Scribble-Guided Diffusion for Training-Free Text-to-Image Generation

Recent advancements in text-to-image diffusion models have shown impressive results but often fail to fully capture user’s intent. Existing methods combining textual inputs with bounding boxes or region masks lack precise spatial guidance, leading to misaligned or unintended object orientations. To address these issues, we propose Scribble-Guided Diffusion (ScribbleDiff), a training-free approach that employs user-provided scribbles as visual prompts for image generation. However, incorporating scribbles poses challenges due to their sparse and thin nature, which complicates accurate alignment. To resolve this, we introduce moment alignment and scribble propagation, enabling effective and flexible alignment between generated images and scribble inputs. Experiments on the PASCAL-Scribble dataset demonstrate notable improvements in spatial control and consistency, validating the effectiveness of our method in scribble-guidance.