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Update to V1.1 (#24)
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* update README

* Update V1.1

* update v2v mask

* update contro model && update comyui && update ui && update readme

* Update README_zh-CN.md

* readme

* Update README.md

* readme

* Update Readme

* update readme

* Update README_TRAIN_CONTROL.md

Add DWPose suggest.

* Update tips in control video

---------

Co-authored-by: yunkchen <[email protected]>
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@bubbliiiing @yunkchen
bubbliiiing and yunkchen authored Sep 30, 2024
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Expand Up @@ -23,6 +23,7 @@ CogVideoX-Fun is a modified pipeline based on the CogVideoX structure, designed
We will support quick pull-ups from different platforms, refer to [Quick Start](#quick-start).

What's New:
- Retrain the i2v model and add noise to increase the motion amplitude of the video. Upload the control model training code and control model. [ 2024.09.29 ]
- Create code! Now supporting Windows and Linux. Supports 2b and 5b models. Supports video generation at any resolution from 256x256x49 to 1024x1024x49. [ 2024.09.18 ]

Function：
Expand Down Expand Up @@ -68,10 +69,10 @@ cd CogVideoX-Fun
mkdir models/Diffusion_Transformer
mkdir models/Personalized_Model
wget https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/cogvideox_fun/Diffusion_Transformer/CogVideoX-Fun-2b-InP.tar.gz -O models/Diffusion_Transformer/CogVideoX-Fun-2b-InP.tar.gz
wget https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/cogvideox_fun/Diffusion_Transformer/CogVideoX-Fun-V1.1-2b-InP.tar.gz -O models/Diffusion_Transformer/CogVideoX-Fun-V1.1-2b-InP.tar.gz
cd models/Diffusion_Transformer/
tar -xvf CogVideoX-Fun-2b-InP.tar.gz
tar -xvf CogVideoX-Fun-V1.1-2b-InP.tar.gz
cd ../../
```

Expand Down Expand Up @@ -103,51 +104,52 @@ We'd better place the [weights](#model-zoo) along the specified path:
```
📦 models/
├── 📂 Diffusion_Transformer/
│ ├── 📂 CogVideoX-Fun-2b-InP/
│ └── 📂 CogVideoX-Fun-5b-InP/
│ ├── 📂 CogVideoX-Fun-V1.1-2b-InP/
│ └── 📂 CogVideoX-Fun-V1.1-5b-InP/
├── 📂 Personalized_Model/
│ └── your trained trainformer model / your trained lora model (for UI load)
```

# Video Result
The results displayed are all based on image.

### CogVideoX-Fun-5B
### CogVideoX-Fun-V1.1-5B

Resolution-1024

<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
<tr>
<td>
<video src="https://github.com/user-attachments/assets/ec749326-b529-453f-a4b4-f587875dff64" width="100%" controls autoplay loop></video>
<video src="https://github.com/user-attachments/assets/34e7ec8f-293e-4655-bb14-5e1ee476f788" width="100%" controls autoplay loop></video>
</td>
<td>
<video src="https://github.com/user-attachments/assets/84df4178-f493-4aa8-a888-d2020338da82" width="100%" controls autoplay loop></video>
<video src="https://github.com/user-attachments/assets/7809c64f-eb8c-48a9-8bdc-ca9261fd5434" width="100%" controls autoplay loop></video>
</td>
<td>
<video src="https://github.com/user-attachments/assets/c66c139d-94d3-4930-985b-60e3e0600d8f" width="100%" controls autoplay loop></video>
<video src="https://github.com/user-attachments/assets/8e76aaa4-c602-44ac-bcb4-8b24b72c386c" width="100%" controls autoplay loop></video>
</td>
<td>
<video src="https://github.com/user-attachments/assets/647c0e0c-28d6-473e-b4eb-a30197dddefc" width="100%" controls autoplay loop></video>
<video src="https://github.com/user-attachments/assets/19dba894-7c35-4f25-b15c-384167ab3b03" width="100%" controls autoplay loop></video>
</td>
</tr>
</table>


Resolution-768

<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
<tr>
<td>
<video src="https://github.com/user-attachments/assets/647d45b0-4253-4438-baf3-f692789bde78" width="100%" controls autoplay loop></video>
<video src="https://github.com/user-attachments/assets/0bc339b9-455b-44fd-8917-80272d702737" width="100%" controls autoplay loop></video>
</td>
<td>
<video src="https://github.com/user-attachments/assets/e5a5a948-5c34-445d-9446-324a666a6a33" width="100%" controls autoplay loop></video>
<video src="https://github.com/user-attachments/assets/70a043b9-6721-4bd9-be47-78b7ec5c27e9" width="100%" controls autoplay loop></video>
</td>
<td>
<video src="https://github.com/user-attachments/assets/0e605797-4a86-4e0c-8589-40ed686d97a4" width="100%" controls autoplay loop></video>
