bugfix

fix qwen_text_encoder bug in transformers>=5.2.0

.github/workflows/publish.yaml

@@ -0,0 +1,29 @@
+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 -m build
+      - 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/

DiffSynth_Studio.py

@@ -1,15 +0,0 @@
-# Set web page format
-import streamlit as st
-st.set_page_config(layout="wide")
-# Diasble 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.
-""")

ExVideo_animatediff_train.py

@@ -1,267 +0,0 @@
-import torch, json, os, imageio
-from torchvision.transforms import v2
-from einops import rearrange
-import lightning as pl
-from diffsynth import ModelManager, EnhancedDDIMScheduler, SDVideoPipeline, SDUNet, load_state_dict, SDMotionModel
-
-
-
-def lets_dance(
-    unet: SDUNet,
-    motion_modules: SDMotionModel,
-    sample,
-    timestep,
-    encoder_hidden_states,
-    use_gradient_checkpointing=False,
-):
-    # 1. ControlNet (skip)
-    # 2. time
-    time_emb = unet.time_proj(timestep[None]).to(sample.dtype)
-    time_emb = unet.time_embedding(time_emb)
-
-    # 3. pre-process
-    hidden_states = unet.conv_in(sample)
-    text_emb = encoder_hidden_states
-    res_stack = [hidden_states]
-
-    # 4. blocks
-    def create_custom_forward(module):
-        def custom_forward(*inputs):
-            return module(*inputs)
-        return custom_forward
-    for block_id, block in enumerate(unet.blocks):
-        # 4.1 UNet
-        if use_gradient_checkpointing:
-            hidden_states, time_emb, text_emb, res_stack = torch.utils.checkpoint.checkpoint(
-                create_custom_forward(block),
-                hidden_states, time_emb, text_emb, res_stack,
-                use_reentrant=False,
-            )
-        else:
-            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
-        # 4.2 AnimateDiff
-        if block_id in motion_modules.call_block_id:
-            motion_module_id = motion_modules.call_block_id[block_id]
-            if use_gradient_checkpointing:
-                hidden_states, time_emb, text_emb, res_stack = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(motion_modules.motion_modules[motion_module_id]),
-                    hidden_states, time_emb, text_emb, res_stack,
-                    use_reentrant=False,
-                )
-            else:
-                hidden_states, time_emb, text_emb, res_stack = motion_modules.motion_modules[motion_module_id](hidden_states, time_emb, text_emb, res_stack)
-    
-    # 5. output
-    hidden_states = unet.conv_norm_out(hidden_states)
-    hidden_states = unet.conv_act(hidden_states)
-    hidden_states = unet.conv_out(hidden_states)
-
-    return hidden_states
-
-
-
-class TextVideoDataset(torch.utils.data.Dataset):
-    def __init__(self, base_path, metadata_path, steps_per_epoch=10000, training_shapes=[(128, 1, 128, 512, 512)]):
-        with open(metadata_path, "r") as f:
-            metadata = json.load(f)
-        self.path = [os.path.join(base_path, i["path"]) for i in metadata]
-        self.text = [i["text"] for i in metadata]
-        self.steps_per_epoch = steps_per_epoch
-        self.training_shapes = training_shapes
-
-        self.frame_process = []
-        for max_num_frames, interval, num_frames, height, width in training_shapes:
-            self.frame_process.append(v2.Compose([
-                v2.Resize(size=max(height, width), antialias=True),
-                v2.CenterCrop(size=(height, width)),
-                v2.Normalize(mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5]),
-            ]))
-
-
-    def load_frames_using_imageio(self, file_path, max_num_frames, start_frame_id, interval, num_frames, frame_process):
-        reader = imageio.get_reader(file_path)
-        if reader.count_frames() < max_num_frames or reader.count_frames() - 1 < start_frame_id + (num_frames - 1) * interval:
-            reader.close()
-            return None
-        
-        frames = []
-        for frame_id in range(num_frames):
-            frame = reader.get_data(start_frame_id + frame_id * interval)
-            frame = torch.tensor(frame, dtype=torch.float32)
-            frame = rearrange(frame, "H W C -> 1 C H W")
-            frame = frame_process(frame)
-            frames.append(frame)
-        reader.close()
-
-        frames = torch.concat(frames, dim=0)
-        frames = rearrange(frames, "T C H W -> C T H W")
-
-        return frames
-
-
-    def load_video(self, file_path, training_shape_id):
-        data = {}
-        max_num_frames, interval, num_frames, height, width = self.training_shapes[training_shape_id]
-        frame_process = self.frame_process[training_shape_id]
-        start_frame_id = torch.randint(0, max_num_frames - (num_frames - 1) * interval, (1,))[0]
-        frames = self.load_frames_using_imageio(file_path, max_num_frames, start_frame_id, interval, num_frames, frame_process)
-        if frames is None:
-            return None
-        else:
-            data[f"frames_{training_shape_id}"] = frames
-            data[f"start_frame_id_{training_shape_id}"] = start_frame_id
-        return data
-
-
-    def __getitem__(self, index):
-        video_data = {}
-        for training_shape_id in range(len(self.training_shapes)):
-            while True:
-                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]
-                if isinstance(text, list):
-                    text = text[torch.randint(0, len(text), (1,))[0]]
-                video_file = self.path[data_id]
-                try:
-                    data = self.load_video(video_file, training_shape_id)
-                except:
-                    data = None
-                if data is not None:
-                    data[f"text_{training_shape_id}"] = text
-                    break
-            video_data.update(data)
-        return video_data
-    
-
-    def __len__(self):
-        return self.steps_per_epoch
-
-
-
-class LightningModel(pl.LightningModule):
-    def __init__(self, learning_rate=1e-5, sd_ckpt_path=None):
-        super().__init__()
-        # Load models
-        model_manager = ModelManager(torch_dtype=torch.float16, device="cpu")
-        model_manager.load_stable_diffusion(load_state_dict(sd_ckpt_path))
-        
-        # Initialize motion modules
-        model_manager.model["motion_modules"] = SDMotionModel().to(dtype=self.dtype, device=self.device)
-
-        # Build pipeline
-        self.pipe = SDVideoPipeline.from_model_manager(model_manager)
-        self.pipe.vae_encoder.eval()
-        self.pipe.vae_encoder.requires_grad_(False)
-
-        self.pipe.vae_decoder.eval()
-        self.pipe.vae_decoder.requires_grad_(False)
-
-        self.pipe.text_encoder.eval()
-        self.pipe.text_encoder.requires_grad_(False)
-
-        self.pipe.unet.eval()
-        self.pipe.unet.requires_grad_(False)
-
-        self.pipe.motion_modules.train()
-        self.pipe.motion_modules.requires_grad_(True)
-
-        # Reset the scheduler
-        self.pipe.scheduler = EnhancedDDIMScheduler(beta_schedule="scaled_linear")
-        self.pipe.scheduler.set_timesteps(1000)
-
-        # Other parameters
-        self.learning_rate = learning_rate
-
-
-    def encode_video_with_vae(self, video):
-        video = video.to(device=self.device, dtype=self.dtype)
-        video = video.unsqueeze(0)
-        latents = self.pipe.vae_encoder.encode_video(video, batch_size=16)
-        latents = rearrange(latents[0], "C T H W -> T C H W")
-        return latents
-        
-
-    def calculate_loss(self, prompt, frames):
-        with torch.no_grad():
-            # Call video encoder
-            latents = self.encode_video_with_vae(frames)
-
-            # Call text encoder
-            prompt_embs = self.pipe.prompter.encode_prompt(self.pipe.text_encoder, prompt, device=self.device, max_length=77)
-            prompt_embs = prompt_embs.repeat(latents.shape[0], 1, 1)
-
-            # Call scheduler
-            timestep = torch.randint(0, len(self.pipe.scheduler.timesteps), (1,), device=self.device)[0]
-            noise = torch.randn_like(latents)
-            noisy_latents = self.pipe.scheduler.add_noise(latents, noise, timestep)
-
-        # Calculate loss
-        model_pred = lets_dance(
-            self.pipe.unet, self.pipe.motion_modules,
-            sample=noisy_latents, encoder_hidden_states=prompt_embs, timestep=timestep
-        )
-        loss = torch.nn.functional.mse_loss(model_pred.float(), noise.float(), reduction="mean")
-        return loss
-    
-
-    def training_step(self, batch, batch_idx):
-        # Loss
-        frames = batch["frames_0"][0]
-        prompt = batch["text_0"][0]
-        loss = self.calculate_loss(prompt, frames)
-
-        # Record log
-        self.log("train_loss", loss, prog_bar=True)
-        return loss
-
-
-    def configure_optimizers(self):
-        optimizer = torch.optim.AdamW(self.pipe.motion_modules.parameters(), lr=self.learning_rate)
-        return optimizer
-    
-
-    def on_save_checkpoint(self, checkpoint):
-        trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.pipe.motion_modules.named_parameters()))
-        trainable_param_names = [named_param[0] for named_param in trainable_param_names]
-        checkpoint["trainable_param_names"] = trainable_param_names
-
-
-
-if __name__ == '__main__':
-    # dataset and data loader
-    dataset = TextVideoDataset(
-        "/data/zhongjie/datasets/opensoraplan/data/processed",
-        "/data/zhongjie/datasets/opensoraplan/data/processed/metadata.json",
-        training_shapes=[(16, 1, 16, 512, 512)],
-        steps_per_epoch=7*10000,
-    )
-    train_loader = torch.utils.data.DataLoader(
-        dataset,
-        shuffle=True,
-        batch_size=1,
-        num_workers=4
-    )
-
-    # model
-    model = LightningModel(
-        learning_rate=1e-5,
-        sd_ckpt_path="models/stable_diffusion/v1-5-pruned-emaonly.safetensors",
-    )
-
-    # train
-    trainer = pl.Trainer(
-        max_epochs=100000,
-        accelerator="gpu",
-        devices="auto",
-        strategy="deepspeed_stage_1",
-        precision="16-mixed",
-        default_root_dir="/data/zhongjie/models/train_extended_animatediff",
-        accumulate_grad_batches=1,
-        callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)]
-    )
-    trainer.fit(
-        model=model,
-        train_dataloaders=train_loader,
-        ckpt_path=None
-    )

configs/hunyuan_dit/tokenizer/special_tokens_map.json

@@ -1,7 +0,0 @@
-{
-  "cls_token": "[CLS]",
-  "mask_token": "[MASK]",
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "unk_token": "[UNK]"
-}

configs/hunyuan_dit/tokenizer/tokenizer_config.json

@@ -1,16 +0,0 @@
-{
-  "cls_token": "[CLS]",
-  "do_basic_tokenize": true,
-  "do_lower_case": true,
-  "mask_token": "[MASK]",
-  "name_or_path": "hfl/chinese-roberta-wwm-ext",
-  "never_split": null,
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "special_tokens_map_file": "/home/chenweifeng/.cache/huggingface/hub/models--hfl--chinese-roberta-wwm-ext/snapshots/5c58d0b8ec1d9014354d691c538661bf00bfdb44/special_tokens_map.json",
-  "strip_accents": null,
-  "tokenize_chinese_chars": true,
-  "tokenizer_class": "BertTokenizer",
-  "unk_token": "[UNK]",
-  "model_max_length": 77
-}

configs/hunyuan_dit/tokenizer_t5/config.json

@@ -1,28 +0,0 @@
-{
-  "_name_or_path": "/home/patrick/t5/mt5-xl",
-  "architectures": [
-    "MT5ForConditionalGeneration"
-  ],
-  "d_ff": 5120,
-  "d_kv": 64,
-  "d_model": 2048,
-  "decoder_start_token_id": 0,
-  "dropout_rate": 0.1,
-  "eos_token_id": 1,
-  "feed_forward_proj": "gated-gelu",
-  "initializer_factor": 1.0,
-  "is_encoder_decoder": true,
-  "layer_norm_epsilon": 1e-06,
-  "model_type": "mt5",
-  "num_decoder_layers": 24,
-  "num_heads": 32,
-  "num_layers": 24,
-  "output_past": true,
-  "pad_token_id": 0,
-  "relative_attention_num_buckets": 32,
-  "tie_word_embeddings": false,
-  "tokenizer_class": "T5Tokenizer",
-  "transformers_version": "4.10.0.dev0",
-  "use_cache": true,
-  "vocab_size": 250112
-}

configs/hunyuan_dit/tokenizer_t5/special_tokens_map.json

@@ -1 +0,0 @@
-{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}

configs/hunyuan_dit/tokenizer_t5/tokenizer_config.json

@@ -1 +0,0 @@
-{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "extra_ids": 0, "additional_special_tokens": null, "special_tokens_map_file": "", "tokenizer_file": null, "name_or_path": "google/mt5-small", "model_max_length": 256, "legacy": true}

configs/stable_diffusion/tokenizer/special_tokens_map.json

@@ -1,24 +0,0 @@
-{
-  "bos_token": {
-    "content": "<|startoftext|>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  },
-  "eos_token": {
-    "content": "<|endoftext|>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  },
-  "pad_token": "<|endoftext|>",
-  "unk_token": {
-    "content": "<|endoftext|>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  }
-}

configs/stable_diffusion/tokenizer/tokenizer_config.json

@@ -1,34 +0,0 @@
-{
-  "add_prefix_space": false,
-  "bos_token": {
-    "__type": "AddedToken",
-    "content": "<|startoftext|>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  },
-  "do_lower_case": true,
-  "eos_token": {
-    "__type": "AddedToken",
-    "content": "<|endoftext|>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  },
-  "errors": "replace",
-  "model_max_length": 77,
-  "name_or_path": "openai/clip-vit-large-patch14",
-  "pad_token": "<|endoftext|>",
-  "special_tokens_map_file": "./special_tokens_map.json",
-  "tokenizer_class": "CLIPTokenizer",
-  "unk_token": {
-    "__type": "AddedToken",
-    "content": "<|endoftext|>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  }
-}

configs/stable_diffusion_xl/tokenizer_2/special_tokens_map.json

@@ -1,24 +0,0 @@
-{
-    "bos_token": {
-      "content": "<|startoftext|>",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false
-    },
-    "eos_token": {
-      "content": "<|endoftext|>",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false
-    },
-    "pad_token": "!",
-    "unk_token": {
-      "content": "<|endoftext|>",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false
-    }
-}

configs/stable_diffusion_xl/tokenizer_2/tokenizer_config.json

@@ -1,38 +0,0 @@
-{
-  "add_prefix_space": false,
-  "added_tokens_decoder": {
-    "0": {
-      "content": "!",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "49406": {
-      "content": "<|startoftext|>",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "49407": {
-      "content": "<|endoftext|>",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    }
-  },
-  "bos_token": "<|startoftext|>",
-  "clean_up_tokenization_spaces": true,
-  "do_lower_case": true,
-  "eos_token": "<|endoftext|>",
-  "errors": "replace",
-  "model_max_length": 77,
-  "pad_token": "!",
-  "tokenizer_class": "CLIPTokenizer",
-  "unk_token": "<|endoftext|>"
-}

diffsynth/__init__.py

@@ -1,6 +1 @@
-from .data import *
-from .models import *
-from .prompts 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, VERSION_CHECKER_MAPS

diffsynth/configs/model_configs.py

@@ -0,0 +1,738 @@
+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},
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="SigLIP2-G384/model.safetensors")
+        "model_hash": "469c78b61e3e31bc9eec0d0af3d3f2f8",
+        "model_name": "siglip2_image_encoder",
+        "model_class": "diffsynth.models.siglip2_image_encoder.Siglip2ImageEncoder",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="DINOv3-7B/model.safetensors")
+        "model_hash": "5722b5c873720009de96422993b15682",
+        "model_name": "dinov3_image_encoder",
+        "model_class": "diffsynth.models.dinov3_image_encoder.DINOv3ImageEncoder",
+    },
+    {
+        # Example: 
+        "model_hash": "a166c33455cdbd89c0888a3645ca5c0f",
+        "model_name": "qwen_image_image2lora_coarse",
+        "model_class": "diffsynth.models.qwen_image_image2lora.QwenImageImage2LoRAModel",
+    },
+    {
+        # Example: 
+        "model_hash": "a5476e691767a4da6d3a6634a10f7408",
+        "model_name": "qwen_image_image2lora_fine",
+        "model_class": "diffsynth.models.qwen_image_image2lora.QwenImageImage2LoRAModel",
+        "extra_kwargs": {"residual_length": 37*37+7, "residual_mid_dim": 64}
+    },
+    {
+        # Example: 
+        "model_hash": "0aad514690602ecaff932c701cb4b0bb",
+        "model_name": "qwen_image_image2lora_style",
+        "model_class": "diffsynth.models.qwen_image_image2lora.QwenImageImage2LoRAModel",
+        "extra_kwargs": {"compress_dim": 64, "use_residual": False}
+    },
+    {
+        # Example: ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors")
+        "model_hash": "8dc8cda05de16c73afa755e2c1ce2839",
+        "model_name": "qwen_image_dit",
+        "model_class": "diffsynth.models.qwen_image_dit.QwenImageDiT",
+        "extra_kwargs": {"use_layer3d_rope": True, "use_additional_t_cond": True}
+    },
+    {
+        # Example: ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors")
+        "model_hash": "44b39ddc499e027cfb24f7878d7416b9",
+        "model_name": "qwen_image_vae",
+        "model_class": "diffsynth.models.qwen_image_vae.QwenImageVAE",
+        "extra_kwargs": {"image_channels": 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",
+    },
+    {
+        # Supported due to historical reasons.
+        "model_hash": "605c56eab23e9e2af863ad8f0813a25d",
+        "model_name": "flux_dit",
+        "model_class": "diffsynth.models.flux_dit.FluxDiT",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_dit.FluxDiTStateDictConverterFromDiffusers",
+    },
+    {
+        # 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},
+    },
+    {
+        # Example: ModelConfig(model_id="MAILAND/majicflus_v1", origin_file_pattern="majicflus_v134.safetensors")
+        "model_hash": "3394f306c4cbf04334b712bf5aaed95f",
+        "model_name": "flux_dit",
+        "model_class": "diffsynth.models.flux_dit.FluxDiT",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_dit.FluxDiTStateDictConverter",
+    },
+]
+
+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",
+    },
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="transformer/*.safetensors")
+        "model_hash": "3bde7b817fec8143028b6825a63180df",
+        "model_name": "flux2_dit",
+        "model_class": "diffsynth.models.flux2_dit.Flux2DiT",
+        "extra_kwargs": {"guidance_embeds": False, "joint_attention_dim": 7680, "num_attention_heads": 24, "num_layers": 5, "num_single_layers": 20}
+    },
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="text_encoder/*.safetensors")
+        "model_hash": "9195f3ea256fcd0ae6d929c203470754",
+        "model_name": "z_image_text_encoder",
+        "model_class": "diffsynth.models.z_image_text_encoder.ZImageTextEncoder",
+        "extra_kwargs": {"model_size": "8B"},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.z_image_text_encoder.ZImageTextEncoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="transformer/*.safetensors")
+        "model_hash": "39c6fc48f07bebecedbbaa971ff466c8",
+        "model_name": "flux2_dit",
+        "model_class": "diffsynth.models.flux2_dit.Flux2DiT",
+        "extra_kwargs": {"guidance_embeds": False, "joint_attention_dim": 12288, "num_attention_heads": 32, "num_layers": 8, "num_single_layers": 24}
+    },
+]
+
+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},
+    },
+    {
+        # Example: ModelConfig(model_id="Tongyi-MAI/Z-Image-Omni-Base", origin_file_pattern="transformer/*.safetensors")
+        "model_hash": "aa3563718e5c3ecde3dfbb020ca61180",
+        "model_name": "z_image_dit",
+        "model_class": "diffsynth.models.z_image_dit.ZImageDiT",
+        "extra_kwargs": {"siglip_feat_dim": 1152},
+    },
+    {
+        # Example: ModelConfig(model_id="Tongyi-MAI/Z-Image-Omni-Base", origin_file_pattern="siglip/model.safetensors")
+        "model_hash": "89d48e420f45cff95115a9f3e698d44a",
+        "model_name": "siglip_vision_model_428m",
+        "model_class": "diffsynth.models.siglip2_image_encoder.Siglip2ImageEncoder428M",
+    },
+    {
+        # Example: ModelConfig(model_id="PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1", origin_file_pattern="Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.safetensors")
+        "model_hash": "1677708d40029ab380a95f6c731a57d7",
+        "model_name": "z_image_controlnet",
+        "model_class": "diffsynth.models.z_image_controlnet.ZImageControlNet",
+    },
+    {
+        # Example: ???
+        "model_hash": "9510cb8cd1dd34ee0e4f111c24905510",
+        "model_name": "z_image_image2lora_style",
+        "model_class": "diffsynth.models.z_image_image2lora.ZImageImage2LoRAModel",
+        "extra_kwargs": {"compress_dim": 128},
+    },
+    {
+        # Example: ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="model.safetensors")
+        "model_hash": "1392adecee344136041e70553f875f31",
+        "model_name": "z_image_text_encoder",
+        "model_class": "diffsynth.models.z_image_text_encoder.ZImageTextEncoder",
+        "extra_kwargs": {"model_size": "0.6B"},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.z_image_text_encoder.ZImageTextEncoderStateDictConverter",
+    },
+]
+"""
+Offical model repo: https://www.modelscope.cn/models/Lightricks/LTX-2
+Repackaged model repo: https://www.modelscope.cn/models/DiffSynth-Studio/LTX-2-Repackage
+For base models of LTX-2, offical checkpoint (with model config ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors"))
+and repackaged checkpoints (with model config ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="*.safetensors")) are both supported.
+We have repackeged the official checkpoints in DiffSynth-Studio/LTX-2-Repackage repo to support separate loading of different submodules,
+and avoid redundant memory usage when users only want to use part of the model.
+"""
+ltx2_series = [
+    {
+        # Example: ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors")
+        "model_hash": "aca7b0bbf8415e9c98360750268915fc",
+        "model_name": "ltx2_dit",
+        "model_class": "diffsynth.models.ltx2_dit.LTXModel",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_dit.LTXModelStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="transformer.safetensors")
+        "model_hash": "c567aaa37d5ed7454c73aa6024458661",
+        "model_name": "ltx2_dit",
+        "model_class": "diffsynth.models.ltx2_dit.LTXModel",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_dit.LTXModelStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors")
+        "model_hash": "aca7b0bbf8415e9c98360750268915fc",
+        "model_name": "ltx2_video_vae_encoder",
+        "model_class": "diffsynth.models.ltx2_video_vae.LTX2VideoEncoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_video_vae.LTX2VideoEncoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_encoder.safetensors")
+        "model_hash": "7f7e904a53260ec0351b05f32153754b",
+        "model_name": "ltx2_video_vae_encoder",
+        "model_class": "diffsynth.models.ltx2_video_vae.LTX2VideoEncoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_video_vae.LTX2VideoEncoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors")
+        "model_hash": "aca7b0bbf8415e9c98360750268915fc",
+        "model_name": "ltx2_video_vae_decoder",
+        "model_class": "diffsynth.models.ltx2_video_vae.LTX2VideoDecoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_video_vae.LTX2VideoDecoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_decoder.safetensors")
+        "model_hash": "dc6029ca2825147872b45e35a2dc3a97",
+        "model_name": "ltx2_video_vae_decoder",
+        "model_class": "diffsynth.models.ltx2_video_vae.LTX2VideoDecoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_video_vae.LTX2VideoDecoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors")
+        "model_hash": "aca7b0bbf8415e9c98360750268915fc",
+        "model_name": "ltx2_audio_vae_decoder",
+        "model_class": "diffsynth.models.ltx2_audio_vae.LTX2AudioDecoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_audio_vae.LTX2AudioDecoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vae_decoder.safetensors")
+        "model_hash": "7d7823dde8f1ea0b50fb07ac329dd4cb",
+        "model_name": "ltx2_audio_vae_decoder",
+        "model_class": "diffsynth.models.ltx2_audio_vae.LTX2AudioDecoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_audio_vae.LTX2AudioDecoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors")
+        "model_hash": "aca7b0bbf8415e9c98360750268915fc",
+        "model_name": "ltx2_audio_vocoder",
+        "model_class": "diffsynth.models.ltx2_audio_vae.LTX2Vocoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_audio_vae.LTX2VocoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vocoder.safetensors")
+        "model_hash": "f471360f6b24bef702ab73133d9f8bb9",
+        "model_name": "ltx2_audio_vocoder",
+        "model_class": "diffsynth.models.ltx2_audio_vae.LTX2Vocoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_audio_vae.LTX2VocoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors")
+        "model_hash": "aca7b0bbf8415e9c98360750268915fc",
+        "model_name": "ltx2_audio_vae_encoder",
+        "model_class": "diffsynth.models.ltx2_audio_vae.LTX2AudioEncoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_audio_vae.LTX2AudioEncoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vae_encoder.safetensors")
+        "model_hash": "29338f3b95e7e312a3460a482e4f4554",
+        "model_name": "ltx2_audio_vae_encoder",
+        "model_class": "diffsynth.models.ltx2_audio_vae.LTX2AudioEncoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_audio_vae.LTX2AudioEncoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors")
+        "model_hash": "aca7b0bbf8415e9c98360750268915fc",
+        "model_name": "ltx2_text_encoder_post_modules",
+        "model_class": "diffsynth.models.ltx2_text_encoder.LTX2TextEncoderPostModules",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_text_encoder.LTX2TextEncoderPostModulesStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="text_encoder_post_modules.safetensors")
+        "model_hash": "981629689c8be92a712ab3c5eb4fc3f6",
+        "model_name": "ltx2_text_encoder_post_modules",
+        "model_class": "diffsynth.models.ltx2_text_encoder.LTX2TextEncoderPostModules",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_text_encoder.LTX2TextEncoderPostModulesStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors")
+        "model_hash": "33917f31c4a79196171154cca39f165e",
+        "model_name": "ltx2_text_encoder",
+        "model_class": "diffsynth.models.ltx2_text_encoder.LTX2TextEncoder",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_text_encoder.LTX2TextEncoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors")
+        "model_hash": "c79c458c6e99e0e14d47e676761732d2",
+        "model_name": "ltx2_latent_upsampler",
+        "model_class": "diffsynth.models.ltx2_upsampler.LTX2LatentUpsampler",
+    },
+]
+anima_series = [
+    {
+        # Example: ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors")
+        "model_hash": "a9995952c2d8e63cf82e115005eb61b9",
+        "model_name": "z_image_text_encoder",
+        "model_class": "diffsynth.models.z_image_text_encoder.ZImageTextEncoder",
+        "extra_kwargs": {"model_size": "0.6B"},
+    },
+    {
+        # Example: ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors")
+        "model_hash": "417673936471e79e31ed4d186d7a3f4a",
+        "model_name": "anima_dit",
+        "model_class": "diffsynth.models.anima_dit.AnimaDiT",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.anima_dit.AnimaDiTStateDictConverter",
+    }
+]
+MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series + z_image_series + ltx2_series + anima_series

diffsynth/configs/vram_management_module_maps.py

@@ -0,0 +1,266 @@
+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",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "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.siglip2_image_encoder.Siglip2ImageEncoder": {
+        "transformers.models.siglip.modeling_siglip.SiglipVisionEmbeddings": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.siglip.modeling_siglip.SiglipMultiheadAttentionPoolingHead": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+    },
+    "diffsynth.models.dinov3_image_encoder.DINOv3ImageEncoder": {
+        "transformers.models.dinov3_vit.modeling_dinov3_vit.DINOv3ViTLayerScale": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.dinov3_vit.modeling_dinov3_vit.DINOv3ViTRopePositionEmbedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.dinov3_vit.modeling_dinov3_vit.DINOv3ViTEmbeddings": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+    },
+    "diffsynth.models.qwen_image_image2lora.QwenImageImage2LoRAModel": {
+        "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.models.z_image_controlnet.ZImageControlNet": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "diffsynth.models.z_image_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.z_image_image2lora.ZImageImage2LoRAModel": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+    },
+    "diffsynth.models.siglip2_image_encoder.Siglip2ImageEncoder428M": {
+        "transformers.models.siglip2.modeling_siglip2.Siglip2VisionEmbeddings": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.siglip2.modeling_siglip2.Siglip2MultiheadAttentionPoolingHead": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+    },
+    "diffsynth.models.ltx2_dit.LTXModel": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.ltx2_upsampler.LTX2LatentUpsampler": {
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Conv3d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.GroupNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.ltx2_video_vae.LTX2VideoEncoder": {
+        "torch.nn.Conv3d": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.ltx2_video_vae.LTX2VideoDecoder": {
+        "torch.nn.Conv3d": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.ltx2_audio_vae.LTX2AudioDecoder": {
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.ltx2_audio_vae.LTX2Vocoder": {
+        "torch.nn.Conv1d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.ConvTranspose1d": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.ltx2_text_encoder.LTX2TextEncoderPostModules": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.ltx2_text_encoder.Embeddings1DConnector": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.ltx2_text_encoder.LTX2TextEncoder": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "transformers.models.gemma3.modeling_gemma3.Gemma3MultiModalProjector": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.gemma3.modeling_gemma3.Gemma3RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.gemma3.modeling_gemma3.Gemma3TextScaledWordEmbedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.anima_dit.AnimaDiT": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+}
+
+def QwenImageTextEncoder_Module_Map_Updater():
+    current = VRAM_MANAGEMENT_MODULE_MAPS["diffsynth.models.qwen_image_text_encoder.QwenImageTextEncoder"]
+    from packaging import version
+    import transformers
+    if version.parse(transformers.__version__) >= version.parse("5.2.0"):
+        # The Qwen2RMSNorm in transformers 5.2.0+ has been renamed to Qwen2_5_VLRMSNorm, so we need to update the module map accordingly
+        current.pop("transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.Qwen2RMSNorm", None)
+        current["transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.Qwen2_5_VLRMSNorm"] = "diffsynth.core.vram.layers.AutoWrappedModule"
+    return current
+
+VERSION_CHECKER_MAPS = {
+    "diffsynth.models.qwen_image_text_encoder.QwenImageTextEncoder": QwenImageTextEncoder_Module_Map_Updater,
+}

diffsynth/controlnets/__init__.py

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

diffsynth/controlnets/controlnet_unit.py

@@ -1,53 +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):
-        self.processor_id = processor_id
-        self.model_path = model_path
-        self.scale = scale
-
-
-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 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
-    ):
-        res_stack = None
-        for conditioning, model, scale in zip(conditionings, self.models, self.scales):
-            res_stack_ = model(
-                sample, timestep, encoder_hidden_states, conditioning,
-                tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
-            )
-            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

diffsynth/controlnets/processors.py

@@ -1,51 +0,0 @@
-from typing_extensions import Literal, TypeAlias
-import warnings
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore")
-    from controlnet_aux.processor import (
-        CannyDetector, MidasDetector, HEDdetector, LineartDetector, LineartAnimeDetector, OpenposeDetector
-    )
-
-
-Processor_id: TypeAlias = Literal[
-    "canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "tile"
-]
-
-class Annotator:
-    def __init__(self, processor_id: Processor_id, model_path="models/Annotators", detect_resolution=None):
-        if processor_id == "canny":
-            self.processor = CannyDetector()
-        elif processor_id == "depth":
-            self.processor = MidasDetector.from_pretrained(model_path).to("cuda")
-        elif processor_id == "softedge":
-            self.processor = HEDdetector.from_pretrained(model_path).to("cuda")
-        elif processor_id == "lineart":
-            self.processor = LineartDetector.from_pretrained(model_path).to("cuda")
-        elif processor_id == "lineart_anime":
-            self.processor = LineartAnimeDetector.from_pretrained(model_path).to("cuda")
-        elif processor_id == "openpose":
-            self.processor = OpenposeDetector.from_pretrained(model_path).to("cuda")
-        elif processor_id == "tile":
-            self.processor = None
-        else:
-            raise ValueError(f"Unsupported processor_id: {processor_id}")
-        
-        self.processor_id = processor_id
-        self.detect_resolution = detect_resolution
-
-    def __call__(self, image):
-        width, height = image.size
-        if self.processor_id == "openpose":
-            kwargs = {
-                "include_body": True,
-                "include_hand": True,
-                "include_face": True
-            }
-        else:
-            kwargs = {}
-        if self.processor is not None:
-            detect_resolution = self.detect_resolution if self.detect_resolution is not None else min(width, height)
-            image = self.processor(image, detect_resolution=detect_resolution, image_resolution=min(width, height), **kwargs)
-        image = image.resize((width, height))
-        return image
-

diffsynth/core/__init__.py

@@ -0,0 +1,6 @@
+from .attention import *
+from .data import *
+from .gradient import *
+from .loader import *
+from .vram import *
+from .device 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"{v_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

@@ -0,0 +1,237 @@
+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
+        
+    def __call__(self, data):
+        for operator in self.operators:
+            data = operator(data)
+        return data
+    
+    def __rshift__(self, pipe):
+        if isinstance(pipe, DataProcessingOperator):
+            pipe = DataProcessingPipeline([pipe])
+        return DataProcessingPipeline(self.operators + pipe.operators)
+
+
+class DataProcessingOperator:
+    def __call__(self, data):
+        raise NotImplementedError("DataProcessingOperator cannot be called directly.")
+    
+    def __rshift__(self, pipe):
+        if isinstance(pipe, DataProcessingOperator):
+            pipe = DataProcessingPipeline([pipe])
+        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
+    
+    def __call__(self, data):
+        if data is None: data = self.none_value
+        return str(data)
+
+
+class LoadImage(DataProcessingOperator):
+    def __init__(self, convert_RGB=True, convert_RGBA=False):
+        self.convert_RGB = convert_RGB
+        self.convert_RGBA = convert_RGBA
+    
+    def __call__(self, data: str):
+        image = Image.open(data)
+        if self.convert_RGB: image = image.convert("RGB")
+        if self.convert_RGBA: image = image.convert("RGBA")
+        return image
+
+
+class ImageCropAndResize(DataProcessingOperator):
+    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
+        self.height_division_factor = height_division_factor
+        self.width_division_factor = width_division_factor
+
+    def crop_and_resize(self, image, target_height, target_width):
+        width, height = image.size
+        scale = max(target_width / width, target_height / height)
+        image = torchvision.transforms.functional.resize(
+            image,
+            (round(height*scale), round(width*scale)),
+            interpolation=torchvision.transforms.InterpolationMode.BILINEAR
+        )
+        image = torchvision.transforms.functional.center_crop(image, (target_height, target_width))
+        return image
+    
+    def get_height_width(self, image):
+        if self.height is None or self.width is None:
+            width, height = image.size
+            if width * height > self.max_pixels:
+                scale = (width * height / self.max_pixels) ** 0.5
+                height, width = int(height / scale), int(width / scale)
+            height = height // self.height_division_factor * self.height_division_factor
+            width = width // self.width_division_factor * self.width_division_factor
+        else:
+            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
+        self.time_division_factor = time_division_factor
+        self.time_division_remainder = time_division_remainder
+        # frame_processor is build in the video loader for high efficiency.
+        self.frame_processor = frame_processor
+        
+    def get_num_frames(self, reader):
+        num_frames = self.num_frames
+        if int(reader.count_frames()) < num_frames:
+            num_frames = int(reader.count_frames())
+            while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder:
+                num_frames -= 1
+        return num_frames
+        
+    def __call__(self, data: str):
+        reader = imageio.get_reader(data)
+        num_frames = self.get_num_frames(reader)
+        frames = []
+        for frame_id in range(num_frames):
+            frame = reader.get_data(frame_id)
+            frame = Image.fromarray(frame)
+            frame = self.frame_processor(frame)
+            frames.append(frame)
+        reader.close()
+        return frames
+
+
+class SequencialProcess(DataProcessingOperator):
+    def __init__(self, operator=lambda x: x):
+        self.operator = operator
+        
+    def __call__(self, data):
+        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
+        self.time_division_factor = time_division_factor
+        self.time_division_remainder = time_division_remainder
+        # frame_processor is build in the video loader for high efficiency.
+        self.frame_processor = frame_processor
+        
+    def get_num_frames(self, path):
+        num_frames = self.num_frames
+        images = iio.imread(path, mode="RGB")
+        if len(images) < num_frames:
+            num_frames = len(images)
+            while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder:
+                num_frames -= 1
+        return num_frames
+        
+    def __call__(self, data: str):
+        num_frames = self.get_num_frames(data)
+        frames = []
+        images = iio.imread(data, mode="RGB")
+        for img in images:
+            frame = Image.fromarray(img)
+            frame = self.frame_processor(frame)
+            frames.append(frame)
+            if len(frames) >= num_frames:
+                break
+        return frames
+
+
+class RouteByExtensionName(DataProcessingOperator):
+    def __init__(self, operator_map):
+        self.operator_map = operator_map
+        
+    def __call__(self, data: str):
+        file_ext_name = data.split(".")[-1].lower()
+        for ext_names, operator in self.operator_map:
+            if ext_names is None or file_ext_name in ext_names:
+                return operator(data)
+        raise ValueError(f"Unsupported file: {data}")
+
+
+class RouteByType(DataProcessingOperator):
+    def __init__(self, operator_map):
+        self.operator_map = operator_map
+        
+    def __call__(self, data):
+        for dtype, operator in self.operator_map:
+            if dtype is None or isinstance(data, dtype):
+                return operator(data)
+        raise ValueError(f"Unsupported data: {data}")
+
+
+class LoadTorchPickle(DataProcessingOperator):
+    def __init__(self, map_location="cpu"):
+        self.map_location = map_location
+        
+    def __call__(self, data):
+        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
+        
+    def __call__(self, data):
+        return os.path.join(self.base_path, data)
+
+
+class LoadAudio(DataProcessingOperator):
+    def __init__(self, sr=16000):
+        self.sr = sr
+    def __call__(self, data: str):
+        import librosa
+        input_audio, sample_rate = librosa.load(data, sr=self.sr)
+        return input_audio
+
+
+class LoadAudioWithTorchaudio(DataProcessingOperator):
+    def __init__(self, duration=5):
+        self.duration = duration
+
+    def __call__(self, data: str):
+        import torchaudio
+        waveform, sample_rate = torchaudio.load(data)
+        target_samples = int(self.duration * sample_rate)
+        current_samples = waveform.shape[-1]
+        if current_samples > target_samples:
+            waveform = waveform[..., :target_samples]
+        elif current_samples < target_samples:
+            padding = target_samples - current_samples
+            waveform = torch.nn.functional.pad(waveform, (0, padding))
+        return waveform, sample_rate

diffsynth/core/data/unified_dataset.py

@@ -0,0 +1,116 @@
+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,
+        max_data_items=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.max_data_items = max_data_items
+        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.max_data_items is not None:
+            return self.max_data_items
+        elif 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/device/__init__.py

