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Release complete Multics inference bundle

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  1. .gitattributes +1 -0
  2. LICENSE +201 -0
  3. README.md +40 -0
  4. SHA256SUMS +15 -0
  5. THIRD_PARTY.md +8 -0
  6. Wan-AI/Wan2.1-T2V-1.3B/google/umt5-xxl/special_tokens_map.json +308 -0
  7. Wan-AI/Wan2.1-T2V-1.3B/google/umt5-xxl/spiece.model +3 -0
  8. Wan-AI/Wan2.1-T2V-1.3B/google/umt5-xxl/tokenizer.json +3 -0
  9. Wan-AI/Wan2.1-T2V-1.3B/google/umt5-xxl/tokenizer_config.json +2748 -0
  10. Wan-AI/Wan2.1-T2V-1.3B/models_t5_umt5-xxl-enc-bf16.pth +3 -0
  11. Wan-AI/Wan2.2-TI2V-5B/Wan2.2_VAE.pth +3 -0
  12. licenses/UMT5-README.md +130 -0
  13. licenses/Wan2.1-LICENSE.txt +201 -0
  14. licenses/Wan2.1-README.md +298 -0
  15. licenses/Wan2.2-README.md +236 -0
  16. multics/4players/checkpoints/model.ckpt +3 -0
  17. multics/4players/hparams.yaml +31 -0
.gitattributes CHANGED
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LICENSE ADDED
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README.md ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: apache-2.0
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+ pipeline_tag: image-to-video
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+ language:
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+ - en
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+ base_model:
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+ - Wan-AI/Wan2.2-TI2V-5B
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+ - Wan-AI/Wan2.1-T2V-1.3B
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+ tags:
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+ - video-generation
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+ - multi-player
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+ - world-model
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+ ---
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+
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+ # Multics
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+
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+ Multics enables scalable multi-player video generation through hierarchical communication.
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+
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+ [Project page](https://karine-huang.github.io/Multics/) · [Code and instructions](https://github.com/TencentARC/Multics)
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+
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+ This repository contains the complete 4-player Multics DiT checkpoint, its configuration, the Wan2.2 VAE, the UMT5 text encoder, and tokenizer. The same checkpoint supports 6-, 8-, and 10-player generation without retraining.
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+
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+ From the Multics code repository, download all inference weights with:
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+
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+ ```bash
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+ hf download TencentARC/Multics --local-dir models
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+ ```
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+
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+ Then generate the first 4-player example:
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+
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+ ```bash
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+ python inference.py --config configs/inference_demo.yaml \
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+ --checkpoint models/multics/4players/checkpoints/model.ckpt \
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+ --hparams models/multics/4players/hparams.yaml \
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+ --case 1
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+ ```
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+
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+ See the code repository for installation, example inputs, and scalability commands.
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+
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+ The weights are distributed under the Apache 2.0 license. See [LICENSE](LICENSE) and [THIRD_PARTY.md](THIRD_PARTY.md) for component attribution.
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+ 683881856f6b3298472b90ae8dc1b707f54875f04028c8a81b26c0f22248cfb3 README.md
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+ e357cb3ac077f8739755b0fb38fe8424c76534b19b4a84f6138856abb00abf69 licenses/Wan2.2-README.md
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+ ef267a5e84c9d220fd4232c575b8fdd9a6d31d89f6be47556e279f5a819cbe8e multics/4players/checkpoints/model.ckpt
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+ 888c5ca7d9d735e2c99fcff626c318893f62c8db82c3fd816733f25af59deb63 multics/4players/hparams.yaml
THIRD_PARTY.md ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
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+ # Component attribution
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+
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+ - `multics/4players/checkpoints/model.ckpt`: Multics weights, fine-tuned from [Wan2.2 TI2V 5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B), with action, camera, and hierarchical communication modules added by the Multics authors. The corresponding public configuration is in `multics/4players/hparams.yaml`.
4
+ - `Wan-AI/Wan2.2-TI2V-5B/Wan2.2_VAE.pth`: unmodified Wan2.2 VAE from [Wan-AI](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B).
5
+ - `Wan-AI/Wan2.1-T2V-1.3B/models_t5_umt5-xxl-enc-bf16.pth`: unmodified text encoder distributed by [Wan-AI](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B).
6
+ - `Wan-AI/Wan2.1-T2V-1.3B/google/umt5-xxl/`: unmodified tokenizer distributed by Wan-AI, based on Google's [UMT5](https://huggingface.co/google/umt5-xxl).
7
+
8
+ These upstream model repositories specify the Apache 2.0 license. The license text is included in `LICENSE`, and upstream model cards and available license files are preserved in `licenses/`.
