CDM migrates distribution matching distillation to continuous time via dynamic random-length schedules and active off-trajectory latent alignment, yielding competitive few-step image fidelity on SD3 and Longcat-Image.
arXiv preprint arXiv:2512.05150 (2025)
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Causal-rCM unifies teacher-forcing and self-forcing distillation for autoregressive video diffusion, delivering a 2-step model with VBench-T2V score 84.63 and enabling interactive world models on Cosmos 3 using only synthetic data.
BudCache optimizes step cache policies for a fixed inference budget in diffusion models via combinatorial search, outperforming threshold heuristics in quality on FLUX.1-dev and Wan2.1.
D-OPSD formulates supervised fine-tuning of step-distilled diffusion models as on-policy self-distillation by having the model act as both teacher (with multimodal context) and student (with text-only context) on its own roll-outs.
AlloSR² claims state-of-the-art one-step real-world super-resolution by SNR-guided trajectory init, velocity regularization (FATC), and allomorphic self-adversarial distillation that preserves flow-matching generative priors.
ReFPO adds explicit Reflow regularization to FPO, stabilizing PPO-style training and supporting high-fidelity one-step inference across GridWorld, MuJoCo, and Humanoid tasks.
citing papers explorer
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Continuous-Time Distribution Matching for Few-Step Diffusion Distillation
CDM migrates distribution matching distillation to continuous time via dynamic random-length schedules and active off-trajectory latent alignment, yielding competitive few-step image fidelity on SD3 and Longcat-Image.
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Causal-rCM: A Unified Teacher-Forcing and Self-Forcing Open Recipe for Autoregressive Diffusion Distillation in Streaming Video Generation and Interactive World Models
Causal-rCM unifies teacher-forcing and self-forcing distillation for autoregressive video diffusion, delivering a 2-step model with VBench-T2V score 84.63 and enabling interactive world models on Cosmos 3 using only synthetic data.
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Budget-Constrained Step-Level Diffusion Caching
BudCache optimizes step cache policies for a fixed inference budget in diffusion models via combinatorial search, outperforming threshold heuristics in quality on FLUX.1-dev and Wan2.1.
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D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models
D-OPSD formulates supervised fine-tuning of step-distilled diffusion models as on-policy self-distillation by having the model act as both teacher (with multimodal context) and student (with text-only context) on its own roll-outs.
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Allo{SR}$^2$: Rectifying One-Step Super-Resolution to Stay Real via Allomorphic Generative Flows
AlloSR² claims state-of-the-art one-step real-world super-resolution by SNR-guided trajectory init, velocity regularization (FATC), and allomorphic self-adversarial distillation that preserves flow-matching generative priors.
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ReFPO: Reflow Regularization for Flow Matching Policy Gradients
ReFPO adds explicit Reflow regularization to FPO, stabilizing PPO-style training and supporting high-fidelity one-step inference across GridWorld, MuJoCo, and Humanoid tasks.