NEWSLETTER
Recent advances in ai laws have changed an increase in model size and training data optimization of the inference time calculation. This approach, exemplified by models such as Openai O1 and Deepseek R1, improves the performance of the model by taking advantage of additional computer resources during inference. The forcing of the trial time budget has emerged as an efficient technique in LLMS, which allows improved performance with a minimum token sampling. Similarly, the inference time scale has gained traction in diffusion models, particularly in rewards -based sampling, where iterative refinement helps generate results that are better align with user preferences. This method is crucial for the generation of text in the image, where naive sampling cannot often capture intricate specifications, such as object relationships and logical limitations.
Inference time scale methods for diffusion models can be classified widely on approaches based on fine adjustment and particle sampling. The fine adjustment improves the alignment of the model with specific tasks, but requires resentment for each case of use, which limits scalability. On the contrary, particle sampling, used in techniques such as SVDD and Code, select high iteration reward samples during dinner, significantly improves the quality of the output. While these methods have been effective for diffusion models, their application to flow models has been limited due to the deterministic nature of its generation process. The recent work, including SOP, has introduced the stochasticity in the flow models, allowing the inference scale based on particle sampling. This study expands in such efforts by modifying the reverse nucleus, further improving sampling diversity and effectiveness in flow -based generative models.
Kaist researchers propose a method of inference time scale for flow models prior to the appearance, addressing their limitations in particle sampling due to a deterministic generative process. They introduce three key innovations: (1) SDE -based generation to enable stochastic sampling, (2) VP interpolation conversion to improve the diversity of samples and (3) budget forcing (RBF) for the adaptive allocation of computational resources. Experimental results show that these techniques improve the alignment of rewards in tasks such as the generation of text in composition image. Its approach exceeds the above methods, which demonstrates the advantages of the inference time scale in the flow models, particularly when combined with gradient -based techniques for differentiable rewards such as the generation of aesthetic images.
The alignment of inference time rewards aims to generate high reward samples based on a flow model prior to the appearance without retention. The objective is to maximize the expected reward while minimizing the deviation of the original data distribution using the regularization of KL. Since direct sampling is challenging, particle sampling techniques, commonly used in diffusion models, adapt. However, the flow models are based on a deterministic sampling, limiting exploration. To address this, the stochastic sampling of inference time is introduced by converting deterministic processes into stochastic. In addition, the interpoling conversion improves the search space by aligning the sampling of the flow model with diffusion models. A dynamic calculation allocation strategy further optimizes efficiency during the inference time scale.
The study presents experimental results in particle sampling methods for the alignment of inference time rewards. The study focuses on the generation of compositional images of text in image and quantity of quantity, using the flow as a flow model prior to the appearance. Metrics such as Vqascore and RSS evaluate alignment and precision. The results indicate that the stochastic sampling of inference time improves efficiency, with an interpolation conversion further improving performance. Flow -based particle sampling produces high reward outputs compared to diffusion models without compromising the image quality. The proposed RBF method optimizes the budget allocation, achieving the best results of alignment of rewards and precision. Qualitative and quantitative findings confirm its effectiveness in generating precise and high quality images.
In conclusion, the study introduces a method of inference time scale for flow models, incorporating three key innovations: (1) ODE to SDE conversion to enable particle sampling, (2) linear interpolation conversion to VP to improve the diversity and search efficiency, and (3) RBF for adaptive update. While diffusion models benefit from stochastic sampling during renewal, flow models require personalized approaches due to their deterministic nature. VP-SD-SD-based generation effectively integrates particle sampling, and RBF optimizes calculation use. Experimental results show that this method exceeds existing inference time scale techniques, improving performance while maintaining high quality outputs in flow generation models and flow -based videos.
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Sana Hassan, a consulting intern in Marktechpost and double grade student in Iit Madras, passionate to apply technology and ai to address real world challenges. With great interest in solving practical problems, it provides a new perspective to the intersection of ai and real -life solutions.
