We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. Given a pair of task-specific example images, such as depth from/to image and scribble from/to image, and a text guidance, our model automatically understands the underlying task and performs the same task on a new query image following the text guidance. To achieve this, we propose a vision-language prompt that can model a wide range of vision-language tasks and a diffusion model that takes it as input. The diffusion model is trained jointly on six different tasks using these prompts. The resulting Prompt Diffusion model becomes the first diffusion-based vision-language foundation model capable of in-context learning. It demonstrates high-quality in-context generation for the trained tasks and effectively generalizes to new, unseen vision tasks using their respective prompts. Our model also shows compelling text-guided image editing results. Our framework aims to facilitate research into in-context learning for computer vision. We share our code and pre-trained models at https://github. com/Zhendong-Wang/Prompt-Diffusion.
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Image synthesis: a review of methods, datasets, evaluation metrics, and future outlook
Image synthesis is a process of converting the input text, sketch, or other sources, i.e., another image or mask, into an image. It is an important problem in the computer vision field, where it has attracted the research community to attempt to solve this challenge at a high level to generate photorealistic images. Different techniques and strategies have been employed to achieve this purpose. Thus, the aim of this paper is to provide a comprehensive review of various image synthesis models covering several aspects. First, the image synthesis concept is introduced. We then review different image synthesis methods divided into three categories: image generation from text, sketch, and other inputs, respectively. Each sub-category is introduced under the proper category based upon the general framework to provide a broad vision of all existing image synthesis methods. Next, brief details of the benchmarked datasets used in image synthesis are discussed along with specifying the image synthesis models that leverage them. Regarding the evaluation, we summarize the metrics used to evaluate the image synthesis models. Moreover, a detailed analysis based on the evaluation metrics of the results of the introduced image synthesis is provided. Finally, we discuss some existing challenges and suggest possible future research directions.
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- Award ID(s):
- 2025234
- NSF-PAR ID:
- 10421011
- Date Published:
- Journal Name:
- Artificial Intelligence Review
- ISSN:
- 0269-2821
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Face sketch-photo synthesis is a critical application in law enforcement and digital entertainment industry. Despite the significant improvements in sketch-to-photo synthesis techniques, existing methods have still serious limitations in practice, such as the need for paired data in the training phase or having no control on enforcing facial attributes over the synthesized image. In this work, we present a new framework, which is a conditional version of Cycle-GAN, conditioned on facial attributes. The proposed network forces facial attributes, such as skin and hair color, on the synthesized photo and does not need a set of aligned face-sketch pairs during its training. We evaluate the proposed network by training on two real and synthetic sketch datasets. The hand-sketch images of the FERET dataset and the color face images from the WVU Multi-modal dataset are used as an unpaired input to the proposed conditional CycleGAN with the skin color as the controlled face attribute. For more attribute guided evaluation, a synthetic sketch dataset is created from the CelebA dataset and used to evaluate the performance of the network by forcing several desired facial attributes on the synthesized faces.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s10462-023-10434-2
