Wan 2.2 T2V A14B Diffusers Reduced To 2layers
💜 Wan | 🖥️ GitHub | 🤗 Hugging Face | 🤖 ModelScope | 📑 Technical Report | 📑 Blog | 💬 WeChat Group | 📖 Discord
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 484 MB–3,49 GB
- Versão
- vRevision 0316937
- Atualizado
- 27 de set. de 2026
- Downloads
- 2
- Fonte
- Hugging Face
Trecho da fonte
💜 Wan | 🖥️ GitHub | 🤗 Hugging Face | 🤖 ModelScope | 📑 Technical Report | 📑 Blog | 💬 WeChat Group | 📖 Discord
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Fontes
1 fonteVerificado 30 de ago.
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Artefatos de modelo
4 artefatos
Artefatos de modelo
4 artefatostext_encoder/model.safetensors
safetensors · 2,67 GB · SHA-256 5e541a653557…538b · Hugging Face
Download filetransformer_2/diffusion_pytorch_model.safetensors
safetensors · 3,49 GB · SHA-256 6b2a2febb8b5…44b1 · Hugging Face
Download filetransformer/diffusion_pytorch_model.safetensors
safetensors · 3,49 GB · SHA-256 e55c1e4fbce6…cd23 · Hugging Face
Download filevae/diffusion_pytorch_model.safetensors
safetensors · 484 MB · SHA-256 d6e524b3fffe…e793 · Hugging Face
Download fileTrechos de fonte
2 trechos
Trechos de fonte
2 trechos--- license: apache-2.0 pipeline_tag: text-to-video --- # Wan2.2 <p align="center"> <img src="assets/logo.png" width="400"/> <p> <p align="center"> 💜 <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>   <br> ----- [**Wan: Open and Advanced Large-Scale Video Generative Models**](https://arxiv.org/abs/2503.20314) <be> 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: - 👍 **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. - 👍 **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. - 👍 **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. - 👍 **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-...
Source context: 2 downloads · 0 likes · Pipeline text-to-video · Library diffusers · Repo xuechendi/Wan2.2-T2V-A14B-Diffusers-reduced-to-2layers
