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exp_code/1_benchmark/CausVid/tests/wan/test_causal_diffusion_timestep_breakdown.py

@@ -0,0 +1,54 @@
+from causvid.models.wan.wan_wrapper import WanDiffusionWrapper, WanTextEncoder, WanVAEWrapper
+from causvid.models.wan.flow_match import FlowMatchScheduler
+from diffusers.utils import export_to_video
+import torch
+
+torch.set_grad_enabled(False)
+
+model = WanDiffusionWrapper().to("cuda").to(torch.bfloat16)
+encoder = WanTextEncoder().to("cuda").to(torch.bfloat16)
+vae = WanVAEWrapper().to("cuda").to(torch.bfloat16)
+
+model.set_module_grad(
+    {
+        "model": False
+    }
+)
+
+text_prompts = [r"""A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about."""] * 2
+
+conditional_dict = encoder(
+    text_prompts=text_prompts
+)
+
+device = "cuda"
+
+noise = torch.randn(
+    1, 21, 16, 60, 104, generator=torch.Generator().manual_seed(42), dtype=torch.float32
+).to("cuda")
+
+scheduler = FlowMatchScheduler(shift=8.0, sigma_min=0.0, extra_one_step=True)
+scheduler.set_timesteps(1000, denoising_strength=1.0)
+scheduler.sigmas = scheduler.sigmas.to(device)
+
+data = torch.load("wan_moviegen_ode/00000.pt")
+prompt = list(data.keys())[0]
+video_latent = data[prompt][:, -1].cuda().to(torch.bfloat16)
+
+for timestep in [100, 200, 300, 400, 500, 600, 700, 800, 900]:
+    timestep = timestep * \
+        torch.ones([1, 21], device="cuda", dtype=torch.float32)
+
+    noisy_latent = scheduler.add_noise(
+        video_latent.flatten(0, 1),
+        noise.flatten(0, 1),
+        timestep.flatten(0, 1)
+    ).unflatten(0, noise.shape[:2]).type_as(video_latent)
+
+    output = model(noisy_latent, conditional_dict, timestep)
+
+    video = vae.decode_to_pixel(output)
+    video = (video * 0.5 + 0.5).cpu().detach().to(torch.float32)[0].permute(0, 2, 3, 1).numpy()
+
+    export_to_video(
+        video, f"one_stpe_output_t={timestep[0, 0].item()}.mp4", fps=16)

exp_code/1_benchmark/CausVid/tests/wan/test_diffusion.py

@@ -0,0 +1,35 @@
+from causvid.models.wan.wan_wrapper import WanDiffusionWrapper, WanTextEncoder, WanVAEWrapper
+from diffusers.utils import export_to_video
+import torch
+
+torch.set_grad_enabled(False)
+
+model = WanDiffusionWrapper().to("cuda").to(torch.float32)
+encoder = WanTextEncoder().to("cuda").to(torch.float32)
+vae = WanVAEWrapper().to("cuda").to(torch.float32)
+
+model.set_module_grad(
+    {
+        "model": False
+    }
+)
+
+text_prompts = [r"""A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about."""]
+
+conditional_dict = encoder(
+    text_prompts=text_prompts
+)
+
+noise = torch.randn(
+    1, 21, 16, 60, 104, generator=torch.Generator().manual_seed(42), dtype=torch.float32
+).to("cuda")
+
+timetep = 999 * torch.ones([1, 21], device="cuda", dtype=torch.long)
+
+with torch.no_grad():
+    output = model(noise, conditional_dict, timetep)
+    video = vae.decode_to_pixel(output)
+
+video = (video * 0.5 + 0.5).cpu().detach().to(torch.float32)[0].permute(0, 2, 3, 1).numpy()
+
+export_to_video(video, "output.mp4", fps=8)

exp_code/1_benchmark/CausVid/tests/wan/test_text_encoder.py

@@ -0,0 +1,16 @@
+from causvid.models.wan.wan_wrapper import WanTextEncoder
+import torch
+
+torch.set_grad_enabled(False)
+
+model = WanTextEncoder().to(device="cuda:0", dtype=torch.bfloat16)
+
+prompt_list = ["a " * 50] * 10 + ["b " * 25] * 10
+
+
+print("Test Text Encoder")
+
+encoded_dict = model(prompt_list)
+
+assert encoded_dict['prompt_embeds'].shape[0] == 20 and encoded_dict[
+    'prompt_embeds'].shape[1] == 512 and encoded_dict['prompt_embeds'].shape[2] == 4096

exp_code/1_benchmark/CausVid/tests/wan/test_wan_ode_pairs.py

@@ -0,0 +1,28 @@
+from causvid.models.wan.wan_wrapper import WanVAEWrapper
+from diffusers.utils import export_to_video
+import argparse
+import torch
+import glob
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--data_path", type=str, required=True)
+
+args = parser.parse_args()
+
+torch.set_grad_enabled(False)
+
+model = WanVAEWrapper().to(device="cuda:0", dtype=torch.bfloat16)
+
+
+for index, video_path in enumerate(sorted(glob.glob(args.data_path + "/*.pt"))):
+    data = torch.load(video_path)
+
+    prompt = list(data.keys())[0]
+
+    video_latent = data[prompt][:, -1].cuda().to(torch.bfloat16)
+
+    video = model.decode_to_pixel(video_latent)
+
+    video = (video * 0.5 + 0.5).clamp(0, 1)[0].permute(0, 2, 3, 1).cpu().numpy()
+    print(index, prompt)
+    export_to_video(video, f"ode_output_{index:03d}.mp4", fps=16)

exp_code/1_benchmark/CausVid/tests/wan/test_wan_teacher_wrapper_many_step_sampling.py

@@ -0,0 +1,90 @@
+from causvid.models.wan.wan_wrapper import WanDiffusionWrapper, WanTextEncoder, WanVAEWrapper
+from causvid.models.wan.flow_match import FlowMatchScheduler
+from diffusers.utils import export_to_video
+from causvid.data import TextDataset
+from tqdm import tqdm
+import torch
+import os
+
+torch.set_grad_enabled(False)
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+model = WanDiffusionWrapper().to("cuda").to(torch.float32)
+encoder = WanTextEncoder().to("cuda").to(torch.float32)
+vae = WanVAEWrapper().to("cuda").to(torch.float32)
+
+model.set_module_grad(
+    {
+        "model": False
+    }
+)
+
+dataset = TextDataset('sample_dataset/MovieGenVideoBench.txt')
+
+os.makedirs('wan_manystep_sampling', exist_ok=True)
+
+device = "cuda"
+num_train_timesteps = 1000
+sampling_steps = 50
+shift = 8
+
+sample_neg_prompt = '色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走'
+
+scheduler = FlowMatchScheduler(shift=8.0, sigma_min=0.0, extra_one_step=True)
+
+scheduler.set_timesteps(num_inference_steps=50, denoising_strength=1.0)
+
+scheduler.sigmas = scheduler.sigmas.to(device)
+
+unconditional_dict = encoder(
+    text_prompts=[sample_neg_prompt]
+)
+
+for index in tqdm(range(len(dataset))):
+    prompt = dataset[index]
+
+    conditional_dict = encoder(
+        text_prompts=prompt
+    )
+
+    latents = torch.randn(
+        1, 16, 21, 60, 104, generator=torch.Generator().manual_seed(42), dtype=torch.float32
+    ).to("cuda").permute(0, 2, 1, 3, 4)
+
+    with torch.amp.autocast(dtype=torch.float32, device_type=torch.device("cuda:0").type):
+
+        for progress_id, t in enumerate(tqdm(scheduler.timesteps)):
+            timestep = t * \
+                torch.ones([1, 21], device=device, dtype=torch.float32)
+
+            x0_pred_cond = model(
+                latents, conditional_dict, timestep
+            )
+
+            x0_pred_uncond = model(
+                latents, unconditional_dict, timestep
+            )
+
+            x0_pred = x0_pred_uncond + 6 * (
+                x0_pred_cond - x0_pred_uncond
+            )
+
+            flow_pred = model._convert_x0_to_flow_pred(
+                x0_pred=x0_pred.flatten(0, 1),
+                xt=latents.flatten(0, 1),
+                timestep=timestep.flatten(0, 1),
+                scheduler=scheduler
+            ).unflatten(0, x0_pred.shape[:2])
+
+            latents = scheduler.step(
+                flow_pred.flatten(0, 1),
+                scheduler.timesteps[progress_id] * torch.ones(
+                    [1, 21], device=device, dtype=torch.long).flatten(0, 1),
+                latents.flatten(0, 1)
+            ).unflatten(dim=0, sizes=flow_pred.shape[:2])
+
+        decoded_video = vae.decode_to_pixel(latents)[0].permute(0, 2, 3, 1).cpu().numpy() * 0.5 + 0.5
+
+    export_to_video(
+        decoded_video, f"test_wanx_wrapper_{index:04d}.mp4", fps=16)

exp_code/1_benchmark/DiffSynth-Studio/.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 setup.py sdist bdist_wheel
+      - name: Publish package to PyPI
+        run: |
+          pip install twine
+          twine upload dist/* --skip-existing -u __token__ -p ${{ secrets.PYPI_API_TOKEN }}

exp_code/1_benchmark/DiffSynth-Studio/LICENSE

@@ -0,0 +1,201 @@
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exp_code/1_benchmark/DiffSynth-Studio/README.md

