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.gitattributes CHANGED
@@ -1 +1,9 @@
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  diffusion_pytorch_model.safetensors filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
 
 
1
  diffusion_pytorch_model.safetensors filter=lfs diff=lfs merge=lfs -text
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+ Wan2.1_VAE.pth filter=lfs diff=lfs merge=lfs -text
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+ google/umt5-xxl/spiece.model filter=lfs diff=lfs merge=lfs -text
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+ google/umt5-xxl/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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+ models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth filter=lfs diff=lfs merge=lfs -text
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+ models_t5_umt5-xxl-enc-bf16.pth filter=lfs diff=lfs merge=lfs -text
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+ optimizer.bin filter=lfs diff=lfs merge=lfs -text
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+ xlm-roberta-large/sentencepiece.bpe.model filter=lfs diff=lfs merge=lfs -text
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+ xlm-roberta-large/tokenizer.json filter=lfs diff=lfs merge=lfs -text
LICENSE.txt ADDED
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README.md CHANGED
@@ -2,64 +2,248 @@
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  license: apache-2.0
3
  language:
4
  - en
5
- library_name: diffusers
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  pipeline_tag: text-to-video
 
7
  tags:
8
  - video
9
  - video-generation
10
  ---
11
 
12
- This repository contains the pretrained checkpoints for:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
13
 
14
- **IllumiCraft: Unified Geometry and Illumination Diffusion for Controllable Video Generation (NeurIPS 2025)**
 
 
 
 
 
15
 
16
- [Yuanze Lin](https://yuanze-lin.me/), [Yi-Wen Chen](https://wenz116.github.io/), [Yi-Hsuan Tsai](https://sites.google.com/site/yihsuantsai/), [Ronald Clark](https://www.ron-clark.com/), [Ming-Hsuan Yang](https://faculty.ucmerced.edu/mhyang/)
 
17
 
18
- ## 🔗 Links
 
19
 
20
- - 📄 Paper: https://arxiv.org/abs/2506.03150
21
- - 🌐 Project Page: https://yuanze-lin.me/IllumiCraft_page/
22
- - 💻 GitHub: https://github.com/yuanze-lin/IllumiCraft
23
- - 🎥 YouTube: https://youtu.be/qAV58sADEzo
24
- - 🤗 Dataset: https://huggingface.co/datasets/YuanzeLin/IllumiCraft
25
 
26
- ## ✨ Overview
 
 
 
27
 
28
- IllumiCraft is a controllable video generation framework that jointly models scene geometry and illumination dynamics. By leveraging foreground appearance, HDR lighting maps, and 3D point tracks, IllumiCraft enables temporally consistent video generation with controllable motion and relighting effects.
 
 
 
29
 
30
- ## 📥 Download Checkpoints
 
 
31
 
32
- Download the pretrained checkpoints:
 
 
 
 
 
 
33
 
34
- ```bash
35
- python download_weights.py
36
- ```
 
 
 
 
37
 
38
- The downloaded checkpoints will be organized as:
39
 
40
- ```text
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- checkpoints/
42
- ├── Wan2.1-Fun-1.3B-Control/
43
- └── illumicraft_pretrained_weights/
 
 
 
 
 
 
 
 
44
  ```
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46
- ## 🚀 Usage
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
47
 
48
- Please refer to the official GitHub repository for installation, dataset preparation, training, and inference instructions:
49
 
50
- https://github.com/yuanze-lin/IllumiCraft
 
 
51
 
52
- ## 📖 Citation
 
 
 
53
 
54
- If you find IllumiCraft useful for your research, please consider citing:
 
55
 
56
- ```bibtex
57
- @article{lin2026illumicraft,
58
- title={IllumiCraft: Unified Geometry and Illumination Diffusion for Controllable Video Generation},
59
- author={Lin, Yuanze and Chen, Yi-Wen and Tsai, Yi-Hsuan and Clark, Ronald and Yang, Ming-Hsuan},
60
- journal={Advances in Neural Information Processing Systems},
61
- volume={38},
62
- pages={27798--27829},
63
- year={2026}
64
- }
65
- ```
 
2
  license: apache-2.0
3
  language:
4
  - en
5
+ - zh
6
  pipeline_tag: text-to-video
7
+ library_name: videox_fun
8
  tags:
9
  - video
10
  - video-generation
11
  ---
12
 
