SDXL in 4 steps with Latent Consistency LoRAs

Exploring simple optimizations for SDXL

Accelerating SD Turbo and SDXL Turbo Inference with ONNX Runtime and Olive

It\^o maps for any-step SDEs

arXiv:2606. 11156v1 Announce Type: cross Abstract: Recent one-step generative models accelerate sampling by learning deterministic flow maps of the underlying dynamics.

Efficient Controllable Generation for SDXL with T2I-Adapters

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling

High-fidelity image generation faces a trade-off between speed and quality. Diffusion models produce strong visuals but require costly iterative sampling.

LayerRoute: Input-Conditioned Adaptive Layer Skipping via LoRA Fine-Tuning for Agentic Language Models

arXiv:2606. 01838v1 Announce Type: cross Abstract: Agentic language model systems alternate between two structurally distinct step types: structured tool calls (short, deterministic, low perplexity) and open-ended planning/reasoning steps (long, complex, high perplexity).

Multi-Mask Diffusion Language Models for Few-Step Generation

arXiv:2607. 19686v1 Announce Type: cross Abstract: Masked diffusion models (MDMs) are a promising family of language generators, but achieving high-quality few-step generation remains challenging.

LiST: Local-Simplex Test-Time LoRA Fusion

arXiv:2608.22370v1 Announce Type: new Abstract: Task-specific LoRA adapters offer a modular way to specialize large language and vision-language models. However, existing adapter composition methods...

Diffusable Latents from Structure-Agnostic Distillation

arXiv:2609.39657v1 Announce Type: new Abstract: Distilling pretrained foundation models into an autoencoder bottleneck improves latent diffusability, enabling diffusion models to converge faster and...

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.

Latent-Kernel Discrete Flow Maps for Few-Step Generation

arXiv:2607. 27529v1 Announce Type: new Abstract: Discrete diffusion and flow-matching models denoise a sequence over many steps, but to keep each step cheap, they factorize the transition across positions and decide every token independently.

The Appeal and Reality of Recycling LoRAs with Adaptive Merging

arXiv:2602. 12323v2 Announce Type: replace Abstract: The widespread availability of fine-tuned LoRA modules for open pre-trained models has led to an interest in methods that can adaptively merge LoRAs to improve performance.