolate_video,
-                inputs=[
-                    video_guide_folder_,
-                    rendered_keyframes_,
-                    output_path_,
-                    fps_,
-                    batch_size_,
-                    tracking_window_size_,
-                    minimum_patch_size_,
-                    num_iter_,
-                    guide_weight_,
-                    initialize_,
-                ],
-                outputs=[output_path_, fps_, video_output_]
-            )
-
-        return [(ui_component, "FastBlend", "FastBlend_ui")]

diffsynth/extensions/FastBlend/cupy_kernels.py

@@ -1,119 +0,0 @@
-import cupy as cp
-
-remapping_kernel = cp.RawKernel(r'''
-extern "C" __global__
-void remap(
-    const int height,
-    const int width,
-    const int channel,
-    const int patch_size,
-    const int pad_size,
-    const float* source_style,
-    const int* nnf,
-    float* target_style
-) {
-    const int r = (patch_size - 1) / 2;
-    const int x = blockDim.x * blockIdx.x + threadIdx.x;
-    const int y = blockDim.y * blockIdx.y + threadIdx.y;
-    if (x >= height or y >= width) return;
-    const int z = blockIdx.z * (height + pad_size * 2) * (width + pad_size * 2) * channel;
-    const int pid = (x + pad_size) * (width + pad_size * 2) + (y + pad_size);
-    const int min_px = x < r ? -x : -r;
-    const int max_px = x + r > height - 1 ? height - 1 - x : r;
-    const int min_py = y < r ? -y : -r;
-    const int max_py = y + r > width - 1 ? width - 1 - y : r;
-    int num = 0;
-    for (int px = min_px; px <= max_px; px++){
-        for (int py = min_py; py <= max_py; py++){
-            const int nid = (x + px) * width + y + py;
-            const int x_ = nnf[blockIdx.z * height * width * 2 + nid*2 + 0] - px;
-            const int y_ = nnf[blockIdx.z * height * width * 2 + nid*2 + 1] - py;
-            if (x_ < 0 or y_ < 0 or x_ >= height or y_ >= width)continue;
-            const int pid_ = (x_ + pad_size) * (width + pad_size * 2) + (y_ + pad_size);
-            num++;
-            for (int c = 0; c < channel; c++){
-                target_style[z + pid * channel + c] += source_style[z + pid_ * channel + c];
-            }
-        }
-    }
-    for (int c = 0; c < channel; c++){
-        target_style[z + pid * channel + c] /= num;
-    }
-}
-''', 'remap')
-
-
-patch_error_kernel = cp.RawKernel(r'''
-extern "C" __global__
-void patch_error(
-    const int height,
-    const int width,
-    const int channel,
-    const int patch_size,
-    const int pad_size,
-    const float* source,
-    const int* nnf,
-    const float* target,
-    float* error
-) {
-    const int r = (patch_size - 1) / 2;
-    const int x = blockDim.x * blockIdx.x + threadIdx.x;
-    const int y = blockDim.y * blockIdx.y + threadIdx.y;
-    const int z = blockIdx.z * (height + pad_size * 2) * (width + pad_size * 2) * channel;
-    if (x >= height or y >= width) return;
-    const int x_ = nnf[blockIdx.z * height * width * 2 + (x * width + y)*2 + 0];
-    const int y_ = nnf[blockIdx.z * height * width * 2 + (x * width + y)*2 + 1];
-    float e = 0;
-    for (int px = -r; px <= r; px++){
-        for (int py = -r; py <= r; py++){
-            const int pid = (x + pad_size + px) * (width + pad_size * 2) + y + pad_size + py;
-            const int pid_ = (x_ + pad_size + px) * (width + pad_size * 2) + y_ + pad_size + py;
-            for (int c = 0; c < channel; c++){
-                const float diff = target[z + pid * channel + c] - source[z + pid_ * channel + c];
-                e += diff * diff;
-            }
-        }
-    }
-    error[blockIdx.z * height * width + x * width + y] = e;
-}
-''', 'patch_error')
-
-
-pairwise_patch_error_kernel = cp.RawKernel(r'''
-extern "C" __global__
-void pairwise_patch_error(
-    const int height,
-    const int width,
-    const int channel,
-    const int patch_size,
-    const int pad_size,
-    const float* source_a,
-    const int* nnf_a,
-    const float* source_b,
-    const int* nnf_b,
-    float* error
-) {
-    const int r = (patch_size - 1) / 2;
-    const int x = blockDim.x * blockIdx.x + threadIdx.x;
-    const int y = blockDim.y * blockIdx.y + threadIdx.y;
-    const int z = blockIdx.z * (height + pad_size * 2) * (width + pad_size * 2) * channel;
-    if (x >= height or y >= width) return;
-    const int z_nnf = blockIdx.z * height * width * 2 + (x * width + y) * 2;
-    const int x_a = nnf_a[z_nnf + 0];
-    const int y_a = nnf_a[z_nnf + 1];
-    const int x_b = nnf_b[z_nnf + 0];
-    const int y_b = nnf_b[z_nnf + 1];
-    float e = 0;
-    for (int px = -r; px <= r; px++){
-        for (int py = -r; py <= r; py++){
-            const int pid_a = (x_a + pad_size + px) * (width + pad_size * 2) + y_a + pad_size + py;
-            const int pid_b = (x_b + pad_size + px) * (width + pad_size * 2) + y_b + pad_size + py;
-            for (int c = 0; c < channel; c++){
-                const float diff = source_a[z + pid_a * channel + c] - source_b[z + pid_b * channel + c];
-                e += diff * diff;
-            }
-        }
-    }
-    error[blockIdx.z * height * width + x * width + y] = e;
-}
-''', 'pairwise_patch_error')

diffsynth/extensions/FastBlend/data.py

@@ -1,146 +0,0 @@
-import imageio, os
-import numpy as np
-from PIL import Image
-
-
-def read_video(file_name):
-    reader = imageio.get_reader(file_name)
-    video = []
-    for frame in reader:
-        frame = np.array(frame)
-        video.append(frame)
-    reader.close()
-    return video
-
-
-def get_video_fps(file_name):
-    reader = imageio.get_reader(file_name)
-    fps = reader.get_meta_data()["fps"]
-    reader.close()
-    return fps
-
-
-def save_video(frames_path, video_path, num_frames, fps):
-    writer = imageio.get_writer(video_path, fps=fps, quality=9)
-    for i in range(num_frames):
-        frame = np.array(Image.open(os.path.join(frames_path, "%05d.png" % i)))
-        writer.append_data(frame)
-    writer.close()
-    return video_path
-
-
-class LowMemoryVideo:
-    def __init__(self, file_name):
-        self.reader = imageio.get_reader(file_name)
-    
-    def __len__(self):
-        return self.reader.count_frames()
-
-    def __getitem__(self, item):
-        return np.array(self.reader.get_data(item))
-
-    def __del__(self):
-        self.reader.close()
-
-
-def split_file_name(file_name):
-    result = []
-    number = -1
-    for i in file_name:
-        if ord(i)>=ord("0") and ord(i)<=ord("9"):
-            if number == -1:
-                number = 0
-            number = number*10 + ord(i) - ord("0")
-        else:
-            if number != -1:
-                result.append(number)
-                number = -1
-            result.append(i)
-    if number != -1:
-        result.append(number)
-    result = tuple(result)
-    return result
-
-
-def search_for_images(folder):
-    file_list = [i for i in os.listdir(folder) if i.endswith(".jpg") or i.endswith(".png")]
-    file_list = [(split_file_name(file_name), file_name) for file_name in file_list]
-    file_list = [i[1] for i in sorted(file_list)]
-    file_list = [os.path.join(folder, i) for i in file_list]
-    return file_list
-
-
-def read_images(folder):
-    file_list = search_for_images(folder)
-    frames = [np.array(Image.open(i)) for i in file_list]
-    return frames
-
-
-class LowMemoryImageFolder:
-    def __init__(self, folder, file_list=None):
-        if file_list is None:
-            self.file_list = search_for_images(folder)
-        else:
-            self.file_list = [os.path.join(folder, file_name) for file_name in file_list]
-    
-    def __len__(self):
-        return len(self.file_list)
-
-    def __getitem__(self, item):
-        return np.array(Image.open(self.file_list[item]))
-
-    def __del__(self):
-        pass
-
-
-class VideoData:
-    def __init__(self, video_file, image_folder, **kwargs):
-        if video_file is not None:
-            self.data_type = "video"
-            self.data = LowMemoryVideo(video_file, **kwargs)
-        elif image_folder is not None:
-            self.data_type = "images"
-            self.data = LowMemoryImageFolder(image_folder, **kwargs)
-        else:
-            raise ValueError("Cannot open video or image folder")
-        self.length = None
-        self.height = None
-        self.width = None
-
-    def raw_data(self):
-        frames = []
-        for i in range(self.__len__()):
-            frames.append(self.__getitem__(i))
-        return frames
-
-    def set_length(self, length):
-        self.length = length
-
-    def set_shape(self, height, width):
-        self.height = height
-        self.width = width
-
-    def __len__(self):
-        if self.length is None:
-            return len(self.data)
-        else:
-            return self.length
-
-    def shape(self):
-        if self.height is not None and self.width is not None:
-            return self.height, self.width
-        else:
-            height, width, _ = self.__getitem__(0).shape
-            return height, width
-
-    def __getitem__(self, item):
-        frame = self.data.__getitem__(item)
-        height, width, _ = frame.shape
-        if self.height is not None and self.width is not None:
-            if self.height != height or self.width != width:
-                frame = Image.fromarray(frame).resize((self.width, self.height))
-                frame = np.array(frame)
-        return frame
-
-    def __del__(self):
-        pass

diffsynth/extensions/FastBlend/patch_match.py

@@ -1,299 +0,0 @@
-from .cupy_kernels import remapping_kernel, patch_error_kernel, pairwise_patch_error_kernel
-import numpy as np
-import cupy as cp
-import cv2
-import torch
-import torch.nn.functional as F
-
-class PatchMatcher:
-    def __init__(
-        self, height, width, channel, minimum_patch_size,
-        threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0,
-        random_search_steps=3, random_search_range=4,
-        use_mean_target_style=False, use_pairwise_patch_error=False,
-        tracking_window_size=0
-    ):
-        self.height = height
-        self.width = width
-        self.channel = channel
-        self.minimum_patch_size = minimum_patch_size
-        self.threads_per_block = threads_per_block
-        self.num_iter = num_iter
-        self.gpu_id = gpu_id
-        self.guide_weight = guide_weight
-        self.random_search_steps = random_search_steps
-        self.random_search_range = random_search_range
-        self.use_mean_target_style = use_mean_target_style
-        self.use_pairwise_patch_error = use_pairwise_patch_error
-        self.tracking_window_size = tracking_window_size
-
-        self.patch_size_list = [minimum_patch_size + i*2 for i in range(num_iter)][::-1]
-        self.pad_size = self.patch_size_list[0] // 2
-        self.grid = (
-            (height + threads_per_block - 1) // threads_per_block,
-            (width + threads_per_block - 1) // threads_per_block
-        )
-        self.block = (threads_per_block, threads_per_block)
-
-    def pad_image(self, image):
-        return cp.pad(image, ((0, 0), (self.pad_size, self.pad_size), (self.pad_size, self.pad_size), (0, 0)))
-
-    def unpad_image(self, image):
-        return image[:, self.pad_size: -self.pad_size, self.pad_size: -self.pad_size, :]
-
-    def apply_nnf_to_image(self, nnf, source):
-        batch_size = source.shape[0]
-        target = cp.zeros((batch_size, self.height + self.pad_size * 2, self.width + self.pad_size * 2, self.channel), dtype=cp.float32)
-        remapping_kernel(
-            self.grid + (batch_size,),
-            self.block,
-            (self.height, self.width, self.channel, self.patch_size, self.pad_size, source, nnf, target)
-        )
-        return target
-
-    def get_patch_error(self, source, nnf, target):
-        batch_size = source.shape[0]
-        error = cp.zeros((batch_size, self.height, self.width), dtype=cp.float32)
-        patch_error_kernel(
-            self.grid + (batch_size,),
-            self.block,
-            (self.height, self.width, self.channel, self.patch_size, self.pad_size, source, nnf, target, error)
-        )
-        return error
-
-    def get_pairwise_patch_error(self, source, nnf):
-        batch_size = source.shape[0]//2
-        error = cp.zeros((batch_size, self.height, self.width), dtype=cp.float32)
-        source_a, nnf_a = source[0::2].copy(), nnf[0::2].copy()
-        source_b, nnf_b = source[1::2].copy(), nnf[1::2].copy()
-        pairwise_patch_error_kernel(
-            self.grid + (batch_size,),
-            self.block,
-            (self.height, self.width, self.channel, self.patch_size, self.pad_size, source_a, nnf_a, source_b, nnf_b, error)
-        )
-        error = error.repeat(2, axis=0)
-        return error
-
-    def get_error(self, source_guide, target_guide, source_style, target_style, nnf):
-        error_guide = self.get_patch_error(source_guide, nnf, target_guide)
-        if self.use_mean_target_style:
-            target_style = self.apply_nnf_to_image(nnf, source_style)
-            target_style = target_style.mean(axis=0, keepdims=True)
-            target_style = target_style.repeat(source_guide.shape[0], axis=0)
-        if self.use_pairwise_patch_error:
-            error_style = self.get_pairwise_patch_error(source_style, nnf)
-        else:
-            error_style = self.get_patch_error(source_style, nnf, target_style)
-        error = error_guide * self.guide_weight + error_style
-        return error
-
-    def clamp_bound(self, nnf):
-        nnf[:,:,:,0] = cp.clip(nnf[:,:,:,0], 0, self.height-1)
-        nnf[:,:,:,1] = cp.clip(nnf[:,:,:,1], 0, self.width-1)
-        return nnf
-
-    def random_step(self, nnf, r):
-        batch_size = nnf.shape[0]
-        step = cp.random.randint(-r, r+1, size=(batch_size, self.height, self.width, 2), dtype=cp.int32)
-        upd_nnf = self.clamp_bound(nnf + step)
-        return upd_nnf
-
-    def neighboor_step(self, nnf, d):
-        if d==0:
-            upd_nnf = cp.concatenate([nnf[:, :1, :], nnf[:, :-1, :]], axis=1)
-            upd_nnf[:, :, :, 0] += 1
-        elif d==1:
-            upd_nnf = cp.concatenate([nnf[:, :, :1], nnf[:, :, :-1]], axis=2)
-            upd_nnf[:, :, :, 1] += 1
-        elif d==2:
-            upd_nnf = cp.concatenate([nnf[:, 1:, :], nnf[:, -1:, :]], axis=1)
-            upd_nnf[:, :, :, 0] -= 1
-        elif d==3:
-            upd_nnf = cp.concatenate([nnf[:, :, 1:], nnf[:, :, -1:]], axis=2)
-            upd_nnf[:, :, :, 1] -= 1
-        upd_nnf = self.clamp_bound(upd_nnf)
-        return upd_nnf
-        
-    def shift_nnf(self, nnf, d):
-        if d>0:
-            d = min(nnf.shape[0], d)
-            upd_nnf = cp.concatenate([nnf[d:]] + [nnf[-1:]] * d, axis=0)
-        else:
-            d = max(-nnf.shape[0], d)
-            upd_nnf = cp.concatenate([nnf[:1]] * (-d) + [nnf[:d]], axis=0)
-        return upd_nnf
-    
-    def track_step(self, nnf, d):
-        if self.use_pairwise_patch_error:
-            upd_nnf = cp.zeros_like(nnf)
-            upd_nnf[0::2] = self.shift_nnf(nnf[0::2], d)
-            upd_nnf[1::2] = self.shift_nnf(nnf[1::2], d)
-        else:
-            upd_nnf = self.shift_nnf(nnf, d)
-        return upd_nnf
-
-    def C(self, n, m):
-        # not used
-        c = 1
-        for i in range(1, n+1):
-            c *= i
-        for i in range(1, m+1):
-            c //= i
-        for i in range(1, n-m+1):
-            c //= i
-        return c
-
-    def bezier_step(self, nnf, r):
-        # not used
-        n = r * 2 - 1
-        upd_nnf = cp.zeros(shape=nnf.shape, dtype=cp.float32)
-        for i, d in enumerate(list(range(-r, 0)) + list(range(1, r+1))):
-            if d>0:
-                ctl_nnf = cp.concatenate([nnf[d:]] + [nnf[-1:]] * d, axis=0)
-            elif d<0:
-                ctl_nnf = cp.concatenate([nnf[:1]] * (-d) + [nnf[:d]], axis=0)
-            upd_nnf += ctl_nnf * (self.C(n, i) / 2**n)
-        upd_nnf = self.clamp_bound(upd_nnf).astype(nnf.dtype)
-        return upd_nnf
-
-    def update(self, source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf):
-        upd_err = self.get_error(source_guide, target_guide, source_style, target_style, upd_nnf)
-        upd_idx = (upd_err < err)
-        nnf[upd_idx] = upd_nnf[upd_idx]
-        err[upd_idx] = upd_err[upd_idx]
-        return nnf, err
-
-    def propagation(self, source_guide, target_guide, source_style, target_style, nnf, err):
-        for d in cp.random.permutation(4):
-            upd_nnf = self.neighboor_step(nnf, d)
-            nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf)
-        return nnf, err
-        
-    def random_search(self, source_guide, target_guide, source_style, target_style, nnf, err):
-        for i in range(self.random_search_steps):
-            upd_nnf = self.random_step(nnf, self.random_search_range)
-            nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf)
-        return nnf, err
-
-    def track(self, source_guide, target_guide, source_style, target_style, nnf, err):
-        for d in range(1, self.tracking_window_size + 1):
-            upd_nnf = self.track_step(nnf, d)
-            nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf)
-            upd_nnf = self.track_step(nnf, -d)
-            nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf)
-        return nnf, err
-
-    def iteration(self, source_guide, target_guide, source_style, target_style, nnf, err):
-        nnf, err = self.propagation(source_guide, target_guide, source_style, target_style, nnf, err)
-        nnf, err = self.random_search(source_guide, target_guide, source_style, target_style, nnf, err)
-        nnf, err = self.track(source_guide, target_guide, source_style, target_style, nnf, err)
-        return nnf, err
-
-    def estimate_nnf(self, source_guide, target_guide, source_style, nnf):
-        with cp.cuda.Device(self.gpu_id):
-            source_guide = self.pad_image(source_guide)
-            target_guide = self.pad_image(target_guide)
-            source_style = self.pad_image(source_style)
-            for it in range(self.num_iter):
-                self.patch_size = self.patch_size_list[it]
-                target_style = self.apply_nnf_to_image(nnf, source_style)
-                err = self.get_error(source_guide, target_guide, source_style, target_style, nnf)
-                nnf, err = self.iteration(source_guide, target_guide, source_style, target_style, nnf, err)
-            target_style = self.unpad_image(self.apply_nnf_to_image(nnf, source_style))
-        return nnf, target_style
-
-
-class PyramidPatchMatcher:
-    def __init__(
-        self, image_height, image_width, channel, minimum_patch_size,
-        threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0,
-        use_mean_target_style=False, use_pairwise_patch_error=False,
-        tracking_window_size=0,
-        initialize="identity"
-    ):
-        maximum_patch_size = minimum_patch_size + (num_iter - 1) * 2
-        self.pyramid_level = int(np.log2(min(image_height, image_width) / maximum_patch_size))
-        self.pyramid_heights = []
-        self.pyramid_widths = []
-        self.patch_matchers = []
-        self.minimum_patch_size = minimum_patch_size
-        self.num_iter = num_iter
-        self.gpu_id = gpu_id
-        self.initialize = initialize
-        for level in range(self.pyramid_level):
-            height = image_height//(2**(self.pyramid_level - 1 - level))
-            width = image_width//(2**(self.pyramid_level - 1 - level))
-            self.pyramid_heights.append(height)
-            self.pyramid_widths.append(width)
-            self.patch_matchers.append(PatchMatcher(
-                height, width, channel, minimum_patch_size=minimum_patch_size,
-                threads_per_block=threads_per_block, num_iter=num_iter, gpu_id=gpu_id, guide_weight=guide_weight,
-                use_mean_target_style=use_mean_target_style, use_pairwise_patch_error=use_pairwise_patch_error,
-                tracking_window_size=tracking_window_size
-            ))
-
-    def resample_image(self, images, level):
-        height, width = self.pyramid_heights[level], self.pyramid_widths[level]
-        images_torch = torch.as_tensor(images, device='cuda', dtype=torch.float32)
-        images_torch = images_torch.permute(0, 3, 1, 2)
-        images_resample = F.interpolate(images_torch, size=(height, width), mode='area', align_corners=None)
-        images_resample = images_resample.permute(0, 2, 3, 1).contiguous()
-        return cp.asarray(images_resample)
-
-    def initialize_nnf(self, batch_size):
-        if self.initialize == "random":
-            height, width = self.pyramid_heights[0], self.pyramid_widths[0]
-            nnf = cp.stack([
-                cp.random.randint(0, height, (batch_size, height, width), dtype=cp.int32),
-                cp.random.randint(0, width, (batch_size, height, width), dtype=cp.int32)
-            ], axis=3)
-        elif self.initialize == "identity":
-            height, width = self.pyramid_heights[0], self.pyramid_widths[0]
-            nnf = cp.stack([
-                cp.repeat(cp.arange(height), width).reshape(height, width),
-                cp.tile(cp.arange(width), height).reshape(height, width)
-            ], axis=2)
-            nnf = cp.stack([nnf] * batch_size)
-        else:
-            raise NotImplementedError()
-        return nnf
-
-    def update_nnf(self, nnf, level):
-        # upscale
-        nnf = nnf.repeat(2, axis=1).repeat(2, axis=2) * 2
-        nnf[:, 1::2, :, 0] += 1
-        nnf[:, :, 1::2, 1] += 1
-        # check if scale is 2
-        height, width = self.pyramid_heights[level], self.pyramid_widths[level]
-        if height != nnf.shape[0] * 2 or width != nnf.shape[1] * 2:
-            nnf_torch = torch.as_tensor(nnf, device='cuda', dtype=torch.float32)
-            nnf_torch = nnf_torch.permute(0, 3, 1, 2)
-            nnf_resized = F.interpolate(nnf_torch, size=(height, width), mode='bilinear', align_corners=False)
-            nnf_resized = nnf_resized.permute(0, 2, 3, 1)
-            nnf = cp.asarray(nnf_resized).astype(cp.int32)
-            nnf = self.patch_matchers[level].clamp_bound(nnf)
-        return nnf
-
-    def apply_nnf_to_image(self, nnf, image):
-        with cp.cuda.Device(self.gpu_id):
-            image = self.patch_matchers[-1].pad_image(image)
-            image = self.patch_matchers[-1].apply_nnf_to_image(nnf, image)
-        return image
-
-    def estimate_nnf(self, source_guide, target_guide, source_style):
-        with cp.cuda.Device(self.gpu_id):
-            if not isinstance(source_guide, cp.ndarray):
-                source_guide = cp.array(source_guide, dtype=cp.float32)
-            if not isinstance(target_guide, cp.ndarray):
-                target_guide = cp.array(target_guide, dtype=cp.float32)
-            if not isinstance(source_style, cp.ndarray):
-                source_style = cp.array(source_style, dtype=cp.float32)
-            for level in range(self.pyramid_level):
-                nnf = self.initialize_nnf(source_guide.shape[0]) if level==0 else self.update_nnf(nnf, level)
-                source_guide_ = self.resample_image(source_guide, level)
-                target_guide_ = self.resample_image(target_guide, level)
-                source_style_ = self.resample_image(source_style, level)
-                nnf, target_style = self.patch_matchers[level].estimate_nnf(
-                    source_guide_, target_guide_, source_style_, nnf
-                )
-        return nnf.get(), target_style.get()

diffsynth/extensions/FastBlend/runners/__init__.py

@@ -1,4 +0,0 @@
-from .accurate import AccurateModeRunner
-from .fast import FastModeRunner
-from .balanced import BalancedModeRunner
-from .interpolation import InterpolationModeRunner, InterpolationModeSingleFrameRunner

diffsynth/extensions/FastBlend/runners/accurate.py

@@ -1,35 +0,0 @@
-from ..patch_match import PyramidPatchMatcher
-import os
-import numpy as np
-from PIL import Image
-from tqdm import tqdm
-
-
-class AccurateModeRunner:
-    def __init__(self):
-        pass
-
-    def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config, desc="Accurate Mode", save_path=None):
-        patch_match_engine = PyramidPatchMatcher(
-            image_height=frames_style[0].shape[0],
-            image_width=frames_style[0].shape[1],
-            channel=3,
-            use_mean_target_style=True,
-            **ebsynth_config
-        )
-        # run
-        n = len(frames_style)
-        for target in tqdm(range(n), desc=desc):
-            l, r = max(target - window_size, 0), min(target + window_size + 1, n)
-            remapped_frames = []
-            for i in range(l, r, batch_size):
-                j = min(i + batch_size, r)
-                source_guide = np.stack([frames_guide[source] for source in range(i, j)])
-                target_guide = np.stack([frames_guide[target]] * (j - i))
-                source_style = np.stack([frames_style[source] for source in range(i, j)])
-                _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style)
-                remapped_frames.append(target_style)
-            frame = np.concatenate(remapped_frames, axis=0).mean(axis=0)
-            frame = frame.clip(0, 255).astype("uint8")
-            if save_path is not None:
-                Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % target))

diffsynth/extensions/FastBlend/runners/balanced.py

@@ -1,46 +0,0 @@
-from ..patch_match import PyramidPatchMatcher
-import os
-import numpy as np
-from PIL import Image
-from tqdm import tqdm
-
-
-class BalancedModeRunner:
-    def __init__(self):
-        pass
-
-    def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config, desc="Balanced Mode", save_path=None):
-        patch_match_engine = PyramidPatchMatcher(
-            image_height=frames_style[0].shape[0],
-            image_width=frames_style[0].shape[1],
-            channel=3,
-            **ebsynth_config
-        )
-        # tasks
-        n = len(frames_style)
-        tasks = []
-        for target in range(n):
-            for source in range(target - window_size, target + window_size + 1):
-                if source >= 0 and source < n and source != target:
-                    tasks.append((source, target))
-        # run
-        frames = [(None, 1) for i in range(n)]
-        for batch_id in tqdm(range(0, len(tasks), batch_size), desc=desc):
-            tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))]
-            source_guide = np.stack([frames_guide[source] for source, target in tasks_batch])
-            target_guide = np.stack([frames_guide[target] for source, target in tasks_batch])
-            source_style = np.stack([frames_style[source] for source, target in tasks_batch])
-            _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style)
-            for (source, target), result in zip(tasks_batch, target_style):
-                frame, weight = frames[target]
-                if frame is None:
-                    frame = frames_style[target]
-                frames[target] = (
-                    frame * (weight / (weight + 1)) + result / (weight + 1),
-                    weight + 1
-                )
-                if weight + 1 == min(n, target + window_size + 1) - max(0, target - window_size):
-                    frame = frame.clip(0, 255).astype("uint8")
-                    if save_path is not None:
-                        Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % target))
-                    frames[target] = (None, 1)

diffsynth/extensions/FastBlend/runners/fast.py

@@ -1,141 +0,0 @@
-from ..patch_match import PyramidPatchMatcher
-import functools, os
-import numpy as np
-from PIL import Image
-from tqdm import tqdm
-
-
-class TableManager:
-    def __init__(self):
-        pass
-
-    def task_list(self, n):
-        tasks = []
-        max_level = 1
-        while (1<<max_level)<=n:
-            max_level += 1
-        for i in range(n):
-            j = i
-            for level in range(max_level):
-                if i&(1<<level):
-                    continue
-                j |= 1<<level
-                if j>=n:
-                    break
-                meta_data = {
-                    "source": i,
-                    "target": j,
-                    "level": level + 1
-                }
-                tasks.append(meta_data)
-        tasks.sort(key=functools.cmp_to_key(lambda u, v: u["level"]-v["level"]))
-        return tasks
-    
-    def build_remapping_table(self, frames_guide, frames_style, patch_match_engine, batch_size, desc=""):
-        n = len(frames_guide)
-        tasks = self.task_list(n)
-        remapping_table = [[(frames_style[i], 1)] for i in range(n)]
-        for batch_id in tqdm(range(0, len(tasks), batch_size), desc=desc):
-            tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))]
-            source_guide = np.stack([frames_guide[task["source"]] for task in tasks_batch])
-            target_guide = np.stack([frames_guide[task["target"]] for task in tasks_batch])
-            source_style = np.stack([frames_style[task["source"]] for task in tasks_batch])
-            _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style)
-            for task, result in zip(tasks_batch, target_style):
-                target, level = task["target"], task["level"]
-                if len(remapping_table[target])==level:
-                    remapping_table[target].append((result, 1))
-                else:
-                    frame, weight = remapping_table[target][level]
-                    remapping_table[target][level] = (
-                        frame * (weight / (weight + 1)) + result / (weight + 1),
-                        weight + 1
-                    )
-        return remapping_table
-
-    def remapping_table_to_blending_table(self, table):
-        for i in range(len(table)):
-            for j in range(1, len(table[i])):
-                frame_1, weight_1 = table[i][j-1]
-                frame_2, weight_2 = table[i][j]
-                frame = (frame_1 + frame_2) / 2
-                weight = weight_1 + weight_2
-                table[i][j] = (frame, weight)
-        return table
-
-    def tree_query(self, leftbound, rightbound):
-        node_list = []
-        node_index = rightbound
-        while node_index>=leftbound:
-            node_level = 0
-            while (1<<node_level)&node_index and node_index-(1<<node_level+1)+1>=leftbound:
-                node_level += 1
-            node_list.append((node_index, node_level))
-            node_index -= 1<<node_level
-        return node_list
-
-    def process_window_sum(self, frames_guide, blending_table, patch_match_engine, window_size, batch_size, desc=""):
-        n = len(blending_table)
-        tasks = []
-        frames_result = []
-        for target in range(n):
-            node_list = self.tree_query(max(target-window_size, 0), target)
-            for source, level in node_list:
-                if source!=target:
-                    meta_data = {
-                        "source": source,
-                        "target": target,
-                        "level": level
-                    }
-                    tasks.append(meta_data)
-                else:
-                    frames_result.append(blending_table[target][level])
-        for batch_id in tqdm(range(0, len(tasks), batch_size), desc=desc):
-            tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))]
-            source_guide = np.stack([frames_guide[task["source"]] for task in tasks_batch])
-            target_guide = np.stack([frames_guide[task["target"]] for task in tasks_batch])
-            source_style = np.stack([blending_table[task["source"]][task["level"]][0] for task in tasks_batch])
-            _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style)
-            for task, frame_2 in zip(tasks_batch, target_style):
-                source, target, level = task["source"], task["target"], task["level"]
-                frame_1, weight_1 = frames_result[target]
-                weight_2 = blending_table[source][level][1]
-                weight = weight_1 + weight_2
-                frame = frame_1 * (weight_1 / weight) + frame_2 * (weight_2 / weight)
-                frames_result[target] = (frame, weight)
-        return frames_result
-
-
-class FastModeRunner:
-    def __init__(self):
-        pass
-
-    def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config, save_path=None):
-        frames_guide = frames_guide.raw_data()
-        frames_style = frames_style.raw_data()
-        table_manager = TableManager()
-        patch_match_engine = PyramidPatchMatcher(
-            image_height=frames_style[0].shape[0],
-            image_width=frames_style[0].shape[1],
-            channel=3,
-            **ebsynth_config
-        )
-        # left part
-        table_l = table_manager.build_remapping_table(frames_guide, frames_style, patch_match_engine, batch_size, desc="Fast Mode Step 1/4")
-        table_l = table_manager.remapping_table_to_blending_table(table_l)
-        table_l = table_manager.process_window_sum(frames_guide, table_l, patch_match_engine, window_size, batch_size, desc="Fast Mode Step 2/4")
-        # right part
-        table_r = table_manager.build_remapping_table(frames_guide[::-1], frames_style[::-1], patch_match_engine, batch_size, desc="Fast Mode Step 3/4")
-        table_r = table_manager.remapping_table_to_blending_table(table_r)
-        table_r = table_manager.process_window_sum(frames_guide[::-1], table_r, patch_match_engine, window_size, batch_size, desc="Fast Mode Step 4/4")[::-1]
-        # merge
-        frames = []
-        for (frame_l, weight_l), frame_m, (frame_r, weight_r) in zip(table_l, frames_style, table_r):
-            weight_m = -1
-            weight = weight_l + weight_m + weight_r
-            frame = frame_l * (weight_l / weight) + frame_m * (weight_m / weight) + frame_r * (weight_r / weight)
-            frames.append(frame)
-        frames = [frame.clip(0, 255).astype("uint8") for frame in frames]
-        if save_path is not None:
-            for target, frame in enumerate(frames):
-                Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % target))

