大きな画像を作る新しい方法 Hires. fixは過去のもの？ 【Stable Diffusion web UI ControlNet Tile】

Signal Flag "Z"
16 Jun 202310:24

TLDRThe video script discusses the evolution of image upscaling techniques, highlighting the transition from using High-Resolution Fixes to Control Net's new Tile feature. It explains how Tile addresses the limitations of traditional upscaling methods, such as the inability to capture small details like eyes and noses, by reconstructing details and maintaining the essence of the original image. The script provides a step-by-step guide on how to use Tile, emphasizing its ability to generate high-resolution images without the need for extensive video memory. It also introduces additional features like Color Fix and Plus Sharp, which enhance color accuracy and definition. The video concludes by encouraging viewers to try Tile for creating intricately detailed images, even with limited resources.

Takeaways

  • 🎨 The script discusses the transition from using High-Resolution Fix (HRF) to a new method for generating detailed images.
  • 🚀 Table Diffusion has limitations in creating small parts of images like eyes and noses, which can be overcome by increasing the image size.
  • 🔍 To generate larger images without breaking the image quality, HRF was used by creating a smaller image and then enlarging it, rather than simply scaling up.
  • 🌟 The script introduces a new feature in Control Net called 'Tile', which helps in generating detailed images while preserving the original image quality.
  • 📌 The 'Tile' control type is demonstrated to be effective in enhancing details such as hair texture and sharpening outlines, even from a small original image size of 64x64 dots.
  • 🔗 Control Net is described as a powerful tool that can control the generated image according to the user's desires, mitigating the strange influences of prompts.
  • 🔧 The process of using Tile involves installing Control Net, confirming the version, downloading the Tile model, and setting the image size to 768x1024 or similar.
  • 🖼️ The script provides a step-by-step guide on how to upscale images using scripts like SD Upscale, Upscale, and Control Net, without the need for HRF.
  • 🌈 The 'Color Fix' and 'Color Fix Plus Sharp' processors are introduced as part of Tile's pre-processors, which can correct colors and enhance outlines for a clearer image.
  • 💻 The script acknowledges the limitations of video memory but suggests that with Tile, high-resolution upscaling is possible even with limited resources.
  • 🎓 The script concludes by encouraging viewers to try the new Tile feature of Control Net for generating high-definition images that were previously considered challenging.

Q & A

  • What was previously recommended for generating detailed images?

    -High-resolution fixes were previously recommended for generating detailed images.

  • Why is High-Resolution Fixes becoming outdated?

    -High-Resolution Fixes is becoming outdated because it struggles with generating larger images without causing the artwork to rupture or break apart.

  • What is the limitation of Stable Diffusion when it comes to generating images?

    -Stable Diffusion has a limitation in that it tends to produce small-sized images, which makes it difficult to accurately render small parts like eyes and noses.

  • How can one address the issue of small image sizes in Stable Diffusion?

    -To address the issue of small image sizes, one can increase the size of the generated image. However, simply enlarging the image can cause it to rupture, so High-Resolution Fixes are used to create a smaller image and then enlarge it without damaging the artwork.

  • What is the new feature introduced to control the generation of detailed images?

    -The new feature introduced is the Control Net, which is a powerful tool that allows for the control of the generated image to match the desired outcome more closely.

  • What does the Tile control type in Control Net do?

    -The Tile control type in Control Net helps to upscale images while preserving the details and reconstructing them, ensuring that the image does not become pixelated or lose its original characteristics.

  • How can one use the Tile feature of Control Net?

    -To use the Tile feature, one must first install Control Net, ensure the version is 1.1.107 or later, download the Tile model, and set the original image size to 768x1024 or a close value. The image is then processed through a series of steps including upscaling and denoising to achieve the final result.

  • What is the role of Denoising Strength in the image upscaling process?

    -Denoising Strength plays a crucial role in the image upscaling process by controlling the level of detail and noise reduction in the final image. Adjusting this value can help achieve a balance between detail preservation and noise reduction.

  • What are the additional features introduced in Tile preprocessors?

    -The additional features introduced in Tile preprocessors include Tile Sample, Tile Color Fix, and Tile Color Fix Plus Sharp. These features help with color correction, maintaining the original colors, and enhancing the clarity of the image轮廓s.

  • How does the Color Fix feature work?

    -The Color Fix feature works by correcting the colors in the image to match the original image more closely. For example, it can make a bright and thin red color in a dress match the exact red from the original image.

  • What is the benefit of using Tile Color Fix Plus Sharp?

    -Tile Color Fix Plus Sharp not only corrects the colors but also adds a sharpness effect to the image轮廓s. This helps to make the outlines more defined and clear, especially when upscaling images that may otherwise become blurry or soft.

  • What is the significance of the new Tile feature in Control Net?

    -The significance of the Tile feature in Control Net is that it allows for the generation of high-resolution images with detailed upscaling, even with limited video memory. It also helps to maintain the original image's fidelity and avoid the common issues associated with traditional upscaling methods.

Outlines

00:00

🖼️ Introduction to High-Resolution Fixing and Table Control Net

The paragraph introduces the concept of generating detailed images using high-resolution fixes and explains the limitations of Table Diffusion, particularly its inability to render small parts like eyes and noses accurately. It then discusses the transition from high-resolution fixes to a new method involving the creation of larger images without breaking the original image. The introduction of Control Net's new feature, Table, is highlighted, which allows for the generation of detailed images without destroying the original.

