Instructions to use BreakpointAI/boxnethd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BreakpointAI/boxnethd 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/boxnethd", 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
|
Download README.md from BreakpointAI/boxnethd: direct link, hf CLI and curl.
- Browser
- Download file 1.3 kB
-
https://huggingface.co/BreakpointAI/boxnethd/resolve/main/README.md
- Command line
-
hf download hf://BreakpointAI/boxnethd/README.md
-
curl -L -o README.md https://huggingface.co/BreakpointAI/boxnethd/resolve/main/README.md
1.3 kB
metadata
library_name: diffusers
license: other
license_name: research-use
tags:
- object-detection
- bounding-boxes
- grounding
- diffusion
- synthetic-data
boxnethd
Higher-resolution joint image + bounding-box diffusion model with a LoRA adapter. Includes EMA weights.
Released by Breakpoint AI as part of open-sourcing the company's research artifacts.
| Training data | BreakpointAI/breakpoint-grounding-55m |
| Checkpoint step | 475,000 |
| Training run | W&B |
Contents
| Path | Size | What it is |
|---|---|---|
ema/ |
8.6 GB | EMA weights for the backbone |
boxnet/ |
7.4 GB | Joint image + bounding-box diffusion backbone |
pytorch_lora_weights.safetensors |
1.2 GB | LoRA adapter weights |
Inference weights only. Optimizer, LR scheduler, RNG and dataloader state were not uploaded, so this checkpoint cannot be used to resume training.
Citation
@misc{breakpoint_boxnethd,
title = {boxnethd},
author = {Wang, Franklin and Zhong, Desmond and Murdoch, Jamie},
year = {2026},
url = {https://huggingface.co/BreakpointAI/boxnethd}
}