13
+ # Wan-Fun
14
+
15
+ 😊 Welcome!
16
+
17
+ [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-yellow)](https://huggingface.co/spaces/alibaba-pai/Wan2.1-Fun-1.3B-InP)
18
+
19
+ [![Github](https://img.shields.io/badge/🎬%20Code-Github-blue)](https://github.com/aigc-apps/VideoX-Fun)
20
+
21
+ [English](./README_en.md) | [简体中文](./README.md)
22
+
23
+ # 目录
24
+ - [目录](#目录)
25
+ - [模型地址](#模型地址)
26
+ - [视频作品](#视频作品)
27
+ - [快速启动](#快速启动)
28
+ - [如何使用](#如何使用)
29
+ - [参考文献](#参考文献)
30
+ - [许可证](#许可证)
31
+
32
+ # 模型地址
33
+ V1.0:
34
+ | 名称 | 存储空间 | Hugging Face | Model Scope | 描述 |
35
+ |--|--|--|--|--|
36
+ | Wan2.1-Fun-1.3B-InP | 19.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/Wan2.1-Fun-1.3B-InP) | [😄Link](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP) | Wan2.1-Fun-1.3B文图生视频权重,以多分辨率训练,支持首尾图预测。 |
37
+ | Wan2.1-Fun-14B-InP | 47.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/Wan2.1-Fun-14B-InP) | [😄Link](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP) | Wan2.1-Fun-14B文图生视频权重,以多分辨率训练,支持首尾图预测。 |
38
+ | Wan2.1-Fun-1.3B-Control | 19.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/Wan2.1-Fun-1.3B-Control) | [😄Link](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control)| Wan2.1-Fun-1.3B视频控制权重,支持不同的控制条件,如Canny、Depth、Pose、MLSD等,同时支持使用轨迹控制。支持多分辨率(512,768,1024)的视频预测,支持多分辨率(512,768,1024)的视频预测,以81帧、每秒16帧进行训练,支持多语言预测 |
39
+ | Wan2.1-Fun-14B-Control | 47.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/Wan2.1-Fun-14B-Control) | [😄Link](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control)| Wan2.1-Fun-14B视频控制权重,支持不同的控制条件,如Canny、Depth、Pose、MLSD等,同时支持使用轨迹控制。支持多分辨率(512,768,1024)的视频预测,支持多分辨率(512,768,1024)的视频预测,以81帧、每秒16帧进行训练,支持多语言预测 |
40
+
41
+ # 视频作品
42
+
43
+ ### Wan2.1-Fun-14B-InP && Wan2.1-Fun-1.3B-InP
44
+
45
+ <table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
46
+ <tr>
47
+ <td>
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+ <video src="https://github.com/user-attachments/assets/bd72a276-e60e-4b5d-86c1-d0f67e7425b9" width="100%" controls autoplay loop></video>
49
+ </td>
50
+ <td>
51
+ <video src="https://github.com/user-attachments/assets/cb7aef09-52c2-4973-80b4-b2fb63425044" width="100%" controls autoplay loop></video>
52
+ </td>
53
+ <td>
54
+ <video src="https://github.com/user-attachments/assets/4e10d491-f1cf-4b08-a7c5-1e01e5418140" width="100%" controls autoplay loop></video>
55
+ </td>
56
+ <td>
57
+ <video src="https://github.com/user-attachments/assets/f7e363a9-be09-4b72-bccf-cce9c9ebeb9b" width="100%" controls autoplay loop></video>
58
+ </td>
59
+ </tr>
60
+ </table>
61
+
62
+ <table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
63
+ <tr>
64
+ <td>
65
+ <video src="https://github.com/user-attachments/assets/28f3e720-8acc-4f22-a5d0-ec1c571e9466" width="100%" controls autoplay loop></video>
66
+ </td>
67
+ <td>
68
+ <video src="https://github.com/user-attachments/assets/fb6e4cb9-270d-47cd-8501-caf8f3e91b5c" width="100%" controls autoplay loop></video>
69
+ </td>
70
+ <td>
71
+ <video src="https://github.com/user-attachments/assets/989a4644-e33b-4f0c-b68e-2ff6ba37ac7e" width="100%" controls autoplay loop></video>
72
+ </td>
73
+ <td>
74
+ <video src="https://github.com/user-attachments/assets/9c604fa7-8657-49d1-8066-b5bb198b28b6" width="100%" controls autoplay loop></video>
75
+ </td>
76
+ </tr>
77
+ </table>
78
+
79
+ ### Wan2.1-Fun-14B-Control && Wan2.1-Fun-1.3B-Control
80
+
81
+ <table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
82
+ <tr>
83
+ <td>
84
+ <video src="https://github.com/user-attachments/assets/f35602c4-9f0a-4105-9762-1e3a88abbac6" width="100%" controls autoplay loop></video>
85
+ </td>
86
+ <td>
87
+ <video src="https://github.com/user-attachments/assets/8b0f0e87-f1be-4915-bb35-2d53c852333e" width="100%" controls autoplay loop></video>
88
+ </td>
89
+ <td>
90
+ <video src="https://github.com/user-attachments/assets/972012c1-772b-427a-bce6-ba8b39edcfad" width="100%" controls autoplay loop></video>
91
+ </td>
92
+ <tr>
93
+ </table>
94
+
95
+ <table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
96
+ <tr>
97
+ <td>
98
+ <video src="https://github.com/user-attachments/assets/53002ce2-dd18-4d4f-8135-b6f68364cabd" width="100%" controls autoplay loop></video>
99
+ </td>
100
+ <td>
101
+ <video src="https://github.com/user-attachments/assets/a1a07cf8-d86d-4cd2-831f-18a6c1ceee1d" width="100%" controls autoplay loop></video>
102
+ </td>
103
+ <td>
104
+ <video src="https://github.com/user-attachments/assets/3224804f-342d-4947-918d-d9fec8e3d273" width="100%" controls autoplay loop></video>
105
+ </td>
106
+ <tr>
107
+ <td>
108
+ <video src="https://github.com/user-attachments/assets/c6c5d557-9772-483e-ae47-863d8a26db4a" width="100%" controls autoplay loop></video>
109
+ </td>
110
+ <td>
111
+ <video src="https://github.com/user-attachments/assets/af617971-597c-4be4-beb5-f9e8aaca2d14" width="100%" controls autoplay loop></video>
112
+ </td>
113
+ <td>
114
+ <video src="https://github.com/user-attachments/assets/8411151e-f491-4264-8368-7fc3c5a6992b" width="100%" controls autoplay loop></video>
115
+ </td>
116
+ </tr>
117
+ </table>
118
+
119
+ # 快速启动
120
+ ### 1. 云使用: AliyunDSW/Docker
121
+ #### a. 通过阿里云 DSW
122
+ DSW 有免费 GPU 时间,用户可申请一次,申请后3个月内有效。
123
+
124
+ 阿里云在[Freetier](https://free.aliyun.com/?product=9602825&crowd=enterprise&spm=5176.28055625.J_5831864660.1.e939154aRgha4e&scm=20140722.M_9974135.P_110.MO_1806-ID_9974135-MID_9974135-CID_30683-ST_8512-V_1)提供免费GPU时间,获取并在阿里云PAI-DSW中使用,5分钟内即可启动CogVideoX-Fun。
125
+
126
+ [![DSW Notebook](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/asset/dsw.png)](https://gallery.pai-ml.com/#/preview/deepLearning/cv/cogvideox_fun)
127
+
128
+ #### b. 通过ComfyUI
129
+ 我们的ComfyUI界面如下,具体查看[ComfyUI README](comfyui/README.md)。
130
+ ![workflow graph](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/cogvideox_fun/asset/v1/cogvideoxfunv1_workflow_i2v.jpg)
131
+
132
+ #### c. 通过docker
133
+ 使用docker的情况下,请保证机器中已经正确安装显卡驱动与CUDA环境,然后以此执行以下命令:
134
 
