abuzreq/model-bending-knowledge-base

Model Bending Knowledge Base

This dataset records what happens when you bend the inside of a diffusion model. Bending means multiplying, rotating, adding noise to or otherwise changing the activations of a layer while the model generates.

Each record names:

• the model and the exact part of it that was bent
• the operation, the amount, and the denoising steps it covered
• the full generation setup
• the output, next to an unbent baseline made with the same setup

Artists can browse it to learn what a model does when bent. Agents, such as the comfyui-model-bending skill, query it to suggest starting recipes ("more abstract on SD1.5" → which bends tend to do that).

Bends are applied with ComfyUI-Model-Bending.

8660 records, 932 cells and 10 findings (index built 2026-10-01T23:11:56Z). Sources: author_experiment 2940, paper 5720. Model families: sd1 8660.

Facts are kept apart from interpretation

Every record folder records/<family>/<source>/<id>/ holds:

file	what	who made it
record.json	facts: model, checkpoint, sampler, scheduler, steps, cfg, seed, size, route; the bends as actually applied (layer path, op, arguments, step window); the output file	the producer named in provenance
measurements.json	numbers computed against the unbent baseline: MAE, latent cosine distance, LPIPS, DINOv2 and CLIP distances, a degeneracy guard	each value names its method, and its model when a learned model computed it
interpretations.jsonl	interpretation: captions, "what changed", keywords, effect tags, concept tags, notes, verdicts	each line names its author: a human ({"type": "human", "name": …}) or an AI ({"type": "ai", "model": <exact model id>, "prompt_version": …})
output.webp	the output image

Other folders:

• baselines/ holds the unbent renders.
• findings/ holds claims about many records, such as a paper's results, with their authors and citation:
  • level: cell findings back the cells they cover.
  • level: general findings give study-wide context.
• vocab/effects.json is the controlled list of effect tags.
• schema/ holds the JSON Schemas.

Prompts and input images are published only when their owner agreed (consent). Otherwise a salted key stands in, so records can still be counted per prompt.

AI-written interpretations are always labelled with the model that wrote them. Treat them as one reading of the image, not ground truth. Humans can add their own readings next to them.

Cells and evidence

index/cells.jsonl groups single-bend records into cells: (model family, layer group, sub-module kind, module type, op, amount bucket, step window, route). Each cell carries:

• record, seed and prompt counts
• the checkpoints it was tested on
• measurement summaries
• effect tags, with who assigned them
• an evidence grade:
  • anecdotal: one seed and one prompt
  • multi-seed or multi-prompt
  • replicated: at least two of each
  • study-backed: a cited finding covers the cell

Sources

source	what
paper	Experiments from Unboxing Diffusion Models for the Arts: Interactive Model Bending and Practice-Based Explainability: RealisticVision v5.1 (SD1.5), all-layer multiply and noise sweeps, and the multi-seed, multi-prompt and timestep studies
author_experiment	Further sweeps by the same author, e.g. rotation across 14 prompts on SD1.4
sweep	Graded sweeps: every cell on several prompts and seeds
run	Bent candidates from agentic novelty-search runs, with their re-rendered baselines
session	Rounds from agent-assisted bending sessions that the artist chose to share
artist	Direct contributions

Contributing

Open a Pull Request on this dataset with the Hugging Face Hub (huggingface_hub.upload_folder(..., create_pr=True)) or the web UI. A contribution adds a record folder that follows schema/record.schema.json. Every interpretation must name its author, and AI-written ones must name their model. PRs are reviewed before merging.

Citation

@misc{abuzuraiq2026unboxing,
  title  = {Unboxing Diffusion Models for the Arts: Interactive Model Bending and Practice-Based Explainability},
  author = {Abuzuraiq, Ahmed M. and Pasquier, Philippe},
  year   = {2026},
  eprint = {2607.22428},
  archivePrefix = {arXiv}
}