@@ -0,0 +1,497 @@
+# DiffSynth-Studio
+
+<a href="https://github.com/modelscope/DiffSynth-Studio"><img src=".github/workflows/logo.gif" title="Logo" style="max-width:100%;" width="55" /></a> <a href="https://trendshift.io/repositories/10946" target="_blank"><img src="https://trendshift.io/api/badge/repositories/10946" alt="modelscope%2FDiffSynth-Studio | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a></p>
+
+[![PyPI](https://img.shields.io/pypi/v/DiffSynth)](https://pypi.org/project/DiffSynth/)
+[![license](https://img.shields.io/github/license/modelscope/DiffSynth-Studio.svg)](https://github.com/modelscope/DiffSynth-Studio/blob/master/LICENSE)
+[![open issues](https://isitmaintained.com/badge/open/modelscope/DiffSynth-Studio.svg)](https://github.com/modelscope/DiffSynth-Studio/issues)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/modelscope/DiffSynth-Studio.svg)](https://GitHub.com/modelscope/DiffSynth-Studio/pull/)
+[![GitHub latest commit](https://badgen.net/github/last-commit/modelscope/DiffSynth-Studio)](https://GitHub.com/modelscope/DiffSynth-Studio/commit/) 
+
+[切换到中文](./README_zh.md)
+
+## Introduction
+
+Welcome to the magic world of Diffusion models! DiffSynth-Studio is an open-source Diffusion model engine developed and maintained by [ModelScope](https://www.modelscope.cn/) team. We aim to foster technical innovation through framework development, bring together the power of the open-source community, and explore the limits of generative models!
+
+DiffSynth currently includes two open-source projects:
+* [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio): Focused on aggressive technical exploration, for academia, providing support for more cutting-edge model capabilities.
+* [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine): Focused on stable model deployment, for industry, offering higher computing performance and more stable features.
+
+[DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) and [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine) are the core projects behind ModelScope [AIGC zone](https://modelscope.cn/aigc/home), offering powerful AI content generation abilities. Come and try our carefully designed features and start your AI creation journey!
+
+## Installation
+
+Install from source (recommended):
+
+```
+git clone https://github.com/modelscope/DiffSynth-Studio.git  
+cd DiffSynth-Studio
+pip install -e .
+```
+
+<details>
+<summary>Other installation methods</summary>
+
+Install from PyPI (version updates may be delayed; for latest features, install from source)
+
+```
+pip install diffsynth
+```
+
+If you meet problems during installation, they might be caused by upstream dependencies. Please check the docs of these packages:
+
+* [torch](https://pytorch.org/get-started/locally/)
+* [sentencepiece](https://github.com/google/sentencepiece)
+* [cmake](https://cmake.org)
+* [cupy](https://docs.cupy.dev/en/stable/install.html)
+
+</details>
+
+## Basic Framework
+
+DiffSynth-Studio redesigns the inference and training pipelines for mainstream Diffusion models (including FLUX, Wan, etc.), enabling efficient memory management and flexible model training.
+
+### Qwen-Image Series (🔥New Model)
+
+Details: [./examples/qwen_image/](./examples/qwen_image/)
+
+![Image](https://github.com/user-attachments/assets/738078d8-8749-4a53-a046-571861541924)
+
+<details>
+
+<summary>Quick Start</summary>
+
+```python
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+import torch
+
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+prompt = "A detailed portrait of a girl underwater, wearing a blue flowing dress, hair gently floating, clear light and shadow, surrounded by bubbles, calm expression, fine details, dreamy and beautiful."
+image = pipe(prompt, seed=0, num_inference_steps=40)
+image.save("image.jpg")
+```
+
+</details>
+
+<details>
+
+<summary>Model Overview</summary>
+
+|Model ID|Inference|Low VRAM Inference|Full Training|Validation after Full Training|LoRA Training|Validation after LoRA Training|
+|-|-|-|-|-|-|-|
+|[Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image)|[code](./examples/qwen_image/model_inference/Qwen-Image.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image.py)|
+|[Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)|[code](./examples/qwen_image/model_inference/Qwen-Image-Edit.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Edit.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py)|
+|[DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2)|[code](./examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
+|[DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)|[code](./examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py)|
+|[DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA)|[code](./examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py)|-|-|-|-|
+|[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](./examples/qwen_image/model_inference/Qwen-Image-EliGen.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
+|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny)|[code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py)|
+|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth)|[code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py)|
+|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint)|[code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|
+|[DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union)|[code](./examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py)|
+|[DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix)|[code](./examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py)|-|-|-|-|
+
+</details>
+
+### FLUX Series
+
+Detail page: [./examples/flux/](./examples/flux/)
+
+![Image](https://github.com/user-attachments/assets/c01258e2-f251-441a-aa1e-ebb22f02594d)
+
+<details>
+
+<summary>Quick Start</summary>
+
+```python
+import torch
+from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
+
+pipe = FluxImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
+        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
+    ],
+)
+
+image = pipe(prompt="a cat", seed=0)
+image.save("image.jpg")
+```
+
+</details>
+
+<details>
+
+<summary>Model Overview</summary>
+
+| Model ID | Extra Parameters | Inference | Low VRAM Inference | Full Training | Validate After Full Training | LoRA Training | Validate After LoRA Training |
+|-|-|-|-|-|-|-|-|
+|[FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev)||[code](./examples/flux/model_inference/FLUX.1-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev.py)|
+|[FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev)||[code](./examples/flux/model_inference/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-Krea-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-Krea-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py)|
+|[FLUX.1-Kontext-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev)|`kontext_images`|[code](./examples/flux/model_inference/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-Kontext-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py)|
+|[FLUX.1-dev-Controlnet-Inpainting-Beta](https://www.modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|
+|[FLUX.1-dev-Controlnet-Union-alpha](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py)|
+|[FLUX.1-dev-Controlnet-Upscaler](https://www.modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py)|
+|[FLUX.1-dev-IP-Adapter](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-IP-Adapter)|`ipadapter_images`, `ipadapter_scale`|[code](./examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py)|
+|[FLUX.1-dev-InfiniteYou](https://www.modelscope.cn/models/ByteDance/InfiniteYou)|`infinityou_id_image`, `infinityou_guidance`, `controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py)|
+|[FLUX.1-dev-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen)|`eligen_entity_prompts`, `eligen_entity_masks`, `eligen_enable_on_negative`, `eligen_enable_inpaint`|[code](./examples/flux/model_inference/FLUX.1-dev-EliGen.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py)|-|-|[code](./examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py)|
+|[FLUX.1-dev-LoRA-Encoder](https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev)|`lora_encoder_inputs`, `lora_encoder_scale`|[code](./examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py)|-|-|
+|[FLUX.1-dev-LoRA-Fusion-Preview](https://modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev)||[code](./examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py)|-|-|-|-|-|
+|[Step1X-Edit](https://www.modelscope.cn/models/stepfun-ai/Step1X-Edit)|`step1x_reference_image`|[code](./examples/flux/model_inference/Step1X-Edit.py)|[code](./examples/flux/model_inference_low_vram/Step1X-Edit.py)|[code](./examples/flux/model_training/full/Step1X-Edit.sh)|[code](./examples/flux/model_training/validate_full/Step1X-Edit.py)|[code](./examples/flux/model_training/lora/Step1X-Edit.sh)|[code](./examples/flux/model_training/validate_lora/Step1X-Edit.py)|
+|[FLEX.2-preview](https://www.modelscope.cn/models/ostris/Flex.2-preview)|`flex_inpaint_image`, `flex_inpaint_mask`, `flex_control_image`, `flex_control_strength`, `flex_control_stop`|[code](./examples/flux/model_inference/FLEX.2-preview.py)|[code](./examples/flux/model_inference_low_vram/FLEX.2-preview.py)|[code](./examples/flux/model_training/full/FLEX.2-preview.sh)|[code](./examples/flux/model_training/validate_full/FLEX.2-preview.py)|[code](./examples/flux/model_training/lora/FLEX.2-preview.sh)|[code](./examples/flux/model_training/validate_lora/FLEX.2-preview.py)|
+|[Nexus-Gen](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2)|`nexus_gen_reference_image`|[code](./examples/flux/model_inference/Nexus-Gen-Editing.py)|[code](./examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py)|[code](./examples/flux/model_training/full/Nexus-Gen.sh)|[code](./examples/flux/model_training/validate_full/Nexus-Gen.py)|[code](./examples/flux/model_training/lora/Nexus-Gen.sh)|[code](./examples/flux/model_training/validate_lora/Nexus-Gen.py)|
+
+</details>
+
+
+
+### Wan Series
+
+Detail page: [./examples/wanvideo/](./examples/wanvideo/)
+
+https://github.com/user-attachments/assets/1d66ae74-3b02-40a9-acc3-ea95fc039314
+
+<details>
+
+<summary>Quick Start</summary>
+
+```python
+import torch
+from diffsynth import save_video
+from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
+    ],
+)
+pipe.enable_vram_management()
+
+video = pipe(
+    prompt="A documentary photography style scene: a lively puppy rapidly running on green grass. The puppy has brown-yellow fur, upright ears, and looks focused and joyful. Sunlight shines on its body, making the fur appear soft and shiny. The background is an open field with occasional wildflowers, and faint blue sky and clouds in the distance. Strong sense of perspective captures the motion of the puppy and the vitality of the surrounding grass. Mid-shot side-moving view.",
+    negative_prompt="Bright colors, overexposed, static, blurry details, subtitles, style, artwork, image, still, overall gray, worst quality, low quality, JPEG compression artifacts, ugly, deformed, extra fingers, poorly drawn hands, poorly drawn face, malformed limbs, fused fingers, still frame, messy background, three legs, crowded background people, walking backwards",
+    seed=0, tiled=True,
+)
+save_video(video, "video1.mp4", fps=15, quality=5)
+```
+
+</details>
+
+<details>
+
+<summary>Model Overview</summary>
+
+| Model ID | Extra Parameters | Inference | Full Training | Validate After Full Training | LoRA Training | Validate After LoRA Training |
+|-|-|-|-|-|-|-|
+|[Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B)|`input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video`|[code](./examples/wanvideo/model_inference/Wan2.2-S2V-14B.py)|-|-|-|-|
+|[Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py)|
+|[Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B)||[code](./examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py)|
+|[Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py)|
+|[Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B)||[code](./examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py)|
+|[Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B)||[code](./examples/wanvideo/model_inference/Wan2.1-T2V-14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py)|
+|[Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py)|
+|[Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py)|
+|[Wan-AI/Wan2.1-FLF2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py)|
+|[PAI/Wan2.1-Fun-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py)|
+|[PAI/Wan2.1-Fun-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control)|`control_video`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py)|
+|[PAI/Wan2.1-Fun-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py)|
+|[PAI/Wan2.1-Fun-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control)|`control_video`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py)|
+|[PAI/Wan2.1-Fun-V1.1-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control)|`control_video`, `reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py)|
+|[PAI/Wan2.1-Fun-V1.1-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control)|`control_video`, `reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](./examples/wanvideo/examples/wanmodel_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py)|
+|[PAI/Wan2.1-Fun-V1.1-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py)|
+|[PAI/Wan2.1-Fun-V1.1-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py)|
+|[PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera)|`control_camera_video`, `input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|
+|[PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera)|`control_camera_video`, `input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|
+|[iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py)|
+|[Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py)|
+|[Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py)|
+|[DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1)|`motion_bucket_id`|[code](./examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py)|
+
+</details>
+
+### More Models
+
+
+
+<details>
+<summary>Image Generation Models</summary>
+
+Detail page: [./examples/image_synthesis/](./examples/image_synthesis/)
+
+|FLUX|Stable Diffusion 3|
+|-|-|
+|![image_1024_cfg](https://github.com/user-attachments/assets/984561e9-553d-4952-9443-79ce144f379f)|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/4df346db-6f91-420a-b4c1-26e205376098)|
+
+|Kolors|Hunyuan-DiT|
+|-|-|
+|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/53ef6f41-da11-4701-8665-9f64392607bf)|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/60b022c8-df3f-4541-95ab-bf39f2fa8bb5)|
+
+|Stable Diffusion|Stable Diffusion XL|
+|-|-|
+|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/6fc84611-8da6-4a1f-8fee-9a34eba3b4a5)|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/67687748-e738-438c-aee5-96096f09ac90)|
+
+</details>
+
+
+
+<details>
+<summary>Video Generation Models</summary>
+
+- HunyuanVideo: [./examples/HunyuanVideo/](./examples/HunyuanVideo/)
+
+https://github.com/user-attachments/assets/48dd24bb-0cc6-40d2-88c3-10feed3267e9  
+
+- StepVideo: [./examples/stepvideo/](./examples/stepvideo/)
+
+https://github.com/user-attachments/assets/5954fdaa-a3cf-45a3-bd35-886e3cc4581b  
+
+- CogVideoX: [./examples/CogVideoX/](./examples/CogVideoX/)
+
+https://github.com/user-attachments/assets/26b044c1-4a60-44a4-842f-627ff289d006  
+
+</details>
+
+
+
+<details>
+<summary>Image Quality Assessment Models</summary>
+
+We have integrated a series of image quality assessment models. These models can be used for evaluating image generation models, alignment training, and similar tasks.
+
+Detail page: [./examples/image_quality_metric/](./examples/image_quality_metric/)
+
+* [ImageReward](https://github.com/THUDM/ImageReward)
+* [Aesthetic](https://github.com/christophschuhmann/improved-aesthetic-predictor)
+* [PickScore](https://github.com/yuvalkirstain/pickscore)
+* [CLIP](https://github.com/openai/CLIP)
+* [HPSv2](https://github.com/tgxs002/HPSv2)
+* [HPSv2.1](https://github.com/tgxs002/HPSv2)
+* [MPS](https://github.com/Kwai-Kolors/MPS)
+
+</details>
+
+
+
+## Innovative Achievements
+
+DiffSynth-Studio is not just an engineering model framework, but also a platform for incubating innovative results.
+
+<details>
+<summary>Nexus-Gen: Unified Architecture for Image Understanding, Generation, and Editing</summary>
+
+- Detail page: https://github.com/modelscope/Nexus-Gen  
+- Paper: [Nexus-Gen: Unified Image Understanding, Generation, and Editing via Prefilled Autoregression in Shared Embedding Space](https://arxiv.org/pdf/2504.21356)
+- Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2), [HuggingFace](https://huggingface.co/modelscope/Nexus-GenV2)
+- Dataset: [ModelScope Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Nexus-Gen-Training-Dataset)
+- Online Demo: [ModelScope Nexus-Gen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/Nexus-Gen)
+
+![](https://github.com/modelscope/Nexus-Gen/raw/main/assets/illustrations/gen_edit.jpg)
+
+</details>
+
+<details>
+<summary>ArtAug: Aesthetic Enhancement for Image Generation Models</summary>
+
+- Detail page: [./examples/ArtAug/](./examples/ArtAug/)
+- Paper: [ArtAug: Enhancing Text-to-Image Generation through Synthesis-Understanding Interaction](https://arxiv.org/abs/2412.12888)
+- Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ArtAug-lora-FLUX.1dev-v1)
+- Online Demo: [ModelScope AIGC Tab](https://www.modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=7228&modelType=LoRA&sdVersion=FLUX_1&modelUrl=modelscope%3A%2F%2FDiffSynth-Studio%2FArtAug-lora-FLUX.1dev-v1%3Frevision%3Dv1.0)
+
+|FLUX.1-dev|FLUX.1-dev + ArtAug LoRA|
+|-|-|
+|![image_1_base](https://github.com/user-attachments/assets/e1d5c505-b423-45fe-be01-25c2758f5417)|![image_1_enhance](https://github.com/user-attachments/assets/335908e3-d0bd-41c2-9d99-d10528a2d719)|
+
+</details>
+
+<details>
+<summary>EliGen: Precise Image Region Control</summary>
+
+- Detail page: [./examples/EntityControl/](./examples/EntityControl/)
+- Paper: [EliGen: Entity-Level Controlled Image Generation with Regional Attention](https://arxiv.org/abs/2501.01097)
+- Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen), [HuggingFace](https://huggingface.co/modelscope/EliGen)
+- Online Demo: [ModelScope EliGen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/EliGen)
+- Dataset: [EliGen Train Set](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet)
+
+|Entity Control Mask|Generated Image|
+|-|-|
+|![eligen_example_2_mask_0](https://github.com/user-attachments/assets/1c6d9445-5022-4d91-ad2e-dc05321883d1)|![eligen_example_2_0](https://github.com/user-attachments/assets/86739945-cb07-4a49-b3b3-3bb65c90d14f)|
+
+</details>
+
+<details>
+<summary>ExVideo: Extended Training for Video Generation Models</summary>
+
+- Project Page: [Project Page](https://ecnu-cilab.github.io/ExVideoProjectPage/)
+- Paper: [ExVideo: Extending Video Diffusion Models via Parameter-Efficient Post-Tuning](https://arxiv.org/abs/2406.14130)
+- Code Example: [./examples/ExVideo/](./examples/ExVideo/)
+- Model: [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1)
+
+https://github.com/modelscope/DiffSynth-Studio/assets/35051019/d97f6aa9-8064-4b5b-9d49-ed6001bb9acc
+
+</details>
+
+<details>
+<summary>Diffutoon: High-Resolution Anime-Style Video Rendering</summary>
+
+- Project Page: [Project Page](https://ecnu-cilab.github.io/DiffutoonProjectPage/)
+- Paper: [Diffutoon: High-Resolution Editable Toon Shading via Diffusion Models](https://arxiv.org/abs/2401.16224)
+- Code Example: [./examples/Diffutoon/](./examples/Diffutoon/)
+
+https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/b54c05c5-d747-4709-be5e-b39af82404dd
+
+</details>
+
+<details>
+<summary>DiffSynth: The Initial Version of This Project</summary>
+
+- Project Page: [Project Page](https://ecnu-cilab.github.io/DiffSynth.github.io/)
+- Paper: [DiffSynth: Latent In-Iteration Deflickering for Realistic Video Synthesis](https://arxiv.org/abs/2308.03463)
+- Code Example: [./examples/diffsynth/](./examples/diffsynth/)
+
+https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-4481-b79f-0c3a7361a1ea
+
+</details>
+
+
+
+## Update History
+- **August 28, 2025** We support Wan2.2-S2V, an audio-driven cinematic video generation model open-sourced by Alibaba. See [./examples/wanvideo/](./examples/wanvideo/).
+
+- **August 21, 2025**: [DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2) is released! Compared to the V1 version, the training dataset has been updated to the [Qwen-Image-Self-Generated-Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Qwen-Image-Self-Generated-Dataset), enabling generated images to better align with the inherent image distribution and style of Qwen-Image. Please refer to [our sample code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py).
+
+- **August 21, 2025**: We open-sourced the [DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union) structure control LoRA model. Following "In Context" routine, it supports various types of structural control conditions, including canny, depth, lineart, softedge, normal, and openpose. Please refer to [our sample code](./examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py).
+
+- **August 20, 2025** We open-sourced [DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix), which improves the editing performance of Qwen-Image-Edit on low-resolution image inputs. Please refer to [our example code](./examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py).
+
+- **August 19, 2025** 🔥 Qwen-Image-Edit is now open source. Welcome the new member to the image editing model family!
+
+- **August 18, 2025** We trained and open-sourced the Inpaint ControlNet model for Qwen-Image, [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint), which adopts a lightweight architectural design. Please refer to [our sample code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py).
+
+- **August 15, 2025** We open-sourced the [Qwen-Image-Self-Generated-Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Qwen-Image-Self-Generated-Dataset). This is an image dataset generated using the Qwen-Image model, with a total of 160,000 `1024 x 1024` images. It includes the general, English text rendering, and Chinese text rendering subsets. We provide caption, entity and control images annotations for each image. Developers can use this dataset to train models such as ControlNet and EliGen for the Qwen-Image model. We aim to promote technological development through open-source contributions!
+
+- **August 13, 2025** We trained and open-sourced the ControlNet model for Qwen-Image, [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth), which adopts a lightweight architectural design. Please refer to [our sample code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py).
+
+- **August 12, 2025** We trained and open-sourced the ControlNet model for Qwen-Image, [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny), which adopts a lightweight architectural design. Please refer to [our sample code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py).
+
+- **August 11, 2025** We released another distilled acceleration model for Qwen-Image, [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA). It uses the same training process as [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full), but the model structure is changed to LoRA. This makes it work better with other open-source models.
+
+- **August 7, 2025** We open-sourced the entity control LoRA of Qwen-Image, [DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen). Qwen-Image-EliGen is able to achieve entity-level controlled text-to-image generation. See the [paper](https://arxiv.org/abs/2501.01097) for technical details. Training dataset: [EliGenTrainSet](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet).
+
+- **August 5, 2025** We open-sourced the distilled acceleration model of Qwen-Image, [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full), achieving approximately 5x speedup.
+
+- **August 4, 2025** 🔥 Qwen-Image is now open source. Welcome the new member to the image generation model family!
+
+- **August 1, 2025** [FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev) with a focus on aesthetic photography is comprehensively supported, including low-GPU-memory layer-by-layer offload, LoRA training and full training. See [./examples/flux/](./examples/flux/).
+
+- **July 28, 2025** With the open-sourcing of Wan 2.2, we immediately provided comprehensive support, including low-GPU-memory layer-by-layer offload, FP8 quantization, sequence parallelism, LoRA training, full training. See [./examples/wanvideo/](./examples/wanvideo/).
+
+- **July 11, 2025** We propose Nexus-Gen, a unified model that synergizes the language reasoning capabilities of LLMs with the image synthesis power of diffusion models. This framework enables seamless image understanding, generation, and editing tasks.
+  - Paper: [Nexus-Gen: Unified Image Understanding, Generation, and Editing via Prefilled Autoregression in Shared Embedding Space](https://arxiv.org/pdf/2504.21356)
+  - Github Repo: https://github.com/modelscope/Nexus-Gen
+  - Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2), [HuggingFace](https://huggingface.co/modelscope/Nexus-GenV2)
+  - Training Dataset: [ModelScope Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Nexus-Gen-Training-Dataset)
+  - Online Demo: [ModelScope Nexus-Gen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/Nexus-Gen)
+
+<details>
+<summary>More</summary>
+
+- **June 15, 2025** ModelScope's official evaluation framework, [EvalScope](https://github.com/modelscope/evalscope), now supports text-to-image generation evaluation. Try it with the [Best Practices](https://evalscope.readthedocs.io/zh-cn/latest/best_practice/t2i_eval.html) guide.
+
+- **March 25, 2025** Our new open-source project, [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine), is now open-sourced! Focused on stable model deployment. Geared towards industry. Offers better engineering support, higher computational performance, and more stable functionality.
+
+- **March 31, 2025** We support InfiniteYou, an identity preserving method for FLUX. Please refer to [./examples/InfiniteYou/](./examples/InfiniteYou/) for more details.
+
+- **March 13, 2025** We support HunyuanVideo-I2V, the image-to-video generation version of HunyuanVideo open-sourced by Tencent. Please refer to [./examples/HunyuanVideo/](./examples/HunyuanVideo/) for more details.
+
+- **February 25, 2025** We support Wan-Video, a collection of SOTA video synthesis models open-sourced by Alibaba. See [./examples/wanvideo/](./examples/wanvideo/).
+
+- **February 17, 2025** We support [StepVideo](https://modelscope.cn/models/stepfun-ai/stepvideo-t2v/summary)! State-of-the-art video synthesis model! See [./examples/stepvideo](./examples/stepvideo/).
+
+- **December 31, 2024** We propose EliGen, a novel framework for precise entity-level controlled text-to-image generation, complemented by an inpainting fusion pipeline to extend its capabilities to image inpainting tasks. EliGen seamlessly integrates with existing community models, such as IP-Adapter and In-Context LoRA, enhancing its versatility. For more details, see [./examples/EntityControl](./examples/EntityControl/).
+  - Paper: [EliGen: Entity-Level Controlled Image Generation with Regional Attention](https://arxiv.org/abs/2501.01097)
+  - Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen), [HuggingFace](https://huggingface.co/modelscope/EliGen)
+  - Online Demo: [ModelScope EliGen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/EliGen)
+  - Training Dataset: [EliGen Train Set](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet)
+
+- **December 19, 2024** We implement advanced VRAM management for HunyuanVideo, making it possible to generate videos at a resolution of 129x720x1280 using 24GB of VRAM, or at 129x512x384 resolution with just 6GB of VRAM. Please refer to [./examples/HunyuanVideo/](./examples/HunyuanVideo/) for more details.
+
+- **December 18, 2024** We propose ArtAug, an approach designed to improve text-to-image synthesis models through synthesis-understanding interactions. We have trained an ArtAug enhancement module for FLUX.1-dev in the format of LoRA. This model integrates the aesthetic understanding of Qwen2-VL-72B into FLUX.1-dev, leading to an improvement in the quality of generated images.
+  - Paper: https://arxiv.org/abs/2412.12888
+  - Examples: https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/ArtAug
+  - Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ArtAug-lora-FLUX.1dev-v1)
+  - Demo: [ModelScope](https://modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=7228&modelType=LoRA&sdVersion=FLUX_1&modelUrl=modelscope%3A%2F%2FDiffSynth-Studio%2FArtAug-lora-FLUX.1dev-v1%3Frevision%3Dv1.0), HuggingFace (Coming soon)
+
+- **October 25, 2024** We provide extensive FLUX ControlNet support. This project supports many different ControlNet models that can be freely combined, even if their structures differ. Additionally, ControlNet models are compatible with high-resolution refinement and partition control techniques, enabling very powerful controllable image generation. See [`./examples/ControlNet/`](./examples/ControlNet/).
+
+- **October 8, 2024.** We release the extended LoRA based on CogVideoX-5B and ExVideo. You can download this model from [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1) or [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1).
+
+- **August 22, 2024.** CogVideoX-5B is supported in this project. See [here](/examples/video_synthesis/). We provide several interesting features for this text-to-video model, including
+  - Text to video
+  - Video editing
+  - Self-upscaling
+  - Video interpolation
+
+- **August 22, 2024.** We have implemented an interesting painter that supports all text-to-image models. Now you can create stunning images using the painter, with assistance from AI!
+  - Use it in our [WebUI](#usage-in-webui).
+
+- **August 21, 2024.** FLUX is supported in DiffSynth-Studio.
+  - Enable CFG and highres-fix to improve visual quality. See [here](/examples/image_synthesis/README.md)
+  - LoRA, ControlNet, and additional models will be available soon.
+
+- **June 21, 2024.** We propose ExVideo, a post-tuning technique aimed at enhancing the capability of video generation models. We have extended Stable Video Diffusion to achieve the generation of long videos up to 128 frames.
+  - [Project Page](https://ecnu-cilab.github.io/ExVideoProjectPage/)
+  - Source code is released in this repo. See [`examples/ExVideo`](./examples/ExVideo/).
+  - Models are released on [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1) and [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1).
+  - Technical report is released on [arXiv](https://arxiv.org/abs/2406.14130).
+  - You can try ExVideo in this [Demo](https://huggingface.co/spaces/modelscope/ExVideo-SVD-128f-v1)!
+
+- **June 13, 2024.** DiffSynth Studio is transferred to ModelScope. The developers have transitioned from "I" to "we". Of course, I will still participate in development and maintenance.
+
+- **Jan 29, 2024.** We propose Diffutoon, a fantastic solution for toon shading.
+  - [Project Page](https://ecnu-cilab.github.io/DiffutoonProjectPage/)
+  - The source codes are released in this project.
+  - The technical report (IJCAI 2024) is released on [arXiv](https://arxiv.org/abs/2401.16224).
+
+- **Dec 8, 2023.** We decide to develop a new Project, aiming to release the potential of diffusion models, especially in video synthesis. The development of this project is started.
+
+- **Nov 15, 2023.** We propose FastBlend, a powerful video deflickering algorithm.
+  - The sd-webui extension is released on [GitHub](https://github.com/Artiprocher/sd-webui-fastblend).
+  - Demo videos are shown on Bilibili, including three tasks.
+    - [Video deflickering](https://www.bilibili.com/video/BV1d94y1W7PE)
+    - [Video interpolation](https://www.bilibili.com/video/BV1Lw411m71p)
+    - [Image-driven video rendering](https://www.bilibili.com/video/BV1RB4y1Z7LF)
+  - The technical report is released on [arXiv](https://arxiv.org/abs/2311.09265).
+  - An unofficial ComfyUI extension developed by other users is released on [GitHub](https://github.com/AInseven/ComfyUI-fastblend).
+
+- **Oct 1, 2023.** We release an early version of this project, namely FastSDXL. A try for building a diffusion engine.
+  - The source codes are released on [GitHub](https://github.com/Artiprocher/FastSDXL).
+  - FastSDXL includes a trainable OLSS scheduler for efficiency improvement.
+    - The original repo of OLSS is [here](https://github.com/alibaba/EasyNLP/tree/master/diffusion/olss_scheduler).
+    - The technical report (CIKM 2023) is released on [arXiv](https://arxiv.org/abs/2305.14677).
+    - A demo video is shown on [Bilibili](https://www.bilibili.com/video/BV1w8411y7uj).
+    - Since OLSS requires additional training, we don't implement it in this project.
+
+- **Aug 29, 2023.** We propose DiffSynth, a video synthesis framework.
+  - [Project Page](https://ecnu-cilab.github.io/DiffSynth.github.io/).
+  - The source codes are released in [EasyNLP](https://github.com/alibaba/EasyNLP/tree/master/diffusion/DiffSynth).
+  - The technical report (ECML PKDD 2024) is released on [arXiv](https://arxiv.org/abs/2308.03463).
+
+</details>

