Less than 8GB VRAM! SVD (Stable Video Diffusion) Demo and detailed tutorial - in Comfy UI

Tech-Practice
26 Nov 202310:25

TLDRThe video script introduces a tutorial on utilizing the latest stable video diffusion with Confy UI, emphasizing the importance of updating the UI and downloading specific models for optimal performance. It guides viewers through the process of installation, model selection, and workflow integration, highlighting the flexibility and automation capabilities of Confy UI. The tutorial also suggests joining a Discord server for further exploration and sharing of workflows, promoting community engagement and knowledge exchange in the realm of AI advancements.

Takeaways

  • πŸ“Ί The video is a tutorial on using the latest stable video diffusion with Confy UI.
  • πŸ”§ It recommends reading the blog for a detailed description and examples before starting.
  • πŸš€ The first step is to install or update Confy UI as per the video's instructions.
  • πŸ“‚ Download several models, including a normal 2-second version and an XT version for 3-second videos.
  • πŸ’Ύ Use the 16bit format for the models to save disk space, requiring only about 4.5 GB.
  • πŸ“ Place the downloaded models in the correct location under the Confy UI's models/checkpoints directory.
  • πŸ’» Start Confy UI and activate the appropriate Python environment to run the main.py script.
  • πŸ”„ Download the official workflow as a JSON file and import it into the Confy UI interface.
  • πŸŽ₯ The video demonstrates the process of generating a 2-second video and also a longer 3-second video using the XT model.
  • 🌐 The tutorial highlights the flexibility of Confy UI in combining different workflows for automation.
  • πŸ“Š The video mentions that the workflow will use about 9 GB of VRAM, suggesting a 12 GB GPU would be sufficient.
  • πŸ“ˆ The speaker shares their excitement for the future of AI advancements and encourages subscription to the channel for updates.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is a tutorial on how to use the latest stable video diffusion with Confy UI.

  • What is recommended before starting with the tutorial?

    -It is recommended to read the introduction blog for detailed descriptions and examples related to the topic.

  • What is the first step in using the video diffusion with Confy UI?

    -The first step is to install or update the Confy UI to the latest version.

  • How many models are mentioned in the video, and what are they used for?

    -Two models are mentioned: one for generating 2-second videos and another XT version for 3-second videos.

  • What format is recommended for saving disk space when downloading the models?

    -The 16-bit format is recommended as it saves a significant amount of disk space.

  • Where should the downloaded models be placed?

    -The downloaded models should be placed in the normal location under the Confy UI's models checkpoints directory.

  • How can one start the Confy UI?

    -To start Confy UI, one can activate their Anaconda environment or Python virtual environment and run the python main.py.

  • What is the official workflow provided in the video?

    -The official workflow is provided as a JSON file that can be downloaded and dragged onto the Confy UI interface.

  • What is the significance of the XT model in the tutorial?

    -The XT model is significant as it is used for generating longer 3-second videos.

  • How does the tutorial demonstrate the combination of texture to image with stable video diffusion?

    -The tutorial demonstrates this by using the power of Confy UI to connect different workflows, starting from a prompt text to image and then from image to video using stable video diffusion.

  • What is the approximate VRAM usage for the model discussed in the video?

    -The model uses about 9 GB of VRAM, suggesting that a 12 GB GPU would be sufficient to run it.

Outlines

00:00

πŸ“š Introduction to Using Stable Video Diffusion with Confy UI

This paragraph introduces the purpose of the tutorial, which is to guide users on how to utilize the latest stable video diffusion technology with the Confy UI. It emphasizes the importance of reading the introduction blog for a better understanding and references a previous video for updating the Confy UI. The first step is to install or update the Confy UI, followed by downloading several models, including a normal 2-second version and an XT version for 3-second videos. The recommendation is to use the 16-bit format to save disk space, which only requires approximately 4.5 GB. After downloading, users are instructed to place the models in the designated location under the Confy UI. The paragraph concludes with the instruction to activate the Anaconda environment or Python virtual environment and run the python main.py to start the Confy UI.

05:03

πŸŽ₯ Demonstration of Video Generation Using XT Model

The second paragraph focuses on demonstrating the process of generating longer videos using the XT model, which was previously downloaded. It mentions that the XT model can generate 2-second videos and encourages users to try this feature. The paragraph also discusses the workflow provided by the official source, which can be downloaded as a JSON file and imported into the Confy UI interface. The summary highlights the simplicity of the interface and the selection of the checkpoint. It also touches on the applause and music that occur during the demonstration, indicating an interactive and engaging tutorial. The paragraph ends with a note on the VRAM usage and a recommendation for users with a 12 GB GPU, suggesting that it is sufficient to run the model. The results of the generated image and video are described, showcasing the capabilities of the technology.

10:06

πŸ€– Combining Texture to Image and Stable Video Diffusion with Comy UI

In this paragraph, the focus shifts to utilizing the power of the Comy UI to combine texture to image with stable video diffusion. Users are instructed to ensure the correct selection of image size for the normal text to image generation pipeline. The paragraph explains how to connect the image with the video diffusion, allowing users to start from a prompt text, create an image, and then generate a video. The flexibility of the Comy UI is praised, allowing users to connect different workflows together for powerful automation. The speaker expresses excitement about sharing their workflow on the Discord server and encourages those interested to join. The paragraph concludes with a note on the VRAM usage and a reminder that the 12 GB GPU is sufficient for this process.

