Instructions to use byliutao/Longcat-Image-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use byliutao/Longcat-Image-Turbo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("byliutao/Longcat-Image-Turbo", 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
- Local Apps
- Draw Things
- DiffusionBee
Add metadata and link to paper
#1
by nielsr HF Staff - opened
Hi there! This PR improves the model card for Longcat-Image-Turbo by adding relevant YAML metadata for better discoverability.
Specifically, I have:
- Added
pipeline_tag: text-to-image. - Added
library_name: diffusers(based on the model structure). - Added
license: mit. - Included a direct link to the paper Continuous-Time Distribution Matching for Few-Step Diffusion Distillation and its GitHub repository.
thanks~
byliutao changed pull request status to merged