Instructions to use neuralvfx/LibreFlux-SAM-ControlNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use neuralvfx/LibreFlux-SAM-ControlNet with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("neuralvfx/LibreFlux-SAM-ControlNet", 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
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README.md
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- Uses Attention Masking
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- Inference runs with CFG
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- Trained on 165K segmented images from Meta's [SA1B Dataset](https://ai.meta.com/datasets/segment-anything/)
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- Trained using [https://github.com/NeuralVFX/LibreFLUX-ControlNet](https://github.com/NeuralVFX/LibreFLUX-ControlNet)
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- [https://huggingface.co/jimmycarter/LibreFLUX](https://huggingface.co/jimmycarter/LibreFLUX)
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- [https://github.com/bghira/SimpleTuner](https://github.com/bghira/SimpleTuner)
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# Compatibility
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- Use these specific library versions:
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```py
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pip install -U diffusers==0.32.0
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pip install -U "transformers @ git+https://github.com/huggingface/transformers@e15687fffe5c9d20598a19aeab721ae0a7580f8a"
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- Uses Attention Masking
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- Inference runs with CFG
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- Trained on 165K segmented images from Meta's [SA1B Dataset](https://ai.meta.com/datasets/segment-anything/)
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- Trained using: [https://github.com/NeuralVFX/LibreFLUX-ControlNet](https://github.com/NeuralVFX/LibreFLUX-ControlNet)
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- Base model used: [https://huggingface.co/jimmycarter/LibreFLUX](https://huggingface.co/jimmycarter/LibreFLUX)
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- Inference code adapted from:[https://github.com/bghira/SimpleTuner](https://github.com/bghira/SimpleTuner)
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# Compatibility
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```py
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pip install -U diffusers==0.32.0
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pip install -U "transformers @ git+https://github.com/huggingface/transformers@e15687fffe5c9d20598a19aeab721ae0a7580f8a"
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