Mirror exact Wan and UMT5 pretrained assets with upstream provenance
Browse files- .gitattributes +1 -0
- README.md +1 -1
- pretrained/wan-2.2-ti2v-5b/LICENSE +202 -0
- pretrained/wan-2.2-ti2v-5b/README.md +11 -0
- pretrained/wan-2.2-ti2v-5b/SHA256SUMS +18 -0
- pretrained/wan-2.2-ti2v-5b/UPSTREAM_README.md +276 -0
- pretrained/wan-2.2-ti2v-5b/provenance.json +44 -0
- pretrained/wan-2.2-ti2v-5b/text_encoder/config.json +34 -0
- pretrained/wan-2.2-ti2v-5b/text_encoder/model-00001-of-00003.safetensors +3 -0
- pretrained/wan-2.2-ti2v-5b/text_encoder/model-00002-of-00003.safetensors +3 -0
- pretrained/wan-2.2-ti2v-5b/text_encoder/model-00003-of-00003.safetensors +3 -0
- pretrained/wan-2.2-ti2v-5b/text_encoder/model.safetensors.index.json +249 -0
- pretrained/wan-2.2-ti2v-5b/tokenizer/special_tokens_map.json +332 -0
- pretrained/wan-2.2-ti2v-5b/tokenizer/spiece.model +3 -0
- pretrained/wan-2.2-ti2v-5b/tokenizer/tokenizer.json +3 -0
- pretrained/wan-2.2-ti2v-5b/tokenizer/tokenizer_config.json +2749 -0
- pretrained/wan-2.2-ti2v-5b/transformer/config.json +24 -0
- pretrained/wan-2.2-ti2v-5b/transformer/diffusion_pytorch_model-00001-of-00005.safetensors +3 -0
- pretrained/wan-2.2-ti2v-5b/transformer/diffusion_pytorch_model-00002-of-00005.safetensors +3 -0
- pretrained/wan-2.2-ti2v-5b/transformer/diffusion_pytorch_model-00003-of-00005.safetensors +3 -0
- pretrained/wan-2.2-ti2v-5b/transformer/diffusion_pytorch_model-00004-of-00005.safetensors +3 -0
- pretrained/wan-2.2-ti2v-5b/transformer/diffusion_pytorch_model-00005-of-00005.safetensors +3 -0
- pretrained/wan-2.2-ti2v-5b/transformer/diffusion_pytorch_model.safetensors.index.json +832 -0
- pretrained/wan-2.2-ti2v-5b/vae/config.json +128 -0
- pretrained/wan-2.2-ti2v-5b/vae/diffusion_pytorch_model.safetensors +3 -0
.gitattributes
CHANGED
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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pretrained/wan-2.2-ti2v-5b/tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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@@ -97,6 +97,6 @@ Download and usage instructions are in the [code repository](https://github.com/
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## Licenses
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Original weights, adapters, Rubik’s Cube Real footage, and training curves: [MIT](./LICENSE).
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[Lichess games](https://database.lichess.org/): [CC0](./LICENSE-CHESS-CC0-1.0.txt). The
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S2PD Kubric renders: [CC BY-SA 4.0](./data/kubric_movi_a/LICENSE.txt); see the dataset cards for upstream attribution.
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## Licenses
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Original weights, adapters, Rubik’s Cube Real footage, and training curves: [MIT](./LICENSE).
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[Lichess games](https://database.lichess.org/): [CC0](./LICENSE-CHESS-CC0-1.0.txt). The [mirrored Wan backbone, VAE, UMT5 encoder, and tokenizer](./pretrained/wan-2.2-ti2v-5b): [Apache 2.0](./LICENSE-WAN-APACHE-2.0.txt).
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S2PD Kubric renders: [CC BY-SA 4.0](./data/kubric_movi_a/LICENSE.txt); see the dataset cards for upstream attribution.
