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| "[Tutorial](https://docs.comfy.org/tutorials/video/wan/vace) | [教程](https://docs.comfy.org/zh-CN/tutorials/video/wan/vace)\n\n\n**diffusion_models** \n\n- [Wan2.1-VACE-14B-GGUF](https://huggingface.co/QuantStack/Wan2.1-VACE-14B-GGUF)\n\n**VAE**\n- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors?download=true)\n\n**Text encoders** Chose one of following model\n- [umt5_xxl_fp16.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp16.safetensors?download=true)\n- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors?download=true)\n- [umt5-xxl-encoder-gguf](https://huggingface.co/city96/umt5-xxl-encoder-gguf/tree/main)\n\n> You can choose between fp16, fp8 and ggufs.\n\n**CausVidLora(Optional for a lot more speed)**\n- [Wan21_CausVid_14B_T2V_lora_rank32.safetensors](https://huggingface.co/Kijai/WanVideo_comfy/resolve/main/Wan21_CausVid_14B_T2V_lora_rank32.safetensors?download=true)\n\n\n\nFile save location\n\n```\nComfyUI/\n├── models/\n│ ├── loras/\n│ │ └─── Wan21_CausVid_14B_T2V_lora_rank32.safetensors\n│ ├── unet/\n│ │ └─── Wan2.1-VACE-14B-Qx_x_x.gguf\n│ ├── text_encoders/\n│ │ └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors # or GGUF (umt5-xxl-encoder-Qx_x.gguf)\n│ └── vae/\n│ └── wan_2.1_vae.safetensors\n\n```\n" |
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| "IF you have 32gb but only 12/16gb or even less vram, you might want to use the \"Low Vram high ram\" loader to offload parts of the model to prevent out of memory errors. You can use this on any card, it can be useful even on a 4090, though be careful with only 16gb ram, you might need to change the virtual vram gb to less." |
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