Self-Adversarial One Step Generation via Condition Shifting

APEX, a new framework for text-to-image synthesis, uses condition shifting to generate adversarial correction signals internally without external discriminators, improving one-step generation quality while maintaining training stability and reducing computational overhead. This innovation allows APEX to outperform models with significantly more parameters in terms of both quality and speed, making it particularly appealing for developers seeking efficient and scalable solutions in generative AI.

Read the full article at arXiv cs.CV (Vision)

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