Fine-tune Stable Diffusion with DDPO via TRL using DDPOTrainer

AI Impact Summary

The content describes enabling fine-tuning of diffusion models using DDPO (Denoising Diffusion Policy Optimization) via the TRL library, applying a reinforcement learning-based alignment workflow across the full denoising trajectory rather than just the final sample. It highlights a practical path to align Stable Diffusion outputs with human aesthetics using a reward model (AVA/CLIP-based) and the DDPOTrainer, with results logged to wandb and eventual model upload to HuggingFace Hub. It also surfaces operational constraints (requires an A100 GPU, specific Python packages, and token-based HF hub uploads), which implies meaningful compute, setup, and cost considerations for production deployments.