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79
+ - sk
80
+ - sl
81
+ - sm
82
+ - sn
83
+ - so
84
+ - sq
85
+ - sr
86
+ - st
87
+ - su
88
+ - sv
89
+ - sw
90
+ - ta
91
+ - te
92
+ - tg
93
+ - th
94
+ - tr
95
+ - uk
96
+ - und
97
+ - ur
98
+ - uz
99
+ - vi
100
+ - xh
101
+ - yi
102
+ - yo
103
+ - zh
104
+ - zu
105
+ datasets:
106
+ - mc4
107
+
108
+ license: apache-2.0
109
+ ---
110
+
111
+ [Google's UMT5](https://github.com/google-research/multilingual-t5)
112
+
113
+ UMT5 is pretrained on the an updated version of [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 107 languages:
114
+
115
+ Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu.
116
+
117
+ **Note**: UMT5 was only pre-trained on mC4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
118
+
119
+ Pretraining Dataset: [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual)
120
+
121
+ Other Community Checkpoints: [here](https://huggingface.co/models?search=umt5)
122
+
123
+ Paper: [UniMax, Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining](https://openreview.net/forum?id=kXwdL1cWOAi)
124
+
125
+ Authors: *by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant*
126
+
127
+
128
+ ## Abstract
129
+
130
+ *Pretrained multilingual large language models have typically used heuristic temperature-based sampling to balance between different languages. However previous work has not systematically evaluated the efficacy of different pretraining language distributions across model scales. In this paper, we propose a new sampling method, UniMax, that delivers more uniform coverage of head languages while mitigating overfitting on tail languages by explicitly capping the number of repeats over each language's corpus. We perform an extensive series of ablations testing a range of sampling strategies on a suite of multilingual benchmarks, while varying model scale. We find that UniMax outperforms standard temperature-based sampling, and the benefits persist as scale increases. As part of our contribution, we release: (i) an improved and refreshed mC4 multilingual corpus consisting of 29 trillion characters across 107 languages, and (ii) a suite of pretrained umT5 model checkpoints trained with UniMax sampling.*
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licenses/Wan2.1-README.md ADDED
@@ -0,0 +1,298 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ # Wan2.1
13
+
14
+ <p align="center">
15
+ <img src="assets/logo.png" width="400"/>
16
+ <p>
17
+
18
+ <p align="center">
19
+ 💜 <a href=""><b>Wan</b></a> &nbsp&nbsp ｜ &nbsp&nbsp 🖥️ <a href="https://github.com/Wan-Video/Wan2.1">GitHub</a> &nbsp&nbsp | &nbsp&nbsp🤗 <a href="https://huggingface.co/Wan-AI/">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/organization/Wan-AI">ModelScope</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="">Paper (Coming soon)</a> &nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://wanxai.com">Blog</a> &nbsp&nbsp | &nbsp&nbsp💬 <a href="https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg">WeChat Group</a>&nbsp&nbsp | &nbsp&nbsp 📖 <a href="https://discord.gg/p5XbdQV7">Discord</a>&nbsp&nbsp
20
+ <br>
21
+
22
+ -----
23
+
24
+ [**Wan: Open and Advanced Large-Scale Video Generative Models**]("#") <be>
25
+
26
+ In this repository, we present **Wan2.1**, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. **Wan2.1** offers these key features:
27
+ - 👍 **SOTA Performance**: **Wan2.1** consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks.
28
+ - 👍 **Supports Consumer-grade GPUs**: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models.
29
+ - 👍 **Multiple Tasks**: **Wan2.1** excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation.
30
+ - 👍 **Visual Text Generation**: **Wan2.1** is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications.
31
+ - 👍 **Powerful Video VAE**: **Wan-VAE** delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation.
32
+
33
+
34
+ This repository hosts our T2V-1.3B model, a versatile solution for video generation that is compatible with nearly all consumer-grade GPUs. In this way, we hope that **Wan2.1** can serve as an easy-to-use tool for more creative teams in video creation, providing a high-quality foundational model for academic teams with limited computing resources. This will facilitate both the rapid development of the video creation community and the swift advancement of video technology.
35
+
36
+
37
+ ## Video Demos
38
+
39
+ <div align="center">
40
+ <video width="80%" controls>
41
+ <source src="https://cloud.video.taobao.com/vod/Jth64Y7wNoPcJki_Bo1ZJTDBvNjsgjlVKsNs05Fqfps.mp4" type="video/mp4">
42
+ Your browser does not support the video tag.
43
+ </video>
44
+ </div>
45
+
46
+
47
+ ## 🔥 Latest News!!
48
+
49
+ * Feb 25, 2025: 👋 We've released the inference code and weights of Wan2.1.
