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.
Phased dmd: Few-step distribution matching distillation via score matching within subintervals.arXiv preprint arXiv:2510.27684
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SGMD uses fake-score optimization toward the teacher with stop-gradient Fisher objective and NR/RC dual potentials to deliver ~3x training speedup and better motion dynamics in 4-step video diffusion models.
Mutual Forcing trains a single native autoregressive audio-video model with mutually reinforcing few-step and multi-step modes via self-distillation to match 50-step baselines at 4-8 steps.
Live Avatar enables 45 FPS real-time streaming infinite-length audio-driven avatar generation from a 14B diffusion model via distillation and timestep-forcing pipeline parallelism.
SenseNova-U1 presents native unified multimodal models that match top understanding VLMs while delivering strong performance in image generation, infographics, and interleaved tasks via the NEO-unify architecture.
A co-designed few-step distillation and low-bit quantization pipeline for Wan2.2-T2V-A14B keeps quantized few-step performance close to or above the full-precision baseline at 8 and 20 steps.
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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SGMD: Score Gradient Matching Distillation for Few-Step Video Diffusion Distillation
SGMD uses fake-score optimization toward the teacher with stop-gradient Fisher objective and NR/RC dual potentials to deliver ~3x training speedup and better motion dynamics in 4-step video diffusion models.
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Mutual Forcing: Dual-Mode Self-Evolution for Fast Autoregressive Audio-Video Character Generation
Mutual Forcing trains a single native autoregressive audio-video model with mutually reinforcing few-step and multi-step modes via self-distillation to match 50-step baselines at 4-8 steps.
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Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length
Live Avatar enables 45 FPS real-time streaming infinite-length audio-driven avatar generation from a 14B diffusion model via distillation and timestep-forcing pipeline parallelism.
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SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture
SenseNova-U1 presents native unified multimodal models that match top understanding VLMs while delivering strong performance in image generation, infographics, and interleaved tasks via the NEO-unify architecture.
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Collaborative Few-Step Distillation and Low-Bit Quantization for Wan2.2 Dual-Expert Video Diffusion Models
A co-designed few-step distillation and low-bit quantization pipeline for Wan2.2-T2V-A14B keeps quantized few-step performance close to or above the full-precision baseline at 8 and 20 steps.