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ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models

Yuxin Zhang, Weiming Dong, Fan Tang, Nisha Huang, Haibin Huang, Chongyang Ma, Tong-Yee Lee, Oliver Deussen, Changsheng Xu

arXiv:2305.16225v3cs.GRcs.CV

TL;DR

Single-image personalization still struggles to disentangle and edit specific visual attributes. ProSpect expands conditioning into stage-specific prompts; experiments report improved disentanglement and controllability for attribute-aware transfer and editing.

  • Problem

    Concept-level personalization methods struggle to represent specific attributes of a single image, while disentangling and recombining attributes such as material, style, and layout is challenging.

    Source: §1 · p. 2

  • Method

    ProSpect learns token embeddings from one image in an expanded conditioning space, using distinct textual conditions for groups of diffusion steps and frequency-based attribute groups.

    Source: §1 · pp. 2–3, §3.1 · p. 5

  • Results

    Experiments report better disentanglement and controllability for P* and effectiveness across attribute-aware generation tasks, with ProSpect separating content from material where Textual Inversion loses fidelity and DreamBooth tends to generate cat-like objects.

    Source: §1 · p. 3

  • Takeaways & Limitations

    ProSpect supports transfer and editing of material, style, content, and layout attributes using representations learned from a single image.

    Source: §1 · p. 3, §6 · p. 13

    ProSpect is slower than encoder-based methods, and transfers across large domain gaps may be unaesthetic or modify similar-scale background objects undesirably.

    Source: §5.5 · p. 12

Abstract

from arXiv · show

Personalizing generative models offers a way to guide image generation with user-provided references. Current personalization methods can invert an object or concept into the textual conditioning space and compose new natural sentences for text-to-image diffusion models. However, representing and editing specific visual attributes such as material, style, and layout remains a challenge, leading to a lack of disentanglement and editability. To address this problem, we propose a novel approach that leverages the step-by-step generation process of diffusion models, which generate images from low to high frequency information, providing a new perspective on representing, generating, and editing images. We develop the Prompt Spectrum Space P*, an expanded textual conditioning space, and a new image representation method called \sysname. ProSpect represents an image as a collection of inverted textual token embeddings encoded from per-stage prompts, where each prompt corresponds to a specific generation stage (i.e., a group of consecutive steps) of the diffusion model. Experimental results demonstrate that P* and ProSpect offer better disentanglement and controllability compared to existing methods. We apply ProSpect in various personalized attribute-aware image generation applications, such as image-guided or text-driven manipulations of materials, style, and layout, achieving previously unattainable results from a single image input without fine-tuning the diffusion models. Our source code is available athttps://github.com/zyxElsa/ProSpect.

1 INTRODUCTION

Concept-level personalization learns a shared concept, but single-image attributes such as material, style, and layout remain difficult to represent and edit. The paper introduces P* and ProSpect to use stage-specific conditioning for attribute-aware generation.

1 INTRODUCTION

  • Diffusion generation proceeds from layout to content to material/style, correlated with visual-signal frequency progressing from low to high.The authors argue that a single textual embedding used across diffusion steps limits visual-attribute disentanglement.Source: §1 · p. 2
  • Prompt Spectrum Space P* expands textual conditioning by assigning a unique textual condition to each group of consecutive diffusion steps.This represents the generation process by stages rather than treating all steps as a whole.Source: §1 · p. 2
  • ProSpect learns token embeddings from a single image in P*, representing the image by frequency to support more flexible and editable attribute generation.The method is presented as a way to separate visual attributes that concept-level personalization treats as a whole.Source: §1 · pp. 2–3
  • ProSpect groups material/style, content, and layout embeddings as high-, medium-, and low-frequency attributes, respectively, and transfers attributes by replacing embeddings.The introduction reports improved transferability, editability, and fidelity for attribute-aware tasks.Source: §1 · p. 3
  • ProSpect separates content from material in attribute-aware generation, while Textual Inversion loses fidelity and DreamBooth tends to produce cat-like objects.The paper also reports effectiveness across various attribute-aware generation tasks.Source: §1 · p. 3

2 RELATED WORK

Related work spans text-to-image synthesis, personalization, image editing, and latent-space approaches. The paper distinguishes its approach by targeting multiple attributes from one image and representing them in the text-conditioning space.

