FeaturedAuto1111 Tutorial Chapter 5 Hi-Res Fix& Stable Diffusion Upscaler

Hi-Res Fix

There is a feature called Hi-Res Fix in text2img, also known as high-resolution restoration. This function helps to improve the quality of low-resolution or damaged images. The "upscale" setting determines how much the image will be enlarged from its original resolution; it's recommended to start with a value of 2. "Hires steps" refer to the number of sampling steps performed during this process, with 0 being consistent with 20 steps. The suggested denoising value ranges from 0.3 to 0.5.

Firstly, you enter the prompt. Then you click generate to generate the image. After that, you click HIres.fix to fix the image into a better quality.

The advantages of this method include maintaining the original image composition and being easy, explicit, and straightforward to use. However, it is limited by video memory and has a relatively slow generation speed.

Now, let's discuss sampling methods. The Latent series can add details to the image but may easily cause distortions. The Gan series, on the other hand, preserves the original image's likeness but lacks in detailed enhancement.

SD Upscale

The essence of SD upscale is to break the image into small pieces, redraw each piece, and then reassemble them into a larger picture.

To maintain similarity to the original image, set the denoising value to 0.5. In the "script" section under the seed, select SD upscale. Keep the values for Tile Overlap and Scale Factor unchanged. Add 64 to both the width and height to improve the image quality; this number matches the Tile Overlap value.

The advantages of SD upscale include overcoming memory limitations to achieve higher resolution and excellent detail enhancement. However, its drawbacks include a cumbersome and relatively unintuitive operation and the potential for introducing unintended elements into the image.

More Upscaler Options

The essence of this method simply improve the resolution of the image through artificial intelligence algorithms, without redrawing.

Click the extra section bseide img2img, upload the image you want to fix, choose the upscaler you want, and then adjust the resize value into 2. Then click generate.

This method is easy to use, preserves the original image, and is fast. However, the effect is minimal, resulting in almost no noticeable change.

How to download Auto1111

For Users Familiar with Python and Git:

1. Download and Install via Git:
2. • If you have a Python environment set up and are comfortable with Git:
   • • Clone the Stable Diffusion repository from GitHub using the following command:
   • git clone https://github.com/StableDiffusion/StableDiffusion.git

• Navigate into the cloned directory (StableDiffusion) and proceed with installation as per the provided documentation.

For Beginners or Those Using an Integration Pack:

1. Download and Install Using an Integration Pack:
2. • Visit the official website of Stable Diffusion or an integration pack provider like auto1111. https://github.com/AUTOMATIC1111/stable-diffusion-webui
   • Download the integration package (typically a zip file).
   • Use decompression software such as Bandzip or WinRAR to extract the downloaded zip file.
   • Or through a cloud service vendor, who generally has Stable diffusion pre-built and setup.
2. Setting Up Stable Diffusion:
3. • Create a new folder on your computer for Stable Diffusion. Ensure the folder path contains only English characters and has sufficient local disk storage.
   • Unzip the contents of the downloaded zip file into this new folder.
3. Running Stable Diffusion:
4. • Locate and double-click the run.bat file within the Stable Diffusion folder.
   • Wait for the application to load; this may take a moment.
   • The Stable Diffusion WebUI homepage should automatically open in your default web browser.
4. Operating Stable Diffusion:
5. 1. Keep the command-line interface (CLI) window open while using the Stable Diffusion WebUI.
   2. Interact with the WebUI through your browser to generate AI images or perform other tasks.
   3. Remember to keep the command-line interface running while using the WebUI, and close it when you're done operating in the browser.

How to download Auto1111

For Users Familiar with Python and Git:

2. Download and Install via Git:
3. • If you have a Python environment set up and are comfortable with Git:
   • • Clone the Stable Diffusion repository from GitHub using the following command:
   • git clone https://github.com/StableDiffusion/StableDiffusion.git

• Navigate into the cloned directory (StableDiffusion) and proceed with installation as per the provided documentation.

For Beginners or Those Using an Integration Pack:

5. Download and Install Using an Integration Pack:
6. • Visit the official website of Stable Diffusion or an integration pack provider like auto1111. https://github.com/AUTOMATIC1111/stable-diffusion-webui
   • Download the integration package (typically a zip file).
   • Use decompression software such as Bandzip or WinRAR to extract the downloaded zip file.
   • Or through a cloud service vendor, who generally has Stable diffusion pre-built and setup.
6. Setting Up Stable Diffusion:
7. • Create a new folder on your computer for Stable Diffusion. Ensure the folder path contains only English characters and has sufficient local disk storage.
   • Unzip the contents of the downloaded zip file into this new folder.
7. Running Stable Diffusion:
8. • Locate and double-click the run.bat file within the Stable Diffusion folder.
   • Wait for the application to load; this may take a moment.
   • The Stable Diffusion WebUI homepage should automatically open in your default web browser.
8. Operating Stable Diffusion:
9. 1. Keep the command-line interface (CLI) window open while using the Stable Diffusion WebUI.
   2. Interact with the WebUI through your browser to generate AI images or perform other tasks.
   3. Remember to keep the command-line interface running while using the WebUI, and close it when you're done operating in the browser.