![]() ![]() Then, at inference, we can implant the unique identifier in different sentences to synthesize the subjects in difference contexts. "dog"), and returns a fine-tuned/"personalized'' text-to-image model that encodes a unique identifier that refers to the subject. Our method takes as input a few images (typically 3-5 images suffice, based on our experiments) of a subject (e.g., a specific dog) and the corresponding class name (e.g. In contrast, our approach (right) can synthesize the clock with high fidelity and in new contexts ( "a clock in the jungle"). Furthermore, even models whose text embedding lies in a shared language-vision space and can create semantic variations of the image, such as DALL-E2, can neither reconstruct the appearance of the given subject nor modify the context (second column). Even with dozens of iterations over a text prompt that contains a detailed description of the appearance of the clock ( "retro style yellow alarm clock with a white clock face and a yellow number three on the right part of the clock face in the jungle"), the Imagen model can't reconstruct its key visual features (third column). ![]() Given a particular subject such as clock (shown in the real images on the left), it is very challenging to generate it in different contexts with state-of-the-art text-to-image models, while maintaining high fidelity to its key visual features. We apply our technique to several previously-unassailable tasks, including subject recontextualization, text-guided view synthesis, appearance modification, and artistic rendering (all while preserving the subject's key features). By leveraging the semantic prior embedded in the model with a new autogenous class-specific prior preservation loss, our technique enables synthesizing the subject in diverse scenes, poses, views, and lighting conditions that do not appear in the reference images. Once the subject is embedded in the output domain of the model, the unique identifier can then be used to synthesize fully-novel photorealistic images of the subject contextualized in different scenes. Given as input just a few images of a subject, we fine-tune a pretrained text-to-image model (Imagen, although our method is not limited to a specific model) such that it learns to bind a unique identifier with that specific subject. In this work, we present a new approach for "personalization" of text-to-image diffusion models (specializing them to users' needs). However, these models lack the ability to mimic the appearance of subjects in a given reference set and synthesize novel renditions of them in different contexts. ![]() Large text-to-image models achieved a remarkable leap in the evolution of AI, enabling high-quality and diverse synthesis of images from a given text prompt. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |