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README.md

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+---
+license: mit
+tags:
+- pytorch
+- diffusers
+- unconditional-image-generation
+- diffusion-models
+- anime
+- anime-faces
+- ddpm
+---
+
+# Anime Face Diffusion Model 🎨
+
+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](https://huggingface.co/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
+
+- 📚 [Diffusion Models Class](https://github.com/huggingface/diffusion-models-class)
+- 📖 [Diffusers Documentation](https://huggingface.co/docs/diffusers)
+- 📄 [DDPM Paper](https://arxiv.org/abs/2006.11239)
+- 🤗 [Hugging Face Hub](https://huggingface.co)
+
+---
+
+**Created**: 2025-12-28
+
+**Model Card Contact**: [Your Name/Username]