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
File size: 735 Bytes
b7f044b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # I2VGen-XL
Official repo for [I2vgen-xl: High-quality image-to-video synthesis via cascaded diffusion models](https://arxiv.org/abs/2311.04145)
Please see [Project Page](https://i2vgen-xl.github.io) 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](https://modelscope.cn/models/damo/Image-to-Video/summary). This project focuses on improving the version, especially in terms of motions and semantics.*
## Examples

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