exp_code/1_benchmark/DiffSynth-Studio/README_zh.md

@@ -0,0 +1,513 @@
+# DiffSynth-Studio
+
+<a href="https://github.com/modelscope/DiffSynth-Studio"><img src=".github/workflows/logo.gif" title="Logo" style="max-width:100%;" width="55" /></a> <a href="https://trendshift.io/repositories/10946" target="_blank"><img src="https://trendshift.io/api/badge/repositories/10946" alt="modelscope%2FDiffSynth-Studio | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a></p>
+
+[![PyPI](https://img.shields.io/pypi/v/DiffSynth)](https://pypi.org/project/DiffSynth/)
+[![license](https://img.shields.io/github/license/modelscope/DiffSynth-Studio.svg)](https://github.com/modelscope/DiffSynth-Studio/blob/master/LICENSE)
+[![open issues](https://isitmaintained.com/badge/open/modelscope/DiffSynth-Studio.svg)](https://github.com/modelscope/DiffSynth-Studio/issues)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/modelscope/DiffSynth-Studio.svg)](https://GitHub.com/modelscope/DiffSynth-Studio/pull/)
+[![GitHub latest commit](https://badgen.net/github/last-commit/modelscope/DiffSynth-Studio)](https://GitHub.com/modelscope/DiffSynth-Studio/commit/) 
+
+[Switch to English](./README.md)
+
+## 简介
+
+欢迎来到 Diffusion 模型的魔法世界！DiffSynth-Studio 是由[魔搭社区](https://www.modelscope.cn/)团队开发和维护的开源 Diffusion 模型引擎。我们期望以框架建设孵化技术创新，凝聚开源社区的力量，探索生成式模型技术的边界！
+
+DiffSynth 目前包括两个开源项目：
+* [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio): 聚焦于激进的技术探索，面向学术界，提供更前沿的模型能力支持。
+* [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine): 聚焦于稳定的模型部署，面向工业界，提供更高的计算性能与更稳定的功能。
+
+[DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) 与 [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine) 作为魔搭社区 [AIGC 专区](https://modelscope.cn/aigc/home) 的核心技术支撑，提供了强大的AI生成内容能力。欢迎体验我们精心打造的产品化功能，开启您的AI创作之旅！
+
+## 安装
+
+从源码安装（推荐）：
+
+```
+git clone https://github.com/modelscope/DiffSynth-Studio.git
+cd DiffSynth-Studio
+pip install -e .
+```
+
+<details>
+<summary>其他安装方式</summary>
+
+从 pypi 安装（存在版本更新延迟，如需使用最新功能，请从源码安装）
+
+```
+pip install diffsynth
+```
+
+如果在安装过程中遇到问题，可能是由上游依赖包导致的，请参考这些包的文档：
+
+* [torch](https://pytorch.org/get-started/locally/)
+* [sentencepiece](https://github.com/google/sentencepiece)
+* [cmake](https://cmake.org)
+* [cupy](https://docs.cupy.dev/en/stable/install.html)
+
+</details>
+
+
+
+## 基础框架
+
+DiffSynth-Studio 为主流 Diffusion 模型（包括 FLUX、Wan 等）重新设计了推理和训练流水线，能够实现高效的显存管理、灵活的模型训练。
+
+### Qwen-Image 系列 (🔥新模型)
+
+详细页面：[./examples/qwen_image/](./examples/qwen_image/)
+
+![Image](https://github.com/user-attachments/assets/738078d8-8749-4a53-a046-571861541924)
+
+<details>
+
+<summary>快速开始</summary>
+
+```python
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+import torch
+
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。"
+image = pipe(prompt, seed=0, num_inference_steps=40)
+image.save("image.jpg")
+```
+
+</details>
+
+<details>
+
+<summary>模型总览</summary>
+
+|模型 ID|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
+|-|-|-|-|-|-|-|
+|[Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image)|[code](./examples/qwen_image/model_inference/Qwen-Image.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image.py)|
+|[Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)|[code](./examples/qwen_image/model_inference/Qwen-Image-Edit.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Edit.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py)|
+|[DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2)|[code](./examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
+|[DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)|[code](./examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py)|
+|[DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA)|[code](./examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py)|-|-|-|-|
+|[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](./examples/qwen_image/model_inference/Qwen-Image-EliGen.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
+|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny)|[code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py)|
+|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth)|[code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py)|
+|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint)|[code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|
+|[DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union)|[code](./examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py)|
+|[DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix)|[code](./examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py)|-|-|-|-|
+
+</details>
+
+### FLUX 系列
+
+详细页面：[./examples/flux/](./examples/flux/)
+
+![Image](https://github.com/user-attachments/assets/c01258e2-f251-441a-aa1e-ebb22f02594d)
+
+<details>
+
+<summary>快速开始</summary>
+
+```python
+import torch
+from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
+
+pipe = FluxImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
+        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
+    ],
+)
+
+image = pipe(prompt="a cat", seed=0)
+image.save("image.jpg")
+```
+
+</details>
+
+<details>
+
+<summary>模型总览</summary>
+
+|模型 ID|额外参数|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
+|-|-|-|-|-|-|-|-|
+|[FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev)||[code](./examples/flux/model_inference/FLUX.1-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev.py)|
+|[FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev)||[code](./examples/flux/model_inference/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-Krea-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-Krea-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py)|
+|[FLUX.1-Kontext-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev)|`kontext_images`|[code](./examples/flux/model_inference/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-Kontext-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py)|
+|[FLUX.1-dev-Controlnet-Inpainting-Beta](https://www.modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|
+|[FLUX.1-dev-Controlnet-Union-alpha](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py)|
+|[FLUX.1-dev-Controlnet-Upscaler](https://www.modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py)|
+|[FLUX.1-dev-IP-Adapter](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-IP-Adapter)|`ipadapter_images`, `ipadapter_scale`|[code](./examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py)|
+|[FLUX.1-dev-InfiniteYou](https://www.modelscope.cn/models/ByteDance/InfiniteYou)|`infinityou_id_image`, `infinityou_guidance`, `controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py)|
+|[FLUX.1-dev-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen)|`eligen_entity_prompts`, `eligen_entity_masks`, `eligen_enable_on_negative`, `eligen_enable_inpaint`|[code](./examples/flux/model_inference/FLUX.1-dev-EliGen.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py)|-|-|[code](./examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py)|
+|[FLUX.1-dev-LoRA-Encoder](https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev)|`lora_encoder_inputs`, `lora_encoder_scale`|[code](./examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py)|-|-|
+|[FLUX.1-dev-LoRA-Fusion-Preview](https://modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev)||[code](./examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py)|-|-|-|-|-|
+|[Step1X-Edit](https://www.modelscope.cn/models/stepfun-ai/Step1X-Edit)|`step1x_reference_image`|[code](./examples/flux/model_inference/Step1X-Edit.py)|[code](./examples/flux/model_inference_low_vram/Step1X-Edit.py)|[code](./examples/flux/model_training/full/Step1X-Edit.sh)|[code](./examples/flux/model_training/validate_full/Step1X-Edit.py)|[code](./examples/flux/model_training/lora/Step1X-Edit.sh)|[code](./examples/flux/model_training/validate_lora/Step1X-Edit.py)|
+|[FLEX.2-preview](https://www.modelscope.cn/models/ostris/Flex.2-preview)|`flex_inpaint_image`, `flex_inpaint_mask`, `flex_control_image`, `flex_control_strength`, `flex_control_stop`|[code](./examples/flux/model_inference/FLEX.2-preview.py)|[code](./examples/flux/model_inference_low_vram/FLEX.2-preview.py)|[code](./examples/flux/model_training/full/FLEX.2-preview.sh)|[code](./examples/flux/model_training/validate_full/FLEX.2-preview.py)|[code](./examples/flux/model_training/lora/FLEX.2-preview.sh)|[code](./examples/flux/model_training/validate_lora/FLEX.2-preview.py)|
+|[Nexus-Gen](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2)|`nexus_gen_reference_image`|[code](./examples/flux/model_inference/Nexus-Gen-Editing.py)|[code](./examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py)|[code](./examples/flux/model_training/full/Nexus-Gen.sh)|[code](./examples/flux/model_training/validate_full/Nexus-Gen.py)|[code](./examples/flux/model_training/lora/Nexus-Gen.sh)|[code](./examples/flux/model_training/validate_lora/Nexus-Gen.py)|
+
+</details>
+
+### Wan 系列
+
+详细页面：[./examples/wanvideo/](./examples/wanvideo/)
+
+https://github.com/user-attachments/assets/1d66ae74-3b02-40a9-acc3-ea95fc039314
+
+<details>
+
+<summary>快速开始</summary>
+
+```python
+import torch
+from diffsynth import save_video
+from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
+        ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
+    ],
+)
+pipe.enable_vram_management()
+
+video = pipe(
+    prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。",
+    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+    seed=0, tiled=True,
+)
+save_video(video, "video1.mp4", fps=15, quality=5)
+```
+
+</details>
+
+<details>
+
+<summary>模型总览</summary>
+
+|模型 ID|额外参数|推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
+|-|-|-|-|-|-|-|
+|[Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B)|`input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video`|[code](./examples/wanvideo/model_inference/Wan2.2-S2V-14B.py)|-|-|-|-|
+|[Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py)|
+|[Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B)||[code](./examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py)|
+|[Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py)|
+|[Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B)||[code](./examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py)|
+|[Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B)||[code](./examples/wanvideo/model_inference/Wan2.1-T2V-14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py)|
+|[Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py)|
+|[Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py)|
+|[Wan-AI/Wan2.1-FLF2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py)|
+|[PAI/Wan2.1-Fun-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py)|
+|[PAI/Wan2.1-Fun-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control)|`control_video`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py)|
+|[PAI/Wan2.1-Fun-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py)|
+|[PAI/Wan2.1-Fun-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control)|`control_video`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py)|
+|[PAI/Wan2.1-Fun-V1.1-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control)|`control_video`, `reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py)|
+|[PAI/Wan2.1-Fun-V1.1-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control)|`control_video`, `reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](./examples/wanvideo/examples/wanmodel_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py)|
+|[PAI/Wan2.1-Fun-V1.1-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py)|
+|[PAI/Wan2.1-Fun-V1.1-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py)|
+|[PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera)|`control_camera_video`, `input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|
+|[PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera)|`control_camera_video`, `input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|
+|[iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py)|
+|[Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py)|
+|[Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py)|
+|[DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1)|`motion_bucket_id`|[code](./examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py)|
+
+</details>
+
+
+
+### 更多模型
+
+
+
+<details>
+<summary>图像生成模型</summary>
+
+详细页面：[./examples/image_synthesis/](./examples/image_synthesis/)
+
+|FLUX|Stable Diffusion 3|
+|-|-|
+|![image_1024_cfg](https://github.com/user-attachments/assets/984561e9-553d-4952-9443-79ce144f379f)|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/4df346db-6f91-420a-b4c1-26e205376098)|
+
+|Kolors|Hunyuan-DiT|
+|-|-|
+|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/53ef6f41-da11-4701-8665-9f64392607bf)|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/60b022c8-df3f-4541-95ab-bf39f2fa8bb5)|
+
+|Stable Diffusion|Stable Diffusion XL|
+|-|-|
+|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/6fc84611-8da6-4a1f-8fee-9a34eba3b4a5)|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/67687748-e738-438c-aee5-96096f09ac90)|
+
+</details>
+
+
+
+<details>
+<summary>视频生成模型</summary>
+
+- HunyuanVideo：[./examples/HunyuanVideo/](./examples/HunyuanVideo/)
+
+https://github.com/user-attachments/assets/48dd24bb-0cc6-40d2-88c3-10feed3267e9
+
+- StepVideo：[./examples/stepvideo/](./examples/stepvideo/)
+
+https://github.com/user-attachments/assets/5954fdaa-a3cf-45a3-bd35-886e3cc4581b
+
+- CogVideoX：[./examples/CogVideoX/](./examples/CogVideoX/)
+
+https://github.com/user-attachments/assets/26b044c1-4a60-44a4-842f-627ff289d006
+
+</details>
+
+
+
+<details>
+<summary>图像质量评估模型</summary>
+
+我们集成了一系列图像质量评估模型，这些模型可以用于图像生成模型的评测、对齐训练等场景中。
+
+详细页面：[./examples/image_quality_metric/](./examples/image_quality_metric/)
+
+* [ImageReward](https://github.com/THUDM/ImageReward)
+* [Aesthetic](https://github.com/christophschuhmann/improved-aesthetic-predictor)
+* [PickScore](https://github.com/yuvalkirstain/pickscore)
+* [CLIP](https://github.com/openai/CLIP)
+* [HPSv2](https://github.com/tgxs002/HPSv2)
+* [HPSv2.1](https://github.com/tgxs002/HPSv2)
+* [MPS](https://github.com/Kwai-Kolors/MPS)
+
+</details>
+
+
+
+## 创新成果
+
+DiffSynth-Studio 不仅仅是一个工程化的模型框架，更是创新成果的孵化器。
+
+<details>
+<summary>Nexus-Gen: 统一架构的图像理解、生成、编辑</summary>
+
+- 详细页面：https://github.com/modelscope/Nexus-Gen
+- 论文：[Nexus-Gen: Unified Image Understanding, Generation, and Editing via Prefilled Autoregression in Shared Embedding Space](https://arxiv.org/pdf/2504.21356)
+- 模型：[ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2), [HuggingFace](https://huggingface.co/modelscope/Nexus-GenV2)
+- 数据集：[ModelScope Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Nexus-Gen-Training-Dataset)
+- 在线体验：[ModelScope Nexus-Gen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/Nexus-Gen)
+
+![](https://github.com/modelscope/Nexus-Gen/raw/main/assets/illustrations/gen_edit.jpg)
+
+</details>
+
+
+
+<details>
+<summary>ArtAug: 图像生成模型的美学提升</summary>
+
+- 详细页面：[./examples/ArtAug/](./examples/ArtAug/)
+- 论文：[ArtAug: Enhancing Text-to-Image Generation through Synthesis-Understanding Interaction](https://arxiv.org/abs/2412.12888)
+- 模型：[ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ArtAug-lora-FLUX.1dev-v1)
+- 在线体验：[ModelScope AIGC Tab](https://www.modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=7228&modelType=LoRA&sdVersion=FLUX_1&modelUrl=modelscope%3A%2F%2FDiffSynth-Studio%2FArtAug-lora-FLUX.1dev-v1%3Frevision%3Dv1.0)
+
+|FLUX.1-dev|FLUX.1-dev + ArtAug LoRA|
+|-|-|
+|![image_1_base](https://github.com/user-attachments/assets/e1d5c505-b423-45fe-be01-25c2758f5417)|![image_1_enhance](https://github.com/user-attachments/assets/335908e3-d0bd-41c2-9d99-d10528a2d719)|
+
+</details>
+
+
+
+<details>
+
+<summary>EliGen: 精准的图像分区控制</summary>
+
+- 详细页面：[./examples/EntityControl/](./examples/EntityControl/)
+- 论文：[EliGen: Entity-Level Controlled Image Generation with Regional Attention](https://arxiv.org/abs/2501.01097)
+- 模型：[ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen), [HuggingFace](https://huggingface.co/modelscope/EliGen)
+- 在线体验：[ModelScope EliGen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/EliGen)
+- 数据集：[EliGen Train Set](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet)
+
+|实体控制区域|生成图像|
+|-|-|
+|![eligen_example_2_mask_0](https://github.com/user-attachments/assets/1c6d9445-5022-4d91-ad2e-dc05321883d1)|![eligen_example_2_0](https://github.com/user-attachments/assets/86739945-cb07-4a49-b3b3-3bb65c90d14f)|
+
+</details>
+
+
+
+<details>
+
+<summary>ExVideo: 视频生成模型的扩展训练</summary>
+
+- 项目页面：[Project Page](https://ecnu-cilab.github.io/ExVideoProjectPage/)
+- 论文：[ExVideo: Extending Video Diffusion Models via Parameter-Efficient Post-Tuning](https://arxiv.org/abs/2406.14130)
+- 代码样例：[./examples/ExVideo/](./examples/ExVideo/)
+- 模型：[ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1)
+
+https://github.com/modelscope/DiffSynth-Studio/assets/35051019/d97f6aa9-8064-4b5b-9d49-ed6001bb9acc
+
+</details>
+
+
+
+<details>
+
+<summary>Diffutoon: 高分辨率动漫风格视频渲染</summary>
+
+- 项目页面：[Project Page](https://ecnu-cilab.github.io/DiffutoonProjectPage/)
+- 论文：[Diffutoon: High-Resolution Editable Toon Shading via Diffusion Models](https://arxiv.org/abs/2401.16224)
+- 代码样例：[./examples/Diffutoon/](./examples/Diffutoon/)
+
+https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/b54c05c5-d747-4709-be5e-b39af82404dd
+
+</details>
+
+
+
+<details>
+
+<summary>DiffSynth: 本项目的初代版本</summary>
+
+- 项目页面：[Project Page](https://ecnu-cilab.github.io/DiffSynth.github.io/)
+- 论文：[DiffSynth: Latent In-Iteration Deflickering for Realistic Video Synthesis](https://arxiv.org/abs/2308.03463)
+- 代码样例：[./examples/diffsynth/](./examples/diffsynth/)
+
+https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-4481-b79f-0c3a7361a1ea
+
+</details>
+
+
+
+## 更新历史
+- **2025年8月28日** 我们支持了Wan2.2-S2V，一个音频驱动的电影级视频生成模型。请参见[./examples/wanvideo/](./examples/wanvideo/)。
+
+- **2025年8月21日** [DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2) 发布！相比于 V1 版本，训练数据集变为 [Qwen-Image-Self-Generated-Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Qwen-Image-Self-Generated-Dataset)，因此，生成的图像更符合 Qwen-Image 本身的图像分布和风格。 请参考[我们的示例代码](./examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py)。
+
+- **2025年8月21日** 我们开源了 [DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union) 结构控制 LoRA 模型，采用 In Context 的技术路线，支持多种类别的结构控制条件，包括 canny, depth, lineart, softedge, normal, openpose。 请参考[我们的示例代码](./examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py)。
+
+- **2025年8月20日** 我们开源了 [DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix) 模型，提升了 Qwen-Image-Edit 对低分辨率图像输入的编辑效果。请参考[我们的示例代码](./examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py)
+
+- **2025年8月19日** 🔥 Qwen-Image-Edit 开源，欢迎图像编辑模型新成员！
+
+- **2025年8月18日** 我们训练并开源了 Qwen-Image 的图像重绘 ControlNet 模型 [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint)，模型结构采用了轻量化的设计，请参考[我们的示例代码](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py)。
+
+- **2025年8月15日** 我们开源了 [Qwen-Image-Self-Generated-Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Qwen-Image-Self-Generated-Dataset) 数据集。这是一个使用 Qwen-Image 模型生成的图像数据集，共包含 160,000 张`1024 x 1024`图像。它包括通用、英文文本渲染和中文文本渲染子集。我们为每张图像提供了图像描述、实体和结构控制图像的标注。开发者可以使用这个数据集来训练 Qwen-Image 模型的 ControlNet 和 EliGen 等模型，我们旨在通过开源推动技术发展！
+
+- **2025年8月13日** 我们训练并开源了 Qwen-Image 的 ControlNet 模型 [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth)，模型结构采用了轻量化的设计，请参考[我们的示例代码](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py)。
+
+- **2025年8月12日** 我们训练并开源了 Qwen-Image 的 ControlNet 模型 [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny)，模型结构采用了轻量化的设计，请参考[我们的示例代码](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py)。
+
+- **2025年8月11日** 我们开源了 Qwen-Image 的蒸馏加速模型 [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA)，沿用了与 [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full) 相同的训练流程，但模型结构修改为了 LoRA，因此能够更好地与其他开源生态模型兼容。
+
+- **2025年8月7日** 我们开源了 Qwen-Image 的实体控制 LoRA 模型 [DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)。Qwen-Image-EliGen 能够实现实体级可控的文生图。技术细节请参见[论文](https://arxiv.org/abs/2501.01097)。训练数据集：[EliGenTrainSet](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet)。
+
+- **2025年8月5日** 我们开源了 Qwen-Image 的蒸馏加速模型 [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)，实现了约 5 倍加速。
+
+- **2025年8月4日** 🔥 Qwen-Image 开源，欢迎图像生成模型家族新成员！
+
+- **2025年8月1日** [FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev) 开源，这是一个专注于美学摄影的文生图模型。我们第一时间提供了全方位支持，包括低显存逐层 offload、LoRA 训练、全量训练。详细信息请参考 [./examples/flux/](./examples/flux/)。
+
+- **2025年7月28日** Wan 2.2 开源，我们第一时间提供了全方位支持，包括低显存逐层 offload、FP8 量化、序列并行、LoRA 训练、全量训练。详细信息请参考 [./examples/wanvideo/](./examples/wanvideo/)。
+
+- **2025年7月11日** 我们提出 Nexus-Gen，一个将大语言模型（LLM）的语言推理能力与扩散模型的图像生成能力相结合的统一框架。该框架支持无缝的图像理解、生成和编辑任务。
+  - 论文: [Nexus-Gen: Unified Image Understanding, Generation, and Editing via Prefilled Autoregression in Shared Embedding Space](https://arxiv.org/pdf/2504.21356)
+  - Github 仓库: https://github.com/modelscope/Nexus-Gen
+  - 模型: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2), [HuggingFace](https://huggingface.co/modelscope/Nexus-GenV2)
+  - 训练数据集: [ModelScope Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Nexus-Gen-Training-Dataset)
+  - 在线体验: [ModelScope Nexus-Gen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/Nexus-Gen)
+
+<details>
+<summary>更多</summary>
+
+- **2025年6月15日** ModelScope 官方评测框架 [EvalScope](https://github.com/modelscope/evalscope) 现已支持文生图生成评测。请参考[最佳实践](https://evalscope.readthedocs.io/zh-cn/latest/best_practice/t2i_eval.html)指南进行尝试。
+
+- **2025年3月25日** 我们的新开源项目 [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine) 现已开源！专注于稳定的模型部署，面向工业界，提供更好的工程支持、更高的计算性能和更稳定的功能。
+
+- **2025年3月31日** 我们支持 InfiniteYou，一种用于 FLUX 的人脸特征保留方法。更多细节请参考 [./examples/InfiniteYou/](./examples/InfiniteYou/)。
+
+- **2025年3月13日** 我们支持 HunyuanVideo-I2V，即腾讯开源的 HunyuanVideo 的图像到视频生成版本。更多细节请参考 [./examples/HunyuanVideo/](./examples/HunyuanVideo/)。
+
+- **2025年2月25日** 我们支持 Wan-Video，这是阿里巴巴开源的一系列最先进的视频合成模型。详见 [./examples/wanvideo/](./examples/wanvideo/)。
+
+- **2025年2月17日** 我们支持 [StepVideo](https://modelscope.cn/models/stepfun-ai/stepvideo-t2v/summary)！先进的视频合成模型！详见 [./examples/stepvideo](./examples/stepvideo/)。
+
+- **2024年12月31日** 我们提出 EliGen，一种用于精确实体级别控制的文本到图像生成的新框架，并辅以修复融合管道，将其能力扩展到图像修复任务。EliGen 可以无缝集成现有的社区模型，如 IP-Adapter 和 In-Context LoRA，提升其通用性。更多详情，请见 [./examples/EntityControl](./examples/EntityControl/)。
+  - 论文: [EliGen: Entity-Level Controlled Image Generation with Regional Attention](https://arxiv.org/abs/2501.01097)
+  - 模型: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen), [HuggingFace](https://huggingface.co/modelscope/EliGen)
+  - 在线体验: [ModelScope EliGen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/EliGen)
+  - 训练数据集: [EliGen Train Set](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet)
+
+- **2024年12月19日** 我们为 HunyuanVideo 实现了高级显存管理，使得在 24GB 显存下可以生成分辨率为 129x720x1280 的视频，或在仅 6GB 显存下生成分辨率为 129x512x384 的视频。更多细节请参考 [./examples/HunyuanVideo/](./examples/HunyuanVideo/)。
+
+- **2024年12月18日** 我们提出 ArtAug，一种通过合成-理解交互来改进文生图模型的方法。我们以 LoRA 格式为 FLUX.1-dev 训练了一个 ArtAug 增强模块。该模型将 Qwen2-VL-72B 的美学理解融入 FLUX.1-dev，从而提升了生成图像的质量。
+  - 论文: https://arxiv.org/abs/2412.12888
+  - 示例: https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/ArtAug
+  - 模型: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ArtAug-lora-FLUX.1dev-v1)
+  - 演示: [ModelScope](https://modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=7228&modelType=LoRA&sdVersion=FLUX_1&modelUrl=modelscope%3A%2F%2FDiffSynth-Studio%2FArtAug-lora-FLUX.1dev-v1%3Frevision%3Dv1.0), HuggingFace (即将上线)
+
+- **2024年10月25日** 我们提供了广泛的 FLUX ControlNet 支持。该项目支持许多不同的 ControlNet 模型，并且可以自由组合，即使它们的结构不同。此外，ControlNet 模型兼容高分辨率优化和分区控制技术，能够实现非常强大的可控图像生成。详见 [`./examples/ControlNet/`](./examples/ControlNet/)。
+
+- **2024年10月8日** 我们发布了基于 CogVideoX-5B 和 ExVideo 的扩展 LoRA。您可以从 [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1) 或 [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1) 下载此模型。
+
+- **2024年8月22日** 本项目现已支持 CogVideoX-5B。详见 [此处](/examples/video_synthesis/)。我们为这个文生视频模型提供了几个有趣的功能，包括：
+  - 文本到视频
+  - 视频编辑
+  - 自我超分
+  - 视频插帧
+
+- **2024年8月22日** 我们实现了一个有趣的画笔功能，支持所有文生图模型。现在，您可以在 AI 的辅助下使用画笔创作惊艳的图像了！
+  - 在我们的 [WebUI](#usage-in-webui) 中使用它。
+
+- **2024年8月21日** DiffSynth-Studio 现已支持 FLUX。
+  - 启用 CFG 和高分辨率修复以提升视觉质量。详见 [此处](/examples/image_synthesis/README.md)
+  - LoRA、ControlNet 和其他附加模型将很快推出。
+
+- **2024年6月21日** 我们提出 ExVideo，一种旨在增强视频生成模型能力的后训练微调技术。我们将 Stable Video Diffusion 进行了扩展，实现了长达 128 帧的长视频生成。
+  - [项目页面](https://ecnu-cilab.github.io/ExVideoProjectPage/)
+  - 源代码已在此仓库中发布。详见 [`examples/ExVideo`](./examples/ExVideo/)。
+  - 模型已发布于 [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1) 和 [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1)。
+  - 技术报告已发布于 [arXiv](https://arxiv.org/abs/2406.14130)。
+  - 您可以在此 [演示](https://huggingface.co/spaces/modelscope/ExVideo-SVD-128f-v1) 中试用 ExVideo！
+
+- **2024年6月13日** DiffSynth Studio 已迁移至 ModelScope。开发团队也从“我”转变为“我们”。当然，我仍会参与后续的开发和维护工作。
+
+- **2024年1月29日** 我们提出 Diffutoon，这是一个出色的卡通着色解决方案。
+  - [项目页面](https://ecnu-cilab.github.io/DiffutoonProjectPage/)
+  - 源代码已在此项目中发布。
+  - 技术报告（IJCAI 2024）已发布于 [arXiv](https://arxiv.org/abs/2401.16224)。
+
+- **2023年12月8日** 我们决定启动一个新项目，旨在释放扩散模型的潜力，尤其是在视频合成方面。该项目的开发工作正式开始。
+
+- **2023年11月15日** 我们提出 FastBlend，一种强大的视频去闪烁算法。
+  - sd-webui 扩展已发布于 [GitHub](https://github.com/Artiprocher/sd-webui-fastblend)。
+  - 演示视频已在 Bilibili 上展示，包含三个任务：
+    - [视频去闪烁](https://www.bilibili.com/video/BV1d94y1W7PE)
+    - [视频插帧](https://www.bilibili.com/video/BV1Lw411m71p)
+    - [图像驱动的视频渲染](https://www.bilibili.com/video/BV1RB4y1Z7LF)
+  - 技术报告已发布于 [arXiv](https://arxiv.org/abs/2311.09265)。
+  - 其他用户开发的非官方 ComfyUI 扩展已发布于 [GitHub](https://github.com/AInseven/ComfyUI-fastblend)。
+
+- **2023年10月1日** 我们发布了该项目的早期版本，名为 FastSDXL。这是构建一个扩散引擎的初步尝试。
+  - 源代码已发布于 [GitHub](https://github.com/Artiprocher/FastSDXL)。
+  - FastSDXL 包含一个可训练的 OLSS 调度器，以提高效率。
+    - OLSS 的原始仓库位于 [此处](https://github.com/alibaba/EasyNLP/tree/master/diffusion/olss_scheduler)。
+    - 技术报告（CIKM 2023）已发布于 [arXiv](https://arxiv.org/abs/2305.14677)。
+    - 演示视频已发布于 [Bilibili](https://www.bilibili.com/video/BV1w8411y7uj)。
+    - 由于 OLSS 需要额外训练，我们未在本项目中实现它。
+
+- **2023年8月29日** 我们提出 DiffSynth，一个视频合成框架。
+  - [项目页面](https://ecnu-cilab.github.io/DiffSynth.github.io/)。
+  - 源代码已发布在 [EasyNLP](https://github.com/alibaba/EasyNLP/tree/master/diffusion/DiffSynth)。
+  - 技术报告（ECML PKDD 2024）已发布于 [arXiv](https://arxiv.org/abs/2308.03463)。
+
+</details>