<video src="https://github.com/user-attachments/assets/d5dd6c09-14f3-40f8-8b6d-91e26519b8ac" width="100%" controls autoplay loop></video>
</td>
<td>
<video src="https://github.com/user-attachments/assets/5356bf79-0a3b-4caf-ac31-2d796e20e429" width="100%" controls autoplay loop></video>
<video src="https://github.com/user-attachments/assets/9327e8bc-4f17-46b0-b50d-38c250a9483a" width="100%" controls autoplay loop></video>
</td>
</tr>
</table>
Expand All @@ -157,35 +159,89 @@ Resolution-512
<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
<tr>
<td>
<video src="https://github.com/user-attachments/assets/5a9f3457-fe82-4082-8494-d8f4f8db75e9" width="100%" controls autoplay loop></video>
<video src="https://github.com/user-attachments/assets/ef407030-8062-454d-aba3-131c21e6b58c" width="100%" controls autoplay loop></video>
</td>
<td>
<video src="https://github.com/user-attachments/assets/ca6874b8-41d1-4f02-bee3-4fc886f309ad" width="100%" controls autoplay loop></video>
<video src="https://github.com/user-attachments/assets/7610f49e-38b6-4214-aa48-723ae4d1b07e" width="100%" controls autoplay loop></video>
</td>
<td>
<video src="https://github.com/user-attachments/assets/9216b348-2c80-4eab-9c1c-dd3a54b7ea1e" width="100%" controls autoplay loop></video>
<video src="https://github.com/user-attachments/assets/1fff0567-1e15-415c-941e-53ee8ae2c841" width="100%" controls autoplay loop></video>
</td>
<td>
<video src="https://github.com/user-attachments/assets/e99ec495-655f-44d8-afa7-3ad0a14f9975" width="100%" controls autoplay loop></video>
<video src="https://github.com/user-attachments/assets/bcec48da-b91b-43a0-9d50-cf026e00fa4f" width="100%" controls autoplay loop></video>
</td>
</tr>
</table>

### CogVideoX-Fun-2B
### CogVideoX-Fun-V1.1-5B-Pose

<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
<tr>
<td>
<video src="https://github.com/user-attachments/assets/d329b4d4-f08f-4e77-887e-049cfc93a908" width="100%" controls autoplay loop></video>
Resolution-512
</td>
<td>
Resolution-768
</td>
<td>
Resolution-1024
</td>
<tr>
<td>
<video src="https://github.com/user-attachments/assets/a746df51-9eb7-4446-bee5-2ee30285c143" width="100%" controls autoplay loop></video>
</td>
<td>
<video src="https://github.com/user-attachments/assets/dd7fa2d5-9871-436c-ae5a-44f1494c9c9f" width="100%" controls autoplay loop></video>
<video src="https://github.com/user-attachments/assets/db295245-e6aa-43be-8c81-32cb411f1473" width="100%" controls autoplay loop></video>
</td>
<td>
<video src="https://github.com/user-attachments/assets/c24a2fa2-2fe3-4277-aa9f-e812a2cf0a4e" width="100%" controls autoplay loop></video>
<video src="https://github.com/user-attachments/assets/ec9875b2-fde0-48e1-ab7e-490cee51ef40" width="100%" controls autoplay loop></video>
</td>
</tr>
</table>

### CogVideoX-Fun-V1.1-2B

Resolution-768

<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
<tr>
<td>
<video src="https://github.com/user-attachments/assets/03235dea-980e-4fc5-9c41-e40a5bc1b6d0" width="100%" controls autoplay loop></video>
</td>
<td>
<video src="https://github.com/user-attachments/assets/573edac3-8bd0-4e95-82df-bcfdcba9a73f" width="100%" controls autoplay loop></video>
<video src="https://github.com/user-attachments/assets/f7302648-5017-47db-bdeb-4d893e620b37" width="100%" controls autoplay loop></video>
</td>
<td>
<video src="https://github.com/user-attachments/assets/cbadf411-28fa-4b87-813d-da63ff481904" width="100%" controls autoplay loop></video>
</td>
<td>
<video src="https://github.com/user-attachments/assets/87cc9d0b-b6fe-4d2d-b447-174513d169ab" width="100%" controls autoplay loop></video>
</td>
</tr>
</table>

### CogVideoX-Fun-V1.1-2B-Pose

<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
<tr>
<td>
Resolution-512
</td>
<td>
Resolution-768
</td>
<td>
Resolution-1024
</td>
<tr>
<td>
<video src="https://github.com/user-attachments/assets/487bcd7b-1b7f-4bb4-95b5-96a6b6548b3e" width="100%" controls autoplay loop></video>
</td>
<td>
<video src="https://github.com/user-attachments/assets/2710fd18-8489-46e4-8086-c237309ae7f6" width="100%" controls autoplay loop></video>
</td>
<td>
<video src="https://github.com/user-attachments/assets/b79513db-7747-4512-b86c-94f9ca447fe2" width="100%" controls autoplay loop></video>
</td>
</tr>
</table>
Expand Down Expand Up @@ -283,11 +339,22 @@ Then, we run scripts/train.sh.
sh scripts/train.sh
```

