Instructions to use MutantSparrow/LowBitMoss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusion Single File
How to use MutantSparrow/LowBitMoss with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 2,644 Bytes
2e7c0b8 9cb174b 2e7c0b8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | # LowBitMoss research data
Licensed under Apache License 2.0; source model attribution is in `THIRD_PARTY_NOTICES.md`.
| Directory or file | Contents |
|---|---|
| `prompts/calib_prompts.json` | General calibration prompt definitions |
| `prompts/calib_text_prompts.json` | Text-rich calibration prompt definitions |
| `prompts/eval_prompts.json` | The held-out 24-prompt evaluation set |
| `data/calib_512/` | Non-distilled teacher sampling captures: prompt text and `{x, t, ctx}` sample records |
| `data/calib_turbo_768/` | Distilled-teacher captures: four conditional sampler states per prompt |
| `data/teacher_out_512.pt`, `data/teacher_out_512_plus.pt` | Non-distilled teacher outputs in sorted capture-file / sample order |
| `data/teacher_out_turbo768.pt` | Distilled-teacher outputs in sorted capture-file / sample order |
| `data/enc_cond/` | Cached text-conditioning tensors for encoder comparisons |
| `eval/` | Generated PNGs, latent tensors, metric JSON, and visual-judge reports |
The `.pt` files use PyTorch's tensor serialization. Use a current PyTorch release and `torch.load(path, map_location="cpu", weights_only=True)` where supported. Capture `samples` are Python dictionaries/lists of tensors; the teacher-output files contain tensors ordered to match the capture scripts. Do not reorder captures when pairing them with outputs.
The supplied `data/calib_512/` directory contains the expanded 240-prompt pool (4,442 captured samples). Pair it with `data/teacher_out_512_plus.pt` when running end-to-end tuning. The legacy `teacher_out_512.pt` belongs to the original 160 general prompts (2,966 samples), so it must not be paired with the expanded directory. For the distilled pool, pair `data/calib_turbo_768/` (456 prompts, four samples each) with `data/teacher_out_turbo768.pt`. If you capture a different pool, regenerate its teacher outputs with the capture files in the same sorted order.
Keep `data/` and `eval/` under the directory selected by `LOWBITMOSS_HOME` when using the GitHub scripts. Downloaded model components go in ComfyUI's model directories, as described in the model card. PNG metadata is removed in the public export; pixels and recorded metric values are preserved. Metadata-only privacy redactions, if required, are recorded in the release manifest without exposing original values.
The paper explains sampler settings, held-out subsets, resolution differences, seeds, failed runs, and configuration-specific evidence. Stored judge reports are historical observations; the corrected paper takes precedence when a historical report uses an unsupported interpretation.
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