LIFT decomposes distillation into coarse linear alignment then fine refinement while PLACE adds error-based local adaptation, allowing stable training of 1.3M-parameter students (1.6% teacher size) to FID 15.73 across diffusion and flow models.
Sdxs: Real- time one-step latent diffusion models with image conditions
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
Systematic benchmarking of diffusion model optimizations on Apple M3 Ultra produces 22.7 FPS real-time img2img at 512x512 and demonstrates that CUDA-derived techniques do not transfer directly to Apple Silicon.
citing papers explorer
-
LIFT and PLACE: A Simple, Stable, and Effective Knowledge Distillation Framework for Lightweight Diffusion Models
LIFT decomposes distillation into coarse linear alignment then fine refinement while PLACE adds error-based local adaptation, allowing stable training of 1.3M-parameter students (1.6% teacher size) to FID 15.73 across diffusion and flow models.
-
Systematic Optimization of Real-Time Diffusion Model Inference on Apple M3 Ultra
Systematic benchmarking of diffusion model optimizations on Apple M3 Ultra produces 22.7 FPS real-time img2img at 512x512 and demonstrates that CUDA-derived techniques do not transfer directly to Apple Silicon.