L2P-Clean — curated (prompt, image) pairs for Latent-to-Pixel transfer on Qwen-Image-2512

Aesthetic-curated synthetic dataset for the L2P distillation recipe (arXiv:2605.12013): the source diffusion model's own outputs, cleaned "to the bones" so the pixel decoder fits an already-smooth, high-quality teacher manifold.

Provenance

Merged and de-conflicted from three independent synthetic runs (each its own prompt corpus — prompt ids are namespaced with a raw_ / part0_ / dataset_ prefix because the numeric ids collide across runs but refer to different prompts):

source	prompts	seeds/prompt	raw images
shauray/l2p-raw	6,172	3	18,516
shauray/l2p-part0	4,421	1	4,421
shauray/l2p-dataset	2,100	2	4,200

(one truncated source shard skipped). All prompt texts are unique across the three runs.

Curation pipeline (clean_l2p.py)

Following Krea's "quality over quantity" philosophy — a human-preference model (PickScore), deliberately not a LAION-aesthetic predictor (which biases toward soft/symmetric/blurry "AI look"):

1. PickScore every image against its own prompt (CLIP-ViT-H backbone).
2. Per-prompt seed selection: keep the top-2 seeds per prompt above a global PickScore floor (p12 — permissive, to preserve the teacher manifold for distillation, cutting only clear failures). Matches the paper's 2-seeds-per-prompt recipe.
3. pHash near-duplicate removal across survivors (Hamming ≤ 6), catching identical look-alikes across prompts/seeds.
4. Diversity audit: k-means over CLIP embeddings → inverse-frequency sampling_weights.jsonl so over-represented visual modes are down-weighted in the dataloader (fixes repetition without deleting anything).

Format

WebDataset shards (shard-*.tar), each sample is <key>.png + <key>.txt (the prompt). Images are ~1472×1104. Plus:

• manifest.jsonl — {id, prompt, files} per sample
• sampling_weights.jsonl — {id, cluster, weight} inverse-frequency sampling weights

Final size

• 18,505 curated (prompt, image) pairs across 75 shards
• per-source survivors: {'raw': 11449, 'part0': 3648, 'dataset': 3408}