I2VGen-XL

Image-to-video generation model by Alibaba DAMO Academy.

Open SourceSelf HostedOffline CapableGPU Required (12GB+ VRAM)
0.0 (0)

About

I2VGen-XL is an image-to-video synthesis method from Alibaba's Tongyi Lab, published within the VGen video generation codebase. It animates a still image into high-definition video through cascaded diffusion: a base stage establishes semantics and motion coherent with the input image, and a refinement stage sharpens detail and raises resolution, optionally guided by a text caption. Trained on WebVid-10M and LAION-400M, the released models are intended for research and non-commercial use, and the authors note weaker results on anime-style images and black backgrounds due to training data coverage. Inference is available through the VGen repository along with a local Gradio demo, plus hosted versions on ModelScope, Hugging Face, and Replicate. Researchers exploring image conditioning in video diffusion use it both as a baseline and as a component of VGen, which bundles several related Tongyi Lab video generation models in one codebase.

Should you use I2VGen-XL?

Pick it when

Pick I2VGen-XL for research on image-conditioned video diffusion, as a paper baseline or a component inside Alibaba's VGen codebase, when you want a cascaded design that separates motion setup from detail refinement.

Look elsewhere when

Skip it for commercial projects: the released models are meant for research and non-commercial use, and results are weaker on anime-style images and black backgrounds. For shippable image-to-video, Wan 2.1 or Stable Video Diffusion XT fit better.

Alternatives to I2VGen-XL

  • DynamiCrafter

    Text-guided image animation that adds two-frame interpolation and looping, with ComfyUI integration, at 12 to 18 GB of VRAM.

  • Stable Video Diffusion XT

    25 frames at 576x1024 from one image, with commercial use allowed under the Stability Community License, but no text caption guidance.

  • Wan 2.1

    Apache-2.0 14B image-to-video at 720p with first-and-last-frame control, the stronger choice when output must ship commercially.

  • FramePack

    Apache-2.0 code, up to a minute of image-to-video on a 6 GB NVIDIA card, and a one-click Windows install instead of a research codebase.

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Details

Price
Free
Platform
Local/Desktop
Difficulty
Advanced (4/5)
License
Apache-2.0
Minimum VRAM
12 GB
Added
Apr 3, 2026

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