Model reference · open weights

Wan2.1-Fun-InP

Video city96 Text→video 1 build Open weights 11k dl/mo

Wan2.1-Fun-InP is an open-weight video model from city96. Wan2.1-Fun-14B-InP-gguf (GGUF) weighs 10.9 GB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • Wan2.1-Fun-InP is a text-to-video model created by city96 and published as a GGUF conversion of the alibaba-pai/Wan2.1-Fun-14B-InP model.
  • It supports English and Chinese and is licensed under apache-2.0.
  • The model files are designed for use with the ComfyUI-GGUF custom node and require additional VAE files from the base Wan2.1 model.

Summary of the city96/Wan2.1-Fun-14B-InP-gguf model card, 2026-10-01

What it is

Released bycity96
Released2025-03-28
VRAM10.9 GB for the weights

What it runs on

Memory and cards for Wan2.1-Fun-14B-InP-gguf (GGUF)

10.9 GBweights, file size
537 MBruntime overhead
CardWeightsMemory
RTX 3060 12 GBtight11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What city96 says about Wan2.1-Fun-InP

Read the model card

This is a direct GGUF conversion of alibaba-pai/Wan2.1-Fun-14B-InP

The model files can be used with the ComfyUI-GGUF custom node.

Place model files in ComfyUI/models/unet - see the GitHub readme for further install instructions.

The VAE and additional files can be downloaded from Comfy-Org/Wan_2.1_ComfyUI_repackaged - they are the same as the base Wan2.1 model.

Please refer to this chart for a basic overview of quantization types.

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

How it works

How video models work

Prompt / imagestart pointTemporal diffusionframes over timeVideoMP4 clipA video model generates a sequence of coherent frames from your prompt or a starting image.

Running it yourself

Run it on a rented GPU

Rent a machine by the hour. ComfyUI is installed on it. Open ComfyUI through the tunnel, then Templates → Wan 2.1 Text to Video. Its model loader takes .safetensors files: replace it with Unet Loader (GGUF) and choose this model's file there.

# on your rented machine (the ssh line is on its page in the console)
# the GGUF loader nodes, once per machine
git clone https://github.com/city96/ComfyUI-GGUF /opt/ComfyUI/custom_nodes/ComfyUI-GGUF
pip install -r /opt/ComfyUI/custom_nodes/ComfyUI-GGUF/requirements.txt

# get REPO FILE FOLDER: one file into /workspace/models/FOLDER, where ComfyUI loads it from
get() { hf download "$1" "$2" --local-dir /workspace/hf-files && mkdir -p "/workspace/models/$3" && mv "/workspace/hf-files/$2" "/workspace/models/$3/$4"; }

# the model (10.1 GB)
get city96/Wan2.1-Fun-14B-InP-gguf wan2.1-fun-14b-inp-Q4_K_M.gguf unet
# what the template also loads “Wan 2.1 Text to Video” (6.5 GB)
get Comfy-Org/Wan_2.1_ComfyUI_repackaged split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors text_encoders
get Comfy-Org/Wan_2.1_ComfyUI_repackaged split_files/vae/wan_2.1_vae.safetensors vae

start-comfyui
Renting a GPU: connect, tunnels, ComfyUI
# on your computer, in a second terminal: ComfyUI in your browser at http://localhost:8188
# HOST, PORT and BOOKING are your machine's, from its page in the console
ssh -o HostKeyAlias=axforge-BOOKING -o StrictHostKeyChecking=accept-new -L 8188:localhost:8188 dev@HOST -p PORT
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