Meet Prompt Diffusion: An AI Framework for Enabling In-Context Learning in Diffusion-Based Generative Models

Next-generation large language models (LLMs), including BERT, GPT-2, BART, T5, GPT-3, and GPT-4, have been developed as a result of recent advances in machine learning, especially in the area of natural language processing (NLP). These models have been used effectively for various tasks, including text production, machine translation, sentiment analysis, and answering questions. Their ability to learn from context, often referred to as learning in context, is one of the emerging behaviors of these LLMs. Without optimizing any model parameters, LLMs with in-context learning capabilities, such as GPT-3, can complete a job by conditioning input-output samples and new query inputs.

Pre-training on numerous language tasks can be combined with learning in context and a well-designed prompt structure, allowing LLMs to successfully generalize to activities they have never encountered. Although learning in context has been extensively investigated in NLP, few applications exist in computer vision. There are two significant difficulties in demonstrating the practicality and promise of learning in context as a standard technique for large vision applications: 1) Creating an effective vision indicator is more difficult than creating indicators for language activities because it requires domain-specific information. : output pairs as examples and image searches as criteria. 2) In computer vision, large models are often trained for specialized tasks, including text-to-image generation, conditional class creation, segmentation, detection, and classification.

These huge vision models need to be more flexible to adapt to new tasks and are not designed for learning in context. Several recent attempts address these problems using NLP responses. Specifically, when a key visual cue is created by merging sample photos, query images, and output images into a bulk realization, a Transformer-based image painting model is trained to anticipate the masked output images. However, stitching together huge photos will significantly increase computational overhead, particularly in high-resolution scenarios. This paper addresses the learning-in-context potential of generative models based on text-guided diffusion by addressing these two issues.

To run learning in context under a vision-language prompt that can handle a wide range of vision-language activities, researchers at Microsoft and UT Austin present a novel model architecture called Prompt Diffusion. Prompt Diffusion undergoes six separate vision and language tasks in tandem. Specifically, they use their vision and language cue to describe a generic vision and language task. Then, using the Stable Diffusion and ControlNet designs as inspiration, they build Prompt Diffusion, which can use their vision language message as input. They suggest Prompt Diffusion as a first step to enable the ability of text-guided diffusion models for in-context learning. You can then use this knowledge to build the output image by remapping the connection to the query image and including the language instructions. More importantly, learning through many tasks endows the model with the ability to learn in context. Immediate diffusion can be successfully generalized to several novel functions that have not yet been observed. This is in addition to his good performance on the six tasks he has seen during the training.

Empirically, Prompt Diffusion works well on familiar and novel and invisible tasks related to learning in context. The effectiveness of Prompt Diffusion is expected to inspire and stimulate further studies on visual learning in diffusion-based context. The following is a summary of his main contributions:

• A state-of-the-art design for vision and language prompts that allows for effective merging of multiple vision and language activities.

• High-quality in-context generation of learned and new, unseen tasks using the rapid diffusion model, the first diffusion-based adaptive vision-language base model capable of learning in context.

• The Pytorch code implementation can be found on GitHub.

review the Paper, Project, and GitHub link. Don’t forget to join our 21k+ ML SubReddit, discord channel, and electronic newsletter, where we share the latest AI research news, exciting AI projects, and more. If you have any questions about the article above or if we missed anything, feel free to email us at [email protected]

🚀 Check out 100 AI tools at AI Tools Club