Abhishek-Thakur
The video introduces the Stable Diffusion Inpainting with Segment Anything Model (SAM), which combines the segment editing model with the stable diffusion model to create a new and exciting model. The presenter demonstrates how to combine the two models and build a simple demo where an input image can have parts replaced with prompts or have the background replaced instead of the object. SAM is also used to inpaint an image using a segment mask created by clicking on the original image or to create a new image with a different background. The video concludes by encouraging viewers to like, subscribe, and share the video.
In this section, the video creator introduces the concept of combining the segment editing model with the stable diffusion model in order to create something new and exciting. The segment anything model is open source and readily available, and it appears to be a very good model. The creator then shows the viewer how to combine the two models, using a GitHub repository for the segment editing model and stable diffusion from Hugging Face Diffusers. They then create a file called "app.py" and import the necessary libraries, including Radio. The creator goes on to define the model type and checkpoint, and then initiates the predictor class and the stable diffusion in painting pipeline.
In this section, the presenter explains how to build a simple demo where an input image is selected and parts of the image can be replaced with prompts or the background can be replaced instead of the object. A row is created with three image components including input image, mask image, and output image. Another row includes a prompt text box and a submit button. The presenter also introduces the 'inpaint' function that takes an image, a mask, and a prompt using stable diffusion 2.1. The image is converted to a PIL image, and the same thing is done with the mask image before proceeding with the function.
In this section, the presenter demonstrates how to use the Stable Diffusion Inpainting with Segment Anything Model (SAM) to inpaint an image using a segment mask created by clicking on the original input image. The presenter resizes the input image and mask to a specific dimension and then writes the function "generate_mask" to handle the pixel coordinate selection event triggered by clicking on the input image. Inside the "generate_mask" function, the selected pixels are stored in a list and used to create input points, and the SAM predictor is used to predict the mask. The presenter shows how to implement the "set_image" and "predict" functions within SAM, which take in the original image and input points to generate the output mask.
In this section of the video, the presenter explains how the Select Anything Model (SAM) can be used for inpainting. The SAM is used to construct input points and input labels, which are then used along with the image to create a mask. The mask is displayed in the mask image, and then stable diffusion is used for inpainting using the mask and prompt text. The demo is launched using the radio demo, and the user can upload an image, click on a specific object in the image to generate a mask, and enhance the mask by clicking more to complete the missing parts.
In this section, the video creator demonstrates the use of the Segment Anything Model (SAM) and stable diffusion inpainting to create a new image with a different background. By selecting different points on the image, the creator is able to create a binary mask that separates the foreground from the background. They then demonstrate the ability to change the background by inverting the mask and using it to select the foreground. The resulting image is then submitted to the model, creating a new image with a different background. The creator concludes the video by encouraging viewers to like, subscribe, and share the video.
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