Datasets:

chs35
/

improving-ca-lora-artifacts

license: other
license_name: research-use-sdxl-openrail-derived
license_link: >-
  https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md
pretty_name: Improving CA-LoRA — CA measurements and generated images
tags:
  - lora
  - sdxl
  - cityscapes
  - concept-aware-lora
  - synthetic-images
  - reproducibility
  - research
task_categories:
  - text-to-image
size_categories:
  - 1K<n<10K

Improving CA-LoRA — CA measurements and generated images

The two large evidence sets behind the reproduction-and-extension study of CA-LoRA (Concept-Aware LoRA) on SDXL / Cityscapes:

measurements/ — the raw head-granularity concept-attribution tensors for a 13-timestep sweep (backs Table 2 and Figure 1 of the report).
generated/ — every image that was scored for the main results (backs Table 3 of the report).

The adapters that produced the images live in chs35/improving-ca-lora-checkpoints.

`measurements/tsweep_sdxl_head/` — 39 files, 191 MB

Head-granularity CA measurements over the timestep grid [1, 41, 81, 121, 201, 301, 401, 481, 601, 701, 801, 901, 981] — 13 timesteps × 3 files:

file	what it is
`t<NNNN>.safetensors`	the full measurement at that timestep (256 samples)
`t<NNNN>_half0.safetensors`	first split half
`t<NNNN>_half1.safetensors`	second split half

The half0 / half1 pair is what the split-half agreement column of Table 2 is computed from (Spearman 0.956 for the primary ews metric); the full files carry the CA magnitudes and direction statistics that rank the timesteps and drive Figure 1's per-head heatmaps.

Each file carries a unit_granularity metadata field (head), which the loading code verifies — head- and module-granularity measurement products cannot be mixed. Measurements were taken at batch_size = 1 with crop="center", and seeds are derived so that pairing holds across concept axes and across timesteps.

`generated/` — 11 runs × 900 PNG + manifest, ~12 GB

The exact 1024×1024 images scored for CMMD and CLIP-Score in Table 3. One directory per evaluated arm:

generated/<RUN>/
  in_domain/   00000.png … 00499.png   (500 images)
  foggy/       00500.png … 00599.png   (100)
  night-time/  00600.png … 00699.png   (100)
  rainy/       00700.png … 00799.png   (100)
  snowy/       00800.png … 00899.png   (100)
  manifest.json

Filenames are a global index 00000–00899 across the whole run, not per-condition counters — the condition directory a file sits in is implied by its index range, as laid out above. 900 images per run, 9,900 in total.

manifest.json records, for every single image: its index, the full prompt actually used, the sampled class_names, the condition, the seed and batch_seed_index, and the relative path. It also records the generation protocol (guidance scale 5.0, 25 steps, EulerDiscreteScheduler, 1024², fp32, seed 0) and which adapter checkpoint was loaded. Per-image scores can therefore be recomputed exactly from these files.

Runs, and the report rows they back

directory	selection criterion	axis	Table 3 row
`A_t81_style`	t = 81 (the original paper's timestep)	style	"t = 81 (paper) / style"
`A_t81_viewpoint`	t = 81 (the original paper's timestep)	viewpoint	"t = 81 (paper) / viewpoint"
`B_top3_style`	multi-t, top-3 timesteps [41, 1, 81]	style	"multi-t [41, 1, 81] / style"
`B_top3_viewpoint`	multi-t, top-3 timesteps [41, 1, 81]	viewpoint	"multi-t [41, 1, 81] / viewpoint"
`C_t41_style`	t = 41 (this study's top-ranked timestep)	style	"t = 41 / style"
`C_t41_viewpoint`	t = 41 (this study's top-ranked timestep)	viewpoint	"t = 41 / viewpoint"
`D_t1_style`	t = 1	style	"t = 1 / style"
`D_t1_viewpoint`	t = 1	viewpoint	"t = 1 / viewpoint"
`E_random`	control: random 2% of heads, no CA	—	"random 2% (no CA)"
`E_all`	control: all attention projections, no CA selection	—	"full attention (no CA)"
`control_base`	control: base SDXL, no adapter at all	—	"0% control (base SDXL)"

The four weather conditions are what the per-condition CLIP-Score columns and the weather-mean column of Table 3 (and the gap analysis of Table 4) are computed over; in_domain is the CMMD set.

⚠️ Not included: the Cityscapes CMMD reference set

CMMD in Table 3 is measured against 500 centre crops of the Cityscapes val split. Those reference images are not distributed here, or anywhere — the Cityscapes license forbids redistribution of the imagery. No real Cityscapes image appears in this repository.

The reference set is rebuildable from your own Cityscapes download: take the first 500 images of the val split in index order, at 1024×1024, centre-cropped and un-mirrored — the same framing the training data was read with, so that CMMD measures a gap in content rather than a difference in cropping. The exporter is scripts/evaluate.py (export_reference) in the code repository, and the relevant config knobs (eval.reference_split, eval.reference_num_images, eval.seed) are in the released config.yaml. Obtain Cityscapes from https://www.cityscapes-dataset.com/. Every other number in the report is reproducible from the artifacts here without it.

License and intended use

Research use. The PNGs are synthetic images generated by SDXL-derived adapters fine-tuned on Cityscapes — they are model output, not photographs, and they inherit the OpenRAIL++ use restrictions of the SDXL base model, which apply to this data as they do to the model that produced it. Because the adapters were fine-tuned on Cityscapes, users should also respect the Cityscapes terms. Again: no real Cityscapes images are included in this repository.

These images exist to make the report's metrics auditable and re-scorable. They are not a general-purpose street-scene dataset, and they carry the domain and quality limitations discussed in the report's Limitations section.

Verifying integrity

The code repository ships results/sha256_measurements.txt and results/sha256_generated_images.csv (path,bytes,sha256), keyed by the same repo-relative paths used here.