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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Support `TwinFlow` via `diffusers`
Unverified signatureMove the weights in `TwinFlow-Qwen-Image-v1.0/TwinFlow-Qwen-Image/*` to the root directory of the repository instead, which allows loading those from `diffusers`, preventing `KeyError: '_class_name'` when reading the `model_index.json` when loading via `AutoPipelineForText2Image.from_pretrained`b277896 -
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Add metadata to TwinFlow model card (#3)
Verified signature- Add metadata to TwinFlow model card (8fb1757881aee3aa9a9ba95ca030fa69a5da7222) Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>58ab18e
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