Introduction to Paint by Example: Exemplar-based Image Editing with Diffusion Models
Overview
Paint by Example is an innovative image editing project that stands out in the field of digital art, leveraging diffusion models for exemplar-guided image manipulation. This approach provides users with precise control over edits by using reference images, allowing for more refined modifications compared to traditional language-guided editing techniques. The project is spearheaded by a team of researchers including Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, and Fang Wen.
Key Features
The fundamental goal of Paint by Example is to offer enhanced control in image editing by using examples or exemplars. This is achieved through a process known as self-supervised training, which disentangles and reorganizes the source image and the reference exemplar image. However, simple techniques often lead to unwanted artifacts during the fusion process. To address this challenge, the project introduces an information bottleneck and robust data augmentations, which together prevent the direct copy-pasting of reference images.
Paint by Example further enhances controllability by incorporating a unique mask of arbitrary shapes for the exemplar image. This mask, together with classifier-free guidance, ensures that the edited image remains similar to the reference example. Importantly, the entire system operates within a single forward pass of the diffusion model, eliminating the need for iterative optimization processes, which can be computationally expensive.
Recent Updates
The project has seen several updates:
- In November 2023, a related work, Asymmetric VQGAN, was introduced to improve detail preservation in non-masked regions.
- The code for generating quantitative results was released in May 2023.
- Additional non-official third-party support was provided through ModelScope in February 2023.
- A demo was launched on Hugging Face in December 2022.
Prerequisites
To explore Paint by Example, users are required to set up an appropriate environment using conda. A comprehensive pretrained model is available for use, which was initially trained on the Open-Images dataset over 40 epochs. Users are expected to download and save this pretrained model in the specified directory.
Testing and Training
Users can test the model utilizing provided scripts, which are straightforward to execute. For example, one can sample from the model using the inference.py script or a simple test script such as test.sh.
The training process involves several steps, including preparing data from the Open-Images dataset, downloading necessary annotations, and utilizing pre-existing models for initialization. The system uses a modified version of the Stable Diffusion model, with an additional set of input channels to accommodate other image data.
Benchmarking and Evaluation
To evaluate the model's performance, the team developed a test benchmark called COCOEE, which is based on the MSCOCO dataset. This benchmark aids in assessing the model's quantitative results by manually selecting and retrieving images and reference patches that are semantically coherent.
Metrics such as FID Score and CLIP Score are used to evaluate the model's output. Scripts are provided to facilitate the calculation of these scores, ensuring thorough evaluation and validation of the model's effectiveness.
Conclusion
Paint by Example offers a novel and precise method for image editing by employing exemplar guidance and diffusion models. It is a promising tool for artists and developers looking for enhanced control over their digital creations. With ongoing updates and community support, it holds a significant place in the evolution of digital image processing techniques.
For more detailed information and access to resources such as demos and the project paper, you can refer to the linked paper on arXiv or explore the project's Hugging Face demo.