【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】

AI is in wonderland
29 Aug 202324:46

TLDRAlice from AI’s in Wonderland introduces the upgraded AnimeteDiff extension for Stable Diffusion WEB UI, a text-to-video tool that AI uses 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, who incorporated 'alphas cumprod' from the original repository into the DDIM schedule of the WEB UI. The tutorial covers the installation process, including downloading necessary modules and adjusting the DDIM.py file for better image quality. The video also demonstrates the use of Control Net to control the start and end of the video, creating a coherent sequence. Alice concludes by showcasing the potential of AnimateDiff and encouraging viewers to stay updated with its development.

Takeaways

  • 🎬 **アニメテディフの紹介**: AliceがAI’s in Wonderlandから、Stable Diffusion WEB UIで使用できるアニメテディフ拡張を紹介。
  • 📹 **ビデオ作成の方法**: プロンプト入力とStable Diffusion WEB UIの設定を通じて、2秒間のビデオが生成される。
  • 🔍 **Control Netの活用**: 新機能で開始と終了の画像を指定し、2秒間のクリップを繋げてシーケンスを作る。
  • 🌟 **画像の質の向上**: TDSによって開発された機能により、以前よりもはっきりとした画像が生成されるようになった。
  • 📈 **AIビデオ作成の進化**: GPUメモリの要求は高く、プログラミング初心者には少し難しいが、AIビデオ作成はまだ初期段階であり、期待される。
  • 🛠️ **インストール手順**: アニメテディフのインストール手順が説明されており、VRAMが12GB以上ある場合、問題なくインストールが可能。
  • 📚 **モジュールのダウンロード**: 必要なモーションモジュールをGoogle Driveからダウンロードし、特定のフォルダに配置する。
  • 🔧 **設定のカスタマイズ**: フレーム数やフレームレート、ループ数などのパラメータを調整して、ビデオの品質と長さを制御。
  • 🎨 **Mistoon Animeモデル**: AnimateDiffに適したとされるMistoon Animeモデルを使用して、ビデオを生成。
  • 📉 **画像のブラー問題**: TDSがオリジナルリポジトリから値を組み込み、Web UIのDDIMスケジュールで画像のブラーを改善。
  • 📝 **コードの編集**: TDSの指示に従って、DDIM.pyファイルにコードを追加し、画像生成の質を向上させる。
  • 🧩 **Control Netのインストール**: TDSのリポジトリからControl Netの特別なブランチをダウンロードして、ビデオの開始と終了を制御。
  • 💫 **LoRAコーナー**: 今回のLoRAはドラゴンボールのエネルギーチャージで、背後にエネルギーがたまっているような画像を生成。
  • 🔥 **ビデオの完成**: シンプルな設定で素晴らしいビデオが作成され、AnimateDiffの驚くべき機能が示されている。

Q & A

  • What is the name of the extension used to create videos from Stable Diffusion images?

    -The extension used to create videos from Stable Diffusion images is called AnimeteDiff.

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

    -AnimeteDiff generates about a 2-second video through the Stable Diffusion WEB UI.

  • What is the new feature introduced in AnimeteDiff that allows for more control over the video creation process?

    -The new feature introduced in AnimeteDiff is the ability to specify the starting and ending images through the Control Net, which allows for more control over the video creation process.

  • What is the name of the person who developed the features for AnimeteDiff?

    -The features for AnimeteDiff were developed by someone named TDS.

  • What is the minimum GPU memory required to use AnimeteDiff?

    -The minimum GPU memory required to use AnimeteDiff is over 12 GB.

  • How can one improve the image quality when using AnimeteDiff?

    -One can improve the image quality by incorporating the value of a variable called 'alphas cumprod' into the DDIM schedule of the stable diffusion Web UI, as provided by TDS.

  • What is the process to install AnimeteDiff on the Stable Diffusion WEB UI?

    -To install AnimeteDiff, one needs to go to the Extensions page on the WEB UI, enter the URL for the Extensions Git Repository, and then press the Install button. After installation, modules need to be downloaded from Google Drive and placed in the correct folder within the WEB UI directory.

