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Phased Consistency Models
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Consistency Models (CMs) have made significant progress in accelerating the generation of diffusion models. However, their application to high-resolution, text-conditioned image generation in the latent space remains unsatisfactory. In this paper, we identify three key flaws in the current design of Latent Consistency Models (LCMs). We investigate the reasons behind these limitations and propose Phased Consistency Models (PCMs), which generalize the design space and address the identified limitations. Our evaluations demonstrate that PCMs outperform LCMs across 1--16 step generation settings. While PCMs are specifically designed for multi-step refinement, they achieve comparable 1-step generation results to previously state-of-the-art specifically designed 1-step methods. Furthermore, we show the methodology of PCMs is versatile and applicable to video generation, enabling us to train the state-of-the-art few-step text-to-video generator. Our code is available at https://github.com/G-U-N/Phased-Consistency-Model.
Forward citations
Cited by 8 Pith papers
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Curvature-Adaptive Consistency Flow Matching: Autonomous Trajectory Optimization via Reinforcement Learning
CACFM applies RL to adaptively select critical regions in probability flow ODE trajectories for consistency distillation, yielding SOTA few-step results on FLUX and SDXL.
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SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices
A compact elastic diffusion transformer with adaptive sparse attention and knowledge-guided distribution-matching distillation achieves 4-step 1K image generation on a phone in roughly 1.8 seconds.
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Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling
Q-Sched's quantization-aware scheduler with a reference-free JAQ loss lets 2-8 step quantized diffusion models reach lower FID than full-precision baselines.
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Adversarial Distribution Matching for Diffusion Distillation Towards Efficient Image and Video Synthesis
A new adversarial distribution matching loss for diffusion distillation gives one-step and few-step generators that match or exceed prior distillation methods on SDXL, SD3, and CogVideoX.
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Dual-Expert Consistency Model for Efficient and High-Quality Video Generation
By training a semantic expert and a LoRA-based detail expert, DCM reaches nearly teacher-level VBench scores with 4-step video sampling on HunyuanVideo and CogVideoX.
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Differentiable Solver Search for Fast Diffusion Sampling
A differentiable search over solver coefficients and sampling timesteps produces a fast diffusion sampler that outperforms DPM-Solver++ and UniPC at 5 to 10 steps.
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UniCMs: A Unified Consistency Model For Efficient Multimodal Generation and Understanding
UniCMs applies consistency distillation to a unified multimodal transformer, treating image mask-diffusion steps and text parallel-decoding steps as one shared denoising trajectory, enabling 2 to 8 step generation and...
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Optimizing Few-Step Generation with Adaptive Matching Distillation
Adaptive Matching Distillation uses reward-model scores to reweight teacher and fake-teacher gradients, improving few-step diffusion distillation on image and video benchmarks.
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