Microsoft’s visual chatGPT takes the industry by storm. Now unveiled, visual chatGPT is a groundbreaking innovation that dives deep into the world of images. Hold your breath as we explore the mesmerizing synergy of four exceptional foundation models: blip stable fusion, pics to picks control net, and the incredible detection algorithm.
Visual chatGPT has a surprise in store for you. Not only does it answer your questions, but it also lets you interact with it in a whole new way. Imagine asking it to create a red flower based on how far away things are in a picture. And that’s not all, it goes beyond your expectations by turning the flower into a fun cartoon, guiding you through the process step by step.
But this is just a sneak peek of what visual chatGPT can do. It’s a work in progress, but it already showcases its potential in talking, drawing, and editing with visual foundation models.
Now let’s dive into some captivating examples. Take a look at this one: when the prompt asks if it can generate a cat, and voila, a cat magically appears. You might wonder how this differs from mid-journey or stable diffusion. Well, here’s the exciting part. Visual chatGPT goes beyond just being a prompt generator. For instance, it can replace the cat with a dog and even remove the book. But that’s not all. You will be amazed as visual chatGPT generates the key edge of an image instantly. And if you thought that was impressive, it doesn’t stop there. It can also generate a yellow dog based on a given image. It’s incredible to see models like visual chatGPT incorporating such capabilities.
And that’s just the beginning. Microsoft is tirelessly working on a multitude of projects integrating various large language models.
Microsoft’s approach to developing visual chatGPT is unique. Rather than starting from scratch, they built it directly based on chatGPT, incorporating a diverse range of visual foundation models (VFMs).
Now let’s clarify one important point. Visual chatGPT’s capabilities are distinct from GPT4’s upcoming multimodal feature. They are not the same, and it’s crucial to avoid any confusion.
Here’s where things get interesting. This is vastly different from traditional chatGPT or prompt image models. Simply asking them to generate an image wouldn’t provide the same level of insight. While tools like mid-journey or stable diffusion can generate images, they wouldn’t be able to accurately describe specific attributes like the color of the background. Mid-journey has a new description feature, but it doesn’t quite match the capability showcased here with visual chatGPT. You can explicitly ask, ‘Can you tell me what color the background is?’ and receive a clear response, such as ‘The background color of this image is blue.’ It even goes beyond that. When you ask it to remove the apple from the picture and describe the resulting image, which now contains a drinking glass against the blue background.
It’s worth noting that at times, the software may encounter some challenges. For example, in one instance, although the apple is removed, the shadow is also missing. However, the user doesn’t give up and asks for help in replacing the table with a black one. Remarkably, the software quickly and accurately fulfills the request, resulting in an image with a black table. These nuances make visual chatGPT truly remarkable.
You’ll be pleased to know that there are ways and workarounds to help you achieve the desired outcome you want. However, it’s important to keep in mind that there can be inconsistencies. Sometimes when you input something in the chat, it may not register for some reason. It could be limited to a certain number of inputs, although the exact limit is uncertain. But when it does work, consider yourself lucky. Simply click on ‘Describe this image,’ and you’ll receive a description like ‘This image shows a cat jumping over a purple flower.’
Now let’s talk about the limitations. Visual chatGPT heavily relies on chatGPT and VFMs. This means the accuracy of the output depends on chatGPT correctly assigning tasks to the VFMs. Additionally, it requires significant prompt engineering, which can be time-consuming and challenging if you’re not familiar with crafting suitable prompts. It also calls for expertise in computer vision and natural language processing to convert VFMs into language and create distinguishable model descriptions.
Keep in mind that real-time capabilities are limited as visual chatGPT is not designed for instantaneous interactions like a dedicated real-time system would be. It’s essential to understand that visual chatGPT won’t be replacing the multimodal features of GPT4 as they serve different purposes and have distinct functionalities.
