Key Points
1. Latent Consistency Models (LCMs) build upon Latent Diffusion Models (LDMs) and have achieved impressive performance in accelerating text-to-image generative tasks by producing high-quality images with minimal inference steps.
2. LCMs are distilled from pretrained LDMs, requiring only approximately 32 A100 GPU training hours, and have been extended to larger models with significantly less memory consumption, achieving superior image generation quality.
3. The LoRA parameters obtained through LCM distillation are identified as a universal Stable-Diffusion acceleration module, named LCM-LoRA, which can be directly plugged into various Stable-Diffusion fine-tuned models or LoRAs without training, representing a universally applicable accelerator for diverse image generation tasks.
4. LCM-LoRA can be viewed as a plug-in neural probability flow ODE (PF-ODE) solver with strong generalization capabilities across various fine-tuned models and LoRAs.
5. Previous numerical PF-ODE solvers such as DDIM and DPM-Solver are compared with LCM-LoRA, highlighting the latter as a novel class of neural network-based PF-ODE solver with robust generalization capabilities.
6. Consistency Models have showcased the potential of enhancing sampling efficiency without sacrificing output quality and have been effective in image generation tasks on ImageNet, LSUN, and other domains.
7. Parameter-Efficient Fine-Tuning (PEFT) enables customization of pre-existing models for particular tasks while limiting the number of parameters that need retraining, thereby enhancing computational efficiency and model refinement with considerably less data.
8. Task arithmetic in pretrained models offers a cost-effective and scalable strategy for direct edits in weight space, though further exploration of its potential and underlying principles remains active.
9. LCM-LoRA represents a universal training-free acceleration module for Stable-Diffusion (SD) and demonstrates strong generalization capabilities and superiority in text-to-image generation tasks.
Summary
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Reference: https://arxiv.org/abs/2311.05556