ReNoise: Real Image Inversion Through Iterative Noising
Recent advancements in text-guided diffusion models have unlocked powerful
image manipulation capabilities. However, applying these methods to real images
necessitates the inversion of the images into the domain of the pretrained
diffusion model. Achieving faithful inversion remains a challenge, particularly
for more recent models trained to generate images with a small number of
denoising steps. In this work, we introduce an inversion method with a high
quality-to-operation ratio, enhancing reconstruction accuracy without
increasing the number of operations. Building on reversing the diffusion
sampling process, our method employs an iterative renoising mechanism at each
inversion sampling step. This mechanism refines the approximation of a
predicted point along the forward diffusion trajectory, by iteratively applying
the pretrained diffusion model, and averaging these predictions. We evaluate
the performance of our ReNoise technique using various sampling algorithms and
models, including recent accelerated diffusion models. Through comprehensive
evaluations and comparisons, we show its effectiveness in terms of both
accuracy and speed. Furthermore, we confirm that our method preserves
editability by demonstrating text-driven image editing on real images.
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Daniel Garibi
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2024-03-22T10:55:01.923Z
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Daniel Garibi
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Or Patashnik
claimed_verified
2024-03-22T09:32:48.654Z
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Or Patashnik
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orpatashnik
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Andrey Voynov
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2024-03-22T10:19:23.588Z
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Andrey Voynov
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avoin
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Hadar Averbuch-Elor
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Daniel Cohen-Or
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2024-03-22T10:19:42.816Z
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Daniel Cohen-Or
user
cohenor
ReNoise: Real Image Inversion Through Iterative Noising
Recent advancements in text-guided diffusion models have unlocked powerful
image manipulation capabilities. However, applying these methods to real images
necessitates the inversion of the images into the domain of the pretrained
diffusion model. Achieving faithful inversion remains a challenge, particularly
for more recent models trained to generate images with a small number of
denoising steps. In this work, we introduce an inversion method with a high
quality-to-operation ratio, enhancing reconstruction accuracy without
increasing the number of operations. Building on reversing the diffusion
sampling process, our method employs an iterative renoising mechanism at each
inversion sampling step. This mechanism refines the approximation of a
predicted point along the forward diffusion trajectory, by iteratively applying
the pretrained diffusion model, and averaging these predictions. We evaluate
the performance of our ReNoise technique using various sampling algorithms and
models, including recent accelerated diffusion models. Through comprehensive
evaluations and comparisons, we show its effectiveness in terms of both
accuracy and speed. Furthermore, we confirm that our method preserves
editability by demonstrating text-driven image editing on real images.
ReNoise method improves image inversion accuracy in text-guided diffusion models without increasing operations, demonstrating effectiveness and editability.
text-guided diffusion models image inversion diffusion sampling process iterative renoising mechanism forward diffusion trajectory accelerated diffusion models