  This paper attempts to address the object repetition issue in patch-wise
higher-resolution image generation. We propose AccDiffusion, an accurate method
for patch-wise higher-resolution image generation without training. An in-depth
analysis in this paper reveals an identical text prompt for different patches
causes repeated object generation, while no prompt compromises the image
details. Therefore, our AccDiffusion, for the first time, proposes to decouple
the vanilla image-content-aware prompt into a set of patch-content-aware
prompts, each of which serves as a more precise description of an image patch.
Besides, AccDiffusion also introduces dilated sampling with window interaction
for better global consistency in higher-resolution image generation.
Experimental comparison with existing methods demonstrates that our
AccDiffusion effectively addresses the issue of repeated object generation and
leads to better performance in higher-resolution image generation.
