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Datasets:
Fanjue
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LITHE-eval

Tasks:
Text-to-Image
Modalities:
Image
Languages:
English
Size:
10K<n<100K
Tags:
flux
lora
peft
personalization
dreambooth
llm-as-a-judge
License:
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metadata 
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
task_categories:
  - text-to-image
language:
  - en
tags:
  - flux
  - lora
  - peft
  - personalization
  - dreambooth
  - llm-as-a-judge
  - evaluation
pretty_name: LITHE evaluation images and judge outputs
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path:
          - metadata.csv
          - images/**/*.jpg

LITHE evaluation images and per-image judge outputs

This dataset contains every generated image behind Table 1 (tab:db30-pareto) and the multi-seed appendix tables of the LITHE paper. Each image comes with the raw output of the three LLM judges that scored it (GPT-5.1, Claude Sonnet 4.6, Gemini 2.5 Flash): the integer score for each rubric dimension and the free-text reason (and, on SC-40, description) fields the judge returned. With these files the paper's judge-based numbers can be audited image by image, and the images can be rescored with a different judge.

Code, prompts, judge prompt templates and the per-prompt score tables: https://github.com/JianJiang1995/LITHE (artifacts/). Pretrained LITHE adapters: https://huggingface.co/Fanjue/LITHE.

Contents

benchmark images methods x runs prompts per concept
DreamBooth-30 (dreambooth30) 4,650 10 x 3, plus vanilla FLUX x 1 5
CC101-20 (cc101_20) 3,100 10 x 3, plus vanilla FLUX x 1 5
SCIdentity (scidentity) 3,100 10 x 3, plus vanilla FLUX x 1 5
SC-40 (sc40) 5,720 10 x 3, plus vanilla FLUX x 1 5 (DyLoRA and LoRA-FA: 3)
total 16,570

Methods (method): lora, lithe, dora, vera, adalora, qlora_nf4, dylora, lora_fa, loraxs, tlora, and vanilla_flux (FLUX.1-dev without an adapter). seed is the training seed of the run: 42, 1 and 7 for LoRA and LITHE; 42, 43 and 44 for the other adapters; 42 for vanilla FLUX. All adapters are rank 16 on FLUX.1-dev.

images/<benchmark>/<method>/seed_<seed>/<concept_id>__p<prompt_idx>.jpg
judges/<benchmark>.jsonl
metadata.csv
SHA256SUMS

The row set is identical to artifacts/results/*_per_prompt.csv in the code repository: one image per (benchmark, method, seed, concept_id, prompt_idx) row, and every row there has an image here.

Images

All images are FLUX.1-dev outputs at 1024 x 1024. They were re-encoded to JPEG (quality 92) from the original output files, writing the pixels only, so no EXIF, XMP, ICC or other metadata is carried over. The original files were JPEGs as well; original_sha256 in metadata.csv is the SHA256 of the original file as it was evaluated, and sha256 is the SHA256 of the file in this dataset. Because of the re-encoding, pixel values differ slightly from the evaluated files, so a rescore of these JPEGs is close to but not bit-identical with what the judges saw.

No training or reference images are included. Images are generated outputs only.

  • DreamBooth-30 and CC101-20 subjects come from the public DreamBooth and CustomConcept101 datasets; their reference images are available from the original authors.
  • SCIdentity subjects are synthetic, rare animal-like individuals.
  • SC-40 covers five axes (outfit, lighting, pose, painter style, anime style). The people shown in SC-40 images (in particular on the outfit and pose axes) are synthetic and were generated by FLUX.1-dev; they do not depict real persons.
  • The SCIdentity and SC-40 training/reference images are not part of this release; their construction is described in the paper appendix.

judges/<benchmark>.jsonl

One JSON object per image:

field meaning
benchmark, method, seed, concept_id, prompt_idx row key, as in the code repository
prompt_set prompt list the image was generated from (see artifacts/results/README.md)
group SC-40 axis, null elsewhere
prompt the generation prompt, including the trigger token
image relative path of the image in this dataset
rubric identity (SI, PF, VQ, SBD, CN), sc40 (TF, PF, VQ), or identity_legacy_si_pf_vq (see below)
judges one entry per judge: gpt51, claude, gemini

Each judge entry has:

field meaning
model GPT-5.1, Claude Sonnet 4.6 or Gemini 2.5 Flash
scores integer 1-5 score per rubric dimension
description SC-40 only: the judge's description of references vs. generated image
reason the judge's justification text, verbatim
raw_response the complete reply text as returned by the API (Claude and Gemini), including any Markdown code fence; null for GPT-5.1
followup_fields present when the first reply omitted these score fields; their values come from the single score-only follow-up request (Claude, 154 images)

GPT-5.1 was run in JSON output mode and its reply was stored as the parsed JSON fields (scores, description, reason); the reply string itself was not kept, so raw_response is null for GPT-5.1. One Claude reply (scidentity, loraxs, seed 43, glass_gill_axolotl_cat, prompt 4) contained scores but no reason; its reason is the empty string.

