title: RL for Consistency Models: Faster Reward Guided Text-to-Image Generation
summary: Reinforcement learning (RL) has improved guided image generation with
  diffusion models by directly optimizing rewards that capture image quality,
  aesthetics, and instruction following capabilities. However, the resulting
  generative policies inherit the same iterative sampling process of diffusion
  models that causes slow generation. To overcome this limitation, consistency
  models proposed learning a new class of generative models that directly map
  noise to data, resulting in a model that can generate an image in as few as one
  sampling iteration. In this work, to optimize text-to-image generative models
  for task specific rewards and enable fast training and inference, we propose a
  framework for fine-tuning consistency models via RL. Our framework, called
  Reinforcement Learning for Consistency Model (RLCM), frames the iterative
  inference process of a consistency model as an RL procedure. RLCM improves upon
  RL fine-tuned diffusion models on text-to-image generation capabilities and
  trades computation during inference time for sample quality. Experimentally, we
  show that RLCM can adapt text-to-image consistency models to objectives that
  are challenging to express with prompting, such as image compressibility, and
  those derived from human feedback, such as aesthetic quality. Comparing to RL
  finetuned diffusion models, RLCM trains significantly faster, improves the
  quality of the generation measured under the reward objectives, and speeds up
  the inference procedure by generating high quality images with as few as two
  inference steps. Our code is available at https://rlcm.owenoertell.com
paper_authors._id: 66134e9487df1b26165ead7b
paper_authors.name: Owen Oertell
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-09T19:22:29.310Z
paper_authors._id: 65786ad2821f439498e5d584
paper_authors.avatarUrl: /avatars/1a85ba5b7f6c6df32d12397057b04566.svg
paper_authors.fullname: Owen Oertell
paper_authors.type: user
paper_authors.user: ojo2
paper_authors._id: 66134e9487df1b26165ead7c
paper_authors.name: Jonathan D. Chang
paper_authors._id: 66134e9487df1b26165ead7d
paper_authors.name: Yiyi Zhang
paper_authors._id: 66134e9487df1b26165ead7e
paper_authors.name: Kianté Brantley
paper_authors._id: 66134e9487df1b26165ead7f
paper_authors.name: Wen Sun
paper_title: RL for Consistency Models: Faster Reward Guided Text-to-Image Generation
paper_summary: Reinforcement learning (RL) has improved guided image generation with
  diffusion models by directly optimizing rewards that capture image quality,
  aesthetics, and instruction following capabilities. However, the resulting
  generative policies inherit the same iterative sampling process of diffusion
  models that causes slow generation. To overcome this limitation, consistency
  models proposed learning a new class of generative models that directly map
  noise to data, resulting in a model that can generate an image in as few as one
  sampling iteration. In this work, to optimize text-to-image generative models
  for task specific rewards and enable fast training and inference, we propose a
  framework for fine-tuning consistency models via RL. Our framework, called
  Reinforcement Learning for Consistency Model (RLCM), frames the iterative
  inference process of a consistency model as an RL procedure. RLCM improves upon
  RL fine-tuned diffusion models on text-to-image generation capabilities and
  trades computation during inference time for sample quality. Experimentally, we
  show that RLCM can adapt text-to-image consistency models to objectives that
  are challenging to express with prompting, such as image compressibility, and
  those derived from human feedback, such as aesthetic quality. Comparing to RL
  finetuned diffusion models, RLCM trains significantly faster, improves the
  quality of the generation measured under the reward objectives, and speeds up
  the inference procedure by generating high quality images with as few as two
  inference steps. Our code is available at https://rlcm.owenoertell.com
paper_ai_summary: A reinforcement learning approach for fine-tuning consistency models enhances text-to-image generation by improving quality and reducing inference time compared to diffusion models.
paper_ai_keywords: reinforcement learning diffusion models consistency models generative policies iterative sampling process text-to-image generation fine-tuning RL for Consistency Model (RLCM) iterative inference human feedback image compressibility aesthetic quality