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The paper proposes CoMA-DiT, a bidirectional cross-modal Diffusion Transformer for latent augmentation in multimodal brain state decoding, which enhances performance in tasks like auditory attention decoding and emotion recognition by using paired modalities as mutual supervisory signals.
Marigold V2 repurposes diffusion transformers for monocular depth estimation via single-step inference and a novel fine-tuning protocol, achieving sharper depth maps and significant improvements on benchmarks like KITTI and ETH3D.
This paper scales the Muon optimizer for Diffusion Transformers from 1.3B to 15B parameters, introducing Periodic Row-wise Muon to reduce computational overhead while preserving generative quality improvements over AdamW.
HelloWorld is a video world model that enables socially interactive characters, allowing users to prompt on-screen characters to respond via a single button press. It uses self-distillation and training-free cross-attention masking to naturalize interactions, and introduces HelloWorldBench for evaluation.
MiniWorld is a reproducible framework for training video world models from scratch using a block-causal Video Diffusion Transformer with Flow Matching, enabling efficient streaming generation and trainable in days on a single 8-GPU server.
Sol-Attn introduces a training-free method to sparsify attention for video generation inference, achieving over 2x speedup by dynamically selecting key-value blocks during online softmax with minimal quality loss.
This paper investigates how text template tokens in diffusion transformers (DiTs) act as implicit semantic registers that causally maintain object identity during denoising, and proposes a training-free pruning rule that removes 20% of attention FLOPs with minimal performance drop.
Introduces appearance pointers, compact tokens that guide Diffusion Transformers to apply correct appearance cues at specified spatial locations, enabling modality-agnostic localized multimodal control without retraining the base model.
ReChannel adapts pretrained diffusion transformers (e.g., FLUX-Klein) for dense prediction tasks by mapping tokens directly to pixel-space patches, achieving state-of-the-art results on trimap-free matting, KITTI depth, and referring segmentation with minimal additional parameters.
This paper explores the use of register tokens in pixel-space Diffusion Transformers (DiTs), finding they improve feature map quality despite DiTs lacking patch-token outliers. The authors propose Register Guidance, a technique to amplify register contributions for better visual structure.
UltraFlux introduces a data-model co-design approach for native 4K text-to-image generation across diverse aspect ratios, addressing positional encoding, VAE compression, and optimization challenges. It outperforms existing open-source baselines and matches proprietary models like Seedream 4.0.
OrbitQuant introduces a data-agnostic quantization method for diffusion transformers that eliminates the need for recalibration across timesteps and modalities, achieving state-of-the-art post-training quantization at low-bit settings for models like FLUX.1 and CogVideoX.
This paper investigates the mechanisms behind self-alignment methods in diffusion transformers, revealing that performance improvements from methods like Self-Flow primarily come from data augmentation along the noise dimension rather than token interactions between noise levels. The authors introduce Attention Separation to demonstrate this and propose an effective design combining self-representation alignment with dual-timestep augmentation.
Proposes Quality Representation Module (QRM), a lightweight transformer module that injects quality-aware signals into diffusion transformer modulation to improve image fidelity and prompt alignment without changing the backbone or sampling schedule.
This paper introduces SpheRoPE, a zero-shot and optimization-free framework that injects spherical priors into pre-trained diffusion transformers to generate 360 panoramic images and videos, overcoming topological constraints without retraining.
PhysiFormer uses coordinate-space diffusion to generate physically-plausible 3D object motions without explicit inductive biases, enabling efficient multi-object reasoning and generalization to complex materials and geometries.
Introduces DiffusionBench, a unified benchmark for holistic evaluation of generative diffusion transformers, supporting multiple generation tasks and providing standardized training and evaluation.
Researchers introduce NanoGen, a unified framework for training and evaluating diffusion transformers, and propose DiffusionBench, a holistic benchmark combining ImageNet class-conditional and text-to-image generation to better assess progress in generative modeling.
A simplified open-source PyTorch implementation of FLUX diffusion transformers with verifiable line-by-line source mappings, designed for educational purposes.
MMDiff extends frozen diffusion transformers into multi-modal generative systems using lightweight decoders, achieving significant improvements in semantic segmentation and other perceptual tasks through multi-timestep feature fusion.