Create README.md

README.md

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+---
+license: mit
+pipeline_tag: unconditional-image-generation
+tags:
+- diffusion
+- rectified-flow
+- patch-diffusion
+- anime
+---
+# Waifu Diffusion
+
+A 130M-parameter diffusion model trained on 10,000 anime faces (90% monochrome) using **rectified flow**, **patch diffusion**, and **CIELAB color space decoupling**.
+
+## Model Details
+
+- **Architecture**: Diffusion Transformer (DiT-B) with Vision RoPE
+- **Parameters**: 130M
+- **Training Data**: 10k anime faces (80×80), 90% corrupted to grayscale
+- **Training Steps**: 1280 epochs × batch 256
+- **Sampling**: 50-step Euler integration
+
+### Versions
+
+| Model | Details |
+|-------|---------|
+| `waifu_diffusion_1280_bs256.safetensors` | Full training (1280 epochs, bs=256) |
+| `waifu_diffusion_128_bs32.safetensors` | Shallow trained version (128 epochs, bs=32) |
+
+## Quick Start
+
+```python
+import torch
+from safetensors.torch import load_file
+from skimage import color
+import numpy as np
+
+# Load model
+model = JiT(
+    input_size=80,
+    patch_size=4,
+    in_channels=3,
+    hidden_size=768,
+    depth=12,
+    num_heads=12,
+    num_classes=1
+)
+state_dict = load_file("waifu_diffusion_1280_bs256.safetensors")
+model.load_state_dict(state_dict)
+model.eval()
+
+# Generate
+device = "cuda"
+model.to(device)
+
+with torch.no_grad():
+    xt = torch.randn((1, 3, 80, 80), device=device)
+    y = torch.zeros(1, dtype=torch.long, device=device)
+    
+    for step in range(50):
+        t = torch.tensor(step / 50, device=device)
+        pred_x1 = model(xt, t, y, top_idx=0, left_idx=0)
+        v = (pred_x1 - xt) / max(1.0 - step / 50, 1e-2)
+        xt = xt + v / 50
+
+# Convert CIELAB → RGB
+lab = torch.clamp(pred_x1[0], -1, 1).cpu().numpy()
+L = (lab[0] + 1) * 50
+a = lab[1] * 128
+b = lab[2] * 128
+rgb = color.lab2rgb(np.stack([L, a, b], axis=-1))
+```
+
+## Key Techniques
+
+- **Rectified Flow**: Straight-line paths from noise to data (50 steps vs. 1000s for DDPM)
+- **CIELAB Decoupling**: Separate luminance from color; mask gradients on monochrome → learn structure from all 10k, color from 1k
+- **Patch Diffusion**: Random 40×80 px crops act as data augmentation; effectively 10k → ~50k samples
+- **Vision RoPE**: 2D rotary embeddings for spatial consistency across patches
+
+## Links
+
+- **GitHub**: https://github.com/ruwwww/waifu_diffusion
+- **Blog Post**: [Training a Waifu Diffusion Model](https://ruwwww.github.io/al-folio/blog/2026/waifu-diffusion/)
+
+## Citation
+
+```bibtex
+@misc{waifu_diffusion_2026,
+  author = {Abdurrahman Izzuddin Al Faruq},
+  title = {Training a Waifu Diffusion Model with Patch Diffusion and Rectified Flow},
+  year = {2026},
+  url = {https://github.com/ruwwww/waifu_diffusion}
+}
+```
+
+## License
+
+MIT