@@ -0,0 +1,2 @@
+from .npu_compatible_device import parse_device_type, parse_nccl_backend, get_available_device_type, get_device_name
+from .npu_compatible_device import IS_NPU_AVAILABLE, IS_CUDA_AVAILABLE

diffsynth/core/device/npu_compatible_device.py

@@ -0,0 +1,107 @@
+import importlib
+import torch
+from typing import Any
+
+
+def is_torch_npu_available():
+    return importlib.util.find_spec("torch_npu") is not None
+
+
+IS_CUDA_AVAILABLE = torch.cuda.is_available()
+IS_NPU_AVAILABLE = is_torch_npu_available() and torch.npu.is_available()
+
+if IS_NPU_AVAILABLE:
+    import torch_npu
+
+    torch.npu.config.allow_internal_format = False
+
+
+def get_device_type() -> str:
+    """Get device type based on current machine, currently only support CPU, CUDA, NPU."""
+    if IS_CUDA_AVAILABLE:
+        device = "cuda"
+    elif IS_NPU_AVAILABLE:
+        device = "npu"
+    else:
+        device = "cpu"
+
+    return device
+
+
+def get_torch_device() -> Any:
+    """Get torch attribute based on device type, e.g. torch.cuda or torch.npu"""
+    device_name = get_device_type()
+
+    try:
+        return getattr(torch, device_name)
+    except AttributeError:
+        print(f"Device namespace '{device_name}' not found in torch, try to load 'torch.cuda'.")
+        return torch.cuda
+
+
+def get_device_id() -> int:
+    """Get current device id based on device type."""
+    return get_torch_device().current_device()
+
+
+def get_device_name() -> str:
+    """Get current device name based on device type."""
+    return f"{get_device_type()}:{get_device_id()}"
+
+
+def synchronize() -> None:
+    """Execute torch synchronize operation."""
+    get_torch_device().synchronize()
+
+
+def empty_cache() -> None:
+    """Execute torch empty cache operation."""
+    get_torch_device().empty_cache()
+
+
+def get_nccl_backend() -> str:
+    """Return distributed communication backend type based on device type."""
+    if IS_CUDA_AVAILABLE:
+        return "nccl"
+    elif IS_NPU_AVAILABLE:
+        return "hccl"
+    else:
+        raise RuntimeError(f"No available distributed communication backend found on device type {get_device_type()}.")
+
+
+def enable_high_precision_for_bf16():
+    """
+    Set high accumulation dtype for matmul and reduction.
+    """
+    if IS_CUDA_AVAILABLE:
+        torch.backends.cuda.matmul.allow_tf32 = False
+        torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False
+
+    if IS_NPU_AVAILABLE:
+        torch.npu.matmul.allow_tf32 = False
+        torch.npu.matmul.allow_bf16_reduced_precision_reduction = False
+
+
+def parse_device_type(device):
+    if isinstance(device, str):
+        if device.startswith("cuda"):
+            return "cuda"
+        elif device.startswith("npu"):
+            return "npu"
+        else:
+            return "cpu"
+    elif isinstance(device, torch.device):
+        return device.type
+
+
+def parse_nccl_backend(device_type):
+    if device_type == "cuda":
+        return "nccl"
+    elif device_type == "npu":
+        return "hccl"
+    else:
+        raise RuntimeError(f"No available distributed communication backend found on device type {device_type}.")
+
+
+def get_available_device_type():
+    return get_device_type()

diffsynth/core/gradient/__init__.py

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

diffsynth/core/gradient/gradient_checkpoint.py

@@ -0,0 +1,34 @@
+import torch
+
+
+def create_custom_forward(module):
+    def custom_forward(*inputs, **kwargs):
+        return module(*inputs, **kwargs)
+    return custom_forward
+
+
+def gradient_checkpoint_forward(
+    model,
+    use_gradient_checkpointing,
+    use_gradient_checkpointing_offload,
+    *args,
+    **kwargs,
+):
+    if use_gradient_checkpointing_offload:
+        with torch.autograd.graph.save_on_cpu():
+            model_output = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(model),
+                *args,
+                **kwargs,
+                use_reentrant=False,
+            )
+    elif use_gradient_checkpointing:
+        model_output = torch.utils.checkpoint.checkpoint(
+            create_custom_forward(model),
+            *args,
+            **kwargs,
+            use_reentrant=False,
+        )
+    else:
+        model_output = model(*args, **kwargs)
+    return model_output

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 +1,119 @@
+import torch, glob, os
+from typing import Optional, Union, Dict
+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
+    state_dict: Dict[str, torch.Tensor] = None
+    
+    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 in [None, "", "./"]:
+            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.path is None:
+            if self.origin_file_pattern in [None, "", "./"]:
+                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 +1,130 @@
+from safetensors import safe_open
+import torch, hashlib
+
+
+def load_state_dict(file_path, torch_dtype=None, device="cpu", pin_memory=False, verbose=0):
+    if isinstance(file_path, list):
+        state_dict = {}
+        for file_path_ in file_path:
+            state_dict.update(load_state_dict(file_path_, torch_dtype, device, pin_memory=pin_memory, verbose=verbose))
+    else:
+        if verbose >= 1:
+            print(f"Loading file [started]: {file_path}")
+        if file_path.endswith(".safetensors"):
+            state_dict = load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype, device=device)
+        else:
+            state_dict = load_state_dict_from_bin(file_path, torch_dtype=torch_dtype, device=device)
+        # If load state dict in CPU memory, `pin_memory=True` will make `model.to("cuda")` faster.
+        if pin_memory:
+            for i in state_dict:
+                state_dict[i] = state_dict[i].pin_memory()
+        if verbose >= 1:
+            print(f"Loading file [done]: {file_path}")
+    return state_dict
+
+
+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 +1,105 @@
+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
+from contextlib import contextmanager
+from transformers.integrations import is_deepspeed_zero3_enabled
+from transformers.utils import ContextManagers
+
+
+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, state_dict=None):
+    config = {} if config is None else config
+    with ContextManagers(get_init_context(torch_dtype=torch_dtype, device=device)):
+        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":
+            if state_dict is None: 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 state_dict is not None:
+            pass
+        elif 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}
+        # Why does DeepSpeed ZeRO Stage 3 need to be handled separately?
+        # Because at this stage, model parameters are partitioned across multiple GPUs.
+        # Loading them directly could lead to excessive GPU memory consumption.
+        if is_deepspeed_zero3_enabled():
+            from transformers.integrations.deepspeed import _load_state_dict_into_zero3_model
+            _load_state_dict_into_zero3_model(model, state_dict)
+        else:
+            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
+
+
+def get_init_context(torch_dtype, device):
+    if is_deepspeed_zero3_enabled():
+        from transformers.modeling_utils import set_zero3_state
+        import deepspeed
+        # Why do we use "deepspeed.zero.Init"?
+        # Weight segmentation of the model can be performed on the CPU side
+        # and loading the segmented weights onto the computing card
+        init_contexts = [deepspeed.zero.Init(remote_device=device, dtype=torch_dtype), set_zero3_state()]
+    else:
+        # 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.
+        init_contexts = [skip_model_initialization()]
+
+    return init_contexts

diffsynth/core/npu_patch/npu_fused_operator.py

@@ -0,0 +1,30 @@
+import torch
+from ..device.npu_compatible_device import get_device_type
+try:
+    import torch_npu
+except:
+    pass
+
+
+def rms_norm_forward_npu(self, hidden_states):
+    "npu rms fused operator for RMSNorm.forward from diffsynth\models\general_modules.py"
+    if hidden_states.dtype != self.weight.dtype:
+        hidden_states = hidden_states.to(self.weight.dtype)
+    return torch_npu.npu_rms_norm(hidden_states, self.weight, self.eps)[0]
+
+
+def rms_norm_forward_transformers_npu(self, hidden_states):
+    "npu rms fused operator for transformers"
+    if hidden_states.dtype != self.weight.dtype:
+        hidden_states = hidden_states.to(self.weight.dtype)
+    return torch_npu.npu_rms_norm(hidden_states, self.weight, self.variance_epsilon)[0]
+
+
+def rotary_emb_Zimage_npu(self, x_in: torch.Tensor, freqs_cis: torch.Tensor):
+    "npu rope fused operator for Zimage"
+    with torch.amp.autocast(get_device_type(), enabled=False):
+        freqs_cis = freqs_cis.unsqueeze(2)
+        cos, sin = torch.chunk(torch.view_as_real(freqs_cis), 2, dim=-1)
+        cos = cos.expand(-1, -1, -1, -1, 2).flatten(-2)
+        sin = sin.expand(-1, -1, -1, -1, 2).flatten(-2)
+        return torch_npu.npu_rotary_mul(x_in, cos, sin, rotary_mode="interleave").to(x_in)

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 +1,479 @@
+import torch, copy
+from typing import Union
+from .initialization import skip_model_initialization
+from .disk_map import DiskMap
+from ..device import parse_device_type, get_device_name, IS_NPU_AVAILABLE
+
+
+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 = ""
+        self.computation_device_type = parse_device_type(self.computation_device)
+
+    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):
+        device = self.computation_device if not IS_NPU_AVAILABLE else get_device_name()
+        gpu_mem_state = getattr(torch, self.computation_device_type).mem_get_info(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]
+        self.computation_device_type = parse_device_type(self.computation_device)
+        
+        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

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 +1,462 @@
+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, parse_device_type
+from ..core.device.npu_compatible_device import get_device_type
+from ..utils.lora import GeneralLoRALoader
+from ..models.model_loader import ModelPool
+from ..utils.controlnet import ControlNetInput
+from ..core.device import get_device_name, IS_NPU_AVAILABLE
+
+
+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=get_device_type(), 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
+        self.device_type = parse_device_type(device)
+        # 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, verbose=1):
+        # Shape check
+        if height % self.height_division_factor != 0:
+            height = (height + self.height_division_factor - 1) // self.height_division_factor * self.height_division_factor
+            if verbose > 0:
+                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
+            if verbose > 0:
+                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
+                if verbose > 0:
+                    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()
+            getattr(torch, self.device_type).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):
+        device = self.device if not IS_NPU_AVAILABLE else get_device_name()
+        return getattr(torch, self.device_type).mem_get_info(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,
+        verbose=1,
+    ):
+        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])
+            if verbose >= 1:
+                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, verbose=1):
+        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()
+        if verbose >= 1:
+            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,
+                state_dict=model_config.state_dict,
+            )
+        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):
+        if inputs_shared.get("positive_only_lora", None) is not None:
+            self.clear_lora(verbose=0)
+            self.load_lora(self.dit, state_dict=inputs_shared["positive_only_lora"], verbose=0)
+        noise_pred_posi = model_fn(**inputs_posi, **inputs_shared, **inputs_others)
+        if cfg_scale != 1.0:
+            if inputs_shared.get("positive_only_lora", None) is not None:
+                self.clear_lora(verbose=0)
+            noise_pred_nega = model_fn(**inputs_nega, **inputs_shared, **inputs_others)
+            if isinstance(noise_pred_posi, tuple):
+                # Separately handling different output types of latents, eg. video and audio latents.
+                noise_pred = tuple(
+                    n_nega + cfg_scale * (n_posi - n_nega)
+                    for n_posi, n_nega in zip(noise_pred_posi, noise_pred_nega)
+                )
+            else:
+                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 +1,236 @@
+import torch, math
+from typing_extensions import Literal
+
+
+class FlowMatchScheduler():
+
+    def __init__(self, template: Literal["FLUX.1", "Wan", "Qwen-Image", "FLUX.2", "Z-Image", "LTX-2", "Qwen-Image-Lightning"] = "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,
+            "LTX-2": FlowMatchScheduler.set_timesteps_ltx2,
+            "Qwen-Image-Lightning": FlowMatchScheduler.set_timesteps_qwen_image_lightning,
+        }.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 set_timesteps_qwen_image_lightning(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
+        base_shift = math.log(3)
+        max_shift = math.log(3)
+        # 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, base_shift=base_shift, max_shift=max_shift)
+        else:
+            mu = 0.8
+        sigmas = math.exp(mu) / (math.exp(mu) + (1 / sigmas - 1))
+        # 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=None):
+        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)
+        if dynamic_shift_len is None:
+            # If you ask me why I set mu=0.8,
+            # I can only say that it yields better training results.
+            mu = 0.8
+        else:
+            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
+
+    @staticmethod
+    def set_timesteps_ltx2(num_inference_steps=100, denoising_strength=1.0, dynamic_shift_len=None, terminal=0.1, special_case=None):
+        num_train_timesteps = 1000
+        if special_case == "stage2":
+            sigmas = torch.Tensor([0.909375, 0.725, 0.421875])
+        elif special_case == "ditilled_stage1":
+            sigmas = torch.Tensor([1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875])
+        else:
+            dynamic_shift_len = dynamic_shift_len or 4096
+            sigma_shift = FlowMatchScheduler._calculate_shift_qwen_image(
+                image_seq_len=dynamic_shift_len,
+                base_seq_len=1024,
+                max_seq_len=4096,
+                base_shift=0.95,
+                max_shift=2.05,
+            )
+            sigma_min = 0.0
+            sigma_max = 1.0
+            sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
+            sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
+            sigmas = math.exp(sigma_shift) / (math.exp(sigma_shift) + (1 / sigmas - 1))
+            # Shift terminal
+            one_minus_z = 1.0 - sigmas
+            scale_factor = one_minus_z[-1] / (1 - terminal)
+            sigmas = 1.0 - (one_minus_z / scale_factor)
+        timesteps = sigmas * num_train_timesteps
+        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, **kwargs):
+        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()
+        state_dict = accelerator.get_state_dict(model)
+        if accelerator.is_main_process:
+            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()
+        state_dict = accelerator.get_state_dict(model)
+        if accelerator.is_main_process:
+            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 +1,158 @@
+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)
+    
+    if "first_frame_latents" in inputs:
+        inputs["latents"][:, :, 0:1] = inputs["first_frame_latents"]
+    
+    models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
+    noise_pred = pipe.model_fn(**models, **inputs, timestep=timestep)
+    
+    if "first_frame_latents" in inputs:
+        noise_pred = noise_pred[:, :, 1:]
+        training_target = training_target[:, :, 1:]
+    
+    loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float())
+    loss = loss * pipe.scheduler.training_weight(timestep)
+    return loss
+
+
+def FlowMatchSFTAudioVideoLoss(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)
+    
+    # video
+    noise = torch.randn_like(inputs["input_latents"])
+    inputs["video_latents"] = pipe.scheduler.add_noise(inputs["input_latents"], noise, timestep)
+    training_target = pipe.scheduler.training_target(inputs["input_latents"], noise, timestep)
+    
+    # audio
+    if inputs.get("audio_input_latents") is not None:
+        audio_noise = torch.randn_like(inputs["audio_input_latents"])
+        inputs["audio_latents"] = pipe.scheduler.add_noise(inputs["audio_input_latents"], audio_noise, timestep)
+        training_target_audio = pipe.scheduler.training_target(inputs["audio_input_latents"], audio_noise, timestep)
+
+    models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
+    noise_pred, noise_pred_audio = 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)
+    if inputs.get("audio_input_latents") is not None:
+        loss_audio = torch.nn.functional.mse_loss(noise_pred_audio.float(), training_target_audio.float())
+        loss_audio = loss_audio * pipe.scheduler.training_weight(timestep)
+        loss = loss + loss_audio
+    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]
+            
+            denom = sigma_ - sigma
+            denom = torch.sign(denom) * torch.clamp(denom.abs(), min=1e-6)
+            target = (latents_ - inputs_shared["latents"]) / denom
+            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 +1,72 @@
+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.to(device=accelerator.device)
+    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, loss=loss)
+                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.to(device=accelerator.device)
+    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 +1,263 @@
+import torch, json, os
+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:
+                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
+                )
+                config = self.parse_path_or_model_id(model_id_with_origin_path)
+                model_configs.append(ModelConfig(model_id=config.model_id, origin_file_pattern=config.origin_file_pattern, **vram_config))
+        return model_configs
+    
+
+    def parse_path_or_model_id(self, model_id_with_origin_path, default_value=None):
+        if model_id_with_origin_path is None:
+            return default_value
+        elif os.path.exists(model_id_with_origin_path):
+            return ModelConfig(path=model_id_with_origin_path)
+        else:
+            if ":" not in model_id_with_origin_path:
+                raise ValueError(f"Failed to parse model config: {model_id_with_origin_path}. This is neither a valid path nor in the format of `model_id/origin_file_pattern`.")
+            split_id = model_id_with_origin_path.rfind(":")
+            model_id = model_id_with_origin_path[:split_id]
+            origin_file_pattern = model_id_with_origin_path[split_id + 1:]
+            return ModelConfig(model_id=model_id, origin_file_pattern=origin_file_pattern)
+
+
+    def auto_detect_lora_target_modules(
+        self,
+        model: torch.nn.Module,
+        search_for_linear=False,
+        linear_detector=lambda x: min(x.weight.shape) >= 512,
+        block_list_detector=lambda x: isinstance(x, torch.nn.ModuleList) and len(x) > 1,
+        name_prefix="",
+    ):
+        lora_target_modules = []
+        if search_for_linear:
+            for name, module in model.named_modules():
+                module_name = name_prefix + ["", "."][name_prefix != ""] + name
+                if isinstance(module, torch.nn.Linear) and linear_detector(module):
+                    lora_target_modules.append(module_name)
+        else:
+            for name, module in model.named_children():
+                module_name = name_prefix + ["", "."][name_prefix != ""] + name
+                lora_target_modules += self.auto_detect_lora_target_modules(
+                    module,
+                    search_for_linear=block_list_detector(module),
+                    linear_detector=linear_detector,
+                    block_list_detector=block_list_detector,
+                    name_prefix=module_name,
+                )
+        return lora_target_modules
+    
+
+    def parse_lora_target_modules(self, model, lora_target_modules):
+        if lora_target_modules == "":
+            print("No LoRA target modules specified. The framework will automatically search for them.")
+            lora_target_modules = self.auto_detect_lora_target_modules(model)
+            print(f"LoRA will be patched at {lora_target_modules}.")
+        else:
+            lora_target_modules = lora_target_modules.split(",")
+        return lora_target_modules
+
+
+    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=self.parse_lora_target_modules(getattr(pipe, lora_base_model), lora_target_modules),
+                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

@@ -1,118 +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):
-        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
-
-
-class ESRGAN(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.model = model
-
-    @staticmethod
-    def from_pretrained(model_path):
-        model = RRDBNet()
-        state_dict = torch.load(model_path, map_location="cpu")["params_ema"]
-        model.load_state_dict(state_dict)
-        model.eval()
-        return ESRGAN(model)
-
-    def process_image(self, image):
-        image = torch.Tensor(np.array(image, dtype=np.float32) / 255).permute(2, 0, 1)
-        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):
-        # 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)
-        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

@@ -1,298 +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
-
-
-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 = images.get()
-        images_resample = []
-        for image in images:
-            image_resample = cv2.resize(image, (width, height), interpolation=cv2.INTER_AREA)
-            images_resample.append(image_resample)
-        images_resample = cp.array(np.stack(images_resample), dtype=cp.float32)
-        return 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[:,[i for i in range(nnf.shape[0]) if i&1],:,0] += 1
-        nnf[:,:,[i for i in range(nnf.shape[0]) if i&1],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 = nnf.get().astype(np.float32)
-            nnf = [cv2.resize(n, (width, height), interpolation=cv2.INTER_LINEAR) for n in nnf]
-            nnf = cp.array(np.stack(nnf), dtype=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/RIFE/__init__.py

@@ -1,241 +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):
-        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
-    
-    def state_dict_converter(self):
-        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)
-
-
-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.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 RIFESmoother(model_manager.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/models/__init__.py

@@ -1,482 +0,0 @@
-import torch, os
-from safetensors import safe_open
-
-from .sd_text_encoder import SDTextEncoder
-from .sd_unet import SDUNet
-from .sd_vae_encoder import SDVAEEncoder
-from .sd_vae_decoder import SDVAEDecoder
-from .sd_lora import SDLoRA
-
-from .sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2
-from .sdxl_unet import SDXLUNet
-from .sdxl_vae_decoder import SDXLVAEDecoder
-from .sdxl_vae_encoder import SDXLVAEEncoder
-
-from .sd_controlnet import SDControlNet
-
-from .sd_motion import SDMotionModel
-from .sdxl_motion import SDXLMotionModel
-
-from .svd_image_encoder import SVDImageEncoder
-from .svd_unet import SVDUNet
-from .svd_vae_decoder import SVDVAEDecoder
-from .svd_vae_encoder import SVDVAEEncoder
-
-from .sd_ipadapter import SDIpAdapter, IpAdapterCLIPImageEmbedder
-from .sdxl_ipadapter import SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder
-
-from .hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder
-from .hunyuan_dit import HunyuanDiT
-
-
-class ModelManager:
-    def __init__(self, torch_dtype=torch.float16, device="cuda"):
-        self.torch_dtype = torch_dtype
-        self.device = device
-        self.model = {}
-        self.model_path = {}
-        self.textual_inversion_dict = {}
-
-    def is_stable_video_diffusion(self, state_dict):
-        param_name = "model.diffusion_model.output_blocks.9.1.time_stack.0.norm_in.weight"
-        return param_name in state_dict
-
-    def is_RIFE(self, state_dict):
-        param_name = "block_tea.convblock3.0.1.weight"
-        return param_name in state_dict or ("module." + param_name) in state_dict
-
-    def is_beautiful_prompt(self, state_dict):
-        param_name = "transformer.h.9.self_attention.query_key_value.weight"
-        return param_name in state_dict
-
-    def is_stabe_diffusion_xl(self, state_dict):
-        param_name = "conditioner.embedders.0.transformer.text_model.embeddings.position_embedding.weight"
-        return param_name in state_dict
-
-    def is_stable_diffusion(self, state_dict):
-        if self.is_stabe_diffusion_xl(state_dict):
-            return False
-        param_name = "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.weight"
-        return param_name in state_dict
-    
-    def is_controlnet(self, state_dict):
-        param_name = "control_model.time_embed.0.weight"
-        param_name_2 = "mid_block.resnets.1.time_emb_proj.weight" # For controlnets in diffusers format
-        return param_name in state_dict or param_name_2 in state_dict
-    
-    def is_animatediff(self, state_dict):
-        param_name = "mid_block.motion_modules.0.temporal_transformer.proj_out.weight"
-        return param_name in state_dict
-    
-    def is_animatediff_xl(self, state_dict):
-        param_name = "up_blocks.2.motion_modules.2.temporal_transformer.transformer_blocks.0.ff_norm.weight"
-        return param_name in state_dict
-    
-    def is_sd_lora(self, state_dict):
-        param_name = "lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2.lora_up.weight"
-        return param_name in state_dict
-    
-    def is_translator(self, state_dict):
-        param_name = "model.encoder.layers.5.self_attn_layer_norm.weight"
-        return param_name in state_dict and len(state_dict) == 254
-    
-    def is_ipadapter(self, state_dict):
-        return "image_proj" in state_dict and "ip_adapter" in state_dict and state_dict["image_proj"]["proj.weight"].shape == torch.Size([3072, 1024])
-    
-    def is_ipadapter_image_encoder(self, state_dict):
-        param_name = "vision_model.encoder.layers.31.self_attn.v_proj.weight"
-        return param_name in state_dict and len(state_dict) == 521
-    
-    def is_ipadapter_xl(self, state_dict):
-        return "image_proj" in state_dict and "ip_adapter" in state_dict and state_dict["image_proj"]["proj.weight"].shape == torch.Size([8192, 1280])
-    
-    def is_ipadapter_xl_image_encoder(self, state_dict):
-        param_name = "vision_model.encoder.layers.47.self_attn.v_proj.weight"
-        return param_name in state_dict and len(state_dict) == 777
-    
-    def is_hunyuan_dit_clip_text_encoder(self, state_dict):
-        param_name = "bert.encoder.layer.23.attention.output.dense.weight"
-        return param_name in state_dict
-    
-    def is_hunyuan_dit_t5_text_encoder(self, state_dict):
-        param_name = "encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight"
-        return param_name in state_dict
-    
-    def is_hunyuan_dit(self, state_dict):
-        param_name = "final_layer.adaLN_modulation.1.weight"
-        return param_name in state_dict
-    
-    def is_diffusers_vae(self, state_dict):
-        param_name = "quant_conv.weight"
-        return param_name in state_dict
-    
-    def is_ExVideo_StableVideoDiffusion(self, state_dict):
-        param_name = "blocks.185.positional_embedding.embeddings"
-        return param_name in state_dict
-    
-    def load_stable_video_diffusion(self, state_dict, components=None, file_path="", add_positional_conv=None):
-        component_dict = {
-            "image_encoder": SVDImageEncoder,
-            "unet": SVDUNet,
-            "vae_decoder": SVDVAEDecoder,
-            "vae_encoder": SVDVAEEncoder,
-        }
-        if components is None:
-            components = ["image_encoder", "unet", "vae_decoder", "vae_encoder"]
-        for component in components:
-            if component == "unet":
-                self.model[component] = component_dict[component](add_positional_conv=add_positional_conv)
-                self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict, add_positional_conv=add_positional_conv), strict=False)
-            else:
-                self.model[component] = component_dict[component]()
-                self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
-            self.model[component].to(self.torch_dtype).to(self.device)
-            self.model_path[component] = file_path
-    
-    def load_stable_diffusion(self, state_dict, components=None, file_path=""):
-        component_dict = {
-            "text_encoder": SDTextEncoder,
-            "unet": SDUNet,
-            "vae_decoder": SDVAEDecoder,
-            "vae_encoder": SDVAEEncoder,
-            "refiner": SDXLUNet,
-        }
-        if components is None:
-            components = ["text_encoder", "unet", "vae_decoder", "vae_encoder"]
-        for component in components:
-            if component == "text_encoder":
-                # Add additional token embeddings to text encoder
-                token_embeddings = [state_dict["cond_stage_model.transformer.text_model.embeddings.token_embedding.weight"]]
-                for keyword in self.textual_inversion_dict:
-                    _, embeddings = self.textual_inversion_dict[keyword]
-                    token_embeddings.append(embeddings.to(dtype=token_embeddings[0].dtype))
-                token_embeddings = torch.concat(token_embeddings, dim=0)
-                state_dict["cond_stage_model.transformer.text_model.embeddings.token_embedding.weight"] = token_embeddings
-                self.model[component] = component_dict[component](vocab_size=token_embeddings.shape[0])
-                self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
-                self.model[component].to(self.torch_dtype).to(self.device)
-            else:
-                self.model[component] = component_dict[component]()
-                self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
-                self.model[component].to(self.torch_dtype).to(self.device)
-            self.model_path[component] = file_path
-
-    def load_stable_diffusion_xl(self, state_dict, components=None, file_path=""):
-        component_dict = {
-            "text_encoder": SDXLTextEncoder,
-            "text_encoder_2": SDXLTextEncoder2,
-            "unet": SDXLUNet,
-            "vae_decoder": SDXLVAEDecoder,
-            "vae_encoder": SDXLVAEEncoder,
-        }
-        if components is None:
-            components = ["text_encoder", "text_encoder_2", "unet", "vae_decoder", "vae_encoder"]
-        for component in components:
-            self.model[component] = component_dict[component]()
-            self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
-            if component in ["vae_decoder", "vae_encoder"]:
-                # These two model will output nan when float16 is enabled.
-                # The precision problem happens in the last three resnet blocks.
-                # I do not know how to solve this problem.
-                self.model[component].to(torch.float32).to(self.device)
-            else:
-                self.model[component].to(self.torch_dtype).to(self.device)
-            self.model_path[component] = file_path
-
-    def load_controlnet(self, state_dict, file_path=""):
-        component = "controlnet"
-        if component not in self.model:
-            self.model[component] = []
-            self.model_path[component] = []
-        model = SDControlNet()
-        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component].append(model)
-        self.model_path[component].append(file_path)
-
-    def load_animatediff(self, state_dict, file_path="", add_positional_conv=None):
-        component = "motion_modules"
-        model = SDMotionModel(add_positional_conv=add_positional_conv)
-        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict, add_positional_conv=add_positional_conv))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_animatediff_xl(self, state_dict, file_path=""):
-        component = "motion_modules_xl"
-        model = SDXLMotionModel()
-        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_beautiful_prompt(self, state_dict, file_path=""):
-        component = "beautiful_prompt"
-        from transformers import AutoModelForCausalLM
-        model_folder = os.path.dirname(file_path)
-        model = AutoModelForCausalLM.from_pretrained(
-            model_folder, state_dict=state_dict, local_files_only=True, torch_dtype=self.torch_dtype
-        ).to(self.device).eval()
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_RIFE(self, state_dict, file_path=""):
-        component = "RIFE"
-        from ..extensions.RIFE import IFNet
-        model = IFNet().eval()
-        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
-        model.to(torch.float32).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_sd_lora(self, state_dict, alpha):
-        SDLoRA().add_lora_to_text_encoder(self.model["text_encoder"], state_dict, alpha=alpha, device=self.device)
-        SDLoRA().add_lora_to_unet(self.model["unet"], state_dict, alpha=alpha, device=self.device)
-
-    def load_translator(self, state_dict, file_path=""):
-        # This model is lightweight, we do not place it on GPU.
-        component = "translator"
-        from transformers import AutoModelForSeq2SeqLM
-        model_folder = os.path.dirname(file_path)
-        model = AutoModelForSeq2SeqLM.from_pretrained(model_folder).eval()
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_ipadapter(self, state_dict, file_path=""):
-        component = "ipadapter"
-        model = SDIpAdapter()
-        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_ipadapter_image_encoder(self, state_dict, file_path=""):
-        component = "ipadapter_image_encoder"
-        model = IpAdapterCLIPImageEmbedder()
-        model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_ipadapter_xl(self, state_dict, file_path=""):
-        component = "ipadapter_xl"
-        model = SDXLIpAdapter()
-        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_ipadapter_xl_image_encoder(self, state_dict, file_path=""):
-        component = "ipadapter_xl_image_encoder"
-        model = IpAdapterXLCLIPImageEmbedder()
-        model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_hunyuan_dit_clip_text_encoder(self, state_dict, file_path=""):
-        component = "hunyuan_dit_clip_text_encoder"
-        model = HunyuanDiTCLIPTextEncoder()
-        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_hunyuan_dit_t5_text_encoder(self, state_dict, file_path=""):
-        component = "hunyuan_dit_t5_text_encoder"
-        model = HunyuanDiTT5TextEncoder()
-        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_hunyuan_dit(self, state_dict, file_path=""):
-        component = "hunyuan_dit"
-        model = HunyuanDiT()
-        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_diffusers_vae(self, state_dict, file_path=""):
-        # TODO: detect SD and SDXL
-        component = "vae_encoder"
-        model = SDXLVAEEncoder()
-        model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-        component = "vae_decoder"
-        model = SDXLVAEDecoder()
-        model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_ExVideo_StableVideoDiffusion(self, state_dict, file_path=""):
-        unet_state_dict = self.model["unet"].state_dict()
-        self.model["unet"].to("cpu")
-        del self.model["unet"]
-        add_positional_conv = state_dict["blocks.185.positional_embedding.embeddings"].shape[0]
-        self.model["unet"] = SVDUNet(add_positional_conv=add_positional_conv)
-        self.model["unet"].load_state_dict(unet_state_dict, strict=False)
-        self.model["unet"].load_state_dict(state_dict, strict=False)
-        self.model["unet"].to(self.torch_dtype).to(self.device)
-
-    def search_for_embeddings(self, state_dict):
-        embeddings = []
-        for k in state_dict:
-            if isinstance(state_dict[k], torch.Tensor):
-                embeddings.append(state_dict[k])
-            elif isinstance(state_dict[k], dict):
-                embeddings += self.search_for_embeddings(state_dict[k])
-        return embeddings
-
-    def load_textual_inversions(self, folder):
-        # Store additional tokens here
-        self.textual_inversion_dict = {}
-
-        # Load every textual inversion file
-        for file_name in os.listdir(folder):
-            if file_name.endswith(".txt"):
-                continue
-            keyword = os.path.splitext(file_name)[0]
-            state_dict = load_state_dict(os.path.join(folder, file_name))
-
-            # Search for embeddings
-            for embeddings in self.search_for_embeddings(state_dict):
-                if len(embeddings.shape) == 2 and embeddings.shape[1] == 768:
-                    tokens = [f"{keyword}_{i}" for i in range(embeddings.shape[0])]
-                    self.textual_inversion_dict[keyword] = (tokens, embeddings)
-                    break
-        
-    def load_model(self, file_path, components=None, lora_alphas=[]):
-        state_dict = load_state_dict(file_path, torch_dtype=self.torch_dtype)
-        if self.is_stable_video_diffusion(state_dict):
-            self.load_stable_video_diffusion(state_dict, file_path=file_path)
-        elif self.is_animatediff(state_dict):
-            self.load_animatediff(state_dict, file_path=file_path)
-        elif self.is_animatediff_xl(state_dict):
-            self.load_animatediff_xl(state_dict, file_path=file_path)
-        elif self.is_controlnet(state_dict):
-            self.load_controlnet(state_dict, file_path=file_path)
-        elif self.is_stabe_diffusion_xl(state_dict):
-            self.load_stable_diffusion_xl(state_dict, components=components, file_path=file_path)
-        elif self.is_stable_diffusion(state_dict):
-            self.load_stable_diffusion(state_dict, components=components, file_path=file_path)
-        elif self.is_sd_lora(state_dict):
-            self.load_sd_lora(state_dict, alpha=lora_alphas.pop(0))
-        elif self.is_beautiful_prompt(state_dict):
-            self.load_beautiful_prompt(state_dict, file_path=file_path)
-        elif self.is_RIFE(state_dict):
-            self.load_RIFE(state_dict, file_path=file_path)
-        elif self.is_translator(state_dict):
-            self.load_translator(state_dict, file_path=file_path)
-        elif self.is_ipadapter(state_dict):
-            self.load_ipadapter(state_dict, file_path=file_path)
-        elif self.is_ipadapter_image_encoder(state_dict):
-            self.load_ipadapter_image_encoder(state_dict, file_path=file_path)
-        elif self.is_ipadapter_xl(state_dict):
-            self.load_ipadapter_xl(state_dict, file_path=file_path)
-        elif self.is_ipadapter_xl_image_encoder(state_dict):
-            self.load_ipadapter_xl_image_encoder(state_dict, file_path=file_path)
-        elif self.is_hunyuan_dit_clip_text_encoder(state_dict):
-            self.load_hunyuan_dit_clip_text_encoder(state_dict, file_path=file_path)
-        elif self.is_hunyuan_dit_t5_text_encoder(state_dict):
-            self.load_hunyuan_dit_t5_text_encoder(state_dict, file_path=file_path)
-        elif self.is_hunyuan_dit(state_dict):
-            self.load_hunyuan_dit(state_dict, file_path=file_path)
-        elif self.is_diffusers_vae(state_dict):
-            self.load_diffusers_vae(state_dict, file_path=file_path)
-        elif self.is_ExVideo_StableVideoDiffusion(state_dict):
-            self.load_ExVideo_StableVideoDiffusion(state_dict, file_path=file_path)
-
-    def load_models(self, file_path_list, lora_alphas=[]):
-        for file_path in file_path_list:
-            self.load_model(file_path, lora_alphas=lora_alphas)
-        
-    def to(self, device):
-        for component in self.model:
-            if isinstance(self.model[component], list):
-                for model in self.model[component]:
-                    model.to(device)
-            else:
-                self.model[component].to(device)
-        torch.cuda.empty_cache()
-
-    def get_model_with_model_path(self, model_path):
-        for component in self.model_path:
-            if isinstance(self.model_path[component], str):
-                if os.path.samefile(self.model_path[component], model_path):
-                    return self.model[component]
-            elif isinstance(self.model_path[component], list):
-                for i, model_path_ in enumerate(self.model_path[component]):
-                    if os.path.samefile(model_path_, model_path):
-                        return self.model[component][i]
-        raise ValueError(f"Please load model {model_path} before you use it.")
-    
-    def __getattr__(self, __name):
-        if __name in self.model:
-            return self.model[__name]
-        else:
-            return super.__getattribute__(__name)
-
-
-def load_state_dict(file_path, torch_dtype=None):
-    if file_path.endswith(".safetensors"):
-        return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype)
-    else:
-        return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype)
-
-
-def load_state_dict_from_safetensors(file_path, torch_dtype=None):
-    state_dict = {}
-    with safe_open(file_path, framework="pt", device="cpu") as f:
-        for k in f.keys():
-            state_dict[k] = f.get_tensor(k)
-            if torch_dtype is not None:
-                state_dict[k] = state_dict[k].to(torch_dtype)
-    return state_dict
-
-
-def load_state_dict_from_bin(file_path, torch_dtype=None):
-    state_dict = torch.load(file_path, map_location="cpu")
-    if torch_dtype is not None:
-        for i in state_dict:
-            if isinstance(state_dict[i], torch.Tensor):
-                state_dict[i] = state_dict[i].to(torch_dtype)
-    return state_dict
-
-
-def search_parameter(param, state_dict):
-    for name, param_ in state_dict.items():
-        if param.numel() == param_.numel():
-            if param.shape == param_.shape:
-                if torch.dist(param, param_) < 1e-6:
-                    return name
-            else:
-                if torch.dist(param.flatten(), param_.flatten()) < 1e-6:
-                    return name
-    return None
-
-
-def build_rename_dict(source_state_dict, target_state_dict, split_qkv=False):
-    matched_keys = set()
-    with torch.no_grad():
-        for name in source_state_dict:
-            rename = search_parameter(source_state_dict[name], target_state_dict)
-            if rename is not None:
-                print(f'"{name}": "{rename}",')
-                matched_keys.add(rename)
-            elif split_qkv and len(source_state_dict[name].shape)>=1 and source_state_dict[name].shape[0]%3==0:
-                length = source_state_dict[name].shape[0] // 3
-                rename = []
-                for i in range(3):
-                    rename.append(search_parameter(source_state_dict[name][i*length: i*length+length], target_state_dict))
-                if None not in rename:
-                    print(f'"{name}": {rename},')
-                    for rename_ in rename:
-                        matched_keys.add(rename_)
-    for name in target_state_dict:
-        if name not in matched_keys:
-            print("Cannot find", name, target_state_dict[name].shape)

