DiTs use either a two-stage cross-attention circuit or text-token fusion circuit for spatial relations depending on the text encoder, achieving near-perfect in-domain accuracy but differing out-of-domain robustness.
Denoising diffu- sion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851, 2020
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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FlowLPS perturbs flow-model estimates with Langevin steps then applies proximal refinement to balance fidelity and perceptual quality on linear inverse problems.
citing papers explorer
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Circuit Mechanisms for Spatial Relation Generation in Diffusion Transformers
DiTs use either a two-stage cross-attention circuit or text-token fusion circuit for spatial relations depending on the text encoder, achieving near-perfect in-domain accuracy but differing out-of-domain robustness.
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FlowLPS: Langevin-Proximal Sampling for Flow-based Inverse Problem Solvers
FlowLPS perturbs flow-model estimates with Langevin steps then applies proximal refinement to balance fidelity and perceptual quality on linear inverse problems.