50
+
51
+
52
+ ## 📑 Todo List
53
+ - Wan2.1 Text-to-Video
54
+ - [x] Multi-GPU Inference code of the 14B and 1.3B models
55
+ - [x] Checkpoints of the 14B and 1.3B models
56
+ - [x] Gradio demo
57
+ - [ ] Diffusers integration
58
+ - [ ] ComfyUI integration
59
+ - Wan2.1 Image-to-Video
60
+ - [x] Multi-GPU Inference code of the 14B model
61
+ - [x] Checkpoints of the 14B model
62
+ - [x] Gradio demo
63
+ - [ ] Diffusers integration
64
+ - [ ] ComfyUI integration
65
+
66
+
67
+ ## Quickstart
68
+
69
+ #### Installation
70
+ Clone the repo:
71
+ ```
72
+ git clone https://github.com/Wan-Video/Wan2.1.git
73
+ cd Wan2.1
74
+ ```
75
+
76
+ Install dependencies:
77
+ ```
78
+ # Ensure torch >= 2.4.0
79
+ pip install -r requirements.txt
80
+ ```
81
+
82
+
83
+ #### Model Download
84
+
85
+ | Models | Download Link | Notes |
86
+ | --------------|-------------------------------------------------------------------------------|-------------------------------|
87
+ | T2V-14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B) | Supports both 480P and 720P
88
+ | I2V-14B-720P | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P) | Supports 720P
89
+ | I2V-14B-480P | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P) | Supports 480P
90
+ | T2V-1.3B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B) | Supports 480P
91
+
92
+
93
+ > 💡Note: The 1.3B model is capable of generating videos at 720P resolution. However, due to limited training at this resolution, the results are generally less stable compared to 480P. For optimal performance, we recommend using 480P resolution.
94
+
95
+
96
+ Download models using 🤗 huggingface-cli:
97
+ ```
98
+ pip install "huggingface_hub[cli]"
99
+ huggingface-cli download Wan-AI/Wan2.1-T2V-1.3B --local-dir ./Wan2.1-T2V-1.3B
100
+ ```
101
+
102
+ Download models using 🤖 modelscope-cli:
103
+ ```
104
+ pip install modelscope
105
+ modelscope download Wan-AI/Wan2.1-T2V-1.3B --local_dir ./Wan2.1-T2V-1.3B
106
+ ```
107
+
108
+ #### Run Text-to-Video Generation
109
+
110
+ This repository supports two Text-to-Video models (1.3B and 14B) and two resolutions (480P and 720P). The parameters and configurations for these models are as follows:
111
+
112
+ <table>
113
+ <thead>
114
+ <tr>
115
+ <th rowspan="2">Task</th>
116
+ <th colspan="2">Resolution</th>
117
+ <th rowspan="2">Model</th>
118
+ </tr>
119
+ <tr>
120
+ <th>480P</th>
121
+ <th>720P</th>
122
+ </tr>
123
+ </thead>
124
+ <tbody>
125
+ <tr>
126
+ <td>t2v-14B</td>
127
+ <td style="color: green;">✔️</td>
128
+ <td style="color: green;">✔️</td>
129
+ <td>Wan2.1-T2V-14B</td>
130
+ </tr>
131
+ <tr>
132
+ <td>t2v-1.3B</td>
133
+ <td style="color: green;">✔️</td>
134
+ <td style="color: red;">❌</td>
135
+ <td>Wan2.1-T2V-1.3B</td>
136
+ </tr>
137
+ </tbody>
138
+ </table>
139
+
140
+
141
+ ##### (1) Without Prompt Extention
142
+
143
+ To facilitate implementation, we will start with a basic version of the inference process that skips the [prompt extension](#2-using-prompt-extention) step.
144
+
145
+ - Single-GPU inference
146
+
147
+ ```
148
+ python generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
149
+ ```
150
+
151
+ If you encounter OOM (Out-of-Memory) issues, you can use the `--offload_model True` and `--t5_cpu` options to reduce GPU memory usage. For example, on an RTX 4090 GPU:
152
+
153
+ ```
154
+ python generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --offload_model True --t5_cpu --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
155
+ ```
156
+
157
+ > 💡Note: If you are using the `T2V-1.3B` model, we recommend setting the parameter `--sample_guide_scale 6`. The `--sample_shift parameter` can be adjusted within the range of 8 to 12 based on the performance.
158
+
159
+ - Multi-GPU inference using FSDP + xDiT USP
160
+
161
+ ```
162
+ pip install "xfuser>=0.4.1"
163
+ torchrun --nproc_per_node=8 generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --dit_fsdp --t5_fsdp --ulysses_size 8 --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
164
+ ```
165
+
166
+
167
+ ##### (2) Using Prompt Extention
168
+
169
+ Extending the prompts can effectively enrich the details in the generated videos, further enhancing the video quality. Therefore, we recommend enabling prompt extension. We provide the following two methods for prompt extension:
170
+
171
+ - Use the Dashscope API for extension.
172
+ - Apply for a `dashscope.api_key` in advance ([EN](https://www.alibabacloud.com/help/en/model-studio/getting-started/first-api-call-to-qwen) | [CN](https://help.aliyun.com/zh/model-studio/getting-started/first-api-call-to-qwen)).
