---
license: apache-2.0
task_categories:
- text-to-image
tags:
- synthetic
- qwen-image
- latent-to-pixel
- l2p
size_categories:
- 10K<n<100K
---

# 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](https://arxiv.org/abs/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}