Instructions to use inclusionAI/TwinFlow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use inclusionAI/TwinFlow with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("inclusionAI/TwinFlow", 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 to TwinFlow model card
#3
by nielsr HF Staff - opened
This PR enhances the model card for the TwinFlow model by adding essential metadata:
pipeline_tag: text-to-imagefor improved discoverability on the Hugging Face Hub.library_name: diffusersto enable the automated "how to use" widget, as the model explicitly usesdiffuserscomponents and providesdiffusers-based inference instructions.license: apache-2.0for clear usage rights.
The existing content, including links to the paper, project page, and GitHub repository, remains unchanged.
Please review and merge if these additions look good.
kenshinn changed pull request status to merged