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Artiprocher
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creating/ToonShading.md
creating/PromptRefine.md
.. toctree::
:maxdepth: 1
:caption: 模型列表
model/StableDiffusion.md
model/StableDiffusionXL.md
model/ControlNet.md
model/AnimateDiff.md
model/IPAdapter.md
model/HunyuanDiT.md
model/Kolors.md
model/StableDiffusion3.md
model/StableVideoDiffusion.md
model/ExVideo.md
model/FLUX.md
model/CogVideo.md
.. toctree::
:maxdepth: 1
:caption: 微调

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# AnimateDiff

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# CogVideoX

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# ControlNet

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# ExVideo
## 相关链接
* 论文:[ExVideo: Extending Video Diffusion Models via Parameter-Efficient Post-Tuning](https://arxiv.org/abs/2406.14130)
* 模型
* ExVideo-CogVideoX
* [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1)
* [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1)
* ExVideo-SVD
* [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1)
* [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1)
## 模型介绍
ExVideo 是一种视频生成模型的后训练post-training方法旨在增强视频生成模型的能力使其能够生成更长的视频。目前ExVideo 已经发布了两个版本,分别将 Stable Video Diffusion 扩展到 128 帧、将 CogVideoX-5B 扩展到 129 帧。
在基于 Stable Video Diffusion 的 ExVideo 扩展模块中静态的位置编码被替换为了可训练的参数矩阵并在时序模块中添加了额外的单位卷积Identidy 3D Convolution在保留预训练模型本身能力的前提下使其能够捕获更长时间尺度上的信息从而生成更长视频。而在基于 CogVideoX-5B 的 ExVideo 扩展模块中,由于模型基础架构为 DiT为保证计算效率扩展模块采用 LoRA 的形式构建。
![](https://github.com/user-attachments/assets/94aa31ba-3ee3-4421-9713-83333a165660)
为了在有限的计算资源上实现长视频的训练ExVideo 做了很多工程优化,包括:
* Parameter freezing冻结除了扩展模块以外的所有参数
* Mixed precision扩展模块部分以全精度维护其他部分以 BFloat16 精度维护
* Gradient checkpointing在前向传播时丢弃中间变量并反向传播时重新计算
* Flash attention在所有注意力机制上启用加速过的注意力实现
* Shard optimizer states and gradients基于 DeepSpeed 把部分参数分拆到多个 GPU 上
Stable Video Diffusion + ExVideo 的生成效果:
<video width="512" height="256" controls>
<source src="https://github.com/modelscope/DiffSynth-Studio/assets/35051019/d97f6aa9-8064-4b5b-9d49-ed6001bb9acc" type="video/mp4">
您的浏览器不支持Video标签。
</video>
CogVideoX-5B + ExVideo 的生成效果:
<video width="512" height="256" controls>
<source src="https://github.com/user-attachments/assets/321ee04b-8c17-479e-8a95-8cbcf21f8d7e" type="video/mp4">
您的浏览器不支持Video标签。
</video>
## 代码样例
ExVideo-SVD
```python
from diffsynth import save_video, ModelManager, SVDVideoPipeline
import torch, requests
from PIL import Image
# Load models
model_manager = ModelManager(torch_dtype=torch.float16, device="cuda",
model_id_list=["stable-video-diffusion-img2vid-xt", "ExVideo-SVD-128f-v1"])
pipe = SVDVideoPipeline.from_model_manager(model_manager)
# Generate a video
torch.manual_seed(0)
image = Image.open(requests.get("https://www.modelscope.cn/api/v1/studio/ECNU-CILab/ExVideo-SVD-128f-v1/repo?Revision=master&FilePath=images%2F0.png", stream=True).raw)
image.save("image.png")
video = pipe(
input_image=image.resize((512, 512)),
num_frames=128, fps=30, height=512, width=512,
motion_bucket_id=127,
num_inference_steps=50,
min_cfg_scale=2, max_cfg_scale=2, contrast_enhance_scale=1.2
)
save_video(video, "video.mp4", fps=30)
```
ExVideo-CogVideoX
```python
from diffsynth import ModelManager, CogVideoPipeline, save_video, download_models
import torch
download_models(["CogVideoX-5B", "ExVideo-CogVideoX-LoRA-129f-v1"])
model_manager = ModelManager(torch_dtype=torch.bfloat16)
model_manager.load_models([
"models/CogVideo/CogVideoX-5b/text_encoder",
"models/CogVideo/CogVideoX-5b/transformer",
"models/CogVideo/CogVideoX-5b/vae/diffusion_pytorch_model.safetensors",
])
model_manager.load_lora("models/lora/ExVideo-CogVideoX-LoRA-129f-v1.safetensors")
pipe = CogVideoPipeline.from_model_manager(model_manager)
torch.manual_seed(6)
video = pipe(
prompt="an astronaut riding a horse on Mars.",
height=480, width=720, num_frames=129,
cfg_scale=7.0, num_inference_steps=100,
)
save_video(video, "video_with_lora.mp4", fps=8, quality=5)
```

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# FLUX

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# Hunyuan-DiT

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# IP-Adapter

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# Kolors

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# Stable Diffusion

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# Stable Diffusion 3

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# Stable Diffusion XL

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# Stable Video Diffusion