173
+ - Configure the environment variable `DASH_API_KEY` to specify the Dashscope API key. For users of Alibaba Cloud's international site, you also need to set the environment variable `DASH_API_URL` to 'https://dashscope-intl.aliyuncs.com/api/v1'. For more detailed instructions, please refer to the [dashscope document](https://www.alibabacloud.com/help/en/model-studio/developer-reference/use-qwen-by-calling-api?spm=a2c63.p38356.0.i1).
174
+ - Use the `qwen-plus` model for text-to-video tasks and `qwen-vl-max` for image-to-video tasks.
175
+ - You can modify the model used for extension with the parameter `--prompt_extend_model`. For example:
176
+ ```
177
+ DASH_API_KEY=your_key python generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" --use_prompt_extend --prompt_extend_method 'dashscope' --prompt_extend_target_lang 'ch'
178
+ ```
179
+
180
+ - Using a local model for extension.
181
+
182
+ - By default, the Qwen model on HuggingFace is used for this extension. Users can choose based on the available GPU memory size.
183
+ - For text-to-video tasks, you can use models like `Qwen/Qwen2.5-14B-Instruct`, `Qwen/Qwen2.5-7B-Instruct` and `Qwen/Qwen2.5-3B-Instruct`
184
+ - For image-to-video tasks, you can use models like `Qwen/Qwen2.5-VL-7B-Instruct` and `Qwen/Qwen2.5-VL-3B-Instruct`.
185
+ - Larger models generally provide better extension results but require more GPU memory.
186
+ - You can modify the model used for extension with the parameter `--prompt_extend_model` , allowing you to specify either a local model path or a Hugging Face model. For example:
187
+
188
+ ```
189
+ python generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" --use_prompt_extend --prompt_extend_method 'local_qwen' --prompt_extend_target_lang 'ch'
190
+ ```
191
+
192
+ ##### (3) Runing local gradio
193
+
194
+ ```
195
+ cd gradio
196
+ # if one uses dashscope’s API for prompt extension
197
+ DASH_API_KEY=your_key python t2v_1.3B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir ./Wan2.1-T2V-1.3B
198
+
199
+ # if one uses a local model for prompt extension
200
+ python t2v_1.3B_singleGPU.py --prompt_extend_method 'local_qwen' --ckpt_dir ./Wan2.1-T2V-1.3B
201
+ ```
202
+
203
+
204
+
205
+ ## Evaluation
206
+
207
+ We employ our **Wan-Bench** framework to evaluate the performance of the T2V-1.3B model, with the results displayed in the table below. The results indicate that our smaller 1.3B model surpasses the overall metrics of larger open-source models, demonstrating the effectiveness of **WanX2.1**'s architecture and the data construction pipeline.
208
+
209
+ <div align="center">
210
+ <img src="assets/vben_1.3b_vs_sota.png" alt="" style="width: 80%;" />
211
+ </div>
212
+
213
+
214
+
215
+ ## Computational Efficiency on Different GPUs
216
+
217
+ We test the computational efficiency of different **Wan2.1** models on different GPUs in the following table. The results are presented in the format: **Total time (s) / peak GPU memory (GB)**.
218
+
219
+
220
+ <div align="center">
221
+ <img src="assets/comp_effic.png" alt="" style="width: 80%;" />
222
+ </div>
223
+
224
+ > The parameter settings for the tests presented in this table are as follows:
225
+ > (1) For the 1.3B model on 8 GPUs, set `--ring_size 8` and `--ulysses_size 1`;
226
+ > (2) For the 14B model on 1 GPU, use `--offload_model True`;
227
+ > (3) For the 1.3B model on a single 4090 GPU, set `--offload_model True --t5_cpu`;
228
+ > (4) For all testings, no prompt extension was applied, meaning `--use_prompt_extend` was not enabled.
229
+
230
+ -------
231
+
232
+ ## Introduction of Wan2.1
233
+
234
+ **Wan2.1** is designed on the mainstream diffusion transformer paradigm, achieving significant advancements in generative capabilities through a series of innovations. These include our novel spatio-temporal variational autoencoder (VAE), scalable training strategies, large-scale data construction, and automated evaluation metrics. Collectively, these contributions enhance the model’s performance and versatility.
235
+
236
+
237
+ ##### (1) 3D Variational Autoencoders
238
+ We propose a novel 3D causal VAE architecture, termed **Wan-VAE** specifically designed for video generation. By combining multiple strategies, we improve spatio-temporal compression, reduce memory usage, and ensure temporal causality. **Wan-VAE** demonstrates significant advantages in performance efficiency compared to other open-source VAEs. Furthermore, our **Wan-VAE** can encode and decode unlimited-length 1080P videos without losing historical temporal information, making it particularly well-suited for video generation tasks.
