Abstract

Diffusion transformers (DiTs) have emerged as a dominant architecture for text-to-image generation, yet their performance drops when generating at resolutions beyond their training range. Existing training-free approaches mitigate this by modifying inference-time attention behavior, often through Rotary Position Embeddings (RoPE) extrapolation combined with attention scaling. However, these strategies apply a uniform and content-agnostic scaling across RoPE components with distinct frequency characteristics, inducing a trade-off between preserving global structure and recovering fine detail. We introduce SEGA, a training-free method that dynamically scales attention across RoPE components according to the latent's spatial-frequency structure at each denoising step. This adaptive scaling improves both structural coherence and fine-detail fidelity. Experiments show that SEGA consistently improves high-resolution synthesis across multiple target resolutions, outperforming state-of-the-art training-free baselines.

Paper: https://arxiv.org/abs/2605.22668

Hugging Face: https://huggingface.co/papers/2605.22668

Blog:

Project Page: https://rajabi2001.github.io/sega/

https://www.youtube.com/watch?v=ARlzfo3MU6U

New models:

• LTX 2.3 Multi-input: https://huggingface.co/Lightricks/LTX-2.3
• Ernie Image: https://huggingface.co/baidu/ERNIE-Image
• Nucleus-Image MOE: https://huggingface.co/NucleusAI/Nucleus-Image
• JoyAI - Image Edit: https://huggingface.co/jdopensource/JoyAI-Image-Edit
• AceStep: https://github.com/ace-step/ACE-Step-1.5

Abstract

Pixel diffusion models have recently regained attention for visual generation. However, training advanced pixel-space models from scratch demands prohibitive computational and data resources. To address this, we propose the Latent-to-Pixel (L2P) transfer paradigm, an efficient framework that directly harnesses the rich knowledge of pre-trained LDMs to build powerful pixel-space models. Specifically, L2P discards the VAE in favor of large-patch tokenization and freezes the source LDM's intermediate layers, exclusively training shallow layers to learn the latent-to-pixel transformation. By utilizing LDM-generated synthetic images as the sole training corpus, L2P fits an already smooth data manifold, enabling rapid convergence with zero real-data collection. This strategy allows L2P to seamlessly migrate massive latent priors to the pixel space using only 8 GPUs. Furthermore, eliminating the VAE memory bottleneck unlocks native 4K ultra-high resolution generation. Extensive experiments across mainstream LDM architectures show that L2P incurs negligible training overhead, yet performs on par with the source LDM on DPG-Bench and reaches 93% performance on GenEval.

Paper: https://arxiv.org/abs/2605.12013

Code: https://github.com/TencentYoutuResearch/T2I-L2P

Model: https://huggingface.co/zhen-nan/L2P/tree/main

Project Page: https://nju-pcalab.github.io/projects/L2P/

Abstract

We introduce Lens, a 3.8B-parameter T2I model that achieves performance competitive with, and in several cases surpassing, state-of-the-art models with more than 6B parameters across various benchmarks, while requiring significantly less training compute. For example, Lens requires only about 19.3% of the training compute used by Z-Image. The training efficiency of Lens stems from two key strategies beyond its compact model size. First, we maximize data information density per training batch by (i) training on Lens-800M, a dataset of 800M densely captioned image-text pairs whose captions are generated by GPT-4.1 and contain approximately 109 words on average, providing richer semantic supervision than conventional short captions, and (ii) constructing each batch from images with multiple resolutions and diverse aspect ratios, thereby enlarging the effective visual coverage of each optimization step. Second, we improve convergence speed through careful architectural choices, including adopting a semantic VAE that provides better latent representations and employing a strong language encoder that accelerates optimization while enabling multilingual generalization from English-only training data. After pre-training, we apply RL with taxonomy-driven prompts (Lens-RL-8K) and structured reward rubrics to suppress artifacts and improve visual quality, a reasoner module with training-free system prompt search to better align user requests with the model, and distillation-based acceleration for 4-step inference. Through efficient training and systematic optimization, Lens generalizes to arbitrary aspect ratios from 1:2 to 2:1 and resolutions up to 1440^2, and supports prompts in several commonly used languages. Thanks to its compact size, Lens generates a 1024^2 image in 3.15 seconds on a single NVIDIA H100 GPU, while its distilled turbo version performs 4-step generation in 0.84 seconds.

