Towards Understanding the Working Mechanism of Text-to-Image Diffusion Model

• URL: http://arxiv.org/abs/2405.15330v3
• Date: Sun, 27 Oct 2024 04:44:41 GMT
• Title: Towards Understanding the Working Mechanism of Text-to-Image Diffusion Model
• Authors: Mingyang Yi, Aoxue Li, Yi Xin, Zhenguo Li,
• Abstract summary: Recently, the strong latent Diffusion Probabilistic Model (DPM) has been applied to high-quality Text-to-Image (T2I) generation. We explore the mechanism behind DPM by examining the intermediate statuses during the gradual denoising generation process. We propose to apply this observation to accelerate the process of T2I generation by properly removing text guidance.
• Score: 57.24046436423511
• License:
• Abstract: Recently, the strong latent Diffusion Probabilistic Model (DPM) has been applied to high-quality Text-to-Image (T2I) generation (e.g., Stable Diffusion), by injecting the encoded target text prompt into the gradually denoised diffusion image generator. Despite the success of DPM in practice, the mechanism behind it remains to be explored. To fill this blank, we begin by examining the intermediate statuses during the gradual denoising generation process in DPM. The empirical observations indicate, the shape of image is reconstructed after the first few denoising steps, and then the image is filled with details (e.g., texture). The phenomenon is because the low-frequency signal (shape relevant) of the noisy image is not corrupted until the final stage in the forward process (initial stage of generation) of adding noise in DPM. Inspired by the observations, we proceed to explore the influence of each token in the text prompt during the two stages. After a series of experiments of T2I generations conditioned on a set of text prompts. We conclude that in the earlier generation stage, the image is mostly decided by the special token [\texttt{EOS}] in the text prompt, and the information in the text prompt is already conveyed in this stage. After that, the diffusion model completes the details of generated images by information from themselves. Finally, we propose to apply this observation to accelerate the process of T2I generation by properly removing text guidance, which finally accelerates the sampling up to 25\%+.

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