239
+
240
+
241
+ <div align="center">
242
+ <img src="assets/video_vae_res.jpg" alt="" style="width: 80%;" />
243
+ </div>
244
+
245
+
246
+ ##### (2) Video Diffusion DiT
247
+
248
+ **Wan2.1** is designed using the Flow Matching framework within the paradigm of mainstream Diffusion Transformers. Our model's architecture uses the T5 Encoder to encode multilingual text input, with cross-attention in each transformer block embedding the text into the model structure. Additionally, we employ an MLP with a Linear layer and a SiLU layer to process the input time embeddings and predict six modulation parameters individually. This MLP is shared across all transformer blocks, with each block learning a distinct set of biases. Our experimental findings reveal a significant performance improvement with this approach at the same parameter scale.
249
+
250
+ <div align="center">
251
+ <img src="assets/video_dit_arch.jpg" alt="" style="width: 80%;" />
252
+ </div>
253
+
254
+
255
+ | Model | Dimension | Input Dimension | Output Dimension | Feedforward Dimension | Frequency Dimension | Number of Heads | Number of Layers |
256
+ |--------|-----------|-----------------|------------------|-----------------------|---------------------|-----------------|------------------|
257
+ | 1.3B | 1536 | 16 | 16 | 8960 | 256 | 12 | 30 |
258
+ | 14B | 5120 | 16 | 16 | 13824 | 256 | 40 | 40 |
259
+
260
+
261
+
262
+ ##### Data
263
+
264
+ We curated and deduplicated a candidate dataset comprising a vast amount of image and video data. During the data curation process, we designed a four-step data cleaning process, focusing on fundamental dimensions, visual quality and motion quality. Through the robust data processing pipeline, we can easily obtain high-quality, diverse, and large-scale training sets of images and videos.
265
+
266
+ ![figure1](assets/data_for_diff_stage.jpg "figure1")
267
+
268
+
269
+ ##### Comparisons to SOTA
270
+ We compared **Wan2.1** with leading open-source and closed-source models to evaluate the performace. Using our carefully designed set of 1,035 internal prompts, we tested across 14 major dimensions and 26 sub-dimensions. Then we calculated the total score through a weighted average based on the importance of each dimension. The detailed results are shown in the table below. These results demonstrate our model's superior performance compared to both open-source and closed-source models.
271
+
272
+ ![figure1](assets/vben_vs_sota.png "figure1")
273
+
274
+
275
+ ## Citation
276
+ If you find our work helpful, please cite us.
277
+
278
+ ```
279
+ @article{wan2.1,
280
+ title = {Wan: Open and Advanced Large-Scale Video Generative Models},
281
+ author = {Wan Team},
282
+ journal = {},
283
+ year = {2025}
284
+ }
285
+ ```
286
+
287
+ ## License Agreement
288
+ The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generate contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the [license](LICENSE.txt).
289
+
290
+
291
+ ## Acknowledgements
292
+
293
+ We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [Qwen](https://huggingface.co/Qwen), [umt5-xxl](https://huggingface.co/google/umt5-xxl), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research.
294
+
295
+
296
+
297
+ ## Contact Us
298
+ If you would like to leave a message to our research or product teams, feel free to join our [Discord](https://discord.gg/p5XbdQV7) or [WeChat groups](https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg)!
licenses/Wan2.2-README.md ADDED
@@ -0,0 +1,236 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ language:
4
+ - en
5
+ - zh
6
+ pipeline_tag: text-to-video
7
+ library_name: wan2.2
8
+ ---
9
+ # Wan2.2
10
+
11
+ <p align="center">
12
+ <img src="assets/logo.png" width="400"/>
13
+ <p>
14
+
15
+ <p align="center">
16
+ 💜 <a href="https://wan.video"><b>Wan</b></a> &nbsp&nbsp ｜ &nbsp&nbsp 🖥️ <a href="https://github.com/Wan-Video/Wan2.2">GitHub</a> &nbsp&nbsp | &nbsp&nbsp🤗 <a href="https://huggingface.co/Wan-AI/">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/organization/Wan-AI">ModelScope</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://arxiv.org/abs/2503.20314">Technical Report</a> &nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://wan.video/welcome?spm=a2ty_o02.30011076.0.0.6c9ee41eCcluqg">Blog</a> &nbsp&nbsp | &nbsp&nbsp💬 <a href="https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg">WeChat Group</a>&nbsp&nbsp | &nbsp&nbsp 📖 <a href="https://discord.gg/AKNgpMK4Yj">Discord</a>&nbsp&nbsp
17
+ <br>
18
+
19
+ -----
20
+
21
+ [**Wan: Open and Advanced Large-Scale Video Generative Models**](https://arxiv.org/abs/2503.20314) <be>
22
+
23
+
24
+ We are excited to introduce **Wan2.2**, a major upgrade to our foundational video models. With **Wan2.2**, we have focused on incorporating the following innovations:
25
+
26
+ - 👍 **Effective MoE Architecture**: Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost.