Paper:

Code: https://github.com/microsoft/Lens

Models: https://huggingface.co/microsoft/Lens

Project Page:

ComfyUI extension & workflows: https://github.com/Lakonik/ComfyUI-piFlow

HF demo: https://huggingface.co/spaces/Lakonik/AsymFLUX.2-klein

Models: https://huggingface.co/Lakonik/AsymFLUX.2-klein-9B https://huggingface.co/Lakonik/AsymFLUX.2-klein-9B-collection

• Variable-length generation up to 6:20, at per-second granularity

• Full song composition on-device (first open model to do this)

• Audio inpainting: single-segment editing, multi-segment editing, and causal continuation (extending a track beyond its original endpoint) LoRA training support with published documentation

• Fully licensed training data

• Commercial use permitted under the Community License

The model family:

• Stable Audio 3.0 Small SFX — on-device sound effects, up to 2 min

• Stable Audio 3.0 Small — full music composition on-device, up to 2 min

• Stable Audio 3.0 Medium — higher musicality and longer tracks, up to 6:20

Is there any simple tutorials for regional promping , for only 2 characters (regions ) that will have different loras ? I am using the Comfyui

Abstract

A LoRA that reproduces its training images perfectly is not a well-trained model. It is a memorization device. This article argues that generalization within the trained concept is the right quality criterion for small-dataset LoRA fine-tuning, and proposes a five-tell diagnostic framework — base capability degradation, concept narrowing, caption rigidity, entanglement leak, and visual signature reproduction — for identifying when a LoRA has crossed from learning into memorization. We then describe a chained training schedule we use in our own work, which rotates through dataset subsets before reintroducing the full combined dataset for a final consolidation phase. This methodology has roots in early Stable Diffusion 1.5-era practitioner trainers, where dataset switching was first-class in the UI; in modern trainers it must be reconstructed manually. We report a paired A/B run on Qwen-Image at two scales — a 244-image illustration style LoRA and a 27-image character LoRA — comparing chained training against a monotonic straight-baseline twin under matched conditions. Both runs produce competent results that pass the five-tell diagnostic without obvious failure on either side, and the hyperparameter recipe we use proves itself viable for Qwen-Image at these scales. We observe specific signals of chained-side flexibility advantages, but the gap is not dramatic enough at these dataset sizes to claim a general methodological advantage from this experiment alone. The natural next test is at smaller and harder dataset sizes where overfitting is more likely to surface; we identify that as the experimental program this position paper opens onto rather than closes.

Abstract

Recent advances in 3D generative models have rapidly improved image-to-3D synthesis quality, enabling higher-resolution geometry and more realistic appearance. Yet fidelity, which measures pixel-level faithfulness of the generated 3D asset to the input image, still remains a central bottleneck. We argue this stems from an implicit 2D-3D correspondence issue: most 3D-native generators synthesize shape in canonical space and inject image cues via attention, leaving pixel-to-3D associations ambiguous. To tackle this issue, we draw inspiration from 3D reconstruction and propose Pixal3D, a pixel-aligned 3D generation paradigm for high-fidelity 3D asset creation from images. Instead of generating in a canonical pose, Pixal3D directly generates 3D in a pixel-aligned way, consistent with the input view. To enable this, we introduce a pixel back-projection conditioning scheme that explicitly lifts multi-scale image features into a 3D feature volume, establishing direct pixel-to-3D correspondence without ambiguity. We show that Pixal3D is not only scalable and capable of producing high-quality 3D assets, but also substantially improves fidelity, approaching the fidelity level of reconstruction. Furthermore, Pixal3D naturally extends to multi-view generation by aggregating back-projected feature volumes across views. Finally, we show pixel-aligned generation benefits scene synthesis, and present a modular pipeline that produces high-fidelity, object-separated 3D scenes from images. Pixal3D for the first time demonstrates 3D-native pixel-aligned generation at scale, and provides a new inspiring way towards high-fidelity 3D generation of object or scene from single or multi-view images. Project page: this https URL

Paper: https://arxiv.org/abs/2605.10922

Code: https://github.com/TencentARC/Pixal3D

Model: https://huggingface.co/TencentARC/Pixal3D

Project Page: https://ldyang694.github.io/projects/pixal3d/

Abstract

Flow-based generation in high-dimensional spaces is difficult because velocity prediction requires modeling high-dimensional noise, even when data has strong low-rank structure. We present Asymmetric Flow Modeling (AsymFlow), a rank-asymmetric velocity parameterization that restricts noise prediction to a low-rank subspace while keeping data prediction full-dimensional. From this asymmetric prediction, AsymFlow analytically recovers the full-dimensional velocity without changing the network architecture or training/sampling procedures. On ImageNet 256 256, AsymFlow achieves a leading 1.57 FID, outperforming prior DiT/JiT-like pixel diffusion models by a large margin. AsymFlow also provides the first-ever route for finetuning pretrained latent flow models into pixel-space models: aligning the low-rank pixel subspace to the latent space gives a seamless initialization that preserves the latent model's high-level semantics and structure, so finetuning mainly improves low-level mismatches rather than relearning pixel generation. We show that the pixel AsymFlow model finetuned from FLUX.2 klein 9B establishes a new state of the art for pixel-space text-to-image generation, beating its latent base on HPSv3, DPG-Bench, and GenEval while qualitatively showing substantially improved visual realism.

Paper: https://arxiv.org/abs/2605.12964

Code: https://github.com/Lakonik/LakonLab

Demo: https://huggingface.co/spaces/Lakonik/AsymFLUX.2-klein

Tutorial: https://youtu.be/fZgtkRcu4_k