This summary of the video was created by an AI. It might contain some inaccuracies.
00:00:00 – 00:05:53
This video tutorial demonstrates how to utilize Stable Diffusion's multi-diffusion extension to upscale and add intricate details to images locally, offering a free alternative to paid cloud-based solutions. The key steps include installing essential extensions like the Control Net extension, Control Net Tile Model, and multi-diffusion/tiled diffusion via the automatic 1111 interface. Initial image creation is done using the zbase XL model with highres fix and low denoising strength, followed by processing in the image-to-image canvas. Optimal results suggest using an SD 1.5 model (like Juggernaut), simplifying prompts, and employing the DPM++ 2M Karras sampling method with appropriate denoising settings.
Further image enhancement involves setting overlap parameters, adjusting batch size based on GPU capacity, and choosing upscalers like 4X Ultra sharp. For added detail, noise inversion with 50 steps and other settings like the tiled VAE and Pixel Perfect options are used. Regenerating the image with reduced denoising strength and deactivated noise inversion/control net is recommended for additional upscaling without extra details. The video emphasizes a balance between retaining the original image quality and adding enhancements while encouraging viewers to subscribe for more content.
00:00:00
In this part of the video, the focus is on using Stable Diffusion’s multi-diffusion extension to upscale and add intricate details to images locally on your computer for free, as an alternative to paid cloud-based solutions. The video highlights the necessary tools, starting with the installation of the Control Net extension and Control Net Tile Model. It then explains how to install the multi-diffusion (or tiled diffusion) extension via the automatic 1111 interface by navigating to the extensions tab, using the GitHub URL, and ensuring the extension is updated.
Creating an initial image using the zbase XL model and enabling highres fix with low denoising strength is the next step. The video advises sending this base image to the image-to-image canvas for further processing. For optimal results, it recommends adjusting the checkpoint to an SD 1.5 model like Juggernaut, simplifying prompts to focus on quality and detail, and using the DPM++ 2M Karras sampling method with 20 sampling steps. Denoising strength should be set between 0.2 to 0.75 to balance original image retention and added detail. Lastly, the tile diffusion extension should be enabled with the mixture of diffusers method selected for enhanced performance, with adjustments to latent tile width and height as needed.
00:03:00
In this part of the video, the presenter provides detailed instructions on how to enhance and upscale an image using various settings and tools. Key actions include setting the overlap to 16, adjusting the batch size based on GPU capacity, and selecting the upscaler, with 4X Ultra sharp and R ESR gen 4X recommended. The scale factor is advised to remain at two for a 2X upscale, and noise inversion is enabled with steps set to 50 for added detail. Denosing strength is adjusted to 0.75, and the tiled VAE extension is enabled with the fast encoder color fix option to maintain image vibrance.
The control net is activated with the Pixel Perfect checkboxes, and the control type is set to tile/blur. After finalizing the settings and generating the image, the result is compared to the original, highlighting the enhanced detail. For further upscaling without adding more details, the process involves reducing the denoising strength, deactivating noise inversion and control net, and regenerating the image. The segment concludes by encouraging viewers to subscribe to the channel for more content and to support reaching a milestone of 10,000 subscribers.