  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\%+.