diffsynth/extensions/FastBlend/runners/interpolation.py

@@ -1,121 +0,0 @@
-from ..patch_match import PyramidPatchMatcher
-import os
-import numpy as np
-from PIL import Image
-from tqdm import tqdm
-
-
-class InterpolationModeRunner:
-    def __init__(self):
-        pass
-
-    def get_index_dict(self, index_style):
-        index_dict = {}
-        for i, index in enumerate(index_style):
-            index_dict[index] = i
-        return index_dict
-
-    def get_weight(self, l, m, r):
-        weight_l, weight_r = abs(m - r), abs(m - l)
-        if weight_l + weight_r == 0:
-            weight_l, weight_r = 0.5, 0.5
-        else:
-            weight_l, weight_r = weight_l / (weight_l + weight_r), weight_r / (weight_l + weight_r)
-        return weight_l, weight_r
-
-    def get_task_group(self, index_style, n):
-        task_group = []
-        index_style = sorted(index_style)
-        # first frame
-        if index_style[0]>0:
-            tasks = []
-            for m in range(index_style[0]):
-                tasks.append((index_style[0], m, index_style[0]))
-            task_group.append(tasks)
-        # middle frames
-        for l, r in zip(index_style[:-1], index_style[1:]):
-            tasks = []
-            for m in range(l, r):
-                tasks.append((l, m, r))
-            task_group.append(tasks)
-        # last frame
-        tasks = []
-        for m in range(index_style[-1], n):
-            tasks.append((index_style[-1], m, index_style[-1]))
-        task_group.append(tasks)
-        return task_group
-
-    def run(self, frames_guide, frames_style, index_style, batch_size, ebsynth_config, save_path=None):
-        patch_match_engine = PyramidPatchMatcher(
-            image_height=frames_style[0].shape[0],
-            image_width=frames_style[0].shape[1],
-            channel=3,
-            use_mean_target_style=False,
-            use_pairwise_patch_error=True,
-            **ebsynth_config
-        )
-        # task
-        index_dict = self.get_index_dict(index_style)
-        task_group = self.get_task_group(index_style, len(frames_guide))
-        # run
-        for tasks in task_group:
-            index_start, index_end = min([i[1] for i in tasks]), max([i[1] for i in tasks])
-            for batch_id in tqdm(range(0, len(tasks), batch_size), desc=f"Rendering frames {index_start}...{index_end}"):
-                tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))]
-                source_guide, target_guide, source_style = [], [], []
-                for l, m, r in tasks_batch:
-                    # l -> m
-                    source_guide.append(frames_guide[l])
-                    target_guide.append(frames_guide[m])
-                    source_style.append(frames_style[index_dict[l]])
-                    # r -> m
-                    source_guide.append(frames_guide[r])
-                    target_guide.append(frames_guide[m])
-                    source_style.append(frames_style[index_dict[r]])
-                source_guide = np.stack(source_guide)
-                target_guide = np.stack(target_guide)
-                source_style = np.stack(source_style)
-                _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style)
-                if save_path is not None:
-                    for frame_l, frame_r, (l, m, r) in zip(target_style[0::2], target_style[1::2], tasks_batch):
-                        weight_l, weight_r = self.get_weight(l, m, r)
-                        frame = frame_l * weight_l + frame_r * weight_r
-                        frame = frame.clip(0, 255).astype("uint8")
-                        Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % m))
-
-
-class InterpolationModeSingleFrameRunner:
-    def __init__(self):
-        pass
-
-    def run(self, frames_guide, frames_style, index_style, batch_size, ebsynth_config, save_path=None):
-        # check input
-        tracking_window_size = ebsynth_config["tracking_window_size"]
-        if tracking_window_size * 2 >= batch_size:
-            raise ValueError("batch_size should be larger than track_window_size * 2")
-        frame_style = frames_style[0]
-        frame_guide = frames_guide[index_style[0]]
-        patch_match_engine = PyramidPatchMatcher(
-            image_height=frame_style.shape[0],
-            image_width=frame_style.shape[1],
-            channel=3,
-            **ebsynth_config
-        )
-        # run
-        frame_id, n = 0, len(frames_guide)
-        for i in tqdm(range(0, n, batch_size - tracking_window_size * 2), desc=f"Rendering frames 0...{n}"):
-            if i + batch_size > n:
-                l, r = max(n - batch_size, 0), n
-            else:
-                l, r = i, i + batch_size
-            source_guide = np.stack([frame_guide] * (r-l))
-            target_guide = np.stack([frames_guide[i] for i in range(l, r)])
-            source_style = np.stack([frame_style] * (r-l))
-            _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style)
-            for i, frame in zip(range(l, r), target_style):
-                if i==frame_id:
-                    frame = frame.clip(0, 255).astype("uint8")
-                    Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % frame_id))
-                    frame_id += 1
-                if r < n and r-frame_id <= tracking_window_size:
-                    break

diffsynth/extensions/ImageQualityMetric/BLIP/__init__.py

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

diffsynth/extensions/ImageQualityMetric/BLIP/blip.py

@@ -1,77 +0,0 @@
-'''
- * Adapted from BLIP (https://github.com/salesforce/BLIP)
-'''
-
-import warnings
-warnings.filterwarnings("ignore")
-
-import torch
-import os
-from urllib.parse import urlparse
-from timm.models.hub import download_cached_file
-from transformers import BertTokenizer
-from .vit import VisionTransformer, interpolate_pos_embed
-
-
-def default_bert():
-    current_dir = os.path.dirname(os.path.abspath(__file__))
-    project_root = os.path.abspath(os.path.join(current_dir, '../../../../'))
-    model_path = os.path.join(project_root, 'models', 'QualityMetric')
-    return os.path.join(model_path, "bert-base-uncased")
-
-
-def init_tokenizer(bert_model_path):
-    tokenizer = BertTokenizer.from_pretrained(bert_model_path)
-    tokenizer.add_special_tokens({'bos_token':'[DEC]'})
-    tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']})       
-    tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]  
-    return tokenizer
-
-
-def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0):
-        
-    assert vit in ['base', 'large'], "vit parameter must be base or large"
-    if vit=='base':
-        vision_width = 768
-        visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12, 
-                                           num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
-                                           drop_path_rate=0 or drop_path_rate
-                                          )   
-    elif vit=='large':
-        vision_width = 1024
-        visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24, 
-                                           num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
-                                           drop_path_rate=0.1 or drop_path_rate
-                                          )   
-    return visual_encoder, vision_width
-
-
-def is_url(url_or_filename):
-    parsed = urlparse(url_or_filename)
-    return parsed.scheme in ("http", "https")
-
-def load_checkpoint(model,url_or_filename):
-    if is_url(url_or_filename):
-        cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
-        checkpoint = torch.load(cached_file, map_location='cpu') 
-    elif os.path.isfile(url_or_filename):        
-        checkpoint = torch.load(url_or_filename, map_location='cpu') 
-    else:
-        raise RuntimeError('checkpoint url or path is invalid')
-        
-    state_dict = checkpoint['model']
-    
-    state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) 
-    if 'visual_encoder_m.pos_embed' in model.state_dict().keys():
-        state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],
-                                                                         model.visual_encoder_m)    
-    for key in model.state_dict().keys():
-        if key in state_dict.keys():
-            if state_dict[key].shape!=model.state_dict()[key].shape:
-                print(key, ": ", state_dict[key].shape, ', ', model.state_dict()[key].shape)
-                del state_dict[key]
-    
-    msg = model.load_state_dict(state_dict,strict=False)
-    print('load checkpoint from %s'%url_or_filename)  
-    return model,msg
-    

diffsynth/extensions/ImageQualityMetric/BLIP/blip_pretrain.py

@@ -1,44 +0,0 @@
-'''
- * Adapted from BLIP (https://github.com/salesforce/BLIP)
-'''
-
-import transformers
-transformers.logging.set_verbosity_error()
-
-from torch import nn
-import os
-from .med import BertConfig, BertModel
-from .blip import create_vit, init_tokenizer
-
-class BLIP_Pretrain(nn.Module):
-    def __init__(self,                 
-                 med_config = "med_config.json",  
-                 image_size = 224,
-                 vit = 'base',
-                 vit_grad_ckpt = False,
-                 vit_ckpt_layer = 0,                    
-                 embed_dim = 256,     
-                 queue_size = 57600,
-                 momentum = 0.995,
-                 bert_model_path = ""
-                 ):
-        """
-        Args:
-            med_config (str): path for the mixture of encoder-decoder model's configuration file
-            image_size (int): input image size
-            vit (str): model size of vision transformer
-        """               
-        super().__init__()
-        
-        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, 0)
-        
-        self.tokenizer = init_tokenizer(bert_model_path)   
-        encoder_config = BertConfig.from_json_file(med_config)
-        encoder_config.encoder_width = vision_width
-        self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False)
-
-        text_width = self.text_encoder.config.hidden_size
-        
-        self.vision_proj = nn.Linear(vision_width, embed_dim)
-        self.text_proj = nn.Linear(text_width, embed_dim)
-

diffsynth/extensions/ImageQualityMetric/BLIP/med.py

@@ -1,947 +0,0 @@
-'''
- * Adapted from BLIP (https://github.com/salesforce/BLIP)
- * Based on huggingface code base
- * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert
-'''
-
-import math
-from typing import Tuple
-
-import torch
-from torch import Tensor, device, nn
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss
-
-from transformers.activations import ACT2FN
-from transformers.file_utils import (
-    ModelOutput,
-)
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPastAndCrossAttentions,
-    BaseModelOutputWithPoolingAndCrossAttentions,
-    CausalLMOutputWithCrossAttentions,
-    MaskedLMOutput,
-    MultipleChoiceModelOutput,
-    NextSentencePredictorOutput,
-    QuestionAnsweringModelOutput,
-    SequenceClassifierOutput,
-    TokenClassifierOutput,
-)
-from transformers.modeling_utils import (
-    PreTrainedModel,
-    apply_chunking_to_forward,
-    find_pruneable_heads_and_indices,
-    prune_linear_layer,
-)
-from transformers.utils import logging
-from transformers.models.bert.configuration_bert import BertConfig
-
-
-logger = logging.get_logger(__name__)
-
-
-class BertEmbeddings(nn.Module):
-    """Construct the embeddings from word and position embeddings."""
-
-    def __init__(self, config):
-        super().__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        
-        self.config = config
-
-    def forward(
-        self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
-    ):
-        if input_ids is not None:
-            input_shape = input_ids.size()
-        else:
-            input_shape = inputs_embeds.size()[:-1]
-
-        seq_length = input_shape[1]
-
-        if position_ids is None:
-            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-
-        embeddings = inputs_embeds
-
-        if self.position_embedding_type == "absolute":
-            position_embeddings = self.position_embeddings(position_ids)
-            embeddings += position_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-class BertSelfAttention(nn.Module):
-    def __init__(self, config, is_cross_attention):
-        super().__init__()
-        self.config = config
-        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
-            raise ValueError(
-                "The hidden size (%d) is not a multiple of the number of attention "
-                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
-            )
-        
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        if is_cross_attention:
-            self.key = nn.Linear(config.encoder_width, self.all_head_size)
-            self.value = nn.Linear(config.encoder_width, self.all_head_size)
-        else:
-            self.key = nn.Linear(config.hidden_size, self.all_head_size)
-            self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            self.max_position_embeddings = config.max_position_embeddings
-            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
-        self.save_attention = False   
-            
-    def save_attn_gradients(self, attn_gradients):
-        self.attn_gradients = attn_gradients
-        
-    def get_attn_gradients(self):
-        return self.attn_gradients
-    
-    def save_attention_map(self, attention_map):
-        self.attention_map = attention_map
-        
-    def get_attention_map(self):
-        return self.attention_map
-    
-    def transpose_for_scores(self, x):
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-    ):
-        mixed_query_layer = self.query(hidden_states)
-
-        # If this is instantiated as a cross-attention module, the keys
-        # and values come from an encoder; the attention mask needs to be
-        # such that the encoder's padding tokens are not attended to.
-        is_cross_attention = encoder_hidden_states is not None
-
-        if is_cross_attention:
-            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
-            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
-            attention_mask = encoder_attention_mask
-        elif past_key_value is not None:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
-            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
-        else:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-
-        past_key_value = (key_layer, value_layer)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            seq_length = hidden_states.size()[1]
-            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
-            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
-            distance = position_ids_l - position_ids_r
-            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
-            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
-
-            if self.position_embedding_type == "relative_key":
-                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores
-            elif self.position_embedding_type == "relative_key_query":
-                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
-
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-        if attention_mask is not None:
-            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
-            attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.Softmax(dim=-1)(attention_scores)
-        
-        if is_cross_attention and self.save_attention:
-            self.save_attention_map(attention_probs)
-            attention_probs.register_hook(self.save_attn_gradients)         
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs_dropped = self.dropout(attention_probs)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            attention_probs_dropped = attention_probs_dropped * head_mask
-
-        context_layer = torch.matmul(attention_probs_dropped, value_layer)
-
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(*new_context_layer_shape)
-
-        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
-
-        outputs = outputs + (past_key_value,)
-        return outputs
-
-
-class BertSelfOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertAttention(nn.Module):
-    def __init__(self, config, is_cross_attention=False):
-        super().__init__()
-        self.self = BertSelfAttention(config, is_cross_attention)
-        self.output = BertSelfOutput(config)
-        self.pruned_heads = set()
-
-    def prune_heads(self, heads):
-        if len(heads) == 0:
-            return
-        heads, index = find_pruneable_heads_and_indices(
-            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
-        )
-
-        # Prune linear layers
-        self.self.query = prune_linear_layer(self.self.query, index)
-        self.self.key = prune_linear_layer(self.self.key, index)
-        self.self.value = prune_linear_layer(self.self.value, index)
-        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
-
-        # Update hyper params and store pruned heads
-        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
-        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
-        self.pruned_heads = self.pruned_heads.union(heads)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-    ):
-        self_outputs = self.self(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            encoder_hidden_states,
-            encoder_attention_mask,
-            past_key_value,
-            output_attentions,
-        )
-        attention_output = self.output(self_outputs[0], hidden_states)
-        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
-        return outputs
-
-
-class BertIntermediate(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        if isinstance(config.hidden_act, str):
-            self.intermediate_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.intermediate_act_fn = config.hidden_act
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-class BertOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertLayer(nn.Module):
-    def __init__(self, config, layer_num):
-        super().__init__()
-        self.config = config
-        self.chunk_size_feed_forward = config.chunk_size_feed_forward
-        self.seq_len_dim = 1
-        self.attention = BertAttention(config)      
-        self.layer_num = layer_num          
-        if self.config.add_cross_attention:
-            self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention)
-        self.intermediate = BertIntermediate(config)
-        self.output = BertOutput(config)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_value=None,
-        output_attentions=False,
-        mode=None,
-    ):
-        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
-        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
-        self_attention_outputs = self.attention(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            output_attentions=output_attentions,
-            past_key_value=self_attn_past_key_value,
-        )
-        attention_output = self_attention_outputs[0]
-
-        outputs = self_attention_outputs[1:-1]
-        present_key_value = self_attention_outputs[-1]
-
-        if mode=='multimodal':
-            assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers"
-
-            cross_attention_outputs = self.crossattention(
-                attention_output,
-                attention_mask,
-                head_mask,
-                encoder_hidden_states,
-                encoder_attention_mask,
-                output_attentions=output_attentions,
-            )
-            attention_output = cross_attention_outputs[0]
-            outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights                               
-        layer_output = apply_chunking_to_forward(
-            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
-        )
-        outputs = (layer_output,) + outputs
-
-        outputs = outputs + (present_key_value,)
-
-        return outputs
-
-    def feed_forward_chunk(self, attention_output):
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.output(intermediate_output, attention_output)
-        return layer_output
-
-
-class BertEncoder(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=False,
-        output_hidden_states=False,
-        return_dict=True,
-        mode='multimodal',
-    ):
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attentions = () if output_attentions else None
-        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
-
-        next_decoder_cache = () if use_cache else None
-               
-        for i in range(self.config.num_hidden_layers):
-            layer_module = self.layer[i]
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            layer_head_mask = head_mask[i] if head_mask is not None else None
-            past_key_value = past_key_values[i] if past_key_values is not None else None
-
-            if self.gradient_checkpointing and self.training:
-
-                if use_cache:
-                    logger.warning(
-                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                    )
-                    use_cache = False
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs, past_key_value, output_attentions)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(layer_module),
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    mode=mode,
-                )
-            else:
-                layer_outputs = layer_module(
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    past_key_value,
-                    output_attentions,
-                    mode=mode,
-                )
-
-            hidden_states = layer_outputs[0]
-            if use_cache:
-                next_decoder_cache += (layer_outputs[-1],)
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (layer_outputs[1],)
-
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [
-                    hidden_states,
-                    next_decoder_cache,
-                    all_hidden_states,
-                    all_self_attentions,
-                    all_cross_attentions,
-                ]
-                if v is not None
-            )
-        return BaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=hidden_states,
-            past_key_values=next_decoder_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
-            cross_attentions=all_cross_attentions,
-        )
-
-
-class BertPooler(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.activation = nn.Tanh()
-
-    def forward(self, hidden_states):
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-        first_token_tensor = hidden_states[:, 0]
-        pooled_output = self.dense(first_token_tensor)
-        pooled_output = self.activation(pooled_output)
-        return pooled_output
-
-
-class BertPredictionHeadTransform(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        if isinstance(config.hidden_act, str):
-            self.transform_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.transform_act_fn = config.hidden_act
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-class BertLMPredictionHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.transform = BertPredictionHeadTransform(config)
-
-        # The output weights are the same as the input embeddings, but there is
-        # an output-only bias for each token.
-        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
-
-        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
-        self.decoder.bias = self.bias
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states)
-        return hidden_states
-
-
-class BertOnlyMLMHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.predictions = BertLMPredictionHead(config)
-
-    def forward(self, sequence_output):
-        prediction_scores = self.predictions(sequence_output)
-        return prediction_scores
-
-
-class BertPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = BertConfig
-    base_model_prefix = "bert"
-    _keys_to_ignore_on_load_missing = [r"position_ids"]
-
-    def _init_weights(self, module):
-        """ Initialize the weights """
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-        if isinstance(module, nn.Linear) and module.bias is not None:
-            module.bias.data.zero_()
-
-
-class BertModel(BertPreTrainedModel):
-    """
-    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
-    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
-    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
-    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
-    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
-    input to the forward pass.
-    """
-
-    def __init__(self, config, add_pooling_layer=True):
-        super().__init__(config)
-        self.config = config
-
-        self.embeddings = BertEmbeddings(config)
-        
-        self.encoder = BertEncoder(config)
-
-        self.pooler = BertPooler(config) if add_pooling_layer else None
-
-        self.init_weights()
- 
-
-    def get_input_embeddings(self):
-        return self.embeddings.word_embeddings
-
-    def set_input_embeddings(self, value):
-        self.embeddings.word_embeddings = value
-
-    def _prune_heads(self, heads_to_prune):
-        """
-        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
-        class PreTrainedModel
-        """
-        for layer, heads in heads_to_prune.items():
-            self.encoder.layer[layer].attention.prune_heads(heads)
-
-    
-    def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor:
-        """
-        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
-
-        Arguments:
-            attention_mask (:obj:`torch.Tensor`):
-                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
-            input_shape (:obj:`Tuple[int]`):
-                The shape of the input to the model.
-            device: (:obj:`torch.device`):
-                The device of the input to the model.
-
-        Returns:
-            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
-        """
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        if attention_mask.dim() == 3:
-            extended_attention_mask = attention_mask[:, None, :, :]
-        elif attention_mask.dim() == 2:
-            # Provided a padding mask of dimensions [batch_size, seq_length]
-            # - if the model is a decoder, apply a causal mask in addition to the padding mask
-            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
-            if is_decoder:
-                batch_size, seq_length = input_shape
-
-                seq_ids = torch.arange(seq_length, device=device)
-                causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
-                # in case past_key_values are used we need to add a prefix ones mask to the causal mask
-                # causal and attention masks must have same type with pytorch version < 1.3
-                causal_mask = causal_mask.to(attention_mask.dtype)
-   
-                if causal_mask.shape[1] < attention_mask.shape[1]:
-                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
-                    causal_mask = torch.cat(
-                        [
-                            torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype),
-                            causal_mask,
-                        ],
-                        axis=-1,
-                    )                     
-
-                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
-            else:
-                extended_attention_mask = attention_mask[:, None, None, :]
-        else:
-            raise ValueError(
-                "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
-                    input_shape, attention_mask.shape
-                )
-            )
-
-        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-        # masked positions, this operation will create a tensor which is 0.0 for
-        # positions we want to attend and -10000.0 for masked positions.
-        # Since we are adding it to the raw scores before the softmax, this is
-        # effectively the same as removing these entirely.
-        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
-        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
-        return extended_attention_mask
-    
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        encoder_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        is_decoder=False,
-        mode='multimodal',
-    ):
-        r"""
-        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
-            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
-            the model is configured as a decoder.
-        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
-            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
-            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
-            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
-        use_cache (:obj:`bool`, `optional`):
-            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
-            decoding (see :obj:`past_key_values`).
-        """
-        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
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if is_decoder:
-            use_cache = use_cache if use_cache is not None else self.config.use_cache
-        else:
-            use_cache = False
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-            batch_size, seq_length = input_shape
-            device = input_ids.device
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-            batch_size, seq_length = input_shape
-            device = inputs_embeds.device
-        elif encoder_embeds is not None:    
-            input_shape = encoder_embeds.size()[:-1]
-            batch_size, seq_length = input_shape 
-            device = encoder_embeds.device
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds")
-
-        # past_key_values_length
-        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
-
-        if attention_mask is None:
-            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
-            
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, 
-                                                                                 device, is_decoder)
-
-        # If a 2D or 3D attention mask is provided for the cross-attention
-        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        if encoder_hidden_states is not None:
-            if type(encoder_hidden_states) == list:
-                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
-            else:
-                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
-            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
-            
-            if type(encoder_attention_mask) == list:
-                encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
-            elif encoder_attention_mask is None:
-                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
-                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-            else:    
-                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
-        else:
-            encoder_extended_attention_mask = None
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-        
-        if encoder_embeds is None:
-            embedding_output = self.embeddings(
-                input_ids=input_ids,
-                position_ids=position_ids,
-                inputs_embeds=inputs_embeds,
-                past_key_values_length=past_key_values_length,
-            )
-        else:
-            embedding_output = encoder_embeds
-            
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask=extended_attention_mask,
-            head_mask=head_mask,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_extended_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            mode=mode,
-        )
-        sequence_output = encoder_outputs[0]
-        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
-
-        if not return_dict:
-            return (sequence_output, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPoolingAndCrossAttentions(
-            last_hidden_state=sequence_output,
-            pooler_output=pooled_output,
-            past_key_values=encoder_outputs.past_key_values,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-            cross_attentions=encoder_outputs.cross_attentions,
-        )
-
-
-
-class BertLMHeadModel(BertPreTrainedModel):
-
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.bert = BertModel(config, add_pooling_layer=False)
-        self.cls = BertOnlyMLMHead(config)
-
-        self.init_weights()
-
-    def get_output_embeddings(self):
-        return self.cls.predictions.decoder
-
-    def set_output_embeddings(self, new_embeddings):
-        self.cls.predictions.decoder = new_embeddings
-
-    def forward(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        labels=None,
-        past_key_values=None,
-        use_cache=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        return_logits=False,            
-        is_decoder=True,
-        reduction='mean',
-        mode='multimodal', 
-    ):
-        r"""
-        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
-            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
-            the model is configured as a decoder.
-        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
-            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
-            ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
-            ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
-        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
-            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
-            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
-            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
-            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
-        use_cache (:obj:`bool`, `optional`):
-            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
-            decoding (see :obj:`past_key_values`).
-        Returns:
-        Example::
-            >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
-            >>> import torch
-            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
-            >>> config = BertConfig.from_pretrained("bert-base-cased")
-            >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
-            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-            >>> outputs = model(**inputs)
-            >>> prediction_logits = outputs.logits
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        if labels is not None:
-            use_cache = False
-
-        outputs = self.bert(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            is_decoder=is_decoder,
-            mode=mode,
-        )
-        
-        sequence_output = outputs[0]
-        prediction_scores = self.cls(sequence_output)
-        
-        if return_logits:
-            return prediction_scores[:, :-1, :].contiguous()  
-
-        lm_loss = None
-        if labels is not None:
-            # we are doing next-token prediction; shift prediction scores and input ids by one
-            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
-            labels = labels[:, 1:].contiguous()
-            loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) 
-            lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
-            if reduction=='none':
-                lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1)               
-
-        if not return_dict:
-            output = (prediction_scores,) + outputs[2:]
-            return ((lm_loss,) + output) if lm_loss is not None else output
-
-        return CausalLMOutputWithCrossAttentions(
-            loss=lm_loss,
-            logits=prediction_scores,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            cross_attentions=outputs.cross_attentions,
-        )
-
-    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
-        input_shape = input_ids.shape
-        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
-        if attention_mask is None:
-            attention_mask = input_ids.new_ones(input_shape)
-
-        # cut decoder_input_ids if past is used
-        if past is not None:
-            input_ids = input_ids[:, -1:]
-
-        return {
-            "input_ids": input_ids, 
-            "attention_mask": attention_mask, 
-            "past_key_values": past,
-            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
-            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
-            "is_decoder": True,
-        }
-
-    def _reorder_cache(self, past, beam_idx):
-        reordered_past = ()
-        for layer_past in past:
-            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
-        return reordered_past

diffsynth/extensions/ImageQualityMetric/BLIP/vit.py

@@ -1,301 +0,0 @@
-'''
- * Adapted from BLIP (https://github.com/salesforce/BLIP)
- * Based on timm code base
- * https://github.com/rwightman/pytorch-image-models/tree/master/timm
-'''
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from functools import partial
-
-from timm.models.vision_transformer import _cfg, PatchEmbed
-from timm.models.registry import register_model
-from timm.models.layers import trunc_normal_, DropPath
-from timm.models.helpers import named_apply, adapt_input_conv
-
-# from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
-
-class Mlp(nn.Module):
-    """ MLP as used in Vision Transformer, MLP-Mixer and related networks
-    """
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
-        self.scale = qk_scale or head_dim ** -0.5
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-        self.attn_gradients = None
-        self.attention_map = None
-        
-    def save_attn_gradients(self, attn_gradients):
-        self.attn_gradients = attn_gradients
-        
-    def get_attn_gradients(self):
-        return self.attn_gradients
-    
-    def save_attention_map(self, attention_map):
-        self.attention_map = attention_map
-        
-    def get_attention_map(self):
-        return self.attention_map
-    
-    def forward(self, x, register_hook=False):
-        B, N, C = x.shape
-        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
-
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-                
-        if register_hook:
-            self.save_attention_map(attn)
-            attn.register_hook(self.save_attn_gradients)        
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class Block(nn.Module):
-
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False):
-        super().__init__()
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        # if use_grad_checkpointing:
-        #     self.attn = checkpoint_wrapper(self.attn)
-        #     self.mlp = checkpoint_wrapper(self.mlp)
-
-    def forward(self, x, register_hook=False):
-        x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        return x
-
-    
-class VisionTransformer(nn.Module):
-    """ Vision Transformer
-    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`  -
-        https://arxiv.org/abs/2010.11929
-    """
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
-                 num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None, 
-                 use_grad_checkpointing=False, ckpt_layer=0):
-        """
-        Args:
-            img_size (int, tuple): input image size
-            patch_size (int, tuple): patch size
-            in_chans (int): number of input channels
-            num_classes (int): number of classes for classification head
-            embed_dim (int): embedding dimension
-            depth (int): depth of transformer
-            num_heads (int): number of attention heads
-            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
-            qkv_bias (bool): enable bias for qkv if True
-            qk_scale (float): override default qk scale of head_dim ** -0.5 if set
-            representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
-            drop_rate (float): dropout rate
-            attn_drop_rate (float): attention dropout rate
-            drop_path_rate (float): stochastic depth rate
-            norm_layer: (nn.Module): normalization layer
-        """
-        super().__init__()
-        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
-        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
-
-        num_patches = self.patch_embed.num_patches
-
-        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
-        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
-        self.blocks = nn.ModuleList([
-            Block(
-                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
-                use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer)
-            )
-            for i in range(depth)])
-        self.norm = norm_layer(embed_dim)
-
-        trunc_normal_(self.pos_embed, std=.02)
-        trunc_normal_(self.cls_token, std=.02)
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'pos_embed', 'cls_token'}
-
-    def forward(self, x, register_blk=-1):
-        B = x.shape[0]
-        x = self.patch_embed(x)
-
-        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
-        x = torch.cat((cls_tokens, x), dim=1)
-  
-        x = x + self.pos_embed[:,:x.size(1),:]
-        x = self.pos_drop(x)
-
-        for i,blk in enumerate(self.blocks):
-            x = blk(x, register_blk==i)
-        x = self.norm(x)
-        
-        return x
-
-    @torch.jit.ignore()
-    def load_pretrained(self, checkpoint_path, prefix=''):
-        _load_weights(self, checkpoint_path, prefix)
-        
-
-@torch.no_grad()
-def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''):
-    """ Load weights from .npz checkpoints for official Google Brain Flax implementation
-    """
-    import numpy as np
-
-    def _n2p(w, t=True):
-        if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
-            w = w.flatten()
-        if t:
-            if w.ndim == 4:
-                w = w.transpose([3, 2, 0, 1])
-            elif w.ndim == 3:
-                w = w.transpose([2, 0, 1])
-            elif w.ndim == 2:
-                w = w.transpose([1, 0])
-        return torch.from_numpy(w)
-
-    w = np.load(checkpoint_path)
-    if not prefix and 'opt/target/embedding/kernel' in w:
-        prefix = 'opt/target/'
-
-    if hasattr(model.patch_embed, 'backbone'):
-        # hybrid
-        backbone = model.patch_embed.backbone
-        stem_only = not hasattr(backbone, 'stem')
-        stem = backbone if stem_only else backbone.stem
-        stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel'])))
-        stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale']))
-        stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias']))
-        if not stem_only:
-            for i, stage in enumerate(backbone.stages):
-                for j, block in enumerate(stage.blocks):
-                    bp = f'{prefix}block{i + 1}/unit{j + 1}/'
-                    for r in range(3):
-                        getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel']))
-                        getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale']))
-                        getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias']))
-                    if block.downsample is not None:
-                        block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel']))
-                        block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale']))
-                        block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias']))
-        embed_conv_w = _n2p(w[f'{prefix}embedding/kernel'])
-    else:
-        embed_conv_w = adapt_input_conv(
-            model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel']))
-    model.patch_embed.proj.weight.copy_(embed_conv_w)
-    model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))
-    model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False))
-    pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False)
-    if pos_embed_w.shape != model.pos_embed.shape:
-        pos_embed_w = resize_pos_embed(  # resize pos embedding when different size from pretrained weights
-            pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)
-    model.pos_embed.copy_(pos_embed_w)
-    model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))
-    model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))
-#     if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
-#         model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
-#         model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
-#     if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
-#         model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
-#         model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
-    for i, block in enumerate(model.blocks.children()):
-        block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
-        mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/'
-        block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))
-        block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))
-        block.attn.qkv.weight.copy_(torch.cat([
-            _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')]))
-        block.attn.qkv.bias.copy_(torch.cat([
-            _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')]))
-        block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))
-        block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))
-        for r in range(2):
-            getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel']))
-            getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias']))
-        block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale']))
-        block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias']))
-
-            
-def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder):        
-    # interpolate position embedding
-    embedding_size = pos_embed_checkpoint.shape[-1]
-    num_patches = visual_encoder.patch_embed.num_patches
-    num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches
-    # height (== width) for the checkpoint position embedding
-    orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
-    # height (== width) for the new position embedding
-    new_size = int(num_patches ** 0.5)
-
-    if orig_size!=new_size:
-        # class_token and dist_token are kept unchanged
-        extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
-        # only the position tokens are interpolated
-        pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
-        pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
-        pos_tokens = torch.nn.functional.interpolate(
-            pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
-        pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
-        new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
-        print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2))
-        
-        return new_pos_embed    
-    else:
-        return pos_embed_checkpoint