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pretrained/wan-2.2-ti2v-5b/LICENSE
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|
| 1 |
+
# Wan 2.2 TI2V-5B assets used by S2PD
|
| 2 |
+
|
| 3 |
+
Byte-identical mirror of the files used from [Wan-AI/Wan2.2-TI2V-5B-Diffusers](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers/tree/b8fff7315c768468a5333511427288870b2e9635), revision `b8fff7315c768468a5333511427288870b2e9635`.
|
| 4 |
+
|
| 5 |
+
Includes the Wan transformer, VAE, UMT5 text encoder, tokenizer, configurations,
|
| 6 |
+
and shard indexes. No precision conversion or weight modifications were made.
|
| 7 |
+
`SHA256SUMS` verifies every model file; `provenance.json` records the upstream
|
| 8 |
+
identity and file sizes. Original attribution is retained in [UPSTREAM_README.md](./UPSTREAM_README.md).
|
| 9 |
+
|
| 10 |
+
Copyright belongs to the upstream authors. Distributed under [Apache 2.0](./LICENSE),
|
| 11 |
+
not the S2PD MIT license. This mirror does not imply upstream endorsement.
|
pretrained/wan-2.2-ti2v-5b/SHA256SUMS
ADDED
|
@@ -0,0 +1,18 @@
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| 1 |
+
a2bcb24699f6c009a2427432bdd483ef8b2b42a712abc9503759cdc77d171f07 text_encoder/config.json
|
| 2 |
+
a8e861969c7433e707cc5a74065d795d36cca07ec96eb6763eb4083df7248f58 text_encoder/model-00001-of-00003.safetensors
|
| 3 |
+
d57d948ece4837d850b7a859a4415121d57cacf8b9ee1d4db200c67f592902d7 text_encoder/model-00002-of-00003.safetensors
|
| 4 |
+
0da9ee284e21d1406df708788db1d502d95d75f69faa25cd26151bf8829b7c5f text_encoder/model-00003-of-00003.safetensors
|
| 5 |
+
31c4c7bcce679eaa0dd4667462394ddb013dc2f748e0bffc893dc9146a320dab text_encoder/model.safetensors.index.json
|
| 6 |
+
456b58fd240a06c743a7c2cf8008bec501240d68ebd1fc4018ea569505fea270 tokenizer/special_tokens_map.json
|
| 7 |
+
e3909a67b780650b35cf529ac782ad2b6b26e6d1f849d3fbb6a872905f452458 tokenizer/spiece.model
|
| 8 |
+
20a46ac256746594ed7e1e3ef733b83fbc5a6f0922aa7480eda961743de080ef tokenizer/tokenizer.json
|
| 9 |
+
1d8d2a216bf8e70ac15b7ddcea566c4dd0433c024b39a58ca5e4c66bd78defbd tokenizer/tokenizer_config.json
|
| 10 |
+
dc00d9866e72cf77db6b531aaa33be4dc7148fef9338442bd0ae9181f7075e9b transformer/config.json
|
| 11 |
+
511bec832a201caa410d09c5ce7dbbf8ad2708c345d82038f684fc74cce982be transformer/diffusion_pytorch_model-00001-of-00005.safetensors
|
| 12 |
+
7c42724912b1911429125dc50c0e9a49ccbada5a601b657d4ed2e15e7597c193 transformer/diffusion_pytorch_model-00002-of-00005.safetensors
|
| 13 |
+
e9c3d0c76de786566382f8258101fea973ae37681c6e9fe0e5fe1fb93b806424 transformer/diffusion_pytorch_model-00003-of-00005.safetensors
|
| 14 |
+
a331121771790939678db6f585553fd5184609f7d02593c699a4d241b0d834c5 transformer/diffusion_pytorch_model-00004-of-00005.safetensors
|
| 15 |
+
