Faster Stable Diffusion with Core ML on iPhone, iPad, and Mac

Using Stable Diffusion with Core ML on Apple Silicon

Swift 🧨Diffusers - Fast Stable Diffusion for Mac

Stable Diffusion XL on Mac with Advanced Core ML Quantization

Accelerating Stable Diffusion Inference on Intel CPUs

Fine-tuning Stable Diffusion models on Intel CPUs

Using LoRA for Efficient Stable Diffusion Fine-Tuning

Optimizing Stable Diffusion for Intel CPUs with NNCF and 🤗 Optimum

🧨 Accelerating Stable Diffusion XL Inference with JAX on Cloud TPU v5e

Japanese Stable Diffusion

Paris: A Decentralized Trained Open-Weight Diffusion Model

arXiv:2510. 03434v3 Announce Type: replace-cross Abstract: We present Paris, the first publicly released diffusion model pre-trained entirely through decentralized computation.

Understanding LoRA Rank Trade-offs in Diffusion Model Fine-Tuning

The study investigates how the rank of Low‑Rank Adaptation (LoRA) affects diffusion model fine‑tuning on CIFAR‑10 using a DDPM U‑Net. Experiments with ranks 2, 4, 8, 16, and 32 show that moderate ranks—particularly rank 4—yield the best FID scores while keeping trainable parameters, runtime, and GPU memory low. Higher ranks offer only marginal improvements at a higher computational cost, suggesting that small‑to‑moderate ranks are efficient defaults for fixed training budgets.

Efficient On-Device Diffusion LLM Inference with Mobile NPU

arXiv:2606. 13740v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) accelerate generation by denoising multiple tokens in parallel, making them attractive for latency-sensitive mobile inference.