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

    -The 'Number of Frames' setting in AnimeteDiff determines the number of images used to create the video. It affects the length of the video and should be set to 16 or fewer to avoid corrupted images.

  • What is the purpose of the 'Display Loop Number' setting in AnimeteDiff?

    -The 'Display Loop Number' setting determines how many times the completed video will loop. A setting of 0 will loop the video indefinitely.

  • What is the recommended model for creating anime-style images with AnimateDiff?

    -The recommended model for creating anime-style images with AnimateDiff is called Mistoon Anime, which is said to be well-suited for the tool.

  • How does the Control Net feature enhance the video creation process in AnimeteDiff?

    -The Control Net feature enhances the video creation process by allowing users to control the very first and last images of the video, enabling the creation of more coherent and intentional video sequences.

  • What is LoRA and how is it used in the video?

    -LoRA is a tool that can generate images with specific effects, such as energy accumulating behind a person, as seen in the Dragon Ball series. In the video, it is used to add a yellow aura effect to the image, which is intended to be used as the last frame of the video.

Outlines

00:00

🎬 Introduction to AnimeteDiff Extension

Alice from AI’s in Wonderland introduces the AnimeteDiff extension, which allows for the creation of videos from Stable Diffusion images using text prompts. The video showcases the process without image adjustments, only through prompt input and settings on the Stable Diffusion WEB UI. The extension is capable of generating 2-second videos and has recently been updated to allow users to specify starting and ending images through the Control Net, enabling the linking of multiple short video clips. The video quality has been improved by TDS, and the process, while requiring some programming knowledge, is made accessible through Alice's guidance. The future of AI video creation is discussed, along with the current requirements for GPU memory and the potential for future updates to streamline the process.

05:01

📚 Installing AnimeteDiff and Modules

The video script provides a step-by-step guide to installing the AnimeteDiff extension and the necessary motion modules. It emphasizes downloading modules from Google Drive to avoid issues and details the process of installing the extension through the Stable Diffusion WEB UI's Extensions page. The script also covers downloading and installing motion modules, potential issues with xformers, and the setup process for using AnimateDiff, including selecting the motion module, setting the number of frames and frames per second, and enabling the AnimateDiff feature. The use of the Mistoon Anime model, suitable for AnimateDiff, is highlighted, along with the prompt and settings used to generate the video.

10:03

🖼️ Enhancing Video Quality with TDS Improvements

The paragraph discusses the improvements made by TDS to enhance the video quality generated by the Stable Diffusion Web UI. It explains the incorporation of 'alphas cumprod' from the original repository into the DDIM schedule of the Web UI to achieve clearer images. The process involves downloading a JSON file called 'new schedule' and adding additional code to the DDIM.py file. The video demonstrates the significant difference in image quality before and after these enhancements, showcasing the potential for high-quality AI-generated videos.

15:07

🔄 Controlling Video Start and End with Control Net

Alice explains the installation and use of a control net to specify the starting and ending images of a video, allowing for greater control over the video's narrative. The script details the process of installing a specific branch of the control net from TDS's repository and replacing the existing hook.py file with a new one. It then demonstrates how to generate base images for the video frames using a specific model and prompt, and how to use the control net to control the start and end of the video. The video showcases the successful creation of a video with a controlled narrative and improved visual appeal.

20:09

🌟 Adding Effects with LoRA and Generating Final Video

The final paragraph of the script focuses on using a LoRA (Low-Rank Adaptation) to add special effects to the generated images and videos. Alice uses the Dragon Ball Energy Charge LoRA to create an image with an energy aura, which is intended to be used as the last frame of the video. She then creates the first frame with a similar composition and pose, using the control net to maintain consistency. The video is generated using AnimateDiff with specific settings, and Alice shares her excitement about the potential of this feature for creating high-quality, creative videos. She concludes by expressing her anticipation for the future development of AnimateDiff and ControlNet technologies.

Mindmap

Keywords

💡AnimeteDiff

AnimeteDiff is a text-to-video tool that utilizes AI to automatically create videos from textual prompts. 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 specifying starting and ending images through the Control Net, allowing for more control over the video creation process.