135
+ ```
136
+ # pull image
137
+ docker pull mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun
138
+
139
+ # enter image
140
+ docker run -it -p 7860:7860 --network host --gpus all --security-opt seccomp:unconfined --shm-size 200g mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun
141
 
142
+ # clone code
143
+ git clone https://github.com/aigc-apps/CogVideoX-Fun.git
144
 
145
+ # enter CogVideoX-Fun's dir
146
+ cd CogVideoX-Fun
147
 
148
+ # download weights
149
+ mkdir models/Diffusion_Transformer
150
+ mkdir models/Personalized_Model
 
 
151
 
152
+ # Please use the hugginface link or modelscope link to download the model.
153
+ # CogVideoX-Fun
154
+ # https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-5b-InP
155
+ # https://modelscope.cn/models/PAI/CogVideoX-Fun-V1.1-5b-InP
156
 
157
+ # Wan
158
+ # https://huggingface.co/alibaba-pai/Wan2.1-Fun-14B-InP
159
+ # https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP
160
+ ```
161
 
162
+ ### 2. 本地安装: 环境检查/下载/安装
163
+ #### a. 环境检查
164
+ 我们已验证该库可在以下环境中执行:
165
 
166
+ Windows 的详细信息:
167
+ - 操作系统 Windows 10
168
+ - python: python3.10 & python3.11
169
+ - pytorch: torch2.2.0
170
+ - CUDA: 11.8 & 12.1
171
+ - CUDNN: 8+
172
+ - GPU: Nvidia-3060 12G & Nvidia-3090 24G
173
 
