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InsertDiffusion: Identity Preserving Visualization of Objects through a Training-Free Diffusion Architecture

Phillip Mueller, Jannik Wiese, Ioan Craciun, Lars Mikelsons

TL;DR

InsertDiffusion addresses realistic object insertion into backgrounds without training or fine-tuning by introducing a training-free, mask-based diffusion pipeline. It blends the object and background through a masked diffusion step guided by CLIP prompts, followed by a SDXL refinement to improve high-frequency realism, all without modifying diffusion weights. Evaluated on real-real benchmarks and a technical-design dataset, it outperforms state-of-the-art training-free and background-replacement baselines in human preference and prompt alignment, while maintaining robust object geometry. The method's modular design and reliance on off-the-shelf diffusion models enable rapid, scalable visualizations for product design and marketing, though automatic object placement and text rendering remain areas for future improvement and caution due to potential misuse.

Abstract

Recent advancements in image synthesis are fueled by the advent of large-scale diffusion models. Yet, integrating realistic object visualizations seamlessly into new or existing backgrounds without extensive training remains a challenge. This paper introduces InsertDiffusion, a novel, training-free diffusion architecture that efficiently embeds objects into images while preserving their structural and identity characteristics. Our approach utilizes off-the-shelf generative models and eliminates the need for fine-tuning, making it ideal for rapid and adaptable visualizations in product design and marketing. We demonstrate superior performance over existing methods in terms of image realism and alignment with input conditions. By decomposing the generation task into independent steps, InsertDiffusion offers a scalable solution that extends the capabilities of diffusion models for practical applications, achieving high-quality visualizations that maintain the authenticity of the original objects.

InsertDiffusion: Identity Preserving Visualization of Objects through a Training-Free Diffusion Architecture

TL;DR

InsertDiffusion addresses realistic object insertion into backgrounds without training or fine-tuning by introducing a training-free, mask-based diffusion pipeline. It blends the object and background through a masked diffusion step guided by CLIP prompts, followed by a SDXL refinement to improve high-frequency realism, all without modifying diffusion weights. Evaluated on real-real benchmarks and a technical-design dataset, it outperforms state-of-the-art training-free and background-replacement baselines in human preference and prompt alignment, while maintaining robust object geometry. The method's modular design and reliance on off-the-shelf diffusion models enable rapid, scalable visualizations for product design and marketing, though automatic object placement and text rendering remain areas for future improvement and caution due to potential misuse.

Abstract

Recent advancements in image synthesis are fueled by the advent of large-scale diffusion models. Yet, integrating realistic object visualizations seamlessly into new or existing backgrounds without extensive training remains a challenge. This paper introduces InsertDiffusion, a novel, training-free diffusion architecture that efficiently embeds objects into images while preserving their structural and identity characteristics. Our approach utilizes off-the-shelf generative models and eliminates the need for fine-tuning, making it ideal for rapid and adaptable visualizations in product design and marketing. We demonstrate superior performance over existing methods in terms of image realism and alignment with input conditions. By decomposing the generation task into independent steps, InsertDiffusion offers a scalable solution that extends the capabilities of diffusion models for practical applications, achieving high-quality visualizations that maintain the authenticity of the original objects.
Paper Structure (18 sections, 4 equations, 9 figures, 6 tables)

This paper contains 18 sections, 4 equations, 9 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Image-to-Image Transformation
  4. Inpainting
  5. Object Insertion
  6. Method
  7. Core Architecture
  8. Optional Additions
  9. Implementation Details
  10. Experiments
  11. Benchmarks
  12. Metrics
  13. Composition into Existing Background
  14. Composition with Generated Background
  15. Ablations
  16. ...and 3 more sections

Figures (9)

  • Figure 1: Realistic object representations in existing and generated backgrounds without the necessity for training or finetuning any parts of the architecture.
  • Figure 2: Local image editing through inpainting as proposed in RePaint lugmayrRePaintInpaintingUsing2022
  • Figure 3: The InsertDiffusion Architecture is designed to seamlessly insert an object into a new background while preserving the geometry and key visual characteristics of the object. After the object is scaled and positioned by the user, an object-mask is created automatically and composed with the background image. The masked background is passed to SD together with the original object image. Using the image-to-image and inpainting functions, the original image is layered onto the background for each denoising step. The resulting intermediate image composition is subsequently refined by a second diffusion model (SDXL).
  • Figure 4: Image Colorization scheme for black-and-white images. Given a mask of the object, SDXL podellSDXLImprovingLatent2023 is prompted to color the object defined by the masked area. If the original image containing the object is of low resolution, we advise upscaling the object by using functionality provided by Stable Diffusion.
  • Figure 5: Qualitative comparison with existing methods for insertion of product images into existing backgrounds, including TF-Icon luTFICONDiffusionBasedTrainingFree2023 and AnyDoor chenAnyDoorZeroshotObjectlevel2023. Our method improves seamless integration of the object into the background while preserving the geometry and structural integrity of the object.
  • ...and 4 more figures