README.md

metadata

library_name: diffusers
license: other
license_name: research-use
tags:
  - object-detection
  - bounding-boxes
  - grounding
  - diffusion
  - synthetic-data

socknetq

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	3,120,000
Training run	W&B

Contents

Path	Size	What it is
pytorch_model/	67.6 GB
boxnet/	20.1 GB	Joint image + bounding-box diffusion backbone
pytorch_lora_weights.safetensors	1.0 GB	LoRA adapter weights
conf_weighter.pth	2.0 KB	Confidence-stream loss-weighting head
weighter.pth	1.9 KB	Loss-weighting head
nash_mtl_weights_conf_ema.safetensors	120 B	Nash-MTL task-weighting coefficients
nash_mtl_weights_ema.safetensors	112 B	Nash-MTL task-weighting coefficients
global_conf_step.txt	7 B	Training step counter
global_nonull_step.txt	7 B	Training step counter
weighter_steps.txt	4 B	Loss-weighter step counter

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_socknetq,
  title  = {socknetq},
  author = {Wang, Franklin and Zhong, Desmond and Murdoch, Jamie},
  year   = {2026},
  url    = {https://huggingface.co/BreakpointAI/socknetq}
}