174
+ Linux 的详细信息:
175
+ - 操作系统 Ubuntu 20.04, CentOS
176
+ - python: python3.10 & python3.11
177
+ - pytorch: torch2.2.0
178
+ - CUDA: 11.8 & 12.1
179
+ - CUDNN: 8+
180
+ - GPU:Nvidia-V100 16G & Nvidia-A10 24G & Nvidia-A100 40G & Nvidia-A100 80G
181
 
182
+ 我们需要大约 60GB 的可用磁盘空间,请检查!
183
 
184
+ #### b. 权重放置
185
+ 我们最好将[权重](#model-zoo)按照指定路径进行放置:
186
+
187
+ ```
188
+ 📦 models/
189
+ ├── 📂 Diffusion_Transformer/
190
+ │ ├── 📂 CogVideoX-Fun-V1.1-2b-InP/
191
+ │ ├── 📂 CogVideoX-Fun-V1.1-5b-InP/
192
+ │ ├── 📂 Wan2.1-Fun-14B-InP
193
+ │ └── 📂 Wan2.1-Fun-1.3B-InP/
194
+ ├── 📂 Personalized_Model/
195
+ │ └── your trained trainformer model / your trained lora model (for UI load)
196
  ```
197
 
198
+ # 如何使用
199
+
200
+ <h3 id="video-gen">1. 生成 </h3>
201
+
202
+ #### a、显存节省方案
203
+ 由于Wan2.1的参数非常大,我们需要考虑显存节省方案,以节省显存适应消费级显卡。我们给每个预测文件都提供了GPU_memory_mode,可以在model_cpu_offload,model_cpu_offload_and_qfloat8,sequential_cpu_offload中进行选择。该方案同样适用于CogVideoX-Fun的生成。
204
+
205
+ - model_cpu_offload代表整个模型在使用后会进入cpu,可以节省部分显存。
206
+ - model_cpu_offload_and_qfloat8代表整个模型在使用后会进入cpu,并且对transformer模型进行了float8的量化,可以节省更多的显存。
207
+ - sequential_cpu_offload代表模型的每一层在使用后会进入cpu,速度较慢,节省大量显存。
208
+
209
+ qfloat8会部分降低模型的性能,但可以节省更多的显存。如果显存足够,推荐使用model_cpu_offload。
210
+
211
+ #### b、通过comfyui
212
+ 具体查看[ComfyUI README](comfyui/README.md)。
213
+
214
+ #### c、运行python文件
215
+ - 步骤1:下载对应[权重](#model-zoo)放入models文件夹。
216
+ - 步骤2:根据不同的权重与预测目标使用不同的文件进行预测。当前该库支持CogVideoX-Fun、Wan2.1和Wan2.1-Fun,在examples文件夹下用文件夹名以区分,不同模型支持的功能不同,请视具体情况予以区分。以CogVideoX-Fun为例。
217
+ - 文生视频:
218
+ - 使用examples/cogvideox_fun/predict_t2v.py文件中修改prompt、neg_prompt、guidance_scale和seed。
219
+ - 而后运行examples/cogvideox_fun/predict_t2v.py文件,等待生成结果,结果保存在samples/cogvideox-fun-videos文件夹中。
220
+ - 图生视频:
221
+ - 使用examples/cogvideox_fun/predict_i2v.py文件中修改validation_image_start、validation_image_end、prompt、neg_prompt、guidance_scale和seed。
222
+ - validation_image_start是视频的开始图片,validation_image_end是视频的结尾图片。
223
+ - 而后运行examples/cogvideox_fun/predict_i2v.py文件,等待生成结果,结果保存在samples/cogvideox-fun-videos_i2v文件夹中。
224
+ - 视频生视频:
225
+ - 使用examples/cogvideox_fun/predict_v2v.py文件中修改validation_video、validation_image_end、prompt、neg_prompt、guidance_scale和seed。
226
+ - validation_video是视频生视频的参考视频。您可以使用以下视频运行演示:[演示视频](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/cogvideox_fun/asset/v1/play_guitar.mp4)
227
+ - 而后运行examples/cogvideox_fun/predict_v2v.py文件,等待生成结果,结果保存在samples/cogvideox-fun-videos_v2v文件夹中。
228
+ - 普通控制生视频(Canny、Pose、Depth等):
229
+ - 使用examples/cogvideox_fun/predict_v2v_control.py文件中修改control_video、validation_image_end、prompt、neg_prompt、guidance_scale和seed。
230
+ - control_video是控制生视频的控制视频,是使用Canny、Pose、Depth等算子提取后的视频。您可以使用以下视频运行演示:[演示视频](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/cogvideox_fun/asset/v1.1/pose.mp4)
231
+ - 而后运行examples/cogvideox_fun/predict_v2v_control.py文件,等待生成结果,结果保存在samples/cogvideox-fun-videos_v2v_control文件夹中。
232
+ - 步骤3:如果想结合自己训练的其他backbone与Lora,则看情况修改examples/{model_name}/predict_t2v.py中的examples/{model_name}/predict_i2v.py和lora_path。
233
+
234
+ #### d、通过ui界面
235
 