27
+
28
+ - 👍 **Cinematic-level Aesthetics**: Wan2.2 incorporates meticulously curated aesthetic data, complete with detailed labels for lighting, composition, contrast, color tone, and more. This allows for more precise and controllable cinematic style generation, facilitating the creation of videos with customizable aesthetic preferences.
29
+
30
+ - 👍 **Complex Motion Generation**: Compared to Wan2.1, Wan2.2 is trained on a significantly larger data, with +65.6% more images and +83.2% more videos. This expansion notably enhances the model's generalization across multiple dimensions such as motions, semantics, and aesthetics, achieving TOP performance among all open-sourced and closed-sourced models.
31
+
32
+ - 👍 **Efficient High-Definition Hybrid TI2V**: Wan2.2 open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of **16×16×4**. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It is one of the fastest **720P@24fps** models currently available, capable of serving both the industrial and academic sectors simultaneously.
33
+
34
+ This repository contains our TI2V-5B model, built with the advanced Wan2.2-VAE that achieves a compression ratio of 16×16×4. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can runs on single consumer-grade GPU such as the 4090. It is one of the fastest 720P@24fps models available, meeting the needs of both industrial applications and academic research.
35
+
36
+
37
+
38
+ ## Video Demos
39
+
40
+ <div align="center">
41
+ <video width="80%" controls>
42
+ <source src="https://cloud.video.taobao.com/vod/4szTT1B0LqXvJzmuEURfGRA-nllnqN_G2AT0ZWkQXoQ.mp4" type="video/mp4">
43
+ Your browser does not support the video tag.
44
+ </video>
45
+ </div>
46
+
47
+
48
+ ## 🔥 Latest News!!
49
+
50
+ * Jul 28, 2025: 👋 We've released the inference code and model weights of **Wan2.2**.
51
+
52
+ ## Community Works
53
+ If your research or project builds upon [**Wan2.1**](https://github.com/Wan-Video/Wan2.1) or Wan2.2, we welcome you to share it with us so we can highlight it for the broader community.
54
+
55
+
56
+ ## 📑 Todo List
57
+ - Wan2.2 Text-to-Video
58
+ - [x] Multi-GPU Inference code of the A14B and 14B models
59
+ - [x] Checkpoints of the A14B and 14B models
60
+ - [x] ComfyUI integration
61
+ - [x] Diffusers integration
62
+ - Wan2.2 Image-to-Video
63
+ - [x] Multi-GPU Inference code of the A14B model
64
+ - [x] Checkpoints of the A14B model
65
+ - [x] ComfyUI integration
66
+ - [x] Diffusers integration
67
+ - Wan2.2 Text-Image-to-Video
68
+ - [x] Multi-GPU Inference code of the 5B model
69
+ - [x] Checkpoints of the 5B model
70
+ - [x] ComfyUI integration
71
+ - [x] Diffusers integration
72
+
73
+ ## Run Wan2.2
74
+
75
+ #### Installation
76
+ Clone the repo:
77
+ ```sh
78
+ git clone https://github.com/Wan-Video/Wan2.2.git
79
+ cd Wan2.2
80
+ ```
81
+
82
+ Install dependencies:
83
+ ```sh
84
+ # Ensure torch >= 2.4.0
85
+ pip install -r requirements.txt
86
+ ```
87
+
88
+
89
+ #### Model Download
90
+
91
+
92
+
93
+
94
+ | Models | Download Links | Description |
95
+ |--------------------|---------------------------------------------------------------------------------------------------------------------------------------------|-------------|
96
+ | T2V-A14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B) 🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B) | Text-to-Video MoE model, supports 480P & 720P |
97
+ | I2V-A14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B) 🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B) | Image-to-Video MoE model, supports 480P & 720P |
98
+ | TI2V-5B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B) 🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B) | High-compression VAE, T2V+I2V, supports 720P |
99
+
100
+
101
+ > 💡Note:
102
+ > The TI2V-5B model supports 720P video generation at **24 FPS**.
103
+
104
+
105
+ Download models using huggingface-cli:
106
+ ``` sh
107
+ pip install "huggingface_hub[cli]"
108
+ huggingface-cli download Wan-AI/Wan2.2-TI2V-5B --local-dir ./Wan2.2-TI2V-5B
109
+ ```
110
+
111
+ Download models using modelscope-cli:
112
+ ``` sh
113
+ pip install modelscope
114
+ modelscope download Wan-AI/Wan2.2-TI2V-5B --local_dir ./Wan2.2-TI2V-5B
115
+ ```
116
+
117
+ #### Run Text-Image-to-Video Generation
118
+
119
+ This repository supports the `Wan2.2-TI2V-5B` Text-Image-to-Video model and can support video generation at 720P resolutions.
120
+
121
+
122
+ - Single-GPU Text-to-Video inference
123
+ ```sh
124
+ python generate.py --task ti2v-5B --size 1280*704 --ckpt_dir ./Wan2.2-TI2V-5B --offload_model True --convert_model_dtype --t5_cpu --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage"
125
+ ```
126
+
127
+ > 💡Unlike other tasks, the 720P resolution of the Text-Image-to-Video task is `1280*704` or `704*1280`.
