SVG-T2I: Scaling Up Text-to-Image Latent Diffusion Model Without Variational Autoencoder

• URL: http://arxiv.org/abs/2512.11749v1
• Date: Fri, 12 Dec 2025 17:45:03 GMT
• Title: SVG-T2I: Scaling Up Text-to-Image Latent Diffusion Model Without Variational Autoencoder
• Abstract summary: We propose SVG-T2I to support high-quality text-to-image synthesis directly in the VFM feature domain.<n>By leveraging a standard text-to-image diffusion pipeline, SVG-T2I achieves competitive performance, reaching 0.75 on GenEval and 85.78 on DPG-Bench.<n>We fully open-source the project, including the autoencoder and generation model, together with their training, inference, evaluation pipelines, and pre-trained weights, to facilitate further research in representation-driven visual generation.
• Score: 90.28800377462197
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual generation grounded in Visual Foundation Model (VFM) representations offers a highly promising unified pathway for integrating visual understanding, perception, and generation. Despite this potential, training large-scale text-to-image diffusion models entirely within the VFM representation space remains largely unexplored. To bridge this gap, we scale the SVG (Self-supervised representations for Visual Generation) framework, proposing SVG-T2I to support high-quality text-to-image synthesis directly in the VFM feature domain. By leveraging a standard text-to-image diffusion pipeline, SVG-T2I achieves competitive performance, reaching 0.75 on GenEval and 85.78 on DPG-Bench. This performance validates the intrinsic representational power of VFMs for generative tasks. We fully open-source the project, including the autoencoder and generation model, together with their training, inference, evaluation pipelines, and pre-trained weights, to facilitate further research in representation-driven visual generation.

Related papers

• Render-in-the-Loop: Vector Graphics Generation via Visual Self-Feedback [29.19392406217364]
  We propose a novel generation paradigm that reformulates SVG synthesis as a step-wise, visual-context-aware process.<n>By rendering intermediate code states into a cumulative canvas, the model explicitly observes the evolving visual context at each step.<n>We show that applying this visual loop naively to off-the-shelf models is suboptimal due to their inability to leverage incremental visual-code mappings.
  arXiv  Detail & Related papers  (2026-04-22T16:15:28Z)
• IntroSVG: Learning from Rendering Feedback for Text-to-SVG Generation via an Introspective Generator-Critic Framework [20.964700751378547]
  Existing text-to-SVG generation methods do not incorporate visual perception of the final rendered image.<n>We propose an Introspective SVG Generation Framework (IntroSVG)<n>We show that our method achieves state-of-the-art performance across several key evaluation metrics.
  arXiv  Detail & Related papers  (2026-03-10T07:44:51Z)
• WildSVG: Towards Reliable SVG Generation Under Real-Word Conditions [15.299111837234678]
  We introduce the task of SVG extraction, which consists in translating specific visual inputs from an image into scalable vector graphics.<n>Existing multimodal models achieve strong results when generating SVGs from clean renderings or textual descriptions, but they fall short in real-world scenarios where natural images introduce noise, clutter, and domain shifts.<n>We benchmark state-of-the-art multimodal models and find that current approaches perform well below what is needed for reliable SVG extraction in real scenarios.
  arXiv  Detail & Related papers  (2026-02-24T22:42:55Z)
• DuetSVG: Unified Multimodal SVG Generation with Internal Visual Guidance [48.98604326855894]
  We introduce DuetSVG, a unified multimodal model that jointly generates image tokens and corresponding SVG tokens in an end-to-end manner.<n>At inference, we apply a novel test-time scaling strategy that leverages the model's native visual predictions as guidance to improve SVG decoding quality.
  arXiv  Detail & Related papers  (2025-12-11T18:23:03Z)
• Visual Generation Tuning [84.50113837230333]
  We propose VGT, Visual Generation Tuning, a novel paradigm designed to stimulate the underlying capabilities of visual generation within vision language models.<n>In image reconstruction tasks, we achieve 26.67 PSNR and 0.50 rFID at a 28x compression ratio, outperforming specialized VAEs.<n>Our proposed VGT showcases significant scaling promise and is versatile for endowing any VLMs trained for multimodal understanding with the capabilities of visual generation.
  arXiv  Detail & Related papers  (2025-11-28T18:57:13Z)
• Latent Diffusion Model without Variational Autoencoder [78.34722551463223]
  SVG is a novel latent diffusion model without variational autoencoders for visual generation.<n>It constructs a feature space with clear semantic discriminability by leveraging frozen DINO features.<n>It enables accelerated diffusion training, supports few-step sampling, and improves generative quality.
  arXiv  Detail & Related papers  (2025-10-17T04:17:44Z)
• Rendering-Aware Reinforcement Learning for Vector Graphics Generation [15.547843461605746]
  We introduce RLRF(Reinforcement Learning from Rendering Feedback), an RL method that enhances SVG generation in vision-language models (VLMs)<n>Given an input image, the model generates SVG roll-outs that are rendered and compared to the original image to compute a reward.<n>This visual fidelity feedback guides the model toward producing more accurate, efficient, and semantically coherent SVGs.
  arXiv  Detail & Related papers  (2025-05-27T06:56:00Z)
• Style Customization of Text-to-Vector Generation with Image Diffusion Priors [27.949704002538944]
  We propose a novel two-stage style customization pipeline for SVG generation.<n>In the first stage, we train a T2V diffusion model with a path-level representation to ensure the structural regularity of SVGs.<n>In the second stage, we customize the T2V diffusion model to different styles by distilling customized T2I models.
  arXiv  Detail & Related papers  (2025-05-15T17:59:21Z)
• IMAGINE-E: Image Generation Intelligence Evaluation of State-of-the-art Text-to-Image Models [52.73820275861131]
  Text-to-image(T2I) models have made significant progress, showcasing impressive abilities in prompt following and image generation.<n>Recent models such as FLUX.1 and Ideogram2.0 have demonstrated exceptional performance across various complex tasks.<n>This study provides valuable insights into the current state and future trajectory of T2I models as they evolve towards general-purpose usability.
  arXiv  Detail & Related papers  (2025-01-23T18:58:33Z)
• NeuralSVG: An Implicit Representation for Text-to-Vector Generation [54.4153300455889]
  We propose NeuralSVG, an implicit neural representation for generating vector graphics from text prompts.<n>To encourage a layered structure in the generated SVG, we introduce a dropout-based regularization technique.<n>We demonstrate that NeuralSVG outperforms existing methods in generating structured and flexible SVG.
  arXiv  Detail & Related papers  (2025-01-07T18:50:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.