Instructions to use rkv1990/FLUX.1-Fill-dev-outpainting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rkv1990/FLUX.1-Fill-dev-outpainting with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rkv1990/FLUX.1-Fill-dev-outpainting", 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
Create README.md
Browse files
README.md
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This is based on Flux.1-Fill-dev model and the FLUX.1-dev Non-Commercial License https://github.com/black-forest-labs/flux/blob/main/model_licenses/LICENSE-FLUX1-dev is applicable.
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The idea is to unlock the full outpainting potential of Flux.1.Fill-dev model.
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The original model parameters have not been finetuned or modified.
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Rather, this simple hack unlocks the full potential of the Flux.1-Fill-dev model.
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Here is a code snippet to use the code.
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TODO
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