Personalization of generative models

  • Many personalization methods need three to five images or model fine-tuning and learn one concept or overall appearance, whereas this approach targets multiple attributes from one image.The cited work also frames the task as representing, disentangling, and recombining those attributes.Source: §2 · p. 3

Image editing

  • Composer decomposes images into factors for recomposition but uses task-specific models for attributes; this approach uses a pre-trained diffusion model alone.The passage gives edge detection and segmentation as examples of auxiliary models used by Composer.Source: §2 · p. 3

Non-diffusion image editing

  • Latent-space methods manipulate encoded image features; unlike those approaches, this method represents attributes with separate text embeddings and supports cross-domain editing.The related-work discussion describes MUNIT's content/style decomposition and notes that latent traversal is usually limited to within-domain editing.Source: §2 · p. 4

3 METHOD

The method uses diffusion’s stage-dependent attribute generation to represent images with separate textual conditions and edit attributes through stage-specific embedding replacement. Its observations motivate Prompt Spectrum Space and ProSpect’s per-stage image inversion.

3 METHOD

  • Initial stages tend to generate layout and color, middle stages structured appearances, and final stages detailed textures, as removal and injection tests illustrate.Stage interventions produced different changes to parrot appearance, feather layering, and yarn texture.Source: §3 · pp. 4–5

3.1 Prompt Spectrum Space

  • Prompt Spectrum Space divides the 1,000 conditioning steps into ten stages on average, each with a unique textual condition and corresponding token embedding.The conditions occupy the CLIP text-image space, with dimensions n × 1 × 768.Source: §3.1 · p. 5

3.2 ProSpect

  • ProSpect maps a single input image to a collection of textual token embeddings in Prompt Spectrum Space.The method extends textual inversion from P to P∗.Source: §3.2 · p. 6
  • A frozen CLIP text embedding initialized from user text feeds a hypernetwork that produces the stage embeddings; only the hypernetwork is trained.Training typically takes 1,000–3,000 iterations, with dropout set to 0.1.Source: §3.2 · p. 6
  • At inference, replacing embeddings associated with different attributes enables attribute control, such as changing layout while retaining content-related embeddings.The barn example retains p3–p10 and replaces p1–p2.Source: §3.2 · p. 6

4 ANALYSIS OF PROMPT SPECTRUM SPACE

The analysis examines how different diffusion stages represent image structure and detail, and tests where visual attributes and content are expressed across stages. It reports an ordering from layout to content to material or style, linked to the growth of high-frequency information during denoising.

4.1 Visualization of Token Embeddings

  • Early-stage token embeddings tend to represent structure, while later-stage embeddings increasingly represent details such as textures or brushstrokes.In the visualization, p2 tends toward layout or content, whereas p8 tends toward textures or brushstrokes.Source: §4.1 · p. 6

4.2 Visualization of Attribute Distribution

  • The attribute-distribution experiment tests 30 attribute–object pairs and replacements on 10 new objects, increasing change intensity when a single-stage edit has little effect.The evaluation covers 10 pairs each for material, style, and layout; increasing intensity continues until other attributes are affected.Source: §4.2 · pp. 6, 8
  • Table 1 reports CLIP-based evaluation results and marks the best and second-best values with bold and underlining, respectively.The accompanying prose identifies the table as a quantitative evaluation of the method and two baselines.Source: Table 1 · p. 7
  • ProSpect’s visual comparisons report stronger controllability and diversity while preserving reference appearance and material, and better identity preservation in one-shot portraits.The portrait comparison is against DreamBooth; the broader personalization comparison includes TI, DreamBooth, XTI, and Perfusion.Source: Figure 9 · p. 7, Figure 10 · p. 8