diffsynth/extensions/ImageQualityMetric/__init__.py

@@ -1,148 +0,0 @@
-from modelscope import snapshot_download
-from typing_extensions import Literal, TypeAlias
-import os
-from diffsynth.extensions.ImageQualityMetric.aesthetic import AestheticScore
-from diffsynth.extensions.ImageQualityMetric.imagereward import ImageRewardScore
-from diffsynth.extensions.ImageQualityMetric.pickscore import PickScore
-from diffsynth.extensions.ImageQualityMetric.clip import CLIPScore
-from diffsynth.extensions.ImageQualityMetric.hps import HPScore_v2
-from diffsynth.extensions.ImageQualityMetric.mps import MPScore
-
-
-preference_model_id: TypeAlias = Literal[
-    "ImageReward",
-    "Aesthetic",
-    "PickScore",
-    "CLIP",
-    "HPSv2",
-    "HPSv2.1",
-    "MPS",
-]
-model_dict = {
-    "ImageReward": {
-        "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained",
-        "allow_file_pattern": [
-            "ImageReward/ImageReward.safetensors",
-            "ImageReward/med_config.json",
-            "bert-base-uncased/config.json",
-            "bert-base-uncased/model.safetensors",
-            "bert-base-uncased/tokenizer.json",
-            "bert-base-uncased/tokenizer_config.json",
-            "bert-base-uncased/vocab.txt",
-        ],
-        "load_path": {
-            "imagereward": "ImageReward/ImageReward.safetensors",
-            "med_config": "ImageReward/med_config.json",
-            "bert_model_path": "bert-base-uncased",
-        },
-        "model_class": ImageRewardScore
-    },
-    "Aesthetic": {
-        "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained",
-        "allow_file_pattern": [
-            "aesthetic-predictor/sac+logos+ava1-l14-linearMSE.safetensors",
-            "clip-vit-large-patch14/config.json",
-            "clip-vit-large-patch14/merges.txt",
-            "clip-vit-large-patch14/model.safetensors",
-            "clip-vit-large-patch14/preprocessor_config.json",
-            "clip-vit-large-patch14/special_tokens_map.json",
-            "clip-vit-large-patch14/tokenizer.json",
-            "clip-vit-large-patch14/tokenizer_config.json",
-            "clip-vit-large-patch14/vocab.json",
-        ],
-        "load_path": {
-            "aesthetic_predictor": "aesthetic-predictor/sac+logos+ava1-l14-linearMSE.safetensors",
-            "clip-large": "clip-vit-large-patch14",
-        },
-        "model_class": AestheticScore
-    },
-    "PickScore": {
-        "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained",
-        "allow_file_pattern": [
-            "PickScore_v1/*",
-            "CLIP-ViT-H-14-laion2B-s32B-b79K/config.json",
-            "CLIP-ViT-H-14-laion2B-s32B-b79K/merges.txt",
-            "CLIP-ViT-H-14-laion2B-s32B-b79K/preprocessor_config.json",
-            "CLIP-ViT-H-14-laion2B-s32B-b79K/special_tokens_map.json",
-            "CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer.json",
-            "CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer_config.json",
-            "CLIP-ViT-H-14-laion2B-s32B-b79K/vocab.json",
-        ],
-        "load_path": {
-            "pickscore": "PickScore_v1",
-            "clip": "CLIP-ViT-H-14-laion2B-s32B-b79K",
-        },
-        "model_class": PickScore
-    },
-    "CLIP": {
-        "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained",
-        "allow_file_pattern": [
-            "CLIP-ViT-H-14-laion2B-s32B-b79K/open_clip_pytorch_model.bin",
-            "bpe_simple_vocab_16e6.txt.gz",
-        ],
-        "load_path": {
-            "open_clip": "CLIP-ViT-H-14-laion2B-s32B-b79K/open_clip_pytorch_model.bin",
-            "open_clip_bpe": "bpe_simple_vocab_16e6.txt.gz",
-        },
-        "model_class": CLIPScore
-    },
-    "HPSv2": {
-        "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained",
-        "allow_file_pattern": [
-            "HPS_v2/HPS_v2_compressed.safetensors",
-            "bpe_simple_vocab_16e6.txt.gz",
-        ],
-        "load_path": {
-            "hpsv2": "HPS_v2/HPS_v2_compressed.safetensors",
-            "open_clip_bpe": "bpe_simple_vocab_16e6.txt.gz",
-        },
-        "model_class": HPScore_v2,
-        "extra_kwargs": {"model_version": "v2"}
-    },
-    "HPSv2.1": {
-        "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained",
-        "allow_file_pattern": [
-            "HPS_v2/HPS_v2.1_compressed.safetensors",
-            "bpe_simple_vocab_16e6.txt.gz",
-        ],
-        "load_path": {
-            "hpsv2.1": "HPS_v2/HPS_v2.1_compressed.safetensors",
-            "open_clip_bpe": "bpe_simple_vocab_16e6.txt.gz",
-        },
-        "model_class": HPScore_v2,
-        "extra_kwargs": {"model_version": "v21"}
-    },
-    "MPS": {
-        "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained",
-        "allow_file_pattern": [
-            "MPS_overall_checkpoint/MPS_overall_checkpoint_diffsynth.safetensors",
-            "CLIP-ViT-H-14-laion2B-s32B-b79K/config.json",
-            "CLIP-ViT-H-14-laion2B-s32B-b79K/merges.txt",
-            "CLIP-ViT-H-14-laion2B-s32B-b79K/preprocessor_config.json",
-            "CLIP-ViT-H-14-laion2B-s32B-b79K/special_tokens_map.json",
-            "CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer.json",
-            "CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer_config.json",
-            "CLIP-ViT-H-14-laion2B-s32B-b79K/vocab.json",
-        ],
-        "load_path": {
-            "mps": "MPS_overall_checkpoint/MPS_overall_checkpoint_diffsynth.safetensors",
-            "clip": "CLIP-ViT-H-14-laion2B-s32B-b79K",
-        },
-        "model_class": MPScore
-    },
-}
-
-
-def download_preference_model(model_name: preference_model_id, cache_dir="models"):
-    metadata = model_dict[model_name]
-    snapshot_download(model_id=metadata["model_id"], allow_file_pattern=metadata["allow_file_pattern"], cache_dir=cache_dir)
-    load_path = metadata["load_path"]
-    load_path = {key: os.path.join(cache_dir, metadata["model_id"], path) for key, path in load_path.items()}
-    return load_path
-
-
-def load_preference_model(model_name: preference_model_id, device = "cuda", path = None):
-    model_class = model_dict[model_name]["model_class"]
-    extra_kwargs = model_dict[model_name].get("extra_kwargs", {})
-    preference_model = model_class(device=device, path=path, **extra_kwargs)
-    return preference_model

diffsynth/extensions/ImageQualityMetric/aesthetic.py

@@ -1,148 +0,0 @@
-from typing import List, Optional
-from PIL import Image
-import torch
-from transformers import AutoProcessor, AutoModel
-from safetensors.torch import load_file
-import os
-from typing import Union, List
-from .config import MODEL_PATHS
-
-class MLP(torch.nn.Module):
-    def __init__(self, input_size: int, xcol: str = "emb", ycol: str = "avg_rating"):
-        super().__init__()
-        self.input_size = input_size
-        self.xcol = xcol
-        self.ycol = ycol
-        self.layers = torch.nn.Sequential(
-            torch.nn.Linear(self.input_size, 1024),
-            #torch.nn.ReLU(),
-            torch.nn.Dropout(0.2),
-            torch.nn.Linear(1024, 128),
-            #torch.nn.ReLU(),
-            torch.nn.Dropout(0.2),
-            torch.nn.Linear(128, 64),
-            #torch.nn.ReLU(),
-            torch.nn.Dropout(0.1),
-            torch.nn.Linear(64, 16),
-            #torch.nn.ReLU(),
-            torch.nn.Linear(16, 1),
-        )
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.layers(x)
-
-    def training_step(self, batch: dict, batch_idx: int) -> torch.Tensor:
-        x = batch[self.xcol]
-        y = batch[self.ycol].reshape(-1, 1)
-        x_hat = self.layers(x)
-        loss = torch.nn.functional.mse_loss(x_hat, y)
-        return loss
-
-    def validation_step(self, batch: dict, batch_idx: int) -> torch.Tensor:
-        x = batch[self.xcol]
-        y = batch[self.ycol].reshape(-1, 1)
-        x_hat = self.layers(x)
-        loss = torch.nn.functional.mse_loss(x_hat, y)
-        return loss
-
-    def configure_optimizers(self) -> torch.optim.Optimizer:
-        return torch.optim.Adam(self.parameters(), lr=1e-3)
-
-
-class AestheticScore(torch.nn.Module):
-    def __init__(self, device: torch.device, path: str = MODEL_PATHS):
-        super().__init__()
-        self.device = device
-        self.aes_model_path = path.get("aesthetic_predictor")
-        # Load the MLP model
-        self.model = MLP(768)
-        try:
-            if self.aes_model_path.endswith(".safetensors"):
-                state_dict = load_file(self.aes_model_path)
-            else:
-                state_dict = torch.load(self.aes_model_path)
-            self.model.load_state_dict(state_dict)
-        except Exception as e:
-            raise ValueError(f"Error loading model weights from {self.aes_model_path}: {e}")
-
-        self.model.to(device)
-        self.model.eval()
-
-        # Load the CLIP model and processor
-        clip_model_name = path.get('clip-large')
-        self.model2 = AutoModel.from_pretrained(clip_model_name).eval().to(device)
-        self.processor = AutoProcessor.from_pretrained(clip_model_name)
-
-    def _calculate_score(self, image: torch.Tensor) -> float:
-        """Calculate the aesthetic score for a single image.
-
-        Args:
-            image (torch.Tensor): The processed image tensor.
-
-        Returns:
-            float: The aesthetic score.
-        """
-        with torch.no_grad():
-            # Get image embeddings
-            image_embs = self.model2.get_image_features(image)
-            image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True)
-
-            # Compute score
-            score = self.model(image_embs).cpu().flatten().item()
-
-        return score
-
-    @torch.no_grad()
-    def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str = "") -> List[float]:
-        """Score the images based on their aesthetic quality.
-
-        Args:
-            images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s).
-
-        Returns:
-            List[float]: List of scores for the images.
-        """
-        try:
-            if isinstance(images, (str, Image.Image)):
-                # Single image
-                if isinstance(images, str):
-                    pil_image = Image.open(images)
-                else:
-                    pil_image = images
-                
-                # Prepare image inputs
-                image_inputs = self.processor(
-                    images=pil_image,
-                    padding=True,
-                    truncation=True,
-                    max_length=77,
-                    return_tensors="pt",
-                ).to(self.device)
-
-                return [self._calculate_score(image_inputs["pixel_values"])]
-            elif isinstance(images, list):
-                # Multiple images
-                scores = []
-                for one_image in images:
-                    if isinstance(one_image, str):
-                        pil_image = Image.open(one_image)
-                    elif isinstance(one_image, Image.Image):
-                        pil_image = one_image
-                    else:
-                        raise TypeError("The type of parameter images is illegal.")
-                    
-                    # Prepare image inputs
-                    image_inputs = self.processor(
-                        images=pil_image,
-                        padding=True,
-                        truncation=True,
-                        max_length=77,
-                        return_tensors="pt",
-                    ).to(self.device)
-
-                    scores.append(self._calculate_score(image_inputs["pixel_values"]))
-                return scores
-            else:
-                raise TypeError("The type of parameter images is illegal.")
-        except Exception as e:
-            raise RuntimeError(f"Error in scoring images: {e}")

diffsynth/extensions/ImageQualityMetric/clip.py

@@ -1,97 +0,0 @@
-from typing import List, Union
-from PIL import Image
-import torch
-from .open_clip import create_model_and_transforms, get_tokenizer
-from .config import MODEL_PATHS
-
-class CLIPScore(torch.nn.Module):
-    def __init__(self, device: torch.device, path: str = MODEL_PATHS):
-        super().__init__()
-        """Initialize the CLIPScore with a model and tokenizer.
-        
-        Args:
-            device (torch.device): The device to load the model on.
-        """
-        self.device = device
-
-        # Create model and transforms
-        self.model, _, self.preprocess_val = create_model_and_transforms(
-            "ViT-H-14",
-            # "laion2B-s32B-b79K",
-            pretrained=path.get("open_clip"),
-            precision="amp",
-            device=device,
-            jit=False,
-            force_quick_gelu=False,
-            force_custom_text=False,
-            force_patch_dropout=False,
-            force_image_size=None,
-            pretrained_image=False,
-            image_mean=None,
-            image_std=None,
-            light_augmentation=True,
-            aug_cfg={},
-            output_dict=True,
-            with_score_predictor=False,
-            with_region_predictor=False,
-        )
-
-        # Initialize tokenizer
-        self.tokenizer = get_tokenizer("ViT-H-14", path["open_clip_bpe"])
-        self.model = self.model.to(device)
-        self.model.eval()
-
-    def _calculate_score(self, image: torch.Tensor, prompt: str) -> float:
-        """Calculate the CLIP score for a single image and prompt.
-
-        Args:
-            image (torch.Tensor): The processed image tensor.
-            prompt (str): The prompt text.
-
-        Returns:
-            float: The CLIP score.
-        """
-        with torch.no_grad():
-            # Process the prompt
-            text = self.tokenizer([prompt]).to(device=self.device, non_blocking=True)
-
-            # Calculate the CLIP score
-            outputs = self.model(image, text)
-            image_features, text_features = outputs["image_features"], outputs["text_features"]
-            logits_per_image = image_features @ text_features.T
-            clip_score = torch.diagonal(logits_per_image).cpu().numpy()
-
-        return clip_score[0].item()
-
-    @torch.no_grad()
-    def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]:
-        """Score the images based on the prompt.
-
-        Args:
-            images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s).
-            prompt (str): The prompt text.
-
-        Returns:
-            List[float]: List of CLIP scores for the images.
-        """
-        if isinstance(images, (str, Image.Image)):
-            # Single image
-            if isinstance(images, str):
-                image = self.preprocess_val(Image.open(images)).unsqueeze(0).to(device=self.device, non_blocking=True)
-            else:
-                image = self.preprocess_val(images).unsqueeze(0).to(device=self.device, non_blocking=True)
-            return [self._calculate_score(image, prompt)]
-        elif isinstance(images, list):
-            # Multiple images
-            scores = []
-            for one_images in images:
-                if isinstance(one_images, str):
-                    image = self.preprocess_val(Image.open(one_images)).unsqueeze(0).to(device=self.device, non_blocking=True)
-                elif isinstance(one_images, Image.Image):
-                    image = self.preprocess_val(one_images).unsqueeze(0).to(device=self.device, non_blocking=True)
-                else:
-                    raise TypeError("The type of parameter images is illegal.")
-                scores.append(self._calculate_score(image, prompt))
-            return scores
-        else:
-            raise TypeError("The type of parameter images is illegal.")

diffsynth/extensions/ImageQualityMetric/config.py

@@ -1,23 +0,0 @@
-import os
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-project_root = os.path.abspath(os.path.join(current_dir, '../../../'))
-model_path = os.path.join(project_root, 'models', 'QualityMetric')
-
-
-def get_model_path(model_name):
-    return os.path.join(model_path, model_name)
-
-
-MODEL_PATHS = {
-    "aesthetic_predictor": get_model_path("aesthetic-predictor/sac+logos+ava1-l14-linearMSE.safetensors"),
-    "open_clip": get_model_path("CLIP-ViT-H-14-laion2B-s32B-b79K/open_clip_pytorch_model.bin"),
-    "hpsv2": get_model_path("HPS_v2/HPS_v2_compressed.safetensors"),
-    "hpsv2.1": get_model_path("HPS_v2/HPS_v2.1_compressed.safetensors"),
-    "imagereward": get_model_path("ImageReward/ImageReward.safetensors"),
-    "med_config": get_model_path("ImageReward/med_config.json"),
-    "clip": get_model_path("CLIP-ViT-H-14-laion2B-s32B-b79K"),
-    "clip-large": get_model_path("clip-vit-large-patch14"),
-    "mps": get_model_path("MPS_overall_checkpoint/MPS_overall_checkpoint_diffsynth.safetensors"),
-    "pickscore": get_model_path("PickScore_v1")
-}

diffsynth/extensions/ImageQualityMetric/hps.py

@@ -1,118 +0,0 @@
-from typing import List, Union
-from PIL import Image
-import torch
-from .open_clip import create_model_and_transforms, get_tokenizer
-from safetensors.torch import load_file
-import os
-from .config import MODEL_PATHS
-
-class HPScore_v2(torch.nn.Module):
-    def __init__(self, device: torch.device, path: str = MODEL_PATHS, model_version: str = "v2"):
-        super().__init__()
-        """Initialize the Selector with a model and tokenizer.
-
-        Args:
-            device (torch.device): The device to load the model on.
-            model_version (str): The version of the model to load. Supports "v2" or "v21". Default is "v2".
-        """
-        self.device = device
-
-        if model_version == "v2":
-            safetensors_path = path.get("hpsv2")
-        elif model_version == "v21":
-            safetensors_path = path.get("hpsv2.1")
-        else:
-            raise ValueError(f"Unsupported model version: {model_version}. Choose 'v2' or 'v21'.")
-
-        # Create model and transforms
-        model, _, self.preprocess_val = create_model_and_transforms(
-            "ViT-H-14",
-            # "laion2B-s32B-b79K",
-            pretrained=path.get("open_clip"),
-            precision="amp",
-            device=device,
-            jit=False,
-            force_quick_gelu=False,
-            force_custom_text=False,
-            force_patch_dropout=False,
-            force_image_size=None,
-            pretrained_image=False,
-            image_mean=None,
-            image_std=None,
-            light_augmentation=True,
-            aug_cfg={},
-            output_dict=True,
-            with_score_predictor=False,
-            with_region_predictor=False,
-        )
-
-        # Load model weights
-        try:
-            state_dict = load_file(safetensors_path)
-            model.load_state_dict(state_dict)
-        except Exception as e:
-            raise ValueError(f"Error loading model weights from {safetensors_path}: {e}")
-
-        # Initialize tokenizer and model
-        self.tokenizer = get_tokenizer("ViT-H-14", path["open_clip_bpe"])
-        model = model.to(device)
-        model.eval()
-        self.model = model
-
-    def _calculate_score(self, image: torch.Tensor, prompt: str) -> float:
-        """Calculate the HPS score for a single image and prompt.
-
-        Args:
-            image (torch.Tensor): The processed image tensor.
-            prompt (str): The prompt text.
-
-        Returns:
-            float: The HPS score.
-        """
-        with torch.no_grad():
-            # Process the prompt
-            text = self.tokenizer([prompt]).to(device=self.device, non_blocking=True)
-
-            # Calculate the HPS score
-            outputs = self.model(image, text)
-            image_features, text_features = outputs["image_features"], outputs["text_features"]
-            logits_per_image = image_features @ text_features.T
-            hps_score = torch.diagonal(logits_per_image).cpu().numpy()
-
-        return hps_score[0].item()
-
-    @torch.no_grad()
-    def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]:
-        """Score the images based on the prompt.
-
-        Args:
-            images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s).
-            prompt (str): The prompt text.
-
-        Returns:
-            List[float]: List of HPS scores for the images.
-        """
-        try:
-            if isinstance(images, (str, Image.Image)):
-                # Single image
-                if isinstance(images, str):
-                    image = self.preprocess_val(Image.open(images)).unsqueeze(0).to(device=self.device, non_blocking=True)
-                else:
-                    image = self.preprocess_val(images).unsqueeze(0).to(device=self.device, non_blocking=True)
-                return [self._calculate_score(image, prompt)]
-            elif isinstance(images, list):
-                # Multiple images
-                scores = []
-                for one_images in images:
-                    if isinstance(one_images, str):
-                        image = self.preprocess_val(Image.open(one_images)).unsqueeze(0).to(device=self.device, non_blocking=True)
-                    elif isinstance(one_images, Image.Image):
-                        image = self.preprocess_val(one_images).unsqueeze(0).to(device=self.device, non_blocking=True)
-                    else:
-                        raise TypeError("The type of parameter images is illegal.")
-                    scores.append(self._calculate_score(image, prompt))
-                return scores
-            else:
-                raise TypeError("The type of parameter images is illegal.")
-        except Exception as e:
-            raise RuntimeError(f"Error in scoring images: {e}")

diffsynth/extensions/ImageQualityMetric/imagereward.py

@@ -1,212 +0,0 @@
-import os
-import torch
-from PIL import Image
-from typing import List, Union
-from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
-from .BLIP.blip_pretrain import BLIP_Pretrain
-from torchvision.transforms import InterpolationMode
-from safetensors.torch import load_file
-from .config import MODEL_PATHS
-BICUBIC = InterpolationMode.BICUBIC
-
-def _convert_image_to_rgb(image):
-    return image.convert("RGB")
-
-def _transform(n_px):
-    return Compose([
-        Resize(n_px, interpolation=BICUBIC),
-        CenterCrop(n_px),
-        _convert_image_to_rgb,
-        ToTensor(),
-        Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
-    ])
-
-class MLP(torch.nn.Module):
-    def __init__(self, input_size):
-        super().__init__()
-        self.input_size = input_size
-        
-        self.layers = torch.nn.Sequential(
-            torch.nn.Linear(self.input_size, 1024),
-            #nn.ReLU(),
-            torch.nn.Dropout(0.2),
-            torch.nn.Linear(1024, 128),
-            #nn.ReLU(),
-            torch.nn.Dropout(0.2),
-            torch.nn.Linear(128, 64),
-            #nn.ReLU(),
-            torch.nn.Dropout(0.1),
-            torch.nn.Linear(64, 16),
-            #nn.ReLU(),
-            torch.nn.Linear(16, 1)
-        )
-        
-        # initial MLP param
-        for name, param in self.layers.named_parameters():
-            if 'weight' in name:
-                torch.nn.init.normal_(param, mean=0.0, std=1.0/(self.input_size+1))
-            if 'bias' in name:
-                torch.nn.init.constant_(param, val=0)
-        
-    def forward(self, input):
-        return self.layers(input)
-
-class ImageReward(torch.nn.Module):
-    def __init__(self, med_config, device='cpu', bert_model_path=""):
-        super().__init__()
-        self.device = device
-        
-        self.blip = BLIP_Pretrain(image_size=224, vit='large', med_config=med_config, bert_model_path=bert_model_path)
-        self.preprocess = _transform(224)
-        self.mlp = MLP(768)
-        
-        self.mean = 0.16717362830052426
-        self.std = 1.0333394966054072
-
-    def score_grad(self, prompt_ids, prompt_attention_mask, image):
-        """Calculate the score with gradient for a single image and prompt.
-
-        Args:
-            prompt_ids (torch.Tensor): Tokenized prompt IDs.
-            prompt_attention_mask (torch.Tensor): Attention mask for the prompt.
-            image (torch.Tensor): The processed image tensor.
-
-        Returns:
-            torch.Tensor: The reward score.
-        """
-        image_embeds = self.blip.visual_encoder(image)
-        image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.device)
-        text_output = self.blip.text_encoder(
-            prompt_ids,
-            attention_mask=prompt_attention_mask,
-            encoder_hidden_states=image_embeds,
-            encoder_attention_mask=image_atts,
-            return_dict=True,
-        )
-        txt_features = text_output.last_hidden_state[:, 0, :]
-        rewards = self.mlp(txt_features)
-        rewards = (rewards - self.mean) / self.std
-        return rewards
-
-    def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str = "") -> List[float]:
-        """Score the images based on the prompt.
-
-        Args:
-            prompt (str): The prompt text.
-            images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s).
-
-        Returns:
-            List[float]: List of scores for the images.
-        """
-        if isinstance(images, (str, Image.Image)):
-            # Single image
-            if isinstance(images, str):
-                pil_image = Image.open(images)
-            else:
-                pil_image = images
-            image = self.preprocess(pil_image).unsqueeze(0).to(self.device)
-            return [self._calculate_score(prompt, image).item()]
-        elif isinstance(images, list):
-            # Multiple images
-            scores = []
-            for one_image in images:
-                if isinstance(one_image, str):
-                    pil_image = Image.open(one_image)
-                elif isinstance(one_image, Image.Image):
-                    pil_image = one_image
-                else:
-                    raise TypeError("The type of parameter images is illegal.")
-                image = self.preprocess(pil_image).unsqueeze(0).to(self.device)
-                scores.append(self._calculate_score(prompt, image).item())
-            return scores
-        else:
-            raise TypeError("The type of parameter images is illegal.")
-
-    def _calculate_score(self, prompt: str, image: torch.Tensor) -> torch.Tensor:
-        """Calculate the score for a single image and prompt.
-
-        Args:
-            prompt (str): The prompt text.
-            image (torch.Tensor): The processed image tensor.
-
-        Returns:
-            torch.Tensor: The reward score.
-        """
-        text_input = self.blip.tokenizer(prompt, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(self.device)
-        image_embeds = self.blip.visual_encoder(image)
-        image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.device)
-        text_output = self.blip.text_encoder(
-            text_input.input_ids,
-            attention_mask=text_input.attention_mask,
-            encoder_hidden_states=image_embeds,
-            encoder_attention_mask=image_atts,
-            return_dict=True,
-        )
-        txt_features = text_output.last_hidden_state[:, 0, :].float()
-        rewards = self.mlp(txt_features)
-        rewards = (rewards - self.mean) / self.std
-        return rewards
-
-    def inference_rank(self, prompt: str, generations_list: List[Union[str, Image.Image]]) -> tuple:
-        """Rank the images based on the prompt.
-
-        Args:
-            prompt (str): The prompt text.
-            generations_list (List[Union[str, Image.Image]]): List of image paths or PIL images.
-
-        Returns:
-            tuple: (indices, rewards) where indices are the ranks and rewards are the scores.
-        """
-        text_input = self.blip.tokenizer(prompt, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(self.device)
-        txt_set = []
-        for generation in generations_list:
-            if isinstance(generation, str):
-                pil_image = Image.open(generation)
-            elif isinstance(generation, Image.Image):
-                pil_image = generation
-            else:
-                raise TypeError("The type of parameter generations_list is illegal.")
-            image = self.preprocess(pil_image).unsqueeze(0).to(self.device)
-            image_embeds = self.blip.visual_encoder(image)
-            image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.device)
-            text_output = self.blip.text_encoder(
-                text_input.input_ids,
-                attention_mask=text_input.attention_mask,
-                encoder_hidden_states=image_embeds,
-                encoder_attention_mask=image_atts,
-                return_dict=True,
-            )
-            txt_set.append(text_output.last_hidden_state[:, 0, :])
-        txt_features = torch.cat(txt_set, 0).float()
-        rewards = self.mlp(txt_features)
-        rewards = (rewards - self.mean) / self.std
-        rewards = torch.squeeze(rewards)
-        _, rank = torch.sort(rewards, dim=0, descending=True)
-        _, indices = torch.sort(rank, dim=0)
-        indices = indices + 1
-        return indices.detach().cpu().numpy().tolist(), rewards.detach().cpu().numpy().tolist()
-
-
-class ImageRewardScore(torch.nn.Module):
-    def __init__(self, device: Union[str, torch.device], path: str = MODEL_PATHS):
-        super().__init__()
-        self.device = device if isinstance(device, torch.device) else torch.device(device)
-        model_path = path.get("imagereward")
-        med_config = path.get("med_config")
-        state_dict = load_file(model_path)
-        self.model = ImageReward(device=self.device, med_config=med_config, bert_model_path=path.get("bert_model_path")).to(self.device)
-        self.model.load_state_dict(state_dict, strict=False)
-        self.model.eval()
-
-    @torch.no_grad()
-    def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]:
-        """Score the images based on the prompt.
-
-        Args:
-            images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s).
-            prompt (str): The prompt text.
-
-        Returns:
-            List[float]: List of scores for the images.
-        """
-        return self.model.score(images, prompt)

diffsynth/extensions/ImageQualityMetric/mps.py

@@ -1,129 +0,0 @@
-import numpy as np
-import torch
-from PIL import Image
-from io import BytesIO
-from tqdm.auto import tqdm
-from transformers import CLIPFeatureExtractor, CLIPImageProcessor
-from transformers import CLIPConfig
-from dataclasses import dataclass
-from transformers import CLIPModel as HFCLIPModel
-from safetensors.torch import load_file
-from torch import nn, einsum
-
-from .trainer.models.base_model import BaseModelConfig
-
-from transformers import CLIPConfig
-from transformers import AutoProcessor, AutoModel, AutoTokenizer
-from typing import Any, Optional, Tuple, Union, List
-import torch
-
-from .trainer.models.cross_modeling import Cross_model
-from .trainer.models import clip_model
-import torch.nn.functional as F
-import gc
-import json
-from .config import MODEL_PATHS
-
-class MPScore(torch.nn.Module):
-    def __init__(self, device: Union[str, torch.device], path: str = MODEL_PATHS, condition: str = 'overall'):
-        super().__init__()
-        """Initialize the MPSModel with a processor, tokenizer, and model.
-
-        Args:
-            device (Union[str, torch.device]): The device to load the model on.
-        """
-        self.device = device
-        processor_name_or_path = path.get("clip")
-        self.image_processor = CLIPImageProcessor.from_pretrained(processor_name_or_path)
-        self.tokenizer = AutoTokenizer.from_pretrained(processor_name_or_path, trust_remote_code=True)
-        self.model = clip_model.CLIPModel(processor_name_or_path, config_file=True)
-        state_dict = load_file(path.get("mps"))
-        self.model.load_state_dict(state_dict, strict=False)
-        self.model.to(device)
-        self.condition = condition
-
-    def _calculate_score(self, image: torch.Tensor, prompt: str) -> float:
-        """Calculate the reward score for a single image and prompt.
-
-        Args:
-            image (torch.Tensor): The processed image tensor.
-            prompt (str): The prompt text.
-
-        Returns:
-            float: The reward score.
-        """
-        def _tokenize(caption):
-            input_ids = self.tokenizer(
-                caption,
-                max_length=self.tokenizer.model_max_length,
-                padding="max_length",
-                truncation=True,
-                return_tensors="pt"
-            ).input_ids
-            return input_ids
-
-        text_input = _tokenize(prompt).to(self.device)
-        if self.condition == 'overall':
-            condition_prompt = 'light, color, clarity, tone, style, ambiance, artistry, shape, face, hair, hands, limbs, structure, instance, texture, quantity, attributes, position, number, location, word, things'
-        elif self.condition == 'aesthetics':
-            condition_prompt = 'light, color, clarity, tone, style, ambiance, artistry'
-        elif self.condition == 'quality':
-            condition_prompt = 'shape, face, hair, hands, limbs, structure, instance, texture'
-        elif self.condition == 'semantic':
-            condition_prompt = 'quantity, attributes, position, number, location'
-        else:
-            raise ValueError(
-                f"Unsupported condition: {self.condition}. Choose 'overall', 'aesthetics', 'quality', or 'semantic'.")
-        condition_batch = _tokenize(condition_prompt).repeat(text_input.shape[0], 1).to(self.device)
-
-        with torch.no_grad():
-            text_f, text_features = self.model.model.get_text_features(text_input)
-
-            image_f = self.model.model.get_image_features(image.half())
-            condition_f, _ = self.model.model.get_text_features(condition_batch)
-
-            sim_text_condition = einsum('b i d, b j d -> b j i', text_f, condition_f)
-            sim_text_condition = torch.max(sim_text_condition, dim=1, keepdim=True)[0]
-            sim_text_condition = sim_text_condition / sim_text_condition.max()
-            mask = torch.where(sim_text_condition > 0.3, 0, float('-inf'))
-            mask = mask.repeat(1, image_f.shape[1], 1)
-            image_features = self.model.cross_model(image_f, text_f, mask.half())[:, 0, :]
-
-            image_features = image_features / image_features.norm(dim=-1, keepdim=True)
-            text_features = text_features / text_features.norm(dim=-1, keepdim=True)
-            image_score = self.model.logit_scale.exp() * text_features @ image_features.T
-
-        return image_score[0].cpu().numpy().item()
-
-    @torch.no_grad()
-    def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]:
-        """Score the images based on the prompt.
-
-        Args:
-            images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s).
-            prompt (str): The prompt text.
-
-        Returns:
-            List[float]: List of reward scores for the images.
-        """
-        if isinstance(images, (str, Image.Image)):
-            # Single image
-            if isinstance(images, str):
-                image = self.image_processor(Image.open(images), return_tensors="pt")["pixel_values"].to(self.device)
-            else:
-                image = self.image_processor(images, return_tensors="pt")["pixel_values"].to(self.device)
-            return [self._calculate_score(image, prompt)]
-        elif isinstance(images, list):
-            # Multiple images
-            scores = []
-            for one_images in images:
-                if isinstance(one_images, str):
-                    image = self.image_processor(Image.open(one_images), return_tensors="pt")["pixel_values"].to(self.device)
-                elif isinstance(one_images, Image.Image):
-                    image = self.image_processor(one_images, return_tensors="pt")["pixel_values"].to(self.device)
-                else:
-                    raise TypeError("The type of parameter images is illegal.")
-                scores.append(self._calculate_score(image, prompt))
-            return scores
-        else:
-            raise TypeError("The type of parameter images is illegal.")

