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Wan 2.2 T2V A14B Diffusers Reduced To 2layers

💜 Wan | 🖥️ GitHub | 🤗 Hugging Face | 🤖 ModelScope | 📑 Technical Report | 📑 Blog | 💬 WeChat Group | 📖 Discord

ArtOtherVersionRevision 0316937CBewertung

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Art
Other
Dateigröße
484 MB–3,49 GB
Version
vRevision 0316937
Aktualisiert
27. Sept. 2026
Downloads
2
Quelle
Hugging Face

Quellenauszug

💜 Wan | 🖥️ GitHub | 🤗 Hugging Face | 🤖 ModelScope | 📑 Technical Report | 📑 Blog | 💬 WeChat Group | 📖 Discord

Quellen

1 Quelle
Hugging FacePrimärVersion 0316937469c047bee3331b30d7b660a84e3d72e6
huggingface.co/xuechendi/Wan2.2-T2V-A14B-Diffusers-reduced-to-2layers/tree/0316937469c047bee3331b30d7b660a84e3d72e6

Verifiziert 30. Aug.

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Modellartefakte

4 Artefakte

text_encoder/model.safetensors

safetensors · 2,67 GB · SHA-256 5e541a653557…538b · Hugging Face

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transformer_2/diffusion_pytorch_model.safetensors

safetensors · 3,49 GB · SHA-256 6b2a2febb8b5…44b1 · Hugging Face

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transformer/diffusion_pytorch_model.safetensors

safetensors · 3,49 GB · SHA-256 e55c1e4fbce6…cd23 · Hugging Face

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vae/diffusion_pytorch_model.safetensors

safetensors · 484 MB · SHA-256 d6e524b3fffe…e793 · Hugging Face

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Quellenauszüge

2 Auszüge

--- 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> &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 <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