Instructions to use BreakpointAI/boxnet-xl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BreakpointAI/boxnet-xl 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/boxnet-xl", 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
File size: 1,106 Bytes
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library_name: diffusers
license: other
license_name: research-use
tags:
- object-detection
- bounding-boxes
- grounding
- diffusion
- synthetic-data
---
# boxnet-xl
SDXL-based UNet fine-tuned for grounded image generation.
Released by [Breakpoint AI](https://huggingface.co/BreakpointAI) as part of open-sourcing the company's research
artifacts.
| | |
|---|---|
| Training data | [`BreakpointAI/breakpoint-grounding-55m`](https://huggingface.co/datasets/BreakpointAI/breakpoint-grounding-55m) |
| Checkpoint step | 260,000 |
| Training run | [W&B](https://wandb.ai/diffusionexp/train_boxnetXL/runs/2y28bu1x) |
## Contents
| Path | Size | What it is |
|---|---|---|
| `unet/` | 7.7 GB | SDXL UNet weights |
**Inference weights only.** Optimizer, LR scheduler, RNG and dataloader state were not
uploaded, so this checkpoint cannot be used to resume training.
## Citation
```bibtex
@misc{breakpoint_boxnet_xl,
title = {boxnet-xl},
author = {Wang, Franklin and Zhong, Desmond and Murdoch, Jamie},
year = {2026},
url = {https://huggingface.co/BreakpointAI/boxnet-xl}
}
```
|