【ai绘画】ControlNet插件超强tile模型！高清修复✓更富细节✓分区放大图片✓
TLDRControlNet插件的新模型Tile Model正在实验阶段，但已可供基本使用。该模型具备两项主要功能：一是忽略图像中的细节并生成新细节，二是在局部语义和提示不匹配时，忽略全局提示并根据局部上下文引导扩散。用户需要更新ControlNet插件至V1.1.107或更高版本，并下载两个必要的文件：pth和yaml文件。这些文件应放置在Webui的ControlNet插件的models目录下。模型的演示展示了如何将低分辨率图像修复为高清大图，以及如何改善图像中不理想的细节。此外，模型还能在保持原始风格的基础上，对细节进行增强或替换，如将眼睛颜色改变。模型还能与SD放大脚本来生成高分辨率的大图，如4K或8K，解决了内存限制的问题。最后，作者还介绍了一个特殊功能——随机提示词转换，通过Control Mode功能，用户可以平衡提示词和ControlNet的重要性，以生成更符合预期的图像。
Takeaways
- 🎨 **ControlNet Plugin Update**: The video introduces a new model in the ControlNet plugin that is currently in the experimental stage but can be used for creating detailed images.
- 🔍 **Model Functionality**: The model has two main functions: ignoring certain details in an image and generating new ones, and focusing on local context if it conflicts with global hints.
- 📈 **Version Requirement**: To use the new model, ControlNet must be updated to at least version 1.1.107, indicating a rapid iteration in updates.
- 📚 **Downloading Model Files**: Users are instructed to download two specific files from Huggingface, a `pth` and a `yaml` file, which are necessary for the model to work.
- 📂 **File Placement**: The downloaded model files should be placed in the root directory of the Webui, within the extensions and ControlNet plugin folders.
- 🔧 **Preprocessor Selection**: The video demonstrates how to select the appropriate preprocessor and model within the ControlNet plugin for image processing.
- 🖼️ **Image Restoration**: The model can take a low-resolution image and restore it to a high-definition image, enhancing details that were not clear in the original.
- 🔵 **Detail Enhancement**: The model is capable of fixing poor details in images, such as those corrupted by other upscaling methods, and generating new, clearer details.
- 🏠 **Stylistic Improvements**: Users can enhance the details of simple images, such as a basic house, to make them more visually appealing and intricate.
- 👁️ **Selective Detail Changes**: The model allows for specific parts of an image to be altered, like changing the eye color in a portrait, without redrawing the entire image.
- 🧩 **Tile-Based Image Processing**: For creating high-resolution images that may be too large for memory constraints, the model can process images in tiles, allowing for zoomed-in details without overwhelming system resources.
- 🔖 **Random Prompt Conversion**: A special feature of the ControlNet plugin is the ability to convert prompt words randomly, offering a unique way to generate varied images based on the same original.
Q & A
What is the current stage of the ControlNet plugin's tile model?
-The ControlNet plugin's tile model is currently in the experimental stage, which means it can be used but may still be undergoing development and testing.
What are the two main functions of the ControlNet plugin's tile model?
-The two main functions of the tile model are: 1) Ignoring details in the image and generating new details, and 2) If local tile semantics and hints do not match, then ignoring the global hint and guiding the diffusion according to the local context.
What is the minimum version of ControlNet required to use the tile model?
-To use the tile model, you must have at least ControlNet version 1.1.107 or higher.
How can one update their ControlNet plugin to the required version?
-To update the ControlNet plugin, you can click on the 'check for updates' option within the plugin interface, and if an update is available, apply it and restart the system.
Where can the necessary files for the tile model be downloaded from?
-The necessary files for the tile model can be downloaded from Huggingface, and you need to download both the 'pth' and 'yaml' files.
How does the tile model handle low-resolution images?
-The tile model can take a low-resolution image and restore it to a high-definition picture by ignoring certain details and generating new ones.
What is the purpose of the 'tile_resample' preprocessor?
-The 'tile_resample' preprocessor is used to resize the image, which can help in generating a more varied output when a photo is created.
How can the tile model be used to fix details in a corrupted image?
-The tile model can be used to fix details in a corrupted image by uploading the image, scaling down its resolution first, and then using the model to generate a new image with improved details.
What is the special function introduced by the author of the ControlNet plugin?
-The special function introduced by the author is the random prompt word conversion, which uses the Control Mode feature to generate images with varying levels of importance given to the cue words and ControlNet guidance.
How does the tile model assist in generating high-definition large pictures like 4K, 8K, or 16K?
-The tile model assists in generating high-definition large pictures by splitting the image into small pieces, zooming in on each piece, and then combining them to form the larger image without consuming too much video memory.
What is the effect of using the ControlNet plugin's tile model in combination with the SD zoom function?
-Using the ControlNet plugin's tile model in combination with the SD zoom function results in a clearer and more detailed enlarged image, as it helps to fix bad details and maintain the overall quality during the zooming process.
