ffjefckds/sd-class-beer-32

Model Card for Unit 1 of the Diffusion Models Class 🧨

This model is a diffusion model for unconditional image generation of beer images 🍺.

Model Description

This model is based on the DDPM (Denoising Diffusion Probabilistic Models) architecture and has been specifically trained to generate images of beer. It employs a UNet architecture with self-attention mechanisms in the middle layers.

Model Architecture

• Type: UNet2DModel with self-attention
• Input Channels: 3 (RGB)
• Output Channels: 3
• Image Resolution: 32x32 pixels
• Layers per Block: 2
• Channel Dimensions: (64, 128, 128, 256)
• Attention Layers: Present in middle down and up blocks

Usage

from diffusers import DDPMPipeline

# Load the model
pipeline = DDPMPipeline.from_pretrained('ffjefckds/sd-class-beer-32')

# Generate an image
image = pipeline().images[0]
image.save("generated_beer.png")

Training

Training Data

The model was trained on a synthetic dataset of beer images, which is available at ffjefckds/small-beer-images.

Training Procedure

• Optimizer: AdamW
• Learning Rate: 4e-4
• Epochs: 500
• Noise Scheduler: DDPM with 1000 timesteps
• Beta Schedule: "squaredcos_cap_v2"
• Batch Size: Dynamic based on dataloader

Training Infrastructure

• Framework: PyTorch
• Library: 🧨 Diffusers

Limitations and Bias

• The model is trained on a low resolution (32x32)
• Synthetic training data might lead to limited diversity
• The generated images are too small for most practical applications due to the low resolution
• The model may exhibit biases present in the training data

License

MIT License