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
Upload README.md
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README.md
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# LibreFLUX-ControlNet Inference (WIP)
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Standalone inference pipeline for LibreFLUX-based ControlNet with classifier-free guidance. Runs a control image and a prompt through a LibreFLUX backbone for quick tests or JSONL batches.
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## Status
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Work in progress. Not fully functional yet. Code is being transferred and adapted from my larger project repo to this inference-only repo.
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## Planned
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- Single image plus prompt inference with CFG
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- Negative prompts and control strength
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- Simple batch mode via JSONL
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- Minimal example inputs
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