05:03

🔍 Process of Upscaling Images with Table Control Type

This paragraph delves into the process of upscaling images using the Table control type in Control Net. It explains the steps involved in the process, including the selection of scripts for upscaling, the use of Control Net to enhance details, and the importance of Denoise Strength in achieving a stable upscaled image. The paragraph also discusses the impact of various settings such as Pixel Perfect, Control Type, and Sampling Steps on the final output.

10:03

🎨 Enhancing Image Details and Color Fix with Table Control Net

The final paragraph discusses the advanced features of Table Control Net, including Color Fix and Plus Sharp, which enhance color accuracy and detail sharpness in upscaled images. It explains how these features can maintain the original colors and contours of the image, even when upscaling. The paragraph concludes with an encouragement to challenge the new high-definition image generation capabilities of Control Net's Table feature.

Mindmap

Keywords

💡High-Resolution Fixes

High-Resolution Fixes refer to techniques or tools used to generate detailed images with high pixel density. In the context of the video, it is mentioned as a method that was previously recommended for creating detailed images but is becoming outdated. The script discusses moving away from relying solely on High-Resolution Fixes to new methods for achieving detailed and enlarged images without breaking the original image's integrity.

💡Table Diffusion

Table Diffusion is a generative model used for creating images; however, it has limitations when it comes to generating small-sized images, as details like eyes and noses may not be rendered well. The video discusses overcoming these limitations by increasing the size of the generated images, which can lead to better detail capture but also introduces the challenge of maintaining image integrity.

💡Control Net

Control Net is a powerful tool introduced in the video that allows for the manipulation and control of the images generated. It has been updated with new features, including the Tile type, which helps in generating high-resolution images while preserving the original details and structure of the image.

💡Tile

Tile is a new type of Control in Control Net that aids in generating detailed and high-resolution images. It works by interpreting what the image is depicting and reconstructing the details, allowing for a more accurate enlargement of the image without the need for high video memory. The Tile function helps in avoiding the common issue of images becoming pixelated or losing detail during enlargement.

💡Denoise Strength

Denoise Strength is a parameter used in image generation processes to control the level of noise reduction in the final image. A higher Denoise Strength value results in a cleaner image with less noise, but it may also lead to a loss of detail. In the video, adjusting Denoise Strength is crucial for achieving a balance between preserving the original image's details and reducing noise in the enlarged image.

💡Pixel Perfect

Pixel Perfect is a term used to describe an image that is sharp and clear, with each pixel being accurately placed and colored. In the context of the video, enabling Pixel Perfect in Control Net ensures that the generated images are as close to the original in terms of detail and quality, especially when using the Tile function for high-resolution enlargement.

💡Singing IR

Singing IR refers to an algorithm used in the image enlargement process. It is mentioned as a choice for the upscaling script, which is responsible for increasing the size of the image. The Singing IR algorithm is part of the process that allows for high-resolution enlargement while keeping video memory usage low.

💡Color Fix

Color Fix is a feature within the Tile function that corrects and enhances the colors in the generated image to match the original more closely. It is designed to address issues where colors may appear washed out or altered during the enlargement process, ensuring that the final image retains the intended color palette.

💡Sharpen

Sharpen is a post-processing effect applied to images to enhance the contrast along the edges and details, making them more defined and clear. In the context of the video, the Sharpen feature is part of the Color Fix Plus option, which not only corrects colors but also adds a sharpening effect to the image, helping to eliminate any soft or blurry areas resulting from the enlargement process.

💡Video Memory

Video Memory refers to the amount of memory available on a graphics card that is used to store and manipulate image data for rendering and display. In the video, it is mentioned as a limiting factor when generating larger images, as some methods require a significant amount of video memory to process high-resolution images.

💡Sample Steps

Sample Steps is a parameter related to the image generation process that determines the number of steps taken to sample and recreate the image during enlargement. Increasing the Sample Steps value can lead to a more detailed image but may also increase the computational resources required. In the context of the video, adjusting Sample Steps is part of optimizing the image generation process for the best balance between detail and resource usage.

Highlights

Introduction to the use of High-Resolution Fixes for generating detailed images.

Discussion on the limitations of Table Diffusion in creating small parts like eyes and noses.

The solution to the limitations of Table Diffusion is to increase the image size, but it risks image degradation.

Explanation of the High-Resolution Fixes process, which involves creating a small image and then enlarging it.

Mention of the new feature in Control Net that allows for detail preservation while enlarging images.

Introduction to the Tile control type in Control Net, which enhances image details and prevents image degradation.

Demonstration of how Tile control interprets and reconstructs image details, unlike simple upscaling.

Control Net's ability to suppress strange influences from prompts, ensuring parts of the image are not overemphasized.

Instructions on how to use Control Net's Tile feature, including installation and model download.

The importance of setting the original image size to 768x1024 for optimal results with Tile.

Explanation of the image generation process using Text2Image and the Image module.

Selection of the SD Upscale script for image enlargement, with a choice of 4x magnification.

Use of the SwingIR algorithm for high-resolution, video memory-efficient image enlargement.

Setting up Control Net with Pixel Perfect and Tile type for detailed image reconstruction.

Adjustment of Denoising Strength and CFG Scale for stabilization and detail enhancement.

Results of the image enlargement process, showcasing added details like eyelashes and sharper outlines.

Comparison of the original and enlarged images, highlighting the fidelity and detail preservation.

Introduction of Tile's preprocessors, including Tile Sample, Tile Color Fix, and Fix Plus Sharp.

Explanation of Color Fix for accurate color reproduction and its potential side effects.

Discussion on the benefits of Color Fix Plus Sharp for enhancing outlines in enlarged images.

Conclusion emphasizing the capabilities of Control Net's Tile in creating high-resolution images without video memory constraints.

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