236
+ webui支持文生视频、图生视频、视频生视频和普通控制生视频(Canny、Pose、Depth等)。当前该库支持CogVideoX-Fun、Wan2.1和Wan2.1-Fun,在examples文件夹下用文件夹名以区分,不同模型支持的功能不同,请视具体情况予以区分。以CogVideoX-Fun为例。
237
 
238
+ - 步骤1:下载对应[权重](#model-zoo)放入models文件夹。
239
+ - 步骤2:运行examples/cogvideox_fun/app.py文件,进入gradio页面。
240
+ - 步骤3:根据页面选择生成模型,填入prompt、neg_prompt、guidance_scale和seed等,点击生成,等待生成结果,结果保存在sample文件夹中。
241
 
242
+ # 参考文献
243
+ - CogVideo: https://github.com/THUDM/CogVideo/
244
+ - EasyAnimate: https://github.com/aigc-apps/EasyAnimate
245
+ - Wan2.1: https://github.com/Wan-Video/Wan2.1/
246
 
247
+ # 许可证
248
+ 本项目采用 [Apache License (Version 2.0)](https://github.com/modelscope/modelscope/blob/master/LICENSE).
249
 
 
 
 
 
 
 
 
 
 
 
README_en.md ADDED
@@ -0,0 +1,249 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ language:
4
+ - en
5
+ - zh
6
+ pipeline_tag: text-to-video
7
+ library_name: diffusers
8
+ tags:
9
+ - video
10
+ - video-generation
11
+ ---
12
+
13
+ # Wan-Fun
14
+
15
+ 😊 Welcome!
16
+
17
+ [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-yellow)](https://huggingface.co/spaces/alibaba-pai/Wan2.1-Fun-1.3B-InP)
18
+
19
+ [![Github](https://img.shields.io/badge/🎬%20Code-Github-blue)](https://github.com/aigc-apps/VideoX-Fun)
20
+
21
+ [English](./README_en.md) | [简体中文](./README.md)
22
+
23
+ # Table of Contents
24
+ - [Table of Contents](#table-of-contents)
25
+ - [Model zoo](#model-zoo)
26
+ - [Video Result](#video-result)
27
+ - [Quick Start](#quick-start)
28
+ - [How to use](#how-to-use)
29
+ - [Reference](#reference)
30
+ - [License](#license)
31
+
32
+ # Model zoo
33
+ V1.0:
34
+ | Name | Storage Space | Hugging Face | Model Scope | Description |
35
+ |--|--|--|--|--|
36
+ | Wan2.1-Fun-1.3B-InP | 19.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/Wan2.1-Fun-1.3B-InP) | [😄Link](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP) | Wan2.1-Fun-1.3B text-to-video weights, trained at multiple resolutions, supporting start and end frame prediction. |
37
+ | Wan2.1-Fun-14B-InP | 47.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/Wan2.1-Fun-14B-InP) | [😄Link](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP) | Wan2.1-Fun-14B text-to-video weights, trained at multiple resolutions, supporting start and end frame prediction. |
38
+ | Wan2.1-Fun-1.3B-Control | 19.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/Wan2.1-Fun-1.3B-Control) | [😄Link](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control) | Wan2.1-Fun-1.3B video control weights, supporting various control conditions such as Canny, Depth, Pose, MLSD, etc., and trajectory control. Supports multi-resolution (512, 768, 1024) video prediction at 81 frames, trained at 16 frames per second, with multilingual prediction support. |
39
+ | Wan2.1-Fun-14B-Control | 47.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/Wan2.1-Fun-14B-Control) | [😄Link](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control) | Wan2.1-Fun-14B video control weights, supporting various control conditions such as Canny, Depth, Pose, MLSD, etc., and trajectory control. Supports multi-resolution (512, 768, 1024) video prediction at 81 frames, trained at 16 frames per second, with multilingual prediction support. |
40
+
41
+ # Video Result
42
+
43
+ ### Wan2.1-Fun-14B-InP && Wan2.1-Fun-1.3B-InP
44
+
45
+ <table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
46
+ <tr>
47
+ <td>
48
+ <video src="https://github.com/user-attachments/assets/bd72a276-e60e-4b5d-86c1-d0f67e7425b9" width="100%" controls autoplay loop></video>
49
+ </td>
50
+ <td>
51
+ <video src="https://github.com/user-attachments/assets/cb7aef09-52c2-4973-80b4-b2fb63425044" width="100%" controls autoplay loop></video>
52
+ </td>
53
+ <td>
54
+ <video src="https://github.com/user-attachments/assets/4e10d491-f1cf-4b08-a7c5-1e01e5418140" width="100%" controls autoplay loop></video>
