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Enhancing Efficiency in Multimodal Models
This chapter explores speculative decoding techniques for multimodal language models, emphasizing efficiency in output generation. It discusses the use of smaller draft models to approximate larger models, enhancing processing speed while balancing accuracy. The chapter also delves into segmentation-free guidance for text-to-image diffusion, showcasing advancements that improve image synthesis without fine-tuning.