78b655685c47efdb2349f36826bb101264e9f212a16325d584aeb5f53c88e719 transformer/diffusion_pytorch_model-00005-of-00005.safetensors
|
| 16 |
+
d6c1f72d86fbddef8fb5ef1c03f5b56b2bfdb6ae58032db98c6019fd1dae9021 transformer/diffusion_pytorch_model.safetensors.index.json
|
| 17 |
+
d996c340fe9a7df5d7371f76a7d8d6956f6c98256080074d8434fa5eeac11360 vae/config.json
|
| 18 |
+
62cd18f19438e35b32ac63020e2852f566e9b02f46b6cdbd87972a356e3c6f4b vae/diffusion_pytorch_model.safetensors
|
pretrained/wan-2.2-ti2v-5b/UPSTREAM_README.md
ADDED
|
@@ -0,0 +1,276 @@
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|
|
|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
language:
|
| 4 |
+
- en
|
| 5 |
+
- zh
|
| 6 |
+
pipeline_tag: text-to-video
|
| 7 |
+
---
|
| 8 |
+
# Wan2.2
|
| 9 |
+
|
| 10 |
+
<p align="center">
|
| 11 |
+
<img src="assets/logo.png" width="400"/>
|
| 12 |
+
<p>
|
| 13 |
+
|
| 14 |
+
<p align="center">
|
| 15 |
+
💜 <a href="https://wan.video"><b>Wan</b></a>    ｜    🖥️ <a href="https://github.com/Wan-Video/Wan2.2">GitHub</a>    |   🤗 <a href="https://huggingface.co/Wan-AI/">Hugging Face</a>   |   🤖 <a href="https://modelscope.cn/organization/Wan-AI">ModelScope</a>   |    📑 <a href="https://arxiv.org/abs/2503.20314">Technical Report</a>    |    📑 <a href="https://wan.video/welcome?spm=a2ty_o02.30011076.0.0.6c9ee41eCcluqg">Blog</a>    |   💬 <a href="https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg">WeChat Group</a>   |    📖 <a href="https://discord.gg/AKNgpMK4Yj">Discord</a>  
|
| 16 |
+
<br>
|
| 17 |
+
|
| 18 |
+
-----
|
| 19 |
+
|
| 20 |
+
[**Wan: Open and Advanced Large-Scale Video Generative Models**](https://arxiv.org/abs/2503.20314) <be>
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
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:
|
| 24 |
+
|
| 25 |
+
- 👍 **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.
|
| 26 |
+
|
| 27 |
+
- 👍 **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.
|
| 28 |
+
|
| 29 |
+
- 👍 **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.
|
| 30 |
+
|
| 31 |
+
- 👍 **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.
|
| 32 |
+
|
| 33 |
+
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.
|
| 34 |
+
|
| 35 |
+
|
| 36 |
+
|
| 37 |
+
## Video Demos
|
| 38 |
+
|
| 39 |
+
<div align="center">
|
| 40 |
+
<video width="80%" controls>
|
| 41 |
+
<source src="https://cloud.video.taobao.com/vod/4szTT1B0LqXvJzmuEURfGRA-nllnqN_G2AT0ZWkQXoQ.mp4" type="video/mp4">
|
| 42 |
+
Your browser does not support the video tag.
|
| 43 |
+
</video>
|
| 44 |
+
</div>
|
| 45 |
+
|
| 46 |
+
|
| 47 |
+
## 🔥 Latest News!!
|
| 48 |
+
|
| 49 |
+
* Jul 28, 2025: 👋 We've released the inference code and model weights of **Wan2.2**.
|
| 50 |
+
|
| 51 |
+
## Community Works
|
| 52 |
+
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.
