Instructions to use X-GenGroup/PaCo-FLUX.1-dev-Lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use X-GenGroup/PaCo-FLUX.1-dev-Lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("X-GenGroup/PaCo-FLUX.1-dev-Lora", 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 model card with metadata and links
#1
by nielsr HF Staff - opened
This PR adds a comprehensive model card to the repository. It includes:
- Relevant metadata:
license: apache-2.0,pipeline_tag: text-to-image, andlibrary_name: diffusers(evidenced byadapter_config.json). - Links to the paper (PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling), project page (https://x-gengroup.github.io/HomePage_PaCo-RL/), and GitHub repository (https://github.com/X-GenGroup/PaCo-RL/).
- A detailed overview, key components, repository structure, model zoo, acknowledgements, and citation, all extracted from the official GitHub README.
Please review and merge if everything looks good.
Jayce-Ping changed pull request status to merged