exp_code/1_benchmark/DiffSynth-Studio/apps/gradio/DiffSynth_Studio.py

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

exp_code/1_benchmark/DiffSynth-Studio/apps/gradio/entity_level_control.py

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

exp_code/1_benchmark/DiffSynth-Studio/apps/gradio/qwen_image_eligen.py

@@ -0,0 +1,382 @@
+import os
+import torch
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+import random
+import json
+import gradio as gr
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from modelscope import dataset_snapshot_download, snapshot_download
+
+# pip install pydantic==2.10.6
+# pip install gradio==5.4.0
+
+snapshot_download("DiffSynth-Studio/Qwen-Image-EliGen", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen", allow_file_pattern="model.safetensors")
+
+dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/qwen-image/*")
+example_json = 'data/examples/eligen/qwen-image/ui_examples.json'
+with open(example_json, 'r') as f:
+    examples = json.load(f)['examples']
+
+for idx in range(len(examples)):
+    example_id = examples[idx]['example_id']
+    entity_prompts = examples[idx]['local_prompt_list']
+    examples[idx]['mask_lists'] = [Image.open(f"data/examples/eligen/qwen-image/example_{example_id}/{i}.png").convert('RGB') for i in range(len(entity_prompts))]
+
+def create_canvas_data(background, masks):
+    if background.shape[-1] == 3:
+        background = np.dstack([background, np.full(background.shape[:2], 255, dtype=np.uint8)])
+    layers = []
+    for mask in masks:
+        if mask is not None:
+            mask_single_channel = mask if mask.ndim == 2 else mask[..., 0]
+            layer = np.zeros((mask_single_channel.shape[0], mask_single_channel.shape[1], 4), dtype=np.uint8)
+            layer[..., -1] = mask_single_channel
+            layers.append(layer)
+        else:
+            layers.append(np.zeros_like(background))
+
+    composite = background.copy()
+    for layer in layers:
+        if layer.size > 0:
+            composite = np.where(layer[..., -1:] > 0, layer, composite)
+    return {
+        "background": background,
+        "layers": layers,
+        "composite": composite,
+    }
+
+def load_example(load_example_button):
+    example_idx = int(load_example_button.split()[-1]) - 1
+    example = examples[example_idx]
+    result = [
+        50,
+        example["global_prompt"],
+        example["negative_prompt"],
+        example["seed"],
+        *example["local_prompt_list"],
+    ]
+    num_entities = len(example["local_prompt_list"])
+    result += [""] * (config["max_num_painter_layers"] - num_entities)
+    masks = []
+    for mask in example["mask_lists"]:
+        mask_single_channel = np.array(mask.convert("L"))
+        masks.append(mask_single_channel)
+    for _ in range(config["max_num_painter_layers"] - len(masks)):
+        blank_mask = np.zeros_like(masks[0]) if masks else np.zeros((512, 512), dtype=np.uint8)
+        masks.append(blank_mask)
+    background = np.ones((masks[0].shape[0], masks[0].shape[1], 4), dtype=np.uint8) * 255
+    canvas_data_list = []
+    for mask in masks:
+        canvas_data = create_canvas_data(background, [mask])
+        canvas_data_list.append(canvas_data)
+    result.extend(canvas_data_list)
+    return result
+
+def save_mask_prompts(masks, mask_prompts, global_prompt, seed=0, random_dir='0000000'):
+    save_dir = os.path.join('workdirs/tmp_mask', random_dir)
+    print(f'save to {save_dir}')
+    os.makedirs(save_dir, exist_ok=True)
+    for i, mask in enumerate(masks):
+        save_path = os.path.join(save_dir, f'{i}.png')
+        mask.save(save_path)
+    sample = {
+        "global_prompt": global_prompt,
+        "mask_prompts": mask_prompts,
+        "seed": seed,
+    }
+    with open(os.path.join(save_dir, f"prompts.json"), 'w', encoding='utf-8') as f:
+        json.dump(sample, f, ensure_ascii=False, indent=4)
+
+def visualize_masks(image, masks, mask_prompts, font_size=35, use_random_colors=False):
+    # Create a blank image for overlays
+    overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))
+    colors = [
+        (165, 238, 173, 80),
+        (76, 102, 221, 80),
+        (221, 160, 77, 80),
+        (204, 93, 71, 80),
+        (145, 187, 149, 80),
+        (134, 141, 172, 80),
+        (157, 137, 109, 80),
+        (153, 104, 95, 80),
+        (165, 238, 173, 80),
+        (76, 102, 221, 80),
+        (221, 160, 77, 80),
+        (204, 93, 71, 80),
+        (145, 187, 149, 80),
+        (134, 141, 172, 80),
+        (157, 137, 109, 80),
+        (153, 104, 95, 80),
+    ]
+    # Generate random colors for each mask
+    if use_random_colors:
+        colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))]
+    # Font settings
+    try:
+        font = ImageFont.truetype("wqy-zenhei.ttc", font_size)  # Adjust as needed
+    except IOError:
+        font = ImageFont.load_default(font_size)
+    # Overlay each mask onto the overlay image
+    for mask, mask_prompt, color in zip(masks, mask_prompts, colors):
+        if mask is None:
+            continue
+        # Convert mask to RGBA mode
+        mask_rgba = mask.convert('RGBA')
+        mask_data = mask_rgba.getdata()
+        new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data]
+        mask_rgba.putdata(new_data)
+        # Draw the mask prompt text on the mask
+        draw = ImageDraw.Draw(mask_rgba)
+        mask_bbox = mask.getbbox()  # Get the bounding box of the mask
+        if mask_bbox is None:
+            continue
+        text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10)  # Adjust text position based on mask position
+        draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font)
+        # Alpha composite the overlay with this mask
+        overlay = Image.alpha_composite(overlay, mask_rgba)
+    # Composite the overlay onto the original image
+    result = Image.alpha_composite(image.convert('RGBA'), overlay)
+    return result
+
+config = {
+    "max_num_painter_layers": 8,
+    "max_num_model_cache": 1,
+}
+
+model_dict = {}
+
+def load_model(model_type='qwen-image'):
+    global model_dict
+    model_key = f"{model_type}"
+    if model_key in model_dict:
+        return model_dict[model_key]
+    pipe = QwenImagePipeline.from_pretrained(
+        torch_dtype=torch.bfloat16,
+        device="cuda",
+        model_configs=[
+            ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+            ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
+            ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+        ],
+        tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+    )
+    pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen/model.safetensors")
+    model_dict[model_key] = pipe
+    return pipe
+
+load_model('qwen-image')
+
+with gr.Blocks() as app:
+    gr.Markdown(
+        """## EliGen: Entity-Level Controllable Text-to-Image Model
+                1. On the left, input the **global prompt** for the overall image, such as "a person stands by the river."
+                2. On the right, input the **local prompt** for each entity, such as "person," and draw the corresponding mask in the **Entity Mask Painter**. Generally, solid rectangular masks yield better results.
+                3. Click the **Generate** button to create the image. By selecting different **random seeds**, you can generate diverse images.
+                4. **You can directly click the "Load Example" button on any sample at the bottom to load example inputs.**
+        """
+    )
+
+    loading_status = gr.Textbox(label="Loading Model...", value="Loading model... Please wait...", visible=True)
+    main_interface = gr.Column(visible=False)
+
+    def initialize_model():
+        try:
+            load_model('qwen-image')
+            return {
+                loading_status: gr.update(value="Model loaded successfully!", visible=False),
+                main_interface: gr.update(visible=True),
+            }
+        except Exception as e:
+            print(f'Failed to load model with error: {e}')
+            return {
+                loading_status: gr.update(value=f"Failed to load model: {str(e)}", visible=True),
+                main_interface: gr.update(visible=True),
+            }
+
+    app.load(initialize_model, inputs=None, outputs=[loading_status, main_interface])
+
+    with main_interface:
+        with gr.Row():
+            local_prompt_list = []
+            canvas_list = []
+            random_mask_dir = gr.State(f'{random.randint(0, 1000000):08d}')
+            with gr.Column(scale=382, min_width=100):
+                model_type = gr.State('qwen-image')
+                with gr.Accordion(label="Global prompt"):
+                    prompt = gr.Textbox(label="Global Prompt", lines=3)
+                    negative_prompt = gr.Textbox(label="Negative prompt", value="", lines=3)
+                with gr.Accordion(label="Inference Options", open=True):
+                    seed = gr.Number(minimum=0, maximum=10**9, value=42, interactive=True, label="Random seed", show_label=True)
+                    num_inference_steps = gr.Slider(minimum=1, maximum=100, value=30, step=1, interactive=True, label="Inference steps")
+                    cfg_scale = gr.Slider(minimum=2.0, maximum=10.0, value=4.0, step=0.1, interactive=True, label="Classifier-free guidance scale")
+                    height = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Height")
+                    width = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Width")
+                with gr.Accordion(label="Inpaint Input Image", open=False, visible=False):
+                    input_image = gr.Image(sources=None, show_label=False, interactive=True, type="pil")
+                    background_weight = gr.Slider(minimum=0.0, maximum=1000., value=0., step=1, interactive=False, label="background_weight", visible=False)
+
+                    with gr.Column():
+                        reset_input_button = gr.Button(value="Reset Inpaint Input")
+                        send_input_to_painter = gr.Button(value="Set as painter's background")
+                    @gr.on(inputs=[input_image], outputs=[input_image], triggers=reset_input_button.click)
+                    def reset_input_image(input_image):
+                        return None
+
+            with gr.Column(scale=618, min_width=100):
+                with gr.Accordion(label="Entity Painter"):
+                    for painter_layer_id in range(config["max_num_painter_layers"]):
+                        with gr.Tab(label=f"Entity {painter_layer_id}"):
+                            local_prompt = gr.Textbox(label="Local prompt", key=f"local_prompt_{painter_layer_id}")
+                            canvas = gr.ImageEditor(
+                                canvas_size=(1024, 1024),
+                                sources=None,
+                                layers=False,
+                                interactive=True,
+                                image_mode="RGBA",
+                                brush=gr.Brush(
+                                    default_size=50,
+                                    default_color="#000000",
+                                    colors=["#000000"],
+                                ),
+                                label="Entity Mask Painter",
+                                key=f"canvas_{painter_layer_id}",
+                                width=width,
+                                height=height,
+                            )
+                            @gr.on(inputs=[height, width, canvas], outputs=canvas, triggers=[height.change, width.change, canvas.clear], show_progress="hidden")
+                            def resize_canvas(height, width, canvas):
+                                if canvas is None or canvas["background"] is None:
+                                    return np.ones((height, width, 3), dtype=np.uint8) * 255
+                                h, w = canvas["background"].shape[:2]
+                                if h != height or width != w:
+                                    return np.ones((height, width, 3), dtype=np.uint8) * 255
+                                else:
+                                    return canvas
+                            local_prompt_list.append(local_prompt)
+                            canvas_list.append(canvas)
+                with gr.Accordion(label="Results"):
+                    run_button = gr.Button(value="Generate", variant="primary")
+                    output_image = gr.Image(sources=None, show_label=False, interactive=False, type="pil")
+                    with gr.Row():
+                        with gr.Column():
+                            output_to_painter_button = gr.Button(value="Set as painter's background")
+                        with gr.Column():
+                            return_with_mask = gr.Checkbox(value=False, interactive=True, label="show result with mask painting")
+                            output_to_input_button = gr.Button(value="Set as input image", visible=False, interactive=False)
+                    real_output = gr.State(None)
+                    mask_out = gr.State(None)
+
+                    @gr.on(
+                        inputs=[model_type, prompt, negative_prompt, cfg_scale, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir] + local_prompt_list + canvas_list,
+                        outputs=[output_image, real_output, mask_out],
+                        triggers=run_button.click
+                    )
+                    def generate_image(model_type, prompt, negative_prompt, cfg_scale, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir, *args, progress=gr.Progress()):
+                        pipe = load_model(model_type)
+                        input_params = {
+                            "prompt": prompt,
+                            "negative_prompt": negative_prompt,
+                            "cfg_scale": cfg_scale,
+                            "num_inference_steps": num_inference_steps,
+                            "height": height,
+                            "width": width,
+                            "progress_bar_cmd": progress.tqdm,
+                        }
+                        # if input_image is not None:
+                        #     input_params["input_image"] = input_image.resize((width, height)).convert("RGB")
+                        #     input_params["enable_eligen_inpaint"] = True
+
+                        local_prompt_list, canvas_list = (
+                            args[0 * config["max_num_painter_layers"]: 1 * config["max_num_painter_layers"]],
+                            args[1 * config["max_num_painter_layers"]: 2 * config["max_num_painter_layers"]],
+                        )
+                        local_prompts, masks = [], []
+                        for local_prompt, canvas in zip(local_prompt_list, canvas_list):
+                            if isinstance(local_prompt, str) and len(local_prompt) > 0:
+                                local_prompts.append(local_prompt)
+                                masks.append(Image.fromarray(canvas["layers"][0][:, :, -1]).convert("RGB"))
+                        entity_prompts = None if len(local_prompts) == 0 else local_prompts
+                        entity_masks = None if len(masks) == 0 or entity_prompts is None else masks
+                        input_params.update({
+                            "eligen_entity_prompts": entity_prompts,
+                            "eligen_entity_masks": entity_masks,
+                        })
+                        torch.manual_seed(seed)
+                        save_mask_prompts(masks, local_prompts, prompt, seed, random_mask_dir)
+                        image = pipe(**input_params)
+                        masks = [mask.resize(image.size) for mask in masks]
+                        image_with_mask = visualize_masks(image, masks, local_prompts)
+
+                        real_output = gr.State(image)
+                        mask_out = gr.State(image_with_mask)
+
+                        if return_with_mask:
+                            return image_with_mask, real_output, mask_out
+                        return image, real_output, mask_out
+
+                    @gr.on(inputs=[input_image] + canvas_list, outputs=canvas_list, triggers=send_input_to_painter.click)
+                    def send_input_to_painter_background(input_image, *canvas_list):
+                        if input_image is None:
+                            return tuple(canvas_list)
+                        for canvas in canvas_list:
+                            h, w = canvas["background"].shape[:2]
+                            canvas["background"] = input_image.resize((w, h))
+                        return tuple(canvas_list)
+                    @gr.on(inputs=[real_output] + canvas_list, outputs=canvas_list, triggers=output_to_painter_button.click)
+                    def send_output_to_painter_background(real_output, *canvas_list):
+                        if real_output is None:
+                            return tuple(canvas_list)
+                        for canvas in canvas_list:
+                            h, w = canvas["background"].shape[:2]
+                            canvas["background"] = real_output.value.resize((w, h))
+                        return tuple(canvas_list)
+                    @gr.on(inputs=[return_with_mask, real_output, mask_out], outputs=[output_image], triggers=[return_with_mask.change], show_progress="hidden")
+                    def show_output(return_with_mask, real_output, mask_out):
+                        if return_with_mask:
+                            return mask_out.value
+                        else:
+                            return real_output.value
+                    @gr.on(inputs=[real_output], outputs=[input_image], triggers=output_to_input_button.click)
+                    def send_output_to_pipe_input(real_output):
+                        return real_output.value
+
+        with gr.Column():
+            gr.Markdown("## Examples")
+            for i in range(0, len(examples), 2):
+                with gr.Row():
+                    if i < len(examples):
+                        example = examples[i]
+                        with gr.Column():
+                            example_image = gr.Image(
+                                value=f"data/examples/eligen/qwen-image/example_{example['example_id']}/example_image.png",
+                                label=example["description"],
+                                interactive=False,
+                                width=1024,
+                                height=512
+                            )
+                            load_example_button = gr.Button(value=f"Load Example {example['example_id']}")
+                            load_example_button.click(
+                                load_example,
+                                inputs=[load_example_button],
+                                outputs=[num_inference_steps, prompt, negative_prompt, seed] + local_prompt_list + canvas_list
+                            )
+
+                    if i + 1 < len(examples):
+                        example = examples[i + 1]
+                        with gr.Column():
+                            example_image = gr.Image(
+                                value=f"data/examples/eligen/qwen-image/example_{example['example_id']}/example_image.png",
+                                label=example["description"],
+                                interactive=False,
+                                width=1024,
+                                height=512
+                            )
+                            load_example_button = gr.Button(value=f"Load Example {example['example_id']}")
+                            load_example_button.click(
+                                load_example,
+                                inputs=[load_example_button],
+                                outputs=[num_inference_steps, prompt, negative_prompt, seed] + local_prompt_list + canvas_list
+                            )
+app.config["show_progress"] = "hidden"
+app.launch(share=False)

