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Prompt Tuning Inversion for Text-Driven Image Editing Using Diffusion Models

Wenkai Dong, Song Xue, Xiaoyue Duan, Shumin Han

arXiv:2305.04441v1cs.CV

TL;DR

Text-driven editing must align images with target prompts while preserving unedited content, but DDIM-based editing can lose fidelity and other approaches may require masks or source descriptions. The paper proposes Prompt Tuning Inversion to learn an image-conditioned embedding and interpolate it with the target embedding; this interpolation is reported to improve the editability-fidelity trade-off.

  • Problem

    Text-driven editing must align an image with a target prompt while preserving unedited content, but DDIM-based editing can distort image structure and other approaches may require masks or source descriptions.

    Source: §1 · p. 2

  • Method

    Prompt Tuning Inversion learns a conditional embedding during image reconstruction, then interpolates it with the target embedding for classifier-free-guided editing.

    Source: §1 · p. 2

  • Results

    The embedding interpolation is reported to boost the trade-off between editability and fidelity.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The approach requires only a target text prompt for editing, unlike the described Null-Text Inversion process that requires an input description.

    Source: §3.5 · p. 6

    The method does not change all instances of a target object in some multi-object images, such as changing all geese to black storks.

    Source: §5 · pp. 8–9

Abstract

from arXiv · show

Recently large-scale language-image models (e.g., text-guided diffusion models) have considerably improved the image generation capabilities to generate photorealistic images in various domains. Based on this success, current image editing methods use texts to achieve intuitive and versatile modification of images. To edit a real image using diffusion models, one must first invert the image to a noisy latent from which an edited image is sampled with a target text prompt. However, most methods lack one of the following: user-friendliness (e.g., additional masks or precise descriptions of the input image are required), generalization to larger domains, or high fidelity to the input image. In this paper, we design an accurate and quick inversion technique, Prompt Tuning Inversion, for text-driven image editing. Specifically, our proposed editing method consists of a reconstruction stage and an editing stage. In the first stage, we encode the information of the input image into a learnable conditional embedding via Prompt Tuning Inversion. In the second stage, we apply classifier-free guidance to sample the edited image, where the conditional embedding is calculated by linearly interpolating between the target embedding and the optimized one obtained in the first stage. This technique ensures a superior trade-off between editability and high fidelity to the input image of our method. For example, we can change the color of a specific object while preserving its original shape and background under the guidance of only a target text prompt. Extensive experiments on ImageNet demonstrate the superior editing performance of our method compared to the state-of-the-art baselines.

1. Introduction

Text-driven editing seeks to align an input image with a target prompt while preserving unedited content, but existing inversion and mask-based approaches can compromise fidelity or ease of use. The paper proposes Prompt Tuning Inversion and a two-stage editing process to balance editability with fidelity.

1. Introduction

  • DDIM-Edit can change the target attribute but may distort the background and object shape because inversion errors accumulate under classifier-free guidance.For example, the desired edit changes a red car to yellow while preserving its shape and background.Source: Figure 1 · p. 1, §1 · p. 2
  • Mask-based editing can preserve the background but may remove structural information, while DiffEdit can require a precise source description and still lose object shape.Without that source description, DiffEdit may fail to locate the object accurately.Source: §1 · p. 2
  • Prompt Tuning Inversion learns a conditional embedding from the input image, then interpolates it with the target embedding before classifier-free-guided editing.The embedding is optimized while reconstructing the image along a trajectory of noisy latents obtained through DDIM inversion.Source: §1 · p. 2
  • The proposed method requires only an input image and target text, without user-provided masks or source descriptions.The stated goal is editing real images across domains while preserving the background and unmodified object attributes.Source: §1 · p. 2
  • The method outperforms state-of-the-art methods in the trade-off between editability and fidelity, according to qualitative and quantitative comparisons.The supplied passage does not report metric values.Source: §1 · p. 2

2. Related work

Related work covers text-to-image synthesis and image editing with GANs, CLIP, and diffusion models. The paper distinguishes its inversion approach by learning a conditional embedding of the input image rather than optimizing an unconditional embedding.

2. Related work

  • Text-to-image synthesis spans GAN-, CLIP-, and diffusion-based approaches; Latent Diffusion Model operates in latent space and adds cross-attention.The paper builds on LDM for its image-generation capability.Source: §2 · p. 3
  • Image-editing approaches include GAN- and CLIP-based methods, diffusion-based methods, model fine-tuning, and mask-guided or automatically masked editing.The paper notes that some fine-tuning methods risk overfitting or language drift, while user-provided masks can hinder interaction.Source: §2 · p. 3
  • Unlike Null-Text Inversion [27], which optimizes the unconditional embedding, this method learns a conditional embedding of the input image.The paper also contrasts its target-prompt-only editing with Null-Text Inversion's use of Prompt-to-Prompt cross-attention control, which requires an input description [16].Source: §2 · p. 3

3. Methodology

The method edits an input image using a target prompt through reconstruction followed by classifier-free-guided sampling with an interpolated embedding. The interpolation balances target-prompt editability with fidelity to the reconstructed image.