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/dinov3_image_encoder.py

@@ -0,0 +1,96 @@
+from transformers import DINOv3ViTModel, DINOv3ViTImageProcessorFast
+from transformers.models.dinov3_vit.modeling_dinov3_vit import DINOv3ViTConfig
+import torch
+
+from ..core.device.npu_compatible_device import get_device_type
+
+
+class DINOv3ImageEncoder(DINOv3ViTModel):
+    def __init__(self):
+        config = DINOv3ViTConfig(
+            architectures = [
+                "DINOv3ViTModel"
+            ],
+            attention_dropout = 0.0,
+            drop_path_rate = 0.0,
+            dtype = "float32",
+            hidden_act = "silu",
+            hidden_size = 4096,
+            image_size = 224,
+            initializer_range = 0.02,
+            intermediate_size = 8192,
+            key_bias = False,
+            layer_norm_eps = 1e-05,
+            layerscale_value = 1.0,
+            mlp_bias = True,
+            model_type = "dinov3_vit",
+            num_attention_heads = 32,
+            num_channels = 3,
+            num_hidden_layers = 40,
+            num_register_tokens = 4,
+            patch_size = 16,
+            pos_embed_jitter = None,
+            pos_embed_rescale = 2.0,
+            pos_embed_shift = None,
+            proj_bias = True,
+            query_bias = False,
+            rope_theta = 100.0,
+            transformers_version = "4.56.1",
+            use_gated_mlp = True,
+            value_bias = False
+        )
+        super().__init__(config)
+        self.processor = DINOv3ViTImageProcessorFast(
+            crop_size = None,
+            data_format = "channels_first",
+            default_to_square = True,
+            device = None,
+            disable_grouping = None,
+            do_center_crop = None,
+            do_convert_rgb = None,
+            do_normalize = True,
+            do_rescale = True,
+            do_resize = True,
+            image_mean = [
+                0.485,
+                0.456,
+                0.406
+            ],
+            image_processor_type = "DINOv3ViTImageProcessorFast",
+            image_std = [
+                0.229,
+                0.224,
+                0.225
+            ],
+            input_data_format = None,
+            resample = 2,
+            rescale_factor = 0.00392156862745098,
+            return_tensors = None,
+            size = {
+                "height": 224,
+                "width": 224
+            }
+        )
+        
+    def forward(self, image, torch_dtype=torch.bfloat16, device=get_device_type()):
+        inputs = self.processor(images=image, return_tensors="pt")
+        pixel_values = inputs["pixel_values"].to(dtype=torch_dtype, device=device)
+        bool_masked_pos = None
+        head_mask = None
+        
+        pixel_values = pixel_values.to(torch_dtype)
+        hidden_states = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)
+        position_embeddings = self.rope_embeddings(pixel_values)
+
+        for i, layer_module in enumerate(self.layer):
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+            hidden_states = layer_module(
+                hidden_states,
+                attention_mask=layer_head_mask,
+                position_embeddings=position_embeddings,
+            )
+
+        sequence_output = self.norm(hidden_states)
+        pooled_output = sequence_output[:, 0, :]
+
+        return pooled_output

diffsynth/models/flux2_text_encoder.py

@@ -0,0 +1,58 @@
+from transformers import Mistral3ForConditionalGeneration, Mistral3Config
+
+
+class Flux2TextEncoder(Mistral3ForConditionalGeneration):
+    def __init__(self):
+        config = Mistral3Config(**{
+            "architectures": [
+                "Mistral3ForConditionalGeneration"
+            ],
+            "dtype": "bfloat16",
+            "image_token_index": 10,
+            "model_type": "mistral3",
+            "multimodal_projector_bias": False,
+            "projector_hidden_act": "gelu",
+            "spatial_merge_size": 2,
+            "text_config": {
+                "attention_dropout": 0.0,
+                "dtype": "bfloat16",
+                "head_dim": 128,
+                "hidden_act": "silu",
+                "hidden_size": 5120,
+                "initializer_range": 0.02,
+                "intermediate_size": 32768,
+                "max_position_embeddings": 131072,
+                "model_type": "mistral",
+                "num_attention_heads": 32,
+                "num_hidden_layers": 40,
+                "num_key_value_heads": 8,
+                "rms_norm_eps": 1e-05,
+                "rope_theta": 1000000000.0,
+                "sliding_window": None,
+                "use_cache": True,
+                "vocab_size": 131072
+            },
+            "transformers_version": "4.57.1",
+            "vision_config": {
+                "attention_dropout": 0.0,
+                "dtype": "bfloat16",
+                "head_dim": 64,
+                "hidden_act": "silu",
+                "hidden_size": 1024,
+                "image_size": 1540,
+                "initializer_range": 0.02,
+                "intermediate_size": 4096,
+                "model_type": "pixtral",
+                "num_attention_heads": 16,
+                "num_channels": 3,
+                "num_hidden_layers": 24,
+                "patch_size": 14,
+                "rope_theta": 10000.0
+            },
+            "vision_feature_layer": -1
+        })
+        super().__init__(config)
+    
+    def forward(self, input_ids = None, pixel_values = None, attention_mask = None, position_ids = None, past_key_values = None, inputs_embeds = None, labels = None, use_cache = None, output_attentions = None, output_hidden_states = None, return_dict = None, cache_position = None, logits_to_keep = 0, image_sizes = None, **kwargs):
+        return super().forward(input_ids, pixel_values, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict, cache_position, logits_to_keep, image_sizes, **kwargs)
+

diffsynth/models/flux_controlnet.py

@@ -0,0 +1,384 @@
+import torch
+from einops import rearrange, repeat
+from .flux_dit import RoPEEmbedding, TimestepEmbeddings, FluxJointTransformerBlock, FluxSingleTransformerBlock, RMSNorm
+# from .utils import hash_state_dict_keys, init_weights_on_device
+from contextlib import contextmanager
+
+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()
+
+@contextmanager
+def init_weights_on_device(device = torch.device("meta"), include_buffers :bool = False):
+    
+    old_register_parameter = torch.nn.Module.register_parameter
+    if include_buffers:
+        old_register_buffer = torch.nn.Module.register_buffer
+    
+    def register_empty_parameter(module, name, param):
+        old_register_parameter(module, name, param)
+        if param is not None:
+            param_cls = type(module._parameters[name])
+            kwargs = module._parameters[name].__dict__
+            kwargs["requires_grad"] = param.requires_grad
+            module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs)
+
+    def register_empty_buffer(module, name, buffer, persistent=True):
+        old_register_buffer(module, name, buffer, persistent=persistent)
+        if buffer is not None:
+            module._buffers[name] = module._buffers[name].to(device)
+            
+    def patch_tensor_constructor(fn):
+        def wrapper(*args, **kwargs):
+            kwargs["device"] = device
+            return fn(*args, **kwargs)
+
+        return wrapper
+    
+    if include_buffers:
+        tensor_constructors_to_patch = {
+            torch_function_name: getattr(torch, torch_function_name)
+            for torch_function_name in ["empty", "zeros", "ones", "full"]
+        }
+    else:
+        tensor_constructors_to_patch = {}
+    
+    try:
+        torch.nn.Module.register_parameter = register_empty_parameter
+        if include_buffers:
+            torch.nn.Module.register_buffer = register_empty_buffer
+        for torch_function_name in tensor_constructors_to_patch.keys():
+            setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name)))
+        yield
+    finally:
+        torch.nn.Module.register_parameter = old_register_parameter
+        if include_buffers:
+            torch.nn.Module.register_buffer = old_register_buffer
+        for torch_function_name, old_torch_function in tensor_constructors_to_patch.items():
+            setattr(torch, torch_function_name, old_torch_function)
+
+class FluxControlNet(torch.nn.Module):
+    def __init__(self, disable_guidance_embedder=False, num_joint_blocks=5, num_single_blocks=10, num_mode=0, mode_dict={}, additional_input_dim=0):
+        super().__init__()
+        self.pos_embedder = RoPEEmbedding(3072, 10000, [16, 56, 56])
+        self.time_embedder = TimestepEmbeddings(256, 3072)
+        self.guidance_embedder = None if disable_guidance_embedder else TimestepEmbeddings(256, 3072)
+        self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(768, 3072), torch.nn.SiLU(), torch.nn.Linear(3072, 3072))
+        self.context_embedder = torch.nn.Linear(4096, 3072)
+        self.x_embedder = torch.nn.Linear(64, 3072)
+
+        self.blocks = torch.nn.ModuleList([FluxJointTransformerBlock(3072, 24) for _ in range(num_joint_blocks)])
+        self.single_blocks = torch.nn.ModuleList([FluxSingleTransformerBlock(3072, 24) for _ in range(num_single_blocks)])
+
+        self.controlnet_blocks = torch.nn.ModuleList([torch.nn.Linear(3072, 3072) for _ in range(num_joint_blocks)])
+        self.controlnet_single_blocks = torch.nn.ModuleList([torch.nn.Linear(3072, 3072) for _ in range(num_single_blocks)])
+        
+        self.mode_dict = mode_dict
+        self.controlnet_mode_embedder = torch.nn.Embedding(num_mode, 3072) if len(mode_dict) > 0 else None
+        self.controlnet_x_embedder = torch.nn.Linear(64 + additional_input_dim, 3072)
+
+
+    def prepare_image_ids(self, latents):
+        batch_size, _, height, width = latents.shape
+        latent_image_ids = torch.zeros(height // 2, width // 2, 3)
+        latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
+        latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :]
+
+        latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
+
+        latent_image_ids = latent_image_ids[None, :].repeat(batch_size, 1, 1, 1)
+        latent_image_ids = latent_image_ids.reshape(
+            batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels
+        )
+        latent_image_ids = latent_image_ids.to(device=latents.device, dtype=latents.dtype)
+
+        return latent_image_ids
+    
+
+    def patchify(self, hidden_states):
+        hidden_states = rearrange(hidden_states, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2)
+        return hidden_states
+    
+
+    def align_res_stack_to_original_blocks(self, res_stack, num_blocks, hidden_states):
+        if len(res_stack) == 0:
+            return [torch.zeros_like(hidden_states)] * num_blocks
+        interval = (num_blocks + len(res_stack) - 1) // len(res_stack)
+        aligned_res_stack = [res_stack[block_id // interval] for block_id in range(num_blocks)]
+        return aligned_res_stack
+
+
+    def forward(
+        self,
+        hidden_states,
+        controlnet_conditioning,
+        timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None,
+        processor_id=None,
+        tiled=False, tile_size=128, tile_stride=64,
+        **kwargs
+    ):
+        if image_ids is None:
+            image_ids = self.prepare_image_ids(hidden_states)
+
+        conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb)
+        if self.guidance_embedder is not None:
+            guidance = guidance * 1000
+            conditioning = conditioning + self.guidance_embedder(guidance, hidden_states.dtype)
+        prompt_emb = self.context_embedder(prompt_emb)
+        if self.controlnet_mode_embedder is not None: # Different from FluxDiT
+            processor_id = torch.tensor([self.mode_dict[processor_id]], dtype=torch.int)
+            processor_id = repeat(processor_id, "D -> B D", B=1).to(text_ids.device)
+            prompt_emb = torch.concat([self.controlnet_mode_embedder(processor_id), prompt_emb], dim=1)
+            text_ids = torch.cat([text_ids[:, :1], text_ids], dim=1)
+        image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
+
+        hidden_states = self.patchify(hidden_states)
+        hidden_states = self.x_embedder(hidden_states)
+        controlnet_conditioning = self.patchify(controlnet_conditioning) # Different from FluxDiT
+        hidden_states = hidden_states + self.controlnet_x_embedder(controlnet_conditioning) # Different from FluxDiT
+
+        controlnet_res_stack = []
+        for block, controlnet_block in zip(self.blocks, self.controlnet_blocks):
+            hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb)
+            controlnet_res_stack.append(controlnet_block(hidden_states))
+
+        controlnet_single_res_stack = []
+        hidden_states = torch.cat([prompt_emb, hidden_states], dim=1)
+        for block, controlnet_block in zip(self.single_blocks, self.controlnet_single_blocks):
+            hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb)
+            controlnet_single_res_stack.append(controlnet_block(hidden_states[:, prompt_emb.shape[1]:]))
+
+        controlnet_res_stack = self.align_res_stack_to_original_blocks(controlnet_res_stack, 19, hidden_states[:, prompt_emb.shape[1]:])
+        controlnet_single_res_stack = self.align_res_stack_to_original_blocks(controlnet_single_res_stack, 38, hidden_states[:, prompt_emb.shape[1]:])
+
+        return controlnet_res_stack, controlnet_single_res_stack
+
+
+    # @staticmethod
+    # def state_dict_converter():
+    #     return FluxControlNetStateDictConverter()
+    
+    def quantize(self):
+        def cast_to(weight, dtype=None, device=None, copy=False):
+            if device is None or weight.device == device:
+                if not copy:
+                    if dtype is None or weight.dtype == dtype:
+                        return weight
+                return weight.to(dtype=dtype, copy=copy)
+
+            r = torch.empty_like(weight, dtype=dtype, device=device)
+            r.copy_(weight)
+            return r
+
+        def cast_weight(s, input=None, dtype=None, device=None):
+            if input is not None:
+                if dtype is None:
+                    dtype = input.dtype
+                if device is None:
+                    device = input.device
+            weight = cast_to(s.weight, dtype, device)
+            return weight
+
+        def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
+            if input is not None:
+                if dtype is None:
+                    dtype = input.dtype
+                if bias_dtype is None:
+                    bias_dtype = dtype
+                if device is None:
+                    device = input.device
+            bias = None
+            weight = cast_to(s.weight, dtype, device)
+            bias = cast_to(s.bias, bias_dtype, device)
+            return weight, bias
+
+        class quantized_layer:
+            class QLinear(torch.nn.Linear):
+                def __init__(self, *args, **kwargs):
+                    super().__init__(*args, **kwargs)
+                    
+                def forward(self,input,**kwargs):
+                    weight,bias= cast_bias_weight(self,input)
+                    return torch.nn.functional.linear(input,weight,bias)
+            
+            class QRMSNorm(torch.nn.Module):
+                def __init__(self, module):
+                    super().__init__()
+                    self.module = module
+                    
+                def forward(self,hidden_states,**kwargs):
+                    weight= cast_weight(self.module,hidden_states)
+                    input_dtype = hidden_states.dtype
+                    variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True)
+                    hidden_states = hidden_states * torch.rsqrt(variance + self.module.eps)
+                    hidden_states = hidden_states.to(input_dtype) * weight
+                    return hidden_states
+            
+            class QEmbedding(torch.nn.Embedding):
+                def __init__(self, *args, **kwargs):
+                    super().__init__(*args, **kwargs)
+                    
+                def forward(self,input,**kwargs):
+                    weight= cast_weight(self,input)
+                    return torch.nn.functional.embedding(
+                        input, weight, self.padding_idx, self.max_norm,
+                        self.norm_type, self.scale_grad_by_freq, self.sparse)
+            
+        def replace_layer(model):
+            for name, module in model.named_children():
+                if isinstance(module,quantized_layer.QRMSNorm):
+                    continue
+                if isinstance(module, torch.nn.Linear):
+                    with init_weights_on_device():
+                        new_layer = quantized_layer.QLinear(module.in_features,module.out_features)
+                    new_layer.weight = module.weight
+                    if module.bias is not None:
+                        new_layer.bias = module.bias
+                    setattr(model, name, new_layer)
+                elif isinstance(module, RMSNorm):
+                    if hasattr(module,"quantized"):
+                        continue
+                    module.quantized= True
+                    new_layer = quantized_layer.QRMSNorm(module)
+                    setattr(model, name, new_layer)
+                elif isinstance(module,torch.nn.Embedding):
+                    rows, cols = module.weight.shape
+                    new_layer = quantized_layer.QEmbedding(
+                        num_embeddings=rows,
+                        embedding_dim=cols,
+                        _weight=module.weight,
+                        # _freeze=module.freeze,
+                        padding_idx=module.padding_idx,
+                        max_norm=module.max_norm,
+                        norm_type=module.norm_type,
+                        scale_grad_by_freq=module.scale_grad_by_freq,
+                        sparse=module.sparse)
+                    setattr(model, name, new_layer)
+                else:
+                    replace_layer(module)
+
+        replace_layer(self)
+    
+
+
+class FluxControlNetStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        hash_value = hash_state_dict_keys(state_dict)
+        global_rename_dict = {
+            "context_embedder": "context_embedder",
+            "x_embedder": "x_embedder",
+            "time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0",
+            "time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2",
+            "time_text_embed.guidance_embedder.linear_1": "guidance_embedder.timestep_embedder.0",
+            "time_text_embed.guidance_embedder.linear_2": "guidance_embedder.timestep_embedder.2",
+            "time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0",
+            "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2",
+            "norm_out.linear": "final_norm_out.linear",
+            "proj_out": "final_proj_out",
+        }
+        rename_dict = {
+            "proj_out": "proj_out",
+            "norm1.linear": "norm1_a.linear",
+            "norm1_context.linear": "norm1_b.linear",
+            "attn.to_q": "attn.a_to_q",
+            "attn.to_k": "attn.a_to_k",
+            "attn.to_v": "attn.a_to_v",
+            "attn.to_out.0": "attn.a_to_out",
+            "attn.add_q_proj": "attn.b_to_q",
+            "attn.add_k_proj": "attn.b_to_k",
+            "attn.add_v_proj": "attn.b_to_v",
+            "attn.to_add_out": "attn.b_to_out",
+            "ff.net.0.proj": "ff_a.0",
+            "ff.net.2": "ff_a.2",
+            "ff_context.net.0.proj": "ff_b.0",
+            "ff_context.net.2": "ff_b.2",
+            "attn.norm_q": "attn.norm_q_a",
+            "attn.norm_k": "attn.norm_k_a",
+            "attn.norm_added_q": "attn.norm_q_b",
+            "attn.norm_added_k": "attn.norm_k_b",
+        }
+        rename_dict_single = {
+            "attn.to_q": "a_to_q",
+            "attn.to_k": "a_to_k",
+            "attn.to_v": "a_to_v",
+            "attn.norm_q": "norm_q_a",
+            "attn.norm_k": "norm_k_a",
+            "norm.linear": "norm.linear",
+            "proj_mlp": "proj_in_besides_attn",
+            "proj_out": "proj_out",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name.endswith(".weight") or name.endswith(".bias"):
+                suffix = ".weight" if name.endswith(".weight") else ".bias"
+                prefix = name[:-len(suffix)]
+                if prefix in global_rename_dict:
+                    state_dict_[global_rename_dict[prefix] + suffix] = param
+                elif prefix.startswith("transformer_blocks."):
+                    names = prefix.split(".")
+                    names[0] = "blocks"
+                    middle = ".".join(names[2:])
+                    if middle in rename_dict:
+                        name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]])
+                        state_dict_[name_] = param
+                elif prefix.startswith("single_transformer_blocks."):
+                    names = prefix.split(".")
+                    names[0] = "single_blocks"
+                    middle = ".".join(names[2:])
+                    if middle in rename_dict_single:
+                        name_ = ".".join(names[:2] + [rename_dict_single[middle]] + [suffix[1:]])
+                        state_dict_[name_] = param
+                    else:
+                        state_dict_[name] = param
+                else:
+                    state_dict_[name] = param
+        for name in list(state_dict_.keys()):
+            if ".proj_in_besides_attn." in name:
+                name_ = name.replace(".proj_in_besides_attn.", ".to_qkv_mlp.")
+                param = torch.concat([
+                    state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_q.")],
+                    state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_k.")],
+                    state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_v.")],
+                    state_dict_[name],
+                ], dim=0)
+                state_dict_[name_] = param
+                state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_q."))
+                state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_k."))
+                state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_v."))
+                state_dict_.pop(name)
+        for name in list(state_dict_.keys()):
+            for component in ["a", "b"]:
+                if f".{component}_to_q." in name:
+                    name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.")
+                    param = torch.concat([
+                        state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")],
+                        state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")],
+                        state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")],
+                    ], dim=0)
+                    state_dict_[name_] = param
+                    state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q."))
+                    state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k."))
+                    state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v."))
+        if hash_value == "78d18b9101345ff695f312e7e62538c0":
+            extra_kwargs = {"num_mode": 10, "mode_dict": {"canny": 0, "tile": 1, "depth": 2, "blur": 3, "pose": 4, "gray": 5, "lq": 6}}
+        elif hash_value == "b001c89139b5f053c715fe772362dd2a":
+            extra_kwargs = {"num_single_blocks": 0}
+        elif hash_value == "52357cb26250681367488a8954c271e8":
+            extra_kwargs = {"num_joint_blocks": 6, "num_single_blocks": 0, "additional_input_dim": 4}
+        elif hash_value == "0cfd1740758423a2a854d67c136d1e8c":
+            extra_kwargs = {"num_joint_blocks": 4, "num_single_blocks": 1}
+        elif hash_value == "7f9583eb8ba86642abb9a21a4b2c9e16":
+            extra_kwargs = {"num_joint_blocks": 4, "num_single_blocks": 10}
+        elif hash_value == "43ad5aaa27dd4ee01b832ed16773fa52":
+            extra_kwargs = {"num_joint_blocks": 6, "num_single_blocks": 0}
+        else:
+            extra_kwargs = {}
+        return state_dict_, extra_kwargs
+    
+
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)

diffsynth/models/flux_dit.py

@@ -0,0 +1,395 @@
+import torch
+from .general_modules import TimestepEmbeddings, AdaLayerNorm, RMSNorm
+from einops import rearrange
+
+
+def interact_with_ipadapter(hidden_states, q, ip_k, ip_v, scale=1.0):
+    batch_size, num_tokens = hidden_states.shape[0:2]
+    ip_hidden_states = torch.nn.functional.scaled_dot_product_attention(q, ip_k, ip_v)
+    ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, num_tokens, -1)
+    hidden_states = hidden_states + scale * ip_hidden_states
+    return hidden_states
+
+
+class RoPEEmbedding(torch.nn.Module):
+    def __init__(self, dim, theta, axes_dim):
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        self.axes_dim = axes_dim
+
+
+    def rope(self, pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor:
+        assert dim % 2 == 0, "The dimension must be even."
+
+        scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
+        omega = 1.0 / (theta**scale)
+
+        batch_size, seq_length = pos.shape
+        out = torch.einsum("...n,d->...nd", pos, omega)
+        cos_out = torch.cos(out)
+        sin_out = torch.sin(out)
+
+        stacked_out = torch.stack([cos_out, -sin_out, sin_out, cos_out], dim=-1)
+        out = stacked_out.view(batch_size, -1, dim // 2, 2, 2)
+        return out.float()
+
+
+    def forward(self, ids):
+        n_axes = ids.shape[-1]
+        emb = torch.cat([self.rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], dim=-3)
+        return emb.unsqueeze(1)
+
+
+
+class FluxJointAttention(torch.nn.Module):
+    def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+        self.only_out_a = only_out_a
+
+        self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3)
+        self.b_to_qkv = torch.nn.Linear(dim_b, dim_b * 3)
+
+        self.norm_q_a = RMSNorm(head_dim, eps=1e-6)
+        self.norm_k_a = RMSNorm(head_dim, eps=1e-6)
+        self.norm_q_b = RMSNorm(head_dim, eps=1e-6)
+        self.norm_k_b = RMSNorm(head_dim, eps=1e-6)
+
+        self.a_to_out = torch.nn.Linear(dim_a, dim_a)
+        if not only_out_a:
+            self.b_to_out = torch.nn.Linear(dim_b, dim_b)
+
+
+    def apply_rope(self, xq, xk, freqs_cis):
+        xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+        xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+        xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+        xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+        return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
+
+    def forward(self, hidden_states_a, hidden_states_b, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
+        batch_size = hidden_states_a.shape[0]
+
+        # Part A
+        qkv_a = self.a_to_qkv(hidden_states_a)
+        qkv_a = qkv_a.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q_a, k_a, v_a = qkv_a.chunk(3, dim=1)
+        q_a, k_a = self.norm_q_a(q_a), self.norm_k_a(k_a)
+
+        # Part B
+        qkv_b = self.b_to_qkv(hidden_states_b)
+        qkv_b = qkv_b.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q_b, k_b, v_b = qkv_b.chunk(3, dim=1)
+        q_b, k_b = self.norm_q_b(q_b), self.norm_k_b(k_b)
+
+        q = torch.concat([q_b, q_a], dim=2)
+        k = torch.concat([k_b, k_a], dim=2)
+        v = torch.concat([v_b, v_a], dim=2)
+
+        q, k = self.apply_rope(q, k, image_rotary_emb)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+        hidden_states_b, hidden_states_a = hidden_states[:, :hidden_states_b.shape[1]], hidden_states[:, hidden_states_b.shape[1]:]
+        if ipadapter_kwargs_list is not None:
+            hidden_states_a = interact_with_ipadapter(hidden_states_a, q_a, **ipadapter_kwargs_list)
+        hidden_states_a = self.a_to_out(hidden_states_a)
+        if self.only_out_a:
+            return hidden_states_a
+        else:
+            hidden_states_b = self.b_to_out(hidden_states_b)
+            return hidden_states_a, hidden_states_b
+
+
+
+class FluxJointTransformerBlock(torch.nn.Module):
+    def __init__(self, dim, num_attention_heads):
+        super().__init__()
+        self.norm1_a = AdaLayerNorm(dim)
+        self.norm1_b = AdaLayerNorm(dim)
+
+        self.attn = FluxJointAttention(dim, dim, num_attention_heads, dim // num_attention_heads)
+
+        self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_a = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim*4),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(dim*4, dim)
+        )
+
+        self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_b = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim*4),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(dim*4, dim)
+        )
+
+
+    def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
+        norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb)
+        norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb)
+
+        # Attention
+        attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b, image_rotary_emb, attn_mask, ipadapter_kwargs_list)
+
+        # Part A
+        hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a
+        norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a
+        hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a)
+
+        # Part B
+        hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b
+        norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b
+        hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b)
+
+        return hidden_states_a, hidden_states_b
+
+
+
+class FluxSingleAttention(torch.nn.Module):
+    def __init__(self, dim_a, dim_b, num_heads, head_dim):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+
+        self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3)
+
+        self.norm_q_a = RMSNorm(head_dim, eps=1e-6)
+        self.norm_k_a = RMSNorm(head_dim, eps=1e-6)
+
+
+    def apply_rope(self, xq, xk, freqs_cis):
+        xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+        xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+        xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+        xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+        return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
+
+
+    def forward(self, hidden_states, image_rotary_emb):
+        batch_size = hidden_states.shape[0]
+
+        qkv_a = self.a_to_qkv(hidden_states)
+        qkv_a = qkv_a.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q_a, k_a, v = qkv_a.chunk(3, dim=1)
+        q_a, k_a = self.norm_q_a(q_a), self.norm_k_a(k_a)
+
+        q, k = self.apply_rope(q_a, k_a, image_rotary_emb)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+        return hidden_states
+
+
+
+class AdaLayerNormSingle(torch.nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.silu = torch.nn.SiLU()
+        self.linear = torch.nn.Linear(dim, 3 * dim, bias=True)
+        self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+
+    def forward(self, x, emb):
+        emb = self.linear(self.silu(emb))
+        shift_msa, scale_msa, gate_msa = emb.chunk(3, dim=1)
+        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
+        return x, gate_msa
+
+
+
+class FluxSingleTransformerBlock(torch.nn.Module):
+    def __init__(self, dim, num_attention_heads):
+        super().__init__()
+        self.num_heads = num_attention_heads
+        self.head_dim = dim // num_attention_heads
+        self.dim = dim
+
+        self.norm = AdaLayerNormSingle(dim)
+        self.to_qkv_mlp = torch.nn.Linear(dim, dim * (3 + 4))
+        self.norm_q_a = RMSNorm(self.head_dim, eps=1e-6)
+        self.norm_k_a = RMSNorm(self.head_dim, eps=1e-6)
+
+        self.proj_out = torch.nn.Linear(dim * 5, dim)
+
+
+    def apply_rope(self, xq, xk, freqs_cis):
+        xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+        xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+        xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+        xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+        return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
+
+
+    def process_attention(self, hidden_states, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
+        batch_size = hidden_states.shape[0]
+
+        qkv = hidden_states.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q, k, v = qkv.chunk(3, dim=1)
+        q, k = self.norm_q_a(q), self.norm_k_a(k)
+
+        q, k = self.apply_rope(q, k, image_rotary_emb)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+        if ipadapter_kwargs_list is not None:
+            hidden_states = interact_with_ipadapter(hidden_states, q, **ipadapter_kwargs_list)
+        return hidden_states
+
+
+    def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
+        residual = hidden_states_a
+        norm_hidden_states, gate = self.norm(hidden_states_a, emb=temb)
+        hidden_states_a = self.to_qkv_mlp(norm_hidden_states)
+        attn_output, mlp_hidden_states = hidden_states_a[:, :, :self.dim * 3], hidden_states_a[:, :, self.dim * 3:]
+
+        attn_output = self.process_attention(attn_output, image_rotary_emb, attn_mask, ipadapter_kwargs_list)
+        mlp_hidden_states = torch.nn.functional.gelu(mlp_hidden_states, approximate="tanh")
+
+        hidden_states_a = torch.cat([attn_output, mlp_hidden_states], dim=2)
+        hidden_states_a = gate.unsqueeze(1) * self.proj_out(hidden_states_a)
+        hidden_states_a = residual + hidden_states_a
+
+        return hidden_states_a, hidden_states_b
+
+
+
+class AdaLayerNormContinuous(torch.nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.silu = torch.nn.SiLU()
+        self.linear = torch.nn.Linear(dim, dim * 2, bias=True)
+        self.norm = torch.nn.LayerNorm(dim, eps=1e-6, elementwise_affine=False)
+
+    def forward(self, x, conditioning):
+        emb = self.linear(self.silu(conditioning))
+        shift, scale = torch.chunk(emb, 2, dim=1)
+        x = self.norm(x) * (1 + scale)[:, None] + shift[:, None]
+        return x
+
+
+
+class FluxDiT(torch.nn.Module):
+    def __init__(self, disable_guidance_embedder=False, input_dim=64, num_blocks=19):
+        super().__init__()
+        self.pos_embedder = RoPEEmbedding(3072, 10000, [16, 56, 56])
+        self.time_embedder = TimestepEmbeddings(256, 3072)
+        self.guidance_embedder = None if disable_guidance_embedder else TimestepEmbeddings(256, 3072)
+        self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(768, 3072), torch.nn.SiLU(), torch.nn.Linear(3072, 3072))
+        self.context_embedder = torch.nn.Linear(4096, 3072)
+        self.x_embedder = torch.nn.Linear(input_dim, 3072)
+
+        self.blocks = torch.nn.ModuleList([FluxJointTransformerBlock(3072, 24) for _ in range(num_blocks)])
+        self.single_blocks = torch.nn.ModuleList([FluxSingleTransformerBlock(3072, 24) for _ in range(38)])
+
+        self.final_norm_out = AdaLayerNormContinuous(3072)
+        self.final_proj_out = torch.nn.Linear(3072, 64)
+        
+        self.input_dim = input_dim
+
+
+    def patchify(self, hidden_states):
+        hidden_states = rearrange(hidden_states, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2)
+        return hidden_states
+
+
+    def unpatchify(self, hidden_states, height, width):
+        hidden_states = rearrange(hidden_states, "B (H W) (C P Q) -> B C (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2)
+        return hidden_states
+
+
+    def prepare_image_ids(self, latents):
+        batch_size, _, height, width = latents.shape
+        latent_image_ids = torch.zeros(height // 2, width // 2, 3)
+        latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
+        latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :]
+
+        latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
+
+        latent_image_ids = latent_image_ids[None, :].repeat(batch_size, 1, 1, 1)
+        latent_image_ids = latent_image_ids.reshape(
+            batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels
+        )
+        latent_image_ids = latent_image_ids.to(device=latents.device, dtype=latents.dtype)
+
+        return latent_image_ids
+
+
+    def construct_mask(self, entity_masks, prompt_seq_len, image_seq_len):
+        N = len(entity_masks)
+        batch_size = entity_masks[0].shape[0]
+        total_seq_len = N * prompt_seq_len + image_seq_len
+        patched_masks = [self.patchify(entity_masks[i]) for i in range(N)]
+        attention_mask = torch.ones((batch_size, total_seq_len, total_seq_len), dtype=torch.bool).to(device=entity_masks[0].device)
+
+        image_start = N * prompt_seq_len
+        image_end = N * prompt_seq_len + image_seq_len
+        # prompt-image mask
+        for i in range(N):
+            prompt_start = i * prompt_seq_len
+            prompt_end = (i + 1) * prompt_seq_len
+            image_mask = torch.sum(patched_masks[i], dim=-1) > 0
+            image_mask = image_mask.unsqueeze(1).repeat(1, prompt_seq_len, 1)
+            # prompt update with image
+            attention_mask[:, prompt_start:prompt_end, image_start:image_end] = image_mask
+            # image update with prompt
+            attention_mask[:, image_start:image_end, prompt_start:prompt_end] = image_mask.transpose(1, 2)
+        # prompt-prompt mask
+        for i in range(N):
+            for j in range(N):
+                if i != j:
+                    prompt_start_i = i * prompt_seq_len
+                    prompt_end_i = (i + 1) * prompt_seq_len
+                    prompt_start_j = j * prompt_seq_len
+                    prompt_end_j = (j + 1) * prompt_seq_len
+                    attention_mask[:, prompt_start_i:prompt_end_i, prompt_start_j:prompt_end_j] = False
+
+        attention_mask = attention_mask.float()
+        attention_mask[attention_mask == 0] = float('-inf')
+        attention_mask[attention_mask == 1] = 0
+        return attention_mask
+
+
+    def process_entity_masks(self, hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids, repeat_dim):
+        max_masks = 0
+        attention_mask = None
+        prompt_embs = [prompt_emb]
+        if entity_masks is not None:
+            # entity_masks
+            batch_size, max_masks = entity_masks.shape[0], entity_masks.shape[1]
+            entity_masks = entity_masks.repeat(1, 1, repeat_dim, 1, 1)
+            entity_masks = [entity_masks[:, i, None].squeeze(1) for i in range(max_masks)]
+            # global mask
+            global_mask = torch.ones_like(entity_masks[0]).to(device=hidden_states.device, dtype=hidden_states.dtype)
+            entity_masks = entity_masks + [global_mask] # append global to last
+            # attention mask
+            attention_mask = self.construct_mask(entity_masks, prompt_emb.shape[1], hidden_states.shape[1])
+            attention_mask = attention_mask.to(device=hidden_states.device, dtype=hidden_states.dtype)
+            attention_mask = attention_mask.unsqueeze(1)
+            # embds: n_masks * b * seq * d
+            local_embs = [entity_prompt_emb[:, i, None].squeeze(1) for i in range(max_masks)]
+            prompt_embs = local_embs + prompt_embs # append global to last
+        prompt_embs = [self.context_embedder(prompt_emb) for prompt_emb in prompt_embs]
+        prompt_emb = torch.cat(prompt_embs, dim=1)
+
+        # positional embedding
+        text_ids = torch.cat([text_ids] * (max_masks + 1), dim=1)
+        image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
+        return prompt_emb, image_rotary_emb, attention_mask
+
+
+    def forward(
+        self,
+        hidden_states,
+        timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None,
+        tiled=False, tile_size=128, tile_stride=64, entity_prompt_emb=None, entity_masks=None,
+        use_gradient_checkpointing=False,
+        **kwargs
+    ):
+        # (Deprecated) The real forward is in `pipelines.flux_image`.
+        return None