|
| 53 |
+
|
| 54 |
+
|
| 55 |
+
## 📑 Todo List
|
| 56 |
+
- Wan2.2 Text-to-Video
|
| 57 |
+
- [x] Multi-GPU Inference code of the A14B and 14B models
|
| 58 |
+
- [x] Checkpoints of the A14B and 14B models
|
| 59 |
+
- [x] ComfyUI integration
|
| 60 |
+
- [x] Diffusers integration
|
| 61 |
+
- Wan2.2 Image-to-Video
|
| 62 |
+
- [x] Multi-GPU Inference code of the A14B model
|
| 63 |
+
- [x] Checkpoints of the A14B model
|
| 64 |
+
- [x] ComfyUI integration
|
| 65 |
+
- [x] Diffusers integration
|
| 66 |
+
- Wan2.2 Text-Image-to-Video
|
| 67 |
+
- [x] Multi-GPU Inference code of the 5B model
|
| 68 |
+
- [x] Checkpoints of the 5B model
|
| 69 |
+
- [x] ComfyUI integration
|
| 70 |
+
- [x] Diffusers integration
|
| 71 |
+
|
| 72 |
+
## Run Wan2.2
|
| 73 |
+
|
| 74 |
+
#### Installation
|
| 75 |
+
Clone the repo:
|
| 76 |
+
```sh
|
| 77 |
+
git clone https://github.com/Wan-Video/Wan2.2.git
|
| 78 |
+
cd Wan2.2
|
| 79 |
+
```
|
| 80 |
+
|
| 81 |
+
Install dependencies:
|
| 82 |
+
```sh
|
| 83 |
+
# Ensure torch >= 2.4.0
|
| 84 |
+
pip install -r requirements.txt
|
| 85 |
+
```
|
| 86 |
+
|
| 87 |
+
|
| 88 |
+
#### Model Download
|
| 89 |
+
|
| 90 |
+
|
| 91 |
+
| Models | Download Links | Description |
|
| 92 |
+
|--------------------|---------------------------------------------------------------------------------------------------------------------------------------------|-------------|
|
| 93 |
+
| 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 |
|
| 94 |
+
| 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 |
|
| 95 |
+
| 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 |
|
| 96 |
+
|
| 97 |
+
|
| 98 |
+
> 💡Note:
|
| 99 |
+
> The TI2V-5B model supports 720P video generation at **24 FPS**.
|
| 100 |
+
|
| 101 |
+
|
| 102 |
+
Download models using huggingface-cli:
|
| 103 |
+
``` sh
|
| 104 |
+
pip install "huggingface_hub[cli]"
|
| 105 |
+
huggingface-cli download Wan-AI/Wan2.2-TI2V-5B --local-dir ./Wan2.2-TI2V-5B
|
| 106 |
+
```
|
| 107 |
+
|
| 108 |
+
Download models using modelscope-cli:
|
| 109 |
+
``` sh
|
| 110 |
+
pip install modelscope
|
| 111 |
+
modelscope download Wan-AI/Wan2.2-TI2V-5B --local_dir ./Wan2.2-TI2V-5B
|
| 112 |
+
```
|
| 113 |
+
|
| 114 |
+
#### Run Text-Image-to-Video Generation
|
| 115 |
+
|
| 116 |
+
This repository supports the `Wan2.2-TI2V-5B` Text-Image-to-Video model and can support video generation at 720P resolutions.
|
| 117 |
+
|
| 118 |
+
|
| 119 |
+
- Single-GPU Text-to-Video inference
|
| 120 |
+
```sh
|
| 121 |
+
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"
|
| 122 |
+
```
|
| 123 |
+
|
| 124 |
+
> 💡Unlike other tasks, the 720P resolution of the Text-Image-to-Video task is `1280*704` or `704*1280`.
|
| 125 |
+
|
| 126 |
+
> This command can run on a GPU with at least 24GB VRAM (e.g, RTX 4090 GPU).
|
| 127 |
+
|
| 128 |
+
> 💡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.
|
| 129 |
+
|
| 130 |
+
|
| 131 |
+
- Single-GPU Image-to-Video inference
|
| 132 |
+
```sh
|
| 133 |
+
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."
|
| 134 |
+
```
|
| 135 |
+
|
| 136 |
+
> 💡If the image parameter is configured, it is an Image-to-Video generation; otherwise, it defaults to a Text-to-Video generation.
|
| 137 |
+
|
| 138 |
+
> 💡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.
|
| 139 |
+
|
| 140 |
+
|
| 141 |
+
- Multi-GPU inference using FSDP + DeepSpeed Ulysses
|
| 142 |
+
|
| 143 |
+
```sh
|
| 144 |
+
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."
|
| 145 |
+
```
|
| 146 |
+
|
| 147 |
+
> The process of prompt extension can be referenced [here](#2-using-prompt-extention).
