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.
Seed-data-edit technical report: A hybrid dataset for instruc- tional image editing, 2024
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.