title: TurboEdit: Instant text-based image editing
summary: We address the challenges of precise image inversion and disentangled image
  editing in the context of few-step diffusion models. We introduce an encoder
  based iterative inversion technique. The inversion network is conditioned on
  the input image and the reconstructed image from the previous step, allowing
  for correction of the next reconstruction towards the input image. We
  demonstrate that disentangled controls can be easily achieved in the few-step
  diffusion model by conditioning on an (automatically generated) detailed text
  prompt. To manipulate the inverted image, we freeze the noise maps and modify
  one attribute in the text prompt (either manually or via instruction based
  editing driven by an LLM), resulting in the generation of a new image similar
  to the input image with only one attribute changed. It can further control the
  editing strength and accept instructive text prompt. Our approach facilitates
  realistic text-guided image edits in real-time, requiring only 8 number of
  functional evaluations (NFEs) in inversion (one-time cost) and 4 NFEs per edit.
  Our method is not only fast, but also significantly outperforms
  state-of-the-art multi-step diffusion editing techniques.
paper_authors._id: 66c2a599a545b0cefe694abd
paper_authors.name: Zongze Wu
paper_authors._id: 66c2a599a545b0cefe694abe
paper_authors.name: Nicholas Kolkin
paper_authors._id: 66c2a599a545b0cefe694abf
paper_authors.name: Jonathan Brandt
paper_authors._id: 66c2a599a545b0cefe694ac0
paper_authors.name: Richard Zhang
paper_authors._id: 66c2a599a545b0cefe694ac1
paper_authors.name: Eli Shechtman
paper_title: TurboEdit: Instant text-based image editing
paper_summary: We address the challenges of precise image inversion and disentangled image
  editing in the context of few-step diffusion models. We introduce an encoder
  based iterative inversion technique. The inversion network is conditioned on
  the input image and the reconstructed image from the previous step, allowing
  for correction of the next reconstruction towards the input image. We
  demonstrate that disentangled controls can be easily achieved in the few-step
  diffusion model by conditioning on an (automatically generated) detailed text
  prompt. To manipulate the inverted image, we freeze the noise maps and modify
  one attribute in the text prompt (either manually or via instruction based
  editing driven by an LLM), resulting in the generation of a new image similar
  to the input image with only one attribute changed. It can further control the
  editing strength and accept instructive text prompt. Our approach facilitates
  realistic text-guided image edits in real-time, requiring only 8 number of
  functional evaluations (NFEs) in inversion (one-time cost) and 4 NFEs per edit.
  Our method is not only fast, but also significantly outperforms
  state-of-the-art multi-step diffusion editing techniques.
paper_ai_keywords: few-step diffusion models encoder based iterative inversion inversion network disentangled controls detailed text prompt noise maps instruction based editing LLM text-guided image edits functional evaluations (NFEs)