Instructions to use MATLOWAI/MOSS-Image-SFT-DeBand with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusion Single File
How to use MATLOWAI/MOSS-Image-SFT-DeBand 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
Add nynxz Destripe and DeBand Strong to edit anatomy
Browse files- research/README.md +1 -1
research/README.md
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@@ -48,7 +48,7 @@ D = Δ · G_num · (G_flat + μ·G_gen + ε·I)⁻¹ ε = ρ · tr(G_flat +
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## Edit anatomy
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`edit_anatomy.py` (+ `edit_anatomy_c.py` for the horizontal-detail measure) computes, for each fix and layer, the weight-change size relative to a full revert and the output-change energy tr(D·G·Dᵀ) on solid-colour vs general activations; `build_anatomy.py` draws it. Measure selectivity on the centred Grams: on raw Grams one per-token colour/brightness/modulation direction holds about half the energy and makes any edit look large.
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## Training (negative result, kept for reproduction)
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## Edit anatomy
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`edit_anatomy.py` (+ `edit_anatomy_c.py` for the horizontal-detail measure) computes, for each fix and layer, the weight-change size relative to a full revert and the output-change energy tr(D·G·Dᵀ) on solid-colour vs general activations; `build_anatomy.py` draws it. `edit_anatomy_lora.py <tag> <lora>` adds any LoRA (lora_up/down or .diff keys; split q/k/v mapped into fused qkv) as another arm. Measure selectivity on the centred Grams: on raw Grams one per-token colour/brightness/modulation direction holds about half the energy and makes any edit look large.
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## Training (negative result, kept for reproduction)
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