diffsynth/extensions/ImageQualityMetric/open_clip/__init__.py

@@ -1,14 +0,0 @@
-from .coca_model import CoCa
-from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
-from .factory import create_model, create_model_and_transforms, create_model_from_pretrained, get_tokenizer, create_loss
-from .factory import list_models, add_model_config, get_model_config, load_checkpoint
-from .loss import ClipLoss, DistillClipLoss, CoCaLoss
-from .model import CLIP, CustomTextCLIP, CLIPTextCfg, CLIPVisionCfg, \
-    convert_weights_to_lp, convert_weights_to_fp16, trace_model, get_cast_dtype
-from .openai import load_openai_model, list_openai_models
-from .pretrained import list_pretrained, list_pretrained_models_by_tag, list_pretrained_tags_by_model, \
-    get_pretrained_url, download_pretrained_from_url, is_pretrained_cfg, get_pretrained_cfg, download_pretrained
-from .push_to_hf_hub import push_pretrained_to_hf_hub, push_to_hf_hub
-from .tokenizer import SimpleTokenizer
-from .transform import image_transform, AugmentationCfg
-from .utils import freeze_batch_norm_2d

diffsynth/extensions/ImageQualityMetric/open_clip/coca_model.py

@@ -1,458 +0,0 @@
-from typing import Optional
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-import numpy as np
-from dataclasses import dataclass
-
-from .transformer import (
-    LayerNormFp32,
-    LayerNorm,
-    QuickGELU,
-    MultimodalTransformer,
-)
-from .model import CLIPTextCfg, CLIPVisionCfg, _build_vision_tower, _build_text_tower
-
-try:
-    from transformers import (
-        BeamSearchScorer,
-        LogitsProcessorList,
-        TopPLogitsWarper,
-        TopKLogitsWarper,
-        RepetitionPenaltyLogitsProcessor,
-        MinLengthLogitsProcessor,
-        MaxLengthCriteria,
-        StoppingCriteriaList
-    )
-
-    GENERATION_TYPES = {
-        "top_k": TopKLogitsWarper,
-        "top_p": TopPLogitsWarper,
-        "beam_search": "beam_search"
-    }
-    _has_transformers = True
-except ImportError as e:
-    GENERATION_TYPES = {
-        "top_k": None,
-        "top_p": None,
-        "beam_search": "beam_search"
-    }
-    _has_transformers = False
-
-
-@dataclass
-class MultimodalCfg(CLIPTextCfg):
-    mlp_ratio: int = 4
-    dim_head: int = 64
-    heads: int = 8
-    n_queries: int = 256
-    attn_pooler_heads: int = 8
-
-
-def _build_text_decoder_tower(
-        embed_dim,
-        multimodal_cfg,
-        quick_gelu: bool = False,
-        cast_dtype: Optional[torch.dtype] = None,
-):
-    multimodal_cfg = MultimodalCfg(**multimodal_cfg) if isinstance(multimodal_cfg, dict) else multimodal_cfg
-    act_layer = QuickGELU if quick_gelu else nn.GELU
-    norm_layer = (
-        LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm
-    )
-
-    decoder = MultimodalTransformer(
-        context_length=multimodal_cfg.context_length,
-        width=multimodal_cfg.width,
-        heads=multimodal_cfg.heads,
-        layers=multimodal_cfg.layers,
-        ls_init_value=multimodal_cfg.ls_init_value,
-        output_dim=embed_dim,
-        act_layer=act_layer,
-        norm_layer=norm_layer,
-    )
-
-    return decoder
-
-
-class CoCa(nn.Module):
-    def __init__(
-            self,
-            embed_dim,
-            multimodal_cfg: MultimodalCfg,
-            text_cfg: CLIPTextCfg,
-            vision_cfg: CLIPVisionCfg,
-            quick_gelu: bool = False,
-            cast_dtype: Optional[torch.dtype] = None,
-            pad_id: int = 0,
-    ):
-        super().__init__()
-        multimodal_cfg = MultimodalCfg(**multimodal_cfg) if isinstance(multimodal_cfg, dict) else multimodal_cfg
-        text_cfg = CLIPTextCfg(**text_cfg) if isinstance(text_cfg, dict) else text_cfg
-        vision_cfg = CLIPVisionCfg(**vision_cfg) if isinstance(vision_cfg, dict) else vision_cfg
-
-        self.text = _build_text_tower(
-            embed_dim=embed_dim,
-            text_cfg=text_cfg,
-            quick_gelu=quick_gelu,
-            cast_dtype=cast_dtype,
-        )
-
-        vocab_size = (
-            text_cfg.vocab_size  # for hf models
-            if hasattr(text_cfg, "hf_model_name") and text_cfg.hf_model_name is not None
-            else text_cfg.vocab_size
-        )
-
-        self.visual = _build_vision_tower(
-            embed_dim=embed_dim,
-            vision_cfg=vision_cfg,
-            quick_gelu=quick_gelu,
-            cast_dtype=cast_dtype,
-        )
-
-        self.text_decoder = _build_text_decoder_tower(
-            vocab_size,
-            multimodal_cfg=multimodal_cfg,
-            quick_gelu=quick_gelu,
-            cast_dtype=cast_dtype,
-        )
-
-        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-        self.pad_id = pad_id
-
-    @torch.jit.ignore
-    def set_grad_checkpointing(self, enable=True):
-        self.visual.set_grad_checkpointing(enable)
-        self.text.set_grad_checkpointing(enable)
-        self.text_decoder.set_grad_checkpointing(enable)
-
-    def _encode_image(self, images, normalize=True):
-        image_latent, tokens_embs = self.visual(images)
-        image_latent = F.normalize(image_latent, dim=-1) if normalize else image_latent
-        return image_latent, tokens_embs
-
-    def _encode_text(self, text, normalize=True, embed_cls=True):
-        text = text[:, :-1] if embed_cls else text # make space for CLS token
-        text_latent, token_emb = self.text(text)
-        text_latent = F.normalize(text_latent, dim=-1) if normalize else text_latent
-        return text_latent, token_emb
-
-    def encode_image(self, images, normalize=True):
-        image_latent, _ = self._encode_image(images, normalize=normalize)
-        return image_latent
-
-    def encode_text(self, text, normalize=True, embed_cls=True):
-        text_latent, _ = self._encode_text(text, normalize=normalize, embed_cls=embed_cls)
-        return text_latent
-
-    def forward(self, image, text, embed_cls=True, image_latent=None, image_embs=None):
-        text_latent, token_embs = self._encode_text(text, embed_cls=embed_cls)
-        if image_latent is None or image_embs is None:
-            image_latent, image_embs = self._encode_image(image)
-
-        # TODO: add assertion to avoid bugs?
-        labels = text[:, -token_embs.shape[1]:]
-
-        logits = self.text_decoder(image_embs, token_embs)
-        return {
-            "image_features": image_latent,
-            "text_features": text_latent,
-            "logits": logits,
-            "labels": labels,
-            "logit_scale": self.logit_scale.exp()
-        }
-
-    def generate(
-        self,
-        image,
-        text=None,
-        seq_len=30,
-        max_seq_len=77,
-        temperature=1.,
-        generation_type="beam_search",
-        top_p=0.1,  # keep tokens in the 1 - top_p quantile
-        top_k=1,  # keeps the top_k most probable tokens
-        pad_token_id=None,
-        eos_token_id=None,
-        sot_token_id=None,
-        num_beams=6,
-        num_beam_groups=3,
-        min_seq_len=5,
-        stopping_criteria=None,
-        repetition_penalty=1.0,
-        fixed_output_length=False # if True output.shape == (batch_size, seq_len)
-    ):
-        # taking many ideas and components from HuggingFace GenerationMixin
-        # https://huggingface.co/docs/transformers/main/en/main_classes/text_generation
-        assert _has_transformers, "Please install transformers for generate functionality. `pip install transformers`."
-        assert seq_len > min_seq_len, "seq_len must be larger than min_seq_len"
-
-        with torch.no_grad():
-            sot_token_id = 49406 if sot_token_id is None else sot_token_id
-            eos_token_id = 49407 if eos_token_id is None else eos_token_id
-            pad_token_id = self.pad_id if pad_token_id is None else pad_token_id
-            logit_processor = LogitsProcessorList(
-                [
-                    MinLengthLogitsProcessor(min_seq_len, eos_token_id),
-                    RepetitionPenaltyLogitsProcessor(repetition_penalty),
-                ]
-            )
-
-            if stopping_criteria is None:
-                stopping_criteria = [MaxLengthCriteria(max_length=seq_len)]
-
-            stopping_criteria = StoppingCriteriaList(
-                stopping_criteria
-            )
-
-            device = image.device
-
-            if generation_type == "beam_search":
-                output = self._generate_beamsearch(
-                    image_inputs = image,
-                    pad_token_id=pad_token_id,
-                    eos_token_id=eos_token_id,
-                    sot_token_id=sot_token_id,
-                    num_beams=num_beams,
-                    num_beam_groups=num_beam_groups,
-                    min_seq_len=min_seq_len,
-                    stopping_criteria=stopping_criteria,
-                    logit_processor=logit_processor,
-                )
-                if fixed_output_length and output.shape[1] < seq_len:
-                    return torch.cat(
-                        (output, torch.ones(output.shape[0], seq_len-output.shape[1], device=device, dtype=output.dtype) * self.pad_id),
-                        dim=1
-                    )
-                return output
-
-            elif generation_type == "top_p":
-                logit_warper = GENERATION_TYPES[generation_type](top_p)
-            elif generation_type == "top_k":
-                logit_warper = GENERATION_TYPES[generation_type](top_k)
-            else:
-                raise ValueError(
-                    f"generation_type has to be one of "
-                    f"{'| ' + ' | '.join(list(GENERATION_TYPES.keys())) + ' |'}."
-                )
-
-            image_latent, image_embs = self._encode_image(image)
-
-            if text is None:
-                text = torch.ones((image.shape[0], 1), device=device, dtype=torch.long) * sot_token_id
-
-            was_training = self.training
-            num_dims = len(text.shape)
-
-            if num_dims == 1:
-                text = text[None, :]
-
-            cur_len = text.shape[1]
-            self.eval()
-            out = text
-
-            while True:
-                x = out[:, -max_seq_len:]
-                cur_len = x.shape[1]
-                logits = self(image, x, image_latent=image_latent, image_embs=image_embs, embed_cls=False)["logits"][:, -1]
-                mask = (out[:, -1] == eos_token_id) | (out[:, -1] == pad_token_id)
-                sample = torch.ones((out.shape[0], 1), device=device, dtype=torch.long) * pad_token_id
-
-                if mask.all():
-                    if not fixed_output_length:
-                        break
-                else:
-                    logits = logits[~mask, :]
-                    filtered_logits = logit_processor(x[~mask, :], logits)
-                    filtered_logits = logit_warper(x[~mask, :], filtered_logits)
-                    probs = F.softmax(filtered_logits / temperature, dim=-1)
-
-                    if (cur_len + 1 == seq_len):
-                        sample[~mask, :] = torch.ones((sum(~mask), 1), device=device, dtype=torch.long) * eos_token_id
-                    else:
-                        sample[~mask, :] = torch.multinomial(probs, 1)
-
-                out = torch.cat((out, sample), dim=-1)
-
-                cur_len += 1
-
-                if stopping_criteria(out, None):
-                    break
-
-            if num_dims == 1:
-                out = out.squeeze(0)
-
-            self.train(was_training)
-            return out
-
-    def _generate_beamsearch(
-            self,
-            image_inputs,
-            pad_token_id=None,
-            eos_token_id=None,
-            sot_token_id=None,
-            num_beams=6,
-            num_beam_groups=3,
-            min_seq_len=5,
-            stopping_criteria=None,
-            logit_processor=None,
-            logit_warper=None,
-    ):
-        device = image_inputs.device
-        batch_size = image_inputs.shape[0]
-        image_inputs = torch.repeat_interleave(image_inputs, num_beams, dim=0)
-        image_latent, image_embs = self._encode_image(image_inputs)
-
-        input_ids = torch.ones((batch_size * num_beams, 1), device=device, dtype=torch.long)
-        input_ids = input_ids * sot_token_id
-        beam_scorer = BeamSearchScorer(
-            batch_size=batch_size,
-            num_beams=num_beams,
-            device=device,
-            num_beam_groups=num_beam_groups,
-        )
-        # instantiate logits processors
-        logits_processor = (
-            LogitsProcessorList([MinLengthLogitsProcessor(min_seq_len, eos_token_id=eos_token_id)])
-            if logit_processor is None
-            else logit_processor
-        )
-
-        batch_size = len(beam_scorer._beam_hyps)
-        num_beams = beam_scorer.num_beams
-        num_beam_groups = beam_scorer.num_beam_groups
-        num_sub_beams = num_beams // num_beam_groups
-        batch_beam_size, cur_len = input_ids.shape
-        beam_indices = None
-
-        if num_beams * batch_size != batch_beam_size:
-            raise ValueError(
-                f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
-            )
-
-        beam_scores = torch.full((batch_size, num_beams), -1e9, dtype=torch.float, device=device)
-        # initialise score of first beam of each group with 0 and the rest with 1e-9. This ensures that the beams in
-        # the same group don't produce same tokens everytime.
-        beam_scores[:, ::num_sub_beams] = 0
-        beam_scores = beam_scores.view((batch_size * num_beams,))
-
-        while True:
-
-            # predicted tokens in cur_len step
-            current_tokens = torch.zeros(batch_size * num_beams, dtype=input_ids.dtype, device=device)
-
-            # indices which will form the beams in the next time step
-            reordering_indices = torch.zeros(batch_size * num_beams, dtype=torch.long, device=device)
-
-            # do one decoder step on all beams of all sentences in batch
-            model_inputs = prepare_inputs_for_generation(input_ids=input_ids, image_inputs=image_inputs)
-            outputs = self(
-                model_inputs['images'],
-                model_inputs['text'],
-                embed_cls=False,
-                image_latent=image_latent,
-                image_embs=image_embs
-            )
-
-            for beam_group_idx in range(num_beam_groups):
-                group_start_idx = beam_group_idx * num_sub_beams
-                group_end_idx = min(group_start_idx + num_sub_beams, num_beams)
-                group_size = group_end_idx - group_start_idx
-
-                # indices of beams of current group among all sentences in batch
-                batch_group_indices = []
-
-                for batch_idx in range(batch_size):
-                    batch_group_indices.extend(
-                        [batch_idx * num_beams + idx for idx in range(group_start_idx, group_end_idx)]
-                    )
-                group_input_ids = input_ids[batch_group_indices]
-
-                # select outputs of beams of currentg group only
-                next_token_logits = outputs['logits'][batch_group_indices, -1, :]
-                vocab_size = next_token_logits.shape[-1]
-
-                next_token_scores_processed = logits_processor(
-                    group_input_ids, next_token_logits, current_tokens=current_tokens, beam_group_idx=beam_group_idx
-                )
-                next_token_scores = next_token_scores_processed + beam_scores[batch_group_indices].unsqueeze(-1)
-                next_token_scores = next_token_scores.expand_as(next_token_scores_processed)
-
-                # reshape for beam search
-                next_token_scores = next_token_scores.view(batch_size, group_size * vocab_size)
-
-                next_token_scores, next_tokens = torch.topk(
-                    next_token_scores, 2 * group_size, dim=1, largest=True, sorted=True
-                )
-
-                next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor")
-                next_tokens = next_tokens % vocab_size
-
-                # stateless
-                process_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None
-                beam_outputs = beam_scorer.process(
-                    group_input_ids,
-                    next_token_scores,
-                    next_tokens,
-                    next_indices,
-                    pad_token_id=pad_token_id,
-                    eos_token_id=eos_token_id,
-                    beam_indices=process_beam_indices,
-                )
-                beam_scores[batch_group_indices] = beam_outputs["next_beam_scores"]
-                beam_next_tokens = beam_outputs["next_beam_tokens"]
-                beam_idx = beam_outputs["next_beam_indices"]
-
-                input_ids[batch_group_indices] = group_input_ids[beam_idx]
-                group_input_ids = torch.cat([group_input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
-                current_tokens[batch_group_indices] = group_input_ids[:, -1]
-
-                # (beam_idx // group_size) -> batch_idx
-                # (beam_idx % group_size) -> offset of idx inside the group
-                reordering_indices[batch_group_indices] = (
-                    num_beams * torch.div(beam_idx, group_size, rounding_mode="floor") + group_start_idx + (beam_idx % group_size)
-                )
-
-            input_ids = torch.cat([input_ids, current_tokens.unsqueeze(-1)], dim=-1)
-
-            # increase cur_len
-            cur_len = cur_len + 1
-            if beam_scorer.is_done or stopping_criteria(input_ids, None):
-                break
-
-        final_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None
-        sequence_outputs = beam_scorer.finalize(
-            input_ids,
-            beam_scores,
-            next_tokens,
-            next_indices,
-            pad_token_id=pad_token_id,
-            eos_token_id=eos_token_id,
-            max_length=stopping_criteria.max_length,
-            beam_indices=final_beam_indices,
-        )
-        return sequence_outputs['sequences']
-
-
-def prepare_inputs_for_generation(input_ids, image_inputs, past=None, **kwargs):
-    if past:
-        input_ids = input_ids[:, -1].unsqueeze(-1)
-
-    attention_mask = kwargs.get("attention_mask", None)
-    position_ids = kwargs.get("position_ids", None)
-
-    if attention_mask is not None and position_ids is None:
-        # create position_ids on the fly for batch generation
-        position_ids = attention_mask.long().cumsum(-1) - 1
-        position_ids.masked_fill_(attention_mask == 0, 1)
-    else:
-        position_ids = None
-    return {
-        "text": input_ids,
-        "images": image_inputs,
-        "past_key_values": past,
-        "position_ids": position_ids,
-        "attention_mask": attention_mask,
-    }

diffsynth/extensions/ImageQualityMetric/open_clip/constants.py

@@ -1,2 +0,0 @@
-OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
-OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)

diffsynth/extensions/ImageQualityMetric/open_clip/factory.py

@@ -1,433 +0,0 @@
-import json
-import logging
-import os
-import pathlib
-import re
-from copy import deepcopy
-from pathlib import Path
-# from turtle import forward
-from typing import Any, Dict, Optional, Tuple, Union
-
-import torch
-
-from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
-from .model import CLIP, CustomTextCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,\
-    resize_pos_embed, get_cast_dtype
-from .coca_model import CoCa
-from .loss import ClipLoss, DistillClipLoss, CoCaLoss
-from .openai import load_openai_model
-from .pretrained import is_pretrained_cfg, get_pretrained_cfg, download_pretrained, list_pretrained_tags_by_model, download_pretrained_from_hf
-from .transform import image_transform, AugmentationCfg
-from .tokenizer import HFTokenizer, SimpleTokenizer
-
-
-HF_HUB_PREFIX = 'hf-hub:'
-_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"]
-_MODEL_CONFIGS = {}  # directory (model_name: config) of model architecture configs
-
-
-def _natural_key(string_):
-    return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
-
-
-def _rescan_model_configs():
-    global _MODEL_CONFIGS
-
-    config_ext = ('.json',)
-    config_files = []
-    for config_path in _MODEL_CONFIG_PATHS:
-        if config_path.is_file() and config_path.suffix in config_ext:
-            config_files.append(config_path)
-        elif config_path.is_dir():
-            for ext in config_ext:
-                config_files.extend(config_path.glob(f'*{ext}'))
-
-    for cf in config_files:
-        with open(cf, 'r') as f:
-            model_cfg = json.load(f)
-            if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')):
-                _MODEL_CONFIGS[cf.stem] = model_cfg
-
-    _MODEL_CONFIGS = {k: v for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))}
-
-
-_rescan_model_configs()  # initial populate of model config registry
-
-
-def list_models():
-    """ enumerate available model architectures based on config files """
-    return list(_MODEL_CONFIGS.keys())
-
-
-def add_model_config(path):
-    """ add model config path or file and update registry """
-    if not isinstance(path, Path):
-        path = Path(path)
-    _MODEL_CONFIG_PATHS.append(path)
-    _rescan_model_configs()
-
-
-def get_model_config(model_name):
-    if model_name in _MODEL_CONFIGS:
-        return deepcopy(_MODEL_CONFIGS[model_name])
-    else:
-        return None
-
-
-def get_tokenizer(model_name, open_clip_bpe_path=None):
-    if model_name.startswith(HF_HUB_PREFIX):
-        tokenizer = HFTokenizer(model_name[len(HF_HUB_PREFIX):])
-    else:
-        config = get_model_config(model_name)
-        tokenizer = HFTokenizer(
-            config['text_cfg']['hf_tokenizer_name']) if 'hf_tokenizer_name' in config['text_cfg'] else SimpleTokenizer(open_clip_bpe_path)
-    return tokenizer
-
-
-def load_state_dict(checkpoint_path: str, map_location='cpu'):
-    checkpoint = torch.load(checkpoint_path, map_location=map_location)
-    if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
-        state_dict = checkpoint['state_dict']
-    else:
-        state_dict = checkpoint
-    if next(iter(state_dict.items()))[0].startswith('module'):
-        state_dict = {k[7:]: v for k, v in state_dict.items()}
-    return state_dict
-
-
-def load_checkpoint(model, checkpoint_path, strict=True):
-    state_dict = load_state_dict(checkpoint_path)
-    # detect old format and make compatible with new format
-    if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'):
-        state_dict = convert_to_custom_text_state_dict(state_dict)
-    resize_pos_embed(state_dict, model)
-    incompatible_keys = model.load_state_dict(state_dict, strict=strict)
-    return incompatible_keys
-
-
-def create_model(
-        model_name: str,
-        pretrained: Optional[str] = None,
-        precision: str = 'fp32',
-        device: Union[str, torch.device] = 'cpu',
-        jit: bool = False,
-        force_quick_gelu: bool = False,
-        force_custom_text: bool = False,
-        force_patch_dropout: Optional[float] = None,
-        force_image_size: Optional[Union[int, Tuple[int, int]]] = None,
-        pretrained_image: bool = False,
-        pretrained_hf: bool = True,
-        cache_dir: Optional[str] = None,
-        output_dict: Optional[bool] = None,
-        require_pretrained: bool = False,
-):
-    has_hf_hub_prefix = model_name.startswith(HF_HUB_PREFIX)
-    if has_hf_hub_prefix:
-        model_id = model_name[len(HF_HUB_PREFIX):]
-        checkpoint_path = download_pretrained_from_hf(model_id, cache_dir=cache_dir)
-        config_path = download_pretrained_from_hf(model_id, filename='open_clip_config.json', cache_dir=cache_dir)
-
-        with open(config_path, 'r', encoding='utf-8') as f:
-            config = json.load(f)
-        pretrained_cfg = config['preprocess_cfg']
-        model_cfg = config['model_cfg']
-    else:
-        model_name = model_name.replace('/', '-')  # for callers using old naming with / in ViT names
-        checkpoint_path = None
-        pretrained_cfg = {}
-        model_cfg = None
-
-    if isinstance(device, str):
-        device = torch.device(device)
-
-    if pretrained and pretrained.lower() == 'openai':
-        logging.info(f'Loading pretrained {model_name} from OpenAI.')
-        model = load_openai_model(
-            model_name,
-            precision=precision,
-            device=device,
-            jit=jit,
-            cache_dir=cache_dir,
-        )
-
-        # to always output dict even if it is clip
-        if output_dict and hasattr(model, "output_dict"):
-            model.output_dict = True
-    else:
-        model_cfg = model_cfg or get_model_config(model_name)
-        if model_cfg is not None:
-            logging.info(f'Loaded {model_name} model config.')
-        else:
-            logging.error(f'Model config for {model_name} not found; available models {list_models()}.')
-            raise RuntimeError(f'Model config for {model_name} not found.')
-
-        if force_quick_gelu:
-            # override for use of QuickGELU on non-OpenAI transformer models
-            model_cfg["quick_gelu"] = True
-
-        if force_patch_dropout is not None:
-            # override the default patch dropout value
-            model_cfg["vision_cfg"]["patch_dropout"] = force_patch_dropout
-
-        if force_image_size is not None:
-            # override model config's image size
-            model_cfg["vision_cfg"]["image_size"] = force_image_size
-
-        if pretrained_image:
-            if 'timm_model_name' in model_cfg.get('vision_cfg', {}):
-                # pretrained weight loading for timm models set via vision_cfg
-                model_cfg['vision_cfg']['timm_model_pretrained'] = True
-            else:
-                assert False, 'pretrained image towers currently only supported for timm models'
-
-        cast_dtype = get_cast_dtype(precision)
-        is_hf_model = 'hf_model_name' in model_cfg.get('text_cfg', {})
-        custom_text = model_cfg.pop('custom_text', False) or force_custom_text or is_hf_model
-
-        if custom_text:
-            if is_hf_model:
-                model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf
-            if "coca" in model_name:
-                model = CoCa(**model_cfg, cast_dtype=cast_dtype)
-            else:
-                model = CustomTextCLIP(**model_cfg, cast_dtype=cast_dtype)
-        else:
-            model = CLIP(**model_cfg, cast_dtype=cast_dtype)
-
-        pretrained_loaded = False
-        if pretrained:
-            checkpoint_path = ''
-            pretrained_cfg = get_pretrained_cfg(model_name, pretrained)
-            if pretrained_cfg:
-                checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir)
-            elif os.path.exists(pretrained):
-                checkpoint_path = pretrained
-
-            if checkpoint_path:
-                logging.info(f'Loading pretrained {model_name} weights ({pretrained}).')
-                load_checkpoint(model, checkpoint_path)
-            else:
-                error_str = (
-                    f'Pretrained weights ({pretrained}) not found for model {model_name}.'
-                    f'Available pretrained tags ({list_pretrained_tags_by_model(model_name)}.')
-                logging.warning(error_str)
-                raise RuntimeError(error_str)
-            pretrained_loaded = True
-        elif has_hf_hub_prefix:
-            logging.info(f'Loading pretrained {model_name} weights ({pretrained}).')
-            load_checkpoint(model, checkpoint_path)
-            pretrained_loaded = True
-
-        if require_pretrained and not pretrained_loaded:
-            # callers of create_model_from_pretrained always expect pretrained weights
-            raise RuntimeError(
-                f'Pretrained weights were required for (model: {model_name}, pretrained: {pretrained}) but not loaded.')
-
-        model.to(device=device)
-        if precision in ("fp16", "bf16"):
-            convert_weights_to_lp(model, dtype=torch.bfloat16 if precision == 'bf16' else torch.float16)
-
-        # set image / mean metadata from pretrained_cfg if available, or use default
-        model.visual.image_mean = pretrained_cfg.get('mean', None) or OPENAI_DATASET_MEAN
-        model.visual.image_std = pretrained_cfg.get('std', None) or OPENAI_DATASET_STD
-
-        # to always output dict even if it is clip
-        if output_dict and hasattr(model, "output_dict"):
-            model.output_dict = True
-
-        if jit:
-            model = torch.jit.script(model)
-
-    return model
-
-
-def create_loss(args):
-    if args.distill:
-        return DistillClipLoss(
-            local_loss=args.local_loss,
-            gather_with_grad=args.gather_with_grad,
-            cache_labels=True,
-            rank=args.rank,
-            world_size=args.world_size,
-            use_horovod=args.horovod,
-        )
-    elif "coca" in args.model.lower():
-        return CoCaLoss(
-            caption_loss_weight=args.coca_caption_loss_weight,
-            clip_loss_weight=args.coca_contrastive_loss_weight,
-            local_loss=args.local_loss,
-            gather_with_grad=args.gather_with_grad,
-            cache_labels=True,
-            rank=args.rank,
-            world_size=args.world_size,
-            use_horovod=args.horovod,
-        )
-    return ClipLoss(
-        local_loss=args.local_loss,
-        gather_with_grad=args.gather_with_grad,
-        cache_labels=True,
-        rank=args.rank,
-        world_size=args.world_size,
-        use_horovod=args.horovod,
-    )
-
-class MLP(torch.nn.Module):
-    def __init__(self, input_size):
-        super().__init__()
-        self.input_size = input_size
-        self.layers = torch.nn.Sequential(
-            torch.nn.Linear(self.input_size, 1024),
-            torch.nn.Dropout(0.2),
-            torch.nn.Linear(1024, 128),
-            torch.nn.Dropout(0.2),
-            torch.nn.Linear(128, 64),
-            torch.nn.Dropout(0.1),
-            torch.nn.Linear(64, 16),
-            torch.nn.Linear(16, 1)
-        )
-
-    def forward(self, x):
-        return self.layers(x)
-
-# class semantic_head(torch.nn.Module):
-#     def __init__(self, input_size):
-#         super().__init__()
-#         self.input_size = input_size  # for ViT-L-14 is 1024
-#         self.seg_head = torch.nn.Sequential(
-#             torch.nn.Linear(input_size, 128),
-#             torch.nn.Dropout(0.2),
-#             torch.nn.Linear(128, 64),
-#             torch.nn.Dropout(0.1),
-#             torch.nn.Linear(64, 16),
-#             torch.nn.Linear(16, 1),
-#         )
-#         self.sigmoid = torch.nn.Sigmoid()
-
-#     def forward(self, x):
-#         return self.sigmoid(self.seg_head(x))
-
-def create_model_and_transforms(
-        model_name: str,
-        pretrained: Optional[str] = None,
-        precision: str = 'fp32',
-        device: Union[str, torch.device] = 'cpu',
-        jit: bool = False,
-        force_quick_gelu: bool = False,
-        force_custom_text: bool = False,
-        force_patch_dropout: Optional[float] = None,
-        force_image_size: Optional[Union[int, Tuple[int, int]]] = None,
-        pretrained_image: bool = False,
-        pretrained_hf: bool = True,
-        image_mean: Optional[Tuple[float, ...]] = None,
-        image_std: Optional[Tuple[float, ...]] = None,
-        aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None,
-        cache_dir: Optional[str] = None,
-        light_augmentation = False,
-        output_dict: Optional[bool] = None,
-        with_score_predictor: bool = False,
-        with_region_predictor: bool = False
-):
-    model = create_model(
-        model_name,
-        pretrained,
-        precision=precision,
-        device=device,
-        jit=jit,
-        force_quick_gelu=force_quick_gelu,
-        force_custom_text=force_custom_text,
-        force_patch_dropout=force_patch_dropout,
-        force_image_size=force_image_size,
-        pretrained_image=pretrained_image,
-        pretrained_hf=pretrained_hf,
-        cache_dir=cache_dir,
-        output_dict=output_dict,
-    )
-
-    image_mean = image_mean or getattr(model.visual, 'image_mean', None)
-    image_std = image_std or getattr(model.visual, 'image_std', None)
-
-    if with_score_predictor:
-        model.score_predictor = MLP(model.visual.proj.size(1)).to(device=device, dtype=model.visual.proj.dtype)
-
-    if with_region_predictor:
-        # model.region_predictor = semantic_head(model.visual.proj.size(1)).to(device=device, dtype=model.visual.proj.dtype)
-        model.region_predictor = torch.nn.Linear(model.visual.proj.size(0), 1).to(device=device, dtype=model.visual.proj.dtype)
-        # preprocess_train = image_transform_region(
-        #     model.visual.image_size,
-        #     is_train=True,
-        #     mean=image_mean,
-        #     std=image_std
-        # )
-        # preprocess_val = image_transform_region(
-        #     model.visual.image_size,
-        #     is_train=False,
-        #     mean=image_mean,
-        #     std=image_std
-        # )
-
-    if light_augmentation:
-        preprocess_val = image_transform(
-            model.visual.image_size,
-            is_train=False,
-            mean=image_mean,
-            std=image_std,
-            resize_longest_max=True,
-        )
-        preprocess_train = preprocess_val
-    else:
-        preprocess_train = image_transform(
-            model.visual.image_size,
-            is_train=True,
-            mean=image_mean,
-            std=image_std
-        )
-        preprocess_val = image_transform(
-            model.visual.image_size,
-            is_train=False,
-            mean=image_mean,
-            std=image_std
-        )
-
-    return model, preprocess_train, preprocess_val
-
-
-def create_model_from_pretrained(
-        model_name: str,
-        pretrained: Optional[str] = None,
-        precision: str = 'fp32',
-        device: Union[str, torch.device] = 'cpu',
-        jit: bool = False,
-        force_quick_gelu: bool = False,
-        force_custom_text: bool = False,
-        force_image_size: Optional[Union[int, Tuple[int, int]]] = None,
-        return_transform: bool = True,
-        image_mean: Optional[Tuple[float, ...]] = None,
-        image_std: Optional[Tuple[float, ...]] = None,
-        cache_dir: Optional[str] = None,
-):
-    model = create_model(
-        model_name,
-        pretrained,
-        precision=precision,
-        device=device,
-        jit=jit,
-        force_quick_gelu=force_quick_gelu,
-        force_custom_text=force_custom_text,
-        force_image_size=force_image_size,
-        cache_dir=cache_dir,
-        require_pretrained=True,
-    )
-
-    if not return_transform:
-        return model
-
-    image_mean = image_mean or getattr(model.visual, 'image_mean', None)
-    image_std = image_std or getattr(model.visual, 'image_std', None)
-    preprocess = image_transform(
-        model.visual.image_size,
-        is_train=False,
-        mean=image_mean,
-        std=image_std,
-    )
-
-    return model, preprocess

diffsynth/extensions/ImageQualityMetric/open_clip/hf_configs.py

@@ -1,45 +0,0 @@
-# HF architecture dict:
-arch_dict = {
-    # https://huggingface.co/docs/transformers/model_doc/roberta#roberta
-    "roberta": {
-        "config_names": {
-            "context_length": "max_position_embeddings",
-            "vocab_size": "vocab_size",
-            "width": "hidden_size",
-            "heads": "num_attention_heads",
-            "layers": "num_hidden_layers",
-            "layer_attr": "layer",
-            "token_embeddings_attr": "embeddings"
-        },
-        "pooler": "mean_pooler",
-    },
-    # https://huggingface.co/docs/transformers/model_doc/xlm-roberta#transformers.XLMRobertaConfig
-    "xlm-roberta": {
-        "config_names": {
-            "context_length": "max_position_embeddings",
-            "vocab_size": "vocab_size",
-            "width": "hidden_size",
-            "heads": "num_attention_heads",
-            "layers": "num_hidden_layers",
-            "layer_attr": "layer",
-            "token_embeddings_attr": "embeddings"
-        },
-        "pooler": "mean_pooler",
-    },
-    # https://huggingface.co/docs/transformers/model_doc/mt5#mt5
-    "mt5": {
-        "config_names": {
-            # unlimited seqlen
-            # https://github.com/google-research/text-to-text-transfer-transformer/issues/273
-            # https://github.com/huggingface/transformers/blob/v4.24.0/src/transformers/models/t5/modeling_t5.py#L374
-            "context_length": "",
-            "vocab_size": "vocab_size",
-            "width": "d_model",
-            "heads": "num_heads",
-            "layers": "num_layers",
-            "layer_attr": "block",
-            "token_embeddings_attr": "embed_tokens"
-        },
-        "pooler": "mean_pooler",
-    },
-}