exp_code/1_benchmark/DiffSynth-Studio/apps/streamlit/DiffSynth_Studio.py

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

exp_code/1_benchmark/DiffSynth-Studio/apps/streamlit/pages/1_Image_Creator.py

@@ -0,0 +1,362 @@
+import torch, os, io, json, time
+import numpy as np
+from PIL import Image
+import streamlit as st
+st.set_page_config(layout="wide")
+from streamlit_drawable_canvas import st_canvas
+from diffsynth.models import ModelManager
+from diffsynth.pipelines import SDImagePipeline, SDXLImagePipeline, SD3ImagePipeline, HunyuanDiTImagePipeline, FluxImagePipeline
+from diffsynth.data.video import crop_and_resize
+
+
+config = {
+    "Stable Diffusion": {
+        "model_folder": "models/stable_diffusion",
+        "pipeline_class": SDImagePipeline,
+        "fixed_parameters": {}
+    },
+    "Stable Diffusion XL": {
+        "model_folder": "models/stable_diffusion_xl",
+        "pipeline_class": SDXLImagePipeline,
+        "fixed_parameters": {}
+    },
+    "Stable Diffusion 3": {
+        "model_folder": "models/stable_diffusion_3",
+        "pipeline_class": SD3ImagePipeline,
+        "fixed_parameters": {}
+    },
+    "Stable Diffusion XL Turbo": {
+        "model_folder": "models/stable_diffusion_xl_turbo",
+        "pipeline_class": SDXLImagePipeline,
+        "fixed_parameters": {
+            "negative_prompt": "",
+            "cfg_scale": 1.0,
+            "num_inference_steps": 1,
+            "height": 512,
+            "width": 512,
+        }
+    },
+    "Kolors": {
+        "model_folder": "models/kolors",
+        "pipeline_class": SDXLImagePipeline,
+        "fixed_parameters": {}
+    },
+    "HunyuanDiT": {
+        "model_folder": "models/HunyuanDiT",
+        "pipeline_class": HunyuanDiTImagePipeline,
+        "fixed_parameters": {
+            "height": 1024,
+            "width": 1024,
+        }
+    },
+    "FLUX": {
+        "model_folder": "models/FLUX",
+        "pipeline_class": FluxImagePipeline,
+        "fixed_parameters": {
+            "cfg_scale": 1.0,
+        }
+    }
+}
+
+
+def load_model_list(model_type):
+    folder = config[model_type]["model_folder"]
+    file_list = [i for i in os.listdir(folder) if i.endswith(".safetensors")]
+    if model_type in ["HunyuanDiT", "Kolors", "FLUX"]:
+        file_list += [i for i in os.listdir(folder) if os.path.isdir(os.path.join(folder, i))]
+    file_list = sorted(file_list)
+    return file_list
+
+
+def release_model():
+    if "model_manager" in st.session_state:
+        st.session_state["model_manager"].to("cpu")
+        del st.session_state["loaded_model_path"]
+        del st.session_state["model_manager"]
+        del st.session_state["pipeline"]
+        torch.cuda.empty_cache()
+
+
+def load_model(model_type, model_path):
+    model_manager = ModelManager()
+    if model_type == "HunyuanDiT":
+        model_manager.load_models([
+            os.path.join(model_path, "clip_text_encoder/pytorch_model.bin"),
+            os.path.join(model_path, "mt5/pytorch_model.bin"),
+            os.path.join(model_path, "model/pytorch_model_ema.pt"),
+            os.path.join(model_path, "sdxl-vae-fp16-fix/diffusion_pytorch_model.bin"),
+        ])
+    elif model_type == "Kolors":
+        model_manager.load_models([
+            os.path.join(model_path, "text_encoder"),
+            os.path.join(model_path, "unet/diffusion_pytorch_model.safetensors"),
+            os.path.join(model_path, "vae/diffusion_pytorch_model.safetensors"),
+        ])
+    elif model_type == "FLUX":
+        model_manager.torch_dtype = torch.bfloat16
+        file_list = [
+            os.path.join(model_path, "text_encoder/model.safetensors"),
+            os.path.join(model_path, "text_encoder_2"),
+        ]
+        for file_name in os.listdir(model_path):
+            if file_name.endswith(".safetensors"):
+                file_list.append(os.path.join(model_path, file_name))
+        model_manager.load_models(file_list)
+    else:
+        model_manager.load_model(model_path)
+    pipeline = config[model_type]["pipeline_class"].from_model_manager(model_manager)
+    st.session_state.loaded_model_path = model_path
+    st.session_state.model_manager = model_manager
+    st.session_state.pipeline = pipeline
+    return model_manager, pipeline
+
+
+def use_output_image_as_input(update=True):
+    # Search for input image
+    output_image_id = 0
+    selected_output_image = None
+    while True:
+        if f"use_output_as_input_{output_image_id}" not in st.session_state:
+            break
+        if st.session_state[f"use_output_as_input_{output_image_id}"]:
+            selected_output_image = st.session_state["output_images"][output_image_id]
+            break
+        output_image_id += 1
+    if update and selected_output_image is not None:
+        st.session_state["input_image"] = selected_output_image
+    return selected_output_image is not None
+
+
+def apply_stroke_to_image(stroke_image, image):
+    image = np.array(image.convert("RGB")).astype(np.float32)
+    height, width, _ = image.shape
+
+    stroke_image = np.array(Image.fromarray(stroke_image).resize((width, height))).astype(np.float32)
+    weight = stroke_image[:, :, -1:] / 255
+    stroke_image = stroke_image[:, :, :-1]
+
+    image = stroke_image * weight + image * (1 - weight)
+    image = np.clip(image, 0, 255).astype(np.uint8)
+    image = Image.fromarray(image)
+    return image
+
+
+@st.cache_data
+def image2bits(image):
+    image_byte = io.BytesIO()
+    image.save(image_byte, format="PNG")
+    image_byte = image_byte.getvalue()
+    return image_byte
+
+
+def show_output_image(image):
+    st.image(image, use_column_width="always")
+    st.button("Use it as input image", key=f"use_output_as_input_{image_id}")
+    st.download_button("Download", data=image2bits(image), file_name="image.png", mime="image/png", key=f"download_output_{image_id}")
+
+
+column_input, column_output = st.columns(2)
+with st.sidebar:
+    # Select a model
+    with st.expander("Model", expanded=True):
+        model_type = st.selectbox("Model type", [model_type_ for model_type_ in config])
+        fixed_parameters = config[model_type]["fixed_parameters"]
+        model_path_list = ["None"] + load_model_list(model_type)
+        model_path = st.selectbox("Model path", model_path_list)
+
+        # Load the model
+        if model_path == "None":
+            # No models are selected. Release VRAM.
+            st.markdown("No models are selected.")
+            release_model()
+        else:
+            # A model is selected.
+            model_path = os.path.join(config[model_type]["model_folder"], model_path)
+            if st.session_state.get("loaded_model_path", "") != model_path:
+                # The loaded model is not the selected model. Reload it.
+                st.markdown(f"Loading model at {model_path}.")
+                st.markdown("Please wait a moment...")
+                release_model()
+                model_manager, pipeline = load_model(model_type, model_path)
+                st.markdown("Done.")
+            else:
+                # The loaded model is not the selected model. Fetch it from `st.session_state`.
+                st.markdown(f"Loading model at {model_path}.")
+                st.markdown("Please wait a moment...")
+                model_manager, pipeline = st.session_state.model_manager, st.session_state.pipeline
+                st.markdown("Done.")
+
+    # Show parameters
+    with st.expander("Prompt", expanded=True):
+        prompt = st.text_area("Positive prompt")
+        if "negative_prompt" in fixed_parameters:
+            negative_prompt = fixed_parameters["negative_prompt"]
+        else:
+            negative_prompt = st.text_area("Negative prompt")
+        if "cfg_scale" in fixed_parameters:
+            cfg_scale = fixed_parameters["cfg_scale"]
+        else:
+            cfg_scale = st.slider("Classifier-free guidance scale", min_value=1.0, max_value=10.0, value=7.5)
+    with st.expander("Image", expanded=True):
+        if "num_inference_steps" in fixed_parameters:
+            num_inference_steps = fixed_parameters["num_inference_steps"]
+        else:
+            num_inference_steps = st.slider("Inference steps", min_value=1, max_value=100, value=20)
+        if "height" in fixed_parameters:
+            height = fixed_parameters["height"]
+        else:
+            height = st.select_slider("Height", options=[256, 512, 768, 1024, 2048], value=512)
+        if "width" in fixed_parameters:
+            width = fixed_parameters["width"]
+        else:
+            width = st.select_slider("Width", options=[256, 512, 768, 1024, 2048], value=512)
+        num_images = st.number_input("Number of images", value=2)
+        use_fixed_seed = st.checkbox("Use fixed seed", value=False)
+        if use_fixed_seed:
+            seed = st.number_input("Random seed", min_value=0, max_value=10**9, step=1, value=0)
+
+    # Other fixed parameters
+    denoising_strength = 1.0
+    repetition = 1
+
+
+# Show input image
+with column_input:
+    with st.expander("Input image (Optional)", expanded=True):
+        with st.container(border=True):
+            column_white_board, column_upload_image = st.columns([1, 2])
+            with column_white_board:
+                create_white_board = st.button("Create white board")
+                delete_input_image = st.button("Delete input image")
+            with column_upload_image:
+                upload_image = st.file_uploader("Upload image", type=["png", "jpg"], key="upload_image")
+
+        if upload_image is not None:
+            st.session_state["input_image"] = crop_and_resize(Image.open(upload_image), height, width)
+        elif create_white_board:
+            st.session_state["input_image"] = Image.fromarray(np.ones((height, width, 3), dtype=np.uint8) * 255)
+        else:
+            use_output_image_as_input()
+
+        if delete_input_image and "input_image" in st.session_state:
+            del st.session_state.input_image
+        if delete_input_image and "upload_image" in st.session_state:
+            del st.session_state.upload_image
+
+        input_image = st.session_state.get("input_image", None)
+        if input_image is not None:
+            with st.container(border=True):
+                column_drawing_mode, column_color_1, column_color_2 = st.columns([4, 1, 1])
+                with column_drawing_mode:
+                    drawing_mode = st.radio("Drawing tool", ["transform", "freedraw", "line", "rect"], horizontal=True, index=1)
+                with column_color_1:
+                    stroke_color = st.color_picker("Stroke color")
+                with column_color_2:
+                    fill_color = st.color_picker("Fill color")
+                stroke_width = st.slider("Stroke width", min_value=1, max_value=50, value=10)
+            with st.container(border=True):
+                denoising_strength = st.slider("Denoising strength", min_value=0.0, max_value=1.0, value=0.7)
+                repetition = st.slider("Repetition", min_value=1, max_value=8, value=1)
+            with st.container(border=True):
+                input_width, input_height = input_image.size
+                canvas_result = st_canvas(
+                    fill_color=fill_color,
+                    stroke_width=stroke_width,
+                    stroke_color=stroke_color,
+                    background_color="rgba(255, 255, 255, 0)",
+                    background_image=input_image,
+                    update_streamlit=True,
+                    height=int(512 / input_width * input_height),
+                    width=512,
+                    drawing_mode=drawing_mode,
+                    key="canvas"
+                )
+
+    num_painter_layer = st.number_input("Number of painter layers", min_value=0, max_value=10, step=1, value=0)
+    local_prompts, masks, mask_scales = [], [], []
+    white_board = Image.fromarray(np.ones((512, 512, 3), dtype=np.uint8) * 255)
+    painter_layers_json_data = []
+    for painter_tab_id in range(num_painter_layer):
+        with st.expander(f"Painter layer {painter_tab_id}", expanded=True):
+            enable_local_prompt = st.checkbox(f"Enable prompt {painter_tab_id}", value=True)
+            local_prompt = st.text_area(f"Prompt {painter_tab_id}")
+            mask_scale = st.slider(f"Mask scale {painter_tab_id}", min_value=0.0, max_value=3.0, value=1.0)
+            stroke_width = st.slider(f"Stroke width {painter_tab_id}", min_value=1, max_value=300, value=100)
+            canvas_result_local = st_canvas(
+                fill_color="#000000",
+                stroke_width=stroke_width,
+                stroke_color="#000000",
+                background_color="rgba(255, 255, 255, 0)",
+                background_image=white_board,
+                update_streamlit=True,
+                height=512,
+                width=512,
+                drawing_mode="freedraw",
+                key=f"canvas_{painter_tab_id}"
+            )
+            if canvas_result_local.json_data is not None:
+                painter_layers_json_data.append(canvas_result_local.json_data.copy())
+                painter_layers_json_data[-1]["prompt"] = local_prompt
+            if enable_local_prompt:
+                local_prompts.append(local_prompt)
+                if canvas_result_local.image_data is not None:
+                    mask = apply_stroke_to_image(canvas_result_local.image_data, white_board)
+                else:
+                    mask = white_board
+                mask = Image.fromarray(255 - np.array(mask))
+                masks.append(mask)
+                mask_scales.append(mask_scale)
+    save_painter_layers = st.button("Save painter layers")
+    if save_painter_layers:
+        os.makedirs("data/painter_layers", exist_ok=True)
+        json_file_path = f"data/painter_layers/{time.time_ns()}.json"
+        with open(json_file_path, "w") as f:
+            json.dump(painter_layers_json_data, f, indent=4)
+            st.markdown(f"Painter layers are saved in {json_file_path}.")
+
+
+with column_output:
+    run_button = st.button("Generate image", type="primary")
+    auto_update = st.checkbox("Auto update", value=False)
+    num_image_columns = st.slider("Columns", min_value=1, max_value=8, value=2)
+    image_columns = st.columns(num_image_columns)
+
+    # Run
+    if (run_button or auto_update) and model_path != "None":
+
+        if input_image is not None:
+            input_image = input_image.resize((width, height))
+            if canvas_result.image_data is not None:
+                input_image = apply_stroke_to_image(canvas_result.image_data, input_image)
+
+        output_images = []
+        for image_id in range(num_images * repetition):
+            if use_fixed_seed:
+                torch.manual_seed(seed + image_id)
+            else:
+                torch.manual_seed(np.random.randint(0, 10**9))
+            if image_id >= num_images:
+                input_image = output_images[image_id - num_images]
+            with image_columns[image_id % num_image_columns]:
+                progress_bar_st = st.progress(0.0)
+                image = pipeline(
+                    prompt, negative_prompt=negative_prompt,
+                    local_prompts=local_prompts, masks=masks, mask_scales=mask_scales,
+                    cfg_scale=cfg_scale, num_inference_steps=num_inference_steps,
+                    height=height, width=width,
+                    input_image=input_image, denoising_strength=denoising_strength,
+                    progress_bar_st=progress_bar_st
+                )
+                output_images.append(image)
+                progress_bar_st.progress(1.0)
+                show_output_image(image)
+                st.session_state["output_images"] = output_images
+
+    elif "output_images" in st.session_state:
+        for image_id in range(len(st.session_state.output_images)):
+            with image_columns[image_id % num_image_columns]:
+                image = st.session_state.output_images[image_id]
+                progress_bar = st.progress(1.0)
+                show_output_image(image)
+    if "upload_image" in st.session_state and use_output_image_as_input(update=False):
+        st.markdown("If you want to use an output image as input image, please delete the uploaded image manually.")

exp_code/1_benchmark/DiffSynth-Studio/apps/streamlit/pages/2_Video_Creator.py

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

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/__init__.py

@@ -0,0 +1,6 @@
+from .data import *
+from .models import *
+from .prompters import *
+from .schedulers import *
+from .pipelines import *
+from .controlnets import *