💡Stable Diffusion WEB UI

Stable Diffusion WEB UI is a user interface for the Stable Diffusion model, which is used for generating images from text descriptions. In the context of the video, it serves as the platform where the AnimeteDiff extension is integrated, enabling users to create videos with greater control and customization options.

💡Control Net

The Control Net is a feature that allows users to specify the starting and ending images for the video created by AnimeteDiff. This provides a level of control over the video generation process, enabling the creation of more coherent and intentional video sequences. In the video, it is used to link together 2-second clips and create a smooth transition between them.

💡TDS

TDS refers to an individual or group responsible for developing and improving the features of AnimeteDiff and the Stable Diffusion WEB UI. They are mentioned as the creators of the improved image quality and the Control Net feature, which are significant advancements in the video creation process discussed in the video.

💡GPU Memory

GPU (Graphics Processing Unit) memory is the dedicated memory within a GPU that allows it to store and manipulate graphical data. In the context of the video, it is highlighted that creating AI-generated videos with AnimeteDiff requires a high amount of GPU memory, specifically over 12 GB, which can be a limitation for users with less powerful hardware.

💡Python

Python is a high-level programming language that is widely used for various types of software development. In the video, it is mentioned in the context of modifying the web UI program to enable the use of AnimeteDiff, indicating that some level of programming knowledge is required to fully utilize the extension's capabilities.

💡VRAM

VRAM, or Video Random Access Memory, is the memory used by graphics cards to store image data. The video script mentions that having more than 12GB of VRAM is a prerequisite for using AnimeteDiff, as it helps in handling the high graphical demands of video generation.

💡Mistoon Anime

Mistoon Anime is a model mentioned in the video that is particularly suited for use with AnimeteDiff. It is part of the Mistoon series and is used to generate anime-style images that can be animated using the AnimeteDiff tool.

💡DDIM

DDIM, or Denoising Diffusion Implicit Models, is a sampling method used in the Stable Diffusion WEB UI for generating images. In the video, it is specified as the preferred method when using AnimeteDiff to ensure compatibility and achieve better image quality.

💡LoRA

LoRA, or Low-Rank Adaptation, is a technique used to modify and adapt pre-trained models, such as those used in image and video generation. In the video, a LoRA called 'Dragon Ball Energy Charge' is used to add special effects like energy accumulation behind a character, demonstrating the creative possibilities of such adaptations.

💡xformers

xformers is a library or tool mentioned in the video that was initially thought to cause issues with AnimeteDiff. However, the speaker later found it to be compatible and used it in their setup. It is part of the technical requirements and considerations when working with the video generation tools discussed.

Highlights

The video was created using the AnimeteDiff extension on Stable Diffusion WEB UI without any image adjustments.

AntimeDiff is a text-to-video tool that automatically creates videos from text input.

Users can now specify starting and ending images through the Control Net for more control over the video creation process.

The video quality has been improved with the incorporation of 'alphas cumprod' from the original repository.

A JSON file called 'new schedule' is provided to enhance the DDIM schedule of the stable diffusion Web UI.

The Control Net allows for the creation of videos with linked 2-second clips and specified start and end images.

The GPU memory requirement for AI video creation is over 12 GB.

The process involves modifying the Python file of the web UI, which may be intimidating for programming beginners.

Easy-to-understand guidance is provided for those who are not very familiar with computers.

The Stable Diffusion WEB UI version 1.5.2 is used for the demonstration.

The Mistoon Anime model is recommended for use with AnimateDiff for creating anime-style images.

The video generation process involves setting the 'Number of Frames' and 'Frames per Second' for the video length.

The 'Display Loop Number' determines how many times the completed video will loop.

The final video is stored in the 'AnimateDiff' folder within the 'Text to Image' folder.

TDS's improvements to image quality and the Control Net are significant contributions to the AnimateDiff tool.

The Control Net can be installed from TDS's repository for more advanced video creation features.

The LoRA corner features a Dragon Ball Energy Charge LoRA for generating images with energy effects.

The potential of AnimateDiff for future AI imaging technology is highlighted, with possible integration into official ControlNet.

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