55
+ </td>
56
+ <td>
57
+ <video src="https://github.com/user-attachments/assets/f7e363a9-be09-4b72-bccf-cce9c9ebeb9b" width="100%" controls autoplay loop></video>
58
+ </td>
59
+ </tr>
60
+ </table>
61
+
62
+ <table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
63
+ <tr>
64
+ <td>
65
+ <video src="https://github.com/user-attachments/assets/28f3e720-8acc-4f22-a5d0-ec1c571e9466" width="100%" controls autoplay loop></video>
66
+ </td>
67
+ <td>
68
+ <video src="https://github.com/user-attachments/assets/fb6e4cb9-270d-47cd-8501-caf8f3e91b5c" width="100%" controls autoplay loop></video>
69
+ </td>
70
+ <td>
71
+ <video src="https://github.com/user-attachments/assets/989a4644-e33b-4f0c-b68e-2ff6ba37ac7e" width="100%" controls autoplay loop></video>
72
+ </td>
73
+ <td>
74
+ <video src="https://github.com/user-attachments/assets/9c604fa7-8657-49d1-8066-b5bb198b28b6" width="100%" controls autoplay loop></video>
75
+ </td>
76
+ </tr>
77
+ </table>
78
+
79
+ ### Wan2.1-Fun-14B-Control && Wan2.1-Fun-1.3B-Control
80
+
81
+ <table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
82
+ <tr>
83
+ <td>
84
+ <video src="https://github.com/user-attachments/assets/f35602c4-9f0a-4105-9762-1e3a88abbac6" width="100%" controls autoplay loop></video>
85
+ </td>
86
+ <td>
87
+ <video src="https://github.com/user-attachments/assets/8b0f0e87-f1be-4915-bb35-2d53c852333e" width="100%" controls autoplay loop></video>
88
+ </td>
89
+ <td>
90
+ <video src="https://github.com/user-attachments/assets/972012c1-772b-427a-bce6-ba8b39edcfad" width="100%" controls autoplay loop></video>
91
+ </td>
92
+ <tr>
93
+ </table>
94
+
95
+ <table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
96
+ <tr>
97
+ <td>
98
+ <video src="https://github.com/user-attachments/assets/53002ce2-dd18-4d4f-8135-b6f68364cabd" width="100%" controls autoplay loop></video>
99
+ </td>
100
+ <td>
101
+ <video src="https://github.com/user-attachments/assets/a1a07cf8-d86d-4cd2-831f-18a6c1ceee1d" width="100%" controls autoplay loop></video>
102
+ </td>
103
+ <td>
104
+ <video src="https://github.com/user-attachments/assets/3224804f-342d-4947-918d-d9fec8e3d273" width="100%" controls autoplay loop></video>
105
+ </td>
106
+ <tr>
107
+ <td>
108
+ <video src="https://github.com/user-attachments/assets/c6c5d557-9772-483e-ae47-863d8a26db4a" width="100%" controls autoplay loop></video>
109
+ </td>
110
+ <td>
111
+ <video src="https://github.com/user-attachments/assets/af617971-597c-4be4-beb5-f9e8aaca2d14" width="100%" controls autoplay loop></video>
112
+ </td>
113
+ <td>
114
+ <video src="https://github.com/user-attachments/assets/8411151e-f491-4264-8368-7fc3c5a6992b" width="100%" controls autoplay loop></video>
115
+ </td>
116
+ </tr>
117
+ </table>
118
+
119
+ # Quick Start
120
+ ### 1. Cloud usage: AliyunDSW/Docker
121
+ #### a. From AliyunDSW
122
+ DSW has free GPU time, which can be applied once by a user and is valid for 3 months after applying.
123
+
124
+ Aliyun provide free GPU time in [Freetier](https://free.aliyun.com/?product=9602825&crowd=enterprise&spm=5176.28055625.J_5831864660.1.e939154aRgha4e&scm=20140722.M_9974135.P_110.MO_1806-ID_9974135-MID_9974135-CID_30683-ST_8512-V_1), get it and use in Aliyun PAI-DSW to start CogVideoX-Fun within 5min!
125
+
126
+ [![DSW Notebook](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/asset/dsw.png)](https://gallery.pai-ml.com/#/preview/deepLearning/cv/cogvideox_fun)
127
+
128
+ #### b. From ComfyUI
129
+ Our ComfyUI is as follows, please refer to [ComfyUI README](comfyui/README.md) for details.
130
+ ![workflow graph](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/cogvideox_fun/asset/v1/cogvideoxfunv1_workflow_i2v.jpg)
131
+
132
+ #### c. From docker
133
