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TurboEdit: Instant text-based image editing

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arxiv 2408.08332 v1 pith:TYFKSCY7 submitted 2024-08-14 cs.CV cs.LG

Zongze Wu , Nicholas Kolkin , Jonathan Brandt , Richard Zhang , Eli Shechtman This is my paper · ORCID
classification cs.CVcs.LG
keywords imageeditinginversiondiffusioninputonlyprompttext
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Instruction-based Image Manipulation by Watching How Things Move

    cs.CV 2024-12 conditional novelty 7.0 of 10

    A diffusion editing model, InstructMove, is trained on video frame pairs annotated by MLLMs using spatial conditioning, enabling non-rigid edits and viewpoint changes.

  2. Steering Rectified Flow Models in the Vector Field for Controlled Image Generation

    cs.CV 2024-11 conditional novelty 6.0 of 10

    FlowChef enables training-free, inversion-free, backprop-free controlled generation for rectified flow models by replacing the gradient through the model with the direct loss gradient on the estimated clean image.

  3. Unveil Inversion and Invariance in Flow Transformer for Versatile Image Editing

    cs.CV 2024-11 conditional novelty 6.0 of 10

    A two-stage flow inversion plus AdaLN text-feature replacement gives tuning-free editing of flow transformers for rigid and non-rigid changes.

  4. ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid Motions

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A released 6.4 million pair dataset and 613 sample benchmark for instruction-guided image editing of non-rigid motions, plus a Flux.1-dev based baseline that outperforms open-source methods on the new benchmark.

  5. Text-to-Image Synthesis: A Decade Survey

    cs.CV 2024-11 conditional novelty 1.0 of 10

    A decade-spanning survey categorizes over 440 text-to-image papers by architecture, research problem, dataset, and evaluation metric.

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