diffsynth/extensions/ImageQualityMetric/open_clip/hf_model.py

@@ -1,176 +0,0 @@
-""" huggingface model adapter
-
-Wraps HuggingFace transformers (https://github.com/huggingface/transformers) models for use as a text tower in CLIP model.
-"""
-
-import re
-
-import torch
-import torch.nn as nn
-from torch import TensorType
-
-try:
-    import transformers
-    from transformers import AutoModel, AutoTokenizer, AutoConfig, PretrainedConfig
-    from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, \
-        BaseModelOutputWithPoolingAndCrossAttentions
-except ImportError as e:
-    transformers = None
-
-
-    class BaseModelOutput:
-        pass
-
-
-    class PretrainedConfig:
-        pass
-
-from .hf_configs import arch_dict
-
-
-# utils
-def _camel2snake(s):
-    return re.sub(r'(?<!^)(?=[A-Z])', '_', s).lower()
-
-
-# TODO: ?last - for gpt-like models
-_POOLERS = {}
-
-
-def register_pooler(cls):
-    """Decorator registering pooler class"""
-    _POOLERS[_camel2snake(cls.__name__)] = cls
-    return cls
-
-
-@register_pooler
-class MeanPooler(nn.Module):
-    """Mean pooling"""
-
-    def forward(self, x: BaseModelOutput, attention_mask: TensorType):
-        masked_output = x.last_hidden_state * attention_mask.unsqueeze(-1)
-        return masked_output.sum(dim=1) / attention_mask.sum(-1, keepdim=True)
-
-
-@register_pooler
-class MaxPooler(nn.Module):
-    """Max pooling"""
-
-    def forward(self, x: BaseModelOutput, attention_mask: TensorType):
-        masked_output = x.last_hidden_state.masked_fill(attention_mask.unsqueeze(-1), -torch.inf)
-        return masked_output.max(1).values
-
-
-@register_pooler
-class ClsPooler(nn.Module):
-    """CLS token pooling"""
-
-    def __init__(self, use_pooler_output=True):
-        super().__init__()
-        self.cls_token_position = 0
-        self.use_pooler_output = use_pooler_output
-
-    def forward(self, x: BaseModelOutput, attention_mask: TensorType):
-        if (self.use_pooler_output and
-            isinstance(x, (BaseModelOutputWithPooling, BaseModelOutputWithPoolingAndCrossAttentions)) and
-            (x.pooler_output is not None)
-        ):
-            return x.pooler_output
-
-        return x.last_hidden_state[:, self.cls_token_position, :]
-
-
-class HFTextEncoder(nn.Module):
-    """HuggingFace model adapter"""
-    output_tokens: torch.jit.Final[bool]
-
-    def __init__(
-            self,
-            model_name_or_path: str,
-            output_dim: int,
-            config: PretrainedConfig = None,
-            pooler_type: str = None,
-            proj: str = None,
-            pretrained: bool = True,
-            output_tokens: bool = False,
-    ):
-        super().__init__()
-        self.output_tokens = output_tokens
-        self.output_dim = output_dim
-
-        # TODO: find better way to get this information
-        uses_transformer_pooler = (pooler_type == "cls_pooler")
-
-        if transformers is None:
-            raise RuntimeError("Please `pip install transformers` to use pre-trained HuggingFace models")
-        if config is None:
-            self.config = AutoConfig.from_pretrained(model_name_or_path)
-            create_func, model_args = (AutoModel.from_pretrained, model_name_or_path) if pretrained else (
-                AutoModel.from_config, self.config)
-            # TODO: do all model configs have this attribute? PretrainedConfig does so yes??
-            if hasattr(self.config, "is_encoder_decoder") and self.config.is_encoder_decoder:
-                self.transformer = create_func(model_args)
-                self.transformer = self.transformer.encoder
-            else:
-                self.transformer = create_func(model_args, add_pooling_layer=uses_transformer_pooler)
-        else:
-            self.config = config
-            self.transformer = AutoModel.from_config(config)
-        if pooler_type is None:  # get default arch pooler
-            pooler_type = (arch_dict[self.config.model_type]["pooler"])
-        
-        self.pooler = _POOLERS[pooler_type]()
-
-        d_model = getattr(self.config, arch_dict[self.config.model_type]["config_names"]["width"])
-        if (d_model == output_dim) and (proj is None):  # do we always need a proj?
-            self.proj = nn.Identity()
-        elif proj == 'linear':
-            self.proj = nn.Linear(d_model, output_dim, bias=False)
-        elif proj == 'mlp':
-            hidden_size = (d_model + output_dim) // 2
-            self.proj = nn.Sequential(
-                nn.Linear(d_model, hidden_size, bias=False),
-                nn.GELU(),
-                nn.Linear(hidden_size, output_dim, bias=False),
-            )
-
-    def forward(self, x: TensorType):
-        attn_mask = (x != self.config.pad_token_id).long()
-        out = self.transformer(input_ids=x, attention_mask=attn_mask)
-        pooled_out = self.pooler(out, attn_mask)
-        projected = self.proj(pooled_out)
-
-        seq_len = out.last_hidden_state.shape[1]
-        tokens = (
-            out.last_hidden_state[:, torch.arange(seq_len) != self.pooler.cls_token_position, :] 
-            if type(self.pooler) == ClsPooler 
-            else out.last_hidden_state
-        )
-        
-        if self.output_tokens:
-            return projected, tokens
-        return projected
-
-    def lock(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True):
-        if not unlocked_layers:  # full freezing
-            for n, p in self.transformer.named_parameters():
-                p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False
-            return
-
-        encoder = self.transformer.encoder if hasattr(self.transformer, 'encoder') else self.transformer
-        layer_list = getattr(encoder, arch_dict[self.config.model_type]["config_names"]["layer_attr"])
-        print(f"Unlocking {unlocked_layers}/{len(layer_list) + 1} layers of hf model")
-        embeddings = getattr(
-            self.transformer, arch_dict[self.config.model_type]["config_names"]["token_embeddings_attr"])
-        modules = [embeddings, *layer_list][:-unlocked_layers]
-        # freeze layers
-        for module in modules:
-            for n, p in module.named_parameters():
-                p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False
-
-    @torch.jit.ignore
-    def set_grad_checkpointing(self, enable=True):
-        self.transformer.gradient_checkpointing_enable()
-
-    def init_parameters(self):
-        pass

diffsynth/extensions/ImageQualityMetric/open_clip/loss.py

@@ -1,270 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-from torch.nn.utils.rnn import pad_sequence
-
-try:
-    import torch.distributed.nn
-    from torch import distributed as dist
-
-    has_distributed = True
-except ImportError:
-    has_distributed = False
-
-try:
-    import horovod.torch as hvd
-except ImportError:
-    hvd = None
-
-
-def gather_features(
-        image_features,
-        text_features,
-        local_loss=False,
-        gather_with_grad=False,
-        rank=0,
-        world_size=1,
-        use_horovod=False
-):
-    assert has_distributed, 'torch.distributed did not import correctly, please use a PyTorch version with support.'
-    if use_horovod:
-        assert hvd is not None, 'Please install horovod'
-        if gather_with_grad:
-            all_image_features = hvd.allgather(image_features)
-            all_text_features = hvd.allgather(text_features)
-        else:
-            with torch.no_grad():
-                all_image_features = hvd.allgather(image_features)
-                all_text_features = hvd.allgather(text_features)
-            if not local_loss:
-                # ensure grads for local rank when all_* features don't have a gradient
-                gathered_image_features = list(all_image_features.chunk(world_size, dim=0))
-                gathered_text_features = list(all_text_features.chunk(world_size, dim=0))
-                gathered_image_features[rank] = image_features
-                gathered_text_features[rank] = text_features
-                all_image_features = torch.cat(gathered_image_features, dim=0)
-                all_text_features = torch.cat(gathered_text_features, dim=0)
-    else:
-        # We gather tensors from all gpus
-        if gather_with_grad:
-            all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features), dim=0)
-            all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0)
-        else:
-            gathered_image_features = [torch.zeros_like(image_features) for _ in range(world_size)]
-            gathered_text_features = [torch.zeros_like(text_features) for _ in range(world_size)]
-            dist.all_gather(gathered_image_features, image_features)
-            dist.all_gather(gathered_text_features, text_features)
-            if not local_loss:
-                # ensure grads for local rank when all_* features don't have a gradient
-                gathered_image_features[rank] = image_features
-                gathered_text_features[rank] = text_features
-            all_image_features = torch.cat(gathered_image_features, dim=0)
-            all_text_features = torch.cat(gathered_text_features, dim=0)
-
-    return all_image_features, all_text_features
-
-
-class ClipLoss(nn.Module):
-
-    def __init__(
-            self,
-            local_loss=False,
-            gather_with_grad=False,
-            cache_labels=False,
-            rank=0,
-            world_size=1,
-            use_horovod=False,
-    ):
-        super().__init__()
-        self.local_loss = local_loss
-        self.gather_with_grad = gather_with_grad
-        self.cache_labels = cache_labels
-        self.rank = rank
-        self.world_size = world_size
-        self.use_horovod = use_horovod
-
-        # cache state
-        self.prev_num_logits = 0
-        self.labels = {}
-
-    def get_ground_truth(self, device, num_logits) -> torch.Tensor:
-        # calculated ground-truth and cache if enabled
-        if self.prev_num_logits != num_logits or device not in self.labels:
-            labels = torch.arange(num_logits, device=device, dtype=torch.long)
-            if self.world_size > 1 and self.local_loss:
-                labels = labels + num_logits * self.rank
-            if self.cache_labels:
-                self.labels[device] = labels
-                self.prev_num_logits = num_logits
-        else:
-            labels = self.labels[device]
-        return labels
-
-    def get_logits(self, image_features, text_features, logit_scale):
-        if self.world_size > 1:
-            all_image_features, all_text_features = gather_features(
-                image_features, text_features,
-                self.local_loss, self.gather_with_grad, self.rank, self.world_size, self.use_horovod)
-
-            if self.local_loss:
-                logits_per_image = logit_scale * image_features @ all_text_features.T
-                logits_per_text = logit_scale * text_features @ all_image_features.T
-            else:
-                logits_per_image = logit_scale * all_image_features @ all_text_features.T
-                logits_per_text = logits_per_image.T
-        else:
-            logits_per_image = logit_scale * image_features @ text_features.T
-            logits_per_text = logit_scale * text_features @ image_features.T
-        
-        return logits_per_image, logits_per_text
-
-    def forward(self, image_features, text_features, logit_scale, output_dict=False):
-        device = image_features.device
-        logits_per_image, logits_per_text = self.get_logits(image_features, text_features, logit_scale)
-
-        labels = self.get_ground_truth(device, logits_per_image.shape[0])
-
-        total_loss = (
-            F.cross_entropy(logits_per_image, labels) +
-            F.cross_entropy(logits_per_text, labels)
-            ) / 2
-        return total_loss
-
-class PreferenceLoss(nn.Module):
-
-    def forward(self, logits_per_image, num_images, labels):
-
-        paired_logits_list = [logit[:,i] for i, logit in enumerate(logits_per_image.split(num_images.tolist()))]
-        paired_logits = pad_sequence(paired_logits_list, batch_first=True, padding_value=-999)
-
-        ce_loss = F.cross_entropy(paired_logits, labels)
-        return ce_loss
-
-class HPSLoss(nn.Module):
-
-    def forward(self, text_logits, labels):
-
-        device = text_logits.device
-        text_0_logits, text_1_logits = text_logits.chunk(2, dim=-1)
-        label_0, label_1 = labels.chunk(2, dim=-1)
-
-        index = torch.arange(text_0_logits.shape[0], device=device, dtype=torch.long)
-        text_0_logits = text_0_logits[index, index]
-        text_1_logits = text_1_logits[index, index]
-        text_logits = torch.stack([text_0_logits, text_1_logits], dim=-1)
-        text_0_labels = torch.zeros(text_logits.shape[0], device=device, dtype=torch.long)
-        text_1_labels = text_0_labels + 1
-
-        text_0_loss = torch.nn.functional.cross_entropy(text_logits, text_0_labels, reduction="none")
-        text_1_loss = torch.nn.functional.cross_entropy(text_logits, text_1_labels, reduction="none")
-
-        text_loss = label_0 * text_0_loss + label_1 * text_1_loss
-
-        # absolute_example_weight = 1 / num_per_prompt
-        # denominator = absolute_example_weight.sum()
-        # weight_per_example = absolute_example_weight / denominator
-        # text_loss *= weight_per_example
-
-        text_loss = text_loss.sum()
-        return text_loss
-
-class RankingLoss(nn.Module):
-
-    def forward(self, logits_per_image, num_images, labels, margin = 1.0):
-        paired_logits_list = [logit[:,i] for i, logit in enumerate(logits_per_image.split(num_images.tolist()))]
-        label_list = [label for label in labels.split(num_images.tolist())]
-        # ranked_logits = [torch.index_select(paired_logits_list[i], 0, rank) for i, rank in enumerate(label_list)]
-
-        paired_logits = pad_sequence(paired_logits_list, batch_first=True, padding_value=-1)
-        padded_labels = pad_sequence(label_list, batch_first=True, padding_value=10)
-
-        # regulized_logits = torch.log(torch.sigmoid(paired_logits))
-
-        diff = paired_logits.unsqueeze(1) - paired_logits.unsqueeze(2)
-        # diff = paired_logits.unsqueeze(1) - paired_logits.unsqueeze(2)
-        # diff_label = torch.clamp(padded_labels.unsqueeze(1) - padded_labels.unsqueeze(2), min=-1, max=1)
-        diff_label = - (padded_labels.unsqueeze(1) - padded_labels.unsqueeze(2))
-        mask = torch.triu(torch.ones(diff.shape[1], diff.shape[1]), diagonal=1).bool().detach()
-
-        loss = torch.clamp(margin - torch.mul(diff[:, ~mask],diff_label[:,~mask]), min=0).mean()
-        return loss
-
-class CoCaLoss(ClipLoss):
-    def __init__(
-            self,
-            caption_loss_weight,
-            clip_loss_weight,
-            pad_id=0,  # pad_token for open_clip custom tokenizer
-            local_loss=False,
-            gather_with_grad=False,
-            cache_labels=False,
-            rank=0,
-            world_size=1,
-            use_horovod=False,
-    ):
-        super().__init__(
-            local_loss=local_loss,
-            gather_with_grad=gather_with_grad,
-            cache_labels=cache_labels,
-            rank=rank,
-            world_size=world_size,
-            use_horovod=use_horovod
-        )
-
-        self.clip_loss_weight = clip_loss_weight
-        self.caption_loss_weight = caption_loss_weight
-        self.caption_loss = nn.CrossEntropyLoss(ignore_index=pad_id)
-
-    def forward(self, image_features, text_features, logits, labels, logit_scale, output_dict=False):
-        clip_loss = super().forward(image_features, text_features, logit_scale)
-        clip_loss = self.clip_loss_weight * clip_loss
-
-        caption_loss = self.caption_loss(
-            logits.permute(0, 2, 1),
-            labels,
-        )
-        caption_loss = caption_loss * self.caption_loss_weight
-
-        if output_dict:
-            return {"contrastive_loss": clip_loss, "caption_loss": caption_loss}
-
-        return clip_loss, caption_loss
-
-
-class DistillClipLoss(ClipLoss):
-
-    def dist_loss(self, teacher_logits, student_logits):
-        return -(teacher_logits.softmax(dim=1) * student_logits.log_softmax(dim=1)).sum(dim=1).mean(dim=0)
-
-    def forward(
-            self,
-            image_features,
-            text_features,
-            logit_scale,
-            dist_image_features,
-            dist_text_features,
-            dist_logit_scale,
-            output_dict=False,
-    ):
-        logits_per_image, logits_per_text = \
-            self.get_logits(image_features, text_features, logit_scale)
-
-        dist_logits_per_image, dist_logits_per_text = \
-            self.get_logits(dist_image_features, dist_text_features, dist_logit_scale)
-
-        labels = self.get_ground_truth(image_features.device, logits_per_image.shape[0])
-
-        contrastive_loss = (
-            F.cross_entropy(logits_per_image, labels) +
-            F.cross_entropy(logits_per_text, labels)
-        ) / 2
-
-        distill_loss = (
-            self.dist_loss(dist_logits_per_image, logits_per_image) +
-            self.dist_loss(dist_logits_per_text, logits_per_text)
-        ) / 2
-
-        if output_dict:
-            return {"contrastive_loss": contrastive_loss, "distill_loss": distill_loss}
-
-        return contrastive_loss, distill_loss

diffsynth/extensions/ImageQualityMetric/open_clip/model.py

@@ -1,461 +0,0 @@
-""" CLIP Model
-
-Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
-"""
-from dataclasses import dataclass
-import logging
-import math
-from typing import Optional, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-from torch.utils.checkpoint import checkpoint
-
-from .hf_model import HFTextEncoder
-from .modified_resnet import ModifiedResNet
-from .timm_model import TimmModel
-from .transformer import LayerNormFp32, LayerNorm, QuickGELU, Attention, VisionTransformer, TextTransformer
-from .utils import to_2tuple
-
-
-@dataclass
-class CLIPVisionCfg:
-    layers: Union[Tuple[int, int, int, int], int] = 12
-    width: int = 768
-    head_width: int = 64
-    mlp_ratio: float = 4.0
-    patch_size: int = 16
-    image_size: Union[Tuple[int, int], int] = 224
-    ls_init_value: Optional[float] = None  # layer scale initial value
-    patch_dropout: float = 0.  # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results
-    input_patchnorm: bool = False # whether to use dual patchnorm - would only apply the input layernorm on each patch, as post-layernorm already exist in original clip vit design
-    global_average_pool: bool = False  # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580)
-    attentional_pool: bool = False # whether to use attentional pooler in the last embedding layer
-    n_queries: int = 256 # n_queries for attentional pooler
-    attn_pooler_heads: int = 8 # n heads for attentional_pooling
-    timm_model_name: str = None  # a valid model name overrides layers, width, patch_size
-    timm_model_pretrained: bool = False  # use (imagenet) pretrained weights for named model
-    timm_pool: str = 'avg'  # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')
-    timm_proj: str = 'linear'  # linear projection for timm model output ('linear', 'mlp', '')
-    timm_proj_bias: bool = False  # enable bias final projection
-    timm_drop: float = 0.  # head dropout
-    timm_drop_path: Optional[float] = None  # backbone stochastic depth
-    output_tokens: bool = False
-
-
-@dataclass
-class CLIPTextCfg:
-    context_length: int = 77
-    vocab_size: int = 49408
-    width: int = 512
-    heads: int = 8
-    layers: int = 12
-    ls_init_value: Optional[float] = None  # layer scale initial value
-    hf_model_name: str = None
-    hf_tokenizer_name: str = None
-    hf_model_pretrained: bool = True
-    proj: str = 'mlp'
-    pooler_type: str = 'mean_pooler'
-    embed_cls: bool = False
-    pad_id: int = 0
-    output_tokens: bool = False
-
-
-def get_cast_dtype(precision: str):
-    cast_dtype = None
-    if precision == 'bf16':
-        cast_dtype = torch.bfloat16
-    elif precision == 'fp16':
-        cast_dtype = torch.float16
-    return cast_dtype
-
-
-def _build_vision_tower(
-        embed_dim: int,
-        vision_cfg: CLIPVisionCfg,
-        quick_gelu: bool = False,
-        cast_dtype: Optional[torch.dtype] = None
-):
-    if isinstance(vision_cfg, dict):
-        vision_cfg = CLIPVisionCfg(**vision_cfg)
-
-    # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more
-    # memory efficient in recent PyTorch releases (>= 1.10).
-    # NOTE: timm models always use native GELU regardless of quick_gelu flag.
-    act_layer = QuickGELU if quick_gelu else nn.GELU
-
-    if vision_cfg.timm_model_name:
-        visual = TimmModel(
-            vision_cfg.timm_model_name,
-            pretrained=vision_cfg.timm_model_pretrained,
-            pool=vision_cfg.timm_pool,
-            proj=vision_cfg.timm_proj,
-            proj_bias=vision_cfg.timm_proj_bias,
-            drop=vision_cfg.timm_drop,
-            drop_path=vision_cfg.timm_drop_path,
-            embed_dim=embed_dim,
-            image_size=vision_cfg.image_size,
-        )
-        act_layer = nn.GELU  # so that text transformer doesn't use QuickGELU w/ timm models
-    elif isinstance(vision_cfg.layers, (tuple, list)):
-        vision_heads = vision_cfg.width * 32 // vision_cfg.head_width
-        visual = ModifiedResNet(
-            layers=vision_cfg.layers,
-            output_dim=embed_dim,
-            heads=vision_heads,
-            image_size=vision_cfg.image_size,
-            width=vision_cfg.width,
-        )
-    else:
-        vision_heads = vision_cfg.width // vision_cfg.head_width
-        norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm
-        visual = VisionTransformer(
-            image_size=vision_cfg.image_size,
-            patch_size=vision_cfg.patch_size,
-            width=vision_cfg.width,
-            layers=vision_cfg.layers,
-            heads=vision_heads,
-            mlp_ratio=vision_cfg.mlp_ratio,
-            ls_init_value=vision_cfg.ls_init_value,
-            patch_dropout=vision_cfg.patch_dropout,
-            input_patchnorm=vision_cfg.input_patchnorm,
-            global_average_pool=vision_cfg.global_average_pool,
-            attentional_pool=vision_cfg.attentional_pool,
-            n_queries=vision_cfg.n_queries,
-            attn_pooler_heads=vision_cfg.attn_pooler_heads,
-            output_tokens=vision_cfg.output_tokens,
-            output_dim=embed_dim,
-            act_layer=act_layer,
-            norm_layer=norm_layer,
-        )
-
-    return visual
-
-
-def _build_text_tower(
-        embed_dim: int,
-        text_cfg: CLIPTextCfg,
-        quick_gelu: bool = False,
-        cast_dtype: Optional[torch.dtype] = None,
-):
-    if isinstance(text_cfg, dict):
-        text_cfg = CLIPTextCfg(**text_cfg)
-
-    if text_cfg.hf_model_name:
-        text = HFTextEncoder(
-            text_cfg.hf_model_name,
-            output_dim=embed_dim,
-            proj=text_cfg.proj,
-            pooler_type=text_cfg.pooler_type,
-            pretrained=text_cfg.hf_model_pretrained,
-            output_tokens=text_cfg.output_tokens,
-        )
-    else:
-        act_layer = QuickGELU if quick_gelu else nn.GELU
-        norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm
-
-        text = TextTransformer(
-            context_length=text_cfg.context_length,
-            vocab_size=text_cfg.vocab_size,
-            width=text_cfg.width,
-            heads=text_cfg.heads,
-            layers=text_cfg.layers,
-            ls_init_value=text_cfg.ls_init_value,
-            output_dim=embed_dim,
-            embed_cls=text_cfg.embed_cls,
-            output_tokens=text_cfg.output_tokens,
-            pad_id=text_cfg.pad_id,
-            act_layer=act_layer,
-            norm_layer=norm_layer,
-        )
-    return text
-
-
-class CLIP(nn.Module):
-    output_dict: torch.jit.Final[bool]
-
-    def __init__(
-            self,
-            embed_dim: int,
-            vision_cfg: CLIPVisionCfg,
-            text_cfg: CLIPTextCfg,
-            quick_gelu: bool = False,
-            cast_dtype: Optional[torch.dtype] = None,
-            output_dict: bool = False,
-    ):
-        super().__init__()
-        self.output_dict = output_dict
-        self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype)
-
-        text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype)
-        self.transformer = text.transformer
-        self.vocab_size = text.vocab_size
-        self.token_embedding = text.token_embedding
-        self.positional_embedding = text.positional_embedding
-        self.ln_final = text.ln_final
-        self.text_projection = text.text_projection
-        self.register_buffer('attn_mask', text.attn_mask, persistent=False)
-
-        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-
-    def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False):
-        # lock image tower as per LiT - https://arxiv.org/abs/2111.07991
-        self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats)
-
-    def lock_text_tower(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True):
-        locked_layers = []
-        locked_layers.append(self.token_embedding)
-        self.positional_embedding.requires_grad = False
-        if unlocked_layers > 0:
-            locked_layers.append(self.transformer.resblocks[:-unlocked_layers])
-        else:
-            locked_layers.append(self.transformer)
-            locked_layers.append(self.ln_final)
-            self.text_projection.requires_grad = False
-
-        # freeze layers
-        for module in locked_layers:
-            for n, p in module.named_parameters():
-                p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False
-
-    @torch.jit.ignore
-    def set_grad_checkpointing(self, enable=True):
-        self.visual.set_grad_checkpointing(enable)
-        self.transformer.grad_checkpointing = enable
-
-    def encode_image(self, image, normalize: bool = False):
-        features = self.visual(image)
-        return F.normalize(features, dim=-1) if normalize else features
-
-    def encode_text(self, text, normalize: bool = False):
-        cast_dtype = self.transformer.get_cast_dtype()
-
-        x = self.token_embedding(text).to(cast_dtype)  # [batch_size, n_ctx, d_model]
-
-        x = x + self.positional_embedding.to(cast_dtype)
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x, attn_mask=self.attn_mask)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-        x = self.ln_final(x)  # [batch_size, n_ctx, transformer.width]
-        # take features from the eot embedding (eot_token is the highest number in each sequence)
-        x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
-        return F.normalize(x, dim=-1) if normalize else x
-
-    def forward(self, image, text):
-        image_features = self.encode_image(image, normalize=True)
-        text_features = self.encode_text(text, normalize=True)
-        if self.output_dict:
-            return {
-                "image_features": image_features,
-                "text_features": text_features,
-                "logit_scale": self.logit_scale.exp()
-            }
-        return image_features, text_features, self.logit_scale.exp()
-
-
-class CustomTextCLIP(nn.Module):
-    output_dict: torch.jit.Final[bool]
-
-    def __init__(
-            self,
-            embed_dim: int,
-            vision_cfg: CLIPVisionCfg,
-            text_cfg: CLIPTextCfg,
-            quick_gelu: bool = False,
-            cast_dtype: Optional[torch.dtype] = None,
-            output_dict: bool = False,
-    ):
-        super().__init__()
-        self.output_dict = output_dict
-        self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype)
-        self.text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype)
-        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-
-    def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False):
-        # lock image tower as per LiT - https://arxiv.org/abs/2111.07991
-        self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats)
-
-    def lock_text_tower(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True):
-        self.text.lock(unlocked_layers, freeze_layer_norm)
-
-    @torch.jit.ignore
-    def set_grad_checkpointing(self, enable=True):
-        self.visual.set_grad_checkpointing(enable)
-        self.text.set_grad_checkpointing(enable)
-
-    def encode_image(self, image, normalize: bool = False):
-        features = self.visual(image)
-        return F.normalize(features, dim=-1) if normalize else features
-
-    def encode_text(self, text, normalize: bool = False):
-        features = self.text(text)
-        return F.normalize(features, dim=-1) if normalize else features
-
-    def forward(self, image, text):
-        image_features = self.encode_image(image, normalize=True)
-        text_features = self.encode_text(text, normalize=True)
-        if self.output_dict:
-            return {
-                "image_features": image_features,
-                "text_features": text_features,
-                "logit_scale": self.logit_scale.exp()
-            }
-        return image_features, text_features, self.logit_scale.exp()
-
-
-def convert_weights_to_lp(model: nn.Module, dtype=torch.float16):
-    """Convert applicable model parameters to low-precision (bf16 or fp16)"""
-
-    def _convert_weights(l):
-        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
-            l.weight.data = l.weight.data.to(dtype)
-            if l.bias is not None:
-                l.bias.data = l.bias.data.to(dtype)
-
-        if isinstance(l, (nn.MultiheadAttention, Attention)):
-            for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
-                tensor = getattr(l, attr)
-                if tensor is not None:
-                    tensor.data = tensor.data.to(dtype)
-
-        for name in ["text_projection", "proj"]:
-            if hasattr(l, name):
-                attr = getattr(l, name)
-                if attr is not None:
-                    attr.data = attr.data.to(dtype)
-
-    model.apply(_convert_weights)
-
-
-convert_weights_to_fp16 = convert_weights_to_lp  # backwards compat
-
-
-# used to maintain checkpoint compatibility
-def convert_to_custom_text_state_dict(state_dict: dict):
-    if 'text_projection' in state_dict:
-        # old format state_dict, move text tower -> .text
-        new_state_dict = {}
-        for k, v in state_dict.items():
-            if any(k.startswith(p) for p in (
-                'text_projection',
-                'positional_embedding',
-                'token_embedding',
-                'transformer',
-                'ln_final',
-            )):
-                k = 'text.' + k
-            new_state_dict[k] = v
-        return new_state_dict
-    return state_dict
-
-
-def build_model_from_openai_state_dict(
-        state_dict: dict,
-        quick_gelu=True,
-        cast_dtype=torch.float16,
-):
-    vit = "visual.proj" in state_dict
-
-    if vit:
-        vision_width = state_dict["visual.conv1.weight"].shape[0]
-        vision_layers = len(
-            [k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
-        vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
-        grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
-        image_size = vision_patch_size * grid_size
-    else:
-        counts: list = [
-            len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
-        vision_layers = tuple(counts)
-        vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
-        output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
-        vision_patch_size = None
-        assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
-        image_size = output_width * 32
-
-    embed_dim = state_dict["text_projection"].shape[1]
-    context_length = state_dict["positional_embedding"].shape[0]
-    vocab_size = state_dict["token_embedding.weight"].shape[0]
-    transformer_width = state_dict["ln_final.weight"].shape[0]
-    transformer_heads = transformer_width // 64
-    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
-
-    vision_cfg = CLIPVisionCfg(
-        layers=vision_layers,
-        width=vision_width,
-        patch_size=vision_patch_size,
-        image_size=image_size,
-    )
-    text_cfg = CLIPTextCfg(
-        context_length=context_length,
-        vocab_size=vocab_size,
-        width=transformer_width,
-        heads=transformer_heads,
-        layers=transformer_layers,
-    )
-    model = CLIP(
-        embed_dim,
-        vision_cfg=vision_cfg,
-        text_cfg=text_cfg,
-        quick_gelu=quick_gelu,  # OpenAI models were trained with QuickGELU
-        cast_dtype=cast_dtype,
-    )
-
-    for key in ["input_resolution", "context_length", "vocab_size"]:
-        state_dict.pop(key, None)
-
-    convert_weights_to_fp16(model)  # OpenAI state dicts are partially converted to float16
-    model.load_state_dict(state_dict)
-    return model.eval()
-
-
-def trace_model(model, batch_size=256, device=torch.device('cpu')):
-    model.eval()
-    image_size = model.visual.image_size
-    example_images = torch.ones((batch_size, 3, image_size, image_size), device=device)
-    example_text = torch.zeros((batch_size, model.context_length), dtype=torch.int, device=device)
-    model = torch.jit.trace_module(
-        model,
-        inputs=dict(
-            forward=(example_images, example_text),
-            encode_text=(example_text,),
-            encode_image=(example_images,)
-        ))
-    model.visual.image_size = image_size
-    return model
-
-
-def resize_pos_embed(state_dict, model, interpolation: str = 'bicubic', antialias: bool = True):
-    # Rescale the grid of position embeddings when loading from state_dict
-    old_pos_embed = state_dict.get('visual.positional_embedding', None)
-    if old_pos_embed is None or not hasattr(model.visual, 'grid_size'):
-        return
-    grid_size = to_2tuple(model.visual.grid_size)
-    extra_tokens = 1  # FIXME detect different token configs (ie no class token, or more)
-    new_seq_len = grid_size[0] * grid_size[1] + extra_tokens
-    if new_seq_len == old_pos_embed.shape[0]:
-        return
-
-    if extra_tokens:
-        pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:]
-    else:
-        pos_emb_tok, pos_emb_img = None, old_pos_embed
-    old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img))))
-
-    logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size)
-    pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2)
-    pos_emb_img = F.interpolate(
-        pos_emb_img,
-        size=grid_size,
-        mode=interpolation,
-        antialias=antialias,
-        align_corners=False,
-    )
-    pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0]
-    if pos_emb_tok is not None:
-        new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0)
-    else:
-        new_pos_embed = pos_emb_img
-    state_dict['visual.positional_embedding'] = new_pos_embed

diffsynth/extensions/ImageQualityMetric/open_clip/model_configs/ViT-H-14.json

@@ -1,17 +0,0 @@
-{
-    "embed_dim": 1024,
-    "vision_cfg": {
-        "image_size": 224,
-        "layers": 32,
-        "width": 1280,
-        "head_width": 80,
-        "patch_size": 14
-    },
-    "text_cfg": {
-        "context_length": 77,
-        "vocab_size": 49408,
-        "width": 1024,
-        "heads": 16,
-        "layers": 24
-    }
-}

