RL for Consistency Models: Faster Reward Guided Text-to-Image Generation
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
66134e9487df1b26165ead7b
Owen Oertell
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2024-04-09T19:22:29.310Z
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Owen Oertell
user
ojo2
66134e9487df1b26165ead7c
Jonathan D. Chang
66134e9487df1b26165ead7d
Yiyi Zhang
66134e9487df1b26165ead7e
Kianté Brantley
66134e9487df1b26165ead7f
Wen Sun
RL for Consistency Models: Faster Reward Guided Text-to-Image Generation
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
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.
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