|
| 148 |
+
|
| 149 |
+
|
| 150 |
+
- Running with Diffusers
|
| 151 |
+
|
| 152 |
+
```py
|
| 153 |
+
import torch
|
| 154 |
+
import numpy as np
|
| 155 |
+
from diffusers import WanPipeline, AutoencoderKLWan, WanTransformer3DModel, UniPCMultistepScheduler
|
| 156 |
+
from diffusers.utils import export_to_video, load_image
|
| 157 |
+
|
| 158 |
+
dtype = torch.bfloat16
|
| 159 |
+
device = "cuda"
|
| 160 |
+
|
| 161 |
+
model_id = "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
|
| 162 |
+
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
|
| 163 |
+
pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=dtype)
|
| 164 |
+
pipe.to(device)
|
| 165 |
+
|
| 166 |
+
height = 704
|
| 167 |
+
width = 1280
|
| 168 |
+
num_frames = 121
|
| 169 |
+
num_inference_steps = 50
|
| 170 |
+
guidance_scale = 5.0
|
| 171 |
+
|
| 172 |
+
|
| 173 |
+
prompt = "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
|
| 174 |
+
negative_prompt = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
|
| 175 |
+
|
| 176 |
+
output = pipe(
|
| 177 |
+
prompt=prompt,
|
| 178 |
+
negative_prompt=negative_prompt,
|
| 179 |
+
height=height,
|
| 180 |
+
width=width,
|
| 181 |
+
num_frames=num_frames,
|
| 182 |
+
guidance_scale=guidance_scale,
|
| 183 |
+
num_inference_steps=num_inference_steps,
|
| 184 |
+
).frames[0]
|
| 185 |
+
export_to_video(output, "5bit2v_output.mp4", fps=24)
|
| 186 |
+
|
| 187 |
+
```
|
| 188 |
+
> 💡**Note**:This model requires features that are currently available only in the main branch of diffusers. The latest stable release on PyPI does not yet include these updates.
|
| 189 |
+
> To use this model, please install the library from source:
|
| 190 |
+
> ```
|
| 191 |
+
> pip install git+https://github.com/huggingface/diffusers
|
| 192 |
+
> ```
|
| 193 |
+
|
| 194 |
+
|
| 195 |
+
## Computational Efficiency on Different GPUs
|
| 196 |
+
|
| 197 |
+
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)**.
|
| 198 |
+
|
| 199 |
+
|
| 200 |
+
<div align="center">
|
| 201 |
+
<img src="assets/comp_effic.png" alt="" style="width: 80%;" />
|
| 202 |
+
</div>
|
| 203 |
+
|
| 204 |
+
> The parameter settings for the tests presented in this table are as follows:
|
| 205 |
+
> (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`
|
| 206 |
+
(--convert_model_dtype converts model parameter types to config.param_dtype);
|
| 207 |
+
> (2) The distributed testing utilizes the built-in FSDP and Ulysses implementations, with FlashAttention3 deployed on Hopper architecture GPUs;
|
| 208 |
+
> (3) Tests were run without the `--use_prompt_extend` flag;
|
| 209 |
+
> (4) Reported results are the average of multiple samples taken after the warm-up phase.
|
| 210 |
+
|
| 211 |
+
|
| 212 |
+
-------
|
| 213 |
+
|
| 214 |
+
## Introduction of Wan2.2
|
| 215 |
+
|
| 216 |
+
**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.
|
| 217 |
+
|
| 218 |
+
##### (1) Mixture-of-Experts (MoE) Architecture
|
| 219 |
+
|
| 220 |
+
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.
|
| 221 |
+
|
| 222 |
+
<div align="center">
|
| 223 |
+
<img src="assets/moe_arch.png" alt="" style="width: 90%;" />
|
| 224 |
+
</div>
|
| 225 |
+
|
| 226 |
+
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 $t<{t}_{moe}$.
|
| 227 |
+
|
| 228 |
+
<div align="center">
|
| 229 |
+
<img src="assets/moe_2.png" alt="" style="width: 90%;" />
|
| 230 |
+
</div>
|
| 231 |
+
|
| 232 |
+
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.
|
| 233 |
+
|
| 234 |
+
|
| 235 |
+
##### (2) Efficient High-Definition Hybrid TI2V
|
| 236 |
+
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.
|
| 237 |
+
|
| 238 |
+
|
| 239 |
+
<div align="center">
|
| 240 |
+
<img src="assets/vae.png" alt="" style="width: 80%;" />
|
| 241 |
+
</div>
|
| 242 |
+
|
| 243 |
+
|
| 244 |
+
|
| 245 |
+
##### Comparisons to SOTAs
|
| 246 |
+
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.
