Instruction-tuning Stable Diffusion with InstructPix2Pix

Fine-tuning Stable Diffusion models on Intel CPUs

Accelerating Stable Diffusion Inference on Intel CPUs

Optimizing Stable Diffusion for Intel CPUs with NNCF and 🤗 Optimum

Using Stable Diffusion with Core ML on Apple Silicon

Using LoRA for Efficient Stable Diffusion Fine-Tuning

Make LLM Fine-tuning 2x faster with Unsloth and 🤗 TRL

An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU

arXiv:2603. 16428v2 Announce Type: replace-cross Abstract: Fine-tuning Large Language Models (LLMs) has become essential for domain adaptation, but its memory-intensive property exceeds the capabilities of most GPUs.

Performance Analysis and Optimization of 3D Generative Diffusion Models across GPU Architectures

arXiv:2606. 19365v1 Announce Type: new Abstract: Diffusion models have become essential for high-fidelity 3D MRI synthesis, yet their deployment remains constrained by substantial GPU resource demands arising from hundreds of U-Net evaluations per sample and a highly heterogeneous kernel behavior.

RotaryQuant: Fitting 120B MoE Models on Consumer Hardware via Fused Compressed-Space Attention

arXiv:2608. 08081v1 Announce Type: cross Abstract: Large mixture-of-experts (MoE) language models with 26--120 billion parameters exceed the memory capacity of consumer devices through three simultaneous pressures: resident weight matrices, key-value (KV) cache state that grows linearly with context, and dozens of expert sublayers that must be paged on demand.

FourTune: Towards Fully 4-Bit Efficient Post-Training for Diffusion Models

arXiv:2607. 05711v1 Announce Type: new Abstract: Diffusion models have become a dominant paradigm for high-quality generative modeling, while post-training is essential for adapting them to diverse downstream applications.

Generalized Discrete Diffusion with Self-Correction

arXiv:2603. 02230v2 Announce Type: replace-cross Abstract: Self-correction is an effective technique for maintaining parallel sampling in discrete diffusion models with minimal performance degradation.

Multi-Block Diffusion Language Models

arXiv:2606. 29215v1 Announce Type: new Abstract: Block Diffusion Language Models (BD-LMs) improve diffusion-based text generation with KV caching and flexible-length generation.