Grouping related text prompts into a tree and sharing early denoising steps with averaged embeddings saves 50 to 74 percent of diffusion compute on image-embedding-conditioned models while keeping VQA quality essentially equal.
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Reusing Computation in Text-to-Image Diffusion for Efficient Generation of Image Sets
Grouping related text prompts into a tree and sharing early denoising steps with averaged embeddings saves 50 to 74 percent of diffusion compute on image-embedding-conditioned models while keeping VQA quality essentially equal.