Datasets:

leffff
/

diffusion-alignment-workshop

license: apache-2.0
task_categories:
  - text-to-image
language:
  - en
tags:
  - diffusion
  - alignment
  - rlhf
  - text-rendering
  - synthetic
  - sana
pretty_name: Diffusion Alignment Workshop (Sana-600M samples with rewards)
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
      - split: eval
        path: data/eval-*.parquet

Diffusion Alignment Workshop dataset

Offline data for a hands-on workshop on aligning a diffusion model with rewards (SFT, RWR, DPO, online DPO, GRPO, DiffusionNFT, on-policy distillation). The model is Sana-600M-512px and the task is text rendering: every prompt asks for a short string to be written in the image.

For every prompt there are 5 samples of the model, each scored with four rewards. With it, a Colab student can run the offline methods without sampling or scoring anything, and the online methods without the 2.6B Gemma-2 text encoder. It is the alignment companion of leffff/diffusion-distillation-workshop (same model, same embedding format).

split	prompts	samples
`train`	2048	5 per prompt
`eval`	64	5 per prompt

Prompts come from leffff/Diffusion-Reward-Modeling-for-Text-Rendering-Dataset (train / test splits), filtered to:

exactly one quoted target, which must be written in the image: a rendering keyword next to the quote (written, spelled, the word, sign, label, a mug that says, ...), and not speech or an attribution (he says "...", "...", says Stan Lee, written by);
short ASCII targets that a 600M model at 512 px can plausibly render: at most 3 words and 16 characters;
at most 2 prompts per target string (there are 219 "Happy Birthday" prompts in the source).

Columns (one row per prompt)

column	content
`prompt`, `text`	the prompt and the string that must appear in the image
`source_id`	the row id in the source dataset
`seeds`	5 seeds, `torch.Generator("cuda").manual_seed(seed)` for the initial noise of each sample
`images`	5 decoded 512×512 JPEGs (quality 95)
`latents`	`float16` bytes, shape `(5, 32, 16, 16)`. DC-AE latents in model space (the x₀ the transformer sees). Decode with `vae.decode(latent / 0.41407)`
`ocr`	5 × OCR-Levenshtein reward in [0, 1]: `1 - min(d, len(t)) / len(t)`, where `d` is the fewest character edits that make the target `t` appear somewhere in the transcription (case, spaces and punctuation ignored)
`ocr_read`	5 × what the OCR model (Qwen3-VL-2B-Instruct, greedy) read
`pickscore`	5 × PickScore (`logit_scale · cos`, ≈ 15–25)
`aesthetic`	5 × LAION aesthetic predictor v2 on CLIP ViT-L/14 (weights, ≈ 1–10)
`clip`	5 × CLIP score, `100 · cos(image, prompt)` with `openai/clip-vit-large-patch14`
`prompt_embeds`	`float16` bytes, shape `(128, 2304)`. Gemma-2 hidden states from Sana's `encode_prompt` with the complex human instruction, `max_sequence_length=128`
`prompt_attention_mask`	`uint8` bytes, shape `(128,)`

null_prompt_embeds.safetensors holds the unconditional (empty negative prompt) embedding and mask for classifier-free guidance. meta.json holds all generation parameters and the reward definitions.

Generation

SanaPipeline default sampler (flow DPM-Solver), 20 steps, CFG 4.5, 512×512, transformer in fp16, Gemma-2 in bf16, DC-AE in fp32, on H100s. The rewards are computed on the decoded fp32 images before JPEG encoding. Code: generate_dataset.py and rewards.py of the workshop.

Loading

import numpy as np, torch, pyarrow.parquet as pq
from huggingface_hub import hf_hub_download

path = hf_hub_download("leffff/diffusion-alignment-workshop", "data/train-00000-of-00008.parquet", repo_type="dataset")
t = pq.read_table(path, columns=["prompt", "text", "latents", "ocr", "prompt_embeds", "prompt_attention_mask"]).to_pydict()
latents = torch.from_numpy(np.stack([np.frombuffer(b, np.float16).reshape(5, 32, 16, 16) for b in t["latents"]]))
embeds = torch.from_numpy(np.stack([np.frombuffer(b, np.float16).reshape(128, 2304) for b in t["prompt_embeds"]]))
ocr = torch.tensor(t["ocr"])        # (n, 5)

License

Apache-2.0 for the images, latents and rewards generated by the apache-2.0 Sana-600M model. The prompts come from the MIT-licensed leffff/Diffusion-Reward-Modeling-for-Text-Rendering-Dataset.