diffsynth/extensions/ImageQualityMetric/open_clip/modified_resnet.py

@@ -1,181 +0,0 @@
-from collections import OrderedDict
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .utils import freeze_batch_norm_2d
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1):
-        super().__init__()
-
-        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
-        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.act1 = nn.ReLU(inplace=True)
-
-        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.act2 = nn.ReLU(inplace=True)
-
-        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
-
-        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-        self.act3 = nn.ReLU(inplace=True)
-
-        self.downsample = None
-        self.stride = stride
-
-        if stride > 1 or inplanes != planes * Bottleneck.expansion:
-            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
-            self.downsample = nn.Sequential(OrderedDict([
-                ("-1", nn.AvgPool2d(stride)),
-                ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
-                ("1", nn.BatchNorm2d(planes * self.expansion))
-            ]))
-
-    def forward(self, x: torch.Tensor):
-        identity = x
-
-        out = self.act1(self.bn1(self.conv1(x)))
-        out = self.act2(self.bn2(self.conv2(out)))
-        out = self.avgpool(out)
-        out = self.bn3(self.conv3(out))
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.act3(out)
-        return out
-
-
-class AttentionPool2d(nn.Module):
-    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
-        self.k_proj = nn.Linear(embed_dim, embed_dim)
-        self.q_proj = nn.Linear(embed_dim, embed_dim)
-        self.v_proj = nn.Linear(embed_dim, embed_dim)
-        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
-        self.num_heads = num_heads
-
-    def forward(self, x):
-        x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1)  # NCHW -> (HW)NC
-        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
-        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
-        x, _ = F.multi_head_attention_forward(
-            query=x, key=x, value=x,
-            embed_dim_to_check=x.shape[-1],
-            num_heads=self.num_heads,
-            q_proj_weight=self.q_proj.weight,
-            k_proj_weight=self.k_proj.weight,
-            v_proj_weight=self.v_proj.weight,
-            in_proj_weight=None,
-            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
-            bias_k=None,
-            bias_v=None,
-            add_zero_attn=False,
-            dropout_p=0.,
-            out_proj_weight=self.c_proj.weight,
-            out_proj_bias=self.c_proj.bias,
-            use_separate_proj_weight=True,
-            training=self.training,
-            need_weights=False
-        )
-
-        return x[0]
-
-
-class ModifiedResNet(nn.Module):
-    """
-    A ResNet class that is similar to torchvision's but contains the following changes:
-    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
-    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
-    - The final pooling layer is a QKV attention instead of an average pool
-    """
-
-    def __init__(self, layers, output_dim, heads, image_size=224, width=64):
-        super().__init__()
-        self.output_dim = output_dim
-        self.image_size = image_size
-
-        # the 3-layer stem
-        self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(width // 2)
-        self.act1 = nn.ReLU(inplace=True)
-        self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(width // 2)
-        self.act2 = nn.ReLU(inplace=True)
-        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(width)
-        self.act3 = nn.ReLU(inplace=True)
-        self.avgpool = nn.AvgPool2d(2)
-
-        # residual layers
-        self._inplanes = width  # this is a *mutable* variable used during construction
-        self.layer1 = self._make_layer(width, layers[0])
-        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
-        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
-        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
-
-        embed_dim = width * 32  # the ResNet feature dimension
-        self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim)
-
-        self.init_parameters()
-
-    def _make_layer(self, planes, blocks, stride=1):
-        layers = [Bottleneck(self._inplanes, planes, stride)]
-
-        self._inplanes = planes * Bottleneck.expansion
-        for _ in range(1, blocks):
-            layers.append(Bottleneck(self._inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    def init_parameters(self):
-        if self.attnpool is not None:
-            std = self.attnpool.c_proj.in_features ** -0.5
-            nn.init.normal_(self.attnpool.q_proj.weight, std=std)
-            nn.init.normal_(self.attnpool.k_proj.weight, std=std)
-            nn.init.normal_(self.attnpool.v_proj.weight, std=std)
-            nn.init.normal_(self.attnpool.c_proj.weight, std=std)
-
-        for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]:
-            for name, param in resnet_block.named_parameters():
-                if name.endswith("bn3.weight"):
-                    nn.init.zeros_(param)
-
-    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
-        assert unlocked_groups == 0, 'partial locking not currently supported for this model'
-        for param in self.parameters():
-            param.requires_grad = False
-        if freeze_bn_stats:
-            freeze_batch_norm_2d(self)
-
-    @torch.jit.ignore
-    def set_grad_checkpointing(self, enable=True):
-        # FIXME support for non-transformer
-        pass
-
-    def stem(self, x):
-        x = self.act1(self.bn1(self.conv1(x)))
-        x = self.act2(self.bn2(self.conv2(x)))
-        x = self.act3(self.bn3(self.conv3(x)))
-        x = self.avgpool(x)
-        return x
-
-    def forward(self, x):
-        x = self.stem(x)
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-        x = self.attnpool(x)
-
-        return x

diffsynth/extensions/ImageQualityMetric/open_clip/openai.py

@@ -1,144 +0,0 @@
-""" OpenAI pretrained model functions
-
-Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
-"""
-
-import os
-import warnings
-from typing import List, Optional, Union
-
-import torch
-
-from .model import build_model_from_openai_state_dict, convert_weights_to_lp, get_cast_dtype
-from .pretrained import get_pretrained_url, list_pretrained_models_by_tag, download_pretrained_from_url
-
-__all__ = ["list_openai_models", "load_openai_model"]
-
-
-def list_openai_models() -> List[str]:
-    """Returns the names of available CLIP models"""
-    return list_pretrained_models_by_tag('openai')
-
-
-def load_openai_model(
-        name: str,
-        precision: Optional[str] = None,
-        device: Optional[Union[str, torch.device]] = None,
-        jit: bool = True,
-        cache_dir: Optional[str] = None,
-):
-    """Load a CLIP model
-
-    Parameters
-    ----------
-    name : str
-        A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
-    precision: str
-        Model precision, if None defaults to 'fp32' if device == 'cpu' else 'fp16'.
-    device : Union[str, torch.device]
-        The device to put the loaded model
-    jit : bool
-        Whether to load the optimized JIT model (default) or more hackable non-JIT model.
-    cache_dir : Optional[str]
-        The directory to cache the downloaded model weights
-
-    Returns
-    -------
-    model : torch.nn.Module
-        The CLIP model
-    preprocess : Callable[[PIL.Image], torch.Tensor]
-        A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
-    """
-    if device is None:
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-    if precision is None:
-        precision = 'fp32' if device == 'cpu' else 'fp16'
-
-    if get_pretrained_url(name, 'openai'):
-        model_path = download_pretrained_from_url(get_pretrained_url(name, 'openai'), cache_dir=cache_dir)
-    elif os.path.isfile(name):
-        model_path = name
-    else:
-        raise RuntimeError(f"Model {name} not found; available models = {list_openai_models()}")
-
-    try:
-        # loading JIT archive
-        model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval()
-        state_dict = None
-    except RuntimeError:
-        # loading saved state dict
-        if jit:
-            warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
-            jit = False
-        state_dict = torch.load(model_path, map_location="cpu")
-
-    if not jit:
-        # Build a non-jit model from the OpenAI jitted model state dict
-        cast_dtype = get_cast_dtype(precision)
-        try:
-            model = build_model_from_openai_state_dict(state_dict or model.state_dict(), cast_dtype=cast_dtype)
-        except KeyError:
-            sd = {k[7:]: v for k, v in state_dict["state_dict"].items()}
-            model = build_model_from_openai_state_dict(sd, cast_dtype=cast_dtype)
-
-        # model from OpenAI state dict is in manually cast fp16 mode, must be converted for AMP/fp32/bf16 use
-        model = model.to(device)
-        if precision.startswith('amp') or precision == 'fp32':
-            model.float()
-        elif precision == 'bf16':
-            convert_weights_to_lp(model, dtype=torch.bfloat16)
-
-        return model
-
-    # patch the device names
-    device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
-    device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]
-
-    def patch_device(module):
-        try:
-            graphs = [module.graph] if hasattr(module, "graph") else []
-        except RuntimeError:
-            graphs = []
-
-        if hasattr(module, "forward1"):
-            graphs.append(module.forward1.graph)
-
-        for graph in graphs:
-            for node in graph.findAllNodes("prim::Constant"):
-                if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"):
-                    node.copyAttributes(device_node)
-
-    model.apply(patch_device)
-    patch_device(model.encode_image)
-    patch_device(model.encode_text)
-
-    # patch dtype to float32 (typically for CPU)
-    if precision == 'fp32':
-        float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
-        float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
-        float_node = float_input.node()
-
-        def patch_float(module):
-            try:
-                graphs = [module.graph] if hasattr(module, "graph") else []
-            except RuntimeError:
-                graphs = []
-
-            if hasattr(module, "forward1"):
-                graphs.append(module.forward1.graph)
-
-            for graph in graphs:
-                for node in graph.findAllNodes("aten::to"):
-                    inputs = list(node.inputs())
-                    for i in [1, 2]:  # dtype can be the second or third argument to aten::to()
-                        if inputs[i].node()["value"] == 5:
-                            inputs[i].node().copyAttributes(float_node)
-
-        model.apply(patch_float)
-        patch_float(model.encode_image)
-        patch_float(model.encode_text)
-        model.float()
-
-    # ensure image_size attr available at consistent location for both jit and non-jit
-    model.visual.image_size = model.input_resolution.item()
-    return model

diffsynth/extensions/ImageQualityMetric/open_clip/pretrained.py

@@ -1,376 +0,0 @@
-import hashlib
-import os
-import urllib
-import warnings
-from functools import partial
-from typing import Dict, Union
-
-from tqdm import tqdm
-
-from .version import __version__
-
-try:
-    from huggingface_hub import hf_hub_download
-    hf_hub_download = partial(hf_hub_download, library_name="open_clip", library_version=__version__)
-    _has_hf_hub = True
-except ImportError:
-    hf_hub_download = None
-    _has_hf_hub = False
-
-
-def _pcfg(url='', hf_hub='', mean=None, std=None):
-    return dict(
-        url=url,
-        hf_hub=hf_hub,
-        mean=mean,
-        std=std,
-    )
-
-
-_RN50 = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt"),
-    yfcc15m=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt"),
-    cc12m=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt"),
-)
-
-_RN50_quickgelu = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt"),
-    yfcc15m=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt"),
-    cc12m=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt"),
-)
-
-_RN101 = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt"),
-    yfcc15m=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt"),
-)
-
-_RN101_quickgelu = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt"),
-    yfcc15m=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt"),
-)
-
-_RN50x4 = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt"),
-)
-
-_RN50x16 = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt"),
-)
-
-_RN50x64 = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt"),
-)
-
-_VITB32 = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"),
-    laion400m_e31=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"),
-    laion400m_e32=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"),
-    laion2b_e16=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-laion2b_e16-af8dbd0c.pth"),
-    laion2b_s34b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-laion2B-s34B-b79K/')
-)
-
-_VITB32_quickgelu = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"),
-    laion400m_e31=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"),
-    laion400m_e32=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"),
-)
-
-_VITB16 = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt"),
-    laion400m_e31=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e31-00efa78f.pt"),
-    laion400m_e32=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e32-55e67d44.pt"),
-    # laion400m_32k=_pcfg(
-    #     url="",
-    #     mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
-    # laion400m_64k=_pcfg(
-    #     url="",
-    #     mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
-    laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-B-16-laion2B-s34B-b88K/'),
-)
-
-_VITB16_PLUS_240 = dict(
-    laion400m_e31=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e31-8fb26589.pt"),
-    laion400m_e32=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e32-699c4b84.pt"),
-)
-
-_VITL14 = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt"),
-    laion400m_e31=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e31-69988bb6.pt"),
-    laion400m_e32=_pcfg(
-        "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e32-3d133497.pt"),
-    laion2b_s32b_b82k=_pcfg(
-        hf_hub='laion/CLIP-ViT-L-14-laion2B-s32B-b82K/',
-        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
-)
-
-_VITL14_336 = dict(
-    openai=_pcfg(
-        "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt"),
-)
-
-_VITH14 = dict(
-    laion2b_s32b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-laion2B-s32B-b79K/'),
-)
-
-_VITg14 = dict(
-    laion2b_s12b_b42k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s12B-b42K/'),
-    laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s34B-b88K/'),
-)
-
-_VITbigG14 = dict(
-    laion2b_s39b_b160k=_pcfg(hf_hub='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/'),
-)
-
-_robertaViTB32 = dict(
-    laion2b_s12b_b32k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-roberta-base-laion2B-s12B-b32k/'),
-)
-
-_xlmRobertaBaseViTB32 = dict(
-    laion5b_s13b_b90k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k/'),
-)
-
-_xlmRobertaLargeFrozenViTH14 = dict(
-    frozen_laion5b_s13b_b90k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-frozen-xlm-roberta-large-laion5B-s13B-b90k/'),
-)
-
-_convnext_base = dict(
-    laion400m_s13b_b51k=_pcfg(hf_hub='laion/CLIP-convnext_base-laion400M-s13B-b51K/'),
-)
-
-_convnext_base_w = dict(
-    laion2b_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion2B-s13B-b82K/'),
-    laion2b_s13b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg/'),
-    laion_aesthetic_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion_aesthetic-s13B-b82K/'),
-)
-
-_convnext_base_w_320 = dict(
-    laion_aesthetic_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K/'),
-    laion_aesthetic_s13b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K-augreg/'),
-)
-
-_convnext_large_d = dict(
-    laion2b_s26b_b102k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg/'),
-)
-
-_convnext_large_d_320 = dict(
-    laion2b_s29b_b131k_ft=_pcfg(hf_hub='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft/'),
-    laion2b_s29b_b131k_ft_soup=_pcfg(hf_hub='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup/'),
-)
-
-_convnext_xxlarge = dict(
-    laion2b_s34b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg/'),
-    laion2b_s34b_b82k_augreg_rewind=_pcfg(hf_hub='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-rewind/'),
-    laion2b_s34b_b82k_augreg_soup=_pcfg(hf_hub='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup/'),
-)
-
-_coca_VITB32 = dict(
-    laion2b_s13b_b90k=_pcfg(hf_hub='laion/CoCa-ViT-B-32-laion2B-s13B-b90k/'),
-    mscoco_finetuned_laion2b_s13b_b90k=_pcfg(hf_hub='laion/mscoco_finetuned_CoCa-ViT-B-32-laion2B-s13B-b90k/')
-)
-
-_coca_VITL14 = dict(
-    laion2b_s13b_b90k=_pcfg(hf_hub='laion/CoCa-ViT-L-14-laion2B-s13B-b90k/'),
-    mscoco_finetuned_laion2b_s13b_b90k=_pcfg(hf_hub='laion/mscoco_finetuned_CoCa-ViT-L-14-laion2B-s13B-b90k/')
-)
-
-
-_PRETRAINED = {
-    "RN50": _RN50,
-    "RN50-quickgelu": _RN50_quickgelu,
-    "RN101": _RN101,
-    "RN101-quickgelu": _RN101_quickgelu,
-    "RN50x4": _RN50x4,
-    "RN50x16": _RN50x16,
-    "RN50x64": _RN50x64,
-    "ViT-B-32": _VITB32,
-    "ViT-B-32-quickgelu": _VITB32_quickgelu,
-    "ViT-B-16": _VITB16,
-    "ViT-B-16-plus-240": _VITB16_PLUS_240,
-    "ViT-L-14": _VITL14,
-    "ViT-L-14-336": _VITL14_336,
-    "ViT-H-14": _VITH14,
-    "ViT-g-14": _VITg14,
-    "ViT-bigG-14": _VITbigG14,
-    "roberta-ViT-B-32": _robertaViTB32,
-    "xlm-roberta-base-ViT-B-32": _xlmRobertaBaseViTB32,
-    "xlm-roberta-large-ViT-H-14": _xlmRobertaLargeFrozenViTH14,
-    "convnext_base": _convnext_base,
-    "convnext_base_w": _convnext_base_w,
-    "convnext_base_w_320": _convnext_base_w_320,
-    "convnext_large_d": _convnext_large_d,
-    "convnext_large_d_320": _convnext_large_d_320,
-    "convnext_xxlarge": _convnext_xxlarge,
-    "coca_ViT-B-32": _coca_VITB32,
-    "coca_ViT-L-14": _coca_VITL14,
-}
-
-
-def _clean_tag(tag: str):
-    # normalize pretrained tags
-    return tag.lower().replace('-', '_')
-
-
-def list_pretrained(as_str: bool = False):
-    """ returns list of pretrained models
-    Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True
-    """
-    return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()]
-
-
-def list_pretrained_models_by_tag(tag: str):
-    """ return all models having the specified pretrain tag """
-    models = []
-    tag = _clean_tag(tag)
-    for k in _PRETRAINED.keys():
-        if tag in _PRETRAINED[k]:
-            models.append(k)
-    return models
-
-
-def list_pretrained_tags_by_model(model: str):
-    """ return all pretrain tags for the specified model architecture """
-    tags = []
-    if model in _PRETRAINED:
-        tags.extend(_PRETRAINED[model].keys())
-    return tags
-
-
-def is_pretrained_cfg(model: str, tag: str):
-    if model not in _PRETRAINED:
-        return False
-    return _clean_tag(tag) in _PRETRAINED[model]
-
-
-def get_pretrained_cfg(model: str, tag: str):
-    if model not in _PRETRAINED:
-        return {}
-    model_pretrained = _PRETRAINED[model]
-    return model_pretrained.get(_clean_tag(tag), {})
-
-
-def get_pretrained_url(model: str, tag: str):
-    cfg = get_pretrained_cfg(model, _clean_tag(tag))
-    return cfg.get('url', '')
-
-
-def download_pretrained_from_url(
-        url: str,
-        cache_dir: Union[str, None] = None,
-):
-    if not cache_dir:
-        cache_dir = os.path.expanduser("~/.cache/clip")
-    os.makedirs(cache_dir, exist_ok=True)
-    filename = os.path.basename(url)
-
-    if 'openaipublic' in url:
-        expected_sha256 = url.split("/")[-2]
-    elif 'mlfoundations' in url:
-        expected_sha256 = os.path.splitext(filename)[0].split("-")[-1]
-    else:
-        expected_sha256 = ''
-
-    download_target = os.path.join(cache_dir, filename)
-
-    if os.path.exists(download_target) and not os.path.isfile(download_target):
-        raise RuntimeError(f"{download_target} exists and is not a regular file")
-
-    if os.path.isfile(download_target):
-        if expected_sha256:
-            if hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256):
-                return download_target
-            else:
-                warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
-        else:
-            return download_target
-
-    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
-        with tqdm(total=int(source.headers.get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop:
-            while True:
-                buffer = source.read(8192)
-                if not buffer:
-                    break
-
-                output.write(buffer)
-                loop.update(len(buffer))
-
-    if expected_sha256 and not hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256):
-        raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match")
-
-    return download_target
-
-
-def has_hf_hub(necessary=False):
-    if not _has_hf_hub and necessary:
-        # if no HF Hub module installed, and it is necessary to continue, raise error
-        raise RuntimeError(
-            'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.')
-    return _has_hf_hub
-
-
-def download_pretrained_from_hf(
-        model_id: str,
-        filename: str = 'open_clip_pytorch_model.bin',
-        revision=None,
-        cache_dir: Union[str, None] = None,
-):
-    has_hf_hub(True)
-    cached_file = hf_hub_download(model_id, filename, revision=revision, cache_dir=cache_dir)
-    return cached_file
-
-
-def download_pretrained(
-        cfg: Dict,
-        force_hf_hub: bool = False,
-        cache_dir: Union[str, None] = None,
-):
-    target = ''
-    if not cfg:
-        return target
-
-    download_url = cfg.get('url', '')
-    download_hf_hub = cfg.get('hf_hub', '')
-    if download_hf_hub and force_hf_hub:
-        # use HF hub even if url exists
-        download_url = ''
-
-    if download_url:
-        target = download_pretrained_from_url(download_url, cache_dir=cache_dir)
-    elif download_hf_hub:
-        has_hf_hub(True)
-        # we assume the hf_hub entries in pretrained config combine model_id + filename in
-        # 'org/model_name/filename.pt' form. To specify just the model id w/o filename and
-        # use 'open_clip_pytorch_model.bin' default, there must be a trailing slash 'org/model_name/'.
-        model_id, filename = os.path.split(download_hf_hub)
-        if filename:
-            target = download_pretrained_from_hf(model_id, filename=filename, cache_dir=cache_dir)
-        else:
-            target = download_pretrained_from_hf(model_id, cache_dir=cache_dir)
-
-    return target

diffsynth/extensions/ImageQualityMetric/open_clip/push_to_hf_hub.py

@@ -1,243 +0,0 @@
-import argparse
-import json
-from pathlib import Path
-from tempfile import TemporaryDirectory
-from typing import Optional, Tuple
-
-import torch
-
-try:
-    from huggingface_hub import (
-        create_repo,
-        get_hf_file_metadata,
-        hf_hub_download,
-        hf_hub_url,
-        repo_type_and_id_from_hf_id,
-        upload_folder,
-    )
-    from huggingface_hub.utils import EntryNotFoundError
-    _has_hf_hub = True
-except ImportError:
-    _has_hf_hub = False
-
-from .factory import create_model_from_pretrained, get_model_config, get_tokenizer
-from .tokenizer import HFTokenizer
-
-
-def save_config_for_hf(
-        model,
-        config_path: str,
-        model_config: Optional[dict]
-):
-    preprocess_cfg = {
-        'mean': model.visual.image_mean,
-        'std': model.visual.image_std,
-    }
-    hf_config = {
-        'model_cfg': model_config,
-        'preprocess_cfg': preprocess_cfg,
-    }
-
-    with config_path.open('w') as f:
-        json.dump(hf_config, f, indent=2)
-
-
-def save_for_hf(
-    model,
-    tokenizer: HFTokenizer,
-    model_config: dict,
-    save_directory: str,
-    weights_filename='open_clip_pytorch_model.bin',
-    config_filename='open_clip_config.json',
-):
-    save_directory = Path(save_directory)
-    save_directory.mkdir(exist_ok=True, parents=True)
-
-    weights_path = save_directory / weights_filename
-    torch.save(model.state_dict(), weights_path)
-
-    tokenizer.save_pretrained(save_directory)
-
-    config_path = save_directory / config_filename
-    save_config_for_hf(model, config_path, model_config=model_config)
-
-
-def push_to_hf_hub(
-    model,
-    tokenizer,
-    model_config: Optional[dict],
-    repo_id: str,
-    commit_message: str = 'Add model',
-    token: Optional[str] = None,
-    revision: Optional[str] = None,
-    private: bool = False,
-    create_pr: bool = False,
-    model_card: Optional[dict] = None,
-):
-    if not isinstance(tokenizer, HFTokenizer):
-        # default CLIP tokenizers use https://huggingface.co/openai/clip-vit-large-patch14
-        tokenizer = HFTokenizer('openai/clip-vit-large-patch14')
-
-    # Create repo if it doesn't exist yet
-    repo_url = create_repo(repo_id, token=token, private=private, exist_ok=True)
-
-    # Infer complete repo_id from repo_url
-    # Can be different from the input `repo_id` if repo_owner was implicit
-    _, repo_owner, repo_name = repo_type_and_id_from_hf_id(repo_url)
-    repo_id = f"{repo_owner}/{repo_name}"
-
-    # Check if README file already exist in repo
-    try:
-        get_hf_file_metadata(hf_hub_url(repo_id=repo_id, filename="README.md", revision=revision))
-        has_readme = True
-    except EntryNotFoundError:
-        has_readme = False
-
-    # Dump model and push to Hub
-    with TemporaryDirectory() as tmpdir:
-        # Save model weights and config.
-        save_for_hf(
-            model,
-            tokenizer=tokenizer,
-            model_config=model_config,
-            save_directory=tmpdir,
-        )
-
-        # Add readme if it does not exist
-        if not has_readme:
-            model_card = model_card or {}
-            model_name = repo_id.split('/')[-1]
-            readme_path = Path(tmpdir) / "README.md"
-            readme_text = generate_readme(model_card, model_name)
-            readme_path.write_text(readme_text)
-
-        # Upload model and return
-        return upload_folder(
-            repo_id=repo_id,
-            folder_path=tmpdir,
-            revision=revision,
-            create_pr=create_pr,
-            commit_message=commit_message,
-        )
-
-
-def push_pretrained_to_hf_hub(
-    model_name,
-    pretrained: str,
-    repo_id: str,
-    image_mean: Optional[Tuple[float, ...]] = None,
-    image_std: Optional[Tuple[float, ...]] = None,
-    commit_message: str = 'Add model',
-    token: Optional[str] = None,
-    revision: Optional[str] = None,
-    private: bool = False,
-    create_pr: bool = False,
-    model_card: Optional[dict] = None,
-):
-    model, preprocess_eval = create_model_from_pretrained(
-        model_name,
-        pretrained=pretrained,
-        image_mean=image_mean,
-        image_std=image_std,
-    )
-
-    model_config = get_model_config(model_name)
-    assert model_config
-
-    tokenizer = get_tokenizer(model_name)
-
-    push_to_hf_hub(
-        model=model,
-        tokenizer=tokenizer,
-        model_config=model_config,
-        repo_id=repo_id,
-        commit_message=commit_message,
-        token=token,
-        revision=revision,
-        private=private,
-        create_pr=create_pr,
-        model_card=model_card,
-    )
-
-
-def generate_readme(model_card: dict, model_name: str):
-    readme_text = "---\n"
-    readme_text += "tags:\n- zero-shot-image-classification\n- clip\n"
-    readme_text += "library_tag: open_clip\n"
-    readme_text += f"license: {model_card.get('license', 'mit')}\n"
-    if 'details' in model_card and 'Dataset' in model_card['details']:
-        readme_text += 'datasets:\n'
-        readme_text += f"- {model_card['details']['Dataset'].lower()}\n"
-    readme_text += "---\n"
-    readme_text += f"# Model card for {model_name}\n"
-    if 'description' in model_card:
-        readme_text += f"\n{model_card['description']}\n"
-    if 'details' in model_card:
-        readme_text += f"\n## Model Details\n"
-        for k, v in model_card['details'].items():
-            if isinstance(v, (list, tuple)):
-                readme_text += f"- **{k}:**\n"
-                for vi in v:
-                    readme_text += f"  - {vi}\n"
-            elif isinstance(v, dict):
-                readme_text += f"- **{k}:**\n"
-                for ki, vi in v.items():
-                    readme_text += f"  - {ki}: {vi}\n"
-            else:
-                readme_text += f"- **{k}:** {v}\n"
-    if 'usage' in model_card:
-        readme_text += f"\n## Model Usage\n"
-        readme_text += model_card['usage']
-        readme_text += '\n'
-
-    if 'comparison' in model_card:
-        readme_text += f"\n## Model Comparison\n"
-        readme_text += model_card['comparison']
-        readme_text += '\n'
-
-    if 'citation' in model_card:
-        readme_text += f"\n## Citation\n"
-        if not isinstance(model_card['citation'], (list, tuple)):
-            citations = [model_card['citation']]
-        else:
-            citations = model_card['citation']
-        for c in citations:
-            readme_text += f"```bibtex\n{c}\n```\n"
-
-    return readme_text
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Push to Hugging Face Hub")
-    parser.add_argument(
-        "--model", type=str, help="Name of the model to use.",
-    )
-    parser.add_argument(
-        "--pretrained", type=str,
-        help="Use a pretrained CLIP model weights with the specified tag or file path.",
-    )
-    parser.add_argument(
-        "--repo-id", type=str,
-        help="Destination HF Hub repo-id ie 'organization/model_id'.",
-    )
-    parser.add_argument(
-        '--image-mean', type=float, nargs='+', default=None, metavar='MEAN',
-        help='Override default image mean value of dataset')
-    parser.add_argument(
-        '--image-std', type=float, nargs='+', default=None, metavar='STD',
-        help='Override default image std deviation of of dataset')
-    args = parser.parse_args()
-
-    print(f'Saving model {args.model} with pretrained weights {args.pretrained} to Hugging Face Hub at {args.repo_id}')
-
-    # FIXME add support to pass model_card json / template from file via cmd line
-
-    push_pretrained_to_hf_hub(
-        args.model,
-        args.pretrained,
-        args.repo_id,
-        image_mean=args.image_mean,  # override image mean/std if trained w/ non defaults
-        image_std=args.image_std,
-    )
-
-    print(f'{args.model} saved.')

diffsynth/extensions/ImageQualityMetric/open_clip/timm_model.py

@@ -1,127 +0,0 @@
-""" timm model adapter
-
-Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model.
-"""
-import logging
-from collections import OrderedDict
-
-import torch
-import torch.nn as nn
-
-try:
-    import timm
-    from timm.models.layers import Mlp, to_2tuple
-    try:
-        # old timm imports < 0.8.1
-        from timm.models.layers.attention_pool2d import RotAttentionPool2d
-        from timm.models.layers.attention_pool2d import AttentionPool2d as AbsAttentionPool2d
-    except ImportError:
-        # new timm imports >= 0.8.1
-        from timm.layers import RotAttentionPool2d
-        from timm.layers import AttentionPool2d as AbsAttentionPool2d
-except ImportError:
-    timm = None
-
-from .utils import freeze_batch_norm_2d
-
-
-class TimmModel(nn.Module):
-    """ timm model adapter
-    # FIXME this adapter is a work in progress, may change in ways that break weight compat
-    """
-
-    def __init__(
-            self,
-            model_name,
-            embed_dim,
-            image_size=224,
-            pool='avg',
-            proj='linear',
-            proj_bias=False,
-            drop=0.,
-            drop_path=None,
-            pretrained=False,
-    ):
-        super().__init__()
-        if timm is None:
-            raise RuntimeError("Please `pip install timm` to use timm models.")
-
-        self.image_size = to_2tuple(image_size)
-        timm_kwargs = {}
-        if drop_path is not None:
-            timm_kwargs['drop_path_rate'] = drop_path
-        self.trunk = timm.create_model(model_name, pretrained=pretrained, **timm_kwargs)
-        feat_size = self.trunk.default_cfg.get('pool_size', None)
-        feature_ndim = 1 if not feat_size else 2
-        if pool in ('abs_attn', 'rot_attn'):
-            assert feature_ndim == 2
-            # if attn pooling used, remove both classifier and default pool
-            self.trunk.reset_classifier(0, global_pool='')
-        else:
-            # reset global pool if pool config set, otherwise leave as network default
-            reset_kwargs = dict(global_pool=pool) if pool else {}
-            self.trunk.reset_classifier(0, **reset_kwargs)
-        prev_chs = self.trunk.num_features
-
-        head_layers = OrderedDict()
-        if pool == 'abs_attn':
-            head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim)
-            prev_chs = embed_dim
-        elif pool == 'rot_attn':
-            head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim)
-            prev_chs = embed_dim
-        else:
-            assert proj, 'projection layer needed if non-attention pooling is used.'
-
-        # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used
-        if proj == 'linear':
-            head_layers['drop'] = nn.Dropout(drop)
-            head_layers['proj'] = nn.Linear(prev_chs, embed_dim, bias=proj_bias)
-        elif proj == 'mlp':
-            head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=(drop, 0), bias=(True, proj_bias))
-
-        self.head = nn.Sequential(head_layers)
-
-    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
-        """ lock modules
-        Args:
-            unlocked_groups (int): leave last n layer groups unlocked (default: 0)
-        """
-        if not unlocked_groups:
-            # lock full model
-            for param in self.trunk.parameters():
-                param.requires_grad = False
-            if freeze_bn_stats:
-                freeze_batch_norm_2d(self.trunk)
-        else:
-            # NOTE: partial freeze requires latest timm (master) branch and is subject to change
-            try:
-                # FIXME import here until API stable and in an official release
-                from timm.models.helpers import group_parameters, group_modules
-            except ImportError:
-                raise RuntimeError(
-                    'Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`')
-            matcher = self.trunk.group_matcher()
-            gparams = group_parameters(self.trunk, matcher)
-            max_layer_id = max(gparams.keys())
-            max_layer_id = max_layer_id - unlocked_groups
-            for group_idx in range(max_layer_id + 1):
-                group = gparams[group_idx]
-                for param in group:
-                    self.trunk.get_parameter(param).requires_grad = False
-            if freeze_bn_stats:
-                gmodules = group_modules(self.trunk, matcher, reverse=True)
-                gmodules = {k for k, v in gmodules.items() if v <= max_layer_id}
-                freeze_batch_norm_2d(self.trunk, gmodules)
-
-    @torch.jit.ignore
-    def set_grad_checkpointing(self, enable=True):
-        try:
-            self.trunk.set_grad_checkpointing(enable)
-        except Exception as e:
-            logging.warning('grad checkpointing not supported for this timm image tower, continuing without...')
-
-    def forward(self, x):
-        x = self.trunk(x)
-        x = self.head(x)
-        return x

