The compressed package contains 2 ComfyUI workflows for running:

1.Wan 2.1 T2V: wan2-t2v-upscale-v1.json

2.Wan 2.1 I2V: wan2-i2v-upscale-v1.json

Reference output:

On my RTX4060 8GB vRAM + 32G RAM i2v: Prompt executed in 2807.42 seconds

On my RTX5080 laptop 16G vRAM + 32G RAM i2v: Prompt executed in 1401.00 seconds

Requirements:

Models:

- wan2.1-t2v-14b-Q3_K_M.gguf (T2V) Put in: ComfyUI\models\unet

https://huggingface.co/city96/Wan2.1-T2V-14B-gguf/resolve/main/wan2.1-t2v-14b-Q3_K_M.gguf

- wan2.1-i2v-14b-480p-Q3_K_M.gguf (I2V) Put in: ComfyUI\models\unet

https://huggingface.co/city96/Wan2.1-I2V-14B-480P-gguf/resolve/main/wan2.1-i2v-14b-480p-Q3_K_M.gguf

- wan2.1_t2v_1.3B_fp16.safetensors (t2v model, used in workflow "v2v") Put in: ComfyUI\models\diffusion_models

https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/diffusion_models/wan2.1_t2v_1.3B_fp16.safetensors

- umt5-xxl-encoder-Q4_K_M.gguf (CLIP) Put in: ComfyUI\models\text_encoders

https://huggingface.co/city96/umt5-xxl-encoder-gguf/resolve/main/umt5-xxl-encoder-Q4_K_M.gguf

- umt5_xxl_fp8_e4m3fn_scaled.safetensors (CLIP, can use above if you modify workflow "v2v") Put in: ComfyUI\models\text_encoders

https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors

- wan_2.1_vae.safetensors (VAE) Put in: ComfyUI\models\vae

https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors

- clip_vision_h.safetensors (CLIP VISION) Put in: ComfyUI\models\clip_vision

https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/clip_vision/clip_vision_h.safetensors

- RealESRGAN_x2plus.pth (Upscale Model) Put in: ComfyUI\models\upscale_models

https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth

ComfyUI Nodes:

- rgthree-comfy

- ComfyUI-KJNodes

- ComfyUI-VideoHelperSuite

- ComfyUI-Frame-Interpolation

- Comfyui-Memory_Cleanup (Not required if you modify the workflow)

If you have higher performance hardware, you can choose higher quantization models.