The model's multi-round interactive editing capability revolutionizes the limitations of traditional single-shot image generation. Users can optimize their work step-by-step through iterative commands: first a base image is generated and then specific elements are adjusted by appending cues such as'Change background from day to dusk'maybe'Add company logo in the bottom right corner'. The technical level adopts the autoregressive token prediction mechanism, together with the hidden space representation of MoVQGAN, so that each editing only requires local computation, which saves 70% computational resources compared with the complete re-generation. Actual tests show that to complete the same complexity of the design task, multi-round editing reduces 3-5 times of re-generation than the traditional way, which greatly improves the efficiency of designers.
This answer comes from the articleLumina-mGPT-2.0: an autoregressive image generation model for handling multiple image generation tasksThe
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