4.3 Explanations

  • Diffusion generation proceeds from layout to content to material/style, corresponding to a progression from low- to high-frequency visual information.The Fourier spectrum shows high-frequency information increasing as denoising steps advance, while structural information appears early and detail grows later.Source: §4.3 · p. 8
  • The Fourier-spectrum analysis links increasing high-frequency information over denoising steps to structural information appearing first and image details accumulating later.The proposed explanation is that attributes arise from low to high signal frequencies.Source: §4.3 · p. 8

5 EXPERIMENTS

The experiments report a balance of object fidelity and editability, supported by qualitative comparisons and user preferences across personalized generation tasks. Applications extend to material, style, layout, and combined-attribute guidance, with stated limits involving speed and difficult image content.

5.1 Quantitative Evaluation

  • ProSpect achieves a better balance of object fidelity and editability than TI and DreamBooth without fine-tuning the model.TI fails to preserve object appearance, while DreamBooth tends to overfit; Table 1 presents the CLIP-based evaluation.Source: §5.1 · pp. 8–9

5.2 Qualitative Evaluation

  • Qualitative comparisons show ProSpect maintaining reference details while varying pose, scene, clothing, hairstyle, or artistic style across object and portrait generation.Examples include cat and teddy prompts, plus one-shot portraits compared with DreamBooth.Source: §5.2 · p. 9

5.3 User Study

  • In user studies, ProSpect receives 51.97% of preferences for personalized objects, 66.36% for material guidance, and 61.67% for style guidance.The respective comparisons are against TI and DreamBooth, DreamBooth, and InST.Source: §5.3 · p. 10

5.4 Applications

  • For material tasks, ProSpect transfers materials between unrelated objects and edits images into unrelated materials while retaining overall appearance and background.The comparisons describe DreamBooth as less flexible with single-image input and InstructPix2Pix as failing on semantically unrelated modifications.Source: Figure 11 · p. 9, §5.4 · pp. 10–11
  • For style tasks, ProSpect transfers style and generates semantically different content in a reference style, with more realistic strokes, fewer artifacts, and better identity maintenance than compared methods.Comparisons include InST, JoJoGAN, CAST, and StyTr2.Source: Figure 12 · p. 10, §5.4 · p. 11
  • Layout references guide text-to-image generation and transfer composition, while multiple references can jointly guide layout, content, and style.Examples include complex or semantically unrelated content, landscape and still-life layout transfers, and combined-attribute generation.Source: §5.4 · pp. 11–12, Figure 13 · p. 11, Figure 14 · p. 12
  • With four sculpture images, ProSpect can produce results with improved fidelity and diversity compared with prior approaches, despite being designed for single-image input.The method can also be applied to model fine-tuning methods.Source: §5.4 · p. 12, Figure 15 · p. 12

5.5 Limitations

  • ProSpect is slower than some encoder-based methods, and transfers across large domain gaps may look unaesthetic or unintentionally affect similar background objects.The background issue is reported for images containing similar objects; the limitation passage also notes ProSpect is faster than TI.Source: §5.5 · p. 12, Figure 16 · p. 12

6 CONCLUSION AND FUTURE WORK

The conclusion presents Prompt Spectrum Space P∗ and ProSpect as a stepwise approach to representing images and editing visual attributes. The reported experiments support its fidelity, expressiveness, and controllability across diverse generation tasks, while future work targets finer attribute division and recombination.

6 CONCLUSION AND FUTURE WORK

  • P∗ expands textual conditioning for diffusion models, and experiments indicate better disentanglement and controllability across image-generation granularities.ProSpect inverts text conditions step by step to represent images in P∗.Source: §6 · p. 13
  • ProSpect supports material-, style-, content-, and layout-related transfer and editing with image representations described as more faithful and editable.The conclusion reports superior fidelity, expressiveness, and controllability across diverse image-generation tasks.Source: §6 · p. 13
  • Future work will investigate more detailed attribute division and recombination, including the mutual impact of different textual conditions.Source: §6 · p. 13

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