diffsynth/models/flux_infiniteyou.py

@@ -0,0 +1,129 @@
+import math
+import torch
+import torch.nn as nn
+
+
+# FFN
+def FeedForward(dim, mult=4):
+    inner_dim = int(dim * mult)
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, inner_dim, bias=False),
+        nn.GELU(),
+        nn.Linear(inner_dim, dim, bias=False),
+    )
+
+
+def reshape_tensor(x, heads):
+    bs, length, width = x.shape
+    #(bs, length, width) --> (bs, length, n_heads, dim_per_head)
+    x = x.view(bs, length, heads, -1)
+    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
+    x = x.transpose(1, 2)
+    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
+    x = x.reshape(bs, heads, length, -1)
+    return x
+
+
+class PerceiverAttention(nn.Module):
+
+    def __init__(self, *, dim, dim_head=64, heads=8):
+        super().__init__()
+        self.scale = dim_head**-0.5
+        self.dim_head = dim_head
+        self.heads = heads
+        inner_dim = dim_head * heads
+
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+    def forward(self, x, latents):
+        """
+        Args:
+            x (torch.Tensor): image features
+                shape (b, n1, D)
+            latent (torch.Tensor): latent features
+                shape (b, n2, D)
+        """
+        x = self.norm1(x)
+        latents = self.norm2(latents)
+
+        b, l, _ = latents.shape
+
+        q = self.to_q(latents)
+        kv_input = torch.cat((x, latents), dim=-2)
+        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+
+        q = reshape_tensor(q, self.heads)
+        k = reshape_tensor(k, self.heads)
+        v = reshape_tensor(v, self.heads)
+
+        # attention
+        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
+        weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        out = weight @ v
+
+        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
+
+        return self.to_out(out)
+
+
+class InfiniteYouImageProjector(nn.Module):
+
+    def __init__(
+        self,
+        dim=1280,
+        depth=4,
+        dim_head=64,
+        heads=20,
+        num_queries=8,
+        embedding_dim=512,
+        output_dim=4096,
+        ff_mult=4,
+    ):
+        super().__init__()
+        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
+        self.proj_in = nn.Linear(embedding_dim, dim)
+
+        self.proj_out = nn.Linear(dim, output_dim)
+        self.norm_out = nn.LayerNorm(output_dim)
+
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                nn.ModuleList([
+                    PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+                    FeedForward(dim=dim, mult=ff_mult),
+                ]))
+
+    def forward(self, x):
+
+        latents = self.latents.repeat(x.size(0), 1, 1)
+        latents = latents.to(dtype=x.dtype, device=x.device)
+
+        x = self.proj_in(x)
+
+        for attn, ff in self.layers:
+            latents = attn(x, latents) + latents
+            latents = ff(latents) + latents
+
+        latents = self.proj_out(latents)
+        return self.norm_out(latents)
+
+    @staticmethod
+    def state_dict_converter():
+        return FluxInfiniteYouImageProjectorStateDictConverter()
+
+
+class FluxInfiniteYouImageProjectorStateDictConverter:
+
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict['image_proj']

diffsynth/models/flux_ipadapter.py

@@ -0,0 +1,110 @@
+from .general_modules import RMSNorm
+from transformers import SiglipVisionModel, SiglipVisionConfig
+import torch
+
+
+class SiglipVisionModelSO400M(SiglipVisionModel):
+    def __init__(self):
+        config = SiglipVisionConfig(
+            hidden_size=1152,
+            image_size=384,
+            intermediate_size=4304,
+            model_type="siglip_vision_model",
+            num_attention_heads=16,
+            num_hidden_layers=27,
+            patch_size=14,
+            architectures=["SiglipModel"],
+            initializer_factor=1.0,
+            torch_dtype="float32",
+            transformers_version="4.37.0.dev0"
+        )
+        super().__init__(config)
+
+class MLPProjModel(torch.nn.Module):
+    def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4):
+        super().__init__()
+        
+        self.cross_attention_dim = cross_attention_dim
+        self.num_tokens = num_tokens
+        
+        self.proj = torch.nn.Sequential(
+            torch.nn.Linear(id_embeddings_dim, id_embeddings_dim*2),
+            torch.nn.GELU(),
+            torch.nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens),
+        )
+        self.norm = torch.nn.LayerNorm(cross_attention_dim)
+        
+    def forward(self, id_embeds):
+        x = self.proj(id_embeds)
+        x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
+        x = self.norm(x)
+        return x
+
+class IpAdapterModule(torch.nn.Module):
+    def __init__(self, num_attention_heads, attention_head_dim, input_dim):
+        super().__init__()
+        self.num_heads = num_attention_heads
+        self.head_dim = attention_head_dim
+        output_dim = num_attention_heads * attention_head_dim
+        self.to_k_ip = torch.nn.Linear(input_dim, output_dim, bias=False)
+        self.to_v_ip = torch.nn.Linear(input_dim, output_dim, bias=False)
+        self.norm_added_k = RMSNorm(attention_head_dim, eps=1e-5, elementwise_affine=False)
+        
+
+    def forward(self, hidden_states):
+        batch_size = hidden_states.shape[0]
+        # ip_k
+        ip_k = self.to_k_ip(hidden_states)
+        ip_k = ip_k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        ip_k = self.norm_added_k(ip_k)
+        # ip_v
+        ip_v = self.to_v_ip(hidden_states)
+        ip_v = ip_v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        return ip_k, ip_v
+
+
+class FluxIpAdapter(torch.nn.Module):
+    def __init__(self, num_attention_heads=24, attention_head_dim=128, cross_attention_dim=4096, num_tokens=128, num_blocks=57):
+        super().__init__()
+        self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(num_attention_heads, attention_head_dim, cross_attention_dim) for _ in range(num_blocks)])
+        self.image_proj = MLPProjModel(cross_attention_dim=cross_attention_dim, id_embeddings_dim=1152, num_tokens=num_tokens)
+        self.set_adapter()
+
+    def set_adapter(self):
+        self.call_block_id = {i:i for i in range(len(self.ipadapter_modules))}
+
+    def forward(self, hidden_states, scale=1.0):
+        hidden_states = self.image_proj(hidden_states)
+        hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1])
+        ip_kv_dict = {}
+        for block_id in self.call_block_id:
+            ipadapter_id = self.call_block_id[block_id]
+            ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states)
+            ip_kv_dict[block_id] = {
+                "ip_k": ip_k,
+                "ip_v": ip_v,
+                "scale": scale
+            }
+        return ip_kv_dict
+
+    @staticmethod
+    def state_dict_converter():
+        return FluxIpAdapterStateDictConverter()
+
+
+class FluxIpAdapterStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = {}
+        for name in state_dict["ip_adapter"]:
+            name_ = 'ipadapter_modules.' + name
+            state_dict_[name_] = state_dict["ip_adapter"][name]
+        for name in state_dict["image_proj"]:
+            name_ = "image_proj." + name
+            state_dict_[name_] = state_dict["image_proj"][name]
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)

diffsynth/models/flux_lora_encoder.py

@@ -0,0 +1,521 @@
+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)
+
+
+
+
+
+class CLIPEncoderLayer(torch.nn.Module):
+    def __init__(self, embed_dim, intermediate_size, num_heads=12, head_dim=64, use_quick_gelu=True):
+        super().__init__()
+        self.attn = Attention(q_dim=embed_dim, num_heads=num_heads, head_dim=head_dim, bias_q=True, bias_kv=True, bias_out=True)
+        self.layer_norm1 = torch.nn.LayerNorm(embed_dim)
+        self.layer_norm2 = torch.nn.LayerNorm(embed_dim)
+        self.fc1 = torch.nn.Linear(embed_dim, intermediate_size)
+        self.fc2 = torch.nn.Linear(intermediate_size, embed_dim)
+
+        self.use_quick_gelu = use_quick_gelu
+
+    def quickGELU(self, x):
+        return x * torch.sigmoid(1.702 * x)
+    
+    def forward(self, hidden_states, attn_mask=None):
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states = self.attn(hidden_states, attn_mask=attn_mask)
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.fc1(hidden_states)
+        if self.use_quick_gelu:
+            hidden_states = self.quickGELU(hidden_states)
+        else:
+            hidden_states = torch.nn.functional.gelu(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+    
+
+class SDTextEncoder(torch.nn.Module):
+    def __init__(self, embed_dim=768, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=12, encoder_intermediate_size=3072):
+        super().__init__()
+
+        # token_embedding
+        self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim)
+
+        # position_embeds (This is a fixed tensor)
+        self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim))
+
+        # encoders
+        self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size) for _ in range(num_encoder_layers)])
+
+        # attn_mask
+        self.attn_mask = self.attention_mask(max_position_embeddings)
+
+        # final_layer_norm
+        self.final_layer_norm = torch.nn.LayerNorm(embed_dim)
+
+    def attention_mask(self, length):
+        mask = torch.empty(length, length)
+        mask.fill_(float("-inf"))
+        mask.triu_(1)
+        return mask
+
+    def forward(self, input_ids, clip_skip=1):
+        embeds = self.token_embedding(input_ids) + self.position_embeds
+        attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype)
+        for encoder_id, encoder in enumerate(self.encoders):
+            embeds = encoder(embeds, attn_mask=attn_mask)
+            if encoder_id + clip_skip == len(self.encoders):
+                break
+        embeds = self.final_layer_norm(embeds)
+        return embeds
+    
+    @staticmethod
+    def state_dict_converter():
+        return SDTextEncoderStateDictConverter()
+
+
+class SDTextEncoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "text_model.embeddings.token_embedding.weight": "token_embedding.weight",
+            "text_model.embeddings.position_embedding.weight": "position_embeds",
+            "text_model.final_layer_norm.weight": "final_layer_norm.weight",
+            "text_model.final_layer_norm.bias": "final_layer_norm.bias"
+        }
+        attn_rename_dict = {
+            "self_attn.q_proj": "attn.to_q",
+            "self_attn.k_proj": "attn.to_k",
+            "self_attn.v_proj": "attn.to_v",
+            "self_attn.out_proj": "attn.to_out",
+            "layer_norm1": "layer_norm1",
+            "layer_norm2": "layer_norm2",
+            "mlp.fc1": "fc1",
+            "mlp.fc2": "fc2",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict[name]] = param
+            elif name.startswith("text_model.encoder.layers."):
+                param = state_dict[name]
+                names = name.split(".")
+                layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1]
+                name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail])
+                state_dict_[name_] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc2.bias": "encoders.0.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc2.weight": "encoders.0.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias": "encoders.0.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight": "encoders.0.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.out_proj.bias": "encoders.0.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.out_proj.weight": "encoders.0.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.q_proj.bias": "encoders.0.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.q_proj.weight": "encoders.0.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.v_proj.bias": "encoders.0.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.v_proj.weight": "encoders.0.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm1.bias": "encoders.1.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm1.weight": "encoders.1.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm2.bias": "encoders.1.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm2.weight": "encoders.1.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc1.bias": "encoders.1.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc1.weight": "encoders.1.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc2.bias": "encoders.1.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc2.weight": "encoders.1.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.k_proj.bias": "encoders.1.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.k_proj.weight": "encoders.1.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.out_proj.bias": "encoders.1.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.out_proj.weight": "encoders.1.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.q_proj.bias": "encoders.1.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.q_proj.weight": "encoders.1.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.v_proj.bias": "encoders.1.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.v_proj.weight": "encoders.1.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.layer_norm1.bias": "encoders.10.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.layer_norm1.weight": "encoders.10.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.layer_norm2.bias": "encoders.10.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.layer_norm2.weight": "encoders.10.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.mlp.fc1.bias": "encoders.10.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.mlp.fc1.weight": "encoders.10.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.mlp.fc2.bias": "encoders.10.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.mlp.fc2.weight": "encoders.10.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.k_proj.bias": "encoders.10.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.k_proj.weight": "encoders.10.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.out_proj.bias": "encoders.10.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.out_proj.weight": "encoders.10.attn.to_out.weight",        
+            "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.q_proj.bias": "encoders.10.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.q_proj.weight": "encoders.10.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.v_proj.bias": "encoders.10.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.v_proj.weight": "encoders.10.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.layer_norm1.bias": "encoders.11.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.layer_norm1.weight": "encoders.11.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.layer_norm2.bias": "encoders.11.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.layer_norm2.weight": "encoders.11.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc1.bias": "encoders.11.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc1.weight": "encoders.11.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc2.bias": "encoders.11.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc2.weight": "encoders.11.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.k_proj.bias": "encoders.11.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.k_proj.weight": "encoders.11.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.out_proj.bias": "encoders.11.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.out_proj.weight": "encoders.11.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.q_proj.bias": "encoders.11.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.q_proj.weight": "encoders.11.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.v_proj.bias": "encoders.11.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.v_proj.weight": "encoders.11.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.layer_norm1.bias": "encoders.2.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.layer_norm1.weight": "encoders.2.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.layer_norm2.bias": "encoders.2.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.layer_norm2.weight": "encoders.2.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc1.bias": "encoders.2.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc1.weight": "encoders.2.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc2.bias": "encoders.2.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc2.weight": "encoders.2.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.k_proj.bias": "encoders.2.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.k_proj.weight": "encoders.2.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.out_proj.bias": "encoders.2.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.out_proj.weight": "encoders.2.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.q_proj.bias": "encoders.2.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.q_proj.weight": "encoders.2.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.v_proj.bias": "encoders.2.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.v_proj.weight": "encoders.2.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.layer_norm1.bias": "encoders.3.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.layer_norm1.weight": "encoders.3.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.layer_norm2.bias": "encoders.3.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.layer_norm2.weight": "encoders.3.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc1.bias": "encoders.3.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc1.weight": "encoders.3.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc2.bias": "encoders.3.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc2.weight": "encoders.3.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.k_proj.bias": "encoders.3.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.k_proj.weight": "encoders.3.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.out_proj.bias": "encoders.3.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.out_proj.weight": "encoders.3.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.q_proj.bias": "encoders.3.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.q_proj.weight": "encoders.3.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.v_proj.bias": "encoders.3.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.v_proj.weight": "encoders.3.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.layer_norm1.bias": "encoders.4.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.layer_norm1.weight": "encoders.4.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.layer_norm2.bias": "encoders.4.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.layer_norm2.weight": "encoders.4.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.mlp.fc1.bias": "encoders.4.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.mlp.fc1.weight": "encoders.4.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.mlp.fc2.bias": "encoders.4.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.mlp.fc2.weight": "encoders.4.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.k_proj.bias": "encoders.4.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.k_proj.weight": "encoders.4.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.out_proj.bias": "encoders.4.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.out_proj.weight": "encoders.4.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.q_proj.bias": "encoders.4.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.q_proj.weight": "encoders.4.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.v_proj.bias": "encoders.4.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.v_proj.weight": "encoders.4.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.layer_norm1.bias": "encoders.5.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.layer_norm1.weight": "encoders.5.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.layer_norm2.bias": "encoders.5.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.layer_norm2.weight": "encoders.5.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.mlp.fc1.bias": "encoders.5.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.mlp.fc1.weight": "encoders.5.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.mlp.fc2.bias": "encoders.5.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.mlp.fc2.weight": "encoders.5.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.k_proj.bias": "encoders.5.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.k_proj.weight": "encoders.5.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.out_proj.bias": "encoders.5.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.out_proj.weight": "encoders.5.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.q_proj.bias": "encoders.5.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.q_proj.weight": "encoders.5.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.v_proj.bias": "encoders.5.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.v_proj.weight": "encoders.5.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.layer_norm1.bias": "encoders.6.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.layer_norm1.weight": "encoders.6.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.layer_norm2.bias": "encoders.6.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.layer_norm2.weight": "encoders.6.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.mlp.fc1.bias": "encoders.6.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.mlp.fc1.weight": "encoders.6.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.mlp.fc2.bias": "encoders.6.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.mlp.fc2.weight": "encoders.6.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.k_proj.bias": "encoders.6.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.k_proj.weight": "encoders.6.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.out_proj.bias": "encoders.6.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.out_proj.weight": "encoders.6.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.q_proj.bias": "encoders.6.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.q_proj.weight": "encoders.6.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.v_proj.bias": "encoders.6.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.v_proj.weight": "encoders.6.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.layer_norm1.bias": "encoders.7.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.layer_norm1.weight": "encoders.7.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.layer_norm2.bias": "encoders.7.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.layer_norm2.weight": "encoders.7.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.mlp.fc1.bias": "encoders.7.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.mlp.fc1.weight": "encoders.7.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.mlp.fc2.bias": "encoders.7.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.mlp.fc2.weight": "encoders.7.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.k_proj.bias": "encoders.7.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.k_proj.weight": "encoders.7.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.out_proj.bias": "encoders.7.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.out_proj.weight": "encoders.7.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.q_proj.bias": "encoders.7.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.q_proj.weight": "encoders.7.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.v_proj.bias": "encoders.7.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.v_proj.weight": "encoders.7.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.layer_norm1.bias": "encoders.8.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.layer_norm1.weight": "encoders.8.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.layer_norm2.bias": "encoders.8.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.layer_norm2.weight": "encoders.8.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.mlp.fc1.bias": "encoders.8.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.mlp.fc1.weight": "encoders.8.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.mlp.fc2.bias": "encoders.8.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.mlp.fc2.weight": "encoders.8.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.k_proj.bias": "encoders.8.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.k_proj.weight": "encoders.8.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.out_proj.bias": "encoders.8.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.out_proj.weight": "encoders.8.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.q_proj.bias": "encoders.8.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.q_proj.weight": "encoders.8.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.v_proj.bias": "encoders.8.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.v_proj.weight": "encoders.8.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.layer_norm1.bias": "encoders.9.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.layer_norm1.weight": "encoders.9.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.layer_norm2.bias": "encoders.9.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.layer_norm2.weight": "encoders.9.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.mlp.fc1.bias": "encoders.9.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.mlp.fc1.weight": "encoders.9.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.mlp.fc2.bias": "encoders.9.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.mlp.fc2.weight": "encoders.9.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.k_proj.bias": "encoders.9.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.k_proj.weight": "encoders.9.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.out_proj.bias": "encoders.9.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.out_proj.weight": "encoders.9.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.q_proj.bias": "encoders.9.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.q_proj.weight": "encoders.9.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.final_layer_norm.bias": "final_layer_norm.bias",
+            "cond_stage_model.transformer.text_model.final_layer_norm.weight": "final_layer_norm.weight",
+            "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight": "position_embeds"
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
+
+
+
+class LoRALayerBlock(torch.nn.Module):
+    def __init__(self, L, dim_in, dim_out):
+        super().__init__()
+        self.x = torch.nn.Parameter(torch.randn(1, L, dim_in))
+        self.layer_norm = torch.nn.LayerNorm(dim_out)
+
+    def forward(self, lora_A, lora_B):
+        x = self.x @ lora_A.T @ lora_B.T
+        x = self.layer_norm(x)
+        return x
+    
+
+class LoRAEmbedder(torch.nn.Module):
+    def __init__(self, lora_patterns=None, L=1, out_dim=2048):
+        super().__init__()
+        if lora_patterns is None:
+            lora_patterns = self.default_lora_patterns()
+            
+        model_dict = {}
+        for lora_pattern in lora_patterns:
+            name, dim = lora_pattern["name"], lora_pattern["dim"]
+            model_dict[name.replace(".", "___")] = LoRALayerBlock(L, dim[0], dim[1])
+        self.model_dict = torch.nn.ModuleDict(model_dict)
+        
+        proj_dict = {}
+        for lora_pattern in lora_patterns:
+            layer_type, dim = lora_pattern["type"], lora_pattern["dim"]
+            if layer_type not in proj_dict:
+                proj_dict[layer_type.replace(".", "___")] = torch.nn.Linear(dim[1], out_dim)
+        self.proj_dict = torch.nn.ModuleDict(proj_dict)
+        
+        self.lora_patterns = lora_patterns
+        
+        
+    def default_lora_patterns(self):
+        lora_patterns = []
+        lora_dict = {
+            "attn.a_to_qkv": (3072, 9216), "attn.a_to_out": (3072, 3072), "ff_a.0": (3072, 12288), "ff_a.2": (12288, 3072), "norm1_a.linear": (3072, 18432),
+            "attn.b_to_qkv": (3072, 9216), "attn.b_to_out": (3072, 3072), "ff_b.0": (3072, 12288), "ff_b.2": (12288, 3072), "norm1_b.linear": (3072, 18432),
+        }
+        for i in range(19):
+            for suffix in lora_dict:
+                lora_patterns.append({
+                    "name": f"blocks.{i}.{suffix}",
+                    "dim": lora_dict[suffix],
+                    "type": suffix,
+                })
+        lora_dict = {"to_qkv_mlp": (3072, 21504), "proj_out": (15360, 3072), "norm.linear": (3072, 9216)}
+        for i in range(38):
+            for suffix in lora_dict:
+                lora_patterns.append({
+                    "name": f"single_blocks.{i}.{suffix}",
+                    "dim": lora_dict[suffix],
+                    "type": suffix,
+                })
+        return lora_patterns
+        
+    def forward(self, lora):
+        lora_emb = []
+        for lora_pattern in self.lora_patterns:
+            name, layer_type = lora_pattern["name"], lora_pattern["type"]
+            lora_A = lora[name + ".lora_A.weight"]
+            lora_B = lora[name + ".lora_B.weight"]
+            lora_out = self.model_dict[name.replace(".", "___")](lora_A, lora_B)
+            lora_out = self.proj_dict[layer_type.replace(".", "___")](lora_out)
+            lora_emb.append(lora_out)
+        lora_emb = torch.concat(lora_emb, dim=1)
+        return lora_emb
+    
+    
+class FluxLoRAEncoder(torch.nn.Module):
+    def __init__(self, embed_dim=4096, encoder_intermediate_size=8192, num_encoder_layers=1, num_embeds_per_lora=16, num_special_embeds=1):
+        super().__init__()
+        self.num_embeds_per_lora = num_embeds_per_lora
+        # embedder
+        self.embedder = LoRAEmbedder(L=num_embeds_per_lora, out_dim=embed_dim)
+        
+        # encoders
+        self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size, num_heads=32, head_dim=128) for _ in range(num_encoder_layers)])
+
+        # special embedding
+        self.special_embeds = torch.nn.Parameter(torch.randn(1, num_special_embeds, embed_dim))
+        self.num_special_embeds = num_special_embeds
+        
+        # final layer
+        self.final_layer_norm = torch.nn.LayerNorm(embed_dim)
+        self.final_linear = torch.nn.Linear(embed_dim, embed_dim)
+
+    def forward(self, lora):
+        lora_embeds = self.embedder(lora)
+        special_embeds = self.special_embeds.to(dtype=lora_embeds.dtype, device=lora_embeds.device)
+        embeds = torch.concat([special_embeds, lora_embeds], dim=1)
+        for encoder_id, encoder in enumerate(self.encoders):
+            embeds = encoder(embeds)
+        embeds = embeds[:, :self.num_special_embeds]
+        embeds = self.final_layer_norm(embeds)
+        embeds = self.final_linear(embeds)
+        return embeds
+    
+    @staticmethod
+    def state_dict_converter():
+        return FluxLoRAEncoderStateDictConverter()
+
+
+class FluxLoRAEncoderStateDictConverter:
+    def from_civitai(self, state_dict):
+        return state_dict

diffsynth/models/flux_lora_patcher.py

@@ -0,0 +1,306 @@
+import torch, math
+from ..core.loader import load_state_dict
+from typing import Union
+
+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.")
+
+class FluxLoRALoader(GeneralLoRALoader):
+    def __init__(self, device="cpu", torch_dtype=torch.float32):
+        super().__init__(device=device, torch_dtype=torch_dtype)
+    
+        self.diffusers_rename_dict = {
+            "transformer.single_transformer_blocks.blockid.attn.to_k.lora_A.weight":"single_blocks.blockid.a_to_k.lora_A.default.weight",
+            "transformer.single_transformer_blocks.blockid.attn.to_k.lora_B.weight":"single_blocks.blockid.a_to_k.lora_B.default.weight",
+            "transformer.single_transformer_blocks.blockid.attn.to_q.lora_A.weight":"single_blocks.blockid.a_to_q.lora_A.default.weight",
+            "transformer.single_transformer_blocks.blockid.attn.to_q.lora_B.weight":"single_blocks.blockid.a_to_q.lora_B.default.weight",
+            "transformer.single_transformer_blocks.blockid.attn.to_v.lora_A.weight":"single_blocks.blockid.a_to_v.lora_A.default.weight",
+            "transformer.single_transformer_blocks.blockid.attn.to_v.lora_B.weight":"single_blocks.blockid.a_to_v.lora_B.default.weight",
+            "transformer.single_transformer_blocks.blockid.norm.linear.lora_A.weight":"single_blocks.blockid.norm.linear.lora_A.default.weight",
+            "transformer.single_transformer_blocks.blockid.norm.linear.lora_B.weight":"single_blocks.blockid.norm.linear.lora_B.default.weight",
+            "transformer.single_transformer_blocks.blockid.proj_mlp.lora_A.weight":"single_blocks.blockid.proj_in_besides_attn.lora_A.default.weight",
+            "transformer.single_transformer_blocks.blockid.proj_mlp.lora_B.weight":"single_blocks.blockid.proj_in_besides_attn.lora_B.default.weight",
+            "transformer.single_transformer_blocks.blockid.proj_out.lora_A.weight":"single_blocks.blockid.proj_out.lora_A.default.weight",
+            "transformer.single_transformer_blocks.blockid.proj_out.lora_B.weight":"single_blocks.blockid.proj_out.lora_B.default.weight",
+            "transformer.transformer_blocks.blockid.attn.add_k_proj.lora_A.weight":"blocks.blockid.attn.b_to_k.lora_A.default.weight",
+            "transformer.transformer_blocks.blockid.attn.add_k_proj.lora_B.weight":"blocks.blockid.attn.b_to_k.lora_B.default.weight",
+            "transformer.transformer_blocks.blockid.attn.add_q_proj.lora_A.weight":"blocks.blockid.attn.b_to_q.lora_A.default.weight",
+            "transformer.transformer_blocks.blockid.attn.add_q_proj.lora_B.weight":"blocks.blockid.attn.b_to_q.lora_B.default.weight",
+            "transformer.transformer_blocks.blockid.attn.add_v_proj.lora_A.weight":"blocks.blockid.attn.b_to_v.lora_A.default.weight",
+            "transformer.transformer_blocks.blockid.attn.add_v_proj.lora_B.weight":"blocks.blockid.attn.b_to_v.lora_B.default.weight",
+            "transformer.transformer_blocks.blockid.attn.to_add_out.lora_A.weight":"blocks.blockid.attn.b_to_out.lora_A.default.weight",
+            "transformer.transformer_blocks.blockid.attn.to_add_out.lora_B.weight":"blocks.blockid.attn.b_to_out.lora_B.default.weight",
+            "transformer.transformer_blocks.blockid.attn.to_k.lora_A.weight":"blocks.blockid.attn.a_to_k.lora_A.default.weight",
+            "transformer.transformer_blocks.blockid.attn.to_k.lora_B.weight":"blocks.blockid.attn.a_to_k.lora_B.default.weight",
+            "transformer.transformer_blocks.blockid.attn.to_out.0.lora_A.weight":"blocks.blockid.attn.a_to_out.lora_A.default.weight",
+            "transformer.transformer_blocks.blockid.attn.to_out.0.lora_B.weight":"blocks.blockid.attn.a_to_out.lora_B.default.weight",
+            "transformer.transformer_blocks.blockid.attn.to_q.lora_A.weight":"blocks.blockid.attn.a_to_q.lora_A.default.weight",
+            "transformer.transformer_blocks.blockid.attn.to_q.lora_B.weight":"blocks.blockid.attn.a_to_q.lora_B.default.weight",
+            "transformer.transformer_blocks.blockid.attn.to_v.lora_A.weight":"blocks.blockid.attn.a_to_v.lora_A.default.weight",
+            "transformer.transformer_blocks.blockid.attn.to_v.lora_B.weight":"blocks.blockid.attn.a_to_v.lora_B.default.weight",
+            "transformer.transformer_blocks.blockid.ff.net.0.proj.lora_A.weight":"blocks.blockid.ff_a.0.lora_A.default.weight",
+            "transformer.transformer_blocks.blockid.ff.net.0.proj.lora_B.weight":"blocks.blockid.ff_a.0.lora_B.default.weight",
+            "transformer.transformer_blocks.blockid.ff.net.2.lora_A.weight":"blocks.blockid.ff_a.2.lora_A.default.weight",
+            "transformer.transformer_blocks.blockid.ff.net.2.lora_B.weight":"blocks.blockid.ff_a.2.lora_B.default.weight",
+            "transformer.transformer_blocks.blockid.ff_context.net.0.proj.lora_A.weight":"blocks.blockid.ff_b.0.lora_A.default.weight",
+            "transformer.transformer_blocks.blockid.ff_context.net.0.proj.lora_B.weight":"blocks.blockid.ff_b.0.lora_B.default.weight",
+            "transformer.transformer_blocks.blockid.ff_context.net.2.lora_A.weight":"blocks.blockid.ff_b.2.lora_A.default.weight",
+            "transformer.transformer_blocks.blockid.ff_context.net.2.lora_B.weight":"blocks.blockid.ff_b.2.lora_B.default.weight",
+            "transformer.transformer_blocks.blockid.norm1.linear.lora_A.weight":"blocks.blockid.norm1_a.linear.lora_A.default.weight",
+            "transformer.transformer_blocks.blockid.norm1.linear.lora_B.weight":"blocks.blockid.norm1_a.linear.lora_B.default.weight",
+            "transformer.transformer_blocks.blockid.norm1_context.linear.lora_A.weight":"blocks.blockid.norm1_b.linear.lora_A.default.weight",
+            "transformer.transformer_blocks.blockid.norm1_context.linear.lora_B.weight":"blocks.blockid.norm1_b.linear.lora_B.default.weight",
+        }
+
+        self.civitai_rename_dict = {
+            "lora_unet_double_blocks_blockid_img_mod_lin.lora_down.weight": "blocks.blockid.norm1_a.linear.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_img_mod_lin.lora_up.weight": "blocks.blockid.norm1_a.linear.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_txt_mod_lin.lora_down.weight": "blocks.blockid.norm1_b.linear.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_txt_mod_lin.lora_up.weight": "blocks.blockid.norm1_b.linear.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_img_attn_qkv.lora_down.weight": "blocks.blockid.attn.a_to_qkv.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_img_attn_qkv.lora_up.weight": "blocks.blockid.attn.a_to_qkv.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_txt_attn_qkv.lora_down.weight": "blocks.blockid.attn.b_to_qkv.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_txt_attn_qkv.lora_up.weight": "blocks.blockid.attn.b_to_qkv.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_img_attn_proj.lora_down.weight": "blocks.blockid.attn.a_to_out.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_img_attn_proj.lora_up.weight": "blocks.blockid.attn.a_to_out.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_txt_attn_proj.lora_down.weight": "blocks.blockid.attn.b_to_out.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_txt_attn_proj.lora_up.weight": "blocks.blockid.attn.b_to_out.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_img_mlp_0.lora_down.weight": "blocks.blockid.ff_a.0.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_img_mlp_0.lora_up.weight": "blocks.blockid.ff_a.0.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_img_mlp_2.lora_down.weight": "blocks.blockid.ff_a.2.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_img_mlp_2.lora_up.weight": "blocks.blockid.ff_a.2.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_txt_mlp_0.lora_down.weight": "blocks.blockid.ff_b.0.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_txt_mlp_0.lora_up.weight": "blocks.blockid.ff_b.0.lora_B.default.weight",
+            "lora_unet_double_blocks_blockid_txt_mlp_2.lora_down.weight": "blocks.blockid.ff_b.2.lora_A.default.weight",
+            "lora_unet_double_blocks_blockid_txt_mlp_2.lora_up.weight": "blocks.blockid.ff_b.2.lora_B.default.weight",
+            "lora_unet_single_blocks_blockid_modulation_lin.lora_down.weight": "single_blocks.blockid.norm.linear.lora_A.default.weight",
+            "lora_unet_single_blocks_blockid_modulation_lin.lora_up.weight": "single_blocks.blockid.norm.linear.lora_B.default.weight",
+            "lora_unet_single_blocks_blockid_linear1.lora_down.weight": "single_blocks.blockid.to_qkv_mlp.lora_A.default.weight",
+            "lora_unet_single_blocks_blockid_linear1.lora_up.weight": "single_blocks.blockid.to_qkv_mlp.lora_B.default.weight",
+            "lora_unet_single_blocks_blockid_linear2.lora_down.weight": "single_blocks.blockid.proj_out.lora_A.default.weight",
+            "lora_unet_single_blocks_blockid_linear2.lora_up.weight": "single_blocks.blockid.proj_out.lora_B.default.weight",
+        }
+
+    def load(self, model: torch.nn.Module, state_dict_lora, alpha=1.0):
+        super().load(model, state_dict_lora, alpha)
+
+    
+    def convert_state_dict(self,state_dict):
+
+        def guess_block_id(name,model_resource):
+            if model_resource == 'civitai':
+                names = name.split("_")
+                for i in names:
+                    if i.isdigit():
+                        return i, name.replace(f"_{i}_", "_blockid_")
+            if model_resource == 'diffusers':
+                names = name.split(".")
+                for i in names:
+                    if i.isdigit():
+                        return i, name.replace(f"transformer_blocks.{i}.", "transformer_blocks.blockid.")
+            return None, None
+
+        def guess_resource(state_dict):
+            for k in state_dict:
+                if "lora_unet_" in k:
+                    return 'civitai'
+                elif k.startswith("transformer."):
+                    return 'diffusers'
+                else:
+                    None
+        
+        model_resource = guess_resource(state_dict)
+        if model_resource is None:
+            return state_dict
+
+        rename_dict = self.diffusers_rename_dict if model_resource == 'diffusers' else self.civitai_rename_dict
+        def guess_alpha(state_dict):
+                for name, param in state_dict.items():
+                    if ".alpha" in name:
+                        for suffix in [".lora_down.weight", ".lora_A.weight"]:
+                            name_ = name.replace(".alpha", suffix)
+                            if name_ in state_dict:
+                                lora_alpha = param.item() / state_dict[name_].shape[0]
+                                lora_alpha = math.sqrt(lora_alpha)
+                                return lora_alpha
+
+                return 1
+        
+        alpha = guess_alpha(state_dict)
+        
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            block_id, source_name = guess_block_id(name,model_resource)
+            if alpha != 1:
+                param *= alpha
+            if source_name in rename_dict:
+                target_name = rename_dict[source_name]
+                target_name = target_name.replace(".blockid.", f".{block_id}.")
+                state_dict_[target_name] = param
+            else:
+                state_dict_[name] = param
+        
+        if model_resource == 'diffusers':
+            for name in list(state_dict_.keys()):
+                if "single_blocks." in name and ".a_to_q." in name:
+                    mlp = state_dict_.get(name.replace(".a_to_q.", ".proj_in_besides_attn."), None)
+                    if mlp is None:
+                        dim = 4
+                        if 'lora_A' in name:
+                            dim = 1
+                        mlp = torch.zeros(dim * state_dict_[name].shape[0],
+                                        *state_dict_[name].shape[1:],
+                                        dtype=state_dict_[name].dtype)
+                    else:
+                        state_dict_.pop(name.replace(".a_to_q.", ".proj_in_besides_attn."))
+                    if 'lora_A' in name:
+                        param = torch.concat([
+                            state_dict_.pop(name),
+                            state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")),
+                            state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")),
+                            mlp,
+                        ], dim=0)
+                    elif 'lora_B' in name:
+                        d, r = state_dict_[name].shape
+                        param = torch.zeros((3*d+mlp.shape[0], 3*r+mlp.shape[1]), dtype=state_dict_[name].dtype, device=state_dict_[name].device)
+                        param[:d, :r] = state_dict_.pop(name)
+                        param[d:2*d, r:2*r] = state_dict_.pop(name.replace(".a_to_q.", ".a_to_k."))
+                        param[2*d:3*d, 2*r:3*r] = state_dict_.pop(name.replace(".a_to_q.", ".a_to_v."))
+                        param[3*d:, 3*r:] = mlp
+                    else:
+                        param = torch.concat([
+                            state_dict_.pop(name),
+                            state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")),
+                            state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")),
+                            mlp,
+                        ], dim=0)
+                    name_ = name.replace(".a_to_q.", ".to_qkv_mlp.")
+                    state_dict_[name_] = param
+            for name in list(state_dict_.keys()):
+                for component in ["a", "b"]:
+                    if f".{component}_to_q." in name:
+                        name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.")
+                        concat_dim = 0
+                        if 'lora_A' in name:
+                            param = torch.concat([
+                                state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")],
+                                state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")],
+                                state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")],
+                            ], dim=0)
+                        elif 'lora_B' in name:
+                            origin = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")]
+                            d, r = origin.shape
+                            # print(d, r)
+                            param = torch.zeros((3*d, 3*r), dtype=origin.dtype, device=origin.device)
+                            param[:d, :r] = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")]
+                            param[d:2*d, r:2*r] = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")]
+                            param[2*d:3*d, 2*r:3*r] = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")]
+                        else:
+                            param = torch.concat([
+                                state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")],
+                                state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")],
+                                state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")],
+                            ], dim=0)
+                        state_dict_[name_] = param
+                        state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q."))
+                        state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k."))
+                        state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v."))  
+        return state_dict_
+
+
+class LoraMerger(torch.nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.weight_base = torch.nn.Parameter(torch.randn((dim,)))
+        self.weight_lora = torch.nn.Parameter(torch.randn((dim,)))
+        self.weight_cross = torch.nn.Parameter(torch.randn((dim,)))
+        self.weight_out = torch.nn.Parameter(torch.ones((dim,)))
+        self.bias = torch.nn.Parameter(torch.randn((dim,)))
+        self.activation = torch.nn.Sigmoid()
+        self.norm_base = torch.nn.LayerNorm(dim, eps=1e-5)
+        self.norm_lora = torch.nn.LayerNorm(dim, eps=1e-5)
+        
+    def forward(self, base_output, lora_outputs):
+        norm_base_output = self.norm_base(base_output)
+        norm_lora_outputs = self.norm_lora(lora_outputs)
+        gate = self.activation(
+            norm_base_output * self.weight_base \
+            + norm_lora_outputs * self.weight_lora \
+            + norm_base_output * norm_lora_outputs * self.weight_cross + self.bias
+        )
+        output = base_output + (self.weight_out * gate * lora_outputs).sum(dim=0)
+        return output
+
+class FluxLoraPatcher(torch.nn.Module):
+    def __init__(self, lora_patterns=None):
+        super().__init__()
+        if lora_patterns is None:
+            lora_patterns = self.default_lora_patterns()
+        model_dict = {}
+        for lora_pattern in lora_patterns:
+            name, dim = lora_pattern["name"], lora_pattern["dim"]
+            model_dict[name.replace(".", "___")] = LoraMerger(dim)
+        self.model_dict = torch.nn.ModuleDict(model_dict)
+        
+    def default_lora_patterns(self):
+        lora_patterns = []
+        lora_dict = {
+            "attn.a_to_qkv": 9216, "attn.a_to_out": 3072, "ff_a.0": 12288, "ff_a.2": 3072, "norm1_a.linear": 18432,
+            "attn.b_to_qkv": 9216, "attn.b_to_out": 3072, "ff_b.0": 12288, "ff_b.2": 3072, "norm1_b.linear": 18432,
+        }
+        for i in range(19):
+            for suffix in lora_dict:
+                lora_patterns.append({
+                    "name": f"blocks.{i}.{suffix}",
+                    "dim": lora_dict[suffix]
+                })
+        lora_dict = {"to_qkv_mlp": 21504, "proj_out": 3072, "norm.linear": 9216}
+        for i in range(38):
+            for suffix in lora_dict:
+                lora_patterns.append({
+                    "name": f"single_blocks.{i}.{suffix}",
+                    "dim": lora_dict[suffix]
+                })
+        return lora_patterns
+        
+    def forward(self, base_output, lora_outputs, name):
+        return self.model_dict[name.replace(".", "___")](base_output, lora_outputs)

