Diffusion Transformer

A flow matching-based diffusion transformer for anime image generation.
This project is for research purposes only.

Links

• GitHub: https://github.com/FREEANIMA/diffusion_model_sampling
• Hugging Face: https://huggingface.co/honghong3/diffusion-transformer

License

This project is licensed under CC BY-NC 4.0.
For research and non-commercial use only.

Training Environment

• GPU: NVIDIA A100 40GB (Google Colab)
• Dataset: ~4.8M anime images
• Processed: ~1.8M images (epoch 0, ongoing)
• Throughput: ~1.3 it/s
• Samples below are intermediate checkpoints — quality will improve as training continues.

Training & Samples

12k images	600k images	1.2M images	1.8M images

# sampler conditional
prompt    = "1girl, red hair, school uniform, happy, red eyes, open mouth, detailed face"
steps     = 100
cfg_scale = 2.0
seed      = 1234

Model Architecture

• Backbone: Diffusion Transformer (DiT) with adaLN modulation
• Parameters: ~550M
• Framework: Flow Matching (velocity prediction)

Components

Component	Model
VAE	stabilityai/sd-vae-ft-mse
Text Encoder	openai/clip-vit-large-patch14
Tokenizer	openai/clip-vit-large-patch14

Sampler Details

• Resolution: 512 × 512 (single bucket)
• Noise Schedule: Log-SNR uniform sampling with resolution-dependent shift
• CFG: Classifier-free guidance
• Prompts are tag-based (comma-separated danbooru-style tags)

Requirements

pip install torch transformers diffusers accelerate torchvision tqdm

Usage

python main.py

C:.
│  main.py
│  output.png
│  README.md
│  requirements.txt
│
├─app
│  │  clip.py
│  │  config.json
│  │  config.py
│  │  model.py
│  │  sampling.py
│  │  sd_vae.py
│  └─ __init__.py
│
├─assets
│      100k.png
│      150k.png
│      1k.png
│      50k.png
│
└─weights
       image.pth