![]() Note that the effect is opposite when you increase text CFG and image CFG: Increasing text CFG changes the image more Increasing the image CFG changes the image less. When CFG values is high, the prompt or the image is closely followed. The meanings of these two knots are the same as in the vanilla Stable Diffusion: When CFG value is low, the prompt or Image is largely ignored. So you will see text CFG and Image CFG in AUTOMATIC1111 GUI which I will show you how to use. But since Instruct pix2pix model has two conditionings (prompt and image), it has two CFG parameters, one for the prompt and the other for the image. The diffusion is guided by the usual classifier free guidance (CFG) mechanism. (Read the article “ How does Stable Diffusion work?” if you need a refresher of model architecture of Stable Diffusion.) ![]() Instruct pix2pix has two conditionings, the text prompt and the input image. Recall that Image-to-image has one conditioning, the text prompt, to steer the image generation. Much like image-to-image, It first encodes the input image into the latent space. The Instruct pix2pix model is a Stable Diffusion model. There are two parts to understand how the model works: (1) model architecture and (2) training data. ![]() We can simply give the image to the model and say “Turn the horse into a dragon.” The model will surgically change the horse into a dragon, while keeping the rest of the image intact. For example, let’s say we want to turn the horse into a dragon in following image. It’s an innovative way of editing photos just by telling it what you want to change. Using Instruct-pix2pix in AUTOMATIC1111.Install Instruct pix2pix in AUTOMATIC1111. ![]()
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