arXiv Computer Vision
Scaling Transformer Energy-based Models For High Quality Image Synthesis
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Machine Learning
Jul 31
Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation
arXiv:2607. 27372v1 Announce Type: new Abstract: The deep learning revolution, kicked off by AlexNet, taught us that end-to-end training beats decomposing a problem into hand-designed stages.
arXiv Computer Vision
Oct 12
Learning Representations to Guide Pixel Diffusion Transformers
The paper investigates how visual representations contribute to pixel diffusion transformers and finds that higher representation quality does not always improve generative performance, and that separating architecture into specialized decoders mainly redistributes modeling rather than enhancing generative suitability. Based on these findings, the authors propose Representation Guidance (RG), a method that compacts generation-relevant information into a low-dimensional bottleneck and explicitly uses it to steer decoder generation. RG achieves state‑of‑the‑art results on ImageNet 256×256 with fewer parameters, scales efficiently, and extends to text‑to‑image tasks with strong benchmark performance.
arXiv Computer Vision
Oct 1
DC-SAE: Deep Compression Semantic Autoencoder for Faster Diffusion Convergence
arXiv:2609.39222v1 Announce Type: new Abstract: High-compression tokenizers are essential for scaling latent image generative models. However, aggressive compression creates a fundamental tradeoff be...
arXiv AI
Oct 19
Rethinking the design space of Vanilla Pixel Diffusion Transformers
The paper revisits pixel-space diffusion models, showing that a vanilla diffusion transformer can generate directly in pixel space by swapping velocity prediction for clean image prediction. It introduces MFlow, an adaptive parameterization that interpolates between these two prediction modes based on data representation and model capacity, and demonstrates competitive performance on ImageNet, UCF‑101, and text‑to‑image tasks. Additionally, the authors add ConvRefiner, a lightweight convolutional module trained post‑hoc to restore high‑frequency details while preserving FID.
arXiv AI
Jun 8
Scalable GANs with Transformers
arXiv:2509. 24935v3 Announce Type: replace-cross Abstract: Scalability has driven recent advances in generative modeling, yet its principles remain underexplored for adversarial learning.
arXiv Computer Vision
3d ago
Efficient Image Synthesis with Sphere Latent Encoder
arXiv:2605.15592v2 Announce Type: replace Abstract: Few-step image generation has seen rapid progress, with consistency and meanflow-based methods significantly reducing the number of sampling steps....