Dream Weaver: Turning Words into Images with Artificial Intelligence Iterative Prompt Relabeling for Text-to-Image Diffusion Model Training

Abstract: Diffusion models have shown impressive performance in many domains. However, the model’s capability to follow natural language instructions (e.g., spatial relationships between objects, generating complex scenes) is still unsatisfactory. In this work, we propose Iterative Prompt Relabeling (IPR), a novel algorithm that aligns images to text through iterative image sampling and prompt relabeling with feedback. IPR first samples a batch of images conditioned on the text, then relabels the text prompts of unmatched text-image pairs with classifier feedback. We conduct thorough experiments on SDv2 and SDXL, testing their capability to follow instructions on spatial relations. With IPR, we improved up to 15.22% (absolute improvement) on the challenging spatial relation VISOR benchmark, demonstrating superior performance compared to previous RL methods.

Keywords: Diffusion Models, Text-to-Image Generation, Iterative Prompt Relabeling, Vision–Language Alignment, Spatial Relationship Understanding, Instruction Following, Self-Supervised Learning, Classifier Feedback, Reinforcement Learning, VISOR Benchmark