| --- |
| 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. |
|
|