diffsynth/models/flux_text_encoder_clip.py

@@ -0,0 +1,112 @@
+import torch
+
+
+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 forward(self, hidden_states, encoder_hidden_states=None, attn_mask=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)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+
+        hidden_states = self.to_out(hidden_states)
+
+        return hidden_states
+
+
+class CLIPEncoderLayer(torch.nn.Module):
+    def __init__(self, embed_dim, intermediate_size, num_heads=12, head_dim=64, use_quick_gelu=True):
+        super().__init__()
+        self.attn = Attention(q_dim=embed_dim, num_heads=num_heads, head_dim=head_dim, bias_q=True, bias_kv=True, bias_out=True)
+        self.layer_norm1 = torch.nn.LayerNorm(embed_dim)
+        self.layer_norm2 = torch.nn.LayerNorm(embed_dim)
+        self.fc1 = torch.nn.Linear(embed_dim, intermediate_size)
+        self.fc2 = torch.nn.Linear(intermediate_size, embed_dim)
+
+        self.use_quick_gelu = use_quick_gelu
+
+    def quickGELU(self, x):
+        return x * torch.sigmoid(1.702 * x)
+    
+    def forward(self, hidden_states, attn_mask=None):
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states = self.attn(hidden_states, attn_mask=attn_mask)
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.fc1(hidden_states)
+        if self.use_quick_gelu:
+            hidden_states = self.quickGELU(hidden_states)
+        else:
+            hidden_states = torch.nn.functional.gelu(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+    
+
+class FluxTextEncoderClip(torch.nn.Module):
+    def __init__(self, embed_dim=768, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=12, encoder_intermediate_size=3072):
+        super().__init__()
+
+        # token_embedding
+        self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim)
+
+        # position_embeds (This is a fixed tensor)
+        self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim))
+
+        # encoders
+        self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size) for _ in range(num_encoder_layers)])
+
+        # attn_mask
+        self.attn_mask = self.attention_mask(max_position_embeddings)
+
+        # final_layer_norm
+        self.final_layer_norm = torch.nn.LayerNorm(embed_dim)
+
+    def attention_mask(self, length):
+        mask = torch.empty(length, length)
+        mask.fill_(float("-inf"))
+        mask.triu_(1)
+        return mask
+
+    def forward(self, input_ids, clip_skip=2, extra_mask=None):
+        embeds = self.token_embedding(input_ids)
+        embeds = embeds + self.position_embeds.to(dtype=embeds.dtype, device=input_ids.device)
+        attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype)
+        if extra_mask is not None:
+            attn_mask[:, extra_mask[0]==0] = float("-inf")
+        for encoder_id, encoder in enumerate(self.encoders):
+            embeds = encoder(embeds, attn_mask=attn_mask)
+            if encoder_id + clip_skip == len(self.encoders):
+                hidden_states = embeds
+        embeds = self.final_layer_norm(embeds)
+        pooled_embeds = embeds[torch.arange(embeds.shape[0]), input_ids.to(dtype=torch.int).argmax(dim=-1)]
+        return pooled_embeds, hidden_states

diffsynth/models/flux_text_encoder_t5.py

@@ -0,0 +1,43 @@
+import torch
+from transformers import T5EncoderModel, T5Config
+
+
+class FluxTextEncoderT5(T5EncoderModel):
+    def __init__(self):
+        config = T5Config(**{
+            "architectures": [
+                "T5EncoderModel"
+            ],
+            "classifier_dropout": 0.0,
+            "d_ff": 10240,
+            "d_kv": 64,
+            "d_model": 4096,
+            "decoder_start_token_id": 0,
+            "dense_act_fn": "gelu_new",
+            "dropout_rate": 0.1,
+            "dtype": "bfloat16",
+            "eos_token_id": 1,
+            "feed_forward_proj": "gated-gelu",
+            "initializer_factor": 1.0,
+            "is_encoder_decoder": True,
+            "is_gated_act": True,
+            "layer_norm_epsilon": 1e-06,
+            "model_type": "t5",
+            "num_decoder_layers": 24,
+            "num_heads": 64,
+            "num_layers": 24,
+            "output_past": True,
+            "pad_token_id": 0,
+            "relative_attention_max_distance": 128,
+            "relative_attention_num_buckets": 32,
+            "tie_word_embeddings": False,
+            "transformers_version": "4.57.1",
+            "use_cache": True,
+            "vocab_size": 32128
+        })
+        super().__init__(config)
+
+    def forward(self, input_ids):
+        outputs = super().forward(input_ids=input_ids)
+        prompt_emb = outputs.last_hidden_state
+        return prompt_emb

diffsynth/models/flux_vae.py

@@ -0,0 +1,451 @@
+import torch
+from einops import rearrange, repeat
+
+
+class TileWorker:
+    def __init__(self):
+        pass
+
+
+    def mask(self, height, width, border_width):
+        # Create a mask with shape (height, width).
+        # The centre area is filled with 1, and the border line is filled with values in range (0, 1].
+        x = torch.arange(height).repeat(width, 1).T
+        y = torch.arange(width).repeat(height, 1)
+        mask = torch.stack([x + 1, height - x, y + 1, width - y]).min(dim=0).values
+        mask = (mask / border_width).clip(0, 1)
+        return mask
+
+
+    def tile(self, model_input, tile_size, tile_stride, tile_device, tile_dtype):
+        # Convert a tensor (b, c, h, w) to (b, c, tile_size, tile_size, tile_num)
+        batch_size, channel, _, _ = model_input.shape
+        model_input = model_input.to(device=tile_device, dtype=tile_dtype)
+        unfold_operator = torch.nn.Unfold(
+            kernel_size=(tile_size, tile_size),
+            stride=(tile_stride, tile_stride)
+        )
+        model_input = unfold_operator(model_input)
+        model_input = model_input.view((batch_size, channel, tile_size, tile_size, -1))
+
+        return model_input
+
+
+    def tiled_inference(self, forward_fn, model_input, tile_batch_size, inference_device, inference_dtype, tile_device, tile_dtype):
+        # Call y=forward_fn(x) for each tile
+        tile_num = model_input.shape[-1]
+        model_output_stack = []
+
+        for tile_id in range(0, tile_num, tile_batch_size):
+
+            # process input
+            tile_id_ = min(tile_id + tile_batch_size, tile_num)
+            x = model_input[:, :, :, :, tile_id: tile_id_]
+            x = x.to(device=inference_device, dtype=inference_dtype)
+            x = rearrange(x, "b c h w n -> (n b) c h w")
+
+            # process output
+            y = forward_fn(x)
+            y = rearrange(y, "(n b) c h w -> b c h w n", n=tile_id_-tile_id)
+            y = y.to(device=tile_device, dtype=tile_dtype)
+            model_output_stack.append(y)
+
+        model_output = torch.concat(model_output_stack, dim=-1)
+        return model_output
+
+
+    def io_scale(self, model_output, tile_size):
+        # Determine the size modification happened in forward_fn
+        # We only consider the same scale on height and width.
+        io_scale = model_output.shape[2] / tile_size
+        return io_scale
+    
+
+    def untile(self, model_output, height, width, tile_size, tile_stride, border_width, tile_device, tile_dtype):
+        # The reversed function of tile
+        mask = self.mask(tile_size, tile_size, border_width)
+        mask = mask.to(device=tile_device, dtype=tile_dtype)
+        mask = rearrange(mask, "h w -> 1 1 h w 1")
+        model_output = model_output * mask
+
+        fold_operator = torch.nn.Fold(
+            output_size=(height, width),
+            kernel_size=(tile_size, tile_size),
+            stride=(tile_stride, tile_stride)
+        )
+        mask = repeat(mask[0, 0, :, :, 0], "h w -> 1 (h w) n", n=model_output.shape[-1])
+        model_output = rearrange(model_output, "b c h w n -> b (c h w) n")
+        model_output = fold_operator(model_output) / fold_operator(mask)
+
+        return model_output
+
+
+    def tiled_forward(self, forward_fn, model_input, tile_size, tile_stride, tile_batch_size=1, tile_device="cpu", tile_dtype=torch.float32, border_width=None):
+        # Prepare
+        inference_device, inference_dtype = model_input.device, model_input.dtype
+        height, width = model_input.shape[2], model_input.shape[3]
+        border_width = int(tile_stride*0.5) if border_width is None else border_width
+
+        # tile
+        model_input = self.tile(model_input, tile_size, tile_stride, tile_device, tile_dtype)
+
+        # inference
+        model_output = self.tiled_inference(forward_fn, model_input, tile_batch_size, inference_device, inference_dtype, tile_device, tile_dtype)
+
+        # resize
+        io_scale = self.io_scale(model_output, tile_size)
+        height, width = int(height*io_scale), int(width*io_scale)
+        tile_size, tile_stride = int(tile_size*io_scale), int(tile_stride*io_scale)
+        border_width = int(border_width*io_scale)
+
+        # untile
+        model_output = self.untile(model_output, height, width, tile_size, tile_stride, border_width, tile_device, tile_dtype)
+        
+        # Done!
+        model_output = model_output.to(device=inference_device, dtype=inference_dtype)
+        return model_output
+
+
+class ConvAttention(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.Conv2d(q_dim, dim_inner, kernel_size=(1, 1), bias=bias_q)
+        self.to_k = torch.nn.Conv2d(kv_dim, dim_inner, kernel_size=(1, 1), bias=bias_kv)
+        self.to_v = torch.nn.Conv2d(kv_dim, dim_inner, kernel_size=(1, 1), bias=bias_kv)
+        self.to_out = torch.nn.Conv2d(dim_inner, q_dim, kernel_size=(1, 1), bias=bias_out)
+
+    def forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None):
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+
+        batch_size = encoder_hidden_states.shape[0]
+
+        conv_input = rearrange(hidden_states, "B L C -> B C L 1")
+        q = self.to_q(conv_input)
+        q = rearrange(q[:, :, :, 0], "B C L -> B L C")
+        conv_input = rearrange(encoder_hidden_states, "B L C -> B C L 1")
+        k = self.to_k(conv_input)
+        v = self.to_v(conv_input)
+        k = rearrange(k[:, :, :, 0], "B C L -> B L C")
+        v = rearrange(v[:, :, :, 0], "B C L -> B L C")
+
+        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)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+
+        conv_input = rearrange(hidden_states, "B L C -> B C L 1")
+        hidden_states = self.to_out(conv_input)
+        hidden_states = rearrange(hidden_states[:, :, :, 0], "B C L -> B L C")
+
+        return hidden_states
+
+
+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 forward(self, hidden_states, encoder_hidden_states=None, attn_mask=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)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+
+        hidden_states = self.to_out(hidden_states)
+
+        return hidden_states
+
+
+class VAEAttentionBlock(torch.nn.Module):
+
+    def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5, use_conv_attention=True):
+        super().__init__()
+        inner_dim = num_attention_heads * attention_head_dim
+
+        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True)
+
+        if use_conv_attention:
+            self.transformer_blocks = torch.nn.ModuleList([
+                ConvAttention(
+                    inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    bias_q=True,
+                    bias_kv=True,
+                    bias_out=True
+                )
+                for d in range(num_layers)
+            ])
+        else:
+            self.transformer_blocks = torch.nn.ModuleList([
+                Attention(
+                    inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    bias_q=True,
+                    bias_kv=True,
+                    bias_out=True
+                )
+                for d in range(num_layers)
+            ])
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack):
+        batch, _, height, width = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
+
+        for block in self.transformer_blocks:
+            hidden_states = block(hidden_states)
+
+        hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
+        hidden_states = hidden_states + residual
+
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class ResnetBlock(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, temb_channels=None, groups=32, eps=1e-5):
+        super().__init__()
+        self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
+        self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        if temb_channels is not None:
+            self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels)
+        self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True)
+        self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.nonlinearity = torch.nn.SiLU()
+        self.conv_shortcut = None
+        if in_channels != out_channels:
+            self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=True)
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        x = hidden_states
+        x = self.norm1(x)
+        x = self.nonlinearity(x)
+        x = self.conv1(x)
+        if time_emb is not None:
+            emb = self.nonlinearity(time_emb)
+            emb = self.time_emb_proj(emb)[:, :, None, None]
+            x = x + emb
+        x = self.norm2(x)
+        x = self.nonlinearity(x)
+        x = self.conv2(x)
+        if self.conv_shortcut is not None:
+            hidden_states = self.conv_shortcut(hidden_states)
+        hidden_states = hidden_states + x
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class UpSampler(torch.nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.conv = torch.nn.Conv2d(channels, channels, 3, padding=1)
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        hidden_states = torch.nn.functional.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
+        hidden_states = self.conv(hidden_states)
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class DownSampler(torch.nn.Module):
+    def __init__(self, channels, padding=1, extra_padding=False):
+        super().__init__()
+        self.conv = torch.nn.Conv2d(channels, channels, 3, stride=2, padding=padding)
+        self.extra_padding = extra_padding
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        if self.extra_padding:
+            hidden_states = torch.nn.functional.pad(hidden_states, (0, 1, 0, 1), mode="constant", value=0)
+        hidden_states = self.conv(hidden_states)
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class FluxVAEDecoder(torch.nn.Module):
+    def __init__(self, use_conv_attention=True):
+        super().__init__()
+        self.scaling_factor = 0.3611
+        self.shift_factor = 0.1159
+        self.conv_in = torch.nn.Conv2d(16, 512, kernel_size=3, padding=1) # Different from SD 1.x
+
+        self.blocks = torch.nn.ModuleList([
+            # UNetMidBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            VAEAttentionBlock(1, 512, 512, 1, eps=1e-6, use_conv_attention=use_conv_attention),
+            ResnetBlock(512, 512, eps=1e-6),
+            # UpDecoderBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            UpSampler(512),
+            # UpDecoderBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            UpSampler(512),
+            # UpDecoderBlock2D
+            ResnetBlock(512, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            UpSampler(256),
+            # UpDecoderBlock2D
+            ResnetBlock(256, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+        ])
+
+        self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-6)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1)
+    
+    def tiled_forward(self, sample, tile_size=64, tile_stride=32):
+        hidden_states = TileWorker().tiled_forward(
+            lambda x: self.forward(x),
+            sample,
+            tile_size,
+            tile_stride,
+            tile_device=sample.device,
+            tile_dtype=sample.dtype
+        )
+        return hidden_states
+
+    def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs):
+        # For VAE Decoder, we do not need to apply the tiler on each layer.
+        if tiled:
+            return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride)
+
+        # 1. pre-process
+        hidden_states = sample / self.scaling_factor + self.shift_factor
+        hidden_states = self.conv_in(hidden_states)
+        time_emb = None
+        text_emb = None
+        res_stack = None
+
+        # 2. blocks
+        for i, block in enumerate(self.blocks):
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+        
+        # 3. output
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+
+        return hidden_states
+
+
+class FluxVAEEncoder(torch.nn.Module):
+    def __init__(self, use_conv_attention=True):
+        super().__init__()
+        self.scaling_factor = 0.3611
+        self.shift_factor = 0.1159
+        self.conv_in = torch.nn.Conv2d(3, 128, kernel_size=3, padding=1)
+
+        self.blocks = torch.nn.ModuleList([
+            # DownEncoderBlock2D
+            ResnetBlock(128, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+            DownSampler(128, padding=0, extra_padding=True),
+            # DownEncoderBlock2D
+            ResnetBlock(128, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            DownSampler(256, padding=0, extra_padding=True),
+            # DownEncoderBlock2D
+            ResnetBlock(256, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            DownSampler(512, padding=0, extra_padding=True),
+            # DownEncoderBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            # UNetMidBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            VAEAttentionBlock(1, 512, 512, 1, eps=1e-6, use_conv_attention=use_conv_attention),
+            ResnetBlock(512, 512, eps=1e-6),
+        ])
+
+        self.conv_norm_out = torch.nn.GroupNorm(num_channels=512, num_groups=32, eps=1e-6)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(512, 32, kernel_size=3, padding=1)
+
+    def tiled_forward(self, sample, tile_size=64, tile_stride=32):
+        hidden_states = TileWorker().tiled_forward(
+            lambda x: self.forward(x),
+            sample,
+            tile_size,
+            tile_stride,
+            tile_device=sample.device,
+            tile_dtype=sample.dtype
+        )
+        return hidden_states
+
+    def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs):
+        # For VAE Decoder, we do not need to apply the tiler on each layer.
+        if tiled:
+            return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride)
+        
+        # 1. pre-process
+        hidden_states = self.conv_in(sample)
+        time_emb = None
+        text_emb = None
+        res_stack = None
+
+        # 2. blocks
+        for i, block in enumerate(self.blocks):
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+        
+        # 3. output
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+        hidden_states = hidden_states[:, :16]
+        hidden_states = (hidden_states - self.shift_factor) * self.scaling_factor
+
+        return hidden_states
+    
+    def encode_video(self, sample, batch_size=8):
+        B = sample.shape[0]
+        hidden_states = []
+
+        for i in range(0, sample.shape[2], batch_size):
+
+            j = min(i + batch_size, sample.shape[2])
+            sample_batch = rearrange(sample[:,:,i:j], "B C T H W -> (B T) C H W")
+
+            hidden_states_batch = self(sample_batch)
+            hidden_states_batch = rearrange(hidden_states_batch, "(B T) C H W -> B C T H W", B=B)
+
+            hidden_states.append(hidden_states_batch)
+        
+        hidden_states = torch.concat(hidden_states, dim=2)
+        return hidden_states

diffsynth/models/flux_value_control.py

@@ -0,0 +1,56 @@
+import torch
+from .general_modules import TemporalTimesteps
+
+
+class MultiValueEncoder(torch.nn.Module):
+    def __init__(self, encoders=()):
+        super().__init__()
+        if not isinstance(encoders, list):
+            encoders = [encoders]
+        self.encoders = torch.nn.ModuleList(encoders)
+
+    def __call__(self, values, dtype):
+        emb = []
+        for encoder, value in zip(self.encoders, values):
+            if value is not None:
+                value = value.unsqueeze(0)
+                emb.append(encoder(value, dtype))
+        emb = torch.concat(emb, dim=0)
+        return emb
+
+
+class SingleValueEncoder(torch.nn.Module):
+    def __init__(self, dim_in=256, dim_out=4096, prefer_len=32, computation_device=None):
+        super().__init__()
+        self.prefer_len = prefer_len
+        self.prefer_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0, computation_device=computation_device)
+        self.prefer_value_embedder = torch.nn.Sequential(
+            torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out)
+        )
+        self.positional_embedding = torch.nn.Parameter(
+            torch.randn(self.prefer_len, dim_out) 
+        )
+
+    def forward(self, value, dtype):
+        value = value * 1000
+        emb = self.prefer_proj(value).to(dtype)
+        emb = self.prefer_value_embedder(emb).squeeze(0)
+        base_embeddings = emb.expand(self.prefer_len, -1)
+        positional_embedding = self.positional_embedding.to(dtype=base_embeddings.dtype, device=base_embeddings.device)
+        learned_embeddings = base_embeddings + positional_embedding
+        return learned_embeddings
+
+    @staticmethod
+    def state_dict_converter():
+        return SingleValueEncoderStateDictConverter()
+
+
+class SingleValueEncoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict
+
+    def from_civitai(self, state_dict):
+        return state_dict

diffsynth/models/general_modules.py

@@ -0,0 +1,146 @@
+import torch, math
+
+
+def get_timestep_embedding(
+    timesteps: torch.Tensor,
+    embedding_dim: int,
+    flip_sin_to_cos: bool = False,
+    downscale_freq_shift: float = 1,
+    scale: float = 1,
+    max_period: int = 10000,
+    computation_device = None,
+    align_dtype_to_timestep = False,
+):
+    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
+
+    half_dim = embedding_dim // 2
+    exponent = -math.log(max_period) * torch.arange(
+        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device if computation_device is None else computation_device
+    )
+    exponent = exponent / (half_dim - downscale_freq_shift)
+
+    emb = torch.exp(exponent)
+    if align_dtype_to_timestep:
+        emb = emb.to(timesteps.dtype)
+    emb = timesteps[:, None].float() * emb[None, :]
+
+    # scale embeddings
+    emb = scale * emb
+
+    # concat sine and cosine embeddings
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
+
+    # flip sine and cosine embeddings
+    if flip_sin_to_cos:
+        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
+
+    # zero pad
+    if embedding_dim % 2 == 1:
+        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
+    return emb
+
+
+class TemporalTimesteps(torch.nn.Module):
+    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, computation_device = None, scale=1, align_dtype_to_timestep=False):
+        super().__init__()
+        self.num_channels = num_channels
+        self.flip_sin_to_cos = flip_sin_to_cos
+        self.downscale_freq_shift = downscale_freq_shift
+        self.computation_device = computation_device
+        self.scale = scale
+        self.align_dtype_to_timestep = align_dtype_to_timestep
+
+    def forward(self, timesteps):
+        t_emb = get_timestep_embedding(
+            timesteps,
+            self.num_channels,
+            flip_sin_to_cos=self.flip_sin_to_cos,
+            downscale_freq_shift=self.downscale_freq_shift,
+            computation_device=self.computation_device,
+            scale=self.scale,
+            align_dtype_to_timestep=self.align_dtype_to_timestep,
+        )
+        return t_emb
+
+
+class DiffusersCompatibleTimestepProj(torch.nn.Module):
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.linear_1 = torch.nn.Linear(dim_in, dim_out)
+        self.act = torch.nn.SiLU()
+        self.linear_2 = torch.nn.Linear(dim_out, dim_out)
+
+    def forward(self, x):
+        x = self.linear_1(x)
+        x = self.act(x)
+        x = self.linear_2(x)
+        return x
+
+
+class TimestepEmbeddings(torch.nn.Module):
+    def __init__(self, dim_in, dim_out, computation_device=None, diffusers_compatible_format=False, scale=1, align_dtype_to_timestep=False, use_additional_t_cond=False):
+        super().__init__()
+        self.time_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0, computation_device=computation_device, scale=scale, align_dtype_to_timestep=align_dtype_to_timestep)
+        if diffusers_compatible_format:
+            self.timestep_embedder = DiffusersCompatibleTimestepProj(dim_in, dim_out)
+        else:
+            self.timestep_embedder = torch.nn.Sequential(
+                torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out)
+            )
+        self.use_additional_t_cond = use_additional_t_cond
+        if use_additional_t_cond:
+            self.addition_t_embedding = torch.nn.Embedding(2, dim_out)
+
+    def forward(self, timestep, dtype, addition_t_cond=None):
+        time_emb = self.time_proj(timestep).to(dtype)
+        time_emb = self.timestep_embedder(time_emb)
+        if addition_t_cond is not None:
+            addition_t_emb = self.addition_t_embedding(addition_t_cond)
+            addition_t_emb = addition_t_emb.to(dtype=dtype)
+            time_emb = time_emb + addition_t_emb
+        return time_emb
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim, eps, elementwise_affine=True):
+        super().__init__()
+        self.eps = eps
+        if elementwise_affine:
+            self.weight = torch.nn.Parameter(torch.ones((dim,)))
+        else:
+            self.weight = None
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
+        hidden_states = hidden_states.to(input_dtype)
+        if self.weight is not None:
+            hidden_states = hidden_states * self.weight
+        return hidden_states
+
+
+class AdaLayerNorm(torch.nn.Module):
+    def __init__(self, dim, single=False, dual=False):
+        super().__init__()
+        self.single = single
+        self.dual = dual
+        self.linear = torch.nn.Linear(dim, dim * [[6, 2][single], 9][dual])
+        self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(self, x, emb):
+        emb = self.linear(torch.nn.functional.silu(emb))
+        if self.single:
+            scale, shift = emb.unsqueeze(1).chunk(2, dim=2)
+            x = self.norm(x) * (1 + scale) + shift
+            return x
+        elif self.dual:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp, shift_msa2, scale_msa2, gate_msa2 = emb.unsqueeze(1).chunk(9, dim=2)
+            norm_x = self.norm(x)
+            x = norm_x * (1 + scale_msa) + shift_msa
+            norm_x2 = norm_x * (1 + scale_msa2) + shift_msa2
+            return x, gate_msa, shift_mlp, scale_mlp, gate_mlp, norm_x2, gate_msa2
+        else:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.unsqueeze(1).chunk(6, dim=2)
+            x = self.norm(x) * (1 + scale_msa) + shift_msa
+            return x, gate_msa, shift_mlp, scale_mlp, gate_mlp