128
+
129
+ > This command can run on a GPU with at least 24GB VRAM (e.g, RTX 4090 GPU).
130
+
131
+ > 💡If you are running on a GPU with at least 80GB VRAM, you can remove the `--offload_model True`, `--convert_model_dtype` and `--t5_cpu` options to speed up execution.
132
+
133
+
134
+ - Single-GPU Image-to-Video inference
135
+ ```sh
136
+ python generate.py --task ti2v-5B --size 1280*704 --ckpt_dir ./Wan2.2-TI2V-5B --offload_model True --convert_model_dtype --t5_cpu --image examples/i2v_input.JPG --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
137
+ ```
138
+
139
+ > 💡If the image parameter is configured, it is an Image-to-Video generation; otherwise, it defaults to a Text-to-Video generation.
140
+
141
+ > 💡Similar to Image-to-Video, the `size` parameter represents the area of the generated video, with the aspect ratio following that of the original input image.
142
+
143
+
144
+ - Multi-GPU inference using FSDP + DeepSpeed Ulysses
145
+
146
+ ```sh
147
+ torchrun --nproc_per_node=8 generate.py --task ti2v-5B --size 1280*704 --ckpt_dir ./Wan2.2-TI2V-5B --dit_fsdp --t5_fsdp --ulysses_size 8 --image examples/i2v_input.JPG --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
148
+ ```
149
+
150
+ > The process of prompt extension can be referenced [here](#2-using-prompt-extention).
151
+
152
+
153
+
154
+
155
+ ## Computational Efficiency on Different GPUs
156
+
157
+ We test the computational efficiency of different **Wan2.2** models on different GPUs in the following table. The results are presented in the format: **Total time (s) / peak GPU memory (GB)**.
158
+
159
+
160
+ <div align="center">
161
+ <img src="assets/comp_effic.png" alt="" style="width: 80%;" />
162
+ </div>
163
+
164
+ > The parameter settings for the tests presented in this table are as follows:
165
+ > (1) Multi-GPU: 14B: `--ulysses_size 4/8 --dit_fsdp --t5_fsdp`, 5B: `--ulysses_size 4/8 --offload_model True --convert_model_dtype --t5_cpu`; Single-GPU: 14B: `--offload_model True --convert_model_dtype`, 5B: `--offload_model True --convert_model_dtype --t5_cpu`
166
+ (--convert_model_dtype converts model parameter types to config.param_dtype);
167
+ > (2) The distributed testing utilizes the built-in FSDP and Ulysses implementations, with FlashAttention3 deployed on Hopper architecture GPUs;
168
+ > (3) Tests were run without the `--use_prompt_extend` flag;
169
+ > (4) Reported results are the average of multiple samples taken after the warm-up phase.
170
+
171
+
172
+ -------
173
+
174
+ ## Introduction of Wan2.2
175
+
176
+ **Wan2.2** builds on the foundation of Wan2.1 with notable improvements in generation quality and model capability. This upgrade is driven by a series of key technical innovations, mainly including the Mixture-of-Experts (MoE) architecture, upgraded training data, and high-compression video generation.
177
+
178
+ ##### (1) Mixture-of-Experts (MoE) Architecture
179
+
180
+ Wan2.2 introduces Mixture-of-Experts (MoE) architecture into the video generation diffusion model. MoE has been widely validated in large language models as an efficient approach to increase total model parameters while keeping inference cost nearly unchanged. In Wan2.2, the A14B model series adopts a two-expert design tailored to the denoising process of diffusion models: a high-noise expert for the early stages, focusing on overall layout; and a low-noise expert for the later stages, refining video details. Each expert model has about 14B parameters, resulting in a total of 27B parameters but only 14B active parameters per step, keeping inference computation and GPU memory nearly unchanged.
181
+
182
+ <div align="center">
183
+ <img src="assets/moe_arch.png" alt="" style="width: 90%;" />
184
+ </div>
185
+
186
+ The transition point between the two experts is determined by the signal-to-noise ratio (SNR), a metric that decreases monotonically as the denoising step $t$ increases. At the beginning of the denoising process, $t$ is large and the noise level is high, so the SNR is at its minimum, denoted as ${SNR}_{min}$. In this stage, the high-noise expert is activated. We define a threshold step ${t}_{moe}$ corresponding to half of the ${SNR}_{min}$, and switch to the low-noise expert when $t<{t}_{moe}$.
