Instructions to use Suleman201/i2vgen-xl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Suleman201/i2vgen-xl with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Suleman201/i2vgen-xl", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
I2VGen-XL
Official repo for I2vgen-xl: High-quality image-to-video synthesis via cascaded diffusion models
Please see Project Page for more examples.
I2VGen-XL is capable of generating high-quality, realistically animated, and temporally coherent high-definition videos from a single input static image, based on user input.
Our initial version has already been open-sourced on Modelscope. This project focuses on improving the version, especially in terms of motions and semantics.