diffsynth/models/hunyuan_dit.py

@@ -1,451 +0,0 @@
-from .attention import Attention
-from .tiler import TileWorker
-from einops import repeat, rearrange
-import math
-import torch
-
-
-class HunyuanDiTRotaryEmbedding(torch.nn.Module):
-
-    def __init__(self, q_norm_shape=88, k_norm_shape=88, rotary_emb_on_k=True):
-        super().__init__()
-        self.q_norm = torch.nn.LayerNorm((q_norm_shape,), elementwise_affine=True, eps=1e-06)
-        self.k_norm = torch.nn.LayerNorm((k_norm_shape,), elementwise_affine=True, eps=1e-06)
-        self.rotary_emb_on_k = rotary_emb_on_k
-        self.k_cache, self.v_cache = [], []
-
-    def reshape_for_broadcast(self, freqs_cis, x):
-        ndim = x.ndim
-        shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
-        return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape)
-
-    def rotate_half(self, x):
-        x_real, x_imag = x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1)
-        return torch.stack([-x_imag, x_real], dim=-1).flatten(3)
-
-    def apply_rotary_emb(self, xq, xk, freqs_cis):
-        xk_out = None
-        cos, sin = self.reshape_for_broadcast(freqs_cis, xq)
-        cos, sin = cos.to(xq.device), sin.to(xq.device)
-        xq_out = (xq.float() * cos + self.rotate_half(xq.float()) * sin).type_as(xq)
-        if xk is not None:
-            xk_out = (xk.float() * cos + self.rotate_half(xk.float()) * sin).type_as(xk)
-        return xq_out, xk_out
-
-    def forward(self, q, k, v, freqs_cis_img, to_cache=False):
-        # norm
-        q = self.q_norm(q)
-        k = self.k_norm(k)
-
-        # RoPE
-        if self.rotary_emb_on_k:
-            q, k = self.apply_rotary_emb(q, k, freqs_cis_img)
-        else:
-            q, _ = self.apply_rotary_emb(q, None, freqs_cis_img)
-        
-        if to_cache:
-            self.k_cache.append(k)
-            self.v_cache.append(v)
-        elif len(self.k_cache) > 0 and len(self.v_cache) > 0:
-            k = torch.concat([k] + self.k_cache, dim=2)
-            v = torch.concat([v] + self.v_cache, dim=2)
-            self.k_cache, self.v_cache = [], []
-        return q, k, v
-
-
-class FP32_Layernorm(torch.nn.LayerNorm):
-    def forward(self, inputs):
-        origin_dtype = inputs.dtype
-        return torch.nn.functional.layer_norm(inputs.float(), self.normalized_shape, self.weight.float(), self.bias.float(), self.eps).to(origin_dtype)
-
-
-class FP32_SiLU(torch.nn.SiLU):
-    def forward(self, inputs):
-        origin_dtype = inputs.dtype
-        return torch.nn.functional.silu(inputs.float(), inplace=False).to(origin_dtype)
-    
-
-class HunyuanDiTFinalLayer(torch.nn.Module):
-    def __init__(self, final_hidden_size=1408, condition_dim=1408, patch_size=2, out_channels=8):
-        super().__init__()
-        self.norm_final = torch.nn.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6)
-        self.linear = torch.nn.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True)
-        self.adaLN_modulation = torch.nn.Sequential(
-            FP32_SiLU(),
-            torch.nn.Linear(condition_dim, 2 * final_hidden_size, bias=True)
-        )
-
-    def modulate(self, x, shift, scale):
-        return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
-
-    def forward(self, hidden_states, condition_emb):
-        shift, scale = self.adaLN_modulation(condition_emb).chunk(2, dim=1)
-        hidden_states = self.modulate(self.norm_final(hidden_states), shift, scale)
-        hidden_states = self.linear(hidden_states)
-        return hidden_states
-
-
-class HunyuanDiTBlock(torch.nn.Module):
-
-    def __init__(
-        self,
-        hidden_dim=1408,
-        condition_dim=1408,
-        num_heads=16,
-        mlp_ratio=4.3637,
-        text_dim=1024,
-        skip_connection=False
-    ):
-        super().__init__()
-        self.norm1 = FP32_Layernorm((hidden_dim,), eps=1e-6, elementwise_affine=True)
-        self.rota1 = HunyuanDiTRotaryEmbedding(hidden_dim//num_heads, hidden_dim//num_heads)
-        self.attn1 = Attention(hidden_dim, num_heads, hidden_dim//num_heads, bias_q=True, bias_kv=True, bias_out=True)
-        self.norm2 = FP32_Layernorm((hidden_dim,), eps=1e-6, elementwise_affine=True)
-        self.rota2 = HunyuanDiTRotaryEmbedding(hidden_dim//num_heads, hidden_dim//num_heads, rotary_emb_on_k=False)
-        self.attn2 = Attention(hidden_dim, num_heads, hidden_dim//num_heads, kv_dim=text_dim, bias_q=True, bias_kv=True, bias_out=True)
-        self.norm3 = FP32_Layernorm((hidden_dim,), eps=1e-6, elementwise_affine=True)
-        self.modulation = torch.nn.Sequential(FP32_SiLU(), torch.nn.Linear(condition_dim, hidden_dim, bias=True))
-        self.mlp = torch.nn.Sequential(
-            torch.nn.Linear(hidden_dim, int(hidden_dim*mlp_ratio), bias=True),
-            torch.nn.GELU(approximate="tanh"),
-            torch.nn.Linear(int(hidden_dim*mlp_ratio), hidden_dim, bias=True)
-        )
-        if skip_connection:
-            self.skip_norm = FP32_Layernorm((hidden_dim * 2,), eps=1e-6, elementwise_affine=True)
-            self.skip_linear = torch.nn.Linear(hidden_dim * 2, hidden_dim, bias=True)
-        else:
-            self.skip_norm, self.skip_linear = None, None
-
-    def forward(self, hidden_states, condition_emb, text_emb, freq_cis_img, residual=None, to_cache=False):
-        # Long Skip Connection
-        if self.skip_norm is not None and self.skip_linear is not None:
-            hidden_states = torch.cat([hidden_states, residual], dim=-1)
-            hidden_states = self.skip_norm(hidden_states)
-            hidden_states = self.skip_linear(hidden_states)
-
-        # Self-Attention
-        shift_msa = self.modulation(condition_emb).unsqueeze(dim=1)
-        attn_input = self.norm1(hidden_states) + shift_msa
-        hidden_states = hidden_states + self.attn1(attn_input, qkv_preprocessor=lambda q, k, v: self.rota1(q, k, v, freq_cis_img, to_cache=to_cache))
-
-        # Cross-Attention
-        attn_input = self.norm3(hidden_states)
-        hidden_states = hidden_states + self.attn2(attn_input, text_emb, qkv_preprocessor=lambda q, k, v: self.rota2(q, k, v, freq_cis_img))
-
-        # FFN Layer
-        mlp_input = self.norm2(hidden_states)
-        hidden_states = hidden_states + self.mlp(mlp_input)
-        return hidden_states
-    
-
-class AttentionPool(torch.nn.Module):
-    def __init__(self, spacial_dim, embed_dim, num_heads, output_dim = None):
-        super().__init__()
-        self.positional_embedding = torch.nn.Parameter(torch.randn(spacial_dim + 1, embed_dim) / embed_dim ** 0.5)
-        self.k_proj = torch.nn.Linear(embed_dim, embed_dim)
-        self.q_proj = torch.nn.Linear(embed_dim, embed_dim)
-        self.v_proj = torch.nn.Linear(embed_dim, embed_dim)
-        self.c_proj = torch.nn.Linear(embed_dim, output_dim or embed_dim)
-        self.num_heads = num_heads
-
-    def forward(self, x):
-        x = x.permute(1, 0, 2)  # NLC -> LNC
-        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (L+1)NC
-        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (L+1)NC
-        x, _ = torch.nn.functional.multi_head_attention_forward(
-            query=x[:1], 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.squeeze(0)
-    
-
-class PatchEmbed(torch.nn.Module):
-    def __init__(
-        self,
-        patch_size=(2, 2),
-        in_chans=4,
-        embed_dim=1408,
-        bias=True,
-    ):
-        super().__init__()
-        self.proj = torch.nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
-
-    def forward(self, x):
-        x = self.proj(x)
-        x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
-        return x
-    
-
-def timestep_embedding(t, dim, max_period=10000, repeat_only=False):
-    # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
-    if not repeat_only:
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period)
-            * torch.arange(start=0, end=half, dtype=torch.float32)
-            / half
-        ).to(device=t.device)   # size: [dim/2], 一个指数衰减的曲线
-        args = t[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat(
-                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
-            )
-    else:
-        embedding = repeat(t, "b -> b d", d=dim)
-    return embedding
-    
-
-class TimestepEmbedder(torch.nn.Module):
-    def __init__(self, hidden_size=1408, frequency_embedding_size=256):
-        super().__init__()
-        self.mlp = torch.nn.Sequential(
-            torch.nn.Linear(frequency_embedding_size, hidden_size, bias=True),
-            torch.nn.SiLU(),
-            torch.nn.Linear(hidden_size, hidden_size, bias=True),
-        )
-        self.frequency_embedding_size = frequency_embedding_size
-
-    def forward(self, t):
-        t_freq = timestep_embedding(t, self.frequency_embedding_size).type(self.mlp[0].weight.dtype)
-        t_emb = self.mlp(t_freq)
-        return t_emb
-
-
-class HunyuanDiT(torch.nn.Module):
-    def __init__(self, num_layers_down=21, num_layers_up=19, in_channels=4, out_channels=8, hidden_dim=1408, text_dim=1024, t5_dim=2048, text_length=77, t5_length=256):
-        super().__init__()
-
-        # Embedders
-        self.text_emb_padding = torch.nn.Parameter(torch.randn(text_length + t5_length, text_dim, dtype=torch.float32))
-        self.t5_embedder = torch.nn.Sequential(
-            torch.nn.Linear(t5_dim, t5_dim * 4, bias=True),
-            FP32_SiLU(),
-            torch.nn.Linear(t5_dim * 4, text_dim, bias=True),
-        )
-        self.t5_pooler = AttentionPool(t5_length, t5_dim, num_heads=8, output_dim=1024)
-        self.style_embedder = torch.nn.Parameter(torch.randn(hidden_dim))
-        self.patch_embedder = PatchEmbed(in_chans=in_channels)
-        self.timestep_embedder = TimestepEmbedder()
-        self.extra_embedder = torch.nn.Sequential(
-            torch.nn.Linear(256 * 6 + 1024 + hidden_dim, hidden_dim * 4),
-            FP32_SiLU(),
-            torch.nn.Linear(hidden_dim * 4, hidden_dim),
-        )
-
-        # Transformer blocks
-        self.num_layers_down = num_layers_down
-        self.num_layers_up = num_layers_up
-        self.blocks = torch.nn.ModuleList(
-            [HunyuanDiTBlock(skip_connection=False) for _ in range(num_layers_down)] + \
-            [HunyuanDiTBlock(skip_connection=True) for _ in range(num_layers_up)]
-        )
-
-        # Output layers
-        self.final_layer = HunyuanDiTFinalLayer()
-        self.out_channels = out_channels
-
-    def prepare_text_emb(self, text_emb, text_emb_t5, text_emb_mask, text_emb_mask_t5):
-        text_emb_mask = text_emb_mask.bool()
-        text_emb_mask_t5 = text_emb_mask_t5.bool()
-        text_emb_t5 = self.t5_embedder(text_emb_t5)
-        text_emb = torch.cat([text_emb, text_emb_t5], dim=1)
-        text_emb_mask = torch.cat([text_emb_mask, text_emb_mask_t5], dim=-1)
-        text_emb = torch.where(text_emb_mask.unsqueeze(2), text_emb, self.text_emb_padding.to(text_emb))
-        return text_emb
-    
-    def prepare_extra_emb(self, text_emb_t5, timestep, size_emb, dtype, batch_size):
-        # Text embedding
-        pooled_text_emb_t5 = self.t5_pooler(text_emb_t5)
-
-        # Timestep embedding
-        timestep_emb = self.timestep_embedder(timestep)
-
-        # Size embedding
-        size_emb = timestep_embedding(size_emb.view(-1), 256).to(dtype)
-        size_emb = size_emb.view(-1, 6 * 256)
-
-        # Style embedding
-        style_emb = repeat(self.style_embedder, "D -> B D", B=batch_size)
-
-        # Concatenate all extra vectors
-        extra_emb = torch.cat([pooled_text_emb_t5, size_emb, style_emb], dim=1)
-        condition_emb = timestep_emb + self.extra_embedder(extra_emb)
-
-        return condition_emb
-
-    def unpatchify(self, x, h, w):
-        return rearrange(x, "B (H W) (P Q C) -> B C (H P) (W Q)", H=h, W=w, P=2, Q=2)
-    
-    def build_mask(self, data, is_bound):
-        _, _, H, W = data.shape
-        h = repeat(torch.arange(H), "H -> H W", H=H, W=W)
-        w = repeat(torch.arange(W), "W -> H W", H=H, W=W)
-        border_width = (H + W) // 4
-        pad = torch.ones_like(h) * border_width
-        mask = torch.stack([
-            pad if is_bound[0] else h + 1,
-            pad if is_bound[1] else H - h,
-            pad if is_bound[2] else w + 1,
-            pad if is_bound[3] else W - w
-        ]).min(dim=0).values
-        mask = mask.clip(1, border_width)
-        mask = (mask / border_width).to(dtype=data.dtype, device=data.device)
-        mask = rearrange(mask, "H W -> 1 H W")
-        return mask
-    
-    def tiled_block_forward(self, block, hidden_states, condition_emb, text_emb, freq_cis_img, residual, torch_dtype, data_device, computation_device, tile_size, tile_stride):
-        B, C, H, W = hidden_states.shape
-
-        weight = torch.zeros((1, 1, H, W), dtype=torch_dtype, device=data_device)
-        values = torch.zeros((B, C, H, W), dtype=torch_dtype, device=data_device)
-
-        # Split tasks
-        tasks = []
-        for h in range(0, H, tile_stride):
-            for w in range(0, W, tile_stride):
-                if (h-tile_stride >= 0 and h-tile_stride+tile_size >= H) or (w-tile_stride >= 0 and w-tile_stride+tile_size >= W):
-                    continue
-                h_, w_ = h + tile_size, w + tile_size
-                if h_ > H: h, h_ = H - tile_size, H
-                if w_ > W: w, w_ = W - tile_size, W
-                tasks.append((h, h_, w, w_))
-
-        # Run
-        for hl, hr, wl, wr in tasks:
-            hidden_states_batch = hidden_states[:, :, hl:hr, wl:wr].to(computation_device)
-            hidden_states_batch = rearrange(hidden_states_batch, "B C H W -> B (H W) C")
-            if residual is not None:
-                residual_batch = residual[:, :, hl:hr, wl:wr].to(computation_device)
-                residual_batch = rearrange(residual_batch, "B C H W -> B (H W) C")
-            else:
-                residual_batch = None
-
-            # Forward
-            hidden_states_batch = block(hidden_states_batch, condition_emb, text_emb, freq_cis_img, residual_batch).to(data_device)
-            hidden_states_batch = rearrange(hidden_states_batch, "B (H W) C -> B C H W", H=hr-hl)
-
-            mask = self.build_mask(hidden_states_batch, is_bound=(hl==0, hr>=H, wl==0, wr>=W))
-            values[:, :, hl:hr, wl:wr] += hidden_states_batch * mask
-            weight[:, :, hl:hr, wl:wr] += mask
-        values /= weight
-        return values
-
-    def forward(
-        self, hidden_states, text_emb, text_emb_t5, text_emb_mask, text_emb_mask_t5, timestep, size_emb, freq_cis_img,
-        tiled=False, tile_size=64, tile_stride=32,
-        to_cache=False,
-        use_gradient_checkpointing=False,
-    ):
-        # Embeddings
-        text_emb = self.prepare_text_emb(text_emb, text_emb_t5, text_emb_mask, text_emb_mask_t5)
-        condition_emb = self.prepare_extra_emb(text_emb_t5, timestep, size_emb, hidden_states.dtype, hidden_states.shape[0])
-        
-        # Input
-        height, width = hidden_states.shape[-2], hidden_states.shape[-1]
-        hidden_states = self.patch_embedder(hidden_states)
-
-        # Blocks
-        def create_custom_forward(module):
-            def custom_forward(*inputs):
-                return module(*inputs)
-            return custom_forward
-        if tiled:
-            hidden_states = rearrange(hidden_states, "B (H W) C -> B C H W", H=height//2)
-            residuals = []
-            for block_id, block in enumerate(self.blocks):
-                residual = residuals.pop() if block_id >= self.num_layers_down else None
-                hidden_states = self.tiled_block_forward(
-                    block, hidden_states, condition_emb, text_emb, freq_cis_img, residual,
-                    torch_dtype=hidden_states.dtype, data_device=hidden_states.device, computation_device=hidden_states.device,
-                    tile_size=tile_size, tile_stride=tile_stride
-                )
-                if block_id < self.num_layers_down - 2:
-                    residuals.append(hidden_states)
-            hidden_states = rearrange(hidden_states, "B C H W -> B (H W) C")
-        else:
-            residuals = []
-            for block_id, block in enumerate(self.blocks):
-                residual = residuals.pop() if block_id >= self.num_layers_down else None
-                if self.training and use_gradient_checkpointing:
-                    hidden_states = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(block),
-                        hidden_states, condition_emb, text_emb, freq_cis_img, residual,
-                        use_reentrant=False,
-                    )
-                else:
-                    hidden_states = block(hidden_states, condition_emb, text_emb, freq_cis_img, residual, to_cache=to_cache)
-                if block_id < self.num_layers_down - 2:
-                    residuals.append(hidden_states)
-
-        # Output
-        hidden_states = self.final_layer(hidden_states, condition_emb)
-        hidden_states = self.unpatchify(hidden_states, height//2, width//2)
-        hidden_states, _ = hidden_states.chunk(2, dim=1)
-        return hidden_states
-    
-    def state_dict_converter(self):
-        return HunyuanDiTStateDictConverter()
-
-
-
-class HunyuanDiTStateDictConverter():
-    def __init__(self):
-        pass
-
-    def from_diffusers(self, state_dict):
-        state_dict_ = {}
-        for name, param in state_dict.items():
-            name_ = name
-            name_ = name_.replace(".default_modulation.", ".modulation.")
-            name_ = name_.replace(".mlp.fc1.", ".mlp.0.")
-            name_ = name_.replace(".mlp.fc2.", ".mlp.2.")
-            name_ = name_.replace(".attn1.q_norm.", ".rota1.q_norm.")
-            name_ = name_.replace(".attn2.q_norm.", ".rota2.q_norm.")
-            name_ = name_.replace(".attn1.k_norm.", ".rota1.k_norm.")
-            name_ = name_.replace(".attn2.k_norm.", ".rota2.k_norm.")
-            name_ = name_.replace(".q_proj.", ".to_q.")
-            name_ = name_.replace(".out_proj.", ".to_out.")
-            name_ = name_.replace("text_embedding_padding", "text_emb_padding")
-            name_ = name_.replace("mlp_t5.0.", "t5_embedder.0.")
-            name_ = name_.replace("mlp_t5.2.", "t5_embedder.2.")
-            name_ = name_.replace("pooler.", "t5_pooler.")
-            name_ = name_.replace("x_embedder.", "patch_embedder.")
-            name_ = name_.replace("t_embedder.", "timestep_embedder.")
-            name_ = name_.replace("t5_pooler.to_q.", "t5_pooler.q_proj.")
-            name_ = name_.replace("style_embedder.weight", "style_embedder")
-            if ".kv_proj." in name_:
-                param_k = param[:param.shape[0]//2]
-                param_v = param[param.shape[0]//2:]
-                state_dict_[name_.replace(".kv_proj.", ".to_k.")] = param_k
-                state_dict_[name_.replace(".kv_proj.", ".to_v.")] = param_v
-            elif ".Wqkv." in name_:
-                param_q = param[:param.shape[0]//3]
-                param_k = param[param.shape[0]//3:param.shape[0]//3*2]
-                param_v = param[param.shape[0]//3*2:]
-                state_dict_[name_.replace(".Wqkv.", ".to_q.")] = param_q
-                state_dict_[name_.replace(".Wqkv.", ".to_k.")] = param_k
-                state_dict_[name_.replace(".Wqkv.", ".to_v.")] = param_v
-            elif "style_embedder" in name_:
-                state_dict_[name_] = param.squeeze()
-            else:
-                state_dict_[name_] = param
-        return state_dict_
-    
-    def from_civitai(self, state_dict):
-        return self.from_diffusers(state_dict)

diffsynth/models/hunyuan_dit_text_encoder.py

@@ -1,161 +0,0 @@
-from transformers import BertModel, BertConfig, T5EncoderModel, T5Config
-import torch
-
-
-
-class HunyuanDiTCLIPTextEncoder(BertModel):
-    def __init__(self):
-        config = BertConfig(
-            _name_or_path = "",
-            architectures = ["BertModel"],
-            attention_probs_dropout_prob = 0.1,
-            bos_token_id = 0,
-            classifier_dropout = None,
-            directionality = "bidi",
-            eos_token_id = 2,
-            hidden_act = "gelu",
-            hidden_dropout_prob = 0.1,
-            hidden_size = 1024,
-            initializer_range = 0.02,
-            intermediate_size = 4096,
-            layer_norm_eps = 1e-12,
-            max_position_embeddings = 512,
-            model_type = "bert",
-            num_attention_heads = 16,
-            num_hidden_layers = 24,
-            output_past = True,
-            pad_token_id = 0,
-            pooler_fc_size = 768,
-            pooler_num_attention_heads = 12,
-            pooler_num_fc_layers = 3,
-            pooler_size_per_head = 128,
-            pooler_type = "first_token_transform",
-            position_embedding_type = "absolute",
-            torch_dtype = "float32",
-            transformers_version = "4.37.2",
-            type_vocab_size = 2,
-            use_cache = True,
-            vocab_size = 47020
-        )
-        super().__init__(config, add_pooling_layer=False)
-        self.eval()
-
-    def forward(self, input_ids, attention_mask, clip_skip=1):
-        input_shape = input_ids.size()
-
-        batch_size, seq_length = input_shape
-        device = input_ids.device
-
-        past_key_values_length = 0
-
-        if attention_mask is None:
-            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
-
-        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
-
-        embedding_output = self.embeddings(
-            input_ids=input_ids,
-            position_ids=None,
-            token_type_ids=None,
-            inputs_embeds=None,
-            past_key_values_length=0,
-        )
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask=extended_attention_mask,
-            head_mask=None,
-            encoder_hidden_states=None,
-            encoder_attention_mask=None,
-            past_key_values=None,
-            use_cache=False,
-            output_attentions=False,
-            output_hidden_states=True,
-            return_dict=True,
-        )
-        all_hidden_states = encoder_outputs.hidden_states
-        prompt_emb = all_hidden_states[-clip_skip]
-        if clip_skip > 1:
-            mean, std = all_hidden_states[-1].mean(), all_hidden_states[-1].std()
-            prompt_emb = (prompt_emb - prompt_emb.mean()) / prompt_emb.std() * std + mean
-        return prompt_emb
-
-    def state_dict_converter(self):
-        return HunyuanDiTCLIPTextEncoderStateDictConverter()
-
-
-
-class HunyuanDiTT5TextEncoder(T5EncoderModel):
-    def __init__(self):
-        config = T5Config(
-            _name_or_path = "../HunyuanDiT/t2i/mt5",
-            architectures = ["MT5ForConditionalGeneration"],
-            classifier_dropout = 0.0,
-            d_ff = 5120,
-            d_kv = 64,
-            d_model = 2048,
-            decoder_start_token_id = 0,
-            dense_act_fn = "gelu_new",
-            dropout_rate = 0.1,
-            eos_token_id = 1,
-            feed_forward_proj = "gated-gelu",
-            initializer_factor = 1.0,
-            is_encoder_decoder = True,
-            is_gated_act = True,
-            layer_norm_epsilon = 1e-06,
-            model_type = "t5",
-            num_decoder_layers = 24,
-            num_heads = 32,
-            num_layers = 24,
-            output_past = True,
-            pad_token_id = 0,
-            relative_attention_max_distance = 128,
-            relative_attention_num_buckets = 32,
-            tie_word_embeddings = False,
-            tokenizer_class = "T5Tokenizer",
-            transformers_version = "4.37.2",
-            use_cache = True,
-            vocab_size = 250112
-        )
-        super().__init__(config)
-        self.eval()
-
-    def forward(self, input_ids, attention_mask, clip_skip=1):
-        outputs = super().forward(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            output_hidden_states=True,
-        )
-        prompt_emb = outputs.hidden_states[-clip_skip]
-        if clip_skip > 1:
-            mean, std = outputs.hidden_states[-1].mean(), outputs.hidden_states[-1].std()
-            prompt_emb = (prompt_emb - prompt_emb.mean()) / prompt_emb.std() * std + mean
-        return prompt_emb
-    
-    def state_dict_converter(self):
-        return HunyuanDiTT5TextEncoderStateDictConverter()
-
-
-
-class HunyuanDiTCLIPTextEncoderStateDictConverter():
-    def __init__(self):
-        pass
-
-    def from_diffusers(self, state_dict):
-        state_dict_ = {name[5:]: param for name, param in state_dict.items() if name.startswith("bert.")}
-        return state_dict_
-    
-    def from_civitai(self, state_dict):
-        return self.from_diffusers(state_dict)
-
-
-class HunyuanDiTT5TextEncoderStateDictConverter():
-    def __init__(self):
-        pass
-
-    def from_diffusers(self, state_dict):
-        state_dict_ = {name: param for name, param in state_dict.items() if name.startswith("encoder.")}
-        state_dict_["shared.weight"] = state_dict["shared.weight"]
-        return state_dict_
-    
-    def from_civitai(self, state_dict):
-        return self.from_diffusers(state_dict)

