生成速度が二桁％アップ【WebUI Forge】の導入/モデルの共有方法/automatic1111と同じUIなのですぐ使いこなせる/stablediffusion

AI Art JAPAN
14 Feb 202411:31

TLDRThe video introduces the new WebUI Stable Diffusion WebUI Forge, a tool that streamlines development, optimizes resource management, and enhances inference speed. It promises significant improvements in inference speed and GPU memory usage across various GPU models. The interface is familiar to users of automatic1111, ensuring ease of use. The video also demonstrates the installation process using a one-click package and highlights features like the inclusion of ControlNet and FreeU, as well as an improved file explorer in the model reference screen.

Takeaways

  • 🔧 Introduction of Stable Diffusion WebUI Forge, a new release by Mr. lllyasviel, aimed at simplifying development and improving inference speed.
  • 🚀 Significant speed improvements are expected with Forge, ranging from 30-75% increase in inference speed depending on the GPU model used.
  • 📈 Resource optimization is evident as GPU memory peak is reduced and maximum spread resolution and batch size are increased.
  • 🌟 The name 'Forge' is inspired by 'Minecraft Forge', emphasizing its role in enhancing the development environment.
  • 🎮 Notable addition of new samplers like DPM++ 2M Turbo for more diverse image generation options.
  • 🛠️ Automatic1111 interface is maintained in Forge, ensuring users can continue using the familiar interface without unnecessary changes.
  • 📊 Comparative analysis shows Forge to be faster than automatic1111, with a 3-second improvement in 512x512 image generation.
  • 📂 Easy installation of Forge is facilitated through a one-click package available on Mr. lllyasviel's github page.
  • 🔄 Instructions for setting up Forge with StabilityMatrix and other extensions like controlnet and FreeU are provided for enhanced functionality.
  • 🔗 The reference screen in Forge has been improved with a tree view for better organization and searchability of model files.
  • 💡 The introduction of WebUI Forge is expected to positively impact the user experience and efficiency of image generation tasks.

Q & A

  • What is the main purpose of the WebUI Stable Diffusion Forge project?

    -The main purpose of the WebUI Stable Diffusion Forge project is to simplify development, optimize resource management, and improve inference speed for users working with Stable Diffusion WebUI.

  • What is the expected performance improvement when using Forge with an 8GB VRAM GPU?

    -With an 8GB VRAM GPU, using Forge is expected to increase inference speed by approximately 30-45%, reduce GPU memory peak by about 1.3GB, improve maximum spread resolution by approximately 2x to 3x, and increase maximum batch size by approximately 4x to 6x.

  • How does Forge perform on a less powerful 6GB VRAM GPU?

    -On a 6GB VRAM GPU, Forge can increase inference speed by approximately 60 to 75%, decrease GPU memory peak by about 800MB to 1.5GB, and improve maximum spread resolution by approximately 3x.

  • What are the benefits of using the 24GB VRAM RTX4090 GPU with Forge?

    -When using the 24GB VRAM RTX4090 GPU, Forge can increase inference speed by about 3 to 6%, reduce GPU memory peak by about 1GB to 1.4GB, and enhance maximum spread resolution by approximately 4x.

  • How does Forge support the use of SDXL's ControlNet?

    -Forge allows the maximum number of ControlNets to approximately double, and it increases the speed of SDXL+ControlNet by about 30 to 45%.

  • What is the key promise made by the developers of Forge regarding the user interface?

    -The developers of Forge promise that they will never make unnecessary opinion changes to the user interface, ensuring that users are using a familiar and consistent automatic1111 WebUI.

  • How does Forge compare to automatic1111 in terms of generation speed and VRAM usage?

    -Forge is about 3 seconds faster than automatic1111 for generating 10 images in about 13 seconds with a 512x512 size. The VRAM usage is reduced from 4.6GB to 4.2GB, which is about a 91% suppression.

  • What are the advantages of using the Hires.fix setting with Forge?

    -Using the Hires.fix setting with Forge at a 720x1280 size, along with ADtailer, results in a generation speed that is about 1.5 seconds faster than the standard settings, indicating a significant performance improvement.

  • How can one install Forge using the one-click package?

    -To install Forge using the one-click package, visit Mr. lllyasviel's github page, go to the webui Forge page, scroll down to find the link for the one-click package, download it, extract the folder, and run the update.bat and then run.bat batch files to complete the installation.

  • What are some of the features included in Forge by default?

    -Forge includes stable video diffusion, controlnet from the beginning, FreeU, and various extensions built-in by default.

  • How does the file organization in Forge's reference screen differ from the conventional model reference screen?

    -Forge's reference screen organizes files in a tree view, making it easier to search for a large number of model files, whereas the conventional reference screen divides files into folders with only top-level folders displayed.

Outlines

00:00

🚀 Introduction to Stable Diffusion WebUI Forge and Its Benefits

This paragraph introduces the new WebUI Stable Diffusion Forge, a tool released by Mr. lllyasviel that aims to simplify development, optimize resource management, and enhance inference speed. It draws inspiration from 'Minecraft Forge' and promises significant improvements over the original WebUI, such as a 30-45% increase in inference speed on an 8GB VRAM GPU, a reduction in GPU memory peak, and an increase in maximum spread resolution and batch size. The Forge also integrates well with SDXL's ControlNet, doubling the number of ControlNets and boosting speed by 30-45%. A key advantage is that Forge maintains the familiar automatic1111 interface, ensuring users can continue generating images with ease post-installation. A comparison of generation speed between automatic1111 and Forge is provided, demonstrating Forge's efficiency.

