Chain-of-Zoom factorizes extreme super-resolution into an autoregressive sequence of intermediate scales using a reused backbone model plus GRPO-tuned multi-scale VLM prompts.
Prompt-tuning latent diffusion models for inverse problems
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Combining diffusion priors as a product-of-experts and optimizing exponents via Bayesian evidence maximization enables prior tuning from one observation in inverse imaging problems.
EmbedOpt optimizes the conditional embedding of protein diffusion models at inference time to shift the structural prior toward experimental constraints, outperforming coordinate-based posterior sampling on cryo-EM fitting while remaining robust across hyperparameter ranges.
citing papers explorer
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Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment
Chain-of-Zoom factorizes extreme super-resolution into an autoregressive sequence of intermediate scales using a reused backbone model plus GRPO-tuned multi-scale VLM prompts.
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Optimizing Diffusion Priors in Image Reconstruction from a Single Observation
Combining diffusion priors as a product-of-experts and optimizing exponents via Bayesian evidence maximization enables prior tuning from one observation in inverse imaging problems.
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Robust Inference-Time Steering of Protein Diffusion Models via Embedding Optimization
EmbedOpt optimizes the conditional embedding of protein diffusion models at inference time to shift the structural prior toward experimental constraints, outperforming coordinate-based posterior sampling on cryo-EM fitting while remaining robust across hyperparameter ranges.