For details on setting some parameters, please refer to [Readme Train](scripts/README_TRAIN.md) and [Readme Lora](scripts/README_TRAIN_LORA.md).
For details on setting some parameters, please refer to [Readme Train](scripts/README_TRAIN.md), [Readme Lora](scripts/README_TRAIN_LORA.md) and [Readme Control](scripts/README_TRAIN_CONTROL.md).


# Model zoo

V1.1:

| 名称 | 存储空间 | Hugging Face | Model Scope | 描述 |
|--|--|--|--|--|
| CogVideoX-Fun-V1.1-2b-InP.tar.gz | Before extraction:9.7 GB \/ After extraction: 13.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-2b-InP) | [😄Link](https://modelscope.cn/models/PAI/CogVideoX-Fun-V1.1-2b-InP) | Our official graph-generated video model is capable of predicting videos at multiple resolutions (512, 768, 1024, 1280) and has been trained on 49 frames at a rate of 8 frames per second. Noise has been added to the reference image, and the amplitude of motion is greater compared to V1.0. |
| CogVideoX-Fun-V1.1-5b-InP.tar.gz | Before extraction:16.0 GB \/ After extraction: 20.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-5b-InP) | [😄Link](https://modelscope.cn/models/PAI/CogVideoX-Fun-V1.1-5b-InP) | Our official graph-generated video model is capable of predicting videos at multiple resolutions (512, 768, 1024, 1280) and has been trained on 49 frames at a rate of 8 frames per second. Noise has been added to the reference image, and the amplitude of motion is greater compared to V1.0. |
| CogVideoX-Fun-V1.1-2b-Pose.tar.gz | Before extraction:9.7 GB \/ After extraction: 13.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose) | [😄Link](https://modelscope.cn/models/PAI/CogVideoX-Fun-V1.1-2b-Pose) | Our official pose-control video model is capable of predicting videos at multiple resolutions (512, 768, 1024, 1280) and has been trained on 49 frames at a rate of 8 frames per second.|
| CogVideoX-Fun-V1.1-5b-Pose.tar.gz | Before extraction:16.0 GB \/ After extraction: 20.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose) | [😄Link](https://modelscope.cn/models/PAI/CogVideoX-Fun-V1.1-5b-Pose) | Our official pose-control video model is capable of predicting videos at multiple resolutions (512, 768, 1024, 1280) and has been trained on 49 frames at a rate of 8 frames per second.|

V1.0:

| Name | Storage Space | Hugging Face | Model Scope | Description |
|--|--|--|--|--|
| CogVideoX-Fun-2b-InP.tar.gz | Before extraction:9.7 GB \/ After extraction: 13.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/CogVideoX-Fun-2b-InP) | [😄Link](https://modelscope.cn/models/PAI/CogVideoX-Fun-2b-InP) | Our official graph-generated video model is capable of predicting videos at multiple resolutions (512, 768, 1024, 1280) and has been trained on 49 frames at a rate of 8 frames per second. |
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