|
| 247 |
+
|
| 248 |
+
|
| 249 |
+
<div align="center">
|
| 250 |
+
<img src="assets/performance.png" alt="" style="width: 90%;" />
|
| 251 |
+
</div>
|
| 252 |
+
|
| 253 |
+
## Citation
|
| 254 |
+
If you find our work helpful, please cite us.
|
| 255 |
+
|
| 256 |
+
```
|
| 257 |
+
@article{wan2025,
|
| 258 |
+
title={Wan: Open and Advanced Large-Scale Video Generative Models},
|
| 259 |
+
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},
|
| 260 |
+
journal = {arXiv preprint arXiv:2503.20314},
|
| 261 |
+
year={2025}
|
| 262 |
+
}
|
| 263 |
+
```
|
| 264 |
+
|
| 265 |
+
## License Agreement
|
| 266 |
+
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).
|
| 267 |
+
|
| 268 |
+
|
| 269 |
+
## Acknowledgements
|
| 270 |
+
|
| 271 |
+
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.
|
| 272 |
+
|
| 273 |
+
|
| 274 |
+
|
| 275 |
+
## Contact Us
|
| 276 |
+
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)!
|
pretrained/wan-2.2-ti2v-5b/provenance.json
ADDED
|
@@ -0,0 +1,44 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"repository": "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
|
| 3 |
+
"revision": "b8fff7315c768468a5333511427288870b2e9635",
|
| 4 |
+
"sha256": {
|
| 5 |
+
"text_encoder/config.json": "a2bcb24699f6c009a2427432bdd483ef8b2b42a712abc9503759cdc77d171f07",
|
| 6 |
+
"text_encoder/model-00001-of-00003.safetensors": "a8e861969c7433e707cc5a74065d795d36cca07ec96eb6763eb4083df7248f58",
|
| 7 |
+
"text_encoder/model-00002-of-00003.safetensors": "d57d948ece4837d850b7a859a4415121d57cacf8b9ee1d4db200c67f592902d7",
|
| 8 |
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"text_encoder/model-00003-of-00003.safetensors": "0da9ee284e21d1406df708788db1d502d95d75f69faa25cd26151bf8829b7c5f",
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| 9 |
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"text_encoder/model.safetensors.index.json": "31c4c7bcce679eaa0dd4667462394ddb013dc2f748e0bffc893dc9146a320dab",
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| 10 |
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|
| 11 |
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"tokenizer/spiece.model": "e3909a67b780650b35cf529ac782ad2b6b26e6d1f849d3fbb6a872905f452458",
|
| 12 |
+
"tokenizer/tokenizer.json": "20a46ac256746594ed7e1e3ef733b83fbc5a6f0922aa7480eda961743de080ef",
|
| 13 |
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"tokenizer/tokenizer_config.json": "1d8d2a216bf8e70ac15b7ddcea566c4dd0433c024b39a58ca5e4c66bd78defbd",
|
| 14 |
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"transformer/config.json": "dc00d9866e72cf77db6b531aaa33be4dc7148fef9338442bd0ae9181f7075e9b",
|
| 15 |
+
"transformer/diffusion_pytorch_model-00001-of-00005.safetensors": "511bec832a201caa410d09c5ce7dbbf8ad2708c345d82038f684fc74cce982be",
|
| 16 |
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"transformer/diffusion_pytorch_model-00002-of-00005.safetensors": "7c42724912b1911429125dc50c0e9a49ccbada5a601b657d4ed2e15e7597c193",
|
| 17 |
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"transformer/diffusion_pytorch_model-00003-of-00005.safetensors": "e9c3d0c76de786566382f8258101fea973ae37681c6e9fe0e5fe1fb93b806424",
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| 18 |
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"transformer/diffusion_pytorch_model-00004-of-00005.safetensors": "a331121771790939678db6f585553fd5184609f7d02593c699a4d241b0d834c5",
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| 19 |
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"transformer/diffusion_pytorch_model-00005-of-00005.safetensors": "78b655685c47efdb2349f36826bb101264e9f212a16325d584aeb5f53c88e719",
|
| 20 |
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"transformer/diffusion_pytorch_model.safetensors.index.json": "d6c1f72d86fbddef8fb5ef1c03f5b56b2bfdb6ae58032db98c6019fd1dae9021",
|
| 21 |
+
"vae/config.json": "d996c340fe9a7df5d7371f76a7d8d6956f6c98256080074d8434fa5eeac11360",
|
| 22 |
+
"vae/diffusion_pytorch_model.safetensors": "62cd18f19438e35b32ac63020e2852f566e9b02f46b6cdbd87972a356e3c6f4b"
|
| 23 |
+
},
|
| 24 |
+
"size_bytes": {
|
| 25 |
+
"text_encoder/config.json": 855,
|
| 26 |
+
"text_encoder/model-00001-of-00003.safetensors": 4935812536,
|
| 27 |
+
"text_encoder/model-00002-of-00003.safetensors": 4983103192,
|
| 28 |
+
"text_encoder/model-00003-of-00003.safetensors": 1442935480,
|
| 29 |
+
"text_encoder/model.safetensors.index.json": 22476,
|
| 30 |
+
"tokenizer/special_tokens_map.json": 7079,
|
| 31 |
+
"tokenizer/spiece.model": 4548313,
|
| 32 |
+
"tokenizer/tokenizer.json": 16837459,
|
| 33 |
+
"tokenizer/tokenizer_config.json": 61758,
|
| 34 |
+
"transformer/config.json": 495,
|
| 35 |
+
"transformer/diffusion_pytorch_model-00001-of-00005.safetensors": 4978254344,
|
| 36 |
+
"transformer/diffusion_pytorch_model-00002-of-00005.safetensors": 4846784976,
