Mastering SDXL Inpainting: Create Stunning Art with Stable Diffusion and Automatic 1111

In this segment, Eric from Alchem Zero introduces a video focused on the inpainting process using Stable Diffusion XL (SDXL) models. He addresses common issues users face, such as edges not blending properly after inpainting. Eric plans to demonstrate his workflow using different inpainting models, including the official SDXL inpainting model which has not been well-received. He emphasizes the use of the 'Sastraran' model from Civit AI for its versatility and begins the process with an image of an aristocratic family, aiming to show how to modify faces and clothing using specific prompts and samplers.

Eric proceeds with the inpainting tutorial by discussing the importance of adjusting the mask blur setting when working with high-resolution SDXL images. He explains that a higher mask blur helps to alleviate the edge effect around the inpainted areas. Demonstrating the process, he uses the control and ALT keys to manipulate the brush and image size within the software. He then focuses on inpainting a family portrait, adjusting the prompts to refine the image and experimenting with different features like adding an aristocratic man with an arrogant and pompous expression.

In this part, Eric discusses the challenges of maintaining the softness and sharpness in AI-generated images, especially when working with faces. He notes the issues with the official SDXL inpainting model, which resulted in low ratings and errors. To address this, he switches to using a 1.5 or 2.1 version of the inpainting model, specifically the 'Zooya' model by RPG artist, which is known for its photorealistic results. Eric illustrates how to adjust settings such as mask blur and resolution to achieve a more natural look in the inpainted areas.

Eric tackles the difficult task of inpainting hands in AI-generated images, noting that hands often present a challenge for AI image generators. He suggests strategies such as hiding hands behind objects or inpainting them holding objects like gloves or flowers. Demonstrating the process, he masks out the hands and attempts to render them as 'gloved hands,' adjusting the settings to improve the outcome. He also touches on the use of the depth library for more accurate hand inpainting.

The video segment shifts focus to adding objects like wall hangings and potted plants to the inpainted image. Eric explains the importance of providing the AI with enough context to understand the scene, such as including surrounding details to help with the sizing and placement of objects. He discusses the challenges of working with flat areas and the need to increase mask blur and sampling steps for better integration of new elements into the image.

Continuing the inpainting process, Eric adds a photograph of a wall hanging and potted plants to the image. He highlights the AI's difficulty with straight lines and consistent details, such as the wall molding. By adjusting the inpainting settings, including mask blur and config scale, he manages to achieve a more integrated and realistic result. The segment emphasizes the iterative process of refining the image through multiple renderings and adjustments.

In the concluding part of the video script, Eric summarizes the key points for successful inpainting with SDXL models. He reiterates the importance of adjusting mask blur and mask padding to control the blending of edges and the context provided to the AI. He demonstrates the impact of these settings with different examples, including inpainting a face with varying mask padding sizes. Eric encourages viewers to consider the context they provide to the AI when inpainting and to experiment with different settings to achieve the desired results.