🌐 Sharing Knowledge and Looking Forward to Future Advancements

The final paragraph wraps up the tutorial by expressing the speaker's happiness in sharing knowledge about stable video diffusion and AI advancements. The speaker looks forward to future developments in the AI field and encourages viewers to subscribe to their channel for updates on the latest advancements in AI and stable diffusion. The paragraph ends on a positive note, with the speaker expressing excitement for the future and bidding farewell to the viewers.

Mindmap

Keywords

πŸ’‘intro videos

Intro videos refer to the introductory or promotional content that is used to present a product, service, or concept. In the context of the video, these are generated using the latest stable video diffusion technology, which is a method for creating realistic video content from scratch. The intro videos serve as an example of the capabilities of this technology, showcasing its potential for generating high-quality visual content quickly and efficiently.

πŸ’‘confy UI

Confy UI is a user interface or graphical interface designed to make the process of using complex software or tools more accessible and user-friendly. In the context of the video, it is used to interact with the video diffusion technology, allowing users to generate their own videos without needing to understand the underlying technical complexities. The tutorial suggests that confy UI is an essential tool for those looking to utilize the latest advancements in video generation.

πŸ’‘models

In the context of the video, models refer to the underlying algorithms or neural networks that are used to generate the video content. These models are trained on vast amounts of data to learn how to produce realistic videos based on given inputs. The script mentions different versions of these models, such as the normal 2-second version and the XT version for 3-second videos, each optimized for different lengths of video generation. The models are a crucial component of the video diffusion technology, as they determine the quality and accuracy of the generated content.

πŸ’‘16bit format

The 16bit format refers to a color depth setting in digital media, where each pixel in an image or video can have one of 65,536 possible colors. This format is often used to save storage space and improve performance, as it requires less memory than higher color depths. In the context of the video, using the 16bit format is recommended to save disk space when downloading and using the video models, making the process more efficient and accessible for users with limited storage or resources.

πŸ’‘checkpoints

Checkpoints in the context of the video refer to specific points or stages in the video generation process where the model's progress is saved. These checkpoints can be used to resume the process from a particular point, ensuring that any interruptions or issues do not result in the loss of work. They are essential for managing and organizing the complex process of video generation, allowing users to pick up where they left off and maintain a smooth workflow.

πŸ’‘workflow

A workflow is a series of connected steps or processes that are followed to complete a specific task or project. In the context of the video, the workflow refers to the sequence of operations required to generate a video using the stable video diffusion technology. This includes selecting the appropriate model, configuring settings, and combining different elements such as text, images, and video content. The script emphasizes the importance of understanding and following the official workflow provided by the developers to ensure successful video generation.

πŸ’‘generator video

A generator video is the output produced by the video diffusion technology, which is created based on input data such as text descriptions or other media. These videos are generated by the models and represent the tangible result of the video generation process. The quality and accuracy of the generator video are indicative of the effectiveness of the models and the input data provided by the user.

πŸ’‘XT model

The XT model is a specific version of the video generation model mentioned in the script, designed to create longer videos of 3 seconds in length. This model is recommended for users who wish to generate more extended video content, offering greater flexibility and creative potential. The XT model represents an advancement in the technology, allowing for more complex and detailed video generation.

πŸ’‘stable video diffusion

Stable video diffusion is a technology or method used for generating realistic and coherent video content from various inputs, such as text descriptions or images. It represents a stable and reliable version of the video generation process, ensuring that the output is of high quality and consistent with the intended content. This technology is central to the video's theme, as it enables the creation of new and engaging video content that can be used for various purposes.

πŸ’‘automation

Automation refers to the process of creating systems or workflows that can perform tasks with minimal human intervention. In the context of the video, automation is achieved through the use of confy UI and the stable video diffusion technology, allowing users to generate videos from text inputs without the need for manual editing or complex technical skills. The script emphasizes the flexibility and power of the UI in automating the video generation process, making it accessible and efficient for users.

πŸ’‘Discord server

A Discord server is an online community platform where users can communicate, share resources, and collaborate on projects. In the context of the video, the Discord server is used as a forum for users interested in the video diffusion technology to connect, share their workflows, and seek assistance or guidance from others in the community. The server serves as a valuable resource for those looking to learn more about the technology and improve their skills in video generation.

Highlights

The introduction of a tutorial on using the latest stable video diffusion with Confy UI.

Recommendation to read the blog introduction for detailed descriptions and examples.

Instructions on installing or updating the Confy UI for use.

The necessity of downloading several models, including a normal 2-second version and an XT version for 3-second videos.

Advantage of using the 16bit format to save disk space, only requiring about 4.5 GB.

The process of placing the downloaded models into the designated location under the Confy UI.

Activation of the Confy UI through the use of an Anaconda environment or Python virtual environment.

Downloading and utilizing the official workflow provided as a JSON file.

Demonstration of the generator video and its capability to produce a 2-second video in about 1 minute.

A demonstration of generating longer videos using the XT model and the previously downloaded files.

Explanation of using the power of Confy UI to combine textures to images with stable video diffusion.

The flexibility of Confy UI in connecting different workflows for automation.

The requirement of approximately 9 GB of VRAM for the model to run, suggesting sufficiency with a 12 GB GPU.

Completion of the results showing the generated image from the SDLX model and the corresponding video.

The sharing of the workflow to a JSON text file and the offer to share it on the Discord server for interested parties.

A strong recommendation to try the model due to positive results and experiences.

Anticipation for the future of AI advancements and a call to subscribe for updates on AI developments.

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