import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("BreakpointAI/socknethd", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]socknethd
Higher-resolution joint image + bounding-box diffusion model with a LoRA adapter, a confidence stream, and Nash-MTL multi-task loss weighting.
Released by Breakpoint AI as part of open-sourcing the company's research artifacts.
| Training data | BreakpointAI/breakpoint-grounding-55m |
| Checkpoint step | 4,125,000 |
| Training run | W&B |
Contents
| Path | Size | What it is |
|---|---|---|
boxnet/ |
16.1 GB | Joint image + bounding-box diffusion backbone |
pytorch_lora_weights.safetensors |
2.8 GB | LoRA adapter weights |
nash_mtl_weights_conf_ema.pkl |
432 B | Nash-MTL task-weighting coefficients |
nash_mtl_weights_ema.pkl |
424 B | Nash-MTL task-weighting coefficients |
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_socknethd,
title = {socknethd},
author = {Wang, Franklin and Zhong, Desmond and Murdoch, Jamie},
year = {2026},
url = {https://huggingface.co/BreakpointAI/socknethd}
}
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