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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base_model: jimmycarter/LibreFLUX
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# LibreFLUX-ControlNet
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, which uses [LibreFLUX](https://huggingface.co/jimmycarter/LibreFLUX) as the underlying Transformer model for the ControlNet. For the dataset, I auto labeled 165K images from the SA1B dataset and trained for 1 epoch. I've tested using this ControlNet model as a base for transfer learning to less generic datasets, the results are good!
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base_model: jimmycarter/LibreFLUX
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# LibreFLUX-ControlNet
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This model/pipeline is the product of my [LibreFlux ControlNet training repo](https://github.com/NeuralVFX/LibreFLUX-ControlNet), which uses [LibreFLUX](https://huggingface.co/jimmycarter/LibreFLUX) as the underlying Transformer model for the ControlNet. For the dataset, I auto labeled 165K images from the SA1B dataset and trained for 1 epoch. I've tested using this ControlNet model as a base for transfer learning to less generic datasets, the results are good!
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