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/configs/model_config.py

@@ -0,0 +1,852 @@
+from typing_extensions import Literal, TypeAlias
+
+from ..models.sd_text_encoder import SDTextEncoder
+from ..models.sd_unet import SDUNet
+from ..models.sd_vae_encoder import SDVAEEncoder
+from ..models.sd_vae_decoder import SDVAEDecoder
+
+from ..models.sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2
+from ..models.sdxl_unet import SDXLUNet
+from ..models.sdxl_vae_decoder import SDXLVAEDecoder
+from ..models.sdxl_vae_encoder import SDXLVAEEncoder
+
+from ..models.sd3_text_encoder import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3
+from ..models.sd3_dit import SD3DiT
+from ..models.sd3_vae_decoder import SD3VAEDecoder
+from ..models.sd3_vae_encoder import SD3VAEEncoder
+
+from ..models.sd_controlnet import SDControlNet
+from ..models.sdxl_controlnet import SDXLControlNetUnion
+
+from ..models.sd_motion import SDMotionModel
+from ..models.sdxl_motion import SDXLMotionModel
+
+from ..models.svd_image_encoder import SVDImageEncoder
+from ..models.svd_unet import SVDUNet
+from ..models.svd_vae_decoder import SVDVAEDecoder
+from ..models.svd_vae_encoder import SVDVAEEncoder
+
+from ..models.sd_ipadapter import SDIpAdapter, IpAdapterCLIPImageEmbedder
+from ..models.sdxl_ipadapter import SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder
+
+from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder
+from ..models.hunyuan_dit import HunyuanDiT
+
+from ..models.flux_dit import FluxDiT
+from ..models.flux_text_encoder import FluxTextEncoder2
+from ..models.flux_vae import FluxVAEEncoder, FluxVAEDecoder
+from ..models.flux_controlnet import FluxControlNet
+from ..models.flux_ipadapter import FluxIpAdapter
+from ..models.flux_infiniteyou import InfiniteYouImageProjector
+
+from ..models.cog_vae import CogVAEEncoder, CogVAEDecoder
+from ..models.cog_dit import CogDiT
+
+from ..models.omnigen import OmniGenTransformer
+
+from ..models.hunyuan_video_vae_decoder import HunyuanVideoVAEDecoder
+from ..models.hunyuan_video_vae_encoder import HunyuanVideoVAEEncoder
+
+from ..extensions.RIFE import IFNet
+from ..extensions.ESRGAN import RRDBNet
+
+from ..models.hunyuan_video_dit import HunyuanVideoDiT
+
+from ..models.stepvideo_vae import StepVideoVAE
+from ..models.stepvideo_dit import StepVideoModel
+
+from ..models.wan_video_dit import WanModel
+from ..models.wan_video_dit_s2v import WanS2VModel
+from ..models.wan_video_text_encoder import WanTextEncoder
+from ..models.wan_video_image_encoder import WanImageEncoder
+from ..models.wan_video_vae import WanVideoVAE, WanVideoVAE38
+from ..models.wan_video_motion_controller import WanMotionControllerModel
+from ..models.wan_video_vace import VaceWanModel
+from ..models.wav2vec import WanS2VAudioEncoder
+
+from ..models.step1x_connector import Qwen2Connector
+
+from ..models.flux_value_control import SingleValueEncoder
+
+from ..lora.flux_lora import FluxLoraPatcher
+from ..models.flux_lora_encoder import FluxLoRAEncoder
+
+from ..models.nexus_gen_projector import NexusGenAdapter, NexusGenImageEmbeddingMerger
+from ..models.nexus_gen import NexusGenAutoregressiveModel
+
+from ..models.qwen_image_dit import QwenImageDiT
+from ..models.qwen_image_text_encoder import QwenImageTextEncoder
+from ..models.qwen_image_vae import QwenImageVAE
+from ..models.qwen_image_controlnet import QwenImageBlockWiseControlNet
+
+model_loader_configs = [
+    # These configs are provided for detecting model type automatically.
+    # The format is (state_dict_keys_hash, state_dict_keys_hash_with_shape, model_names, model_classes, model_resource)
+    (None, "091b0e30e77c76626b3ba62acdf95343", ["sd_controlnet"], [SDControlNet], "civitai"),
+    (None, "4a6c8306a27d916dea81263c8c88f450", ["hunyuan_dit_clip_text_encoder"], [HunyuanDiTCLIPTextEncoder], "civitai"),
+    (None, "f4aec400fe394297961218c768004521", ["hunyuan_dit"], [HunyuanDiT], "civitai"),
+    (None, "9e6e58043a5a2e332803ed42f6ee7181", ["hunyuan_dit_t5_text_encoder"], [HunyuanDiTT5TextEncoder], "civitai"),
+    (None, "13115dd45a6e1c39860f91ab073b8a78", ["sdxl_vae_encoder", "sdxl_vae_decoder"], [SDXLVAEEncoder, SDXLVAEDecoder], "diffusers"),
+    (None, "d78aa6797382a6d455362358a3295ea9", ["sd_ipadapter_clip_image_encoder"], [IpAdapterCLIPImageEmbedder], "diffusers"),
+    (None, "e291636cc15e803186b47404262ef812", ["sd_ipadapter"], [SDIpAdapter], "civitai"),
+    (None, "399c81f2f8de8d1843d0127a00f3c224", ["sdxl_ipadapter_clip_image_encoder"], [IpAdapterXLCLIPImageEmbedder], "diffusers"),
+    (None, "a64eac9aa0db4b9602213bc0131281c7", ["sdxl_ipadapter"], [SDXLIpAdapter], "civitai"),
+    (None, "52817e4fdd89df154f02749ca6f692ac", ["sdxl_unet"], [SDXLUNet], "diffusers"),
+    (None, "03343c606f16d834d6411d0902b53636", ["sd_text_encoder", "sd_unet", "sd_vae_decoder", "sd_vae_encoder"], [SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder], "civitai"),
+    (None, "d4ba77a7ece070679b4a987f58f201e9", ["sd_text_encoder"], [SDTextEncoder], "civitai"),
+    (None, "d0c89e55c5a57cf3981def0cb1c9e65a", ["sd_vae_decoder", "sd_vae_encoder"], [SDVAEDecoder, SDVAEEncoder], "civitai"),
+    (None, "3926bf373b39a67eeafd7901478a47a7", ["sd_unet"], [SDUNet], "civitai"),
+    (None, "1e0c39ec176b9007c05f76d52b554a4d", ["sd3_text_encoder_1", "sd3_text_encoder_2", "sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3TextEncoder1, SD3TextEncoder2, SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"),
+    (None, "d9e0290829ba8d98e28e1a2b1407db4a", ["sd3_text_encoder_1", "sd3_text_encoder_2", "sd3_text_encoder_3", "sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3, SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"),
+    (None, "5072d0b24e406b49507abe861cf97691", ["sd3_text_encoder_3"], [SD3TextEncoder3], "civitai"),
+    (None, "4cf64a799d04260df438c6f33c9a047e", ["sdxl_text_encoder", "sdxl_text_encoder_2", "sdxl_unet", "sdxl_vae_decoder", "sdxl_vae_encoder"], [SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder], "civitai"),
+    (None, "d9b008a867c498ab12ad24042eff8e3f", ["sdxl_text_encoder", "sdxl_text_encoder_2", "sdxl_unet", "sdxl_vae_decoder", "sdxl_vae_encoder"], [SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder], "civitai"), # SDXL-Turbo
+    (None, "025bb7452e531a3853d951d77c63f032", ["sdxl_text_encoder", "sdxl_text_encoder_2"], [SDXLTextEncoder, SDXLTextEncoder2], "civitai"),
+    (None, "298997b403a4245c04102c9f36aac348", ["sdxl_unet"], [SDXLUNet], "civitai"),
+    (None, "2a07abce74b4bdc696b76254ab474da6", ["svd_image_encoder", "svd_unet", "svd_vae_decoder", "svd_vae_encoder"], [SVDImageEncoder, SVDUNet, SVDVAEDecoder, SVDVAEEncoder], "civitai"),
+    (None, "c96a285a6888465f87de22a984d049fb", ["sd_motion_modules"], [SDMotionModel], "civitai"),
+    (None, "72907b92caed19bdb2adb89aa4063fe2", ["sdxl_motion_modules"], [SDXLMotionModel], "civitai"),
+    (None, "31d2d9614fba60511fc9bf2604aa01f7", ["sdxl_controlnet"], [SDXLControlNetUnion], "diffusers"),
+    (None, "94eefa3dac9cec93cb1ebaf1747d7b78", ["sd3_text_encoder_1"], [SD3TextEncoder1], "diffusers"),
+    (None, "1aafa3cc91716fb6b300cc1cd51b85a3", ["flux_vae_encoder", "flux_vae_decoder"], [FluxVAEEncoder, FluxVAEDecoder], "diffusers"),
+    (None, "21ea55f476dfc4fd135587abb59dfe5d", ["flux_vae_encoder", "flux_vae_decoder"], [FluxVAEEncoder, FluxVAEDecoder], "civitai"),
+    (None, "a29710fea6dddb0314663ee823598e50", ["flux_dit"], [FluxDiT], "civitai"),
+    (None, "57b02550baab820169365b3ee3afa2c9", ["flux_dit"], [FluxDiT], "civitai"),
+    (None, "3394f306c4cbf04334b712bf5aaed95f", ["flux_dit"], [FluxDiT], "civitai"),
+    (None, "023f054d918a84ccf503481fd1e3379e", ["flux_dit"], [FluxDiT], "civitai"),
+    (None, "d02f41c13549fa5093d3521f62a5570a", ["flux_dit"], [FluxDiT], "civitai"),
+    (None, "605c56eab23e9e2af863ad8f0813a25d", ["flux_dit"], [FluxDiT], "diffusers"),
+    (None, "0629116fce1472503a66992f96f3eb1a", ["flux_value_controller"], [SingleValueEncoder], "civitai"),
+    (None, "280189ee084bca10f70907bf6ce1649d", ["cog_vae_encoder", "cog_vae_decoder"], [CogVAEEncoder, CogVAEDecoder], "diffusers"),
+    (None, "9b9313d104ac4df27991352fec013fd4", ["rife"], [IFNet], "civitai"),
+    (None, "6b7116078c4170bfbeaedc8fe71f6649", ["esrgan"], [RRDBNet], "civitai"),
+    (None, "61cbcbc7ac11f169c5949223efa960d1", ["omnigen_transformer"], [OmniGenTransformer], "diffusers"),
+    (None, "78d18b9101345ff695f312e7e62538c0", ["flux_controlnet"], [FluxControlNet], "diffusers"),
+    (None, "b001c89139b5f053c715fe772362dd2a", ["flux_controlnet"], [FluxControlNet], "diffusers"),
+    (None, "52357cb26250681367488a8954c271e8", ["flux_controlnet"], [FluxControlNet], "diffusers"),
+    (None, "0cfd1740758423a2a854d67c136d1e8c", ["flux_controlnet"], [FluxControlNet], "diffusers"),
+    (None, "7f9583eb8ba86642abb9a21a4b2c9e16", ["flux_controlnet"], [FluxControlNet], "diffusers"),
+    (None, "43ad5aaa27dd4ee01b832ed16773fa52", ["flux_controlnet"], [FluxControlNet], "diffusers"),
+    (None, "c07c0f04f5ff55e86b4e937c7a40d481", ["infiniteyou_image_projector"], [InfiniteYouImageProjector], "diffusers"),
+    (None, "4daaa66cc656a8fe369908693dad0a35", ["flux_ipadapter"], [FluxIpAdapter], "diffusers"),
+    (None, "51aed3d27d482fceb5e0739b03060e8f", ["sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"),
+    (None, "98cc34ccc5b54ae0e56bdea8688dcd5a", ["sd3_text_encoder_2"], [SD3TextEncoder2], "civitai"),
+    (None, "77ff18050dbc23f50382e45d51a779fe", ["sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"),
+    (None, "5da81baee73198a7c19e6d2fe8b5148e", ["sd3_text_encoder_1"], [SD3TextEncoder1], "diffusers"),
+    (None, "aeb82dce778a03dcb4d726cb03f3c43f", ["hunyuan_video_vae_decoder", "hunyuan_video_vae_encoder"], [HunyuanVideoVAEDecoder, HunyuanVideoVAEEncoder], "diffusers"),
+    (None, "b9588f02e78f5ccafc9d7c0294e46308", ["hunyuan_video_dit"], [HunyuanVideoDiT], "civitai"),
+    (None, "84ef4bd4757f60e906b54aa6a7815dc6", ["hunyuan_video_dit"], [HunyuanVideoDiT], "civitai"),
+    (None, "68beaf8429b7c11aa8ca05b1bd0058bd", ["stepvideo_vae"], [StepVideoVAE], "civitai"),
+    (None, "5c0216a2132b082c10cb7a0e0377e681", ["stepvideo_dit"], [StepVideoModel], "civitai"),
+    (None, "9269f8db9040a9d860eaca435be61814", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "aafcfd9672c3a2456dc46e1cb6e52c70", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "6bfcfb3b342cb286ce886889d519a77e", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "6d6ccde6845b95ad9114ab993d917893", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "6bfcfb3b342cb286ce886889d519a77e", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "349723183fc063b2bfc10bb2835cf677", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "efa44cddf936c70abd0ea28b6cbe946c", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "3ef3b1f8e1dab83d5b71fd7b617f859f", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "70ddad9d3a133785da5ea371aae09504", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "26bde73488a92e64cc20b0a7485b9e5b", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "ac6a5aa74f4a0aab6f64eb9a72f19901", ["wan_video_dit"], [WanModel], "civitai"), 
+    (None, "b61c605c2adbd23124d152ed28e049ae", ["wan_video_dit"], [WanModel], "civitai"), 
+    (None, "1f5ab7703c6fc803fdded85ff040c316", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "5b013604280dd715f8457c6ed6d6a626", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "2267d489f0ceb9f21836532952852ee5", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "47dbeab5e560db3180adf51dc0232fb1", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "a61453409b67cd3246cf0c3bebad47ba", ["wan_video_dit", "wan_video_vace"], [WanModel, VaceWanModel], "civitai"),
+    (None, "7a513e1f257a861512b1afd387a8ecd9", ["wan_video_dit", "wan_video_vace"], [WanModel, VaceWanModel], "civitai"),
+    (None, "cb104773c6c2cb6df4f9529ad5c60d0b", ["wan_video_dit"], [WanModel], "diffusers"),
+    (None, "966cffdcc52f9c46c391768b27637614", ["wan_video_dit"], [WanS2VModel], "civitai"),
+    (None, "9c8818c2cbea55eca56c7b447df170da", ["wan_video_text_encoder"], [WanTextEncoder], "civitai"),
+    (None, "5941c53e207d62f20f9025686193c40b", ["wan_video_image_encoder"], [WanImageEncoder], "civitai"),
+    (None, "1378ea763357eea97acdef78e65d6d96", ["wan_video_vae"], [WanVideoVAE], "civitai"),
+    (None, "ccc42284ea13e1ad04693284c7a09be6", ["wan_video_vae"], [WanVideoVAE], "civitai"),
+    (None, "e1de6c02cdac79f8b739f4d3698cd216", ["wan_video_vae"], [WanVideoVAE38], "civitai"),
+    (None, "dbd5ec76bbf977983f972c151d545389", ["wan_video_motion_controller"], [WanMotionControllerModel], "civitai"),
+    (None, "d30fb9e02b1dbf4e509142f05cf7dd50", ["flux_dit", "step1x_connector"], [FluxDiT, Qwen2Connector], "civitai"),
+    (None, "30143afb2dea73d1ac580e0787628f8c", ["flux_lora_patcher"], [FluxLoraPatcher], "civitai"),
+    (None, "77c2e4dd2440269eb33bfaa0d004f6ab", ["flux_lora_encoder"], [FluxLoRAEncoder], "civitai"),
+    (None, "3e6c61b0f9471135fc9c6d6a98e98b6d", ["flux_dit", "nexus_gen_generation_adapter"], [FluxDiT, NexusGenAdapter], "civitai"),
+    (None, "63c969fd37cce769a90aa781fbff5f81", ["flux_dit", "nexus_gen_editing_adapter"], [FluxDiT, NexusGenImageEmbeddingMerger], "civitai"),
+    (None, "2bd19e845116e4f875a0a048e27fc219", ["nexus_gen_llm"], [NexusGenAutoregressiveModel], "civitai"),
+    (None, "0319a1cb19835fb510907dd3367c95ff", ["qwen_image_dit"], [QwenImageDiT], "civitai"),
+    (None, "8004730443f55db63092006dd9f7110e", ["qwen_image_text_encoder"], [QwenImageTextEncoder], "diffusers"),
+    (None, "ed4ea5824d55ec3107b09815e318123a", ["qwen_image_vae"], [QwenImageVAE], "diffusers"),
+    (None, "073bce9cf969e317e5662cd570c3e79c", ["qwen_image_blockwise_controlnet"], [QwenImageBlockWiseControlNet], "civitai"),
+    (None, "a9e54e480a628f0b956a688a81c33bab", ["qwen_image_blockwise_controlnet"], [QwenImageBlockWiseControlNet], "civitai"),
+    (None, "06be60f3a4526586d8431cd038a71486", ["wans2v_audio_encoder"], [WanS2VAudioEncoder], "civitai"),
+]
+huggingface_model_loader_configs = [
+    # These configs are provided for detecting model type automatically.
+    # The format is (architecture_in_huggingface_config, huggingface_lib, model_name, redirected_architecture)
+    ("ChatGLMModel", "diffsynth.models.kolors_text_encoder", "kolors_text_encoder", None),
+    ("MarianMTModel", "transformers.models.marian.modeling_marian", "translator", None),
+    ("BloomForCausalLM", "transformers.models.bloom.modeling_bloom", "beautiful_prompt", None),
+    ("Qwen2ForCausalLM", "transformers.models.qwen2.modeling_qwen2", "qwen_prompt", None),
+    # ("LlamaForCausalLM", "transformers.models.llama.modeling_llama", "omost_prompt", None),
+    ("T5EncoderModel", "diffsynth.models.flux_text_encoder", "flux_text_encoder_2", "FluxTextEncoder2"),
+    ("CogVideoXTransformer3DModel", "diffsynth.models.cog_dit", "cog_dit", "CogDiT"),
+    ("SiglipModel", "transformers.models.siglip.modeling_siglip", "siglip_vision_model", "SiglipVisionModel"),
+    ("LlamaForCausalLM", "diffsynth.models.hunyuan_video_text_encoder", "hunyuan_video_text_encoder_2", "HunyuanVideoLLMEncoder"),
+    ("LlavaForConditionalGeneration", "diffsynth.models.hunyuan_video_text_encoder", "hunyuan_video_text_encoder_2", "HunyuanVideoMLLMEncoder"),
+    ("Step1Model", "diffsynth.models.stepvideo_text_encoder", "stepvideo_text_encoder_2", "STEP1TextEncoder"),
+    ("Qwen2_5_VLForConditionalGeneration", "diffsynth.models.qwenvl", "qwenvl", "Qwen25VL_7b_Embedder"),
+]
+patch_model_loader_configs = [
+    # These configs are provided for detecting model type automatically.
+    # The format is (state_dict_keys_hash_with_shape, model_name, model_class, extra_kwargs)
+    ("9a4ab6869ac9b7d6e31f9854e397c867", ["svd_unet"], [SVDUNet], {"add_positional_conv": 128}),
+]
+
+preset_models_on_huggingface = {
+    "HunyuanDiT": [
+        ("Tencent-Hunyuan/HunyuanDiT", "t2i/clip_text_encoder/pytorch_model.bin", "models/HunyuanDiT/t2i/clip_text_encoder"),
+        ("Tencent-Hunyuan/HunyuanDiT", "t2i/mt5/pytorch_model.bin", "models/HunyuanDiT/t2i/mt5"),
+        ("Tencent-Hunyuan/HunyuanDiT", "t2i/model/pytorch_model_ema.pt", "models/HunyuanDiT/t2i/model"),
+        ("Tencent-Hunyuan/HunyuanDiT", "t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix"),
+    ],
+    "stable-video-diffusion-img2vid-xt": [
+        ("stabilityai/stable-video-diffusion-img2vid-xt", "svd_xt.safetensors", "models/stable_video_diffusion"),
+    ],
+    "ExVideo-SVD-128f-v1": [
+        ("ECNU-CILab/ExVideo-SVD-128f-v1", "model.fp16.safetensors", "models/stable_video_diffusion"),
+    ],
+    # Stable Diffusion
+    "StableDiffusion_v15": [
+        ("benjamin-paine/stable-diffusion-v1-5", "v1-5-pruned-emaonly.safetensors", "models/stable_diffusion"),
+    ],
+    "DreamShaper_8": [
+        ("Yntec/Dreamshaper8", "dreamshaper_8.safetensors", "models/stable_diffusion"),
+    ],
+    # Textual Inversion
+    "TextualInversion_VeryBadImageNegative_v1.3": [
+        ("gemasai/verybadimagenegative_v1.3", "verybadimagenegative_v1.3.pt", "models/textual_inversion"),
+    ],
+    # Stable Diffusion XL
+    "StableDiffusionXL_v1": [
+        ("stabilityai/stable-diffusion-xl-base-1.0", "sd_xl_base_1.0.safetensors", "models/stable_diffusion_xl"),
+    ],
+    "BluePencilXL_v200": [
+        ("frankjoshua/bluePencilXL_v200", "bluePencilXL_v200.safetensors", "models/stable_diffusion_xl"),
+    ],
+    "StableDiffusionXL_Turbo": [
+        ("stabilityai/sdxl-turbo", "sd_xl_turbo_1.0_fp16.safetensors", "models/stable_diffusion_xl_turbo"),
+    ],
+    # Stable Diffusion 3
+    "StableDiffusion3": [
+        ("stabilityai/stable-diffusion-3-medium", "sd3_medium_incl_clips_t5xxlfp16.safetensors", "models/stable_diffusion_3"),
+    ],
+    "StableDiffusion3_without_T5": [
+        ("stabilityai/stable-diffusion-3-medium", "sd3_medium_incl_clips.safetensors", "models/stable_diffusion_3"),
+    ],
+    # ControlNet
+    "ControlNet_v11f1p_sd15_depth": [
+        ("lllyasviel/ControlNet-v1-1", "control_v11f1p_sd15_depth.pth", "models/ControlNet"),
+        ("lllyasviel/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators")
+    ],
+    "ControlNet_v11p_sd15_softedge": [
+        ("lllyasviel/ControlNet-v1-1", "control_v11p_sd15_softedge.pth", "models/ControlNet"),
+        ("lllyasviel/Annotators", "ControlNetHED.pth", "models/Annotators")
+    ],
+    "ControlNet_v11f1e_sd15_tile": [
+        ("lllyasviel/ControlNet-v1-1", "control_v11f1e_sd15_tile.pth", "models/ControlNet")
+    ],
+    "ControlNet_v11p_sd15_lineart": [
+        ("lllyasviel/ControlNet-v1-1", "control_v11p_sd15_lineart.pth", "models/ControlNet"),
+        ("lllyasviel/Annotators", "sk_model.pth", "models/Annotators"),
+        ("lllyasviel/Annotators", "sk_model2.pth", "models/Annotators")
+    ],
+    "ControlNet_union_sdxl_promax": [
+        ("xinsir/controlnet-union-sdxl-1.0", "diffusion_pytorch_model_promax.safetensors", "models/ControlNet/controlnet_union"),
+        ("lllyasviel/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators")
+    ],
+    # AnimateDiff
+    "AnimateDiff_v2": [
+        ("guoyww/animatediff", "mm_sd_v15_v2.ckpt", "models/AnimateDiff"),
+    ],
+    "AnimateDiff_xl_beta": [
+        ("guoyww/animatediff", "mm_sdxl_v10_beta.ckpt", "models/AnimateDiff"),
+    ],
+
+    # Qwen Prompt
+    "QwenPrompt": [
+        ("Qwen/Qwen2-1.5B-Instruct", "config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+        ("Qwen/Qwen2-1.5B-Instruct", "generation_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+        ("Qwen/Qwen2-1.5B-Instruct", "model.safetensors", "models/QwenPrompt/qwen2-1.5b-instruct"),
+        ("Qwen/Qwen2-1.5B-Instruct", "special_tokens_map.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+        ("Qwen/Qwen2-1.5B-Instruct", "tokenizer.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+        ("Qwen/Qwen2-1.5B-Instruct", "tokenizer_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+        ("Qwen/Qwen2-1.5B-Instruct", "merges.txt", "models/QwenPrompt/qwen2-1.5b-instruct"),
+        ("Qwen/Qwen2-1.5B-Instruct", "vocab.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+    ],
+    # Beautiful Prompt
+    "BeautifulPrompt": [
+        ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
+        ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "generation_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
+        ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "model.safetensors", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
+        ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "special_tokens_map.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
+        ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "tokenizer.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
+        ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "tokenizer_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
+    ],
+    # Omost prompt
+    "OmostPrompt":[
+        ("lllyasviel/omost-llama-3-8b-4bits", "model-00001-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+        ("lllyasviel/omost-llama-3-8b-4bits", "model-00002-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+        ("lllyasviel/omost-llama-3-8b-4bits", "tokenizer.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+        ("lllyasviel/omost-llama-3-8b-4bits", "tokenizer_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),  
+        ("lllyasviel/omost-llama-3-8b-4bits", "config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+        ("lllyasviel/omost-llama-3-8b-4bits", "generation_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+        ("lllyasviel/omost-llama-3-8b-4bits", "model.safetensors.index.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+        ("lllyasviel/omost-llama-3-8b-4bits", "special_tokens_map.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+    ],
+    # Translator
+    "opus-mt-zh-en": [
+        ("Helsinki-NLP/opus-mt-zh-en", "config.json", "models/translator/opus-mt-zh-en"),
+        ("Helsinki-NLP/opus-mt-zh-en", "generation_config.json", "models/translator/opus-mt-zh-en"),
+        ("Helsinki-NLP/opus-mt-zh-en", "metadata.json", "models/translator/opus-mt-zh-en"),
+        ("Helsinki-NLP/opus-mt-zh-en", "pytorch_model.bin", "models/translator/opus-mt-zh-en"),
+        ("Helsinki-NLP/opus-mt-zh-en", "source.spm", "models/translator/opus-mt-zh-en"),
+        ("Helsinki-NLP/opus-mt-zh-en", "target.spm", "models/translator/opus-mt-zh-en"),
+        ("Helsinki-NLP/opus-mt-zh-en", "tokenizer_config.json", "models/translator/opus-mt-zh-en"),
+        ("Helsinki-NLP/opus-mt-zh-en", "vocab.json", "models/translator/opus-mt-zh-en"),
+    ],
+    # IP-Adapter
+    "IP-Adapter-SD": [
+        ("h94/IP-Adapter", "models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion/image_encoder"),
+        ("h94/IP-Adapter", "models/ip-adapter_sd15.bin", "models/IpAdapter/stable_diffusion"),
+    ],
+    "IP-Adapter-SDXL": [
+        ("h94/IP-Adapter", "sdxl_models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion_xl/image_encoder"),
+        ("h94/IP-Adapter", "sdxl_models/ip-adapter_sdxl.bin", "models/IpAdapter/stable_diffusion_xl"),
+    ],
+    "SDXL-vae-fp16-fix": [
+        ("madebyollin/sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors", "models/sdxl-vae-fp16-fix")
+    ],
+    # Kolors
+    "Kolors": [
+        ("Kwai-Kolors/Kolors", "text_encoder/config.json", "models/kolors/Kolors/text_encoder"),
+        ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model.bin.index.json", "models/kolors/Kolors/text_encoder"),
+        ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00001-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+        ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00002-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+        ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00003-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+        ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00004-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+        ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00005-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+        ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00006-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+        ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00007-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+        ("Kwai-Kolors/Kolors", "unet/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/unet"),
+        ("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/vae"),
+    ],
+    # FLUX
+    "FLUX.1-dev": [
+        ("black-forest-labs/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"),
+        ("black-forest-labs/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+        ("black-forest-labs/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+        ("black-forest-labs/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+        ("black-forest-labs/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+        ("black-forest-labs/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"),
+        ("black-forest-labs/FLUX.1-dev", "flux1-dev.safetensors", "models/FLUX/FLUX.1-dev"),
+    ],
+    "InstantX/FLUX.1-dev-IP-Adapter": {
+        "file_list": [
+            ("InstantX/FLUX.1-dev-IP-Adapter", "ip-adapter.bin", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter"),
+            ("google/siglip-so400m-patch14-384", "model.safetensors", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"),
+            ("google/siglip-so400m-patch14-384", "config.json", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"),
+        ],
+        "load_path": [
+            "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/ip-adapter.bin",
+            "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder",
+        ],
+    },
+    # RIFE
+    "RIFE": [
+        ("AlexWortega/RIFE", "flownet.pkl", "models/RIFE"),
+    ],
+    # CogVideo
+    "CogVideoX-5B": [
+        ("THUDM/CogVideoX-5b", "text_encoder/config.json", "models/CogVideo/CogVideoX-5b/text_encoder"),
+        ("THUDM/CogVideoX-5b", "text_encoder/model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/text_encoder"),
+        ("THUDM/CogVideoX-5b", "text_encoder/model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"),