+ If you are using docker, please make sure that the graphics card driver and CUDA environment have been installed correctly in your machine.
134
+
135
+ Then execute the following commands in this way:
136
+
137
+ ```
138
+ # pull image
139
+ docker pull mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun
140
+
141
+ # enter image
142
+ docker run -it -p 7860:7860 --network host --gpus all --security-opt seccomp:unconfined --shm-size 200g mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun
143
+
144
+ # clone code
145
+ git clone https://github.com/aigc-apps/CogVideoX-Fun.git
146
+
147
+ # enter CogVideoX-Fun's dir
148
+ cd CogVideoX-Fun
149
+
150
+ # download weights
151
+ mkdir models/Diffusion_Transformer
152
+ mkdir models/Personalized_Model
153
+
154
+ # Please use the hugginface link or modelscope link to download the model.
155
+ # CogVideoX-Fun
156
+ # https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-5b-InP
157
+ # https://modelscope.cn/models/PAI/CogVideoX-Fun-V1.1-5b-InP
158
+
159
+ # Wan
160
+ # https://huggingface.co/alibaba-pai/Wan2.1-Fun-14B-InP
161
+ # https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP
162
+ ```
163
+
164
+ ### 2. Local install: Environment Check/Downloading/Installation
165
+ #### a. Environment Check
166
+ We have verified this repo execution on the following environment:
167
+
168
+ The detailed of Windows:
169
+ - OS: Windows 10
170
+ - python: python3.10 & python3.11
171
+ - pytorch: torch2.2.0
172
+ - CUDA: 11.8 & 12.1
173
+ - CUDNN: 8+
174
+ - GPU: Nvidia-3060 12G & Nvidia-3090 24G
175
+
176
+ The detailed of Linux:
177
+ - OS: Ubuntu 20.04, CentOS
178
+ - python: python3.10 & python3.11
179
+ - pytorch: torch2.2.0
180
+ - CUDA: 11.8 & 12.1
181
+ - CUDNN: 8+
182
+ - GPU:Nvidia-V100 16G & Nvidia-A10 24G & Nvidia-A100 40G & Nvidia-A100 80G
183
+
184
+ We need about 60GB available on disk (for saving weights), please check!
185
+
186
+ #### b. Weights
187
+ We'd better place the [weights](#model-zoo) along the specified path:
188
+
189
+ ```
190
+ 📦 models/
191
+ ├── 📂 Diffusion_Transformer/
192
+ │ ├── 📂 CogVideoX-Fun-V1.1-2b-InP/
193
+ │ ├── 📂 CogVideoX-Fun-V1.1-5b-InP/
194
+ │ ├── 📂 Wan2.1-Fun-14B-InP
195
+ │ └── 📂 Wan2.1-Fun-1.3B-InP/
196
+ ├── 📂 Personalized_Model/
197
+ │ └── your trained trainformer model / your trained lora model (for UI load)
198
+ ```
199
+
200
+ # How to Use
201
+
202
+ <h3 id="video-gen">1. Generation</h3>
203
+
204
+ #### a. GPU Memory Optimization
205
+ Since Wan2.1 has a very large number of parameters, we need to consider memory optimization strategies to adapt to consumer-grade GPUs. We provide `GPU_memory_mode` for each prediction file, allowing you to choose between `model_cpu_offload`, `model_cpu_offload_and_qfloat8`, and `sequential_cpu_offload`. This solution is also applicable to CogVideoX-Fun generation.
206
+
207
+ - `model_cpu_offload`: The entire model is moved to the CPU after use, saving some GPU memory.
208
+ - `model_cpu_offload_and_qfloat8`: The entire model is moved to the CPU after use, and the transformer model is quantized to float8, saving more GPU memory.
209
+ - `sequential_cpu_offload`: Each layer of the model is moved to the CPU after use. It is slower but saves a significant amount of GPU memory.
210
+
211
+ `qfloat8` may slightly reduce model performance but saves more GPU memory. If you have sufficient GPU memory, it is recommended to use `model_cpu_offload`.
212
+
213
+ #### b. Using ComfyUI
214
+ For details, refer to [ComfyUI README](comfyui/README.md).
215
+
216
+ #### c. Running Python Files
217
+ - **Step 1**: Download the corresponding [weights](#model-zoo) and place them in the `models` folder.
218