Rubric dimensions. Identity benchmarks: SI subject identity, PF prompt following, VQ visual quality, SBD subject-background disentanglement, CN class naturalness. SC-40: TF target fidelity, PF prompt following, VQ visual quality.

Vanilla FLUX is scored by GPT-5.1 only. On DreamBooth-30 and CC101-20 its images were generated from the vanilla_sks prompts and scored with an earlier GPT-5.1 rubric that returned SI, PF and VQ plus description and reason (rubric = identity_legacy_si_pf_vq); on SCIdentity and SC-40 it uses the same rubrics as the adapters.

Each judge entry is the call whose scores enter the paper tables, taken from the same result files as artifacts/results/*_per_prompt.csv, and raw_response is the reply of that same call. For 706 images (GPT-5.1 44, Claude 642, Gemini 20 judge entries) the evaluation logs also hold earlier or partial re-runs with the same scores and different text; those are not included.

metadata.csv

One row per image: the row key, prompt, file_name (image path), width, height, original_sha256, sha256, rubric, all per-judge rubric scores and averages (gpt51_*, claude_*, gemini_*, llm_avg) and the embedding metrics (dreamsim, clip_i, clip_t), with the same values as artifacts/results/*_per_prompt.csv. Columns that do not apply to a row are empty. artifacts/results/aggregate.py in the code repository reproduces Table 1 and the appendix tables from these values.

Judging protocol

GPT-5.1 was called through the OpenAI chat-completions API at temperature 0 with JSON output mode, image detail high, and max_completion_tokens 300 (identity) / 400 (SC-40). Claude Sonnet 4.6 and Gemini 2.5 Flash were called through an OpenAI-compatible endpoint at temperature 0 with the same prompt text and image order, without JSON mode; when a reply omitted a score field, the missing fields were requested once more with a short score-only follow-up on the same images.

The identity rubric shows the judge the subject's first training image (REFERENCE:) and the generated image (GENERATED:). The SC-40 rubric shows two reference images of the concept and the generated image. Per-image LLM-avg is the mean of the three judges' dimension means.

Rescoring with another judge

The judge prompt templates and a minimal judge script are in artifacts/judge/ of https://github.com/JianJiang1995/LITHE. The script talks to any OpenAI-compatible chat-completions endpoint, configured through OPENAI_API_KEY and OPENAI_BASE_URL:

git clone https://github.com/JianJiang1995/LITHE && cd LITHE/artifacts/judge
export OPENAI_API_KEY=...            # and OPENAI_BASE_URL for a non-OpenAI endpoint
# identity benchmarks: one reference image (the subject's first training image)
python judge.py --rubric identity --model <judge-model> \
    --image <dataset>/images/dreambooth30/lithe/seed_42/db_cat__p0.jpg \
    --ref <dreambooth>/cat/00.jpg --prompt "a photo of a <v_sub_001> cat" --class-name cat
# SC-40: two reference images of the concept
python judge.py --rubric sc40 --model <judge-model> --image <image> --ref <ref1> --ref <ref2> \
    --group anime_style --concept candy_glass_cyber_cel --prompt "<prompt from the jsonl>"

Take prompt from judges/<benchmark>.jsonl, the class word and trigger from artifacts/prompts/<benchmark>.csv, and pass --no-json-mode --detail '' for endpoints that reject JSON mode. Reference images are needed for the identity and target-fidelity dimensions and are not part of this dataset (see Images above). Prompt following and visual quality can be rescored from the prompt and image alone with a rubric of your own.

License

The images are outputs of FLUX.1-dev and are released under the FLUX.1 [dev] Non-Commercial License (https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). The judge texts are model outputs released for research use under the same non-commercial terms. DreamBooth and CustomConcept101 subjects remain subject to the terms of their original datasets.