![]() Using the only masked option can create artifacts like the image below. On the other hand, you should inpaint the whole picture when regenerating part of the background. Inpaint the whole picture won’t fix the face: Original inpaint whole picture inpaint only masked Inpainting only masked fixes the face. ![]() Use the whole picture option if global consistency is important, such as fixing blemishes in the background. It is often at the expense of the consistency of the rest of the image since you crop out the masked area for inpainting. The most common use case of the only masked option is to regenerate faces in finer detail. The whole picture option takes the input picture and masks as is without cropping. It fixes the issues of painting small faces or objects. It then scales the result back to its original size. It crops out the masked area and uses the whole resolution for that area. The only masked option is designed to fix this. It cannot generate a face not covered by enough pixels. The native resolution of Stable Diffusion v1 is 512×512 pixels. Original 0.1 0.5 0.9 Denoising strength Inpaint the whole picture vs only maskedĭo you wonder why Stable Diffusion is not able to generate the face correctly in the first place? It is because the face is too small. Setting denoising strength to 0.5 is a good starting point. (There’s a way to fix it in the later part of this article) Too low a value could result in a blurry result. It controls how much the masked area should change.Īs a rule of thumbnail, too high a value causes the inpainting result to be inconsistent with the rest of the image. Denoising strengthĭenoising strength is the most important setting in inpainting. We will go through the essential settings of inpainting in this section. But random noise is added only to the masked area in inpainting. Inpainting is similar to image-to-image, where random noise is added to the whole image in the latent space before denoising. You get images with the face fixed while keeping everything outside the mask the same. Denoising strength: 0.75 – This is the most critical parameter controlling how much the masked area will change.Batch size: 4 – How many inpainting images to generate each time.Inpaint area: Only masked – We want to regenerate the masked area. ![]() Use the paintbrush tool to create a mask on the face. You should now be on the img2img page and Inpaint tab. Click the Send to Inpaint icon below the image to send the image to img2img > inpainting. Luckily, you can use inpainting to fix it. This is because the face is too small to be generated correctly. But the faces are not quite right in full-body images. Let’s say you used the txt2img page to generate an image using the following settings.įull body, audrey hepburn, black hair, 18 years old, 1940’s, photoshoot, Fujifilm XT3 Viltrox, posing, instagram, happy smile, stand up, ultra detailed, sharp focus, elegant, jewels, urban background, rim lighting, short beige dress, beige kitten-heels, black gloves, pearl tiara, pearls necklace, brilliant pearl earrings, hdr, high contrast, sunlight,, shadows, skin pore, pretty, beautiful, feminine, loving, in love, adorable, fashion, chic, excellence, leg, dress We will go through the basic usage of inpainting in this section. A basic example of inpainting Step-by-step workflow Check out the AUTOMATIC1111 Guide if you are new to AUTOMATIC1111. (This site earns a small commission if you sign up.)Ĭheck out the Quick Start Guide if you are new to Stable Diffusion. They offer 20% extra credits to our readers. You can use this GUI on Windows, Mac, or Google Colab.Ĭheck out Think Diffusion if you are looking for a fully managed AUTOMATIC1111 online service. We will use AUTOMATIC1111 Stable Diffusion WebUI, a popular and free open-source software.
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