diffsynth/extensions/ImageQualityMetric/open_clip/tokenizer.py

@@ -1,211 +0,0 @@
-""" CLIP tokenizer
-
-Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
-"""
-import gzip
-import html
-import os
-from functools import lru_cache
-from typing import Union, List
-
-import ftfy
-import regex as re
-import torch
-
-# https://stackoverflow.com/q/62691279
-import os
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-
-@lru_cache()
-def default_bpe():
-    current_dir = os.path.dirname(os.path.abspath(__file__))
-    project_root = os.path.abspath(os.path.join(current_dir, '../../../../'))
-    quality_metric_path = os.path.join(project_root, 'models', 'QualityMetric')
-    return os.path.join(quality_metric_path, "bpe_simple_vocab_16e6.txt.gz")
-
-
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a significant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8+n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """Return set of symbol pairs in a word.
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-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
-
-
-class SimpleTokenizer(object):
-    def __init__(self, bpe_path: str = default_bpe(), special_tokens=None):
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
-        merges = merges[1:49152-256-2+1]
-        merges = [tuple(merge.split()) for merge in merges]
-        vocab = list(bytes_to_unicode().values())
-        vocab = vocab + [v+'</w>' for v in vocab]
-        for merge in merges:
-            vocab.append(''.join(merge))
-        if not special_tokens:
-            special_tokens = ['<start_of_text>', '<end_of_text>']
-        else:
-            special_tokens = ['<start_of_text>', '<end_of_text>'] + special_tokens
-        vocab.extend(special_tokens)
-        self.encoder = dict(zip(vocab, range(len(vocab))))
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.bpe_ranks = dict(zip(merges, range(len(merges))))
-        self.cache = {t:t for t in special_tokens}
-        special = "|".join(special_tokens)
-        self.pat = re.compile(special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
-
-        self.vocab_size = len(self.encoder)
-        self.all_special_ids = [self.encoder[t] for t in special_tokens]
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token[:-1]) + ( token[-1] + '</w>',)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token+'</w>'
-
-        while True:
-            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                    new_word.extend(word[i:j])
-                    i = j
-                except:
-                    new_word.extend(word[i:])
-                    break
-
-                if word[i] == first and i < len(word)-1 and word[i+1] == second:
-                    new_word.append(first+second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = ' '.join(word)
-        self.cache[token] = word
-        return word
-
-    def encode(self, text):
-        bpe_tokens = []
-        text = whitespace_clean(basic_clean(text)).lower()
-        for token in re.findall(self.pat, text):
-            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
-            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
-        return bpe_tokens
-
-    def decode(self, tokens):
-        text = ''.join([self.decoder[token] for token in tokens])
-        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
-        return text
-    
-    def __call__(self, texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor:
-        """
-        Returns the tokenized representation of given input string(s)
-
-        Parameters
-        ----------
-        texts : Union[str, List[str]]
-            An input string or a list of input strings to tokenize
-        context_length : int
-            The context length to use; all CLIP models use 77 as the context length
-
-        Returns
-        -------
-        A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]
-        """
-        if isinstance(texts, str):
-            texts = [texts]
-
-        sot_token = self.encoder["<start_of_text>"]
-        eot_token = self.encoder["<end_of_text>"]
-        all_tokens = [[sot_token] + self.encode(text) + [eot_token] for text in texts]
-        result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
-
-        for i, tokens in enumerate(all_tokens):
-            if len(tokens) > context_length:
-                tokens = tokens[:context_length]  # Truncate
-                tokens[-1] = eot_token
-            result[i, :len(tokens)] = torch.tensor(tokens)
-
-        return result
-
-
-
-class HFTokenizer:
-    """HuggingFace tokenizer wrapper"""
-
-    def __init__(self, tokenizer_name: str):
-        from transformers import AutoTokenizer
-        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
-
-    def save_pretrained(self, dest):
-        self.tokenizer.save_pretrained(dest)
-
-    def __call__(self, texts: Union[str, List[str]], context_length: int = 77) -> torch.Tensor:
-        # same cleaning as for default tokenizer, except lowercasing
-        # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance
-        if isinstance(texts, str):
-            texts = [texts]
-        texts = [whitespace_clean(basic_clean(text)) for text in texts]
-        input_ids = self.tokenizer(
-            texts,
-            return_tensors='pt',
-            max_length=context_length,
-            padding='max_length',
-            truncation=True,
-        ).input_ids
-        return input_ids

diffsynth/extensions/ImageQualityMetric/open_clip/transform.py

@@ -1,216 +0,0 @@
-import warnings
-from dataclasses import dataclass, asdict
-from typing import Any, Dict, Optional, Sequence, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torchvision.transforms.functional as F
-from functools import partial
-from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \
-    CenterCrop
-
-from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
-
-
-@dataclass
-class AugmentationCfg:
-    scale: Tuple[float, float] = (0.9, 1.0)
-    ratio: Optional[Tuple[float, float]] = None
-    color_jitter: Optional[Union[float, Tuple[float, float, float]]] = None
-    interpolation: Optional[str] = None
-    re_prob: Optional[float] = None
-    re_count: Optional[int] = None
-    use_timm: bool = False
-
-
-class ResizeMaxSize(nn.Module):
-
-    def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=0):
-        super().__init__()
-        if not isinstance(max_size, int):
-            raise TypeError(f"Size should be int. Got {type(max_size)}")
-        self.max_size = max_size
-        self.interpolation = interpolation
-        self.fn = min if fn == 'min' else min
-        self.fill = fill
-
-    def forward(self, img):
-        if isinstance(img, torch.Tensor):
-            height, width = img.shape[1:]
-        else:
-            width, height = img.size
-        scale = self.max_size / float(max(height, width))
-        if scale != 1.0:
-            new_size = tuple(round(dim * scale) for dim in (height, width))
-            img = F.resize(img, new_size, self.interpolation)
-            pad_h = self.max_size - new_size[0]
-            pad_w = self.max_size - new_size[1]
-            img = F.pad(img, padding=[pad_w//2, pad_h//2, pad_w - pad_w//2, pad_h - pad_h//2], fill=self.fill)
-        return img
-
-
-def _convert_to_rgb_or_rgba(image):
-    if image.mode == 'RGBA':
-        return image
-    else:
-        return image.convert('RGB')
-
-# def transform_and_split(merged, transform_fn, normalize_fn):
-#     transformed = transform_fn(merged)
-#     crop_img, crop_label = torch.split(transformed, [3,1], dim=0)
-
-#     # crop_img = _convert_to_rgb(crop_img)
-#     crop_img = normalize_fn(ToTensor()(crop_img))
-#     return crop_img, crop_label
-
-class MaskAwareNormalize(nn.Module):
-    def __init__(self, mean, std):
-        super().__init__()
-        self.normalize = Normalize(mean=mean, std=std)
-
-    def forward(self, tensor):
-        if tensor.shape[0] == 4:
-            return torch.cat([self.normalize(tensor[:3]), tensor[3:]], dim=0)
-        else:
-            return self.normalize(tensor)
-
-def image_transform(
-        image_size: int,
-        is_train: bool,
-        mean: Optional[Tuple[float, ...]] = None,
-        std: Optional[Tuple[float, ...]] = None,
-        resize_longest_max: bool = False,
-        fill_color: int = 0,
-        aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None,
-):
-    mean = mean or OPENAI_DATASET_MEAN
-    if not isinstance(mean, (list, tuple)):
-        mean = (mean,) * 3
-
-    std = std or OPENAI_DATASET_STD
-    if not isinstance(std, (list, tuple)):
-        std = (std,) * 3
-
-    if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]:
-        # for square size, pass size as int so that Resize() uses aspect preserving shortest edge
-        image_size = image_size[0]
-
-    if isinstance(aug_cfg, dict):
-        aug_cfg = AugmentationCfg(**aug_cfg)
-    else:
-        aug_cfg = aug_cfg or AugmentationCfg()
-    normalize = MaskAwareNormalize(mean=mean, std=std)
-    if is_train:
-        aug_cfg_dict = {k: v for k, v in asdict(aug_cfg).items() if v is not None}
-        use_timm = aug_cfg_dict.pop('use_timm', False)
-        if use_timm:
-            assert False, "not tested for augmentation with mask"
-            from timm.data import create_transform  # timm can still be optional
-            if isinstance(image_size, (tuple, list)):
-                assert len(image_size) >= 2
-                input_size = (3,) + image_size[-2:]
-            else:
-                input_size = (3, image_size, image_size)
-            # by default, timm aug randomly alternates bicubic & bilinear for better robustness at inference time
-            aug_cfg_dict.setdefault('interpolation', 'random')
-            aug_cfg_dict.setdefault('color_jitter', None)  # disable by default
-            train_transform = create_transform(
-                input_size=input_size,
-                is_training=True,
-                hflip=0.,
-                mean=mean,
-                std=std,
-                re_mode='pixel',
-                **aug_cfg_dict,
-            )
-        else:
-            train_transform = Compose([
-                _convert_to_rgb_or_rgba,
-                ToTensor(),
-                RandomResizedCrop(
-                    image_size,
-                    scale=aug_cfg_dict.pop('scale'),
-                    interpolation=InterpolationMode.BICUBIC,
-                ),
-                normalize,
-            ])
-            if aug_cfg_dict:
-                warnings.warn(f'Unused augmentation cfg items, specify `use_timm` to use ({list(aug_cfg_dict.keys())}).')
-        return train_transform
-    else:
-        transforms = [
-            _convert_to_rgb_or_rgba,
-            ToTensor(),
-        ]
-        if resize_longest_max:
-            transforms.extend([
-                ResizeMaxSize(image_size, fill=fill_color)
-            ])
-        else:
-            transforms.extend([
-                Resize(image_size, interpolation=InterpolationMode.BICUBIC),
-                CenterCrop(image_size),
-            ])
-        transforms.extend([
-            normalize,
-        ])
-        return Compose(transforms)
-
-
-# def image_transform_region(
-#         image_size: int,
-#         is_train: bool,
-#         mean: Optional[Tuple[float, ...]] = None,
-#         std: Optional[Tuple[float, ...]] = None,
-#         resize_longest_max: bool = False,
-#         fill_color: int = 0,
-#         aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None,
-# ):
-#     mean = mean or OPENAI_DATASET_MEAN
-#     if not isinstance(mean, (list, tuple)):
-#         mean = (mean,) * 3
-
-#     std = std or OPENAI_DATASET_STD
-#     if not isinstance(std, (list, tuple)):
-#         std = (std,) * 3
-
-#     if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]:
-#         # for square size, pass size as int so that Resize() uses aspect preserving shortest edge
-#         image_size = image_size[0]
-
-#     if isinstance(aug_cfg, dict):
-#         aug_cfg = AugmentationCfg(**aug_cfg)
-#     else:
-#         aug_cfg = aug_cfg or AugmentationCfg()
-#     normalize = Normalize(mean=mean, std=std)
-#     if is_train:
-#         aug_cfg_dict = {k: v for k, v in asdict(aug_cfg).items() if v is not None}
-        
-#         transform = Compose([
-#                 RandomResizedCrop(
-#                     image_size,
-#                     scale=aug_cfg_dict.pop('scale'),
-#                     interpolation=InterpolationMode.BICUBIC,
-#                 ),
-#                 ])
-#         train_transform = Compose([
-#             partial(transform_and_split, transform_fn=transform,normalize_fn=normalize)
-#         ])
-#         return train_transform
-#     else:
-#         if resize_longest_max:
-#             transform = [
-#                 ResizeMaxSize(image_size, fill=fill_color)
-#             ]
-#             val_transform = Compose([
-#                 partial(transform_and_split, transform_fn=transform,normalize_fn=normalize),
-#             ])
-#         else:
-#             transform = [
-#                 Resize(image_size, interpolation=InterpolationMode.BICUBIC),
-#                 CenterCrop(image_size),
-#             ]
-#             val_transform = Compose([
-#                 partial(transform_and_split, transform_fn=transform,normalize_fn=normalize),
-#             ])
-#         return val_transform

diffsynth/extensions/ImageQualityMetric/open_clip/transformer.py

@@ -1,727 +0,0 @@
-from collections import OrderedDict
-import math
-from typing import Callable, Optional, Sequence, Tuple
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.utils.checkpoint import checkpoint
-
-from .utils import to_2tuple
-
-
-class LayerNormFp32(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back)."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        x = F.layer_norm(x.to(torch.float32), self.normalized_shape, self.weight, self.bias, self.eps)
-        return x.to(orig_type)
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm (with cast back to input dtype)."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
-        return x.to(orig_type)
-
-
-class QuickGELU(nn.Module):
-    # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory
-    def forward(self, x: torch.Tensor):
-        return x * torch.sigmoid(1.702 * x)
-
-
-class LayerScale(nn.Module):
-    def __init__(self, dim, init_values=1e-5, inplace=False):
-        super().__init__()
-        self.inplace = inplace
-        self.gamma = nn.Parameter(init_values * torch.ones(dim))
-
-    def forward(self, x):
-        return x.mul_(self.gamma) if self.inplace else x * self.gamma
-
-
-class PatchDropout(nn.Module):
-    """
-    https://arxiv.org/abs/2212.00794
-    """
-
-    def __init__(self, prob, exclude_first_token=True):
-        super().__init__()
-        assert 0 <= prob < 1.
-        self.prob = prob
-        self.exclude_first_token = exclude_first_token  # exclude CLS token
-
-    def forward(self, x):
-        if not self.training or self.prob == 0.:
-            return x
-
-        if self.exclude_first_token:
-            cls_tokens, x = x[:, :1], x[:, 1:]
-        else:
-            cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1])
-
-        batch = x.size()[0]
-        num_tokens = x.size()[1]
-
-        batch_indices = torch.arange(batch)
-        batch_indices = batch_indices[..., None]
-
-        keep_prob = 1 - self.prob
-        num_patches_keep = max(1, int(num_tokens * keep_prob))
-
-        rand = torch.randn(batch, num_tokens)
-        patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices
-
-        x = x[batch_indices, patch_indices_keep]
-
-        if self.exclude_first_token:
-            x = torch.cat((cls_tokens, x), dim=1)
-
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(
-            self,
-            dim,
-            num_heads=8,
-            qkv_bias=True,
-            scaled_cosine=False,
-            scale_heads=False,
-            logit_scale_max=math.log(1. / 0.01),
-            attn_drop=0.,
-            proj_drop=0.
-    ):
-        super().__init__()
-        self.scaled_cosine = scaled_cosine
-        self.scale_heads = scale_heads
-        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-        self.scale = self.head_dim ** -0.5
-        self.logit_scale_max = logit_scale_max
-
-        # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original
-        self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale)
-        if qkv_bias:
-            self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3))
-        else:
-            self.in_proj_bias = None
-
-        if self.scaled_cosine:
-            self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))))
-        else:
-            self.logit_scale = None
-        self.attn_drop = nn.Dropout(attn_drop)
-        if self.scale_heads:
-            self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1)))
-        else:
-            self.head_scale = None
-        self.out_proj = nn.Linear(dim, dim)
-        self.out_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x, attn_mask: Optional[torch.Tensor] = None):
-        L, N, C = x.shape
-        q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk(3, dim=-1)
-        q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)
-        k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)
-        v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)
-
-        if self.logit_scale is not None:
-            attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2))
-            logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp()
-            attn = attn.view(N, self.num_heads, L, L) * logit_scale
-            attn = attn.view(-1, L, L)
-        else:
-            q = q * self.scale
-            attn = torch.bmm(q, k.transpose(-1, -2))
-
-        if attn_mask is not None:
-            if attn_mask.dtype == torch.bool:
-                new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype)
-                new_attn_mask.masked_fill_(attn_mask, float("-inf"))
-                attn_mask = new_attn_mask
-            attn += attn_mask
-
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = torch.bmm(attn, v)
-        if self.head_scale is not None:
-            x = x.view(N, self.num_heads, L, C) * self.head_scale
-            x = x.view(-1, L, C)
-        x = x.transpose(0, 1).reshape(L, N, C)
-        x = self.out_proj(x)
-        x = self.out_drop(x)
-        return x
-
-
-class AttentionalPooler(nn.Module):
-    def __init__(
-            self,
-            d_model: int,
-            context_dim: int,
-            n_head: int = 8,
-            n_queries: int = 256,
-            norm_layer: Callable = LayerNorm
-    ):
-        super().__init__()
-        self.query = nn.Parameter(torch.randn(n_queries, d_model))
-        self.attn = nn.MultiheadAttention(d_model, n_head, kdim=context_dim, vdim=context_dim)
-        self.ln_q = norm_layer(d_model)
-        self.ln_k = norm_layer(context_dim)
-
-    def forward(self, x: torch.Tensor):
-        x = self.ln_k(x).permute(1, 0, 2)  # NLD -> LND
-        N = x.shape[1]
-        q = self.ln_q(self.query)
-        out = self.attn(self._repeat(q, N), x, x, need_weights=False)[0]
-        return out.permute(1, 0, 2)  # LND -> NLD
-
-    def _repeat(self, query, N: int):
-        return query.unsqueeze(1).repeat(1, N, 1)
-
-
-class ResidualAttentionBlock(nn.Module):
-    def __init__(
-            self,
-            d_model: int,
-            n_head: int,
-            mlp_ratio: float = 4.0,
-            ls_init_value: float = None,
-            act_layer: Callable = nn.GELU,
-            norm_layer: Callable = LayerNorm,
-            is_cross_attention: bool = False,
-    ):
-        super().__init__()
-
-        self.ln_1 = norm_layer(d_model)
-        self.attn = nn.MultiheadAttention(d_model, n_head)
-        self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
-        if is_cross_attention:
-            self.ln_1_kv = norm_layer(d_model)
-
-        self.ln_2 = norm_layer(d_model)
-        mlp_width = int(d_model * mlp_ratio)
-        self.mlp = nn.Sequential(OrderedDict([
-            ("c_fc", nn.Linear(d_model, mlp_width)),
-            ("gelu", act_layer()),
-            ("c_proj", nn.Linear(mlp_width, d_model))
-        ]))
-        self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
-
-    def attention(
-            self,
-            q_x: torch.Tensor,
-            k_x: Optional[torch.Tensor] = None,
-            v_x: Optional[torch.Tensor] = None,
-            attn_mask: Optional[torch.Tensor] = None,
-    ):
-        k_x = k_x if k_x is not None else q_x
-        v_x = v_x if v_x is not None else q_x
-
-        attn_mask = attn_mask.to(q_x.dtype) if attn_mask is not None else None
-        return self.attn(
-            q_x, k_x, v_x, need_weights=False, attn_mask=attn_mask
-        )[0]
-
-    def forward(
-            self,
-            q_x: torch.Tensor,
-            k_x: Optional[torch.Tensor] = None,
-            v_x: Optional[torch.Tensor] = None,
-            attn_mask: Optional[torch.Tensor] = None,
-    ):
-        k_x = self.ln_1_kv(k_x) if hasattr(self, "ln_1_kv") and k_x is not None else None
-        v_x = self.ln_1_kv(v_x) if hasattr(self, "ln_1_kv") and v_x is not None else None
-
-        x = q_x + self.ls_1(self.attention(q_x=self.ln_1(q_x), k_x=k_x, v_x=v_x, attn_mask=attn_mask))
-        x = x + self.ls_2(self.mlp(self.ln_2(x)))
-        return x
-
-
-class CustomResidualAttentionBlock(nn.Module):
-    def __init__(
-            self,
-            d_model: int,
-            n_head: int,
-            mlp_ratio: float = 4.0,
-            ls_init_value: float = None,
-            act_layer: Callable = nn.GELU,
-            norm_layer: Callable = LayerNorm,
-            scale_cosine_attn: bool = False,
-            scale_heads: bool = False,
-            scale_attn: bool = False,
-            scale_fc: bool = False,
-    ):
-        super().__init__()
-
-        self.ln_1 = norm_layer(d_model)
-        self.attn = Attention(
-            d_model, n_head,
-            scaled_cosine=scale_cosine_attn,
-            scale_heads=scale_heads,
-        )
-        self.ln_attn = norm_layer(d_model) if scale_attn else nn.Identity()
-        self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
-
-        self.ln_2 = norm_layer(d_model)
-        mlp_width = int(d_model * mlp_ratio)
-        self.mlp = nn.Sequential(OrderedDict([
-            ("c_fc", nn.Linear(d_model, mlp_width)),
-            ('ln', norm_layer(mlp_width) if scale_fc else nn.Identity()),
-            ("gelu", act_layer()),
-            ("c_proj", nn.Linear(mlp_width, d_model))
-        ]))
-        self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
-
-    def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
-        x = x + self.ls_1(self.ln_attn(self.attn(self.ln_1(x), attn_mask=attn_mask)))
-        x = x + self.ls_2(self.mlp(self.ln_2(x)))
-        return x
-
-
-class Transformer(nn.Module):
-    def __init__(
-            self,
-            width: int,
-            layers: int,
-            heads: int,
-            mlp_ratio: float = 4.0,
-            ls_init_value: float = None,
-            act_layer: Callable = nn.GELU,
-            norm_layer: Callable = LayerNorm,
-    ):
-        super().__init__()
-        self.width = width
-        self.layers = layers
-        self.grad_checkpointing = False
-
-        self.resblocks = nn.ModuleList([
-            ResidualAttentionBlock(
-                width, heads, mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer)
-            for _ in range(layers)
-        ])
-
-    def get_cast_dtype(self) -> torch.dtype:
-        return self.resblocks[0].mlp.c_fc.weight.dtype
-
-    def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
-        for r in self.resblocks:
-            if self.grad_checkpointing and not torch.jit.is_scripting():
-                # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372
-                x = checkpoint(r, x, None, None, attn_mask)
-            else:
-                x = r(x, attn_mask=attn_mask)
-        return x
-
-
-class VisionTransformer(nn.Module):
-    output_tokens: torch.jit.Final[bool]
-
-    def __init__(
-            self,
-            image_size: int,
-            patch_size: int,
-            width: int,
-            layers: int,
-            heads: int,
-            mlp_ratio: float,
-            ls_init_value: float = None,
-            global_average_pool: bool = False,
-            attentional_pool: bool = False,
-            n_queries: int = 256,
-            attn_pooler_heads: int = 8,
-            output_dim: int = 512,
-            patch_dropout: float = 0.,
-            input_patchnorm: bool = False,
-            act_layer: Callable = nn.GELU,
-            norm_layer: Callable = LayerNorm,
-            output_tokens: bool = False
-    ):
-        super().__init__()
-        self.output_tokens = output_tokens
-        image_height, image_width = self.image_size = to_2tuple(image_size)
-        patch_height, patch_width = self.patch_size = to_2tuple(patch_size)
-        self.grid_size = (image_height // patch_height, image_width // patch_width)
-        self.output_dim = output_dim
-
-        # whether to layernorm each patch, as done in dual patchnorm paper - https://arxiv.org/abs/2302.01327v1
-        self.input_patchnorm = input_patchnorm
-
-        if input_patchnorm:
-            patch_input_dim = patch_height * patch_width * 3
-            self.patchnorm_pre_ln = LayerNorm(patch_input_dim)
-            self.conv1 = nn.Linear(patch_input_dim, width)
-        else:
-            self.patchnorm_pre_ln = nn.Identity()
-            self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
-
-        # class embeddings and positional embeddings
-        scale = width ** -0.5
-        self.class_embedding = nn.Parameter(scale * torch.randn(width))
-        self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width))
-
-        # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn
-        self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity()
-
-        self.ln_pre = norm_layer(width)
-        self.transformer = Transformer(
-            width,
-            layers,
-            heads,
-            mlp_ratio,
-            ls_init_value=ls_init_value,
-            act_layer=act_layer,
-            norm_layer=norm_layer,
-        )
-
-        self.global_average_pool = global_average_pool
-        if attentional_pool:
-            self.attn_pool = AttentionalPooler(output_dim, width, n_head=attn_pooler_heads, n_queries=n_queries)
-            self.ln_post = norm_layer(output_dim)
-            self.proj = nn.Parameter(scale * torch.randn(output_dim, output_dim))
-        else:
-            self.attn_pool = None
-            self.ln_post = norm_layer(width)
-            self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
-
-        self.init_parameters()
-
-    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
-        for param in self.parameters():
-            param.requires_grad = False
-
-        if unlocked_groups != 0:
-            groups = [
-                [
-                    self.conv1,
-                    self.class_embedding,
-                    self.positional_embedding,
-                    self.ln_pre,
-                ],
-                *self.transformer.resblocks[:-1],
-                [
-                    self.transformer.resblocks[-1],
-                    self.ln_post,
-                ],
-                self.proj,
-            ]
-
-            def _unlock(x):
-                if isinstance(x, Sequence):
-                    for g in x:
-                        _unlock(g)
-                else:
-                    if isinstance(x, torch.nn.Parameter):
-                        x.requires_grad = True
-                    else:
-                        for p in x.parameters():
-                            p.requires_grad = True
-
-            _unlock(groups[-unlocked_groups:])
-
-    def init_parameters(self):
-        # FIXME OpenAI CLIP did not define an init for the VisualTransformer
-        # TODO experiment if default PyTorch init, below, or alternate init is best.
-
-        # nn.init.normal_(self.class_embedding, std=self.scale)
-        # nn.init.normal_(self.positional_embedding, std=self.scale)
-        #
-        # proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
-        # attn_std = self.transformer.width ** -0.5
-        # fc_std = (2 * self.transformer.width) ** -0.5
-        # for block in self.transformer.resblocks:
-        #     nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-        #     nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-        #     nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-        #     nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-        #
-        # if self.text_projection is not None:
-        #     nn.init.normal_(self.text_projection, std=self.scale)
-        pass
-
-    @torch.jit.ignore
-    def set_grad_checkpointing(self, enable=True):
-        self.transformer.grad_checkpointing = enable
-
-    def _global_pool(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        if self.global_average_pool:
-            return x.mean(dim=1), x
-        else:
-            return x[:, 0], x[:, 1:]
-
-    def forward(self, x: torch.Tensor, skip_pool: bool = False):
-
-        # to patches - whether to use dual patchnorm - https://arxiv.org/abs/2302.01327v1
-        if self.input_patchnorm:
-            # einops - rearrange(x, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)')
-            x = x.reshape(x.shape[0], x.shape[1], self.grid_size[0], self.patch_size[0], self.grid_size[1], self.patch_size[1])
-            x = x.permute(0, 2, 4, 1, 3, 5)
-            x = x.reshape(x.shape[0], self.grid_size[0] * self.grid_size[1], -1)
-            x = self.patchnorm_pre_ln(x)
-            x = self.conv1(x)
-        else:
-            x = self.conv1(x)  # shape = [*, width, grid, grid]
-            x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
-            x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
-
-        # class embeddings and positional embeddings
-        x = torch.cat(
-            [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),
-             x], dim=1)  # shape = [*, grid ** 2 + 1, width]
-        x = x + self.positional_embedding.to(x.dtype)
-
-        # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in
-        x = self.patch_dropout(x)
-        x = self.ln_pre(x)
-
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-
-        if skip_pool:
-            return x
-
-        if self.attn_pool is not None:
-            x = self.attn_pool(x)
-            x = self.ln_post(x)
-            pooled, tokens = self._global_pool(x)
-        else:
-            pooled, tokens = self._global_pool(x)
-            pooled = self.ln_post(pooled)
-
-        if self.proj is not None:
-            pooled = pooled @ self.proj
-
-        if self.output_tokens:
-            return pooled, tokens
-        
-        return pooled
-
-
-class TextTransformer(nn.Module):
-    output_tokens: torch.jit.Final[bool]
-
-    def __init__(
-            self,
-            context_length: int = 77,
-            vocab_size: int = 49408,
-            width: int = 512,
-            heads: int = 8,
-            layers: int = 12,
-            ls_init_value: float = None,
-            output_dim: int = 512,
-            act_layer: Callable = nn.GELU,
-            norm_layer: Callable = LayerNorm,
-            embed_cls: bool = False,
-            pad_id: int = 0,
-            output_tokens: bool = False,
-    ):
-        super().__init__()
-        self.output_tokens = output_tokens
-        self.num_pos = self.context_length = context_length
-        self.vocab_size = vocab_size
-        self.width = width
-        self.output_dim = output_dim
-        self.heads = heads
-        self.pad_id = pad_id
-
-        self.text_projection = nn.Parameter(torch.empty(width, output_dim))
-
-        if embed_cls:
-            self.cls_emb = nn.Parameter(torch.empty(width))
-            self.num_pos += 1
-        else:
-            self.cls_emb = None
-
-        self.token_embedding = nn.Embedding(vocab_size, width)
-        self.positional_embedding = nn.Parameter(torch.empty(self.num_pos, width))
-        self.transformer = Transformer(
-            width=width,
-            layers=layers,
-            heads=heads,
-            ls_init_value=ls_init_value,
-            act_layer=act_layer,
-            norm_layer=norm_layer,
-        )
-        self.ln_final = norm_layer(width)
-
-        self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False)
-
-        self.init_parameters()
-
-    def init_parameters(self):
-        nn.init.normal_(self.token_embedding.weight, std=0.02)
-        nn.init.normal_(self.positional_embedding, std=0.01)
-        if self.cls_emb is not None:
-            nn.init.normal_(self.cls_emb, std=0.01)
-
-        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
-        attn_std = self.transformer.width ** -0.5
-        fc_std = (2 * self.transformer.width) ** -0.5
-        for block in self.transformer.resblocks:
-            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-
-        if self.text_projection is not None:
-            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
-
-    @torch.jit.ignore
-    def set_grad_checkpointing(self, enable=True):
-        self.transformer.grad_checkpointing = enable
-
-    def build_attention_mask(self):
-        # lazily create causal attention mask, with full attention between the tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(self.num_pos, self.num_pos)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)  # zero out the lower diagonal
-        return mask
-
-    def build_cls_mask(self, text, cast_dtype: torch.dtype):
-        cls_mask = (text != self.pad_id).unsqueeze(1)
-        cls_mask = F.pad(cls_mask, (1, 0, cls_mask.shape[2], 0), value=1.0)
-        additive_mask = torch.empty(cls_mask.shape, dtype=cast_dtype, device=cls_mask.device)
-        additive_mask.fill_(0)
-        additive_mask.masked_fill_(~cls_mask, float("-inf"))
-        additive_mask = torch.repeat_interleave(additive_mask, self.heads, 0)
-        return additive_mask
-
-    def _repeat(self, t, N: int):
-        return t.reshape(1, 1, -1).repeat(N, 1, 1)
-
-    def forward(self, text):
-        cast_dtype = self.transformer.get_cast_dtype()
-        seq_len = text.shape[1]
-
-        x = self.token_embedding(text).to(cast_dtype)  # [batch_size, n_ctx, d_model]
-        attn_mask = self.attn_mask
-        if self.cls_emb is not None:
-            seq_len += 1
-            x = torch.cat([x, self._repeat(self.cls_emb, x.shape[0])], dim=1)
-            cls_mask = self.build_cls_mask(text, cast_dtype)
-            attn_mask = attn_mask[None, :seq_len, :seq_len] + cls_mask[:, :seq_len, :seq_len]
-
-        x = x + self.positional_embedding[:seq_len].to(cast_dtype)
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x, attn_mask=attn_mask)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-
-        # x.shape = [batch_size, n_ctx, transformer.width]
-        # take features from the eot embedding (eot_token is the highest number in each sequence)
-        if self.cls_emb is not None:
-            pooled, tokens = x[:, -1], x[:, :-1]
-            pooled = self.ln_final(pooled)
-        else:
-            x = self.ln_final(x)
-            pooled, tokens = x[torch.arange(x.shape[0]), text.argmax(dim=-1)], x
-
-        if self.text_projection is not None:
-            pooled = pooled @ self.text_projection
-
-        if self.output_tokens:
-            return pooled, tokens
-
-        return pooled
-
-
-class MultimodalTransformer(Transformer):
-    def __init__(
-            self,
-            width: int,
-            layers: int,
-            heads: int,
-            context_length: int = 77,
-            mlp_ratio: float = 4.0,
-            ls_init_value: float = None,
-            act_layer: Callable = nn.GELU,
-            norm_layer: Callable = LayerNorm,
-            output_dim: int = 512,
-    ):
-
-        super().__init__(
-            width=width,
-            layers=layers,
-            heads=heads,
-            mlp_ratio=mlp_ratio,
-            ls_init_value=ls_init_value,
-            act_layer=act_layer,
-            norm_layer=norm_layer,
-        )
-        self.context_length = context_length
-        self.cross_attn = nn.ModuleList([
-            ResidualAttentionBlock(
-                width,
-                heads,
-                mlp_ratio,
-                ls_init_value=ls_init_value,
-                act_layer=act_layer,
-                norm_layer=norm_layer,
-                is_cross_attention=True,
-            )
-            for _ in range(layers)
-        ])
-
-        self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False)
-
-        self.ln_final = norm_layer(width)
-        self.text_projection = nn.Parameter(torch.empty(width, output_dim))
-
-    def init_parameters(self):
-        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
-        attn_std = self.transformer.width ** -0.5
-        fc_std = (2 * self.transformer.width) ** -0.5
-        for block in self.transformer.resblocks:
-            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-        for block in self.transformer.cross_attn:
-            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-
-        if self.text_projection is not None:
-            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
-
-    def build_attention_mask(self):
-        # lazily create causal attention mask, with full attention between the tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(self.context_length, self.context_length)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)  # zero out the lower diagonal
-        return mask
-
-    def forward(self, image_embs, text_embs):
-        text_embs = text_embs.permute(1, 0, 2)  # NLD -> LNDsq
-        image_embs = image_embs.permute(1, 0, 2)  # NLD -> LND
-        seq_len = text_embs.shape[0]
-
-        for resblock, cross_attn in zip(self.resblocks, self.cross_attn):
-            if self.grad_checkpointing and not torch.jit.is_scripting():
-                # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372
-                text_embs = checkpoint(resblock, text_embs, None, None, self.attn_mask[:seq_len, :seq_len])
-                text_embs = checkpoint(cross_attn, text_embs, image_embs, image_embs, None)
-            else:
-                text_embs = resblock(text_embs, attn_mask=self.attn_mask[:seq_len, :seq_len])
-                text_embs = cross_attn(text_embs, k_x=image_embs, v_x=image_embs)
-
-        x = text_embs.permute(1, 0, 2)  # LND -> NLD
-        x = self.ln_final(x)
-
-        if self.text_projection is not None:
-            x = x @ self.text_projection
-
-        return x
-
-    @torch.jit.ignore
-    def set_grad_checkpointing(self, enable=True):
-        self.grad_checkpointing = enable

