【必見！】進化版のAnimeteDiffが一気にレベルアップしたので紹介します！ 【stable diffusion】

In this video, Alice from AI’s in Wonderland introduces the upgraded AnimeteDiff extension for Stable Diffusion WEB UI, a text-to-video tool that uses AI to create videos from text prompts. The new feature allows users to specify starting and ending images through Control Net, enabling the linking of 2-second video clips. The video quality has been improved by TDS, with better image clarity and reduced blurriness. Although the process requires some programming knowledge and a high GPU memory of over 12 GB, Alice provides a step-by-step guide for beginners. She also discusses the potential of future developments and the integration of ControlNet, highlighting the exciting possibilities of AI video creation with AnimateDiff.

Takeaways

• {"📚":"アニメテディフ（AnimeteDiff）は、テキストからビデオを自動生成するAIツールで、Stable Diffusionの拡張機能です。"}
• {"🎬":"Stable Diffusion WEB UIを使用して、プロンプトを入力することで約2秒のビデオが生成できます。"}
• {"🔄":"Control Netを使用して、ビデオの開始および終了画像を指定できるようになりました。"}
• {"🖼️":"以前のStable Diffusion WEB UIで生成された潰れた画像は、改善され、より鮮明になりました。"}
• {"👩‍🏫":"TDSという方が、Xとnoteで方法を教えてくれている開発者による機能追加です。"}
• {"💻":"AIビデオ作成はまだ初期段階であり、GPUメモリの必要量が12GB以上と高めです。"}
• {"📝":"初心者には少し怖いかもしれないが、Pythonファイルを少し変更する必要があります。"}
• {"🔗":"アニメテディフを試すだけであれば、拡張子とモデルをダウンロードするだけで簡単に使用できます。"}
• {"📈":"TDSによって提案された改善により、ビデオの質が向上し、より鮮明な画像が生成されるようになりました。"}
• {"🛠️":"Control Netを使用することで、ビデオの開始と終了のフレームを制御し、シークエンスをリンクできます。"}
• {"🌟":"LoRAコーナーでは、ドラゴンボールのエネルギーチャージのような効果を生成することができるLoRAが紹介されました。"}
• {"⚙️":"アニメテディフの進化は驚くべきものであり、将来的にはAI画像技術においてゲームチェンジャーになる可能性があります。"}

Q & A

What is the purpose of the AnimeteDiff extension on Stable Diffusion WEB UI?

AnimeteDiff is a text-to-video tool that uses AI to automatically create videos from text input. It allows users to generate short videos by specifying prompts and settings on the Stable Diffusion WEB UI.

How long are the videos generated by AnimeteDiff using the Stable Diffusion WEB UI?

AnimeteDiff generates about 2-second long videos for each input prompt.

What is the significance of the Control Net in the context of AnimeteDiff?

The Control Net allows users to specify the starting and ending images for the video, enabling them to create a sequence of linked 2-second clips and have more control over the video outcome.

Who developed the features of AnimeteDiff that are discussed in the video?

The features were developed by someone named TDS, who shares the methods on X and note.

What is the current limitation regarding the GPU memory requirement for using AnimeteDiff?

The required GPU memory for using AnimeteDiff is a bit high, at over 12 GB.

How has the image quality of videos created with AnimeteDiff been improved?

TDS improved the image quality by incorporating the value of a variable called 'alphas cumprod' from the original repository into the DDIM schedule of the stable diffusion Web UI and providing a JSON file with the values.

What is the process for installing AnimeteDiff on Stable Diffusion WEB UI?

The installation process involves downloading a dedicated Stable Diffusion WEB UI, copying the URL from the AnimeteDiff homepage, installing it from the Extensions page using the 'Install from URL' option, and downloading necessary modules from Google Drive.

What is the role of the 'Number of Frames' setting in AnimeteDiff?

The 'Number of Frames' setting determines the number of images used to create the video. It affects the length and quality of the generated video.