diffsynth/models/longcat_video_dit.py

@@ -0,0 +1,902 @@
+from typing import List, Optional, Tuple
+
+import math
+import torch
+import torch.nn as nn
+import torch.amp as amp
+
+import numpy as np
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from .wan_video_dit import flash_attention
+from ..core.device.npu_compatible_device import get_device_type
+from ..core.gradient import gradient_checkpoint_forward
+
+
+class RMSNorm_FP32(torch.nn.Module):
+    def __init__(self, dim: int, eps: float):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight
+
+
+def broadcat(tensors, dim=-1):
+    num_tensors = len(tensors)
+    shape_lens = set(list(map(lambda t: len(t.shape), 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(*map(lambda t: list(t.shape), tensors)))
+    expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim]
+    assert all(
+        [*map(lambda t: len(set(t[1])) <= 2, expandable_dims)]
+    ), "invalid dimensions for broadcastable concatentation"
+    max_dims = list(map(lambda t: (t[0], max(t[1])), expandable_dims))
+    expanded_dims = list(map(lambda t: (t[0], (t[1],) * num_tensors), max_dims))
+    expanded_dims.insert(dim, (dim, dims[dim]))
+    expandable_shapes = list(zip(*map(lambda t: t[1], expanded_dims)))
+    tensors = list(map(lambda t: t[0].expand(*t[1]), zip(tensors, expandable_shapes)))
+    return torch.cat(tensors, dim=dim)
+
+
+def rotate_half(x):
+    x = rearrange(x, "... (d r) -> ... d r", r=2)
+    x1, x2 = x.unbind(dim=-1)
+    x = torch.stack((-x2, x1), dim=-1)
+    return rearrange(x, "... d r -> ... (d r)")
+
+
+class RotaryPositionalEmbedding(nn.Module):
+
+    def __init__(self,
+                 head_dim,
+                 cp_split_hw=None
+                 ):
+        """Rotary positional embedding for 3D
+        Reference : https://blog.eleuther.ai/rotary-embeddings/
+        Paper: https://arxiv.org/pdf/2104.09864.pdf
+        Args:
+            dim: Dimension of embedding
+            base: Base value for exponential
+        """
+        super().__init__()
+        self.head_dim = head_dim
+        assert self.head_dim % 8 == 0, 'Dim must be a multiply of 8 for 3D RoPE.'
+        self.cp_split_hw = cp_split_hw
+        # We take the assumption that the longest side of grid will not larger than 512, i.e, 512 * 8 = 4098 input pixels
+        self.base = 10000
+        self.freqs_dict = {}
+
+    def register_grid_size(self, grid_size):
+        if grid_size not in self.freqs_dict:
+            self.freqs_dict.update({
+                grid_size: self.precompute_freqs_cis_3d(grid_size)
+            })
+
+    def precompute_freqs_cis_3d(self, grid_size):
+        num_frames, height, width = grid_size     
+        dim_t = self.head_dim - 4 * (self.head_dim // 6)
+        dim_h = 2 * (self.head_dim // 6)
+        dim_w = 2 * (self.head_dim // 6)
+        freqs_t = 1.0 / (self.base ** (torch.arange(0, dim_t, 2)[: (dim_t // 2)].float() / dim_t))
+        freqs_h = 1.0 / (self.base ** (torch.arange(0, dim_h, 2)[: (dim_h // 2)].float() / dim_h))
+        freqs_w = 1.0 / (self.base ** (torch.arange(0, dim_w, 2)[: (dim_w // 2)].float() / dim_w))
+        grid_t = np.linspace(0, num_frames, num_frames, endpoint=False, dtype=np.float32)
+        grid_h = np.linspace(0, height, height, endpoint=False, dtype=np.float32)
+        grid_w = np.linspace(0, width, width, endpoint=False, dtype=np.float32)
+        grid_t = torch.from_numpy(grid_t).float()
+        grid_h = torch.from_numpy(grid_h).float()
+        grid_w = torch.from_numpy(grid_w).float()
+        freqs_t = torch.einsum("..., f -> ... f", grid_t, freqs_t)
+        freqs_h = torch.einsum("..., f -> ... f", grid_h, freqs_h)
+        freqs_w = torch.einsum("..., f -> ... f", grid_w, freqs_w)
+        freqs_t = repeat(freqs_t, "... n -> ... (n r)", r=2)
+        freqs_h = repeat(freqs_h, "... n -> ... (n r)", r=2)
+        freqs_w = repeat(freqs_w, "... n -> ... (n r)", r=2)
+        freqs = broadcat((freqs_t[:, None, None, :], freqs_h[None, :, None, :], freqs_w[None, None, :, :]), dim=-1)
+        # (T H W D)
+        freqs = rearrange(freqs, "T H W D -> (T H W) D")
+        # if self.cp_split_hw[0] * self.cp_split_hw[1] > 1:
+        #     with torch.no_grad():
+        #         freqs = rearrange(freqs, "(T H W) D -> T H W D", T=num_frames, H=height, W=width)
+        #         freqs = context_parallel_util.split_cp_2d(freqs, seq_dim_hw=(1, 2), split_hw=self.cp_split_hw)
+        #         freqs = rearrange(freqs, "T H W D -> (T H W) D")
+
+        return freqs
+
+    def forward(self, q, k, grid_size):
+        """3D RoPE.
+
+        Args:
+            query: [B, head, seq, head_dim]
+            key: [B, head, seq, head_dim]
+        Returns:
+            query and key with the same shape as input.
+        """
+
+        if grid_size not in self.freqs_dict:
+            self.register_grid_size(grid_size)
+
+        freqs_cis = self.freqs_dict[grid_size].to(q.device)
+        q_, k_ = q.float(), k.float()
+        freqs_cis = freqs_cis.float().to(q.device)
+        cos, sin = freqs_cis.cos(), freqs_cis.sin()
+        cos, sin = rearrange(cos, 'n d -> 1 1 n d'), rearrange(sin, 'n d -> 1 1 n d')
+        q_ = (q_ * cos) + (rotate_half(q_) * sin)
+        k_ = (k_ * cos) + (rotate_half(k_) * sin)
+
+        return q_.type_as(q), k_.type_as(k)
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        enable_flashattn3: bool = False,
+        enable_flashattn2: bool = False,
+        enable_xformers: bool = False,
+        enable_bsa: bool = False,
+        bsa_params: dict = None,
+        cp_split_hw: Optional[List[int]] = None
+    ) -> None:
+        super().__init__()
+        assert dim % num_heads == 0, "dim should be divisible by num_heads"
+        self.dim = dim
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim**-0.5
+        self.enable_flashattn3 = enable_flashattn3
+        self.enable_flashattn2 = enable_flashattn2
+        self.enable_xformers = enable_xformers
+        self.enable_bsa = enable_bsa
+        self.bsa_params = bsa_params
+        self.cp_split_hw = cp_split_hw
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=True)
+        self.q_norm = RMSNorm_FP32(self.head_dim, eps=1e-6)
+        self.k_norm = RMSNorm_FP32(self.head_dim, eps=1e-6)
+        self.proj = nn.Linear(dim, dim)
+
+        self.rope_3d = RotaryPositionalEmbedding(
+            self.head_dim,
+            cp_split_hw=cp_split_hw
+        )
+
+    def _process_attn(self, q, k, v, shape):
+        q = rearrange(q, "B H S D -> B S (H D)")
+        k = rearrange(k, "B H S D -> B S (H D)")
+        v = rearrange(v, "B H S D -> B S (H D)")
+        x = flash_attention(q, k, v, num_heads=self.num_heads)
+        x = rearrange(x, "B S (H D) -> B H S D", H=self.num_heads)
+        return x
+
+    def forward(self, x: torch.Tensor, shape=None, num_cond_latents=None, return_kv=False) -> torch.Tensor:
+        """
+        """
+        B, N, C = x.shape
+        qkv = self.qkv(x)
+
+        qkv_shape = (B, N, 3, self.num_heads, self.head_dim)
+        qkv = qkv.view(qkv_shape).permute((2, 0, 3, 1, 4)) # [3, B, H, N, D]
+        q, k, v = qkv.unbind(0)
+        q, k = self.q_norm(q), self.k_norm(k)
+
+        if return_kv:
+            k_cache, v_cache = k.clone(), v.clone()
+
+        q, k = self.rope_3d(q, k, shape)
+
+        # cond mode
+        if num_cond_latents is not None and num_cond_latents > 0:
+            num_cond_latents_thw = num_cond_latents * (N // shape[0])
+            # process the condition tokens
+            q_cond = q[:, :, :num_cond_latents_thw].contiguous()
+            k_cond = k[:, :, :num_cond_latents_thw].contiguous()
+            v_cond = v[:, :, :num_cond_latents_thw].contiguous()
+            x_cond = self._process_attn(q_cond, k_cond, v_cond, shape)
+            # process the noise tokens
+            q_noise = q[:, :, num_cond_latents_thw:].contiguous()
+            x_noise = self._process_attn(q_noise, k, v, shape)
+            # merge x_cond and x_noise
+            x = torch.cat([x_cond, x_noise], dim=2).contiguous()
+        else:
+            x = self._process_attn(q, k, v, shape)
+
+        x_output_shape = (B, N, C)
+        x = x.transpose(1, 2) # [B, H, N, D] --> [B, N, H, D]
+        x = x.reshape(x_output_shape) # [B, N, H, D] --> [B, N, C]
+        x = self.proj(x)
+
+        if return_kv:
+            return x, (k_cache, v_cache)
+        else:
+            return x
+
+    def forward_with_kv_cache(self, x: torch.Tensor, shape=None, num_cond_latents=None, kv_cache=None) -> torch.Tensor:
+        """
+        """
+        B, N, C = x.shape
+        qkv = self.qkv(x)
+        
+        qkv_shape = (B, N, 3, self.num_heads, self.head_dim)
+        qkv = qkv.view(qkv_shape).permute((2, 0, 3, 1, 4)) # [3, B, H, N, D]
+        q, k, v = qkv.unbind(0)
+        q, k = self.q_norm(q), self.k_norm(k)
+
+        T, H, W = shape
+        k_cache, v_cache = kv_cache
+        assert k_cache.shape[0] == v_cache.shape[0] and k_cache.shape[0] in [1, B]
+        if k_cache.shape[0] == 1:
+            k_cache = k_cache.repeat(B, 1, 1, 1)
+            v_cache = v_cache.repeat(B, 1, 1, 1)
+        
+        if num_cond_latents is not None and num_cond_latents > 0:
+            k_full = torch.cat([k_cache, k], dim=2).contiguous()
+            v_full = torch.cat([v_cache, v], dim=2).contiguous()
+            q_padding = torch.cat([torch.empty_like(k_cache), q], dim=2).contiguous()
+            q_padding, k_full = self.rope_3d(q_padding, k_full, (T + num_cond_latents, H, W))
+            q = q_padding[:, :, -N:].contiguous()
+            
+        x = self._process_attn(q, k_full, v_full, shape)
+        
+        x_output_shape = (B, N, C)
+        x = x.transpose(1, 2) # [B, H, N, D] --> [B, N, H, D]
+        x = x.reshape(x_output_shape) # [B, N, H, D] --> [B, N, C]
+        x = self.proj(x)
+
+        return x
+
+
+class MultiHeadCrossAttention(nn.Module):
+    def __init__(
+            self,
+            dim,
+            num_heads,
+            enable_flashattn3=False,
+            enable_flashattn2=False,
+            enable_xformers=False,
+        ):
+        super(MultiHeadCrossAttention, self).__init__()
+        assert dim % num_heads == 0, "d_model must be divisible by num_heads"
+
+        self.dim = dim
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+
+        self.q_linear = nn.Linear(dim, dim)
+        self.kv_linear = nn.Linear(dim, dim * 2)
+        self.proj = nn.Linear(dim, dim)
+
+        self.q_norm = RMSNorm_FP32(self.head_dim, eps=1e-6)
+        self.k_norm = RMSNorm_FP32(self.head_dim, eps=1e-6)
+
+        self.enable_flashattn3 = enable_flashattn3
+        self.enable_flashattn2 = enable_flashattn2
+        self.enable_xformers = enable_xformers
+
+    def _process_cross_attn(self, x, cond, kv_seqlen):
+        B, N, C = x.shape
+        assert C == self.dim and cond.shape[2] == self.dim
+
+        q = self.q_linear(x).view(1, -1, self.num_heads, self.head_dim)
+        kv = self.kv_linear(cond).view(1, -1, 2, self.num_heads, self.head_dim)
+        k, v = kv.unbind(2)
+
+        q, k = self.q_norm(q), self.k_norm(k)
+
+        q = rearrange(q, "B S H D -> B S (H D)")
+        k = rearrange(k, "B S H D -> B S (H D)")
+        v = rearrange(v, "B S H D -> B S (H D)")
+        x = flash_attention(q, k, v, num_heads=self.num_heads)
+
+        x = x.view(B, -1, C)
+        x = self.proj(x)
+        return x
+
+    def forward(self, x, cond, kv_seqlen, num_cond_latents=None, shape=None):
+        """
+            x: [B, N, C]
+            cond: [B, M, C]
+        """
+        if num_cond_latents is None or num_cond_latents == 0:
+            return self._process_cross_attn(x, cond, kv_seqlen)
+        else:
+            B, N, C = x.shape
+            if num_cond_latents is not None and num_cond_latents > 0:
+                assert shape is not None, "SHOULD pass in the shape"
+                num_cond_latents_thw = num_cond_latents * (N // shape[0])
+                x_noise = x[:, num_cond_latents_thw:] # [B, N_noise, C]
+                output_noise = self._process_cross_attn(x_noise, cond, kv_seqlen) # [B, N_noise, C]
+                output = torch.cat([
+                    torch.zeros((B, num_cond_latents_thw, C), dtype=output_noise.dtype, device=output_noise.device),
+                    output_noise
+                ], dim=1).contiguous()
+            else:
+                raise NotImplementedError
+                
+            return output
+
+
+class LayerNorm_FP32(nn.LayerNorm):
+    def __init__(self, dim, eps, elementwise_affine):
+        super().__init__(dim, eps=eps, elementwise_affine=elementwise_affine)
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        origin_dtype = inputs.dtype
+        out = F.layer_norm(
+            inputs.float(), 
+            self.normalized_shape, 
+            None if self.weight is None else self.weight.float(), 
+            None if self.bias is None else self.bias.float() ,
+            self.eps
+        ).to(origin_dtype)
+        return out
+
+
+def modulate_fp32(norm_func, x, shift, scale):
+    # Suppose x is (B, N, D), shift is (B, -1, D), scale is (B, -1, D)
+    # ensure the modulation params be fp32
+    assert shift.dtype == torch.float32, scale.dtype == torch.float32
+    dtype = x.dtype
+    x = norm_func(x.to(torch.float32))
+    x = x * (scale + 1) + shift
+    x = x.to(dtype)
+    return x
+
+
+class FinalLayer_FP32(nn.Module):
+    """
+    The final layer of DiT.
+    """
+
+    def __init__(self, hidden_size, num_patch, out_channels, adaln_tembed_dim):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.num_patch = num_patch
+        self.out_channels = out_channels
+        self.adaln_tembed_dim = adaln_tembed_dim
+
+        self.norm_final = LayerNorm_FP32(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = nn.Linear(hidden_size, num_patch * out_channels, bias=True)
+        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(adaln_tembed_dim, 2 * hidden_size, bias=True))
+
+    def forward(self, x, t, latent_shape):
+        # timestep shape: [B, T, C]
+        assert t.dtype == torch.float32
+        B, N, C = x.shape
+        T, _, _ = latent_shape
+
+        with amp.autocast(get_device_type(), dtype=torch.float32):
+            shift, scale = self.adaLN_modulation(t).unsqueeze(2).chunk(2, dim=-1) # [B, T, 1, C]
+            x = modulate_fp32(self.norm_final, x.view(B, T, -1, C), shift, scale).view(B, N, C)
+            x = self.linear(x)
+        return x
+
+
+class FeedForwardSwiGLU(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        hidden_dim: int,
+        multiple_of: int = 256,
+        ffn_dim_multiplier: Optional[float] = None,
+    ):
+        super().__init__()
+        hidden_dim = int(2 * hidden_dim / 3)
+        # custom dim factor multiplier
+        if ffn_dim_multiplier is not None:
+            hidden_dim = int(ffn_dim_multiplier * hidden_dim)
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+        self.dim = dim
+        self.hidden_dim = hidden_dim
+        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
+        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
+        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
+
+    def forward(self, x):
+        return self.w2(F.silu(self.w1(x)) * self.w3(x))
+
+
+class TimestepEmbedder(nn.Module):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+
+    def __init__(self, t_embed_dim, frequency_embedding_size=256):
+        super().__init__()
+        self.t_embed_dim = t_embed_dim
+        self.frequency_embedding_size = frequency_embedding_size
+        self.mlp = nn.Sequential(
+            nn.Linear(frequency_embedding_size, t_embed_dim, bias=True),
+            nn.SiLU(),
+            nn.Linear(t_embed_dim, t_embed_dim, bias=True),
+        )
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        """
+        Create sinusoidal timestep embeddings.
+        :param t: a 1-D Tensor of N indices, one per batch element.
+                          These may be fractional.
+        :param dim: the dimension of the output.
+        :param max_period: controls the minimum frequency of the embeddings.
+        :return: an (N, D) Tensor of positional embeddings.
+        """
+        half = dim // 2
+        freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half)
+        freqs = freqs.to(device=t.device)
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+        return embedding
+
+    def forward(self, t, dtype):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
+        if t_freq.dtype != dtype:
+            t_freq = t_freq.to(dtype)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class CaptionEmbedder(nn.Module):
+    """
+    Embeds class labels into vector representations.
+    """
+
+    def __init__(self, in_channels, hidden_size):
+        super().__init__()
+        self.in_channels = in_channels
+        self.hidden_size = hidden_size
+        self.y_proj = nn.Sequential(
+            nn.Linear(in_channels, hidden_size, bias=True),
+            nn.GELU(approximate="tanh"),
+            nn.Linear(hidden_size, hidden_size, bias=True),
+        )
+
+    def forward(self, caption):
+        B, _, N, C = caption.shape
+        caption = self.y_proj(caption)
+        return caption
+
+
+class PatchEmbed3D(nn.Module):
+    """Video to Patch Embedding.
+
+    Args:
+        patch_size (int): Patch token size. Default: (2,4,4).
+        in_chans (int): Number of input video channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(
+        self,
+        patch_size=(2, 4, 4),
+        in_chans=3,
+        embed_dim=96,
+        norm_layer=None,
+        flatten=True,
+    ):
+        super().__init__()
+        self.patch_size = patch_size
+        self.flatten = flatten
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        """Forward function."""
+        # padding
+        _, _, D, H, W = x.size()
+        if W % self.patch_size[2] != 0:
+            x = F.pad(x, (0, self.patch_size[2] - W % self.patch_size[2]))
+        if H % self.patch_size[1] != 0:
+            x = F.pad(x, (0, 0, 0, self.patch_size[1] - H % self.patch_size[1]))
+        if D % self.patch_size[0] != 0:
+            x = F.pad(x, (0, 0, 0, 0, 0, self.patch_size[0] - D % self.patch_size[0]))
+
+        B, C, T, H, W = x.shape
+        x = self.proj(x)  # (B C T H W)
+        if self.norm is not None:
+            D, Wh, Ww = x.size(2), x.size(3), x.size(4)
+            x = x.flatten(2).transpose(1, 2)
+            x = self.norm(x)
+            x = x.transpose(1, 2).view(-1, self.embed_dim, D, Wh, Ww)
+        if self.flatten:
+            x = x.flatten(2).transpose(1, 2)  # BCTHW -> BNC
+        return x
+
+
+class LongCatSingleStreamBlock(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        mlp_ratio: int,
+        adaln_tembed_dim: int,
+        enable_flashattn3: bool = False,
+        enable_flashattn2: bool = False,
+        enable_xformers: bool = False,
+        enable_bsa: bool = False,
+        bsa_params=None,
+        cp_split_hw=None
+    ):
+        super().__init__()
+
+        self.hidden_size = hidden_size
+
+        # scale and gate modulation
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(adaln_tembed_dim, 6 * hidden_size, bias=True)
+        )
+
+        self.mod_norm_attn = LayerNorm_FP32(hidden_size, eps=1e-6, elementwise_affine=False)
+        self.mod_norm_ffn  = LayerNorm_FP32(hidden_size, eps=1e-6, elementwise_affine=False)
+        self.pre_crs_attn_norm = LayerNorm_FP32(hidden_size, eps=1e-6, elementwise_affine=True)
+
+        self.attn = Attention(
+            dim=hidden_size,
+            num_heads=num_heads,
+            enable_flashattn3=enable_flashattn3,
+            enable_flashattn2=enable_flashattn2,
+            enable_xformers=enable_xformers,
+            enable_bsa=enable_bsa,
+            bsa_params=bsa_params,
+            cp_split_hw=cp_split_hw
+        )
+        self.cross_attn = MultiHeadCrossAttention(
+            dim=hidden_size,
+            num_heads=num_heads,
+            enable_flashattn3=enable_flashattn3,
+            enable_flashattn2=enable_flashattn2,
+            enable_xformers=enable_xformers,
+        )
+        self.ffn = FeedForwardSwiGLU(dim=hidden_size, hidden_dim=int(hidden_size * mlp_ratio))
+
+    def forward(self, x, y, t, y_seqlen, latent_shape, num_cond_latents=None, return_kv=False, kv_cache=None, skip_crs_attn=False):
+        """
+            x: [B, N, C]
+            y: [1, N_valid_tokens, C]
+            t: [B, T, C_t]
+            y_seqlen: [B]; type of a list
+            latent_shape: latent shape of a single item
+        """
+        x_dtype = x.dtype
+
+        B, N, C = x.shape
+        T, _, _ = latent_shape # S != T*H*W in case of CP split on H*W.
+
+        # compute modulation params in fp32
+        with amp.autocast(device_type=get_device_type(), dtype=torch.float32):
+            shift_msa, scale_msa, gate_msa, \
+            shift_mlp, scale_mlp, gate_mlp = \
+                self.adaLN_modulation(t).unsqueeze(2).chunk(6, dim=-1) # [B, T, 1, C]
+
+        # self attn with modulation
+        x_m = modulate_fp32(self.mod_norm_attn, x.view(B, T, -1, C), shift_msa, scale_msa).view(B, N, C)
+
+        if kv_cache is not None:
+            kv_cache = (kv_cache[0].to(x.device), kv_cache[1].to(x.device))
+            attn_outputs = self.attn.forward_with_kv_cache(x_m, shape=latent_shape, num_cond_latents=num_cond_latents, kv_cache=kv_cache)
+        else:
+            attn_outputs = self.attn(x_m, shape=latent_shape, num_cond_latents=num_cond_latents, return_kv=return_kv)
+        
+        if return_kv:
+            x_s, kv_cache = attn_outputs
+        else:
+            x_s = attn_outputs
+
+        with amp.autocast(device_type=get_device_type(), dtype=torch.float32):
+            x = x + (gate_msa * x_s.view(B, -1, N//T, C)).view(B, -1, C) # [B, N, C]
+        x = x.to(x_dtype)
+
+        # cross attn
+        if not skip_crs_attn:
+            if kv_cache is not None:
+                num_cond_latents = None
+            x = x + self.cross_attn(self.pre_crs_attn_norm(x), y, y_seqlen, num_cond_latents=num_cond_latents, shape=latent_shape)
+
+        # ffn with modulation
+        x_m = modulate_fp32(self.mod_norm_ffn, x.view(B, -1, N//T, C), shift_mlp, scale_mlp).view(B, -1, C)
+        x_s = self.ffn(x_m)
+        with amp.autocast(device_type=get_device_type(), dtype=torch.float32):
+            x = x + (gate_mlp * x_s.view(B, -1, N//T, C)).view(B, -1, C) # [B, N, C]
+        x = x.to(x_dtype)
+
+        if return_kv:
+            return x, kv_cache
+        else:
+            return x
+
+
+class LongCatVideoTransformer3DModel(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels: int = 16,
+        out_channels: int = 16,
+        hidden_size: int = 4096,
+        depth: int = 48,
+        num_heads: int = 32,
+        caption_channels: int = 4096,
+        mlp_ratio: int = 4,
+        adaln_tembed_dim: int = 512,
+        frequency_embedding_size: int = 256,
+        # default params
+        patch_size: Tuple[int] = (1, 2, 2),
+        # attention config
+        enable_flashattn3: bool = False,
+        enable_flashattn2: bool = True,
+        enable_xformers: bool = False,
+        enable_bsa: bool = False,
+        bsa_params: dict = {'sparsity': 0.9375, 'chunk_3d_shape_q': [4, 4, 4], 'chunk_3d_shape_k': [4, 4, 4]},
+        cp_split_hw: Optional[List[int]] = [1, 1],
+        text_tokens_zero_pad: bool = True,
+    ) -> None:
+        super().__init__()
+
+        self.patch_size = patch_size
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.cp_split_hw = cp_split_hw
+
+        self.x_embedder = PatchEmbed3D(patch_size, in_channels, hidden_size)
+        self.t_embedder = TimestepEmbedder(t_embed_dim=adaln_tembed_dim, frequency_embedding_size=frequency_embedding_size)
+        self.y_embedder = CaptionEmbedder(
+            in_channels=caption_channels,
+            hidden_size=hidden_size,
+        )
+
+        self.blocks = nn.ModuleList(
+            [
+                LongCatSingleStreamBlock(
+                    hidden_size=hidden_size,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    adaln_tembed_dim=adaln_tembed_dim,
+                    enable_flashattn3=enable_flashattn3,
+                    enable_flashattn2=enable_flashattn2,
+                    enable_xformers=enable_xformers,
+                    enable_bsa=enable_bsa,
+                    bsa_params=bsa_params,
+                    cp_split_hw=cp_split_hw
+                )
+                for i in range(depth)
+            ]
+        )
+
+        self.final_layer = FinalLayer_FP32(
+            hidden_size,
+            np.prod(self.patch_size),
+            out_channels,
+            adaln_tembed_dim,
+        )
+
+        self.gradient_checkpointing = False
+        self.text_tokens_zero_pad = text_tokens_zero_pad
+
+        self.lora_dict = {}
+        self.active_loras = []
+
+    def enable_loras(self, lora_key_list=[]):
+        self.disable_all_loras()
+    
+        module_loras = {}  # {module_name: [lora1, lora2, ...]}
+        model_device = next(self.parameters()).device
+        model_dtype = next(self.parameters()).dtype
+        
+        for lora_key in lora_key_list:
+            if lora_key in self.lora_dict:
+                for lora in self.lora_dict[lora_key].loras:
+                    lora.to(model_device, dtype=model_dtype, non_blocking=True)
+                    module_name = lora.lora_name.replace("lora___lorahyphen___", "").replace("___lorahyphen___", ".")
+                    if module_name not in module_loras:
+                        module_loras[module_name] = []
+                    module_loras[module_name].append(lora)
+                self.active_loras.append(lora_key)
+    
+        for module_name, loras in module_loras.items():
+            module = self._get_module_by_name(module_name)
+            if not hasattr(module, 'org_forward'):
+                module.org_forward = module.forward
+            module.forward = self._create_multi_lora_forward(module, loras)
+    
+    def _create_multi_lora_forward(self, module, loras):
+        def multi_lora_forward(x, *args, **kwargs):
+            weight_dtype = x.dtype
+            org_output = module.org_forward(x, *args, **kwargs)
+            
+            total_lora_output = 0
+            for lora in loras:
+                if lora.use_lora:
+                    lx = lora.lora_down(x.to(lora.lora_down.weight.dtype))
+                    lx = lora.lora_up(lx)
+                    lora_output = lx.to(weight_dtype) * lora.multiplier * lora.alpha_scale
+                    total_lora_output += lora_output
+            
+            return org_output + total_lora_output
+        
+        return multi_lora_forward
+    
+    def _get_module_by_name(self, module_name):
+        try:
+            module = self
+            for part in module_name.split('.'):
+                module = getattr(module, part)
+            return module
+        except AttributeError as e:
+            raise ValueError(f"Cannot find module: {module_name}, error: {e}")
+    
+    def disable_all_loras(self):
+        for name, module in self.named_modules():
+            if hasattr(module, 'org_forward'):
+                module.forward = module.org_forward
+                delattr(module, 'org_forward')
+        
+        for lora_key, lora_network in self.lora_dict.items():
+            for lora in lora_network.loras:
+                lora.to("cpu")
+        
+        self.active_loras.clear()
+
+    def enable_bsa(self,):
+        for block in self.blocks:
+            block.attn.enable_bsa = True
+    
+    def disable_bsa(self,):
+        for block in self.blocks:
+            block.attn.enable_bsa = False    
+
+    def forward(
+        self, 
+        hidden_states, 
+        timestep, 
+        encoder_hidden_states, 
+        encoder_attention_mask=None, 
+        num_cond_latents=0,
+        return_kv=False, 
+        kv_cache_dict={},
+        skip_crs_attn=False, 
+        offload_kv_cache=False,
+        use_gradient_checkpointing=False,
+        use_gradient_checkpointing_offload=False,
+    ):
+
+        B, _, T, H, W = hidden_states.shape
+
+        N_t = T // self.patch_size[0]
+        N_h = H // self.patch_size[1]
+        N_w = W // self.patch_size[2]
+
+        assert self.patch_size[0]==1, "Currently, 3D x_embedder should not compress the temporal dimension."
+
+        # expand the shape of timestep from [B] to [B, T]
+        if len(timestep.shape) == 1:
+            timestep = timestep.unsqueeze(1).expand(-1, N_t).clone() # [B, T]
+        timestep[:, :num_cond_latents] = 0
+
+        dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(dtype)
+        timestep = timestep.to(dtype)
+        encoder_hidden_states = encoder_hidden_states.to(dtype)
+
+        hidden_states = self.x_embedder(hidden_states)  # [B, N, C]
+
+        with amp.autocast(device_type=get_device_type(), dtype=torch.float32):
+            t = self.t_embedder(timestep.float().flatten(), dtype=torch.float32).reshape(B, N_t, -1)  # [B, T, C_t]
+
+        encoder_hidden_states = self.y_embedder(encoder_hidden_states)  # [B, 1, N_token, C]
+
+        if self.text_tokens_zero_pad and encoder_attention_mask is not None:
+            encoder_hidden_states = encoder_hidden_states * encoder_attention_mask[:, None, :, None]
+            encoder_attention_mask = (encoder_attention_mask * 0 + 1).to(encoder_attention_mask.dtype)
+
+        if encoder_attention_mask is not None:
+            encoder_attention_mask = encoder_attention_mask.squeeze(1).squeeze(1)
+            encoder_hidden_states = encoder_hidden_states.squeeze(1).masked_select(encoder_attention_mask.unsqueeze(-1) != 0).view(1, -1, hidden_states.shape[-1]) # [1, N_valid_tokens, C]
+            y_seqlens = encoder_attention_mask.sum(dim=1).tolist() # [B]
+        else:
+            y_seqlens = [encoder_hidden_states.shape[2]] * encoder_hidden_states.shape[0]
+            encoder_hidden_states = encoder_hidden_states.squeeze(1).view(1, -1, hidden_states.shape[-1])
+
+        # if self.cp_split_hw[0] * self.cp_split_hw[1] > 1:
+        #     hidden_states = rearrange(hidden_states, "B (T H W) C -> B T H W C", T=N_t, H=N_h, W=N_w)
+        #     hidden_states = context_parallel_util.split_cp_2d(hidden_states, seq_dim_hw=(2, 3), split_hw=self.cp_split_hw)
+        #     hidden_states = rearrange(hidden_states, "B T H W C -> B (T H W) C")
+
+        # blocks
+        kv_cache_dict_ret = {}
+        for i, block in enumerate(self.blocks):
+            block_outputs = gradient_checkpoint_forward(
+                block,
+                use_gradient_checkpointing=use_gradient_checkpointing,
+                use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+                x=hidden_states,
+                y=encoder_hidden_states,
+                t=t,
+                y_seqlen=y_seqlens,
+                latent_shape=(N_t, N_h, N_w),
+                num_cond_latents=num_cond_latents,
+                return_kv=return_kv,
+                kv_cache=kv_cache_dict.get(i, None),
+                skip_crs_attn=skip_crs_attn,
+            )
+            
+            if return_kv:
+                hidden_states, kv_cache = block_outputs
+                if offload_kv_cache:
+                    kv_cache_dict_ret[i] = (kv_cache[0].cpu(), kv_cache[1].cpu())
+                else:
+                    kv_cache_dict_ret[i] = (kv_cache[0].contiguous(), kv_cache[1].contiguous())
+            else:
+                hidden_states = block_outputs
+
+        hidden_states = self.final_layer(hidden_states, t, (N_t, N_h, N_w))  # [B, N, C=T_p*H_p*W_p*C_out]
+
+        # if self.cp_split_hw[0] * self.cp_split_hw[1] > 1:
+        #     hidden_states = context_parallel_util.gather_cp_2d(hidden_states, shape=(N_t, N_h, N_w), split_hw=self.cp_split_hw)
+
+        hidden_states = self.unpatchify(hidden_states, N_t, N_h, N_w)  # [B, C_out, H, W]
+
+        # cast to float32 for better accuracy
+        hidden_states = hidden_states.to(torch.float32)
+
+        if return_kv:
+            return hidden_states, kv_cache_dict_ret
+        else:
+            return hidden_states
+    
+
+    def unpatchify(self, x, N_t, N_h, N_w):
+        """
+        Args:
+            x (torch.Tensor): of shape [B, N, C]
+
+        Return:
+            x (torch.Tensor): of shape [B, C_out, T, H, W]
+        """
+        T_p, H_p, W_p = self.patch_size
+        x = rearrange(
+            x,
+            "B (N_t N_h N_w) (T_p H_p W_p C_out) -> B C_out (N_t T_p) (N_h H_p) (N_w W_p)",
+            N_t=N_t,
+            N_h=N_h,
+            N_w=N_w,
+            T_p=T_p,
+            H_p=H_p,
+            W_p=W_p,
+            C_out=self.out_channels,
+        )
+        return x
+
+    @staticmethod
+    def state_dict_converter():
+        return LongCatVideoTransformer3DModelDictConverter()
+
+
+class LongCatVideoTransformer3DModelDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict
+    
+    def from_civitai(self, state_dict):
+        return state_dict
+

diffsynth/models/ltx2_common.py

@@ -0,0 +1,371 @@
+from dataclasses import dataclass
+from typing import NamedTuple, Protocol, Tuple
+import torch
+from torch import nn
+from enum import Enum
+
+
+class VideoPixelShape(NamedTuple):
+    """
+    Shape of the tensor representing the video pixel array. Assumes BGR channel format.
+    """
+
+    batch: int
+    frames: int
+    height: int
+    width: int
+    fps: float
+
+
+class SpatioTemporalScaleFactors(NamedTuple):
+    """
+    Describes the spatiotemporal downscaling between decoded video space and
+    the corresponding VAE latent grid.
+    """
+
+    time: int
+    width: int
+    height: int
+
+    @classmethod
+    def default(cls) -> "SpatioTemporalScaleFactors":
+        return cls(time=8, width=32, height=32)
+
+
+VIDEO_SCALE_FACTORS = SpatioTemporalScaleFactors.default()
+
+
+class VideoLatentShape(NamedTuple):
+    """
+    Shape of the tensor representing video in VAE latent space.
+    The latent representation is a 5D tensor with dimensions ordered as
+    (batch, channels, frames, height, width). Spatial and temporal dimensions
+    are downscaled relative to pixel space according to the VAE's scale factors.
+    """
+
+    batch: int
+    channels: int
+    frames: int
+    height: int
+    width: int
+
+    def to_torch_shape(self) -> torch.Size:
+        return torch.Size([self.batch, self.channels, self.frames, self.height, self.width])
+
+    @staticmethod
+    def from_torch_shape(shape: torch.Size) -> "VideoLatentShape":
+        return VideoLatentShape(
+            batch=shape[0],
+            channels=shape[1],
+            frames=shape[2],
+            height=shape[3],
+            width=shape[4],
+        )
+
+    def mask_shape(self) -> "VideoLatentShape":
+        return self._replace(channels=1)
+
+    @staticmethod
+    def from_pixel_shape(
+        shape: VideoPixelShape,
+        latent_channels: int = 128,
+        scale_factors: SpatioTemporalScaleFactors = VIDEO_SCALE_FACTORS,
+    ) -> "VideoLatentShape":
+        frames = (shape.frames - 1) // scale_factors[0] + 1
+        height = shape.height // scale_factors[1]
+        width = shape.width // scale_factors[2]
+
+        return VideoLatentShape(
+            batch=shape.batch,
+            channels=latent_channels,
+            frames=frames,
+            height=height,
+            width=width,
+        )
+
+    def upscale(self, scale_factors: SpatioTemporalScaleFactors = VIDEO_SCALE_FACTORS) -> "VideoLatentShape":
+        return self._replace(
+            channels=3,
+            frames=(self.frames - 1) * scale_factors.time + 1,
+            height=self.height * scale_factors.height,
+            width=self.width * scale_factors.width,
+        )
+
+
+class AudioLatentShape(NamedTuple):
+    """
+    Shape of audio in VAE latent space: (batch, channels, frames, mel_bins).
+    mel_bins is the number of frequency bins from the mel-spectrogram encoding.
+    """
+
+    batch: int
+    channels: int
+    frames: int
+    mel_bins: int
+
+    def to_torch_shape(self) -> torch.Size:
+        return torch.Size([self.batch, self.channels, self.frames, self.mel_bins])
+
+    def mask_shape(self) -> "AudioLatentShape":
+        return self._replace(channels=1, mel_bins=1)
+
+    @staticmethod
+    def from_torch_shape(shape: torch.Size) -> "AudioLatentShape":
+        return AudioLatentShape(
+            batch=shape[0],
+            channels=shape[1],
+            frames=shape[2],
+            mel_bins=shape[3],
+        )
+
+    @staticmethod
+    def from_duration(
+        batch: int,
+        duration: float,
+        channels: int = 8,
+        mel_bins: int = 16,
+        sample_rate: int = 16000,
+        hop_length: int = 160,
+        audio_latent_downsample_factor: int = 4,
+    ) -> "AudioLatentShape":
+        latents_per_second = float(sample_rate) / float(hop_length) / float(audio_latent_downsample_factor)
+
+        return AudioLatentShape(
+            batch=batch,
+            channels=channels,
+            frames=round(duration * latents_per_second),
+            mel_bins=mel_bins,
+        )
+
+    @staticmethod
+    def from_video_pixel_shape(
+        shape: VideoPixelShape,
+        channels: int = 8,
+        mel_bins: int = 16,
+        sample_rate: int = 16000,
+        hop_length: int = 160,
+        audio_latent_downsample_factor: int = 4,
+    ) -> "AudioLatentShape":
+        return AudioLatentShape.from_duration(
+            batch=shape.batch,
+            duration=float(shape.frames) / float(shape.fps),
+            channels=channels,
+            mel_bins=mel_bins,
+            sample_rate=sample_rate,
+            hop_length=hop_length,
+            audio_latent_downsample_factor=audio_latent_downsample_factor,
+        )
+
+
+@dataclass(frozen=True)
+class LatentState:
+    """
+    State of latents during the diffusion denoising process.
+    Attributes:
+        latent: The current noisy latent tensor being denoised.
+        denoise_mask: Mask encoding the denoising strength for each token (1 = full denoising, 0 = no denoising).
+        positions: Positional indices for each latent element, used for positional embeddings.
+        clean_latent: Initial state of the latent before denoising, may include conditioning latents.
+    """
+
+    latent: torch.Tensor
+    denoise_mask: torch.Tensor
+    positions: torch.Tensor
+    clean_latent: torch.Tensor
+
+    def clone(self) -> "LatentState":
+        return LatentState(
+            latent=self.latent.clone(),
+            denoise_mask=self.denoise_mask.clone(),
+            positions=self.positions.clone(),
+            clean_latent=self.clean_latent.clone(),
+        )
+
+
+class NormType(Enum):
+    """Normalization layer types: GROUP (GroupNorm) or PIXEL (per-location RMS norm)."""
+
+    GROUP = "group"
+    PIXEL = "pixel"
+
+
+class PixelNorm(nn.Module):
+    """
+    Per-pixel (per-location) RMS normalization layer.
+    For each element along the chosen dimension, this layer normalizes the tensor
+    by the root-mean-square of its values across that dimension:
+        y = x / sqrt(mean(x^2, dim=dim, keepdim=True) + eps)
+    """
+
+    def __init__(self, dim: int = 1, eps: float = 1e-8) -> None:
+        """
+        Args:
+            dim: Dimension along which to compute the RMS (typically channels).
+            eps: Small constant added for numerical stability.
+        """
+        super().__init__()
+        self.dim = dim
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Apply RMS normalization along the configured dimension.
+        """
+        # Compute mean of squared values along `dim`, keep dimensions for broadcasting.
+        mean_sq = torch.mean(x**2, dim=self.dim, keepdim=True)
+        # Normalize by the root-mean-square (RMS).
+        rms = torch.sqrt(mean_sq + self.eps)
+        return x / rms
+
+
+def build_normalization_layer(
+    in_channels: int, *, num_groups: int = 32, normtype: NormType = NormType.GROUP
+) -> nn.Module:
+    """
+    Create a normalization layer based on the normalization type.
+    Args:
+        in_channels: Number of input channels
+        num_groups: Number of groups for group normalization
+        normtype: Type of normalization: "group" or "pixel"
+    Returns:
+        A normalization layer
+    """
+    if normtype == NormType.GROUP:
+        return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+    if normtype == NormType.PIXEL:
+        return PixelNorm(dim=1, eps=1e-6)
+    raise ValueError(f"Invalid normalization type: {normtype}")
+
+
+def rms_norm(x: torch.Tensor, weight: torch.Tensor | None = None, eps: float = 1e-6) -> torch.Tensor:
+    """Root-mean-square (RMS) normalize `x` over its last dimension.
+    Thin wrapper around `torch.nn.functional.rms_norm` that infers the normalized
+    shape and forwards `weight` and `eps`.
+    """
+    return torch.nn.functional.rms_norm(x, (x.shape[-1],), weight=weight, eps=eps)
+
+
+@dataclass(frozen=True)
+class Modality:
+    """
+    Input data for a single modality (video or audio) in the transformer.
+    Bundles the latent tokens, timestep embeddings, positional information,
+    and text conditioning context for processing by the diffusion transformer.
+    """
+
+    latent: (
+        torch.Tensor
+    )  # Shape: (B, T, D) where B is the batch size, T is the number of tokens, and D is input dimension
+    timesteps: torch.Tensor  # Shape: (B, T) where T is the number of timesteps
+    positions: (
+        torch.Tensor
+    )  # Shape: (B, 3, T) for video, where 3 is the number of dimensions and T is the number of tokens
+    context: torch.Tensor
+    enabled: bool = True
+    context_mask: torch.Tensor | None = None
+
+
+def to_denoised(
+    sample: torch.Tensor,
+    velocity: torch.Tensor,
+    sigma: float | torch.Tensor,
+    calc_dtype: torch.dtype = torch.float32,
+) -> torch.Tensor:
+    """
+    Convert the sample and its denoising velocity to denoised sample.
+    Returns:
+        Denoised sample
+    """
+    if isinstance(sigma, torch.Tensor):
+        sigma = sigma.to(calc_dtype)
+    return (sample.to(calc_dtype) - velocity.to(calc_dtype) * sigma).to(sample.dtype)
+
+
+
+class Patchifier(Protocol):
+    """
+    Protocol for patchifiers that convert latent tensors into patches and assemble them back.
+    """
+
+    def patchify(
+        self,
+        latents: torch.Tensor,
+    ) -> torch.Tensor:
+        ...
+        """
+        Convert latent tensors into flattened patch tokens.
+        Args:
+            latents: Latent tensor to patchify.
+        Returns:
+            Flattened patch tokens tensor.
+        """
+
+    def unpatchify(
+        self,
+        latents: torch.Tensor,
+        output_shape: AudioLatentShape | VideoLatentShape,
+    ) -> torch.Tensor:
+        """
+        Converts latent tensors between spatio-temporal formats and flattened sequence representations.
+        Args:
+            latents: Patch tokens that must be rearranged back into the latent grid constructed by `patchify`.
+            output_shape: Shape of the output tensor. Note that output_shape is either AudioLatentShape or
+            VideoLatentShape.
+        Returns:
+            Dense latent tensor restored from the flattened representation.
+        """
+
+    @property
+    def patch_size(self) -> Tuple[int, int, int]:
+        ...
+        """
+        Returns the patch size as a tuple of (temporal, height, width) dimensions
+        """
+
+    def get_patch_grid_bounds(
+        self,
+        output_shape: AudioLatentShape | VideoLatentShape,
+        device: torch.device | None = None,
+    ) -> torch.Tensor:
+        ...
+        """
+        Compute metadata describing where each latent patch resides within the
+        grid specified by `output_shape`.
+        Args:
+            output_shape: Target grid layout for the patches.
+            device: Target device for the returned tensor.
+        Returns:
+            Tensor containing patch coordinate metadata such as spatial or temporal intervals.
+        """
+
+
+def get_pixel_coords(
+    latent_coords: torch.Tensor,
+    scale_factors: SpatioTemporalScaleFactors,
+    causal_fix: bool = False,
+) -> torch.Tensor:
+    """
+    Map latent-space `[start, end)` coordinates to their pixel-space equivalents by scaling
+    each axis (frame/time, height, width) with the corresponding VAE downsampling factors.
+    Optionally compensate for causal encoding that keeps the first frame at unit temporal scale.
+    Args:
+        latent_coords: Tensor of latent bounds shaped `(batch, 3, num_patches, 2)`.
+        scale_factors: SpatioTemporalScaleFactors tuple `(temporal, height, width)` with integer scale factors applied
+        per axis.
+        causal_fix: When True, rewrites the temporal axis of the first frame so causal VAEs
+            that treat frame zero differently still yield non-negative timestamps.
+    """
+    # Broadcast the VAE scale factors so they align with the `(batch, axis, patch, bound)` layout.
+    broadcast_shape = [1] * latent_coords.ndim
+    broadcast_shape[1] = -1  # axis dimension corresponds to (frame/time, height, width)
+    scale_tensor = torch.tensor(scale_factors, device=latent_coords.device).view(*broadcast_shape)
+
+    # Apply per-axis scaling to convert latent bounds into pixel-space coordinates.
+    pixel_coords = latent_coords * scale_tensor
+
+    if causal_fix:
+        # VAE temporal stride for the very first frame is 1 instead of `scale_factors[0]`.
+        # Shift and clamp to keep the first-frame timestamps causal and non-negative.
+        pixel_coords[:, 0, ...] = (pixel_coords[:, 0, ...] + 1 - scale_factors[0]).clamp(min=0)
+
+    return pixel_coords

