Pallaidium: https://github.com/tin2tin/Pallaidium

Abstract

Diffusion Transformers (DiT) have demonstrated remarkable generative capabilities but remain highly computationally expensive. Previous acceleration methods, such as pruning and distillation, typically rely on a fixed computational capacity, leading to insufficient acceleration and degraded generation quality. To address this limitation, we propose \textbf{Elastic Diffusion Transformer (E-DiT)}, an adaptive acceleration framework for DiT that effectively improves efficiency while maintaining generation quality. Specifically, we observe that the generative process of DiT exhibits substantial sparsity (i.e., some computations can be skipped with minimal impact on quality), and this sparsity varies significantly across samples. Motivated by this observation, E-DiT equips each DiT block with a lightweight router that dynamically identifies sample-dependent sparsity from the input latent. Each router adaptively determines whether the corresponding block can be skipped. If the block is not skipped, the router then predicts the optimal MLP width reduction ratio within the block. During inference, we further introduce a block-level feature caching mechanism that leverages router predictions to eliminate redundant computations in a training-free manner. Extensive experiments across 2D image (Qwen-Image and FLUX) and 3D asset (Hunyuan3D-3.0) demonstrate the effectiveness of E-DiT, achieving up to ∼2× speedup with negligible loss in generation quality. Code will be available at this https URL.

Paper: https://arxiv.org/abs/2602.13993

Code: https://github.com/wangjiangshan0725/Elastic-DiT

Model MD: https://github.com/OpenSenseNova/SenseNova-U1/blob/main/docs/u1_infographic_model.md

https://xcancel.com/fal/status/2070523006770630813

Easy Install: https://github.com/AHEKOT/VNCCS_Easy-Install

Technical report: https://www.krea.ai/blog/krea-2-technical-report

Code: http://github.com/krea-ai/krea-2

Code and weights: http://krea.ai/krea-2-open-source

Hugging Face: huggingface.co/krea/Krea-2-Raw, huggingface.co/krea/Krea-2-Turbo

Discord Server: https://discord.gg/EwKE8KBDqD

Abstract

While 10B-level industrial foundation models have pushed the boundaries of image inpainting, their prohibitive computational costs severely hinder practical deployment. Constructing a highly optimized task-specific specialist offers a promising solution; however, extreme structural compression inevitably triggers a severe representation bottleneck. To conquer this, we propose Moebius, a highly efficient lightweight inpainting framework. We systematically reconstruct the diffusion backbone by introducing the Local-λ Mix Interaction (LλMI) block. Comprising Local-λ and Interactive-λ modules, it elegantly summarizes spatial contexts and global semantic priors into fixed-size linear matrices, preserving complex latent interactions while drastically shedding parameters. Furthermore, to unlock the full representational capacity of this highly compact architecture, we synergistically pair it with an adaptive multi-granularity distillation strategy. Operating strictly within the latent space to avoid expensive pixel-space decoding, this strategy dynamically balances multiple gradient-based losses to achieve high-fidelity alignment. Extensive experiments across natural and portrait benchmarks demonstrate that this optimal synergy enables Moebius to rival or even surpass the generation quality of the 10B-level industrial generalist FLUX.1-Fill-Dev. Remarkably, Moebius achieves this using less than 2% of the parameters (0.22B vs. 11.9B) while delivering a >15× acceleration in total inference time, setting a new efficiency standard for high-fidelity inpainting.

Paper: https://arxiv.org/abs/2606.19195

Code: https://github.com/hustvl/Moebius

Weights: https://huggingface.co/hustvl/Moebius

Project Page: https://hustvl.github.io/Moebius/

Release: https://github.com/rupeshs/fastsdcpu/releases/tag/v1.0.0-beta.500

Video: https://www.youtube.com/watch?v=vGxZIS4J0Mg

Workflows: https://github.com/WhatDreamsCost/WhatDreamsCost-ComfyUI/tree/main/example_workflows