Let’s first compare realistic images generated using the prompt in the realistic people tutorial. I tested the SDXL model, so you don’t have to. You will find a series of images generated with the same prompts from the v1.5 and SDXL models. Users overwhelmingly prefer the SDXL model over the 1.5 model (figure from the research article)Īccording to Stability AI’s own study, most users prefer the images from the SDXL model over the v1.5 base model. This is 4 times larger than v1.5 model’s 512×512. The default image size of SDXL is 1024×1024. Together with the larger language model, the SDXL model generates high-quality images matching the prompt closely. The U-Net, the most crucial part of the diffusion model, is now 3 times larger. This significantly increases the training data by not discarding 39% of the images. The SDXL model has a new image size conditioning that aims to use training images smaller than 256×256. The prompts that work on v1.5 will have a good chance to work on SDXL. Bringing back OpenAI’s CLIP makes prompting easier. This is a smart choice because Stable Diffusion v2 uses OpenClip alone and is hard to prompt. The language model (the module that understands your prompts) is a combination of the largest OpenClip model (ViT-G/14) and OpenAI’s proprietary CLIP ViT-L. (You can optionally run the base model alone.) The base model sets the global composition. You run the base model, followed by the refiner model. The SDXL model is, in practice, two models. Differences between SDXL and v1.5 models The SDXL model consists of two models – The base model and the refiner model. The total number of parameters of the SDXL model is 6.6 billion, compared with 0.98 billion for the v1.5 model. In the AI world, we can expect it to be better. The model is released as open-source software. The Stable Diffusion XL (SDXL) model is the official upgrade to the v1.5 model. What image sizes should I use with SDXL models?.Installing SDXL 1.0 models on Windows or Mac.Installing SDXL 1.0 models on Google Colab.
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