How does the 'Display Loop Number' setting work in AnimeteDiff?

The 'Display Loop Number' setting controls how many times the completed video will loop. A setting of 0 will loop the video indefinitely, while a specific number will loop the video for that many times before stopping.

What is the LoRA featured in the video, and what effect does it produce?

The featured LoRA is a Dragon Ball Energy Charge, which can generate images with an energy accumulation effect behind a person, similar to the visual style in the Dragon Ball series.

What are the potential future developments mentioned for AnimeteDiff and ControlNet?

The author of ControlNet, lllyasviel, mentioned on TDS's git page that after updating for SDXL compatibility, they would be looking into incorporating AnimeteDiff technology into the official ControlNet, which could be a game changer in AI imaging technology.

Outlines

00:00

🎬 Introduction to AnimeteDiff for Video Creation

Alice introduces the AnimeteDiff extension, a text-to-video tool that uses Stable Diffusion images to create videos. She explains that it's possible to generate videos through prompt input and settings on the Stable Diffusion WEB UI without manual image adjustments. The video showcases the ability to specify starting and ending images through the Control Net, which allows for the creation of linked 2-second video clips. Alice also discusses improvements made by TDS, the developer behind the tool, and provides guidance for beginners interested in trying out AnimeteDiff.

05:01

📚 Installing AnimeteDiff and Creating a Video

The paragraph details the process of installing AnimeteDiff and the necessary motion modules from Google Drive. It emphasizes the importance of selecting the correct model and settings within the Stable Diffusion WEB UI to generate a video. The video's parameters, such as the number of frames and frames per second, are explained. Alice also shares her experience with the Mistoon Anime model, which is suitable for AnimateDiff, and provides a step-by-step guide for generating a video using specific prompts and settings.

10:03

🖼️ Enhancing Video Quality with TDS's Improvements

Alice discusses the improvements made by TDS to enhance the video quality of the Stable Diffusion WEB UI. She explains the incorporation of 'alphas cumprod' from the original repository into the DDIM schedule, which results in cleaner images. The process of downloading and implementing a JSON file called 'new schedule' and additional code into the DDIM.py file is outlined. The paragraph concludes with a comparison of video quality before and after the implementation of these improvements.

15:07

🎭 Using Control Net for Video Framing

The paragraph explains the installation and use of a control net to affect only the first and last images of a video, allowing for control over the start and end of the video sequence. Alice guides viewers through the process of installing a specific branch of the control net from TDS's repository and replacing the hook.py file to enable AnimateDiff support. She then demonstrates how to generate base images for video frames using a specific model and settings, and how to use the control net to control the start and end of the video.

20:09

🌟 Creating a Video with LoRA and Control Units

Alice concludes the video by showcasing the use of LoRA (Low-Rank Adaptation) to generate images with a Dragon Ball Energy Charge effect. She uses the Mainamix model and specific prompts to create an image with a yellow aura, which she plans to use as the last frame of a video. The process of generating the first frame with a similar composition and using AnimateDiff with control units to create a video is detailed. Alice expresses excitement about the potential of AnimateDiff and the future integration of these features into official ControlNet, encouraging viewers to stay informed about the technology's development.

Keywords

💡AnimeteDiff

AnimeteDiff is a text-to-video tool that uses AI to automatically create videos from text inputs. It is an extension for the Stable Diffusion WEB UI and represents a significant upgrade in the capability to generate videos from static images. In the video, it is used to create short, animated clips by linking together 2-second sequences with specified starting and ending images, showcasing its potential for video creation.

💡Stable Diffusion WEB UI

Stable Diffusion WEB UI is a user interface for the Stable Diffusion model, which is used for generating images from textual descriptions. The video discusses the use of this interface with the AnimeteDiff extension to produce videos, emphasizing its role in facilitating the creation process without the need for manual image adjustments.

