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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.

Sourcehuggingface.co/ruwwww/waifu_diffusion/tree/42f4abebacc8e8244935d48bf9856770919cea40

TypeOtherVersionRevision 42f4abeCClasse

Source du modèle

Type: Other
Taille du fichier: 496 MB
Version: vRevision 42f4abe
Mis à jour: 27 oct. 2026
Téléchargements: 0
Source: Hugging Face

Description de la source

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

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

Citation

@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

Sources

1 source

Hugging FacePrincipalVersion 42f4abebacc8e8244935d48bf9856770919cea40

huggingface.co/ruwwww/waifu_diffusion/tree/42f4abebacc8e8244935d48bf9856770919cea40

Vérifié 27 oct.

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waifu_diffusion_128_bs32.safetensors

safetensors · 496 MB · SHA-256 8e4ae0d8e368…641e · Hugging Face

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waifu_diffusion_1280_bs256.safetensors

safetensors · 496 MB · SHA-256 89061ef0fad6…5a3b · Hugging Face

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Extraits de sources

3 extraits

Readme8 aoûthuggingface.co/ruwwww/waifu_diffusion

--- 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 = {Abdurrah...

Note27 oct.huggingface.co/ruwwww/waifu_diffusion

Source context: 0 downloads · 2 likes · Pipeline unconditional-image-generation · Repo ruwwww/waifu_diffusion

Note8 aoûthuggingface.co/ruwwww/waifu_diffusion

Source context: 0 downloads · 1 likes · Pipeline unconditional-image-generation · Repo ruwwww/waifu_diffusion