Reinforcement Learning from Diffusion Feedback: Q* for Image Search

• URL: http://arxiv.org/abs/2311.15648v1
• Date: Mon, 27 Nov 2023 09:20:12 GMT
• Title: Reinforcement Learning from Diffusion Feedback: Q* for Image Search
• Authors: Aboli Marathe
• Abstract summary: We present two models for image generation using model-agnostic learning. RLDF is a singular approach for visual imitation through prior-preserving reward function guidance. It generates high-quality images over varied domains showcasing class-consistency and strong visual diversity.
• Score: 2.5835347022640254
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large vision-language models are steadily gaining personalization capabilities at the cost of fine-tuning or data augmentation. We present two models for image generation using model-agnostic learning that align semantic priors with generative capabilities. RLDF, or Reinforcement Learning from Diffusion Feedback, is a singular approach for visual imitation through prior-preserving reward function guidance. This employs Q-learning (with standard Q*) for generation and follows a semantic-rewarded trajectory for image search through finite encoding-tailored actions. The second proposed method, noisy diffusion gradient, is optimization driven. At the root of both methods is a special CFG encoding that we propose for continual semantic guidance. Using only a single input image and no text input, RLDF generates high-quality images over varied domains including retail, sports and agriculture showcasing class-consistency and strong visual diversity. Project website is available at https://infernolia.github.io/RLDF.

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