Add dataset card

README.md

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+---
+license: apache-2.0
+task_categories:
+- text-to-image
+language:
+- en
+tags:
+- diffusion
+- distillation
+- synthetic
+- sana
+pretty_name: Diffusion Distillation Workshop (Sana-600M teacher samples)
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*.parquet
+  - split: eval
+    path: data/eval-*.parquet
+---
+
+# Diffusion Distillation Workshop dataset
+
+Teacher samples for a hands-on workshop on **diffusion step distillation** (Consistency Distillation, LADD, DMD, PDD). The teacher is [Sana-600M-512px](https://huggingface.co/Efficient-Large-Model/Sana_600M_512px_diffusers).
+
+Every row contains the **text prompt**, the teacher's **prompt embeddings**, the **generated latent**, and the **decoded 512×512 image**. With it, a Colab student can skip the 2.6B-parameter Gemma-2 text encoder and the teacher sampling and go straight to distillation.
+
+| split | rows | prompts from |
+|---|---|---|
+| `train` | 4096 | [open-image-preferences-v1](https://huggingface.co/datasets/data-is-better-together/open-image-preferences-v1) `simplified_prompt`, balanced over its 12 style categories (≈341 each) |
+| `eval` | 64 | [PartiPrompts](https://huggingface.co/datasets/nateraw/parti-prompts), balanced over its 12 categories |
+
+## Columns
+
+| column | content |
+|---|---|
+| `prompt` | the text actually fed to the teacher |
+| `source_prompt`, `category`, `subcategory`, `source` | provenance (for PartiPrompts, `subcategory` is the *Challenge*) |
+| `seed` | `torch.Generator("cuda").manual_seed(seed)` for the initial noise |
+| `image` | decoded 512×512 PNG |
+| `latent` | `float16` bytes, shape `(32, 16, 16)`. DC-AE latent in **model space**: the x₀ the transformer sees. Decode with `vae.decode(latent / 0.41407)` |
+| `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.
+
+## 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. Generated on a single H100.
+
+## Loading
+
+```python
+import numpy as np, torch, pyarrow.parquet as pq
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download("leffff/diffusion-distillation-workshop", "data/train-00000-of-00008.parquet", repo_type="dataset")
+t = pq.read_table(path, columns=["prompt", "latent", "prompt_embeds", "prompt_attention_mask"]).to_pydict()
+latents = torch.from_numpy(np.stack([np.frombuffer(b, np.float16).reshape(32, 16, 16) for b in t["latent"]]))
+embeds = torch.from_numpy(np.stack([np.frombuffer(b, np.float16).reshape(128, 2304) for b in t["prompt_embeds"]]))
+masks = torch.from_numpy(np.stack([np.frombuffer(b, np.uint8) for b in t["prompt_attention_mask"]])).long()
+```
+
+## License
+
+Apache-2.0. The prompts come from apache-2.0 datasets, and the images and latents were generated by the apache-2.0 Sana-600M model.