|
| 37 |
+
"transformer/diffusion_pytorch_model-00003-of-00005.safetensors": 4972658392,
|
| 38 |
+
"transformer/diffusion_pytorch_model-00004-of-00005.safetensors": 4846785080,
|
| 39 |
+
"transformer/diffusion_pytorch_model-00005-of-00005.safetensors": 354751840,
|
| 40 |
+
"transformer/diffusion_pytorch_model.safetensors.index.json": 73297,
|
| 41 |
+
"vae/config.json": 1701,
|
| 42 |
+
"vae/diffusion_pytorch_model.safetensors": 2818777808
|
| 43 |
+
}
|
| 44 |
+
}
|
pretrained/wan-2.2-ti2v-5b/text_encoder/config.json
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "google/umt5-xxl",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"UMT5EncoderModel"
|
| 5 |
+
],
|
| 6 |
+
"classifier_dropout": 0.0,
|
| 7 |
+
"d_ff": 10240,
|
| 8 |
+
"d_kv": 64,
|
| 9 |
+
"d_model": 4096,
|
| 10 |
+
"decoder_start_token_id": 0,
|
| 11 |
+
"dense_act_fn": "gelu_new",
|
| 12 |
+
"dropout_rate": 0.1,
|
| 13 |
+
"eos_token_id": 1,
|
| 14 |
+
"feed_forward_proj": "gated-gelu",
|
| 15 |
+
"initializer_factor": 1.0,
|
| 16 |
+
"is_encoder_decoder": true,
|
| 17 |
+
"is_gated_act": true,
|
| 18 |
+
"layer_norm_epsilon": 1e-06,
|
| 19 |
+
"model_type": "umt5",
|
| 20 |
+
"num_decoder_layers": 24,
|
| 21 |
+
"num_heads": 64,
|
| 22 |
+
"num_layers": 24,
|
| 23 |
+
"output_past": true,
|
| 24 |
+
"pad_token_id": 0,
|
| 25 |
+
"relative_attention_max_distance": 128,
|
| 26 |
+
"relative_attention_num_buckets": 32,
|
| 27 |
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"scalable_attention": true,
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pretrained/wan-2.2-ti2v-5b/tokenizer/special_tokens_map.json
ADDED
|
@@ -0,0 +1,332 @@
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| 1 |
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{
|
| 2 |
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| 3 |
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| 28 |
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| 29 |
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| 30 |
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| 31 |
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| 33 |
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| 34 |
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| 35 |
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| 37 |
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| 38 |
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| 39 |
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| 40 |
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| 42 |
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| 46 |
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| 47 |
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| 48 |
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| 67 |
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| 70 |
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| 119 |
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| 120 |
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| 121 |
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| 122 |
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| 123 |
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| 125 |
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| 126 |
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| 129 |
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| 130 |
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| 133 |
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| 134 |
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| 136 |
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| 137 |
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| 138 |
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| 139 |
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| 141 |
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| 144 |
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| 146 |
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| 147 |
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| 149 |
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| 198 |
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| 302 |
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|
| 303 |
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],
|
| 304 |
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"bos_token": {
|
| 305 |
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"content": "<s>",
|
| 306 |
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"lstrip": false,
|
| 307 |
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"normalized": false,
|
| 308 |
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"rstrip": false,
|
| 309 |
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"single_word": false
|
| 310 |
+
},
|
| 311 |
+
"eos_token": {
|
| 312 |
+
"content": "</s>",
|
| 313 |
+
