# E-Commerce Inpainting with Mask Guidance in Controlnet for Reducing Overcompletion

arXiv: https://arxiv.org/abs/2409.09681 · HTML: https://mlsanity.com/paper/2409.09681/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 12 of its 15 references that are in the map.
- First posted on arXiv 15 Sep 2024.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 12 of 12)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Blended Latent Diffusion](https://mlsanity.com/paper/2206.02779/) (2022)
- [Segment Anything in High Quality](https://mlsanity.com/paper/2306.01567/) (2023)
- [BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion](https://mlsanity.com/paper/2403.06976/) (2024)
- [Smartbanner: Intelligent banner design framework that strikes a balance between creative freedom and design rules](https://mlsanity.com/paper/2303.10325/) (2023)
- [Enhancing Prompt Following with Visual Control Through Training-Free Mask-Guided Diffusion](https://mlsanity.com/paper/2404.14768/) (2024)
- [Two-stage dynamic creative optimization under sparse ambiguous samples for e-commerce advertising](https://mlsanity.com/paper/2312.01295/) (2023)
- [Training-Free Style Consistent Image Synthesis with Condition and Mask Guidance in E-Commerce](https://mlsanity.com/paper/2409.04750/) (2024)
- [Intelligent Parsing: An Automated Parsing Framework for Extracting Design Semantics from E-commerce Creatives](https://mlsanity.com/paper/2312.17283/) (2023)

## Built on by (most cited first, 4 of 4)

- [Dual-Channel Attention Guidance for Training-Free Image Editing Control in Diffusion Transformers](https://mlsanity.com/paper/2602.18022/) (2026)
- [Layout Control and Semantic Guidance with Attention Loss Backward for T2I Diffusion Model](https://mlsanity.com/paper/2411.06692/) (2024)
- [FlexID: Training-Free Flexible Identity Injection via Intent-Aware Modulation for Text-to-Image Generation](https://mlsanity.com/paper/2602.07554/) (2026)
- [Inject Where It Matters: Training-Free Spatially-Adaptive Identity Preservation for Text-to-Image Personalization](https://mlsanity.com/paper/2602.13994/) (2026)

## Abstract (arXiv)

E-commerce image generation has always been one of the core demands in the e-commerce field. The goal is to restore the missing background that matches the main product given. In the post-AIGC era, diffusion models are primarily used to generate product images, achieving impressive results. This paper systematically analyzes and addresses a core pain point in diffusion model generation: overcompletion, which refers to the difficulty in maintaining product features. We propose two solutions: 1. Using an instance mask fine-tuned inpainting model to mitigate this phenomenon; 2. Adopting a train-free mask guidance approach, which incorporates refined product masks as constraints when combining ControlNet and UNet to generate the main product, thereby avoiding overcompletion of the product. Our method has achieved promising results in practical applications and we hope it can serve as an inspiring technical report in this field.