🤖🧠 Diffusion Transformers with Representation Autoencoders (RAE): The Next Leap in Generative AI

🗓️ 14 Oct 2025
📚 AI News & Trends

Diffusion Transformers (DiTs) have revolutionized image and video generation enabling stunningly realistic outputs in systems like Stable Diffusion and Imagen. However, despite innovations in transformer architectures and training methods, one crucial element of the diffusion pipeline has remained largely stagnant- the autoencoder that defines the latent space. Most current diffusion models still depend on Variational ...

#DiffusionTransformers #RAE #GenerativeAI #StableDiffusion #Imagen #LatentSpace

🔥 DiffusionBench: On Holistic Evaluation of Diffusion Transformers

💡 The paper introduces a unified framework called NanoGen for training and evaluating diffusion transformers, which are used in image generation tasks. The current evaluation setup for diffusion transformers is limited to class-conditional generation on ImageNet, which may not reflect real progress in generative modeling. The authors argue that text-to-image generation is a more comprehensive task, but it is often skipped due to perceived high costs and inconvenience. However, the authors show that with NanoGen, training and evaluating text-to-image models requires comparable compute to ImageNet.

The NanoGen framework supports various diffusion methods and can be easily configured to train models on both ImageNet and text-to-image tasks. The authors trained 21 latent diffusion models using NanoGen and found that the ranking of methods on ImageNet and text-to-image tasks shows no strong correlation. This suggests that a method that improves performance on ImageNet may not necessarily improve performance on text-to-image generation.

To address this issue, the authors propose a holistic benchmark called DiffusionBench, which summarizes results on both ImageNet and text-to-image tasks. The authors recommend reporting DiffusionBench in place of ImageNet alone, as methods that improve DiffusionBench are more likely to reflect broader progress in generative modeling. The main contribution of the paper is the introduction of NanoGen and DiffusionBench, which provide a more comprehensive evaluation setup for diffusion transformers and can help to advance research in generative modeling.

📅 Published on Jun 23

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.24888
• PDF: https://arxiv.org/pdf/2606.24888
• Project Page: https://end2end-diffusion.github.io/diffusion-bench/

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📢 By: https://xn--r1a.website/PaperNexus

#DiffusionTransformers #ImageGenerationTasks #TextToImageGeneration #GenerativeModeling #DiffusionBasedArchitectures

🔥 Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing

💡 The paper introduces Mage-Flow, a compact 4B-scale generative stack for efficient text-to-image generation and instruction-based image editing. The stack is built from two co-designed components: Mage-VAE, a lightweight high-fidelity latent tokenizer, and a Native-Resolution Multimodal Diffusion Transformer trained with rectified flow matching. Mage-VAE uses one-step diffusion-style encoding and decoding with anchor-latent regularization, preserving the reconstruction quality of strong public VAEs while reducing tokenization cost by more than an order of magnitude.

The stack supports flexible-resolution training and improves end-to-end training throughput by about 2.5 times, thanks to native-resolution packing and stack-level CUDA kernel fusion. Built on this foundation, the authors develop a complete model family with Base, RL-aligned, and Turbo variants for both generation and editing. The results show that Mage-Flow and Mage-Flow-Edit achieve competitive performance across standard generation and editing benchmarks, despite their compact scale.

The Turbo variants make high-resolution generation and editing practical for interactive use, generating an image in 0.59 seconds and editing an image in 1.02 seconds at 1024^2 resolution on a single NVIDIA A100 GPU, while maintaining a small memory footprint. The results demonstrate that careful tokenizer-backbone-system co-design can deliver strong high-resolution generation and editing within an efficient 4B model family. The paper addresses the problem of large-scale visual generators being costly to train, fine-tune, and deploy, and provides a solution with Mage-Flow, which is a more efficient and compact alternative.

📅 Published on Jul 21

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.19064
• PDF: https://arxiv.org/pdf/2607.19064
• Project Page: https://microsoft.github.io/Mage/

🤖 Models citing this paper:
• https://huggingface.co/microsoft/Mage-Flow
• https://huggingface.co/microsoft/Mage-Flow-Edit-Turbo
• https://huggingface.co/microsoft/Mage-Flow-Base

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/microsoft/mage-flow
• https://huggingface.co/spaces/hugging-apps/mage-flow-base

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📢 By: https://xn--r1a.website/PaperNexus

#ImageGeneration #TextToImageSynthesis #DiffusionTransformers #LatentTokenization #MultimodalLearning

🔥 AURORA-LM: Autoencoding Unified Representation for Continuous-Latent Diffusion Language Modeling

💡 The paper introduces AURORA-LM, a continuous-latent diffusion language model that addresses the problem of text generation in continuous latent spaces. Unlike existing models that either inherit embedding spaces not designed for joint generation and decoding or compress autoencoded latent spaces to ease diffusion, sacrificing token-level fidelity, AURORA-LM preserves a high-capacity, decodable text latent and designs the diffusion model to learn its distribution directly.

The model consists of a query-based encoder-decoder that organizes text into a high-capacity, prefix-aligned latent sequence, and a block-causal diffusion transformer that learns its distribution through flow matching, generating blocks left to right while denoising positions within each block in parallel. To accommodate the harder-to-model latent, AURORA-LM restricts only the noisy-input pathway while retaining the full clean-latent prediction target, accommodating full-width latents without reducing decoder-facing capacity.

The authors further calibrate the noise-level distribution to the latent width and introduce self-trajectory consistency to bridge independently sampled training noise and iterative denoising at inference. The results show that AURORA-LM achieves the strongest performance among evaluated continuous and diffusion-based language models on OpenWebText free generation and XSum summarization. Scaling to 1 billion parameters with about 1500 EFLOPs of total compute yields further gains, surpassing a larger publicly released latent-diffusion language model under a matched evaluation protocol. All experiments are conducted on Ascend NPUs.

The key contributions of the paper are the introduction of a novel continuous-latent diffusion language model that preserves high-capacity, decodable text latents and designs the diffusion model to learn its distribution directly, and the demonstration of its effectiveness in achieving state-of-the-art results on text generation and summarization tasks.

📅 Published on Aug 3

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2608.02602
• PDF: https://arxiv.org/pdf/2608.02602
• Project Page: https://aurora-lm-project.github.io/

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📢 By: https://xn--r1a.website/PaperNexus

#ContinuousLatentDiffusion #LanguageModeling #Autoencoding #DiffusionTransformers #TextGeneration