"lstrip": false,
|
| 314 |
+
"normalized": false,
|
| 315 |
+
"rstrip": false,
|
| 316 |
+
"single_word": false
|
| 317 |
+
},
|
| 318 |
+
"pad_token": {
|
| 319 |
+
"content": "<pad>",
|
| 320 |
+
"lstrip": false,
|
| 321 |
+
"normalized": false,
|
| 322 |
+
"rstrip": false,
|
| 323 |
+
"single_word": false
|
| 324 |
+
},
|
| 325 |
+
"unk_token": {
|
| 326 |
+
"content": "<unk>",
|
| 327 |
+
"lstrip": false,
|
| 328 |
+
"normalized": false,
|
| 329 |
+
"rstrip": false,
|
| 330 |
+
"single_word": false
|
| 331 |
+
}
|
| 332 |
+
}
|
pretrained/wan-2.2-ti2v-5b/tokenizer/spiece.model
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e3909a67b780650b35cf529ac782ad2b6b26e6d1f849d3fbb6a872905f452458
|
| 3 |
+
size 4548313
|
pretrained/wan-2.2-ti2v-5b/tokenizer/tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:20a46ac256746594ed7e1e3ef733b83fbc5a6f0922aa7480eda961743de080ef
|
| 3 |
+
size 16837459
|
pretrained/wan-2.2-ti2v-5b/tokenizer/tokenizer_config.json
ADDED
|
@@ -0,0 +1,2749 @@
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| 1 |
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{
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| 2 |
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| 3 |
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| 4 |
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| 5 |
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| 9 |
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| 10 |
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| 11 |
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| 12 |
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| 13 |
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| 14 |
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| 16 |
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| 18 |
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| 19 |
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| 25 |
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| 26 |
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| 28 |
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| 36 |
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| 37 |
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pretrained/wan-2.2-ti2v-5b/transformer/config.json
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pretrained/wan-2.2-ti2v-5b/transformer/diffusion_pytorch_model-00001-of-00005.safetensors
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| 1 |
+
{
|
| 2 |
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"metadata": {
|
| 3 |
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"total_size": 19999150848
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| 4 |
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| 5 |
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|
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|
| 814 |
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|
| 815 |
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|
| 816 |
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|
| 819 |
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|
| 820 |
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|
| 821 |
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|
| 822 |
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|
| 823 |
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|
| 824 |
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|
| 825 |
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|
| 826 |
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|
| 827 |
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|
| 828 |
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|
| 829 |
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|
| 830 |
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|
| 831 |
+
}
|
| 832 |
+
}
|
pretrained/wan-2.2-ti2v-5b/vae/config.json
ADDED
|
@@ -0,0 +1,128 @@
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|
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|
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|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_class_name": "AutoencoderKLWan",
|
| 3 |
+
"_diffusers_version": "0.35.0.dev0",
|
| 4 |
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|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
pretrained/wan-2.2-ti2v-5b/vae/diffusion_pytorch_model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
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|
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|
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|
| 1 |
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version https://git-lfs.github.com/spec/v1
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| 3 |
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size 2818777808
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