These models are the result of a partial NVFP4 quantization of <a target="_blank" rel="ugc" href="https://huggingface.co/lightx2v/Wan2.2-I2V-A14B-Moe-Distill-Lightx2v">Wan2.2-I2V-A14B-Moe-Distill-Lightx2v</a><a target="_blank" rel="ugc" href="https://huggingface.co/lightx2v/Wan2.1-I2V-14B-480P-StepDistill-CfgDistill-Lightx2v"> </a>by lightx2v, produced using <a target="_blank" rel="ugc" href="https://github.com/silveroxides/convert_to_quant">convert_to_quant</a> by <a target="_blank" rel="ugc" href="https://huggingface.co/silveroxides">silveroxides</a>. Some layers have been kept on their original BF16 format, while others were quantized as MXFP8 or NVFP4, mostly.<a target="_blank" rel="ugc" href="https://huggingface.co/lightx2v/Wan2.2-I2V-A14B-Moe-Distill-Lightx2v">Wan2.2-I2V-A14B-Moe-Distill-Lightx2v</a> is an image-to-video generation model built on <a target="_blank" rel="ugc" href="https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B">Wan2.2-I2V-A14B</a>. It applies step distillation and a MoE architecture to reduce inference to 4 steps without CFG, cutting generation time substantially while preserving output quality.Remember that you need to download the high and low noise models in order for your Wan 2.2 workflow to work. IMPORTANTSince NVFP4 is only supported on NVIDIA Blackwell architecture GPUs, running this model requires a Blackwell GPU with its corresponding support enabled in torch, along with a recent version of ComfyUI and <a target="_blank" rel="ugc" href="https://github.com/Comfy-Org/comfy-kitchen">comfy-kitchen</a> built against CUDA 13.

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