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sha256:56f42c63d1164252d5bcd4cc31235d60926265f9f3aa2448ae702d2813ae03e0 +oid sha256:454df06ac547ac9e61e574fd23b6cb22e02e7cfd18547491f6c54ea73b8f56a6 size 66621600 diff --git a/README.md b/README.md index 38c357a89fc185166d3ecac7c31e808659670370..d8e48521cb78f256e13bf783c6ee611344bc10ec 100644 --- a/README.md +++ b/README.md @@ -20,9 +20,17 @@ SDXL / Cityscapes. Every adapter here is a **head-sliced** LoRA: instead of adap attention projection matrices, it adapts only the ~2% of *individual attention heads* that a concept-attribution (CA) measurement picked out at a chosen diffusion timestep. -These are the exact adapters that produced the numbers in Tables 3 and 5 of the report, and the -images in the companion dataset repo -[`chs35/improving-ca-lora-artifacts`](https://huggingface.co/datasets/chs35/improving-ca-lora-artifacts). +These are the exact adapters that produced the numbers in Tables 3 and 5 of the current report +(the **few-shot experiment**: trained on 10 randomly drawn Cityscapes images, 2026-11). Their +evidence files are in the code repository's `results/sdxl_cityscapes_fewshot10/`. + +> **Earlier experiment.** Until 2026-11-04 this repo held the adapters of the earlier experiment, +> trained on the full 2,975-image `train` split. They are still available at revision +> [`db005e2c124b6666c27b8a788cd5a1f6c1f3f080`](https://huggingface.co/chs35/improving-ca-lora-checkpoints/tree/db005e2c124b6666c27b8a788cd5a1f6c1f3f080). +> The generated images in the companion dataset repo +> [`chs35/improving-ca-lora-artifacts`](https://huggingface.co/datasets/chs35/improving-ca-lora-artifacts) +> come from that **earlier** experiment, not from the adapters here; the head CA measurements +> there are shared by both experiments. ## ⚠️ Custom format — not loadable with `peft` or `diffusers` @@ -78,9 +86,9 @@ measurement was taken on; the letter/timestep prefix is the **timestep selection | `E_all` | **control**: all attention projections, full LoRA, no CA selection | — | "full attention (no CA)" | The same ten directories back the drift rows of **Table 5** (per-snapshot Jaccard / Spearman of the -selected set against the trained set). The report's third control, `control_base` (0%, base SDXL -with no adapter), needs no checkpoint and so has none here; its generated images are in the dataset -repo. +CA top-2% against its step-0 baseline, recorded on both concept axes, with each run read on its own +selection axis). The report's third control, `control_base` (0%, base SDXL with no adapter), needs +no checkpoint and so has none here. ## Checkpoints per run @@ -103,23 +111,28 @@ An **undocumented earlier pilot run at *module* granularity** (whole projection not individual heads), kept here as a granularity-ablation data point. It is *not* the source of any number in the report — the reported `A_t81_*` runs are the head-granularity ones above. -It differs in format as well as in granularity: it took the `peft` path (`granularity=module` is the +It is also from the earlier full-data period, and it differs in format as well as in granularity: it took the `peft` path (`granularity=module` is the only mode `peft` supports here), so its keys are peft-style `...lora_A.default.weight`, it has no `lora_head_indices` tensors, and its metadata predates the `granularity`/`axis` fields. 20 tensors vs. the 168–1680 of the head-sliced runs; 34 MB for all five checkpoints. ## Training setup -SDXL base 1.0 in **fp32**; Cityscapes `train` split (2,975 images) at 1024²; **10,000 steps**, -batch size 1, constant lr **1e-4** (AdamW, β = 0.9/0.999, weight decay 0.01, grad clip 1.0), LoRA -**rank 64 / alpha 64**, diffusion loss only, random crop + random horizontal flip, seed 0. Selection -budget: 2% of attention units by unit count. Full config is released as `config.yaml` in the code -repository's `results/sdxl_cityscapes/`. +SDXL base 1.0 with fp32 weights, trained under **bf16 autocast + TF32** (LoRA weights and Adam +state in fp32); **10 images** drawn at random (seed 0) from the Cityscapes `train` split, the same +10 for every run (listed in `results/sdxl_cityscapes_fewshot10/training_subset.json`), at 1024²; +**10,000 steps** (~1,000 epochs), batch size 1, constant lr **1e-4** (AdamW, β = 0.9/0.999, weight +decay 0.01, grad clip 1.0), LoRA **rank 64 / alpha 64**, diffusion loss only, random crop + random +horizontal flip, seed 0. The head selections are the earlier experiment's, measured on 256 images of +the full split. Selection budget: 2% of attention units by unit count. Full config is released as +`config.yaml` in the code repository's `results/sdxl_cityscapes_fewshot10/`. ## Intended use and limitations Research artifacts for reproducing and auditing the report — not production models. They are -fine-tuned on **Cityscapes** (German urban street scenes, daytime, vehicle-mounted camera), so they +fine-tuned on 10 **Cityscapes** frames (German urban street scenes, daytime, vehicle-mounted +camera) for ~1,000 epochs, which makes memorization of those frames a real risk that has not been +audited; they absorb that domain narrowly and measurably lose prompt controllability relative to base SDXL; that loss is the subject of the study, not a defect to work around. Anyone using these adapters must respect the [Cityscapes dataset terms](https://www.cityscapes-dataset.com/license/) as well as the