Custom image generation is the process of generating images of certain personal objects in different contexts specified by the user. For example, one may want to visualize the different ways their dog would look in different scenarios. In addition to personal experiences, this method also has use cases in personalized storytelling, interactive designs, etc. Although current text-to-image generation models have demonstrated exceptional performance, they fail to customize image generation to the specific topic and often fall short. in terms of fidelity to the reference object.
In this research work, a team of Salesforce ai researchers attempted to address the above issues and introduced a novel architecture. starter pork, which enables custom image generation capabilities in text-to-image models. The idea behind the architecture is to insert the appearance of the reference object into the features of a pre-trained diffusion model so that the generated images imitate the reference object. This process is done by replacing all self-attention (SA) layers with an operation that the authors call reference self-attention (RSA).
BootPIG is built on top of existing diffusion models and its architecture consists of two replicas of a latent diffusion model: Reference UNet and Base UNet. The first is used to process the reference image and collect its features before each SA layer. UNet Base SA layers are modified to RSA layers, use the reference features as input and guide the image generation towards the reference object.
To train BootPIG, the researchers used an automated synthetic data generation pipeline that leverages the capabilities of ChatGPT, Stable Diffusion, and the Segment Anything model. ChatGPT is used to generate subtitles, Stable Diffusion for image generation, and the Segment Anything model to segment the foreground of the image, which is then used as a reference image. The most important thing is that it can be trained in just approximately 1 hour.
For evaluation, the authors compared the performance of BootPIG with that of existing methods such as BLIP-Diffusion, ELITE, and Dreambooth. The results of the qualitative comparison show that BootPIG outperforms the other methods in terms of subject and cue fidelity and avoids adjustments at the time of testing. Furthermore, human evaluation highlights the superiority of BootPIG over other methods. Human raters consistently preferred frame-generated images and found significantly higher theme and caption fidelity.
BootPIG also has some limitations that are common to existing methods. In many cases, it fails to represent the fine details of the subject and has difficulty strictly complying with the user's instructions. However, some of its flaws are also inherited from the underlying models. However, BootPIG shows impressive results when it comes to custom image generation. The authors believe their method can help learn new capabilities and unlock other imaging modalities.
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