---
license: cc-by-nc-4.0
pipeline_tag: text-to-image
library_name: pytorch
tags:
- diffusion-transformer
- rectified-flow
- text-to-image
- from-scratch
base_model:
- black-forest-labs/FLUX.2-dev
- google/flan-t5-base
datasets:
- LucasFang/FLUX-Reason-6M
- bghira/photo-concept-bucket
- detection-datasets/coco
---

# tinydit-256

A 210M-parameter text-to-image diffusion transformer trained from scratch on one GPU (3.5 days, 400k steps,
4.2M images at 256² area with aspect-ratio buckets). Frozen FLUX.2 autoencoder (32-channel latent) and frozen
flan-t5-base text encoder; the transformer, recipe, data pipeline and evaluation are original.

- 📝 Write-up: https://huggingface.co/blog/ivanmikhnenkov/tinydit-text-to-image-from-scratch-one-gpu
- 🎨 Try it in the browser: https://huggingface.co/spaces/ivanmikhnenkov/tinydit
- 💻 Code, figures and every design decision with sources: https://github.com/ivanmikhnenkov/tinydit (tag `v1-pretrain`)
- 🌐 Author: https://ivanmikhnenkov.com

![samples](grid_objects.jpg)

![same prompt over training](progress.jpg)

## Results (held-out prompts, 20 Euler steps, CFG 4, shift 2.8)

| FID | FD-DINOv2 | object accuracy (COCO classes, Faster R-CNN) | CLIP | PickScore | HPSv2.1 |
|---|---|---|---|---|---|
| 27.0 | 218 | 90% | 0.322 | 20.9 | 0.254 |

Strengths: single objects, animals, scenes, colours and simple relations, five aspect ratios. Known limits: readable
text, close faces, crowds, counts above three.

## Files

- `model.safetensors`: EMA weights (bf16), step 400k
- `config.json`: architecture and sampler defaults
- `stats.json`: per-channel latent mean/std; latents are whitened with these after encoding and un-whitened before decoding

## Usage

Clone the GitHub repo, fetch the frozen FLUX.2 VAE (gated, needs your own HF token; it is not redistributed here)
and flan-t5-base, then:

```bash
python -m tinydit.sample --ckpt model.safetensors --config run1 --sets NOVEL=prompts/novel.txt --out out.png --shape 320x208
```

or run the local playground (`python -m tinydit.playground`) for interactive prompts, sampling trajectories and
attention maps. Architecture: adaLN-single DiT, dim 896 × 16 blocks, 2D RoPE, QK-norm, SwiGLU, 16 register tokens
and 2 learned null key/value slots per cross-attention; rectified flow with a 2.8 timestep shift, cosine and
dispersive auxiliary losses.

## License

Weights are released under CC BY-NC 4.0. They depend at inference on the FLUX.2 autoencoder (FLUX.2-dev
non-commercial licence, gated) and were trained in part on images generated by FLUX.1-dev (FLUX-Reason-6M);
use is non-commercial accordingly.