Instructions to use rzgar/Qwen-Image-WAN2.2-VAE-Collection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Wan2.2
How to use rzgar/Qwen-Image-WAN2.2-VAE-Collection with Wan2.2:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
input image: Wan2_1_VAE_fp32 | VAE: Wan21_QwenHDR_hybrid_head_blend50
Hybrid VAE Collection
Experimental Wan 2.1 video VAE variants for use with Wan 2.2 or Qwen-Image.
Built by merging the native Wan decoder with weights from the Qwen Image HDR VAE.
All files share the same architecture as Wan2_1_VAE (16-channel latent, 4× temporal / 8× spatial compression). Drop-in replacements for the VAEDecode node — no workflow changes needed beyond picking a file.
What’s in this repo
Decoder fusions that keep Wan’s temporal decoding (correct video motion) while borrowing Qwen HDR detail and tone from the output head/decoder body. Use these when you want richer contrast, analog-ish character, or a touch of HDR punch without the slow-motion artifacts of the full Qwen HDR VAE.
Strength varies by file (head-only, partial blends).
Grayscale variants: the final RGB output layer is collapsed to luminance so every frame decodes as true black & white with Wan-correct motion.
Notes
- These are decode-time stylization VAEs, not a retrained model. Latent space matches the official Wan 2.1 VAE.
- Full Qwen Image HDR VAE alone can cause slow-motion / smeared motion on video, these hybrids avoid that by preserving Wan’s decoder body.
- Selective color ( red dress stays red, rest goes grayscale) is not possible at the VAE level, use post-decode color grading or a selective-color node after VAEDecode.
Credits
- Wan 2.1 VAE - Alibaba Wan Team / Comfy-Org
- Qwen Image HDR VAE - Felldude
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Model tree for rzgar/Qwen-Image-WAN2.2-VAE-Collection
Base model
Wan-AI/Wan2.2-I2V-A14B