SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization

• URL: http://arxiv.org/abs/2510.04961v1
• Date: Mon, 06 Oct 2025 15:57:31 GMT
• Title: SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization
• Authors: Théophane Vallaeys, Jakob Verbeek, Matthieu Cord,
• Abstract summary: We introduce a new pixel diffusion decoder architecture for improved scaling and training stability.<n>We use distillation to replicate the performance of the diffusion decoder in an efficient single-step decoder.<n>This makes SSDD the first diffusion decoder optimized for single-step reconstruction trained without adversarial losses.
• Score: 56.12853087022071
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tokenizers are a key component of state-of-the-art generative image models, extracting the most important features from the signal while reducing data dimension and redundancy. Most current tokenizers are based on KL-regularized variational autoencoders (KL-VAE), trained with reconstruction, perceptual and adversarial losses. Diffusion decoders have been proposed as a more principled alternative to model the distribution over images conditioned on the latent. However, matching the performance of KL-VAE still requires adversarial losses, as well as a higher decoding time due to iterative sampling. To address these limitations, we introduce a new pixel diffusion decoder architecture for improved scaling and training stability, benefiting from transformer components and GAN-free training. We use distillation to replicate the performance of the diffusion decoder in an efficient single-step decoder. This makes SSDD the first diffusion decoder optimized for single-step reconstruction trained without adversarial losses, reaching higher reconstruction quality and faster sampling than KL-VAE. In particular, SSDD improves reconstruction FID from $0.87$ to $0.50$ with $1.4\times$ higher throughput and preserve generation quality of DiTs with $3.8\times$ faster sampling. As such, SSDD can be used as a drop-in replacement for KL-VAE, and for building higher-quality and faster generative models.

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