6/25/2023 0 Comments Make art text cloud in idraw![]() So this whole model learns on the fly over many iterations, optimizing two losses we see here. This simple process is repeated until we are satisfied with the results! It will basically create multiple variations of the image and allow the model to converge on results that look right to humans and not simply on the right numerical values for the machine. Here you can see a very important step called image augmentation. We utilize this information to orient the next generation and repeat the process until satisfying results. Then, we compare its encodings with the image’s style and the text. First, we generate an image with random lines. ![]() So using the same image and same text as inputs, you will end up with different results that may look even better! StyleCLIPDraw’s model architecture. This random process is quite cool since it will also allow each new test to look different. At each iteration, which are shown above in the gif, we draw random curves again oriented by the two losses we will see in a second. This new image is then sent back to the algorithm and compared with both the style image and the text, which will generate another version. Image credits to the great CLIPDraw author’s blog post. CLIPDraw synthesizes novel drawings from text. It will just draw random lines at first and create an initial image, as shown below before converging into a final picture. Rather, it will simply draw on a canvas and get better and better over time. It won’t simply generate an image right away. Well, the image generation process is quite unique. Indeed, we also need something else to capture the style of the image sent in both textures and shapes. If you are not familiar with CLIP yet, I would recommend reading the article I wrote on Toward’s AI about it together with DALL-E earlier this year.īut then, how did they apply a new style to it? CLIP is just linking existing images to texts. Using CLIP, the researchers could understand the text from the user input and generate an image out of it. Both the text and images will be encoded similarly so that they will be very close to each other in the new space they are encoded in if they mean the same thing. Quickly, CLIP is a model developed by OpenAI that can basically associate a line of text with an image. CLIP encoding system to compare text inputs with images. As you may suspect, this is incredibly challenging, but we are fortunate enough to have a lot of researchers working on so many different challenges, like trying to link text with images, which is what CLIP can do. So the model has to understand what’s in the text and the image to correctly copy its style. I will quickly show how you can use it and play with their code easily, but first, let’s see how they achieved that. Simply take a picture of the style you want to copy, enter the text you want to generate, and this algorithm will generate a new picture out of it! Just look back at the results above, such a big step forward! The results are extremely impressive, especially if you consider that they were made from a single line of text! To be honest, sometimes it may look a bit all over the place if you select a more complicated or messy drawing style like this one.Īs we said, this new model by Peter Schaldenbrand et al., called StyleCLIPDraw, which is an improvement upon CLIPDraw by Kevin Frans et al., takes an image and text as inputs and can generate a new image based on your text and following the style in the image. ![]() In fact, you can even achieve that from only text and can try it right now with this new method and their Google Colab notebook available for everyone. Have you ever dreamed of taking the style of a picture, like this cool TikTok drawing style, and applying it to a new picture of your choice? Well, I did, and it has never been easier to do. 6 min read Example results of input text and style (left), baseline comparisons (middle two columns), and StyleCLIPDraw results (right).
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