3.1. Background and preliminaries

  • Latent diffusion operates on encoded image representations, while classifier-free guidance combines conditional and unconditional predictions during text-guided sampling.The paper uses Stable Diffusion and describes guidance as amplifying the effect of conditional text.Source: §3.1 · p. 4

3.2. Problems of DDIM inversion

  • DDIM inversion can nearly reconstruct an input at guidance scale ω = 0, but larger sampling scales can reduce fidelity; matching forward and sampling scales gives the best reconstruction in each table row.The paper notes that PSNR still decreases as ω increases even when the forward and sampling scales match.Source: §3.2 · p. 5, Figure 3 · p. 5, Table 1 · p. 5

3.3. Prompt tuning for inversion

  • Prompt Tuning Inversion optimizes only each image’s conditional embedding along a DDIM-inverted trajectory, while keeping the diffusion network and text encoder frozen.The optimization uses ω > 1 at each timestep to reconstruct the input.Source: §3.3 · p. 5

3.4. Prompt tuning for editing

  • For editing, the method linearly interpolates the optimized embedding with the target-text embedding before classifier-free-guided sampling.The target term supports editability, while the optimized term supports reconstruction fidelity; η = 0 or η = 1 yields the reconstructed image or DDIM-Edit output, respectively.Source: §3 · p. 3, §3.4 · pp. 5–6
  • Latent interpolation is a simpler alternative that can clutter images because it mixes source and edited objects spatially rather than semantically.This alternative does not require prompt tuning.Source: §3.4 · p. 6

3.5. Discussion

  • Unlike Imagic and UniTune, the method optimizes a conditional embedding rather than fine-tuning the diffusion model, reducing computational budgets.Unlike Null-Text Inversion, it optimizes the conditional rather than unconditional embedding and requires only a target prompt, not an input-image description.Source: §3.5 · p. 6

4. Experiments

Experiments compare editing trade-offs on ImageNet and reconstruction quality on COCO. The method achieves the best reported editability–fidelity trade-off against DiffEdit and DDIM-Edit, while its inversion and interpolation settings affect reconstruction and editing balance.

4.2. Comparison with other methods on ImageNet

  • Our method achieves the best CSFID-LPIPS trade-off against DiffEdit and DDIM-Edit on ImageNet.The comparison fixes the DDIM encoding ratio at 0.8 and varies the mask threshold to draw trade-off curves.Source: Figure 4 · p. 7, §4.2 · p. 7

4.1. Setup

  • The ImageNet evaluation measures editability with LPIPS and fidelity with CSFID, with lower values indicating better performance on both metrics.The experiments compare trade-off curves for edits between ImageNet classes; the implementation uses Stable Diffusion at 512 × 512 resolution and reports editing in approximately one minute on a Tesla V100.Source: §4.1 · pp. 6–7

4.2. Comparison with other methods on ImageNet

  • With a learnable conditional embedding, the method produces realistic edits and preserves original details, unlike global DDIM-Edit edits in complex examples.The examples compare the proposed method with other methods, including DDIM-G and DDIM-L variants.Source: Figure 5 · p. 7, §4.2 · p. 7
  • DDIM-Edit achieves a better trade-off than DiffEdit, which the authors attribute to its cross-attention-map masks being more appropriate.The comparison methods share the DDIM forward process and publicly available pretrained weights.Source: §4.2 · p. 7

4.3. Ablation study

  • 25.71 vs. 24.45 PSNR: PTI reconstructs images better than NTI, while DDIM inversion scores 13.64 under the reported guidance settings.Across optimizing settings with N from 1 to 5 and β from 0.01 to 0.1, PTI's reconstruction quality remains higher than NTI's, indicating faster convergence.Source: §4.3 · p. 8, Table 2 · p. 8, Table 3 · p. 8
  • Reducing η from 1.0 to 0.9 improves the CSFID-LPIPS trade-off, but lower η values or reducing β from 0.1 to 0.05 or 0.01 worsens the balance.Figure 6 presents the η and learning-rate ablations.Source: §4.3 · p. 8, Figure 6 · p. 8

5. Conclusion and future work

Prompt Tuning Inversion combines image information with the target text to balance editability and fidelity, and the method achieves superior editing performance over previous methods. In multi-object scenes, it may fail to edit every instance; more precise attention maps or additional conditional controls are proposed as future directions.

5. Conclusion and future work

  • The method achieves superior editing performance over previous methods while balancing editability and fidelity.It interpolates the target text embedding with the optimized embedding from reconstruction.Source: §5 · p. 8
  • In multi-object images, neither DiffEdit nor the proposed method changes every goose to a black stork.The authors identify this as a limitation of their method.Source: §5 · p. 8
  • More precise attention maps or different modes of conditional control are possible future directions for addressing the limitation.Source: §5 · p. 9

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