title: AMO Sampler: Enhancing Text Rendering with Overshooting
summary: Achieving precise alignment between textual instructions and generated images
  in text-to-image generation is a significant challenge, particularly in
  rendering written text within images. Sate-of-the-art models like Stable
  Diffusion 3 (SD3), Flux, and AuraFlow still struggle with accurate text
  depiction, resulting in misspelled or inconsistent text. We introduce a
  training-free method with minimal computational overhead that significantly
  enhances text rendering quality. Specifically, we introduce an overshooting
  sampler for pretrained rectified flow (RF) models, by alternating between
  over-simulating the learned ordinary differential equation (ODE) and
  reintroducing noise. Compared to the Euler sampler, the overshooting sampler
  effectively introduces an extra Langevin dynamics term that can help correct
  the compounding error from successive Euler steps and therefore improve the
  text rendering. However, when the overshooting strength is high, we observe
  over-smoothing artifacts on the generated images. To address this issue, we
  propose an Attention Modulated Overshooting sampler (AMO), which adaptively
  controls the strength of overshooting for each image patch according to their
  attention score with the text content. AMO demonstrates a 32.3% and 35.9%
  improvement in text rendering accuracy on SD3 and Flux without compromising
  overall image quality or increasing inference cost.
paper_authors._id: 674fac3ae3ba9e6539cda7c0
paper_authors.name: Xixi Hu
paper_authors._id: 674fac3ae3ba9e6539cda7c1
paper_authors.name: Keyang Xu
paper_authors._id: 674fac3ae3ba9e6539cda7c2
paper_authors.name: Bo Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-06T09:09:50.276Z
paper_authors._id: 64375a5ab8ea86e50329d2da
paper_authors.avatarUrl: /avatars/b3dcef9e2e0234156a22d699a30b6f1f.svg
paper_authors.fullname: Bo Liu
paper_authors.type: user
paper_authors.user: cranial-xix
paper_authors._id: 674fac3ae3ba9e6539cda7c3
paper_authors.name: Qiang Liu
paper_authors._id: 674fac3ae3ba9e6539cda7c4
paper_authors.name: Hongliang Fei
paper_title: AMO Sampler: Enhancing Text Rendering with Overshooting
paper_summary: Achieving precise alignment between textual instructions and generated images
  in text-to-image generation is a significant challenge, particularly in
  rendering written text within images. Sate-of-the-art models like Stable
  Diffusion 3 (SD3), Flux, and AuraFlow still struggle with accurate text
  depiction, resulting in misspelled or inconsistent text. We introduce a
  training-free method with minimal computational overhead that significantly
  enhances text rendering quality. Specifically, we introduce an overshooting
  sampler for pretrained rectified flow (RF) models, by alternating between
  over-simulating the learned ordinary differential equation (ODE) and
  reintroducing noise. Compared to the Euler sampler, the overshooting sampler
  effectively introduces an extra Langevin dynamics term that can help correct
  the compounding error from successive Euler steps and therefore improve the
  text rendering. However, when the overshooting strength is high, we observe
  over-smoothing artifacts on the generated images. To address this issue, we
  propose an Attention Modulated Overshooting sampler (AMO), which adaptively
  controls the strength of overshooting for each image patch according to their
  attention score with the text content. AMO demonstrates a 32.3% and 35.9%
  improvement in text rendering accuracy on SD3 and Flux without compromising
  overall image quality or increasing inference cost.
paper_ai_summary: A novel training-free method using an overshooting sampler enhances text rendering quality in text-to-image generation without increasing computational overhead.
paper_ai_keywords: Stable Diffusion 3 Flux AuraFlow pretrained rectified flow ordinary differential equation Euler sampler Langevin dynamics Attention Modulated Overshooting sampler text rendering accuracy inference cost