diffsynth/extensions/ImageQualityMetric/open_clip/utils.py

@@ -1,60 +0,0 @@
-from itertools import repeat
-import collections.abc
-
-from torch import nn as nn
-from torchvision.ops.misc import FrozenBatchNorm2d
-
-
-def freeze_batch_norm_2d(module, module_match={}, name=''):
-    """
-    Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is
-    itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and
-    returned. Otherwise, the module is walked recursively and submodules are converted in place.
-
-    Args:
-        module (torch.nn.Module): Any PyTorch module.
-        module_match (dict): Dictionary of full module names to freeze (all if empty)
-        name (str): Full module name (prefix)
-
-    Returns:
-        torch.nn.Module: Resulting module
-
-    Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762
-    """
-    res = module
-    is_match = True
-    if module_match:
-        is_match = name in module_match
-    if is_match and isinstance(module, (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm)):
-        res = FrozenBatchNorm2d(module.num_features)
-        res.num_features = module.num_features
-        res.affine = module.affine
-        if module.affine:
-            res.weight.data = module.weight.data.clone().detach()
-            res.bias.data = module.bias.data.clone().detach()
-        res.running_mean.data = module.running_mean.data
-        res.running_var.data = module.running_var.data
-        res.eps = module.eps
-    else:
-        for child_name, child in module.named_children():
-            full_child_name = '.'.join([name, child_name]) if name else child_name
-            new_child = freeze_batch_norm_2d(child, module_match, full_child_name)
-            if new_child is not child:
-                res.add_module(child_name, new_child)
-    return res
-
-
-# From PyTorch internals
-def _ntuple(n):
-    def parse(x):
-        if isinstance(x, collections.abc.Iterable):
-            return x
-        return tuple(repeat(x, n))
-    return parse
-
-
-to_1tuple = _ntuple(1)
-to_2tuple = _ntuple(2)
-to_3tuple = _ntuple(3)
-to_4tuple = _ntuple(4)
-to_ntuple = lambda n, x: _ntuple(n)(x)

diffsynth/extensions/ImageQualityMetric/open_clip/version.py

@@ -1 +0,0 @@
-__version__ = '2.16.0'

diffsynth/extensions/ImageQualityMetric/pickscore.py

@@ -1,112 +0,0 @@
-import torch
-from PIL import Image
-from transformers import AutoProcessor, AutoModel
-from typing import List, Union
-import os
-from .config import MODEL_PATHS
-
-class PickScore(torch.nn.Module):
-    def __init__(self, device: Union[str, torch.device], path: str = MODEL_PATHS):
-        super().__init__()
-        """Initialize the Selector with a processor and model.
-
-        Args:
-            device (Union[str, torch.device]): The device to load the model on.
-        """
-        self.device = device if isinstance(device, torch.device) else torch.device(device)
-        processor_name_or_path = path.get("clip")
-        model_pretrained_name_or_path = path.get("pickscore")
-        self.processor = AutoProcessor.from_pretrained(processor_name_or_path)
-        self.model = AutoModel.from_pretrained(model_pretrained_name_or_path).eval().to(self.device)
-
-    def _calculate_score(self, image: torch.Tensor, prompt: str, softmax: bool = False) -> float:
-        """Calculate the score for a single image and prompt.
-
-        Args:
-            image (torch.Tensor): The processed image tensor.
-            prompt (str): The prompt text.
-            softmax (bool): Whether to apply softmax to the scores.
-
-        Returns:
-            float: The score for the image.
-        """
-        with torch.no_grad():
-            # Prepare text inputs
-            text_inputs = self.processor(
-                text=prompt,
-                padding=True,
-                truncation=True,
-                max_length=77,
-                return_tensors="pt",
-            ).to(self.device)
-
-            # Embed images and text
-            image_embs = self.model.get_image_features(pixel_values=image)
-            image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True)
-            text_embs = self.model.get_text_features(**text_inputs)
-            text_embs = text_embs / torch.norm(text_embs, dim=-1, keepdim=True)
-
-            # Compute score
-            score = (text_embs @ image_embs.T)[0]
-            if softmax:
-                # Apply logit scale and softmax
-                score = torch.softmax(self.model.logit_scale.exp() * score, dim=-1)
-
-        return score.cpu().item()
-
-    @torch.no_grad()
-    def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str, softmax: bool = False) -> List[float]:
-        """Score the images based on the prompt.
-
-        Args:
-            images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s).
-            prompt (str): The prompt text.
-            softmax (bool): Whether to apply softmax to the scores.
-
-        Returns:
-            List[float]: List of scores for the images.
-        """
-        try:
-            if isinstance(images, (str, Image.Image)):
-                # Single image
-                if isinstance(images, str):
-                    pil_image = Image.open(images)
-                else:
-                    pil_image = images
-
-                # Prepare image inputs
-                image_inputs = self.processor(
-                    images=pil_image,
-                    padding=True,
-                    truncation=True,
-                    max_length=77,
-                    return_tensors="pt",
-                ).to(self.device)
-
-                return [self._calculate_score(image_inputs["pixel_values"], prompt, softmax)]
-            elif isinstance(images, list):
-                # Multiple images
-                scores = []
-                for one_image in images:
-                    if isinstance(one_image, str):
-                        pil_image = Image.open(one_image)
-                    elif isinstance(one_image, Image.Image):
-                        pil_image = one_image
-                    else:
-                        raise TypeError("The type of parameter images is illegal.")
-
-                    # Prepare image inputs
-                    image_inputs = self.processor(
-                        images=pil_image,
-                        padding=True,
-                        truncation=True,
-                        max_length=77,
-                        return_tensors="pt",
-                    ).to(self.device)
-
-                    scores.append(self._calculate_score(image_inputs["pixel_values"], prompt, softmax))
-                return scores
-            else:
-                raise TypeError("The type of parameter images is illegal.")
-        except Exception as e:
-            raise RuntimeError(f"Error in scoring images: {e}")

diffsynth/extensions/ImageQualityMetric/trainer/__init__.py

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

diffsynth/extensions/ImageQualityMetric/trainer/models/__init__.py

@@ -1,3 +0,0 @@
-from .base_model import *
-from .clip_model import *
-from .cross_modeling import *

diffsynth/extensions/ImageQualityMetric/trainer/models/base_model.py

@@ -1,7 +0,0 @@
-from dataclasses import dataclass
-
-
-
-@dataclass
-class BaseModelConfig:
-    pass

diffsynth/extensions/ImageQualityMetric/trainer/models/clip_model.py

@@ -1,146 +0,0 @@
-from dataclasses import dataclass
-from transformers import CLIPModel as HFCLIPModel
-from transformers import AutoTokenizer
-
-from torch import nn, einsum
-
-from .base_model import BaseModelConfig
-
-from transformers import CLIPConfig
-from typing import Any, Optional, Tuple, Union
-import torch
-
-from .cross_modeling import Cross_model
-
-import json, os
-
-class XCLIPModel(HFCLIPModel):
-    def __init__(self, config: CLIPConfig):
-        super().__init__(config)
-    
-    def get_text_features(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> torch.FloatTensor:
-
-        # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
-        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
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        text_outputs = self.text_model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        # pooled_output = text_outputs[1]
-        # text_features = self.text_projection(pooled_output)
-        last_hidden_state = text_outputs[0]
-        text_features = self.text_projection(last_hidden_state)
-
-        pooled_output = text_outputs[1]
-        text_features_EOS = self.text_projection(pooled_output)
-
-
-        # del last_hidden_state, text_outputs
-        # gc.collect()
-
-        return text_features, text_features_EOS
-
-    def get_image_features(
-        self,
-        pixel_values: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> torch.FloatTensor:
-        
-        # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
-        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
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        vision_outputs = self.vision_model(
-            pixel_values=pixel_values,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        # pooled_output = vision_outputs[1]  # pooled_output
-        # image_features = self.visual_projection(pooled_output)
-        last_hidden_state = vision_outputs[0]
-        image_features = self.visual_projection(last_hidden_state)  
-
-        return image_features
-
-
-
-@dataclass
-class ClipModelConfig(BaseModelConfig):
-    _target_: str = "diffsynth.extensions.QualityMetric.trainer.models.clip_model.CLIPModel"
-    pretrained_model_name_or_path: str ="checkpoints/clip-vit-base-patch32"
-
-
-class CLIPModel(nn.Module):
-    def __init__(self, ckpt, config_file=False):
-        super().__init__()
-        if config_file is None:
-            self.model = XCLIPModel.from_pretrained(ckpt)
-        else:
-            with open(os.path.join(ckpt, "config.json"), "r", encoding="utf-8") as f:
-                config = json.load(f)
-            config = CLIPConfig(**config)
-            self.model = XCLIPModel._from_config(config)
-        self.cross_model = Cross_model(dim=1024, layer_num=4, heads=16)
-    
-    def get_text_features(self, *args, **kwargs):
-        return self.model.get_text_features(*args, **kwargs)
-
-    def get_image_features(self, *args, **kwargs):
-        return self.model.get_image_features(*args, **kwargs)
-
-    def forward(self, text_inputs=None, image_inputs=None, condition_inputs=None):
-        outputs = ()
-
-        text_f, text_EOS = self.model.get_text_features(text_inputs) # B*77*1024
-        outputs += text_EOS,
-
-        image_f = self.model.get_image_features(image_inputs.half()) # 2B*257*1024
-        condition_f, _ = self.model.get_text_features(condition_inputs) # B*5*1024
-
-        sim_text_condition = einsum('b i d, b j d -> b j i', text_f, condition_f)
-        sim_text_condition = torch.max(sim_text_condition, dim=1, keepdim=True)[0]
-        sim_text_condition = sim_text_condition / sim_text_condition.max()
-        mask = torch.where(sim_text_condition > 0.01, 0, float('-inf')) # B*1*77
-
-        mask = mask.repeat(1,image_f.shape[1],1) # B*257*77
-        bc = int(image_f.shape[0]/2)
-
-        sim0 = self.cross_model(image_f[:bc,:,:], text_f,mask.half())
-        sim1 = self.cross_model(image_f[bc:,:,:], text_f,mask.half())
-        outputs += sim0[:,0,:],
-        outputs += sim1[:,0,:],
-
-        return outputs
-
-    @property
-    def logit_scale(self):
-        return self.model.logit_scale
-
-    def save(self, path):
-        self.model.save_pretrained(path)
-

diffsynth/extensions/ImageQualityMetric/trainer/models/cross_modeling.py

@@ -1,292 +0,0 @@
-import torch
-from torch import einsum, nn
-import torch.nn.functional as F
-from einops import rearrange, repeat
-
-# helper functions
-
-def exists(val):
-    return val is not None
-
-def default(val, d):
-    return val if exists(val) else d
-
-# normalization
-# they use layernorm without bias, something that pytorch does not offer
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(dim))
-        self.register_buffer("bias", torch.zeros(dim))
-
-    def forward(self, x):
-        return F.layer_norm(x, x.shape[-1:], self.weight, self.bias)
-
-# residual
-
-
-class Residual(nn.Module):
-    def __init__(self, fn):
-        super().__init__()
-        self.fn = fn
-
-    def forward(self, x, *args, **kwargs):
-        return self.fn(x, *args, **kwargs) + x
-
-
-# rotary positional embedding
-# https://arxiv.org/abs/2104.09864
-
-
-class RotaryEmbedding(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
-        self.register_buffer("inv_freq", inv_freq)
-
-    def forward(self, max_seq_len, *, device):
-        seq = torch.arange(max_seq_len, device=device, dtype=self.inv_freq.dtype)
-        freqs = einsum("i , j -> i j", seq, self.inv_freq)
-        return torch.cat((freqs, freqs), dim=-1)
-
-
-def rotate_half(x):
-    x = rearrange(x, "... (j d) -> ... j d", j=2)
-    x1, x2 = x.unbind(dim=-2)
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(pos, t):
-    return (t * pos.cos()) + (rotate_half(t) * pos.sin())
-
-
-# classic Noam Shazeer paper, except here they use SwiGLU instead of the more popular GEGLU for gating the feedforward
-# https://arxiv.org/abs/2002.05202
-
-
-class SwiGLU(nn.Module):
-    def forward(self, x):
-        x, gate = x.chunk(2, dim=-1)
-        return F.silu(gate) * x
-
-
-# parallel attention and feedforward with residual
-# discovered by Wang et al + EleutherAI from GPT-J fame
-
-class ParallelTransformerBlock(nn.Module):
-    def __init__(self, dim, dim_head=64, heads=8, ff_mult=4):
-        super().__init__()
-        self.norm = LayerNorm(dim)
-
-        attn_inner_dim = dim_head * heads
-        ff_inner_dim = dim * ff_mult
-        self.fused_dims = (attn_inner_dim, dim_head, dim_head, (ff_inner_dim * 2))
-
-        self.heads = heads
-        self.scale = dim_head**-0.5
-        self.rotary_emb = RotaryEmbedding(dim_head)
-
-        self.fused_attn_ff_proj = nn.Linear(dim, sum(self.fused_dims), bias=False)
-        self.attn_out = nn.Linear(attn_inner_dim, dim, bias=False)
-
-        self.ff_out = nn.Sequential(
-            SwiGLU(),
-            nn.Linear(ff_inner_dim, dim, bias=False)
-        )
-
-        self.register_buffer("pos_emb", None, persistent=False)
-
-
-    def get_rotary_embedding(self, n, device):
-        if self.pos_emb is not None and self.pos_emb.shape[-2] >= n:
-            return self.pos_emb[:n]
-
-        pos_emb = self.rotary_emb(n, device=device)
-        self.register_buffer("pos_emb", pos_emb, persistent=False)
-        return pos_emb
-
-    def forward(self, x, attn_mask=None):
-        """
-        einstein notation
-        b - batch
-        h - heads
-        n, i, j - sequence length (base sequence length, source, target)
-        d - feature dimension
-        """
-
-        n, device, h = x.shape[1], x.device, self.heads
-
-        # pre layernorm
-
-        x = self.norm(x)
-
-        # attention queries, keys, values, and feedforward inner
-
-        q, k, v, ff = self.fused_attn_ff_proj(x).split(self.fused_dims, dim=-1)
-
-        # split heads
-        # they use multi-query single-key-value attention, yet another Noam Shazeer paper
-        # they found no performance loss past a certain scale, and more efficient decoding obviously
-        # https://arxiv.org/abs/1911.02150
-
-        q = rearrange(q, "b n (h d) -> b h n d", h=h)
-
-        # rotary embeddings
-
-        positions = self.get_rotary_embedding(n, device)
-        q, k = map(lambda t: apply_rotary_pos_emb(positions, t), (q, k))
-
-        # scale
-
-        q = q * self.scale
-
-        # similarity
-
-        sim = einsum("b h i d, b j d -> b h i j", q, k)
-
-
-        # extra attention mask - for masking out attention from text CLS token to padding
-
-        if exists(attn_mask):
-            attn_mask = rearrange(attn_mask, 'b i j -> b 1 i j')
-            sim = sim.masked_fill(~attn_mask, -torch.finfo(sim.dtype).max)
-
-        # attention
-
-        sim = sim - sim.amax(dim=-1, keepdim=True).detach()
-        attn = sim.softmax(dim=-1)
-
-        # aggregate values
-
-        out = einsum("b h i j, b j d -> b h i d", attn, v)
-
-        # merge heads
-
-        out = rearrange(out, "b h n d -> b n (h d)")
-        return self.attn_out(out) + self.ff_out(ff)
-
-# cross attention - using multi-query + one-headed key / values as in PaLM w/ optional parallel feedforward
-
-class CrossAttention(nn.Module):
-    def __init__(
-        self,
-        dim,
-        *,
-        context_dim=None,
-        dim_head=64,
-        heads=12,
-        parallel_ff=False,
-        ff_mult=4,
-        norm_context=False
-    ):
-        super().__init__()
-        self.heads = heads
-        self.scale = dim_head ** -0.5
-        inner_dim = heads * dim_head
-        context_dim = default(context_dim, dim)
-
-        self.norm = LayerNorm(dim)
-        self.context_norm = LayerNorm(context_dim) if norm_context else nn.Identity()
-
-        self.to_q = nn.Linear(dim, inner_dim, bias=False)
-        self.to_kv = nn.Linear(context_dim, dim_head * 2, bias=False)
-        self.to_out = nn.Linear(inner_dim, dim, bias=False)
-
-        # whether to have parallel feedforward
-
-        ff_inner_dim = ff_mult * dim
-
-        self.ff = nn.Sequential(
-            nn.Linear(dim, ff_inner_dim * 2, bias=False),
-            SwiGLU(),
-            nn.Linear(ff_inner_dim, dim, bias=False)
-        ) if parallel_ff else None
-
-    def forward(self, x, context, mask):
-        """
-        einstein notation
-        b - batch
-        h - heads
-        n, i, j - sequence length (base sequence length, source, target)
-        d - feature dimension
-        """
-
-        # pre-layernorm, for queries and context
-
-        x = self.norm(x)
-        context = self.context_norm(context)
-
-        # get queries
-
-        q = self.to_q(x)
-        q = rearrange(q, 'b n (h d) -> b h n d', h = self.heads)
-
-        # scale
-
-        q = q * self.scale
-
-        # get key / values
-
-        k, v = self.to_kv(context).chunk(2, dim=-1)
-
-        # query / key similarity
-
-        sim = einsum('b h i d, b j d -> b h i j', q, k)
-
-        # attention
-        mask = mask.unsqueeze(1).repeat(1,self.heads,1,1)
-        sim = sim + mask  # context mask
-        sim = sim - sim.amax(dim=-1, keepdim=True)
-        attn = sim.softmax(dim=-1)
-
-        # aggregate
-
-        out = einsum('b h i j, b j d -> b h i d', attn, v)
-
-        # merge and combine heads
-
-        out = rearrange(out, 'b h n d -> b n (h d)')
-        out = self.to_out(out)
-
-        # add parallel feedforward (for multimodal layers)
-
-        if exists(self.ff):
-            out = out + self.ff(x)
-
-        return out
-
-
-class Cross_model(nn.Module):
-    def __init__(
-        self,
-        dim=512,
-        layer_num=4,
-        dim_head=64,
-        heads=8,
-        ff_mult=4
-    ):
-        super().__init__()
-
-        self.layers = nn.ModuleList([])
-
-
-        for ind in range(layer_num):
-            self.layers.append(nn.ModuleList([
-                Residual(CrossAttention(dim=dim, dim_head=dim_head, heads=heads, parallel_ff=True, ff_mult=ff_mult)),
-                Residual(ParallelTransformerBlock(dim=dim, dim_head=dim_head, heads=heads, ff_mult=ff_mult))
-            ]))
-
-    def forward(
-        self,
-        query_tokens,
-        context_tokens,
-        mask
-    ):
-
-        for cross_attn, self_attn_ff in self.layers:
-            query_tokens = cross_attn(query_tokens, context_tokens,mask)
-            query_tokens = self_attn_ff(query_tokens)
-
-        return query_tokens

diffsynth/extensions/RIFE/__init__.py

@@ -1,242 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import numpy as np
-from PIL import Image
-
-
-def warp(tenInput, tenFlow, device):
-    backwarp_tenGrid = {}
-    k = (str(tenFlow.device), str(tenFlow.size()))
-    if k not in backwarp_tenGrid:
-        tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device).view(
-            1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1)
-        tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=device).view(
-            1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3])
-        backwarp_tenGrid[k] = torch.cat(
-            [tenHorizontal, tenVertical], 1).to(device)
-
-    tenFlow = torch.cat([tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0),
-                         tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1)
-
-    g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1)
-    return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True)
-
-
-def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
-    return nn.Sequential(
-        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
-                  padding=padding, dilation=dilation, bias=True),        
-        nn.PReLU(out_planes)
-    )
-
-
-class IFBlock(nn.Module):
-    def __init__(self, in_planes, c=64):
-        super(IFBlock, self).__init__()
-        self.conv0 = nn.Sequential(conv(in_planes, c//2, 3, 2, 1), conv(c//2, c, 3, 2, 1),)
-        self.convblock0 = nn.Sequential(conv(c, c), conv(c, c))
-        self.convblock1 = nn.Sequential(conv(c, c), conv(c, c))
-        self.convblock2 = nn.Sequential(conv(c, c), conv(c, c))
-        self.convblock3 = nn.Sequential(conv(c, c), conv(c, c))
-        self.conv1 = nn.Sequential(nn.ConvTranspose2d(c, c//2, 4, 2, 1), nn.PReLU(c//2), nn.ConvTranspose2d(c//2, 4, 4, 2, 1))
-        self.conv2 = nn.Sequential(nn.ConvTranspose2d(c, c//2, 4, 2, 1), nn.PReLU(c//2), nn.ConvTranspose2d(c//2, 1, 4, 2, 1))
-
-    def forward(self, x, flow, scale=1):
-        x = F.interpolate(x, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False)
-        flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 1. / scale
-        feat = self.conv0(torch.cat((x, flow), 1))
-        feat = self.convblock0(feat) + feat
-        feat = self.convblock1(feat) + feat
-        feat = self.convblock2(feat) + feat
-        feat = self.convblock3(feat) + feat        
-        flow = self.conv1(feat)
-        mask = self.conv2(feat)
-        flow = F.interpolate(flow, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * scale
-        mask = F.interpolate(mask, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False)
-        return flow, mask
-
-
-class IFNet(nn.Module):
-    def __init__(self, **kwargs):
-        super(IFNet, self).__init__()
-        self.block0 = IFBlock(7+4, c=90)
-        self.block1 = IFBlock(7+4, c=90)
-        self.block2 = IFBlock(7+4, c=90)
-        self.block_tea = IFBlock(10+4, c=90)
-
-    def forward(self, x, scale_list=[4, 2, 1], training=False):
-        if training == False:
-            channel = x.shape[1] // 2
-            img0 = x[:, :channel]
-            img1 = x[:, channel:]
-        flow_list = []
-        merged = []
-        mask_list = []
-        warped_img0 = img0
-        warped_img1 = img1
-        flow = (x[:, :4]).detach() * 0
-        mask = (x[:, :1]).detach() * 0
-        block = [self.block0, self.block1, self.block2]
-        for i in range(3):
-            f0, m0 = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], mask), 1), flow, scale=scale_list[i])
-            f1, m1 = block[i](torch.cat((warped_img1[:, :3], warped_img0[:, :3], -mask), 1), torch.cat((flow[:, 2:4], flow[:, :2]), 1), scale=scale_list[i])
-            flow = flow + (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2
-            mask = mask + (m0 + (-m1)) / 2
-            mask_list.append(mask)
-            flow_list.append(flow)
-            warped_img0 = warp(img0, flow[:, :2], device=x.device)
-            warped_img1 = warp(img1, flow[:, 2:4], device=x.device)
-            merged.append((warped_img0, warped_img1))
-        '''
-        c0 = self.contextnet(img0, flow[:, :2])
-        c1 = self.contextnet(img1, flow[:, 2:4])
-        tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
-        res = tmp[:, 1:4] * 2 - 1
-        '''
-        for i in range(3):
-            mask_list[i] = torch.sigmoid(mask_list[i])
-            merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])    
-        return flow_list, mask_list[2], merged
-    
-    @staticmethod
-    def state_dict_converter():
-        return IFNetStateDictConverter()
-
-
-class IFNetStateDictConverter:
-    def __init__(self):
-        pass
-
-    def from_diffusers(self, state_dict):
-        state_dict_ = {k.replace("module.", ""): v for k, v in state_dict.items()}
-        return state_dict_
-    
-    def from_civitai(self, state_dict):
-        return self.from_diffusers(state_dict), {"upcast_to_float32": True}
-
-
-class RIFEInterpolater:
-    def __init__(self, model, device="cuda"):
-        self.model = model
-        self.device = device
-        # IFNet only does not support float16
-        self.torch_dtype = torch.float32
-
-    @staticmethod
-    def from_model_manager(model_manager):
-        return RIFEInterpolater(model_manager.fetch_model("rife"), device=model_manager.device)
-
-    def process_image(self, image):
-        width, height = image.size
-        if width % 32 != 0 or height % 32 != 0:
-            width = (width + 31) // 32
-            height = (height + 31) // 32
-            image = image.resize((width, height))
-        image = torch.Tensor(np.array(image, dtype=np.float32)[:, :, [2,1,0]] / 255).permute(2, 0, 1)
-        return image
-    
-    def process_images(self, images):
-        images = [self.process_image(image) for image in images]
-        images = torch.stack(images)
-        return images
-    
-    def decode_images(self, images):
-        images = (images[:, [2,1,0]].permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8)
-        images = [Image.fromarray(image) for image in images]
-        return images
-    
-    def add_interpolated_images(self, images, interpolated_images):
-        output_images = []
-        for image, interpolated_image in zip(images, interpolated_images):
-            output_images.append(image)
-            output_images.append(interpolated_image)
-        output_images.append(images[-1])
-        return output_images
-    
-
-    @torch.no_grad()
-    def interpolate_(self, images, scale=1.0):
-        input_tensor = self.process_images(images)
-        input_tensor = torch.cat((input_tensor[:-1], input_tensor[1:]), dim=1)
-        input_tensor = input_tensor.to(device=self.device, dtype=self.torch_dtype)
-        flow, mask, merged = self.model(input_tensor, [4/scale, 2/scale, 1/scale])
-        output_images = self.decode_images(merged[2].cpu())
-        if output_images[0].size != images[0].size:
-            output_images = [image.resize(images[0].size) for image in output_images]
-        return output_images
-    
-
-    @torch.no_grad()
-    def interpolate(self, images, scale=1.0, batch_size=4, num_iter=1, progress_bar=lambda x:x):
-        # Preprocess
-        processed_images = self.process_images(images)
-
-        for iter in range(num_iter):
-            # Input
-            input_tensor = torch.cat((processed_images[:-1], processed_images[1:]), dim=1)
-
-            # Interpolate
-            output_tensor = []
-            for batch_id in progress_bar(range(0, input_tensor.shape[0], batch_size)):
-                batch_id_ = min(batch_id + batch_size, input_tensor.shape[0])
-                batch_input_tensor = input_tensor[batch_id: batch_id_]
-                batch_input_tensor = batch_input_tensor.to(device=self.device, dtype=self.torch_dtype)
-                flow, mask, merged = self.model(batch_input_tensor, [4/scale, 2/scale, 1/scale])
-                output_tensor.append(merged[2].cpu())
-            
-            # Output
-            output_tensor = torch.concat(output_tensor, dim=0).clip(0, 1)
-            processed_images = self.add_interpolated_images(processed_images, output_tensor)
-            processed_images = torch.stack(processed_images)
-
-        # To images
-        output_images = self.decode_images(processed_images)
-        if output_images[0].size != images[0].size:
-            output_images = [image.resize(images[0].size) for image in output_images]
-        return output_images
-
-
-class RIFESmoother(RIFEInterpolater):
-    def __init__(self, model, device="cuda"):
-        super(RIFESmoother, self).__init__(model, device=device)
-
-    @staticmethod
-    def from_model_manager(model_manager):
-        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 = []
-        for batch_id in range(0, input_tensor.shape[0], batch_size):
-            batch_id_ = min(batch_id + batch_size, input_tensor.shape[0])
-            batch_input_tensor = input_tensor[batch_id: batch_id_]
-            batch_input_tensor = batch_input_tensor.to(device=self.device, dtype=self.torch_dtype)
-            flow, mask, merged = self.model(batch_input_tensor, [4/scale, 2/scale, 1/scale])
-            output_tensor.append(merged[2].cpu())
-        output_tensor = torch.concat(output_tensor, dim=0)
-        return output_tensor
-
-    @torch.no_grad()
-    def __call__(self, rendered_frames, scale=1.0, batch_size=4, num_iter=1, **kwargs):
-        # Preprocess
-        processed_images = self.process_images(rendered_frames)
-
-        for iter in range(num_iter):
-            # Input
-            input_tensor = torch.cat((processed_images[:-2], processed_images[2:]), dim=1)
-
-            # Interpolate
-            output_tensor = self.process_tensors(input_tensor, scale=scale, batch_size=batch_size)
-            
-            # Blend
-            input_tensor = torch.cat((processed_images[1:-1], output_tensor), dim=1)
-            output_tensor = self.process_tensors(input_tensor, scale=scale, batch_size=batch_size)
-
-            # Add to frames
-            processed_images[1:-1] = output_tensor
-
-        # To images
-        output_images = self.decode_images(processed_images)
-        if output_images[0].size != rendered_frames[0].size:
-            output_images = [image.resize(rendered_frames[0].size) for image in output_images]
-        return output_images

diffsynth/lora/__init__.py

@@ -1,45 +0,0 @@
-import torch
-
-
-
-class GeneralLoRALoader:
-    def __init__(self, device="cpu", torch_dtype=torch.float32):
-        self.device = device
-        self.torch_dtype = torch_dtype
-    
-    
-    def get_name_dict(self, lora_state_dict):
-        lora_name_dict = {}
-        for key in lora_state_dict:
-            if ".lora_B." not in key:
-                continue
-            keys = key.split(".")
-            if len(keys) > keys.index("lora_B") + 2:
-                keys.pop(keys.index("lora_B") + 1)
-            keys.pop(keys.index("lora_B"))
-            if keys[0] == "diffusion_model":
-                keys.pop(0)
-            keys.pop(-1)
-            target_name = ".".join(keys)
-            lora_name_dict[target_name] = (key, key.replace(".lora_B.", ".lora_A."))
-        return lora_name_dict
-
-
-    def load(self, model: torch.nn.Module, state_dict_lora, alpha=1.0):
-        updated_num = 0
-        lora_name_dict = self.get_name_dict(state_dict_lora)
-        for name, module in model.named_modules():
-            if name in lora_name_dict:
-                weight_up = state_dict_lora[lora_name_dict[name][0]].to(device=self.device, dtype=self.torch_dtype)
-                weight_down = state_dict_lora[lora_name_dict[name][1]].to(device=self.device, dtype=self.torch_dtype)
-                if len(weight_up.shape) == 4:
-                    weight_up = weight_up.squeeze(3).squeeze(2)
-                    weight_down = weight_down.squeeze(3).squeeze(2)
-                    weight_lora = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
-                else:
-                    weight_lora = alpha * torch.mm(weight_up, weight_down)
-                state_dict = module.state_dict()
-                state_dict["weight"] = state_dict["weight"].to(device=self.device, dtype=self.torch_dtype) + weight_lora
-                module.load_state_dict(state_dict)
-                updated_num += 1
-        print(f"{updated_num} tensors are updated by LoRA.")

diffsynth/models/__init__.py

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

diffsynth/models/attention.py

@@ -1,89 +0,0 @@
-import torch
-from einops import rearrange
-
-
-def low_version_attention(query, key, value, attn_bias=None):
-    scale = 1 / query.shape[-1] ** 0.5
-    query = query * scale
-    attn = torch.matmul(query, key.transpose(-2, -1))
-    if attn_bias is not None:
-        attn = attn + attn_bias
-    attn = attn.softmax(-1)
-    return attn @ value
-
-
-class Attention(torch.nn.Module):
-
-    def __init__(self, q_dim, num_heads, head_dim, kv_dim=None, bias_q=False, bias_kv=False, bias_out=False):
-        super().__init__()
-        dim_inner = head_dim * num_heads
-        kv_dim = kv_dim if kv_dim is not None else q_dim
-        self.num_heads = num_heads
-        self.head_dim = head_dim
-
-        self.to_q = torch.nn.Linear(q_dim, dim_inner, bias=bias_q)
-        self.to_k = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
-        self.to_v = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
-        self.to_out = torch.nn.Linear(dim_inner, q_dim, bias=bias_out)
-
-    def interact_with_ipadapter(self, hidden_states, q, ip_k, ip_v, scale=1.0):
-        batch_size = q.shape[0]
-        ip_k = ip_k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
-        ip_v = ip_v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
-        ip_hidden_states = torch.nn.functional.scaled_dot_product_attention(q, ip_k, ip_v)
-        hidden_states = hidden_states + scale * ip_hidden_states
-        return hidden_states
-
-    def torch_forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None, ipadapter_kwargs=None, qkv_preprocessor=None):
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-
-        batch_size = encoder_hidden_states.shape[0]
-
-        q = self.to_q(hidden_states)
-        k = self.to_k(encoder_hidden_states)
-        v = self.to_v(encoder_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 qkv_preprocessor is not None:
-            q, k, v = qkv_preprocessor(q, k, v)
-
-        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
-        if ipadapter_kwargs is not None:
-            hidden_states = self.interact_with_ipadapter(hidden_states, q, **ipadapter_kwargs)
-        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.to_out(hidden_states)
-
-        return hidden_states
-    
-    def xformers_forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None):
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-
-        q = self.to_q(hidden_states)
-        k = self.to_k(encoder_hidden_states)
-        v = self.to_v(encoder_hidden_states)
-
-        q = rearrange(q, "b f (n d) -> (b n) f d", n=self.num_heads)
-        k = rearrange(k, "b f (n d) -> (b n) f d", n=self.num_heads)
-        v = rearrange(v, "b f (n d) -> (b n) f d", n=self.num_heads)
-
-        if attn_mask is not None:
-            hidden_states = low_version_attention(q, k, v, attn_bias=attn_mask)
-        else:
-            import xformers.ops as xops
-            hidden_states = xops.memory_efficient_attention(q, k, v)
-        hidden_states = rearrange(hidden_states, "(b n) f d -> b f (n d)", n=self.num_heads)
-
-        hidden_states = hidden_states.to(q.dtype)
-        hidden_states = self.to_out(hidden_states)
-
-        return hidden_states
-
-    def forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None, ipadapter_kwargs=None, qkv_preprocessor=None):
-        return self.torch_forward(hidden_states, encoder_hidden_states=encoder_hidden_states, attn_mask=attn_mask, ipadapter_kwargs=ipadapter_kwargs, qkv_preprocessor=qkv_preprocessor)

diffsynth/models/cog_dit.py

@@ -1,408 +0,0 @@
-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

@@ -1,518 +0,0 @@
-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)
-