+        ("THUDM/CogVideoX-5b", "text_encoder/model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"),
+        ("THUDM/CogVideoX-5b", "transformer/config.json", "models/CogVideo/CogVideoX-5b/transformer"),
+        ("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/transformer"),
+        ("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"),
+        ("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"),
+        ("THUDM/CogVideoX-5b", "vae/diffusion_pytorch_model.safetensors", "models/CogVideo/CogVideoX-5b/vae"),
+    ],
+    # Stable Diffusion 3.5
+    "StableDiffusion3.5-large": [
+        ("stabilityai/stable-diffusion-3.5-large", "sd3.5_large.safetensors", "models/stable_diffusion_3"),
+        ("stabilityai/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"),
+        ("stabilityai/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"),
+        ("stabilityai/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"),
+    ],
+}
+preset_models_on_modelscope = {
+    # Hunyuan DiT
+    "HunyuanDiT": [
+        ("modelscope/HunyuanDiT", "t2i/clip_text_encoder/pytorch_model.bin", "models/HunyuanDiT/t2i/clip_text_encoder"),
+        ("modelscope/HunyuanDiT", "t2i/mt5/pytorch_model.bin", "models/HunyuanDiT/t2i/mt5"),
+        ("modelscope/HunyuanDiT", "t2i/model/pytorch_model_ema.pt", "models/HunyuanDiT/t2i/model"),
+        ("modelscope/HunyuanDiT", "t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix"),
+    ],
+    # Stable Video Diffusion
+    "stable-video-diffusion-img2vid-xt": [
+        ("AI-ModelScope/stable-video-diffusion-img2vid-xt", "svd_xt.safetensors", "models/stable_video_diffusion"),
+    ],
+    # ExVideo
+    "ExVideo-SVD-128f-v1": [
+        ("ECNU-CILab/ExVideo-SVD-128f-v1", "model.fp16.safetensors", "models/stable_video_diffusion"),
+    ],
+    "ExVideo-CogVideoX-LoRA-129f-v1": [
+        ("ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1", "ExVideo-CogVideoX-LoRA-129f-v1.safetensors", "models/lora"),
+    ],
+    # Stable Diffusion
+    "StableDiffusion_v15": [
+        ("AI-ModelScope/stable-diffusion-v1-5", "v1-5-pruned-emaonly.safetensors", "models/stable_diffusion"),
+    ],
+    "DreamShaper_8": [
+        ("sd_lora/dreamshaper_8", "dreamshaper_8.safetensors", "models/stable_diffusion"),
+    ],
+    "AingDiffusion_v12": [
+        ("sd_lora/aingdiffusion_v12", "aingdiffusion_v12.safetensors", "models/stable_diffusion"),
+    ],
+    "Flat2DAnimerge_v45Sharp": [
+        ("sd_lora/Flat-2D-Animerge", "flat2DAnimerge_v45Sharp.safetensors", "models/stable_diffusion"),
+    ],
+    # Textual Inversion
+    "TextualInversion_VeryBadImageNegative_v1.3": [
+        ("sd_lora/verybadimagenegative_v1.3", "verybadimagenegative_v1.3.pt", "models/textual_inversion"),
+    ],
+    # Stable Diffusion XL
+    "StableDiffusionXL_v1": [
+        ("AI-ModelScope/stable-diffusion-xl-base-1.0", "sd_xl_base_1.0.safetensors", "models/stable_diffusion_xl"),
+    ],
+    "BluePencilXL_v200": [
+        ("sd_lora/bluePencilXL_v200", "bluePencilXL_v200.safetensors", "models/stable_diffusion_xl"),
+    ],
+    "StableDiffusionXL_Turbo": [
+        ("AI-ModelScope/sdxl-turbo", "sd_xl_turbo_1.0_fp16.safetensors", "models/stable_diffusion_xl_turbo"),
+    ],
+    "SDXL_lora_zyd232_ChineseInkStyle_SDXL_v1_0": [
+        ("sd_lora/zyd232_ChineseInkStyle_SDXL_v1_0", "zyd232_ChineseInkStyle_SDXL_v1_0.safetensors", "models/lora"),
+    ],
+    # Stable Diffusion 3
+    "StableDiffusion3": [
+        ("AI-ModelScope/stable-diffusion-3-medium", "sd3_medium_incl_clips_t5xxlfp16.safetensors", "models/stable_diffusion_3"),
+    ],
+    "StableDiffusion3_without_T5": [
+        ("AI-ModelScope/stable-diffusion-3-medium", "sd3_medium_incl_clips.safetensors", "models/stable_diffusion_3"),
+    ],
+    # ControlNet
+    "ControlNet_v11f1p_sd15_depth": [
+        ("AI-ModelScope/ControlNet-v1-1", "control_v11f1p_sd15_depth.pth", "models/ControlNet"),
+        ("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators")
+    ],
+    "ControlNet_v11p_sd15_softedge": [
+        ("AI-ModelScope/ControlNet-v1-1", "control_v11p_sd15_softedge.pth", "models/ControlNet"),
+        ("sd_lora/Annotators", "ControlNetHED.pth", "models/Annotators")
+    ],
+    "ControlNet_v11f1e_sd15_tile": [
+        ("AI-ModelScope/ControlNet-v1-1", "control_v11f1e_sd15_tile.pth", "models/ControlNet")
+    ],
+    "ControlNet_v11p_sd15_lineart": [
+        ("AI-ModelScope/ControlNet-v1-1", "control_v11p_sd15_lineart.pth", "models/ControlNet"),
+        ("sd_lora/Annotators", "sk_model.pth", "models/Annotators"),
+        ("sd_lora/Annotators", "sk_model2.pth", "models/Annotators")
+    ],
+    "ControlNet_union_sdxl_promax": [
+        ("AI-ModelScope/controlnet-union-sdxl-1.0", "diffusion_pytorch_model_promax.safetensors", "models/ControlNet/controlnet_union"),
+        ("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators")
+    ],
+    "Annotators:Depth": [
+        ("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators"),
+    ],
+    "Annotators:Softedge": [
+        ("sd_lora/Annotators", "ControlNetHED.pth", "models/Annotators"),
+    ],
+    "Annotators:Lineart": [
+        ("sd_lora/Annotators", "sk_model.pth", "models/Annotators"),
+        ("sd_lora/Annotators", "sk_model2.pth", "models/Annotators"),
+    ],
+    "Annotators:Normal": [
+        ("sd_lora/Annotators", "scannet.pt", "models/Annotators"),
+    ],
+    "Annotators:Openpose": [
+        ("sd_lora/Annotators", "body_pose_model.pth", "models/Annotators"),
+        ("sd_lora/Annotators", "facenet.pth", "models/Annotators"),
+        ("sd_lora/Annotators", "hand_pose_model.pth", "models/Annotators"),
+    ],
+    # AnimateDiff
+    "AnimateDiff_v2": [
+        ("Shanghai_AI_Laboratory/animatediff", "mm_sd_v15_v2.ckpt", "models/AnimateDiff"),
+    ],
+    "AnimateDiff_xl_beta": [
+        ("Shanghai_AI_Laboratory/animatediff", "mm_sdxl_v10_beta.ckpt", "models/AnimateDiff"),
+    ],
+    # RIFE
+    "RIFE": [
+        ("Damo_XR_Lab/cv_rife_video-frame-interpolation", "flownet.pkl", "models/RIFE"),
+    ],
+    # Qwen Prompt
+    "QwenPrompt": {
+        "file_list": [
+            ("qwen/Qwen2-1.5B-Instruct", "config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+            ("qwen/Qwen2-1.5B-Instruct", "generation_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+            ("qwen/Qwen2-1.5B-Instruct", "model.safetensors", "models/QwenPrompt/qwen2-1.5b-instruct"),
+            ("qwen/Qwen2-1.5B-Instruct", "special_tokens_map.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+            ("qwen/Qwen2-1.5B-Instruct", "tokenizer.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+            ("qwen/Qwen2-1.5B-Instruct", "tokenizer_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+            ("qwen/Qwen2-1.5B-Instruct", "merges.txt", "models/QwenPrompt/qwen2-1.5b-instruct"),
+            ("qwen/Qwen2-1.5B-Instruct", "vocab.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
+        ],
+        "load_path": [
+            "models/QwenPrompt/qwen2-1.5b-instruct",
+        ],
+    },
+    # Beautiful Prompt
+    "BeautifulPrompt": {
+        "file_list": [
+            ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
+            ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "generation_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
+            ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "model.safetensors", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
+            ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "special_tokens_map.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
+            ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "tokenizer.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
+            ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "tokenizer_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
+        ],
+        "load_path": [
+            "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd",
+        ],
+    },
+    # Omost prompt
+    "OmostPrompt": {
+        "file_list": [
+            ("Omost/omost-llama-3-8b-4bits", "model-00001-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+            ("Omost/omost-llama-3-8b-4bits", "model-00002-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+            ("Omost/omost-llama-3-8b-4bits", "tokenizer.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+            ("Omost/omost-llama-3-8b-4bits", "tokenizer_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),  
+            ("Omost/omost-llama-3-8b-4bits", "config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+            ("Omost/omost-llama-3-8b-4bits", "generation_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+            ("Omost/omost-llama-3-8b-4bits", "model.safetensors.index.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+            ("Omost/omost-llama-3-8b-4bits", "special_tokens_map.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
+        ],
+        "load_path": [
+            "models/OmostPrompt/omost-llama-3-8b-4bits",
+        ],
+    },
+    # Translator
+    "opus-mt-zh-en": {
+        "file_list": [
+            ("moxying/opus-mt-zh-en", "config.json", "models/translator/opus-mt-zh-en"),
+            ("moxying/opus-mt-zh-en", "generation_config.json", "models/translator/opus-mt-zh-en"),
+            ("moxying/opus-mt-zh-en", "metadata.json", "models/translator/opus-mt-zh-en"),
+            ("moxying/opus-mt-zh-en", "pytorch_model.bin", "models/translator/opus-mt-zh-en"),
+            ("moxying/opus-mt-zh-en", "source.spm", "models/translator/opus-mt-zh-en"),
+            ("moxying/opus-mt-zh-en", "target.spm", "models/translator/opus-mt-zh-en"),
+            ("moxying/opus-mt-zh-en", "tokenizer_config.json", "models/translator/opus-mt-zh-en"),
+            ("moxying/opus-mt-zh-en", "vocab.json", "models/translator/opus-mt-zh-en"),
+        ],
+        "load_path": [
+            "models/translator/opus-mt-zh-en",
+        ],
+    },
+    # IP-Adapter
+    "IP-Adapter-SD": [
+        ("AI-ModelScope/IP-Adapter", "models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion/image_encoder"),
+        ("AI-ModelScope/IP-Adapter", "models/ip-adapter_sd15.bin", "models/IpAdapter/stable_diffusion"),
+    ],
+    "IP-Adapter-SDXL": [
+        ("AI-ModelScope/IP-Adapter", "sdxl_models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion_xl/image_encoder"),
+        ("AI-ModelScope/IP-Adapter", "sdxl_models/ip-adapter_sdxl.bin", "models/IpAdapter/stable_diffusion_xl"),
+    ],
+    # Kolors
+    "Kolors": {
+        "file_list": [
+            ("Kwai-Kolors/Kolors", "text_encoder/config.json", "models/kolors/Kolors/text_encoder"),
+            ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model.bin.index.json", "models/kolors/Kolors/text_encoder"),
+            ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00001-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+            ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00002-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+            ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00003-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+            ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00004-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+            ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00005-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+            ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00006-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+            ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00007-of-00007.bin", "models/kolors/Kolors/text_encoder"),
+            ("Kwai-Kolors/Kolors", "unet/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/unet"),
+            ("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/vae"),
+        ],
+        "load_path": [
+            "models/kolors/Kolors/text_encoder",
+            "models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors",
+            "models/kolors/Kolors/vae/diffusion_pytorch_model.safetensors",
+        ],
+    },
+    "SDXL-vae-fp16-fix": [
+        ("AI-ModelScope/sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors", "models/sdxl-vae-fp16-fix")
+    ],
+    # FLUX
+    "FLUX.1-dev": {
+        "file_list": [
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"),
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+            ("AI-ModelScope/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"),
+            ("AI-ModelScope/FLUX.1-dev", "flux1-dev.safetensors", "models/FLUX/FLUX.1-dev"),
+        ],
+        "load_path": [
+            "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
+            "models/FLUX/FLUX.1-dev/text_encoder_2",
+            "models/FLUX/FLUX.1-dev/ae.safetensors",
+            "models/FLUX/FLUX.1-dev/flux1-dev.safetensors"
+        ],
+    },
+    "FLUX.1-schnell": {
+        "file_list": [
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"),
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+            ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+            ("AI-ModelScope/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"),
+            ("AI-ModelScope/FLUX.1-schnell", "flux1-schnell.safetensors", "models/FLUX/FLUX.1-schnell"),
+        ],
+        "load_path": [
+            "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
+            "models/FLUX/FLUX.1-dev/text_encoder_2",
+            "models/FLUX/FLUX.1-dev/ae.safetensors",
+            "models/FLUX/FLUX.1-schnell/flux1-schnell.safetensors"
+        ],
+    },
+    "InstantX/FLUX.1-dev-Controlnet-Union-alpha": [
+        ("InstantX/FLUX.1-dev-Controlnet-Union-alpha", "diffusion_pytorch_model.safetensors", "models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha"),
+    ],
+    "jasperai/Flux.1-dev-Controlnet-Depth": [
+        ("jasperai/Flux.1-dev-Controlnet-Depth", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Depth"),
+    ],
+    "jasperai/Flux.1-dev-Controlnet-Surface-Normals": [
+        ("jasperai/Flux.1-dev-Controlnet-Surface-Normals", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Surface-Normals"),
+    ],
+    "jasperai/Flux.1-dev-Controlnet-Upscaler": [
+        ("jasperai/Flux.1-dev-Controlnet-Upscaler", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler"),
+    ],
+    "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha": [
+        ("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha", "diffusion_pytorch_model.safetensors", "models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha"),
+    ],
+    "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta": [
+        ("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", "diffusion_pytorch_model.safetensors", "models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta"),
+    ],
+    "Shakker-Labs/FLUX.1-dev-ControlNet-Depth": [
+        ("Shakker-Labs/FLUX.1-dev-ControlNet-Depth", "diffusion_pytorch_model.safetensors", "models/ControlNet/Shakker-Labs/FLUX.1-dev-ControlNet-Depth"),
+    ],
+    "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro": [
+        ("Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", "diffusion_pytorch_model.safetensors", "models/ControlNet/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro"),
+    ],
+    "InstantX/FLUX.1-dev-IP-Adapter": {
+        "file_list": [
+            ("InstantX/FLUX.1-dev-IP-Adapter", "ip-adapter.bin", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter"),
+            ("AI-ModelScope/siglip-so400m-patch14-384", "model.safetensors", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"),
+            ("AI-ModelScope/siglip-so400m-patch14-384", "config.json", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"),
+        ],
+        "load_path": [
+            "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/ip-adapter.bin",
+            "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder",
+        ],
+    },
+    "InfiniteYou":{
+        "file_list":[
+            ("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/InfuseNetModel/diffusion_pytorch_model-00001-of-00002.safetensors", "models/InfiniteYou/InfuseNetModel"),
+            ("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/InfuseNetModel/diffusion_pytorch_model-00002-of-00002.safetensors", "models/InfiniteYou/InfuseNetModel"),
+            ("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/image_proj_model.bin", "models/InfiniteYou"),
+            ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/1k3d68.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
+            ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/2d106det.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
+            ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/genderage.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
+            ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/glintr100.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
+            ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/scrfd_10g_bnkps.onnx", "models/InfiniteYou/insightface/models/antelopev2"),            
+        ],
+        "load_path":[
+            [
+                "models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00001-of-00002.safetensors",
+                "models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00002-of-00002.safetensors"
+            ],
+            "models/InfiniteYou/image_proj_model.bin",
+            ],
+    },
+    # ESRGAN
+    "ESRGAN_x4": [
+        ("AI-ModelScope/Real-ESRGAN", "RealESRGAN_x4.pth", "models/ESRGAN"),
+    ],
+    # RIFE
+    "RIFE": [
+        ("AI-ModelScope/RIFE", "flownet.pkl", "models/RIFE"),
+    ],
+    # Omnigen
+    "OmniGen-v1": {
+        "file_list": [
+            ("BAAI/OmniGen-v1", "vae/diffusion_pytorch_model.safetensors", "models/OmniGen/OmniGen-v1/vae"),
+            ("BAAI/OmniGen-v1", "model.safetensors", "models/OmniGen/OmniGen-v1"),
+            ("BAAI/OmniGen-v1", "config.json", "models/OmniGen/OmniGen-v1"),
+            ("BAAI/OmniGen-v1", "special_tokens_map.json", "models/OmniGen/OmniGen-v1"),
+            ("BAAI/OmniGen-v1", "tokenizer_config.json", "models/OmniGen/OmniGen-v1"),
+            ("BAAI/OmniGen-v1", "tokenizer.json", "models/OmniGen/OmniGen-v1"),
+        ],
+        "load_path": [
+            "models/OmniGen/OmniGen-v1/vae/diffusion_pytorch_model.safetensors",
+            "models/OmniGen/OmniGen-v1/model.safetensors",
+        ]
+    },
+    # CogVideo
+    "CogVideoX-5B": {
+        "file_list": [
+            ("ZhipuAI/CogVideoX-5b", "text_encoder/config.json", "models/CogVideo/CogVideoX-5b/text_encoder"),
+            ("ZhipuAI/CogVideoX-5b", "text_encoder/model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/text_encoder"),
+            ("ZhipuAI/CogVideoX-5b", "text_encoder/model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"),
+            ("ZhipuAI/CogVideoX-5b", "text_encoder/model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"),
+            ("ZhipuAI/CogVideoX-5b", "transformer/config.json", "models/CogVideo/CogVideoX-5b/transformer"),
+            ("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/transformer"),
+            ("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"),
+            ("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"),
+            ("ZhipuAI/CogVideoX-5b", "vae/diffusion_pytorch_model.safetensors", "models/CogVideo/CogVideoX-5b/vae"),
+        ],
+        "load_path": [
+            "models/CogVideo/CogVideoX-5b/text_encoder",
+            "models/CogVideo/CogVideoX-5b/transformer",
+            "models/CogVideo/CogVideoX-5b/vae/diffusion_pytorch_model.safetensors",
+        ],
+    },
+    # Stable Diffusion 3.5
+    "StableDiffusion3.5-large": [
+        ("AI-ModelScope/stable-diffusion-3.5-large", "sd3.5_large.safetensors", "models/stable_diffusion_3"),
+        ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"),
+        ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"),
+        ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"),
+    ],
+    "StableDiffusion3.5-medium": [
+        ("AI-ModelScope/stable-diffusion-3.5-medium", "sd3.5_medium.safetensors", "models/stable_diffusion_3"),
+        ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"),
+        ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"),
+        ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"),
+    ],
+    "StableDiffusion3.5-large-turbo": [
+        ("AI-ModelScope/stable-diffusion-3.5-large-turbo", "sd3.5_large_turbo.safetensors", "models/stable_diffusion_3"),
+        ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"),
+        ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"),
+        ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"),
+    ],
+    "HunyuanVideo":{
+        "file_list": [
+            ("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideo/text_encoder"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00001-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00002-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00003-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00004-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "config.json", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model.safetensors.index.json", "models/HunyuanVideo/text_encoder_2"),
+            ("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/vae/pytorch_model.pt", "models/HunyuanVideo/vae"),
+            ("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt", "models/HunyuanVideo/transformers")
+        ],
+        "load_path": [
+            "models/HunyuanVideo/text_encoder/model.safetensors",
+            "models/HunyuanVideo/text_encoder_2",
+            "models/HunyuanVideo/vae/pytorch_model.pt",
+            "models/HunyuanVideo/transformers/mp_rank_00_model_states.pt"
+        ],
+    },
+    "HunyuanVideoI2V":{
+        "file_list": [
+            ("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideoI2V/text_encoder"),
+            ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00001-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"),
+            ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00002-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"),
+            ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00003-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"),
+            ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00004-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"),
+            ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "config.json", "models/HunyuanVideoI2V/text_encoder_2"),
+            ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model.safetensors.index.json", "models/HunyuanVideoI2V/text_encoder_2"),
+            ("AI-ModelScope/HunyuanVideo-I2V", "hunyuan-video-i2v-720p/vae/pytorch_model.pt", "models/HunyuanVideoI2V/vae"),
+            ("AI-ModelScope/HunyuanVideo-I2V", "hunyuan-video-i2v-720p/transformers/mp_rank_00_model_states.pt", "models/HunyuanVideoI2V/transformers")
+        ],
+        "load_path": [
+            "models/HunyuanVideoI2V/text_encoder/model.safetensors",
+            "models/HunyuanVideoI2V/text_encoder_2",
+            "models/HunyuanVideoI2V/vae/pytorch_model.pt",
+            "models/HunyuanVideoI2V/transformers/mp_rank_00_model_states.pt"
+        ],
+    },
+    "HunyuanVideo-fp8":{
+        "file_list": [
+            ("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideo/text_encoder"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00001-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00002-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00003-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00004-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "config.json", "models/HunyuanVideo/text_encoder_2"),
+            ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model.safetensors.index.json", "models/HunyuanVideo/text_encoder_2"),
+            ("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/vae/pytorch_model.pt", "models/HunyuanVideo/vae"),
+            ("DiffSynth-Studio/HunyuanVideo-safetensors", "model.fp8.safetensors", "models/HunyuanVideo/transformers")
+        ],
+        "load_path": [
+            "models/HunyuanVideo/text_encoder/model.safetensors",
+            "models/HunyuanVideo/text_encoder_2",
+            "models/HunyuanVideo/vae/pytorch_model.pt",
+            "models/HunyuanVideo/transformers/model.fp8.safetensors"
+        ],
+    },
+}
+Preset_model_id: TypeAlias = Literal[
+    "HunyuanDiT",
+    "stable-video-diffusion-img2vid-xt",
+    "ExVideo-SVD-128f-v1",
+    "ExVideo-CogVideoX-LoRA-129f-v1",
+    "StableDiffusion_v15",
+    "DreamShaper_8",
+    "AingDiffusion_v12",
+    "Flat2DAnimerge_v45Sharp",
+    "TextualInversion_VeryBadImageNegative_v1.3",
+    "StableDiffusionXL_v1",
+    "BluePencilXL_v200",
+    "StableDiffusionXL_Turbo",
+    "ControlNet_v11f1p_sd15_depth",
+    "ControlNet_v11p_sd15_softedge",
+    "ControlNet_v11f1e_sd15_tile",
+    "ControlNet_v11p_sd15_lineart",
+    "AnimateDiff_v2",
+    "AnimateDiff_xl_beta",
+    "RIFE",
+    "BeautifulPrompt",
+    "opus-mt-zh-en",
+    "IP-Adapter-SD",
+    "IP-Adapter-SDXL",
+    "StableDiffusion3",
+    "StableDiffusion3_without_T5",
+    "Kolors",
+    "SDXL-vae-fp16-fix",
+    "ControlNet_union_sdxl_promax",
+    "FLUX.1-dev",
+    "FLUX.1-schnell",
+    "InstantX/FLUX.1-dev-Controlnet-Union-alpha",
+    "jasperai/Flux.1-dev-Controlnet-Depth",
+    "jasperai/Flux.1-dev-Controlnet-Surface-Normals",
+    "jasperai/Flux.1-dev-Controlnet-Upscaler",
+    "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha",
+    "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta",
+    "Shakker-Labs/FLUX.1-dev-ControlNet-Depth",
+    "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro",
+    "InstantX/FLUX.1-dev-IP-Adapter",
+    "InfiniteYou",
+    "SDXL_lora_zyd232_ChineseInkStyle_SDXL_v1_0",
+    "QwenPrompt",
+    "OmostPrompt",
+    "ESRGAN_x4",
+    "RIFE",
+    "OmniGen-v1",
+    "CogVideoX-5B",
+    "Annotators:Depth",
+    "Annotators:Softedge",
+    "Annotators:Lineart",
+    "Annotators:Normal",
+    "Annotators:Openpose",
+    "StableDiffusion3.5-large",
+    "StableDiffusion3.5-medium",
+    "HunyuanVideo",
+    "HunyuanVideo-fp8",
+    "HunyuanVideoI2V",
+]