How can the tile model be used to modify specific details in an image, such as changing the eye color?
-The tile model can be used to modify specific details in an image by adjusting the prompt words and using the model to generate a new image with the desired changes, such as changing the eye color to blue.
Outlines
🚀 Introduction to the ControlNet Plugin and Model
The video, presented by ControlNet, introduces a new model in the experimental stage with intriguing features. The model's capabilities include ignoring image details and generating new ones, and adjusting diffusion based on local context if it conflicts with global hints. The presenter guides viewers on updating the ControlNet plugin to at least version 1.1.107, downloading necessary model files from huggingface, and placing them in the correct directory for use. The first function of the model is demonstrated by enhancing a low-resolution image to a high-definition image, showcasing impressive detail restoration.
🖼️ Enhancing Image Resolution and Details with the Model
The second paragraph delves into the model's ability to repair and enhance image details. Using a small 64x64 resolution image as an example, the presenter shows how the model can upscale the image to 512x512 and even 1024x1024 resolutions, significantly improving the image's quality and details. The model is not a super-resolution tool but rather generates new details where the original image lacks, which can be used to fix poor details in images. The video also compares the model's output with that of Real-ESRGAN, highlighting the model's effectiveness in restoring details like the eyes, hair, and legs of a dog in the example image.
🏡 Adding Details to Simple Structures and Modifying Image Features
The third paragraph showcases the model's second function, which is adding details to simple images, such as a rudimentary house, to generate more complex and visually appealing pictures. The presenter uses a prompt to guide the model to generate a log cabin and adjusts the image resolution to 1024x1024 for better detail. The model can also modify specific features of an image, such as changing the color of the eyes in a portrait, without the need for a complete redraw. This is achieved by using a preprocessor model and providing specific prompt words to guide the generation process.
🔍 High-Definition Image Generation and Zooming Techniques
The fourth paragraph discusses the challenges of generating high-definition large images like 4K, 8K, or 16K due to memory limitations. The presenter suggests a method to overcome this by splitting the image into smaller pieces and zooming in on each block. The ControlNet plugin's tile model is used in conjunction with a script to enhance the zooming process and improve the quality of the enlarged image. The presenter demonstrates the difference between using and not using the ControlNet plugin when zooming in on images and emphasizes the improved detail and quality achieved with the plugin.
📈 Comparing Zooming Methods and ControlNet's Impact on Image Quality
The fifth paragraph compares different zooming methods and the impact of using the ControlNet plugin on image quality. The presenter disables the ControlNet plugin to show the default zooming effect and then re-enables it to demonstrate the improved results. The video explains the process of changing the sampling method and using a script to achieve better zooming results. Two images, one using the ControlNet plugin and one without, are compared to illustrate the significant improvement in detail and quality when the plugin is used.
⚙️ ControlNet's Special Features and Balancing Model Influence
The final paragraph introduces a special function of the ControlNet plugin: random prompt word conversion, which allows for more versatile image generation. The presenter discusses the 'Balance' mode, which considers both the cue words and the ControlNet guidance to generate an image that balances the two influences. The video also explores settings where the cue words or ControlNet have a more significant impact on the generated image, showing how these adjustments can lead to different results. The presenter encourages viewers to experiment with these settings to achieve the desired outcome, highlighting the fun and creative potential of the model.
Mindmap
Keywords
💡ControlNet plugin
💡Tile model
💡High-definition (HD) repair
💡Preprocessor
💡Stable diffusion
💡Huggingface
💡Resolution
💡Super-resolution
💡Semantics and hints
💡Real-ESRGAN
💡ControlNet
Highlights
ControlNet插件超强tile模型，功能强大且在实验阶段。
模型有两个主要功能：一是忽略图像中的细节并生成新细节；二是根据局部语境指导扩散。
需要更新ControlNet插件至V1.1.107版本以上。
下载模型需要两个文件：pth文件和yaml文件。
模型下载后需要放置在Webui根目录的extensions文件夹下的ControlNet插件的models文件夹中。
模型操作步骤：选择预处理器、选择模型、设置提示词、设置输出图像分辨率、生成修复图像。
模型的第一功能是将低分辨率图像恢复为高清大图。
第二个功能是修复细节不佳的图像。
第三个功能是根据提示词修改图像细节，例如将眼睛变成蓝色。
第四个功能是通过分块放大图像以实现高分辨率放大，以解决内存限制的问题。
使用ControlNet插件结合脚本进行放大时，可以修复图像中的细节。
通过平衡模式、提示词重要性设置、ControlNet重要性设置等功能，可以控制生成图像的样式。
Control Mode功能能够在生成图像时综合考虑提示词和ControlNet引导。
平衡模式下生成的图像同时考虑了提示词和ControlNet引导，使生成图像更加均衡。
提示词重要性设置下生成的图像更加注重提示词，生成的图像会更加接近提示词的要求。