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This paper proposes a latent-to-pixel training strategy for pixel-space text-to-image diffusion models, accelerating convergence and improving inference speed while matching or surpassing latent-space counterparts.
This paper introduces Synthetic Self-Guidance (SSG), a method that attaches a lightweight prediction head to a frozen pretrained pixel-space diffusion model, using the discrepancy between intermediate and final predictions as self-guidance during sampling. It shows that model-generated samples suffice for training the head, improving FID by over 50% on several variants without classifier-free guidance and enhancing strong baselines with CFG.
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.
PixWorld presents a unified pixel-space diffusion approach for 3D scene reconstruction and generation, overcoming limitations of latent-space methods by using direct image-level supervision and geometry-aware feature alignment. The method outperforms prior generation methods and matches state-of-the-art reconstruction methods.
Parallel Rollout Approximation (PRA) improves pixel-space autoregressive image generation by using low-dimensional intermediate states and parallel training, achieving new state-of-the-art results on ImageNet-1K generation.
A new technique called Spectral Forcing applies a time-conditional 2D-DCT low-pass operator to pixel-space diffusion models, improving efficiency by explicitly separating signal from noise and outperforming baselines on ImageNet and text-to-image tasks.
AsymFlow is a new method from Stanford that converts latent diffusion models to pixel space, achieving more realistic images by avoiding information loss from compression. It surpasses FLUX.2 klein on benchmarks with lower computational cost.
Asymmetric Flow Modeling (AsymFlow) restricts noise prediction to low-rank subspaces for efficient high-dimensional flow-based generation, achieving state-of-the-art results on ImageNet and text-to-image tasks by fine-tuning from latent flow models.
The L2P paper introduces a Latent-to-Pixel transfer paradigm that leverages pre-trained latent diffusion models to create efficient pixel-space models capable of 4K generation with minimal training overhead.