Papers › Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference
Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference
Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, Hang Zhao
Latent Diffusion models (LDMs) have achieved remarkable results in synthesizing high-resolution images. However, the iterative sampling process is computationally intensive and leads to slow generation. Inspired by Consistency Models (song et al.), we propose Latent Consistency Models (LCMs), enabling swift inference with minimal steps on any pre-trained LDMs, including Stable Diffusion (rombach et al). Viewing the guided reverse diffusion process as solving an augmented probability flow ODE (PF-ODE), LCMs are designed to directly predict the solution of such ODE in latent space, mitigating the need for numerous iterations and allowing rapid, high-fidelity sampling. Efficiently distilled from pre-trained classifier-free guided diffusion models, a high-quality 768 x 768 2~4-step LCM takes only 32 A100 GPU hours for training. Furthermore, we introduce Latent Consistency Fine-tuning (LCF), a novel method that is tailored for fine-tuning LCMs on customized image datasets. Evaluation on the LAION-5B-Aesthetics dataset demonstrates that LCMs achieve state-of-the-art text-to-image generation performance with few-step inference. Project Page: https://latent-consistency-models.github.io/
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text-to-Image Generation | DrawBench | LCM | Aesthetics (Laion Aesthtetics Predictor) | 5.8038 | #4 of 8 | Archive leaderboard | report |
| Text-to-Image Generation | DrawBench | LCM | Human Preference Alignement (HPSv2) | 0.2610 | #4 of 8 | Archive leaderboard | report |
| Text-to-Image Generation | DrawBench | LCM | Text Alignement (SentenceBERT) | 0.5602 | #4 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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