Summary of Tlcm: Training-efficient Latent Consistency Model For Image Generation with 2-8 Steps, by Qingsong Xie et al.
TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps
by Qingsong Xie, Zhenyi Liao, Zhijie Deng, Chen chen, Haonan Lu
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Latent Consistency Model (TLCM) aims to accelerate latent diffusion models (LDMs) by overcoming two critical challenges: requiring long training times on large datasets and degrading quality for text-image alignment. To achieve this, TLCM employs a novel training-efficient approach that combines data-free multistep latent consistency distillation (MLCD) and latent consistency distillation to guarantee inter-segment consistency in MLCD. The model is further enhanced through distribution matching, adversarial learning, and preference learning techniques. TLCM demonstrates flexibility by allowing adjustment of sampling steps while producing competitive outputs compared to full-step approaches. Notably, TLCM enjoys the data-free merit by using synthetic data from a teacher for distillation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TLCM is a new way to make latent diffusion models faster and better. This model helps solve two big problems: it takes too long to train on lots of real data and the quality gets worse when generating text-image pairs. TLCM uses a special training method that doesn’t need real data, which makes it more efficient. It also includes techniques like distribution matching and adversarial learning to make the model better. The model can adjust how many steps it takes to generate an image while still producing good results. This is important because some models take too long to train or produce low-quality images. |
Keywords
» Artificial intelligence » Alignment » Diffusion » Distillation » Synthetic data
