| --- |
| license: mit |
| task_categories: |
| - text-to-image |
| tags: |
| - t2i-compbench |
| - compositional-generation |
| - grpo |
| - ablation |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # CompGen-GRPO Eval Samples |
|
|
| Pre-generated images from a series of **CompGen-GRPO** checkpoints (fine-tuned Janus-Pro-1B with GRPO), evaluated on **T2I-CompBench** (color / shape / texture / spatial / non_spatial / complex). |
| |
| Every image is a 384×384 PNG rendered with the same sampling config so results are directly comparable: |
| |
| - **Prompt set**: T2I-CompBench eval split (300 prompts × 6 categories) |
| - **Samples per prompt**: 10 |
| - **Resolution**: 384×384 |
| - **CFG scale**: 5.0 |
| - **Seed**: 42 |
| - **Reasoning prompt**: Bi-CoT template (see companion code repo) |
| |
| Total: **~162,000 images** across 9 models × 6 categories × 3000 imgs. |
| |
| ## Repo layout |
| |
| ``` |
| CompGen-GRPO-eval-samples/ |
| ├── baseline/ # Janus-Pro-1B (no RL, deepseek-ai release) |
| │ ├── color/samples.tar |
| │ ├── shape/samples.tar |
| │ └── ... |
| ├── full_400/ |
| ├── full_800/ |
| ├── wo_gdino_400/ # ablation: no GroundingDINO |
| ├── wo_gdino_600/ |
| ├── wo_gdino_800/ |
| ├── wo_attr_400/ # ablation: no VLM Attr |
| ├── wo_attr_600/ |
| ├── wo_attr_800/ |
| └── <MODEL>/<CATEGORY>/samples.tar |
| ``` |
| |
| Each `samples.tar` contains a `samples/<PROMPT_ID>_<SAMPLE_ID>.png` directory (uncompressed tar — PNG is already compressed). |
|
|
| Categories: |
|
|
| | Category | Prompts | Total imgs | Eval metric | |
| |---|---|---|---| |
| | `color` | 300 | 3000 | BLIP-VQA | |
| | `shape` | 300 | 3000 | BLIP-VQA | |
| | `texture` | 300 | 3000 | BLIP-VQA | |
| | `spatial` | 300 | 3000 | UniDet-2D | |
| | `non_spatial` | 300 | 3000 | CLIPScore | |
| | `complex` | 300 | 3000 | 3-in-1 (BLIP+UniDet+CLIP) | |
|
|
| Naming convention: `<PROMPT_ID>_<SAMPLE_ID>.png` — `PROMPT_ID ∈ [0, 300)`, `SAMPLE_ID ∈ [0, 10)`. |
|
|
| ## Note on `full_400` |
| |
| For historical reasons `full_400/{color,shape,texture}/samples/` contains **6000** images (20 samples / prompt) instead of the standard 3000. This does not change the mean score meaningfully (n=3000 is already low-variance), but be aware if you rely on per-sample paired comparisons. |
|
|
| ## How to download |
|
|
| ```bash |
| pip install -U huggingface_hub |
| |
| # Full dataset (all zips) |
| hf download <HF_USER>/CompGen-GRPO-eval-samples \ |
| --repo-type dataset \ |
| --local-dir ./eval_samples |
| |
| # Just one model |
| hf download <HF_USER>/CompGen-GRPO-eval-samples \ |
| --repo-type dataset \ |
| --include "full_800/*" \ |
| --local-dir ./eval_samples |
| |
| # Unzip all |
| find ./eval_samples -name "samples.tar" | while read t; do |
| (cd "$(dirname "$t")" && tar -xf "$(basename "$t")" && rm "$(basename "$t")") |
| done |
| ``` |
|
|
| ## How to reproduce eval |
|
|
| ```bash |
| git clone https://github.com/xiezifan/CompGen-GRPO |
| cd CompGen-GRPO |
| # After unzip, samples land at <MODEL>/<CATEGORY>/samples/*.png — just move |
| # that tree under eval_results/ and run: |
| bash src/t2i-r1/src/run_eval.sh --model full_800,wo_gdino_600 --task all --gpu 0 |
| ``` |
|
|
| See [`run_eval.sh`](https://github.com/xiezifan/CompGen-GRPO/blob/main/src/t2i-r1/src/run_eval.sh) for the full T2I-CompBench eval pipeline (BLIP-VQA + UniDet + CLIPScore + 3-in-1). |
|
|
| ## License |
|
|
| Images are released under **CC-BY-4.0**. See paper for detailed attribution. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{compgen-grpo-2026, |
| title = {Compositional Text-to-Image Generation via Multi-Reward GRPO}, |
| author = {Xie, Zifan and others}, |
| year = {2026}, |
| note = {In preparation} |
| } |
| ``` |
|
|
