arXiv Computer Vision
Decompose the Distillation: Interpretable Single-Pass Guidance for Diffusion Models
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arXiv Computer Vision
Sep 30
LongLive-Plug: Once-for-All Distillation for Video Generation
LongLive‑Plug is a once‑for‑all distillation framework that learns reusable LoRA adapters on a base video diffusion model, enabling training‑free, plug‑and‑play deployment to a wide range of downstream models. These adapters provide single‑pass classifier‑free guidance, few‑step sampling, and long‑context error correction for autoregressive generation, and remain effective even when downstream models add conditioning branches or expand output channels. The authors demonstrate that the approach works on 54 downstream models across three backbone families and eight task categories, including world modeling, robotics, editing, and multimodal generation.
arXiv Machine Learning
Jul 9
Guidance Breaks the Fitted Operator: A Terminal-Fitted Repair for Classifier-Free Guidance
arXiv:2607. 07665v1 Announce Type: new Abstract: Classifier-free guidance (CFG) is the standard way to strengthen class-conditioning in diffusion and flow-matching samplers, yet at large guidance it oversaturates and destabilizes, symptoms practitioners suppress with more steps or limited-interval schedules.
Hugging Face Trending Papers
Jul 8
Guidance Breaks the Fitted Operator: A Terminal-Fitted Repair for Classifier-Free Guidance
Classifier-free guidance (CFG) is the standard way to strengthen class-conditioning in diffusion and flow-matching samplers, yet at large guidance it oversaturates and destabilizes, symptoms practitioners suppress with more steps or limited-interval schedules. We analyze CFG through an asymptotic-preserving, numerical-analysis lens.
arXiv Machine Learning
Jun 30
Momentum Guidance: Plug-and-Play Guidance for Flow Models
arXiv:2602. 20360v2 Announce Type: replace Abstract: Flow-based generative methods offer a simple and effective framework for high-fidelity generation, yet pretrained flow models are rarely used in their vanilla conditional form: in image generation, samples without guidance often appear diffuse and lack fine-grained detail.
arXiv Computer Vision
Oct 23
TrajCFG: Token-Wise Classifier-Free Guidance via Denoising Trajectory Feedback
TrajCFG introduces a training‑free, token‑wise adaptation of classifier‑free guidance for flow‑matching models, leveraging historical changes in guided velocity direction to assign bounded scales. By mapping directional feedback to stronger guidance for larger changes and weaker guidance for consistent directions, it fuses adjacent‑step and interval‑normalized longer‑span signals without extra model evaluations. Experiments across text‑to‑video, image‑to‑video, text‑to‑audio‑video, text‑to‑image, and image‑to‑3D tasks demonstrate consistent improvements over base samplers, notably raising VBench Total scores by 0.30‑0.75 points on three backbones for text‑to‑video generation.
arXiv Machine Learning
Sep 1
PathGuide: Dynamic Classifier-Free Guidance via On-Policy Transport Alignment
arXiv:2608.29107v1 Announce Type: new Abstract: While modern generative models excel at modeling complex data, precise inference-time control in conditional generation remains a critical challenge. C...