187
+
188
+ <div align="center">
189
+ <img src="assets/moe_2.png" alt="" style="width: 90%;" />
190
+ </div>
191
+
192
+ To validate the effectiveness of the MoE architecture, four settings are compared based on their validation loss curves. The baseline **Wan2.1** model does not employ the MoE architecture. Among the MoE-based variants, the **Wan2.1 & High-Noise Expert** reuses the Wan2.1 model as the low-noise expert while uses the Wan2.2's high-noise expert, while the **Wan2.1 & Low-Noise Expert** uses Wan2.1 as the high-noise expert and employ the Wan2.2's low-noise expert. The **Wan2.2 (MoE)** (our final version) achieves the lowest validation loss, indicating that its generated video distribution is closest to ground-truth and exhibits superior convergence.
193
+
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+
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+ ##### (2) Efficient High-Definition Hybrid TI2V
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+ To enable more efficient deployment, Wan2.2 also explores a high-compression design. In addition to the 27B MoE models, a 5B dense model, i.e., TI2V-5B, is released. It is supported by a high-compression Wan2.2-VAE, which achieves a $T\times H\times W$ compression ratio of $4\times16\times16$, increasing the overall compression rate to 64 while maintaining high-quality video reconstruction. With an additional patchification layer, the total compression ratio of TI2V-5B reaches $4\times32\times32$. Without specific optimization, TI2V-5B can generate a 5-second 720P video in under 9 minutes on a single consumer-grade GPU, ranking among the fastest 720P@24fps video generation models. This model also natively supports both text-to-video and image-to-video tasks within a single unified framework, covering both academic research and practical applications.
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+
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+
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+ <div align="center">
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+ <img src="assets/vae.png" alt="" style="width: 80%;" />
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+ </div>
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+
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+
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+
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+ ##### Comparisons to SOTAs
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+ We compared Wan2.2 with leading closed-source commercial models on our new Wan-Bench 2.0, evaluating performance across multiple crucial dimensions. The results demonstrate that Wan2.2 achieves superior performance compared to these leading models.
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+
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+
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+ <div align="center">
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+ <img src="assets/performance.png" alt="" style="width: 90%;" />
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+ </div>
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+
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+ ## Citation
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+ If you find our work helpful, please cite us.
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+
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+ ```
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+ @article{wan2025,
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+ title={Wan: Open and Advanced Large-Scale Video Generative Models},
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+ author={Team Wan and Ang Wang and Baole Ai and Bin Wen and Chaojie Mao and Chen-Wei Xie and Di Chen and Feiwu Yu and Haiming Zhao and Jianxiao Yang and Jianyuan Zeng and Jiayu Wang and Jingfeng Zhang and Jingren Zhou and Jinkai Wang and Jixuan Chen and Kai Zhu and Kang Zhao and Keyu Yan and Lianghua Huang and Mengyang Feng and Ningyi Zhang and Pandeng Li and Pingyu Wu and Ruihang Chu and Ruili Feng and Shiwei Zhang and Siyang Sun and Tao Fang and Tianxing Wang and Tianyi Gui and Tingyu Weng and Tong Shen and Wei Lin and Wei Wang and Wei Wang and Wenmeng Zhou and Wente Wang and Wenting Shen and Wenyuan Yu and Xianzhong Shi and Xiaoming Huang and Xin Xu and Yan Kou and Yangyu Lv and Yifei Li and Yijing Liu and Yiming Wang and Yingya Zhang and Yitong Huang and Yong Li and You Wu and Yu Liu and Yulin Pan and Yun Zheng and Yuntao Hong and Yupeng Shi and Yutong Feng and Zeyinzi Jiang and Zhen Han and Zhi-Fan Wu and Ziyu Liu},
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+ journal = {arXiv preprint arXiv:2503.20314},
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+ year={2025}
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+ }
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+ ```
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+
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+ ## License Agreement
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+ The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generated contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the [license](LICENSE.txt).
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+
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+
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+ ## Acknowledgements
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+
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+ We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [Qwen](https://huggingface.co/Qwen), [umt5-xxl](https://huggingface.co/google/umt5-xxl), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research.
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+
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+
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+
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+ ## Contact Us
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+ If you would like to leave a message to our research or product teams, feel free to join our [Discord](https://discord.gg/AKNgpMK4Yj) or [WeChat groups](https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg)!
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+ attention_mode: hier_hub
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+ camera_dim: 12
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+ camera_dropout_p: 0.1
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+ group_label_player_ids: null
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+ hier_direct_frac: 1.0
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+ local_attn_size: 7
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+ local_attn_size_after: 21
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+ local_attn_switch_step: 10000
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+ loss_type: diffusion_forcing
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+ num_frame_per_block: 3
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+ num_global_hub_tokens: 6
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+ num_groups: 2
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+ num_hub_tokens: 16
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+ num_local_hub_tokens: 5
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+ num_players: 4
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+ simplex_gamma: 1.0
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+ simplex_pool_size: 10
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+ target_rgb_frames: 165
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+ train_step_offset: 10000
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+ training_stage: stage2
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+ use_camera_pose: true
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+ use_simplex_rope: true
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+ vae_latent_channels: 48
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+ hier_local_hub_mode: shared
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+ hier_no_local_hub: false