RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction

• URL: http://arxiv.org/abs/2412.18390v2
• Date: Wed, 25 Dec 2024 12:12:10 GMT
• Title: RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction
• Authors: Xiaoping Wu, Jie Hu, Xiaoming Wei,
• Abstract summary: Diffusion Probabilistic Models (DPMs) have emerged as the de facto approach for high-fidelity image synthesis.<n>We introduce a novel generative framework, the Recurrent Diffusion Probabilistic Model (RDPM), which enhances the diffusion process through a recurrent token prediction mechanism.
• Score: 17.005198258689035
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion Probabilistic Models (DPMs) have emerged as the de facto approach for high-fidelity image synthesis, operating diffusion processes on continuous VAE latent, which significantly differ from the text generation methods employed by Large Language Models (LLMs). In this paper, we introduce a novel generative framework, the Recurrent Diffusion Probabilistic Model (RDPM), which enhances the diffusion process through a recurrent token prediction mechanism, thereby pioneering the field of Discrete Diffusion. By progressively introducing Gaussian noise into the latent representations of images and encoding them into vector-quantized tokens in a recurrent manner, RDPM facilitates a unique diffusion process on discrete-value domains. This process iteratively predicts the token codes for subsequent timesteps, transforming the initial standard Gaussian noise into the source data distribution, aligning with GPT-style models in terms of the loss function. RDPM demonstrates superior performance while benefiting from the speed advantage of requiring only a few inference steps. This model not only leverages the diffusion process to ensure high-quality generation but also converts continuous signals into a series of high-fidelity discrete tokens, thereby maintaining a unified optimization strategy with other discrete tokens, such as text. We anticipate that this work will contribute to the development of a unified model for multimodal generation, specifically by integrating continuous signal domains such as images, videos, and audio with text. We will release the code and model weights to the open-source community.

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