Instructions to use BreakpointAI/socknetq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BreakpointAI/socknetq 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/socknetq", 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
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Download README.md from BreakpointAI/socknetq: direct link, hf CLI and curl.
- Browser
- Download file 1.78 kB
-
https://huggingface.co/BreakpointAI/socknetq/resolve/main/README.md
- Command line
-
hf download hf://BreakpointAI/socknetq/README.md
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curl -L -o README.md https://huggingface.co/BreakpointAI/socknetq/resolve/main/README.md
1.78 kB
| 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](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 | 3,120,000 | | |
| | Training run | [W&B](https://wandb.ai/diffusionexp/train_socknetq/runs/7pqundo1) | | |
| ## 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 | |
| ```bibtex | |
| @misc{breakpoint_socknetq, | |
| title = {socknetq}, | |
| author = {Wang, Franklin and Zhong, Desmond and Murdoch, Jamie}, | |
| year = {2026}, | |
| url = {https://huggingface.co/BreakpointAI/socknetq} | |
| } | |
| ``` | |