【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】
TLDRAlice from AI’s in Wonderland introduces the upgraded AnimeteDiff extension for Stable Diffusion WEB UI, a text-to-video tool that allows users to create videos from text prompts. The new feature enables specifying starting and ending images through Control Net, linking 2-second clips to form a sequence. The video quality has been improved by TDS, with clearer images and a new JSON file 'new schedule' for better image generation. The tutorial covers the installation process, including downloading modules and setting up the WEB UI. It also demonstrates how to generate a video using the Mistoon Anime model, with customization options like frame number and looping. The video concludes with an exploration of LoRA for adding special effects like energy charges, showcasing the potential of AnimateDiff for creative video generation.
Takeaways
- 🎬 The video was created using the AnimeteDiff extension on Stable Diffusion WEB UI, showcasing its ability to generate videos from text prompts.
- 📈 AnimeteDiff has been upgraded to allow users to specify starting and ending images through the Control Net, enabling the creation of more controlled video sequences.
- 🔍 The image quality has been improved by TDS, who integrated 'alphas cumprod' from the original repository into the DDIM schedule of the Stable Diffusion WEB UI.
- 🚀 Users can now generate higher quality videos with clearer images, thanks to the new developments by TDS.
- 📉 The required GPU memory for AI video creation is still high, at over 12 GB, which might be a limitation for some users.
- 🛠️ The process involves some programming, as users need to modify the Python file of the web UI, which could be challenging for beginners.
- 💡 Despite the current complexity, the potential of AnimeteDiff is significant, and it's expected to become more user-friendly with future updates.
- 📚 The video provides detailed guidance for users who wish to try out AnimeteDiff, including how to install the extension and model.
- 🔗 TDS's developments are available on X and note, and the community is encouraged to follow these resources for the latest updates.
- 🧩 By using a control net, users can now influence the start and end of a video, creating a more coherent narrative within the generated clips.
- ⚙️ The installation process for AnimeteDiff and the Control Net is outlined in the video, with specific instructions for downloading and configuring the necessary components.
Q & A
What is the AnimeteDiff extension used for?
-AnimeteDiff is a text-to-video tool that uses AI to automatically create videos from text input. It is an extension for making videos using Stable Diffusion images.
How long is the video generated by the AnimeteDiff extension on Stable Diffusion WEB UI?
-The AnimeteDiff extension generates a video that is about 2 seconds long.
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 the creation of more controlled and interconnected video sequences.
Who developed the features that improved the image quality and how did they achieve it?
-The features were developed by someone named TDS. They 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.
What is the GPU memory requirement for using AnimeteDiff?
-The required GPU memory for using AnimeteDiff is a bit high, at over 12 GB.
How can one install AnimeteDiff?
-To install AnimeteDiff, one needs to go to the sd web UI and AnimeteDiff homepage, copy the URL from the dropdown window, go to the Extensions page on the web UI, enter the URL in the 'URL for Extensions Git Repository' section, and then press the Install button.
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.
What is the recommended approach if one encounters issues during the installation or use of AnimeteDiff?
-If one encounters issues, it is recommended to try using AnimeteDiff without installing xformers from the command line.
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.
How does the Control Net help in creating videos with AnimeteDiff?
-The Control Net allows for the control of the very first and last images of the video, enabling users to dictate the start and end of the video sequence more precisely.
What is LoRA and how is it used in the context of the video?
-LoRA is a tool that can generate images with specific effects, such as an energy charge as seen in Dragon Ball. In the video, it is used to add a yellow aura effect to the generated image, which is intended to be used as the last frame of a video sequence.
Outlines
🎬 Introduction to AnimeteDiff and Stable Diffusion WEB UI
Alice introduces the audience to AnimeteDiff, an extension for creating videos using Stable Diffusion images. She explains that the tool generates short videos from text prompts and has been enhanced to allow users to specify starting and ending images through the Control Net. The video demonstrates the process of installing AnimeteDiff and using it to create a 2-second video. Alice also discusses improvements made by TDS to the image quality and provides guidance for beginners interested in trying out the tool.
