This model is a fine-tuned Denoising Diffusion Probabilistic Model (DDPM) for generating images of flowers using the Oxford Flowers dataset. It builds upon the pretrained google/ddpm-cifar10-32 model and is optimized for training on a GPU.
Model Details
Architecture: UNet2DModel
Noise Scheduler: DDPMScheduler
Training Data: Oxford Flowers dataset (nelorth/oxford-flowers)
Optimizer: AdamW
Learning Rate: 1e-4, adjusted using a cosine scheduler
Training Steps: 100 epochs
Batch Size: 64
Image Size: 32x32 pixels
Training Configuration
The training process involves the following steps:
Data Preprocessing:
Images resized to 32x32.
Random horizontal flipping applied for augmentation.
Normalized to the range [-1, 1].
Noise Addition:
Random noise added to images using a linear beta schedule.
Model Training:
The UNet model predicts the noise added to images.
The Mean Squared Error (MSE) loss is used.
The learning rate is adjusted with a cosine scheduler.
Checkpointing:
Model checkpoints are saved every 1000 steps.
Usage
Once trained, the model can be used for generating images of flowers. The trained model is saved as a DDPMPipeline and can be loaded for inference.
