Instructions to use BreakpointAI/breaknet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BreakpointAI/breaknet with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BreakpointAI/breaknet", 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
Add model card
Browse files
README.md
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---
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library_name: diffusers
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license: other
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license_name: research-use
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tags:
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- object-detection
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- bounding-boxes
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- grounding
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- diffusion
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- synthetic-data
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---
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# breaknet
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SDXL ControlNet trained for grounded image generation from bounding-box conditioning.
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Released by [Breakpoint AI](https://huggingface.co/BreakpointAI) as part of open-sourcing the company's research
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artifacts.
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|---|---|
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| Training data | [`BreakpointAI/breakpoint-grounding-55m`](https://huggingface.co/datasets/BreakpointAI/breakpoint-grounding-55m) |
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| Checkpoint step | 140,000 |
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| Training run | not recorded |
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## Contents
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| Path | Size | What it is |
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|---|---|---|
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| `controlnet/` | 5.0 GB | ControlNet weights |
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**Inference weights only.** Optimizer, LR scheduler, RNG and dataloader state were not
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uploaded, so this checkpoint cannot be used to resume training.
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## Citation
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```bibtex
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@misc{breakpoint_breaknet,
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title = {breaknet},
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author = {Wang, Franklin and Zhong, Desmond and Murdoch, Jamie},
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year = {2026},
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url = {https://huggingface.co/BreakpointAI/breaknet}
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}
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```
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