Optimizing Stable Diffusion Models with DDPO via TRL for Automated Workflows
Compute & Experimental Workflow Note: This analysis is based on the TRL and DDPO frameworks as they existed in October 2023. Fine-tuning diffusion models via reinforcement learning is computationally expensive and remains an experimental workflow. Results depend heavily on the quality of the “Reward Model” (e.g., aesthetic scores) and can be vulnerable to “reward hacking,” where the system optimizes the score rather than visual quality. Performance outcomes vary by hardware, datasets, and sampling settings. Use this information at your own discretion; we can’t accept responsibility for decisions made based on it. Stable Diffusion models generate images from text prompts using diffusion-based denoising. By late 2023, many teams are no longer satisfied with “generic” image generation that only follows prompt text—they want models to align with a specific environment’s taste and constraints: brand style, compressibility requirements for delivery, or human preference in ...