+ - **Step 2**: Use different files for prediction based on the weights and prediction goals. This library currently supports CogVideoX-Fun, Wan2.1, and Wan2.1-Fun. Different models are distinguished by folder names under the `examples` folder, and their supported features vary. Use them accordingly. Below is an example using CogVideoX-Fun:
219
+ - **Text-to-Video**:
220
+ - Modify `prompt`, `neg_prompt`, `guidance_scale`, and `seed` in the file `examples/cogvideox_fun/predict_t2v.py`.
221
+ - Run the file `examples/cogvideox_fun/predict_t2v.py` and wait for the results. The generated videos will be saved in the folder `samples/cogvideox-fun-videos`.
222
+ - **Image-to-Video**:
223
+ - Modify `validation_image_start`, `validation_image_end`, `prompt`, `neg_prompt`, `guidance_scale`, and `seed` in the file `examples/cogvideox_fun/predict_i2v.py`.
224
+ - `validation_image_start` is the starting image of the video, and `validation_image_end` is the ending image of the video.
225
+ - Run the file `examples/cogvideox_fun/predict_i2v.py` and wait for the results. The generated videos will be saved in the folder `samples/cogvideox-fun-videos_i2v`.
226
+ - **Video-to-Video**:
227
+ - Modify `validation_video`, `validation_image_end`, `prompt`, `neg_prompt`, `guidance_scale`, and `seed` in the file `examples/cogvideox_fun/predict_v2v.py`.
228
+ - `validation_video` is the reference video for video-to-video generation. You can use the following demo video: [Demo Video](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/cogvideox_fun/asset/v1/play_guitar.mp4).
229
+ - Run the file `examples/cogvideox_fun/predict_v2v.py` and wait for the results. The generated videos will be saved in the folder `samples/cogvideox-fun-videos_v2v`.
230
+ - **Controlled Video Generation (Canny, Pose, Depth, etc.)**:
231
+ - Modify `control_video`, `validation_image_end`, `prompt`, `neg_prompt`, `guidance_scale`, and `seed` in the file `examples/cogvideox_fun/predict_v2v_control.py`.
232
+ - `control_video` is the control video extracted using operators such as Canny, Pose, or Depth. You can use the following demo video: [Demo Video](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/cogvideox_fun/asset/v1.1/pose.mp4).
233
+ - Run the file `examples/cogvideox_fun/predict_v2v_control.py` and wait for the results. The generated videos will be saved in the folder `samples/cogvideox-fun-videos_v2v_control`.
234
+ - **Step 3**: If you want to integrate other backbones or Loras trained by yourself, modify `lora_path` and relevant paths in `examples/{model_name}/predict_t2v.py` or `examples/{model_name}/predict_i2v.py` as needed.
235
+
236
+ #### d. Using the Web UI
237
+ The web UI supports text-to-video, image-to-video, video-to-video, and controlled video generation (Canny, Pose, Depth, etc.). This library currently supports CogVideoX-Fun, Wan2.1, and Wan2.1-Fun. Different models are distinguished by folder names under the `examples` folder, and their supported features vary. Use them accordingly. Below is an example using CogVideoX-Fun:
238
+
239
+ - **Step 1**: Download the corresponding [weights](#model-zoo) and place them in the `models` folder.
240
+ - **Step 2**: Run the file `examples/cogvideox_fun/app.py` to access the Gradio interface.
241
+ - **Step 3**: Select the generation model on the page, fill in `prompt`, `neg_prompt`, `guidance_scale`, and `seed`, click "Generate," and wait for the results. The generated videos will be saved in the `sample` folder.
242
+
243
+ # Reference
244
+ - CogVideo: https://github.com/THUDM/CogVideo/
245
+ - EasyAnimate: https://github.com/aigc-apps/EasyAnimate
246
+ - Wan2.1: https://github.com/Wan-Video/Wan2.1/
247
+
248
+ # License
249
+ This project is licensed under the [Apache License (Version 2.0)](https://github.com/modelscope/modelscope/blob/master/LICENSE).
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