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Wan 2.2 I2V A14B logo

Wan 2.2 I2V A14B Image-to-Video on Modular

Wan 2.2 I2V A14B is an image-to-video diffusion model from Wan AI. It animates a source image with prompt guidance and supports 480P and 720P video generation using the Wan A14B MoE architecture.

Example Usage

Video output
Code to use
Python

  import base64
  from openai import OpenAI
  
  client = OpenAI(
      base_url="https://model.api.modular.com",
      api_key="<your_api_token>",
  )
  
  prompt = """A 2D pop art comic book animation. Thick black ink outlines,
  flat color fills, Ben-Day halftone dots covering every surface,
  limited palette of pink, cyan, red, and black. A rainy back alley
  in Shinjuku at night, rendered as a comic panel: kanji neon signs
  drawn as flat graphic shapes with halftone glow, puddles drawn as
  flat cyan shapes with white highlight marks, rain shown as diagonal
  ink lines. A helmeted astronaut figure in a black raincoat walks
  away from the viewer down the alley, never turning. Steam from a
  ramen vent drawn as cartoon curls. The viewpoint pushes slowly
  forward through the alley. Hand-drawn cel animation feel, no
  photorealism, no 3D shading, no lens effects."""
  
  response = client.responses.create(
      model="Wan-AI/Wan2.2-I2V-A14B-Diffusers",
      input=prompt,
      extra_body={
          "provider_options": {
              "video": {
                  "height": 512,
                  "width": 512,
                  "steps": 28,
                  "num_frames": 81,
                  "frames_per_second": 16,
                  "response_format": "b64_json",
              }
          }
      },
  )
  
  video_data = response.output[0].content[0].video_data
  
  with open("output.mp4", "wb") as f:
      f.write(base64.b64decode(video_data))
Model Details
  • Developed by
    Wan AI
  • Model family
    Wan-AI/Wan2.2-I2V-A14B-Diffusers
  • Modality
    Video,
  • Total Params
    27B
  • Precision
    BF16
  • Deployment options
    Shared, Dedicated, Self-hosted

Why choose Wan 2.2 I2V A14B on Modular?

  • High performance, out of the box

    Run leading open models with strong default performance and the ability to optimize down to the kernel — extracting more from every GPU.

  • Lower Infrastructure Costs

    Deploy efficiently across NVIDIA and AMD hardware to reduce GPU count, increase throughput, and avoid expensive closed-model licensing.

  • Easy Integration

    Integrate through an OpenAI-compatible endpoint, swap models freely, and scale across clouds or hardware without redesigning your application stack.

Wan 2.2 I2V A14B
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