05:03

📦 Installation Guide for WebUI Forge and Interface Comparison

The paragraph outlines the installation process for WebUI Forge using a one-click package. It directs users to Mr. lllyasviel's GitHub page and provides step-by-step instructions for downloading, extracting, and running the software. The guide also covers updating the software and setting up the run batch file. Additionally, it discusses the UI's similarities to automatic1111 and the inclusion of features like stable video diffusion, controlnet, and FreeU. The paragraph further explains how to configure the software to work with StabilityMatrix by editing the webui user batch file and setting the correct paths for data and models. It also touches on the model and LoRA reference screen's tree view feature and the controlnet's accordion format.

10:04

🌟 WebUI Forge Features and User Experience Enhancements

This section highlights additional features of WebUI Forge, such as the tree view for model files, which simplifies the search process, and the accordion format for controlnets, which may be challenging to use with multiple units open. It also mentions the inclusion of extended functions from Mendokusai and encourages users to provide feedback by rating the software. The paragraph concludes with an invitation for users to explore WebUI Forge and share their experiences.

Mindmap

Keywords

💡WebUI

WebUI stands for Web User Interface, which refers to the visual and interactive design through which users access and interact with web applications. In the context of the video, it is specifically about the user interface for a project called 'Stable Diffusion WebUI Forge', which is designed to streamline the development process, optimize resource management, and enhance inference speed for users working with AI models.

💡Forge

In the video, 'Forge' is the name of a project inspired by 'Minecraft Forge', aiming to serve as a powerful tool for the Stable Diffusion WebUI. It signifies a platform that facilitates development, improves resource allocation, and increases the speed of AI model inference. The term 'forge' metaphorically suggests a place of creation and enhancement, aligning with the project's goal to refine and optimize the user experience.

💡Checkpoint

A checkpoint in the context of AI and machine learning refers to a snapshot of the model's state during the training process. These snapshots can be used to resume training from a specific point or to evaluate the model's performance at different stages. In the video, the checkpoint is used in relation to the StabilityMatrix, indicating a specific state of the AI model that can be loaded for continued use or analysis.

💡LoRA

LoRA, or Low-Rank Adaptation, is a method for efficiently adapting large pre-trained models to new tasks or datasets with minimal computational resources. It involves modifying a small portion of the model's parameters while retaining the majority of the original model. In the video, LoRA is discussed as a sharing method, implying that it allows users to share or utilize adapted models in a more efficient manner.

💡Inference Speed

Inference speed refers to the rate at which an AI model can process input data and produce output predictions or results. It is a critical factor in determining the usability and efficiency of AI systems, especially in real-time applications. The video emphasizes the improvements in inference speed achieved through the use of the Forge project, showcasing its benefits for users working with AI models.

💡GPU Memory Peak

GPU Memory Peak refers to the maximum amount of memory utilized by the GPU during the execution of a particular task or process. In the context of the video, reducing the GPU memory peak is one of the improvements offered by the Forge project, which allows for more efficient use of the GPU's resources and can enable running more complex models or multiple instances concurrently.

💡Spread Resolution

Spread resolution refers to the maximum dimensions or size of the images that an AI model can generate. An increase in spread resolution indicates that the model can produce images with larger dimensions, which can be important for creating high-resolution content. The video highlights the improvements in spread resolution when using the Forge project, showcasing its capability to handle larger image sizes.

💡Batch Size

Batch size in the context of AI and machine learning refers to the number of samples processed by the model in one go. An increase in batch size means that the model can handle more samples simultaneously, which can lead to faster processing times and improved efficiency. The video emphasizes the increase in maximum batch size when using Forge, indicating its potential for higher throughput.

💡ControlNet

ControlNet is a feature or module within AI models that provides additional control over the generation process, often used to guide the model's output according to specific criteria or constraints. In the video, the use of ControlNet with SDXL (Stable Diffusion XL) is mentioned, highlighting the ability to double the number of ControlNets and increase the speed of the combined system.

💡Automatic1111

Automatic1111, as mentioned in the video, seems to be the previous version or a related project of the Stable Diffusion WebUI. It is used as a point of comparison to demonstrate the improvements and new features introduced with the Forge project. The term suggests an automatic or user-friendly interface for AI model interaction.

💡One-Click Package

A one-click package refers to a software distribution method that allows users to install and set up applications with a single click or action, simplifying the installation process. In the context of the video, it describes the ease of installing the Forge project through a one-click package available on the public page, making it accessible and user-friendly.

Highlights

Introduction of the new WebUI Stable Diffusion WebUI Forge by Mr. lllyasviel.

Forge simplifies development, optimizes resource management, and improves inference speed.

The project aims to be the Forge of SD WebUI, with the name inspired by 'Minecraft Forge'.

Inference speed increases by 30-45% on an 8GB VRAM GPU.

GPU memory peak is reduced by approximately 1.3GB on an 8GB VRAM GPU.

Maximum spread resolution and batch size increase significantly on various GPUs.

Usage of SDXL's ControlNet approximately doubles the number of ControlNets and increases speed by 30-45%.

Forge introduces new samplers, such as DPM++ 2M Turbo.

Forge promises no unnecessary opinion changes to the user interface, maintaining 100% UI consistency with automatic1111.

Comparative analysis shows Forge is faster than automatic1111 in image generation.

VRAM usage is reduced when using Forge for image generation.

Instructions on installing Forge using a one-click package from Mr. lllyasviel's GitHub page.

Details on updating Forge before launching for the latest features and improvements.

Forge's WebUI starts up with a familiar interface and includes controlnet and FreeU from the beginning.

Explanation of how to configure the one-click package to work with StabilityMatrix and shared model files.

The new reference screen in Forge supports tree view, making it easier to search for model files.

Introduction of the WebUI Forge and a call to action for users to provide feedback and ratings.