Image-to-Text
Diffusers
Safetensors
English
uniar
image-generation
image-understanding
image-editing
multimodal
autoregressive
text-to-image
unified-model
Instructions to use ShareLab-SII/UniAR-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ShareLab-SII/UniAR-RL with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ShareLab-SII/UniAR-RL", 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
- Xet hash:
- 69a7b1feb614d93d9901421ed6fa0eede9cfb5536c704e97d96443986fd93327
- Size of remote file:
- 4.96 GB
- SHA256:
- 7bfc3ce6379861b9bd928ff49915a5b97187ef7c770303d140b69be1ad8a1535
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.