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 Settings
- Draw Things
- DiffusionBee
Commit History
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Add metadata to TwinFlow model card
Verified signatureThis PR enhances the model card for the TwinFlow model by adding essential metadata: - `pipeline_tag: text-to-image` for improved discoverability on the Hugging Face Hub. - `library_name: diffusers` to enable the automated "how to use" widget, as the model explicitly uses `diffusers` components and provides `diffusers`-based inference instructions. - `license: apache-2.0` for 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.8fb1757 -
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Upload folder using huggingface_hub
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