💡Control Net

Control Net is a feature that allows users to specify the starting and ending images for a video sequence created by AnimeteDiff. This capability is significant as it provides more control over the video outcome, enabling the creation of coherent video sequences that transition from one scene to another, as demonstrated in the video with the linking of 2-second video clips.

💡TDS

TDS refers to an individual or group responsible for developing and improving the features of AnimeteDiff and Stable Diffusion WEB UI. They are mentioned as the developers behind the image quality enhancements and the Control Net feature, which are crucial for the video's theme of advanced video creation using AI.

💡GPU Memory

GPU (Graphics Processing Unit) memory is the dedicated memory within a GPU that is used for rendering images, videos, and scenes. The video mentions that AI video creation with AnimeteDiff requires a high GPU memory, specifically over 12 GB, which is a technical requirement for handling the computationally intensive task of video generation.

💡Python

Python is a high-level programming language often used for web development and scripting. In the context of the video, Python is essential for modifying the web UI program to integrate the AnimeteDiff extension, which can be intimidating for beginners but is a necessary step for customizing the video creation process.

💡VRAM

VRAM, or Video RAM, is the memory used by the GPU to store image data. The video script specifies that having more than 12GB of VRAM is a prerequisite for using AnimeteDiff, highlighting the importance of sufficient VRAM for smooth video generation without running into performance issues.

💡DDIM Sampling Method

DDIM (Denoising Diffusion Implicit Models) is a sampling method used in the Stable Diffusion WEB UI for generating images. The video discusses setting the DDIM sampling method for creating videos with AnimeteDiff, emphasizing its role in the image generation process.

💡LoRA

LoRA (Low-Rank Adaptation) is a technique used to modify the behavior of a pre-trained model, such as generating images with specific styles or attributes. In the video, a Dragon Ball Energy Charge LoRA is used to create an image with an energy effect behind the subject, demonstrating the customization options available through LoRA.

💡Mistoon Anime

Mistoon Anime is a model mentioned in the video that is well-suited for use with AnimeteDiff. It is used to generate anime-style images, which are then animated using the AnimeteDiff tool. The video script discusses using this model to create the base images for the video, showcasing its compatibility with the video creation process.

💡xformers

xformers is a library or tool mentioned in the video that was initially used without issues and later installed by the presenter. It is implied that xformers might have been part of the setup for using AnimeteDiff, although the exact role is not explicitly detailed in the script.

Highlights

Introducing the upgraded AnimeteDiff extension for Stable Diffusion WEB UI, which allows for text-to-video AI creation.

The new version of AnimeteDiff enables users to specify starting and ending images through the Control Net, linking multiple 2-second clips together.

The video quality on Stable Diffusion WEB UI has been improved, with reduced blurriness in the generated images.

TDS, the developer behind these improvements, provides tutorials on X and note for learning how to use these features.

AI video creation is in its early stages, with high GPU memory requirements of over 12 GB.

The tutorial provides guidance for beginners to get started with AnimeteDiff, despite potential programming intimidation.

The Stable Diffusion WEB UI version 1.5.2 is used in the tutorial, with a dedicated WEB UI for AnimeteDiff.

Control Net and AnimeteDiff extensions are used, with a special branch for Control Net.

Detailed installation instructions are provided, including downloading and placing motion modules in the correct folder.

The video showcases the process of generating a 2-second video using the Mistoon Anime model and AnimateDiff settings.

TDS's improvements to image quality involve incorporating 'alphas cumprod' values from the original repository into the DDIM schedule.

A comparison demonstrates the significant difference in image quality between the improved and unimproved DDIM files.

The tutorial introduces using a control net to affect only the first and last images of a video, providing more control over the video's start and end.

The process of installing the control net and replacing the hook.py file is explained in detail.

A demonstration of generating a video with a controlled start and end using specific images and settings is provided.

The potential of AnimateDiff and its evolution is discussed, with anticipation for future developments and official ControlNet integration.

The video concludes with encouragement for viewers to subscribe and follow for updates on AI imaging technology.