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/controlnets/__init__.py

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

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/controlnets/controlnet_unit.py

@@ -0,0 +1,91 @@
+import torch
+import numpy as np
+from .processors import Processor_id
+
+
+class ControlNetConfigUnit:
+    def __init__(self, processor_id: Processor_id, model_path, scale=1.0, skip_processor=False):
+        self.processor_id = processor_id
+        self.model_path = model_path
+        self.scale = scale
+        self.skip_processor = skip_processor
+
+
+class ControlNetUnit:
+    def __init__(self, processor, model, scale=1.0):
+        self.processor = processor
+        self.model = model
+        self.scale = scale
+
+
+class MultiControlNetManager:
+    def __init__(self, controlnet_units=[]):
+        self.processors = [unit.processor for unit in controlnet_units]
+        self.models = [unit.model for unit in controlnet_units]
+        self.scales = [unit.scale for unit in controlnet_units]
+
+    def cpu(self):
+        for model in self.models:
+            model.cpu()
+
+    def to(self, device):
+        for model in self.models:
+            model.to(device)
+        for processor in self.processors:
+            processor.to(device)
+    
+    def process_image(self, image, processor_id=None):
+        if processor_id is None:
+            processed_image = [processor(image) for processor in self.processors]
+        else:
+            processed_image = [self.processors[processor_id](image)]
+        processed_image = torch.concat([
+            torch.Tensor(np.array(image_, dtype=np.float32) / 255).permute(2, 0, 1).unsqueeze(0)
+            for image_ in processed_image
+        ], dim=0)
+        return processed_image
+    
+    def __call__(
+        self,
+        sample, timestep, encoder_hidden_states, conditionings,
+        tiled=False, tile_size=64, tile_stride=32, **kwargs
+    ):
+        res_stack = None
+        for processor, conditioning, model, scale in zip(self.processors, conditionings, self.models, self.scales):
+            res_stack_ = model(
+                sample, timestep, encoder_hidden_states, conditioning, **kwargs,
+                tiled=tiled, tile_size=tile_size, tile_stride=tile_stride,
+                processor_id=processor.processor_id
+            )
+            res_stack_ = [res * scale for res in res_stack_]
+            if res_stack is None:
+                res_stack = res_stack_
+            else:
+                res_stack = [i + j for i, j in zip(res_stack, res_stack_)]
+        return res_stack
+
+
+class FluxMultiControlNetManager(MultiControlNetManager):
+    def __init__(self, controlnet_units=[]):
+        super().__init__(controlnet_units=controlnet_units)
+
+    def process_image(self, image, processor_id=None):
+        if processor_id is None:
+            processed_image = [processor(image) for processor in self.processors]
+        else:
+            processed_image = [self.processors[processor_id](image)]
+        return processed_image
+
+    def __call__(self, conditionings, **kwargs):
+        res_stack, single_res_stack = None, None
+        for processor, conditioning, model, scale in zip(self.processors, conditionings, self.models, self.scales):
+            res_stack_, single_res_stack_ = model(controlnet_conditioning=conditioning, processor_id=processor.processor_id, **kwargs)
+            res_stack_ = [res * scale for res in res_stack_]
+            single_res_stack_ = [res * scale for res in single_res_stack_]
+            if res_stack is None:
+                res_stack = res_stack_
+                single_res_stack = single_res_stack_
+            else:
+                res_stack = [i + j for i, j in zip(res_stack, res_stack_)]
+                single_res_stack = [i + j for i, j in zip(single_res_stack, single_res_stack_)]
+        return res_stack, single_res_stack

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/controlnets/processors.py

@@ -0,0 +1,62 @@
+from typing_extensions import Literal, TypeAlias
+
+
+Processor_id: TypeAlias = Literal[
+    "canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "normal", "tile", "none", "inpaint"
+]
+
+class Annotator:
+    def __init__(self, processor_id: Processor_id, model_path="models/Annotators", detect_resolution=None, device='cuda', skip_processor=False):
+        if not skip_processor:
+            if processor_id == "canny":
+                from controlnet_aux.processor import CannyDetector
+                self.processor = CannyDetector()
+            elif processor_id == "depth":
+                from controlnet_aux.processor import MidasDetector
+                self.processor = MidasDetector.from_pretrained(model_path).to(device)
+            elif processor_id == "softedge":
+                from controlnet_aux.processor import HEDdetector
+                self.processor = HEDdetector.from_pretrained(model_path).to(device)
+            elif processor_id == "lineart":
+                from controlnet_aux.processor import LineartDetector
+                self.processor = LineartDetector.from_pretrained(model_path).to(device)
+            elif processor_id == "lineart_anime":
+                from controlnet_aux.processor import LineartAnimeDetector
+                self.processor = LineartAnimeDetector.from_pretrained(model_path).to(device)
+            elif processor_id == "openpose":
+                from controlnet_aux.processor import OpenposeDetector
+                self.processor = OpenposeDetector.from_pretrained(model_path).to(device)
+            elif processor_id == "normal":
+                from controlnet_aux.processor import NormalBaeDetector
+                self.processor = NormalBaeDetector.from_pretrained(model_path).to(device)
+            elif processor_id == "tile" or processor_id == "none" or processor_id == "inpaint":
+                self.processor = None
+            else:
+                raise ValueError(f"Unsupported processor_id: {processor_id}")
+        else:
+            self.processor = None
+
+        self.processor_id = processor_id
+        self.detect_resolution = detect_resolution
+    
+    def to(self,device):
+        if hasattr(self.processor,"model") and hasattr(self.processor.model,"to"):
+
+            self.processor.model.to(device)
+
+    def __call__(self, image, mask=None):
+        width, height = image.size
+        if self.processor_id == "openpose":
+            kwargs = {
+                "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
+

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/data/__init__.py

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

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/data/simple_text_image.py

@@ -0,0 +1,41 @@
+import torch, os, torchvision
+from torchvision import transforms
+import pandas as pd
+from PIL import Image
+
+
+
+class TextImageDataset(torch.utils.data.Dataset):
+    def __init__(self, dataset_path, steps_per_epoch=10000, height=1024, width=1024, center_crop=True, random_flip=False):
+        self.steps_per_epoch = steps_per_epoch
+        metadata = pd.read_csv(os.path.join(dataset_path, "train/metadata.csv"))
+        self.path = [os.path.join(dataset_path, "train", file_name) for file_name in metadata["file_name"]]
+        self.text = metadata["text"].to_list()
+        self.height = height
+        self.width = width
+        self.image_processor = transforms.Compose(
+            [
+                transforms.CenterCrop((height, width)) if center_crop else transforms.RandomCrop((height, width)),
+                transforms.RandomHorizontalFlip() if random_flip else transforms.Lambda(lambda x: x),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+        )
+
+
+    def __getitem__(self, index):
+        data_id = torch.randint(0, len(self.path), (1,))[0]
+        data_id = (data_id + index) % len(self.path) # For fixed seed.
+        text = self.text[data_id]
+        image = Image.open(self.path[data_id]).convert("RGB")
+        target_height, target_width = self.height, self.width
+        width, height = image.size
+        scale = max(target_width / width, target_height / height)
+        shape = [round(height*scale),round(width*scale)]
+        image = torchvision.transforms.functional.resize(image,shape,interpolation=transforms.InterpolationMode.BILINEAR)
+        image = self.image_processor(image)
+        return {"text": text, "image": image}
+
+
+    def __len__(self):
+        return self.steps_per_epoch

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/data/video.py

@@ -0,0 +1,217 @@
+import imageio, os
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+import subprocess
+import shutil
+
+
+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 Image.fromarray(np.array(self.reader.get_data(item))).convert("RGB")
+
+    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
+
+
+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 Image.open(self.file_list[item]).convert("RGB")
+
+    def __del__(self):
+        pass
+
+
+def crop_and_resize(image, height, width):
+    image = np.array(image)
+    image_height, image_width, _ = image.shape
+    if image_height / image_width < height / width:
+        croped_width = int(image_height / height * width)
+        left = (image_width - croped_width) // 2
+        image = image[:, left: left+croped_width]
+        image = Image.fromarray(image).resize((width, height))
+    else:
+        croped_height = int(image_width / width * height)
+        left = (image_height - croped_height) // 2
+        image = image[left: left+croped_height, :]
+        image = Image.fromarray(image).resize((width, height))
+    return image
+
+
+class VideoData:
+    def __init__(self, video_file=None, image_folder=None, height=None, width=None, **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.set_shape(height, width)
+
+    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)
+        width, height = frame.size
+        if self.height is not None and self.width is not None:
+            if self.height != height or self.width != width:
+                frame = crop_and_resize(frame, self.height, self.width)
+        return frame
+
+    def __del__(self):
+        pass
+
+    def save_images(self, folder):
+        os.makedirs(folder, exist_ok=True)
+        for i in tqdm(range(self.__len__()), desc="Saving images"):
+            frame = self.__getitem__(i)
+            frame.save(os.path.join(folder, f"{i}.png"))
+
+
+def save_video(frames, save_path, fps, quality=9, ffmpeg_params=None):
+    writer = imageio.get_writer(save_path, fps=fps, quality=quality, ffmpeg_params=ffmpeg_params)
+    for frame in tqdm(frames, desc="Saving video"):
+        frame = np.array(frame)
+        writer.append_data(frame)
+    writer.close()
+
+def save_frames(frames, save_path):
+    os.makedirs(save_path, exist_ok=True)
+    for i, frame in enumerate(tqdm(frames, desc="Saving images")):
+        frame.save(os.path.join(save_path, f"{i}.png"))
+
+
+def merge_video_audio(video_path: str, audio_path: str):
+    # TODO: may need a in-python implementation to avoid subprocess dependency
+    """
+    Merge the video and audio into a new video, with the duration set to the shorter of the two,
+    and overwrite the original video file.
+
+    Parameters:
+    video_path (str): Path to the original video file
+    audio_path (str): Path to the audio file
+    """
+
+    # check
+    if not os.path.exists(video_path):
+        raise FileNotFoundError(f"video file {video_path} does not exist")
+    if not os.path.exists(audio_path):
+        raise FileNotFoundError(f"audio file {audio_path} does not exist")
+
+    base, ext = os.path.splitext(video_path)
+    temp_output = f"{base}_temp{ext}"
+
+    try:
+        # create ffmpeg command
+        command = [
+            'ffmpeg',
+            '-y',  # overwrite
+            '-i',
+            video_path,
+            '-i',
+            audio_path,
+            '-c:v',
+            'copy',  # copy video stream
+            '-c:a',
+            'aac',  # use AAC audio encoder
+            '-b:a',
+            '192k',  # set audio bitrate (optional)
+            '-map',
+            '0:v:0',  # select the first video stream
+            '-map',
+            '1:a:0',  # select the first audio stream
+            '-shortest',  # choose the shortest duration
+            temp_output
+        ]
+
+        # execute the command
+        result = subprocess.run(
+            command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+
+        # check result
+        if result.returncode != 0:
+            error_msg = f"FFmpeg execute failed: {result.stderr}"
+            print(error_msg)
+            raise RuntimeError(error_msg)
+
+        shutil.move(temp_output, video_path)
+        print(f"Merge completed, saved to {video_path}")
+
+    except Exception as e:
+        if os.path.exists(temp_output):
+            os.remove(temp_output)
+        print(f"merge_video_audio failed with error: {e}")
+
+
+def save_video_with_audio(frames, save_path, audio_path, fps=16, quality=9, ffmpeg_params=None):
+    save_video(frames, save_path, fps, quality, ffmpeg_params)
+    merge_video_audio(save_path, audio_path)

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/distributed/xdit_context_parallel.py

@@ -0,0 +1,131 @@
+import torch
+from typing import Optional
+from einops import rearrange
+from xfuser.core.distributed import (get_sequence_parallel_rank,
+                                     get_sequence_parallel_world_size,
+                                     get_sp_group)
+from xfuser.core.long_ctx_attention import xFuserLongContextAttention
+
+def sinusoidal_embedding_1d(dim, position):
+    sinusoid = torch.outer(position.type(torch.float64), torch.pow(
+        10000, -torch.arange(dim//2, dtype=torch.float64, device=position.device).div(dim//2)))
+    x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
+    return x.to(position.dtype)
+
+def pad_freqs(original_tensor, target_len):
+    seq_len, s1, s2 = original_tensor.shape
+    pad_size = target_len - seq_len
+    padding_tensor = torch.ones(
+        pad_size,
+        s1,
+        s2,
+        dtype=original_tensor.dtype,
+        device=original_tensor.device)
+    padded_tensor = torch.cat([original_tensor, padding_tensor], dim=0)
+    return padded_tensor
+    
+def rope_apply(x, freqs, num_heads):
+    x = rearrange(x, "b s (n d) -> b s n d", n=num_heads)
+    s_per_rank = x.shape[1]
+
+    x_out = torch.view_as_complex(x.to(torch.float64).reshape(
+        x.shape[0], x.shape[1], x.shape[2], -1, 2))
+
+    sp_size = get_sequence_parallel_world_size()
+    sp_rank = get_sequence_parallel_rank()
+    freqs = pad_freqs(freqs, s_per_rank * sp_size)
+    freqs_rank = freqs[(sp_rank * s_per_rank):((sp_rank + 1) * s_per_rank), :, :]
+
+    x_out = torch.view_as_real(x_out * freqs_rank).flatten(2)
+    return x_out.to(x.dtype)
+
+def usp_dit_forward(self,
+            x: torch.Tensor,
+            timestep: torch.Tensor,
+            context: torch.Tensor,
+            clip_feature: Optional[torch.Tensor] = None,
+            y: Optional[torch.Tensor] = None,
+            use_gradient_checkpointing: bool = False,
+            use_gradient_checkpointing_offload: bool = False,
+            **kwargs,
+            ):
+    t = self.time_embedding(
+        sinusoidal_embedding_1d(self.freq_dim, timestep))
+    t_mod = self.time_projection(t).unflatten(1, (6, self.dim))
+    context = self.text_embedding(context)
+    
+    if self.has_image_input:
+        x = torch.cat([x, y], dim=1)  # (b, c_x + c_y, f, h, w)
+        clip_embdding = self.img_emb(clip_feature)
+        context = torch.cat([clip_embdding, context], dim=1)
+    
+    x, (f, h, w) = self.patchify(x)
+    
+    freqs = torch.cat([
+        self.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
+        self.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
+        self.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
+    ], dim=-1).reshape(f * h * w, 1, -1).to(x.device)
+    
+    def create_custom_forward(module):
+        def custom_forward(*inputs):
+            return module(*inputs)
+        return custom_forward
+
+    # Context Parallel
+    chunks = torch.chunk(x, get_sequence_parallel_world_size(), dim=1)
+    pad_shape = chunks[0].shape[1] - chunks[-1].shape[1]
+    chunks = [torch.nn.functional.pad(chunk, (0, 0, 0, chunks[0].shape[1]-chunk.shape[1]), value=0) for chunk in chunks]
+    x = chunks[get_sequence_parallel_rank()]
+
+    for block in self.blocks:
+        if self.training and use_gradient_checkpointing:
+            if use_gradient_checkpointing_offload:
+                with torch.autograd.graph.save_on_cpu():
+                    x = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(block),
+                        x, context, t_mod, freqs,
+                        use_reentrant=False,
+                    )
+            else:
+                x = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    x, context, t_mod, freqs,
+                    use_reentrant=False,
+                )
+        else:
+            x = block(x, context, t_mod, freqs)
+
+    x = self.head(x, t)
+
+    # Context Parallel
+    x = get_sp_group().all_gather(x, dim=1)
+    x = x[:, :-pad_shape] if pad_shape > 0 else x
+
+    # unpatchify
+    x = self.unpatchify(x, (f, h, w))
+    return x
+
+
+def usp_attn_forward(self, x, freqs):
+    q = self.norm_q(self.q(x))
+    k = self.norm_k(self.k(x))
+    v = self.v(x)
+
+    q = rope_apply(q, freqs, self.num_heads)
+    k = rope_apply(k, freqs, self.num_heads)
+    q = rearrange(q, "b s (n d) -> b s n d", n=self.num_heads)
+    k = rearrange(k, "b s (n d) -> b s n d", n=self.num_heads)
+    v = rearrange(v, "b s (n d) -> b s n d", n=self.num_heads)
+
+    x = xFuserLongContextAttention()(
+        None,
+        query=q,
+        key=k,
+        value=v,
+    )
+    x = x.flatten(2)
+
+    del q, k, v
+    torch.cuda.empty_cache()
+    return self.o(x)

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ESRGAN/__init__.py

@@ -0,0 +1,137 @@
+import torch
+from einops import repeat
+from PIL import Image
+import numpy as np
+
+
+class ResidualDenseBlock(torch.nn.Module):
+
+    def __init__(self, num_feat=64, num_grow_ch=32):
+        super(ResidualDenseBlock, self).__init__()
+        self.conv1 = torch.nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
+        self.conv2 = torch.nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv3 = torch.nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv4 = torch.nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv5 = torch.nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
+        self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x):
+        x1 = self.lrelu(self.conv1(x))
+        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
+        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
+        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
+        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
+        return x5 * 0.2 + x
+
+
+class RRDB(torch.nn.Module):
+
+    def __init__(self, num_feat, num_grow_ch=32):
+        super(RRDB, self).__init__()
+        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
+        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
+        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
+
+    def forward(self, x):
+        out = self.rdb1(x)
+        out = self.rdb2(out)
+        out = self.rdb3(out)
+        return out * 0.2 + x
+
+
+class RRDBNet(torch.nn.Module):
+
+    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, **kwargs):
+        super(RRDBNet, self).__init__()
+        self.conv_first = torch.nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+        self.body = torch.torch.nn.Sequential(*[RRDB(num_feat=num_feat, num_grow_ch=num_grow_ch) for _ in range(num_block)])
+        self.conv_body = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        # upsample
+        self.conv_up1 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_up2 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_hr = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_last = torch.nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+        self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x):
+        feat = x
+        feat = self.conv_first(feat)
+        body_feat = self.conv_body(self.body(feat))
+        feat = feat + body_feat
+        # upsample
+        feat = repeat(feat, "B C H W -> B C (H 2) (W 2)")
+        feat = self.lrelu(self.conv_up1(feat))
+        feat = repeat(feat, "B C H W -> B C (H 2) (W 2)")
+        feat = self.lrelu(self.conv_up2(feat))
+        out = self.conv_last(self.lrelu(self.conv_hr(feat)))
+        return out
+    
+    @staticmethod
+    def state_dict_converter():
+        return RRDBNetStateDictConverter()
+    
+
+class RRDBNetStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict, {"upcast_to_float32": True}
+    
+    def from_civitai(self, state_dict):
+        return state_dict, {"upcast_to_float32": True}
+
+
+class ESRGAN(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    @staticmethod
+    def from_model_manager(model_manager):
+        return ESRGAN(model_manager.fetch_model("esrgan"))
+
+    def process_image(self, image):
+        image = torch.Tensor(np.array(image, dtype=np.float32) / 255).permute(2, 0, 1)
+        return image
+    
+    def process_images(self, images):
+        images = [self.process_image(image) for image in images]
+        images = torch.stack(images)
+        return images
+    
+    def decode_images(self, images):
+        images = (images.permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8)
+        images = [Image.fromarray(image) for image in images]
+        return images
+    
+    @torch.no_grad()
+    def upscale(self, images, batch_size=4, progress_bar=lambda x:x):
+        if not isinstance(images, list):
+            images = [images]
+            is_single_image = True
+        else:
+            is_single_image = False
+
+        # Preprocess
+        input_tensor = self.process_images(images)
+
+        # Interpolate
+        output_tensor = []
+        for batch_id in progress_bar(range(0, input_tensor.shape[0], batch_size)):
+            batch_id_ = min(batch_id + batch_size, input_tensor.shape[0])
+            batch_input_tensor = input_tensor[batch_id: batch_id_]
+            batch_input_tensor = batch_input_tensor.to(
+                device=self.model.conv_first.weight.device,
+                dtype=self.model.conv_first.weight.dtype)
+            batch_output_tensor = self.model(batch_input_tensor)
+            output_tensor.append(batch_output_tensor.cpu())
+        
+        # Output
+        output_tensor = torch.concat(output_tensor, dim=0)
+
+        # To images
+        output_images = self.decode_images(output_tensor)
+        if is_single_image:
+            output_images = output_images[0]
+        return output_images

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/__init__.py

@@ -0,0 +1,63 @@
+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

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/api.py

@@ -0,0 +1,397 @@
+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")]

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/cupy_kernels.py

@@ -0,0 +1,119 @@
+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')

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/data.py

@@ -0,0 +1,146 @@
+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

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/patch_match.py

@@ -0,0 +1,298 @@
+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()

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/runners/__init__.py

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

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/runners/accurate.py

@@ -0,0 +1,35 @@
+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))

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/runners/balanced.py

@@ -0,0 +1,46 @@
+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)

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/runners/fast.py

@@ -0,0 +1,141 @@
+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))

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/runners/interpolation.py

@@ -0,0 +1,121 @@
+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

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/BLIP/__init__.py

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

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/BLIP/blip.py

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

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/BLIP/blip_pretrain.py

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

exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/BLIP/med.py

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