📚 Downloading and Installing Motion Modules
The paragraph details the process of downloading motion modules from Google Drive, as the ones from CIVITAI were reported to be unusable. It provides instructions on where to place the downloaded modules within the Stable Diffusion WEB UI folder structure. The paragraph also mentions a potential issue with the AnimateDiff extension when xformers is installed but notes that the presenter had no problems using both. The setup is finalized with a restart of the UI and the enabling of AnimateDiff within the interface.
🖼️ Enhancing Video Quality with TDS Improvements
Alice discusses the improvements made by TDS to the video quality in Stable Diffusion WEB UI. She explains that TDS incorporated a variable from the original repository to enhance image clarity. The audience is guided to download a JSON file and additional code to modify the DDIM.py file for better image quality. A comparison is shown between the original and improved images, highlighting the significant visual upgrade.
🔍 Control Net Installation and Usage
The paragraph explains how to install and use a control net for more precise control over the video generation process. It details the process of downloading a specific branch of the control net from TDS's repository and replacing a file in the Stable Diffusion Web UI folder. The paragraph then demonstrates how to generate base images for video frames and use the control net to control the starting and ending frames of the video, resulting in a coherent sequence.
🌟 Creating Dynamic Videos with LoRA and Control Units
Alice concludes the video by showcasing how to use LoRA (Low-Rank Adaptation) to add dynamic effects like an energy charge from Dragon Ball to the video. She uses the Mainamix model and a specific prompt to generate an image with a yellow aura, which she plans to use as the last frame of the video. The first frame is created with a similar outfit and pose, and then AnimateDiff is used to generate the video. The video turns out to be satisfactory despite some timing issues with the aura effect. Alice expresses excitement about the potential of AnimateDiff and encourages viewers to stay updated with the technology's developments.
Mindmap
Keywords
💡AnimeteDiff
💡Stable Diffusion WEB UI
💡Control Net
💡TDS
💡GPU Memory
💡Python
💡VRAM
💡Mistoon Anime
💡DDIM Sampling Method
💡LoRA
💡xformers
Highlights
The AnimeteDiff extension on Stable Diffusion WEB UI has been upgraded, allowing users to create videos through prompt input and settings.
AnimeteDiff is a text-to-video tool that automatically generates videos from text.
Users can now specify starting and ending images using the Control Net, enabling the creation of linked 2-second video clips.
The image quality has been improved on Stable Diffusion WEB UI, with the help of TDS's development.
TDS has integrated 'alphas cumprod' from the original repository to enhance the DDIM schedule of the Stable Diffusion WEB UI.
A JSON file named 'new schedule' is provided by TDS for improved image quality.
The Control Net allows for the control of the first and last images of a video, providing more creative freedom.
The installation process for AnimeteDiff and Control Net is detailed, requiring a certain level of expertise.
AnimateDiff can be used with more than 12GB of VRAM, but Control Net is tested with 24GB for better performance.
The video creation process involves selecting motion modules, enabling AnimateDiff, and setting the number of frames and display loop number.
The Mistoon Anime model is recommended for use with AnimateDiff for creating anime-style images.
The final video is stored in the 'AnimateDiff' folder within the 'Text to Image' folder.
TDS's modifications to the DDIM.py file result in a significant improvement in image clarity.
The use of a control net allows for the creation of videos with controlled start and end frames, leading to more coherent storytelling.
The LoRA corner features a Dragon Ball Energy Charge LoRA that can generate images with energy accumulating behind a person.
The video demonstrates the potential of AnimateDiff and Control Net for creating high-quality, controlled anime-style videos.
The future development of AnimateDiff and its potential incorporation into official ControlNet is a significant area of interest.
The presenter encourages viewers to keep an eye on the evolution of AnimateDiff as it may become a game changer in AI imaging technology.