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RL for Consistency Models: Faster Reward Guided Text-to-Image Generation (2404.03673v2)

Published 25 Mar 2024 in cs.CV, cs.AI, and cs.LG

Abstract: 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. 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. 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. Our code is available at https://rlcm.owenoertell.com.

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Authors (5)
  1. Owen Oertell (8 papers)
  2. Jonathan D. Chang (10 papers)
  3. Yiyi Zhang (16 papers)
  4. Kianté Brantley (25 papers)
  5. Wen Sun (124 papers)
Citations (3)
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Summary

An Overview of "RL for Consistency Models: Faster Reward Guided Text-to-Image Generation"

In this paper, the authors propose a novel approach to improve the efficiency of text-to-image generative models by integrating reinforcement learning (RL) with consistency models. The approach, termed Reinforcement Learning for Consistency Model (RLCM), addresses key limitations of existing diffusion models, particularly the slow iterative sampling process which hinders their practical utility in generating images quickly in response to textual descriptions.

Background and Motivation

Diffusion models have seen broad applicability due to their high-quality image generation capabilities, particularly when conditioned on text. Despite their success, these models suffer from slow inference times as they necessitate multiple iterative steps to refine a noisy input into a coherent image. This problem is exacerbated when trying to align the output image closely with complex or nuanced textual inputs that are not easily expressible via simple text prompts.

Consistency models come into play by offering a more efficient alternative, transforming noise directly into data in potentially a single operation, which markedly reduces inference time. The integration of RL into this framework aims to align the generative process with specific, often downstream, reward functions that capture desired properties of the output images.

Methodology

The authors reformulate the text-to-image generation task with consistency models into a Markov Decision Process (MDP). This formulation allows the application of RL techniques to optimize the generation process against specified rewards, which can represent various qualities such as aesthetic appeal, image compressibility, fidelity to human feedback, or alignment with textual prompts.

The key innovation here is the framing of the consistency model as an RL problem with a much reduced time horizon compared to diffusion models. This is accomplished by considering the process of consistency function's inference as a multi-step decision-making task, where each step involves applying a learned policy to iteratively refine the output starting from an initial noise sample. The objective is to optimize this policy to maximize a reward function indicative of high-quality image generation.

Experimental Results

The authors present experimental results showing that RLCM can be trained significantly faster than RL-tuned diffusion models while maintaining, and in some cases, improving the quality of the generated images. Specifically, RLCM demonstrates superior performance in scenarios where rewards are challenging to express explicitly through input prompts, such as aesthetic quality rated by human labels and new tasks derived from human feedback.

Quantitatively, RLCM achieves notable reductions in training time and an enhancement in generation speed by producing high-quality images in as few as two inference steps. These improvements are attributed to the reduced complexity and shorter trajectory lengths native to the consistency model's design.

Implications and Future Directions

The work presented marks a significant step forward in making guided image generation more efficient and accessible, particularly in real-time or resource-constrained settings. The ability to rapidly adapt generative models to task-specific rewards through RLCM opens up numerous possibilities for personalized content creation, real-world interactions through augmented reality, and other applications requiring quick feedback loops between user inputs and model outputs.

Future research may expand on integrating more complex reward structures or exploring different RL methodologies suited to this framework, potentially refining the trade-offs between inference speed and image quality. Another exciting prospect involves leveraging this approach in multimodal settings where models learn from both visual and textual data, further blurring the lines between creative input and sophisticated machine-generated content.

Through RLCM, the authors pave the way for faster, more flexible generative models that can cater to niche user demands while maintaining state-of-the-art output quality.

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