Method:
Framework Design
The design of our framework centers on two key components: validators, which provide search feedback, and algorithms, which seek better noise candidates. The approach is aimed at optimizing noise candidates through search.
Validators are configured in three scenarios: with privileged information, with conditional information, and without any additional information. On the algorithmic front, we employ three strategies: random search, zeroth-order search, and path search.
Our design choices were first validated on the category conditional generation task on ImageNet and further extended to a larger-scale text conditional generation task.
We evaluate the quality of generation using multiple validators, analyzing the bias of each validator and its alignment with the generation task.
Results:
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Star Performances in ImageNet Task
Our proposed framework significantly enhances the quality of generation, particularly shining with zeroth-order and path search strategies. The results underscore a remarkable improvement in the generation quality, showcasing the effectiveness of our approach.
Generalization in Text Conditional Generation
In the realm of text conditional generation tasks, our validator ensemble method demonstrates excellent generalization across multiple benchmarks, outperforming single validators. This indicates a robust and versatile performance that speaks to the broader applicability of our framework.
Optimizing Noise Candidates
Our experimental results reveal that searching for better noise candidates can effectively utilize computational resources during inference, enhancing the performance and scalability of diffusion models. This finding highlights the potential of our method in resource optimization and model enhancement.
Overall, the content is delivered with a blend of scientific rigor and a touch of humanistic warmth, ensuring that the emotional value is maintained without exaggeration.
