A fine-tuned diffusion model for generating high-quality anime faces using DDPM. This model is based on Google's pre-trained ddpm-celebahq-256 model and fine-tuned on 7,000+ anime face images.

Model Details

Model Type: Denoising Diffusion Probabilistic Model (DDPM)
Base Model: google/ddpm-celebahq-256
Task: Unconditional Image Generation (256×256 anime faces)
Training Data: 7,000+ high-quality anime face images
Framework: 🧨 Diffusers
License: MIT

Training Parameters

Learning Rate: 2e-5
Epochs: 15
Batch Size: 4
Gradient Accumulation Steps: 2
Training Steps: ~26,250 (1750 steps/epoch × 15 epochs)
Optimizer: AdamW
Loss: MSE (Mean Squared Error)

Usage

Basic Usage

python

from diffusers import DDPMPipeline
import torch

# Load the model
pipeline = DDPMPipeline.from_pretrained("abcd2019/Anime-face-generation")
device = "cuda" if torch.cuda.is_available() else "cpu"
pipeline = pipeline.to(device)

# Generate a single image
image = pipeline(num_inference_steps=100).images[0]
image.save("anime_face.png")

Generate Multiple Images

python

from diffusers import DDPMPipeline

pipeline = DDPMPipeline.from_pretrained("abcd2019/Anime-face-generation")
pipeline = pipeline.to("cuda")

# Generate 5 anime faces
images = pipeline(batch_size=5, num_inference_steps=100).images

for i, image in enumerate(images):
    image.save(f"anime_face_{i}.png")

Adjust Inference Steps for Quality vs Speed

python

# Fast generation (fewer steps, less quality)
fast_image = pipeline(num_inference_steps=50).images[0]

# High quality (more steps, slower)
quality_image = pipeline(num_inference_steps=150).images[0]

# Recommended: 100 steps for good balance
balanced_image = pipeline(num_inference_steps=100).images[0]

Use Different Scheduler

python

from diffusers import DDPMPipeline, DDIMScheduler

pipeline = DDPMPipeline.from_pretrained("abcd2019/Anime-face-generation")

# Switch to DDIM for faster sampling
scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
scheduler.set_timesteps(num_inference_steps=50)
pipeline.scheduler = scheduler

fast_image = pipeline().images[0]  # Generates in ~50 steps instead of 1000

Model Performance

Training Loss: ~0.0077 (final epoch)
Image Resolution: 256×256 pixels
Inference Speed: ~30-60 seconds per image (depending on steps)
Recommended Inference Steps: 100 (for best quality)
Generated Face Styles: Wide diversity of anime faces with various:
- Hair colors and styles
- Eye colors and expressions
- Face shapes and features
- Skin tones

Limitations & Bias

Resolution: Limited to 256×256 pixels (inherent to model architecture)
Style: Specifically trained on anime faces, may not generate realistic/photorealistic faces
Diversity: Generated faces are limited to patterns in training data
Quality Variation: Face shape clarity depends on inference steps (higher = better)

Training Details

Data Preparation

Dataset: Anime Face Dataset (Kaggle)
Total Images: 7,000
Selection Method: Top quality images by file size
Preprocessing:
- Resized to 256×256
- Random horizontal flip (50% probability)
- Normalized to [-1, 1]

Fine-tuning Approach

Started from pre-trained ddpm-celebahq-256
Fine-tuned with low learning rate to preserve general face generation knowledge
Adapted to anime-specific features (large eyes, stylized features, etc.)

Training Dynamics

Epoch 0-3: Model adapts from photorealistic to anime style
Epoch 4-8: Loss continues to decrease, anime features solidify
Epoch 9+: Marginal improvements, risk of overfitting

Ethical Considerations

This model generates synthetic anime faces and should not be used to:

Create misleading/deceptive content
Generate non-consensual images of real people
Violate any local laws or regulations

Recommended Citation

If you use this model in your research or project, please credit:

The original DDPM paper
Google's pre-trained ddpm-celebahq-256 model
This fine-tuned adaptation

Future Improvements

Potential enhancements for future versions:

Higher resolution (512×512 or more)
Conditional generation (text-to-image for anime faces)
Better diversity through larger training datasets
Improved training with advanced schedulers or techniques

Resources

Created: 2025-12-28

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