How to deploy Wan2.2-I2V-A14B on a GPU cloud
A 14.3B-parameter image-to-video MoE diffusion model. Full specs, license and use cases.
Wan2.2-I2V-A14B size and hardware requirements
How to run Wan2.2-I2V-A14B
Run Wan2.2-I2V-A14B with Diffusers (Python)
From Wan-AI/Wan2.2-I2V-A14B-Diffusers's own model card (requires diffusers installed from source as of this model's release).
import torch
from diffusers import WanImageToVideoPipeline
from diffusers.utils import export_to_video, load_image
model_id = "Wan-AI/Wan2.2-I2V-A14B-Diffusers"
pipe = WanImageToVideoPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
pipe.to("cuda")
image = load_image("input.jpg")
output = pipe(image=image, prompt="a description of the motion").frames[0]
export_to_video(output, "i2v_out.mp4", fps=16)Source: https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers/raw/main/README.md
Run Wan2.2-I2V-A14B with ComfyUI
Aquanode's ComfyUI template comes with ComfyUI preinstalled. Download Wan-AI/Wan2.2-I2V-A14B's checkpoint into the models folder and load it in a workflow; this is a real Aquanode template, but loading this specific checkpoint is a manual step, not a one-click deploy.
Deploy Wan2.2-I2V-A14B on Aquanode
Aquanode has no one-click deploy template for Wan2.2-I2V-A14B; it comes with ComfyUI preinstalled, so you only need to load the checkpoint, not install anything. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch the ComfyUI template sized to the requirement above (1× A100 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch the ComfyUI template