diffsynth/models/ltx2_text_encoder.py

@@ -0,0 +1,366 @@
+import torch
+from transformers import Gemma3ForConditionalGeneration, Gemma3Config, AutoTokenizer
+from .ltx2_dit import (LTXRopeType, generate_freq_grid_np, generate_freq_grid_pytorch, precompute_freqs_cis, Attention,
+                       FeedForward)
+from .ltx2_common import rms_norm
+
+
+class LTX2TextEncoder(Gemma3ForConditionalGeneration):
+    def __init__(self):
+        config = Gemma3Config(
+            **{
+                "architectures": ["Gemma3ForConditionalGeneration"],
+                "boi_token_index": 255999,
+                "dtype": "bfloat16",
+                "eoi_token_index": 256000,
+                "eos_token_id": [1, 106],
+                "image_token_index": 262144,
+                "initializer_range": 0.02,
+                "mm_tokens_per_image": 256,
+                "model_type": "gemma3",
+                "text_config": {
+                    "_sliding_window_pattern": 6,
+                    "attention_bias": False,
+                    "attention_dropout": 0.0,
+                    "attn_logit_softcapping": None,
+                    "cache_implementation": "hybrid",
+                    "dtype": "bfloat16",
+                    "final_logit_softcapping": None,
+                    "head_dim": 256,
+                    "hidden_activation": "gelu_pytorch_tanh",
+                    "hidden_size": 3840,
+                    "initializer_range": 0.02,
+                    "intermediate_size": 15360,
+                    "layer_types": [
+                        "sliding_attention", "sliding_attention", "sliding_attention", "sliding_attention",
+                        "sliding_attention", "full_attention", "sliding_attention", "sliding_attention",
+                        "sliding_attention", "sliding_attention", "sliding_attention", "full_attention",
+                        "sliding_attention", "sliding_attention", "sliding_attention", "sliding_attention",
+                        "sliding_attention", "full_attention", "sliding_attention", "sliding_attention",
+                        "sliding_attention", "sliding_attention", "sliding_attention", "full_attention",
+                        "sliding_attention", "sliding_attention", "sliding_attention", "sliding_attention",
+                        "sliding_attention", "full_attention", "sliding_attention", "sliding_attention",
+                        "sliding_attention", "sliding_attention", "sliding_attention", "full_attention",
+                        "sliding_attention", "sliding_attention", "sliding_attention", "sliding_attention",
+                        "sliding_attention", "full_attention", "sliding_attention", "sliding_attention",
+                        "sliding_attention", "sliding_attention", "sliding_attention", "full_attention"
+                    ],
+                    "max_position_embeddings": 131072,
+                    "model_type": "gemma3_text",
+                    "num_attention_heads": 16,
+                    "num_hidden_layers": 48,
+                    "num_key_value_heads": 8,
+                    "query_pre_attn_scalar": 256,
+                    "rms_norm_eps": 1e-06,
+                    "rope_local_base_freq": 10000,
+                    "rope_scaling": {
+                        "factor": 8.0,
+                        "rope_type": "linear"
+                    },
+                    "rope_theta": 1000000,
+                    "sliding_window": 1024,
+                    "sliding_window_pattern": 6,
+                    "use_bidirectional_attention": False,
+                    "use_cache": True,
+                    "vocab_size": 262208
+                },
+                "transformers_version": "4.57.3",
+                "vision_config": {
+                    "attention_dropout": 0.0,
+                    "dtype": "bfloat16",
+                    "hidden_act": "gelu_pytorch_tanh",
+                    "hidden_size": 1152,
+                    "image_size": 896,
+                    "intermediate_size": 4304,
+                    "layer_norm_eps": 1e-06,
+                    "model_type": "siglip_vision_model",
+                    "num_attention_heads": 16,
+                    "num_channels": 3,
+                    "num_hidden_layers": 27,
+                    "patch_size": 14,
+                    "vision_use_head": False
+                }
+            })
+        super().__init__(config)
+
+
+class LTXVGemmaTokenizer:
+    """
+    Tokenizer wrapper for Gemma models compatible with LTXV processes.
+    This class wraps HuggingFace's `AutoTokenizer` for use with Gemma text encoders,
+    ensuring correct settings and output formatting for downstream consumption.
+    """
+
+    def __init__(self, tokenizer_path: str, max_length: int = 1024):
+        """
+        Initialize the tokenizer.
+        Args:
+            tokenizer_path (str): Path to the pretrained tokenizer files or model directory.
+            max_length (int, optional): Max sequence length for encoding. Defaults to 256.
+        """
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            tokenizer_path, local_files_only=True, model_max_length=max_length
+        )
+        # Gemma expects left padding for chat-style prompts; for plain text it doesn't matter much.
+        self.tokenizer.padding_side = "left"
+        if self.tokenizer.pad_token is None:
+            self.tokenizer.pad_token = self.tokenizer.eos_token
+
+        self.max_length = max_length
+
+    def tokenize_with_weights(self, text: str, return_word_ids: bool = False) -> dict[str, list[tuple[int, int]]]:
+        """
+        Tokenize the given text and return token IDs and attention weights.
+        Args:
+            text (str): The input string to tokenize.
+            return_word_ids (bool, optional): If True, includes the token's position (index) in the output tuples.
+                                              If False (default), omits the indices.
+        Returns:
+            dict[str, list[tuple[int, int]]] OR dict[str, list[tuple[int, int, int]]]:
+                A dictionary with a "gemma" key mapping to:
+                    - a list of (token_id, attention_mask) tuples if return_word_ids is False;
+                    - a list of (token_id, attention_mask, index) tuples if return_word_ids is True.
+        Example:
+            >>> tokenizer = LTXVGemmaTokenizer("path/to/tokenizer", max_length=8)
+            >>> tokenizer.tokenize_with_weights("hello world")
+            {'gemma': [(1234, 1), (5678, 1), (2, 0), ...]}
+        """
+        text = text.strip()
+        encoded = self.tokenizer(
+            text,
+            padding="max_length",
+            max_length=self.max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        input_ids = encoded.input_ids
+        attention_mask = encoded.attention_mask
+        tuples = [
+            (token_id, attn, i) for i, (token_id, attn) in enumerate(zip(input_ids[0], attention_mask[0], strict=True))
+        ]
+        out = {"gemma": tuples}
+
+        if not return_word_ids:
+            # Return only (token_id, attention_mask) pairs, omitting token position
+            out = {k: [(t, w) for t, w, _ in v] for k, v in out.items()}
+
+        return out
+
+
+class GemmaFeaturesExtractorProjLinear(torch.nn.Module):
+    """
+    Feature extractor module for Gemma models.
+    This module applies a single linear projection to the input tensor.
+    It expects a flattened feature tensor of shape (batch_size, 3840*49).
+    The linear layer maps this to a (batch_size, 3840) embedding.
+    Attributes:
+        aggregate_embed (torch.nn.Linear): Linear projection layer.
+    """
+
+    def __init__(self) -> None:
+        """
+        Initialize the GemmaFeaturesExtractorProjLinear module.
+        The input dimension is expected to be 3840 * 49, and the output is 3840.
+        """
+        super().__init__()
+        self.aggregate_embed = torch.nn.Linear(3840 * 49, 3840, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass for the feature extractor.
+        Args:
+            x (torch.Tensor): Input tensor of shape (batch_size, 3840 * 49).
+        Returns:
+            torch.Tensor: Output tensor of shape (batch_size, 3840).
+        """
+        return self.aggregate_embed(x)
+
+
+class _BasicTransformerBlock1D(torch.nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        heads: int,
+        dim_head: int,
+        rope_type: LTXRopeType = LTXRopeType.INTERLEAVED,
+    ):
+        super().__init__()
+
+        self.attn1 = Attention(
+            query_dim=dim,
+            heads=heads,
+            dim_head=dim_head,
+            rope_type=rope_type,
+        )
+
+        self.ff = FeedForward(
+            dim,
+            dim_out=dim,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        pe: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        # Notice that normalization is always applied before the real computation in the following blocks.
+
+        # 1. Normalization Before Self-Attention
+        norm_hidden_states = rms_norm(hidden_states)
+
+        norm_hidden_states = norm_hidden_states.squeeze(1)
+
+        # 2. Self-Attention
+        attn_output = self.attn1(norm_hidden_states, mask=attention_mask, pe=pe)
+
+        hidden_states = attn_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        # 3. Normalization before Feed-Forward
+        norm_hidden_states = rms_norm(hidden_states)
+
+        # 4. Feed-forward
+        ff_output = self.ff(norm_hidden_states)
+
+        hidden_states = ff_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        return hidden_states
+
+
+class Embeddings1DConnector(torch.nn.Module):
+    """
+    Embeddings1DConnector applies a 1D transformer-based processing to sequential embeddings (e.g., for video, audio, or
+    other modalities). It supports rotary positional encoding (rope), optional causal temporal positioning, and can
+    substitute padded positions with learnable registers. The module is highly configurable for head size, number of
+    layers, and register usage.
+    Args:
+        attention_head_dim (int): Dimension of each attention head (default=128).
+        num_attention_heads (int): Number of attention heads (default=30).
+        num_layers (int): Number of transformer layers (default=2).
+        positional_embedding_theta (float): Scaling factor for position embedding (default=10000.0).
+        positional_embedding_max_pos (list[int] | None): Max positions for positional embeddings (default=[1]).
+        causal_temporal_positioning (bool): If True, uses causal attention (default=False).
+        num_learnable_registers (int | None): Number of learnable registers to replace padded tokens. If None, disables
+            register replacement. (default=128)
+        rope_type (LTXRopeType): The RoPE variant to use (default=DEFAULT_ROPE_TYPE).
+        double_precision_rope (bool): Use double precision rope calculation (default=False).
+    """
+
+    _supports_gradient_checkpointing = True
+
+    def __init__(
+        self,
+        attention_head_dim: int = 128,
+        num_attention_heads: int = 30,
+        num_layers: int = 2,
+        positional_embedding_theta: float = 10000.0,
+        positional_embedding_max_pos: list[int] | None = [4096],
+        causal_temporal_positioning: bool = False,
+        num_learnable_registers: int | None = 128,
+        rope_type: LTXRopeType = LTXRopeType.SPLIT,
+        double_precision_rope: bool = True,
+    ):
+        super().__init__()
+        self.num_attention_heads = num_attention_heads
+        self.inner_dim = num_attention_heads * attention_head_dim
+        self.causal_temporal_positioning = causal_temporal_positioning
+        self.positional_embedding_theta = positional_embedding_theta
+        self.positional_embedding_max_pos = (
+            positional_embedding_max_pos if positional_embedding_max_pos is not None else [1]
+        )
+        self.rope_type = rope_type
+        self.double_precision_rope = double_precision_rope
+        self.transformer_1d_blocks = torch.nn.ModuleList(
+            [
+                _BasicTransformerBlock1D(
+                    dim=self.inner_dim,
+                    heads=num_attention_heads,
+                    dim_head=attention_head_dim,
+                    rope_type=rope_type,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        self.num_learnable_registers = num_learnable_registers
+        if self.num_learnable_registers:
+            self.learnable_registers = torch.nn.Parameter(
+                torch.rand(self.num_learnable_registers, self.inner_dim, dtype=torch.bfloat16) * 2.0 - 1.0
+            )
+
+    def _replace_padded_with_learnable_registers(
+        self, hidden_states: torch.Tensor, attention_mask: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        assert hidden_states.shape[1] % self.num_learnable_registers == 0, (
+            f"Hidden states sequence length {hidden_states.shape[1]} must be divisible by num_learnable_registers "
+            f"{self.num_learnable_registers}."
+        )
+
+        num_registers_duplications = hidden_states.shape[1] // self.num_learnable_registers
+        learnable_registers = torch.tile(self.learnable_registers, (num_registers_duplications, 1))
+        attention_mask_binary = (attention_mask.squeeze(1).squeeze(1).unsqueeze(-1) >= -9000.0).int()
+
+        non_zero_hidden_states = hidden_states[:, attention_mask_binary.squeeze().bool(), :]
+        non_zero_nums = non_zero_hidden_states.shape[1]
+        pad_length = hidden_states.shape[1] - non_zero_nums
+        adjusted_hidden_states = torch.nn.functional.pad(non_zero_hidden_states, pad=(0, 0, 0, pad_length), value=0)
+        flipped_mask = torch.flip(attention_mask_binary, dims=[1])
+        hidden_states = flipped_mask * adjusted_hidden_states + (1 - flipped_mask) * learnable_registers
+
+        attention_mask = torch.full_like(
+            attention_mask,
+            0.0,
+            dtype=attention_mask.dtype,
+            device=attention_mask.device,
+        )
+
+        return hidden_states, attention_mask
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Forward pass of Embeddings1DConnector.
+        Args:
+            hidden_states (torch.Tensor): Input tensor of embeddings (shape [batch, seq_len, feature_dim]).
+            attention_mask (torch.Tensor|None): Optional mask for valid tokens (shape compatible with hidden_states).
+        Returns:
+            tuple[torch.Tensor, torch.Tensor]: Processed features and the corresponding (possibly modified) mask.
+        """
+        if self.num_learnable_registers:
+            hidden_states, attention_mask = self._replace_padded_with_learnable_registers(hidden_states, attention_mask)
+
+        indices_grid = torch.arange(hidden_states.shape[1], dtype=torch.float32, device=hidden_states.device)
+        indices_grid = indices_grid[None, None, :]
+        freq_grid_generator = generate_freq_grid_np if self.double_precision_rope else generate_freq_grid_pytorch
+        freqs_cis = precompute_freqs_cis(
+            indices_grid=indices_grid,
+            dim=self.inner_dim,
+            out_dtype=hidden_states.dtype,
+            theta=self.positional_embedding_theta,
+            max_pos=self.positional_embedding_max_pos,
+            num_attention_heads=self.num_attention_heads,
+            rope_type=self.rope_type,
+            freq_grid_generator=freq_grid_generator,
+        )
+
+        for block in self.transformer_1d_blocks:
+            hidden_states = block(hidden_states, attention_mask=attention_mask, pe=freqs_cis)
+
+        hidden_states = rms_norm(hidden_states)
+
+        return hidden_states, attention_mask
+
+
+class LTX2TextEncoderPostModules(torch.nn.Module):
+    def __init__(self,):
+        super().__init__()
+        self.feature_extractor_linear = GemmaFeaturesExtractorProjLinear()
+        self.embeddings_connector = Embeddings1DConnector()
+        self.audio_embeddings_connector = Embeddings1DConnector()

diffsynth/models/ltx2_upsampler.py

@@ -0,0 +1,313 @@
+import math
+from typing import Optional, Tuple
+import torch
+from einops import rearrange
+import torch.nn.functional as F
+from .ltx2_video_vae import LTX2VideoEncoder
+
+class PixelShuffleND(torch.nn.Module):
+    """
+    N-dimensional pixel shuffle operation for upsampling tensors.
+    Args:
+        dims (int): Number of dimensions to apply pixel shuffle to.
+            - 1: Temporal (e.g., frames)
+            - 2: Spatial (e.g., height and width)
+            - 3: Spatiotemporal (e.g., depth, height, width)
+        upscale_factors (tuple[int, int, int], optional): Upscaling factors for each dimension.
+            For dims=1, only the first value is used.
+            For dims=2, the first two values are used.
+            For dims=3, all three values are used.
+    The input tensor is rearranged so that the channel dimension is split into
+    smaller channels and upscaling factors, and the upscaling factors are moved
+    into the corresponding spatial/temporal dimensions.
+    Note:
+    This operation is equivalent to the patchifier operation in for the models. Consider
+    using this class instead.
+    """
+
+    def __init__(self, dims: int, upscale_factors: tuple[int, int, int] = (2, 2, 2)):
+        super().__init__()
+        assert dims in [1, 2, 3], "dims must be 1, 2, or 3"
+        self.dims = dims
+        self.upscale_factors = upscale_factors
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.dims == 3:
+            return rearrange(
+                x,
+                "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)",
+                p1=self.upscale_factors[0],
+                p2=self.upscale_factors[1],
+                p3=self.upscale_factors[2],
+            )
+        elif self.dims == 2:
+            return rearrange(
+                x,
+                "b (c p1 p2) h w -> b c (h p1) (w p2)",
+                p1=self.upscale_factors[0],
+                p2=self.upscale_factors[1],
+            )
+        elif self.dims == 1:
+            return rearrange(
+                x,
+                "b (c p1) f h w -> b c (f p1) h w",
+                p1=self.upscale_factors[0],
+            )
+        else:
+            raise ValueError(f"Unsupported dims: {self.dims}")
+
+
+class ResBlock(torch.nn.Module):
+    """
+    Residual block with two convolutional layers, group normalization, and SiLU activation.
+    Args:
+        channels (int): Number of input and output channels.
+        mid_channels (Optional[int]): Number of channels in the intermediate convolution layer. Defaults to `channels`
+        if not specified.
+        dims (int): Dimensionality of the convolution (2 for Conv2d, 3 for Conv3d). Defaults to 3.
+    """
+
+    def __init__(self, channels: int, mid_channels: Optional[int] = None, dims: int = 3):
+        super().__init__()
+        if mid_channels is None:
+            mid_channels = channels
+
+        conv = torch.nn.Conv2d if dims == 2 else torch.nn.Conv3d
+
+        self.conv1 = conv(channels, mid_channels, kernel_size=3, padding=1)
+        self.norm1 = torch.nn.GroupNorm(32, mid_channels)
+        self.conv2 = conv(mid_channels, channels, kernel_size=3, padding=1)
+        self.norm2 = torch.nn.GroupNorm(32, channels)
+        self.activation = torch.nn.SiLU()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        residual = x
+        x = self.conv1(x)
+        x = self.norm1(x)
+        x = self.activation(x)
+        x = self.conv2(x)
+        x = self.norm2(x)
+        x = self.activation(x + residual)
+        return x
+
+
+class BlurDownsample(torch.nn.Module):
+    """
+    Anti-aliased spatial downsampling by integer stride using a fixed separable binomial kernel.
+    Applies only on H,W. Works for dims=2 or dims=3 (per-frame).
+    """
+
+    def __init__(self, dims: int, stride: int, kernel_size: int = 5) -> None:
+        super().__init__()
+        assert dims in (2, 3)
+        assert isinstance(stride, int)
+        assert stride >= 1
+        assert kernel_size >= 3
+        assert kernel_size % 2 == 1
+        self.dims = dims
+        self.stride = stride
+        self.kernel_size = kernel_size
+
+        # 5x5 separable binomial kernel using binomial coefficients [1, 4, 6, 4, 1] from
+        # the 4th row of Pascal's triangle. This kernel is used for anti-aliasing and
+        # provides a smooth approximation of a Gaussian filter (often called a "binomial filter").
+        # The 2D kernel is constructed as the outer product and normalized.
+        k = torch.tensor([math.comb(kernel_size - 1, k) for k in range(kernel_size)])
+        k2d = k[:, None] @ k[None, :]
+        k2d = (k2d / k2d.sum()).float()  # shape (kernel_size, kernel_size)
+        self.register_buffer("kernel", k2d[None, None, :, :])  # (1, 1, kernel_size, kernel_size)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.stride == 1:
+            return x
+
+        if self.dims == 2:
+            return self._apply_2d(x)
+        else:
+            # dims == 3: apply per-frame on H,W
+            b, _, f, _, _ = x.shape
+            x = rearrange(x, "b c f h w -> (b f) c h w")
+            x = self._apply_2d(x)
+            h2, w2 = x.shape[-2:]
+            x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f, h=h2, w=w2)
+            return x
+
+    def _apply_2d(self, x2d: torch.Tensor) -> torch.Tensor:
+        c = x2d.shape[1]
+        weight = self.kernel.expand(c, 1, self.kernel_size, self.kernel_size)  # depthwise
+        x2d = F.conv2d(x2d, weight=weight, bias=None, stride=self.stride, padding=self.kernel_size // 2, groups=c)
+        return x2d
+
+
+def _rational_for_scale(scale: float) -> Tuple[int, int]:
+    mapping = {0.75: (3, 4), 1.5: (3, 2), 2.0: (2, 1), 4.0: (4, 1)}
+    if float(scale) not in mapping:
+        raise ValueError(f"Unsupported scale {scale}. Choose from {list(mapping.keys())}")
+    return mapping[float(scale)]
+
+
+class SpatialRationalResampler(torch.nn.Module):
+    """
+    Fully-learned rational spatial scaling: up by 'num' via PixelShuffle, then anti-aliased
+    downsample by 'den' using fixed blur + stride. Operates on H,W only.
+    For dims==3, work per-frame for spatial scaling (temporal axis untouched).
+    Args:
+        mid_channels (`int`): Number of intermediate channels for the convolution layer
+        scale (`float`): Spatial scaling factor. Supported values are:
+            - 0.75: Downsample by 3/4 (reduce spatial size)
+            - 1.5: Upsample by 3/2 (increase spatial size)
+            - 2.0: Upsample by 2x (double spatial size)
+            - 4.0: Upsample by 4x (quadruple spatial size)
+            Any other value will raise a ValueError.
+    """
+
+    def __init__(self, mid_channels: int, scale: float):
+        super().__init__()
+        self.scale = float(scale)
+        self.num, self.den = _rational_for_scale(self.scale)
+        self.conv = torch.nn.Conv2d(mid_channels, (self.num**2) * mid_channels, kernel_size=3, padding=1)
+        self.pixel_shuffle = PixelShuffleND(2, upscale_factors=(self.num, self.num))
+        self.blur_down = BlurDownsample(dims=2, stride=self.den)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        b, _, f, _, _ = x.shape
+        x = rearrange(x, "b c f h w -> (b f) c h w")
+        x = self.conv(x)
+        x = self.pixel_shuffle(x)
+        x = self.blur_down(x)
+        x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f)
+        return x
+
+
+class LTX2LatentUpsampler(torch.nn.Module):
+    """
+    Model to upsample VAE latents spatially and/or temporally.
+    Args:
+        in_channels (`int`): Number of channels in the input latent
+        mid_channels (`int`): Number of channels in the middle layers
+        num_blocks_per_stage (`int`): Number of ResBlocks to use in each stage (pre/post upsampling)
+        dims (`int`): Number of dimensions for convolutions (2 or 3)
+        spatial_upsample (`bool`): Whether to spatially upsample the latent
+        temporal_upsample (`bool`): Whether to temporally upsample the latent
+        spatial_scale (`float`): Scale factor for spatial upsampling
+        rational_resampler (`bool`): Whether to use a rational resampler for spatial upsampling
+    """
+    def __init__(
+        self,
+        in_channels: int = 128,
+        mid_channels: int = 1024,
+        num_blocks_per_stage: int = 4,
+        dims: int = 3,
+        spatial_upsample: bool = True,
+        temporal_upsample: bool = False,
+        spatial_scale: float = 2.0,
+        rational_resampler: bool = True,
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.mid_channels = mid_channels
+        self.num_blocks_per_stage = num_blocks_per_stage
+        self.dims = dims
+        self.spatial_upsample = spatial_upsample
+        self.temporal_upsample = temporal_upsample
+        self.spatial_scale = float(spatial_scale)
+        self.rational_resampler = rational_resampler
+
+        conv = torch.nn.Conv2d if dims == 2 else torch.nn.Conv3d
+
+        self.initial_conv = conv(in_channels, mid_channels, kernel_size=3, padding=1)
+        self.initial_norm = torch.nn.GroupNorm(32, mid_channels)
+        self.initial_activation = torch.nn.SiLU()
+
+        self.res_blocks = torch.nn.ModuleList([ResBlock(mid_channels, dims=dims) for _ in range(num_blocks_per_stage)])
+
+        if spatial_upsample and temporal_upsample:
+            self.upsampler = torch.nn.Sequential(
+                torch.nn.Conv3d(mid_channels, 8 * mid_channels, kernel_size=3, padding=1),
+                PixelShuffleND(3),
+            )
+        elif spatial_upsample:
+            if rational_resampler:
+                self.upsampler = SpatialRationalResampler(mid_channels=mid_channels, scale=self.spatial_scale)
+            else:
+                self.upsampler = torch.nn.Sequential(
+                    torch.nn.Conv2d(mid_channels, 4 * mid_channels, kernel_size=3, padding=1),
+                    PixelShuffleND(2),
+                )
+        elif temporal_upsample:
+            self.upsampler = torch.nn.Sequential(
+                torch.nn.Conv3d(mid_channels, 2 * mid_channels, kernel_size=3, padding=1),
+                PixelShuffleND(1),
+            )
+        else:
+            raise ValueError("Either spatial_upsample or temporal_upsample must be True")
+
+        self.post_upsample_res_blocks = torch.nn.ModuleList(
+            [ResBlock(mid_channels, dims=dims) for _ in range(num_blocks_per_stage)]
+        )
+
+        self.final_conv = conv(mid_channels, in_channels, kernel_size=3, padding=1)
+
+    def forward(self, latent: torch.Tensor) -> torch.Tensor:
+        b, _, f, _, _ = latent.shape
+
+        if self.dims == 2:
+            x = rearrange(latent, "b c f h w -> (b f) c h w")
+            x = self.initial_conv(x)
+            x = self.initial_norm(x)
+            x = self.initial_activation(x)
+
+            for block in self.res_blocks:
+                x = block(x)
+
+            x = self.upsampler(x)
+
+            for block in self.post_upsample_res_blocks:
+                x = block(x)
+
+            x = self.final_conv(x)
+            x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f)
+        else:
+            x = self.initial_conv(latent)
+            x = self.initial_norm(x)
+            x = self.initial_activation(x)
+
+            for block in self.res_blocks:
+                x = block(x)
+
+            if self.temporal_upsample:
+                x = self.upsampler(x)
+                # remove the first frame after upsampling.
+                # This is done because the first frame encodes one pixel frame.
+                x = x[:, :, 1:, :, :]
+            elif isinstance(self.upsampler, SpatialRationalResampler):
+                x = self.upsampler(x)
+            else:
+                x = rearrange(x, "b c f h w -> (b f) c h w")
+                x = self.upsampler(x)
+                x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f)
+
+            for block in self.post_upsample_res_blocks:
+                x = block(x)
+
+            x = self.final_conv(x)
+
+        return x
+
+
+def upsample_video(latent: torch.Tensor, video_encoder: LTX2VideoEncoder, upsampler: "LTX2LatentUpsampler") -> torch.Tensor:
+    """
+    Apply upsampling to the latent representation using the provided upsampler,
+    with normalization and un-normalization based on the video encoder's per-channel statistics.
+    Args:
+        latent: Input latent tensor of shape [B, C, F, H, W].
+        video_encoder: VideoEncoder with per_channel_statistics for normalization.
+        upsampler: LTX2LatentUpsampler module to perform upsampling.
+    Returns:
+        torch.Tensor: Upsampled and re-normalized latent tensor.
+    """
+    latent = video_encoder.per_channel_statistics.un_normalize(latent)
+    latent = upsampler(latent)
+    latent = video_encoder.per_channel_statistics.normalize(latent)
+    return latent

diffsynth/models/model_loader.py

@@ -0,0 +1,113 @@
+from ..core.loader import load_model, hash_model_file
+from ..core.vram import AutoWrappedModule
+from ..configs import MODEL_CONFIGS, VRAM_MANAGEMENT_MODULE_MAPS, VERSION_CHECKER_MAPS
+import importlib, json, torch
+
+
+class ModelPool:
+    def __init__(self):
+        self.model = []
+        self.model_name = []
+        self.model_path = []
+        
+    def import_model_class(self, model_class):
+        split = model_class.rfind(".")
+        model_resource, model_class = model_class[:split], model_class[split+1:]
+        model_class = importlib.import_module(model_resource).__getattribute__(model_class)
+        return model_class
+    
+    def need_to_enable_vram_management(self, vram_config):
+        return vram_config["offload_dtype"] is not None and vram_config["offload_device"] is not None
+    
+    def fetch_module_map(self, model_class, vram_config):
+        if self.need_to_enable_vram_management(vram_config):
+            if model_class in VRAM_MANAGEMENT_MODULE_MAPS:
+                vram_module_map = VRAM_MANAGEMENT_MODULE_MAPS[model_class] if model_class not in VERSION_CHECKER_MAPS else VERSION_CHECKER_MAPS[model_class]()
+                module_map = {self.import_model_class(source): self.import_model_class(target) for source, target in vram_module_map.items()}
+            else:
+                module_map = {self.import_model_class(model_class): AutoWrappedModule}
+        else:
+            module_map = None
+        return module_map
+    
+    def load_model_file(self, config, path, vram_config, vram_limit=None, state_dict=None):
+        model_class = self.import_model_class(config["model_class"])
+        model_config = config.get("extra_kwargs", {})
+        if "state_dict_converter" in config:
+            state_dict_converter = self.import_model_class(config["state_dict_converter"])
+        else:
+            state_dict_converter = None
+        module_map = self.fetch_module_map(config["model_class"], vram_config)
+        model = load_model(
+            model_class, path, model_config,
+            vram_config["computation_dtype"], vram_config["computation_device"],
+            state_dict_converter,
+            use_disk_map=True,
+            vram_config=vram_config, module_map=module_map, vram_limit=vram_limit,
+            state_dict=state_dict,
+        )
+        return model
+    
+    def default_vram_config(self):
+        vram_config = {
+            "offload_dtype": None,
+            "offload_device": None,
+            "onload_dtype": torch.bfloat16,
+            "onload_device": "cpu",
+            "preparing_dtype": torch.bfloat16,
+            "preparing_device": "cpu",
+            "computation_dtype": torch.bfloat16,
+            "computation_device": "cpu",
+        }
+        return vram_config
+    
+    def auto_load_model(self, path, vram_config=None, vram_limit=None, clear_parameters=False, state_dict=None):
+        print(f"Loading models from: {json.dumps(path, indent=4)}")
+        if vram_config is None:
+            vram_config = self.default_vram_config()
+        model_hash = hash_model_file(path)
+        loaded = False
+        for config in MODEL_CONFIGS:
+            if config["model_hash"] == model_hash:
+                model = self.load_model_file(config, path, vram_config, vram_limit=vram_limit, state_dict=state_dict)
+                if clear_parameters: self.clear_parameters(model)
+                self.model.append(model)
+                model_name = config["model_name"]
+                self.model_name.append(model_name)
+                self.model_path.append(path)
+                model_info = {"model_name": model_name, "model_class": config["model_class"], "extra_kwargs": config.get("extra_kwargs")}
+                print(f"Loaded model: {json.dumps(model_info, indent=4)}")
+                loaded = True
+        if not loaded:
+            raise ValueError(f"Cannot detect the model type. File: {path}. Model hash: {model_hash}")
+    
+    def fetch_model(self, model_name, index=None):
+        fetched_models = []
+        fetched_model_paths = []
+        for model, model_path, model_name_ in zip(self.model, self.model_path, self.model_name):
+            if model_name == model_name_:
+                fetched_models.append(model)
+                fetched_model_paths.append(model_path)
+        if len(fetched_models) == 0:
+            print(f"No {model_name} models available. This is not an error.")
+            model = None
+        elif len(fetched_models) == 1:
+            print(f"Using {model_name} from {json.dumps(fetched_model_paths[0], indent=4)}.")
+            model = fetched_models[0]
+        else:
+            if index is None:
+                model = fetched_models[0]
+                print(f"More than one {model_name} models are loaded: {fetched_model_paths}. Using {model_name} from {json.dumps(fetched_model_paths[0], indent=4)}.")
+            elif isinstance(index, int):
+                model = fetched_models[:index]
+                print(f"More than one {model_name} models are loaded: {fetched_model_paths}. Using {model_name} from {json.dumps(fetched_model_paths[:index], indent=4)}.")
+            else:
+                model = fetched_models
+                print(f"More than one {model_name} models are loaded: {fetched_model_paths}. Using {model_name} from {json.dumps(fetched_model_paths, indent=4)}.")
+        return model
+
+    def clear_parameters(self, model: torch.nn.Module):
+        for name, module in model.named_children():
+            self.clear_parameters(module)
+        for name, param in model.named_parameters(recurse=False):
+            setattr(model, name, None)

diffsynth/models/nexus_gen.py

@@ -0,0 +1,161 @@
+import torch
+from PIL import Image
+
+
+class NexusGenAutoregressiveModel(torch.nn.Module):
+    def __init__(self, max_length=1024, max_pixels=262640):
+        super(NexusGenAutoregressiveModel, self).__init__()
+        from .nexus_gen_ar_model import Qwen2_5_VLForConditionalGeneration
+        from transformers import Qwen2_5_VLConfig
+        self.max_length = max_length
+        self.max_pixels = max_pixels
+        model_config = Qwen2_5_VLConfig(**{
+            "_name_or_path": "DiffSynth-Studio/Nexus-GenV2",
+            "architectures": [
+                "Qwen2_5_VLForConditionalGeneration"
+            ],
+            "attention_dropout": 0.0,
+            "auto_map": {
+                "AutoConfig": "configuration_qwen2_5_vl.Qwen2_5_VLConfig",
+                "AutoModel": "modeling_qwen2_5_vl.Qwen2_5_VLModel",
+                "AutoModelForCausalLM": "modeling_qwen2_5_vl.Qwen2_5_VLForConditionalGeneration"
+            },
+            "bos_token_id": 151643,
+            "eos_token_id": 151645,
+            "hidden_act": "silu",
+            "hidden_size": 3584,
+            "image_token_id": 151655,
+            "initializer_range": 0.02,
+            "intermediate_size": 18944,
+            "max_position_embeddings": 128000,
+            "max_window_layers": 28,
+            "model_type": "qwen2_5_vl",
+            "num_attention_heads": 28,
+            "num_hidden_layers": 28,
+            "num_key_value_heads": 4,
+            "pad_token_id": 151643,
+            "rms_norm_eps": 1e-06,
+            "rope_scaling": {
+                "mrope_section": [
+                16,
+                24,
+                24
+                ],
+                "rope_type": "default",
+                "type": "default"
+            },
+            "rope_theta": 1000000.0,
+            "sliding_window": 32768,
+            "tie_word_embeddings": False,
+            "torch_dtype": "bfloat16",
+            "transformers_version": "4.49.0",
+            "use_cache": False,
+            "use_sliding_window": False,
+            "video_token_id": 151656,
+            "vision_config": {
+                "hidden_size": 1280,
+                "in_chans": 3,
+                "model_type": "qwen2_5_vl",
+                "spatial_patch_size": 14,
+                "tokens_per_second": 2,
+                "torch_dtype": "bfloat16"
+            },
+            "vision_end_token_id": 151653,
+            "vision_start_token_id": 151652,
+            "vision_token_id": 151654,
+            "vocab_size": 152064
+        })
+        self.model = Qwen2_5_VLForConditionalGeneration(model_config)
+        self.processor = None
+        
+        
+    def load_processor(self, path):
+        from .nexus_gen_ar_model import Qwen2_5_VLProcessor
+        self.processor = Qwen2_5_VLProcessor.from_pretrained(path)
+
+
+    @staticmethod
+    def state_dict_converter():
+        return NexusGenAutoregressiveModelStateDictConverter()
+
+    def bound_image(self, image, max_pixels=262640):
+        from qwen_vl_utils import smart_resize
+        resized_height, resized_width = smart_resize(
+            image.height,
+            image.width,
+            max_pixels=max_pixels,
+        )
+        return image.resize((resized_width, resized_height))
+
+    def get_editing_msg(self, instruction):
+        if '<image>' not in instruction:
+            instruction = '<image> ' + instruction
+        messages = [{"role":"user", "content":instruction}, {"role":"assistant", "content":"Here is the image: <image>"}]
+        return messages
+
+    def get_generation_msg(self, instruction):
+        instruction = "Generate an image according to the following description: {}".format(instruction)
+        messages = [{"role":"user", "content":instruction}, {"role":"assistant", "content":"Here is an image based on the description: <image>"}]
+        return messages
+
+    def forward(self, instruction, ref_image=None, num_img_tokens=81):
+        """
+        Generate target embeddings for the given instruction and reference image.
+        """
+        if ref_image is not None:
+            messages = self.get_editing_msg(instruction)
+            images = [self.bound_image(ref_image)] + [Image.new(mode='RGB', size=(252, 252), color=(255, 255, 255))]
+            output_image_embeddings = self.get_target_embeddings(images, messages, self.processor, self.model, num_img_tokens)
+        else:
+            messages = self.get_generation_msg(instruction)
+            images = [Image.new(mode='RGB', size=(252, 252), color=(255, 255, 255))]
+            output_image_embeddings = self.get_target_embeddings(images, messages, self.processor, self.model, num_img_tokens)
+
+        return output_image_embeddings
+
+    def get_target_embeddings(self, images, messages, processor, model, num_img_tokens=81):
+        text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
+        text = text.replace('<image>', '<|vision_start|><|image_pad|><|vision_end|>')
+        inputs = processor(
+            text=[text],
+            images=images,
+            padding=True,
+            return_tensors="pt",
+        )
+        inputs = inputs.to(model.device)
+
+        input_embeds = model.model.embed_tokens(inputs['input_ids'])
+        image_embeds = model.visual(inputs['pixel_values'], grid_thw=inputs['image_grid_thw'])
+        ground_truth_image_embeds = image_embeds[-num_img_tokens:]
+        input_image_embeds = image_embeds[:-num_img_tokens]
+
+        image_mask = inputs['input_ids'] == model.config.image_token_id
+        indices = image_mask.cumsum(dim=1)
+        input_image_mask = torch.logical_and(indices <= (image_embeds.shape[0] - ground_truth_image_embeds.shape[0]), image_mask)
+        gt_image_mask = torch.logical_and(image_mask, ~input_image_mask)
+        input_image_mask = input_image_mask.unsqueeze(-1).expand_as(input_embeds)
+        input_embeds = input_embeds.masked_scatter(input_image_mask, input_image_embeds)
+
+        image_prefill_embeds = model.image_prefill_embeds(
+            torch.arange(81, device=model.device).long()
+        )
+        input_embeds = input_embeds.masked_scatter(gt_image_mask.unsqueeze(-1).expand_as(input_embeds), image_prefill_embeds)
+
+        position_ids, _ = model.get_rope_index(
+            inputs['input_ids'],
+            inputs['image_grid_thw'],
+            attention_mask=inputs['attention_mask'])
+        position_ids = position_ids.contiguous()
+        outputs = model(inputs_embeds=input_embeds, position_ids=position_ids, attention_mask=inputs['attention_mask'], return_dict=True)
+        output_image_embeddings = outputs.image_embeddings[:, :-1, :]
+        output_image_embeddings = output_image_embeddings[gt_image_mask[:, 1:]]
+        return output_image_embeddings, input_image_embeds, inputs['image_grid_thw']
+
+
+class NexusGenAutoregressiveModelStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        state_dict = {"model." + key: value for key, value in state_dict.items()}
+        return state_dict