ELECT selects the best random seed for instruction-guided image editing by scoring background consistency from early diffusion latents, reducing inference cost by about 41 percent and turning about 40 percent of previously failed edits into successes.
Guiding instruction-based image editing via multimodal large language models
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Early Timestep Zero-Shot Candidate Selection for Instruction-Guided Image Editing
ELECT selects the best random seed for instruction-guided image editing by scoring background consistency from early diffusion latents, reducing inference cost by about 41 percent and turning about 40 percent of previously failed edits into successes.