Easiest Way to Install Stable Video Diffusion ComfyUI

Table of Contents

1. Introduction
2. Updating Comfy UI and Custom Nodes
3. Text-to-Image Workflow
4. Stable Video Diffusion (SVD)
5. Loading SVD Checkpoint Models
6. SVD Image to Video Conditioning
7. Augmentation Level in SVD
8. Augmentation Level and Animation Effects
9. Image to Video Conditioning for Different Animations
10. Results and Examples

Introduction

In this article, we will explore how to use KY UI for stable video diffusion. Many YouTube videos demonstrate image-to-video workflows, but we will take it a step further by incorporating text into our workflow. We will guide you through the process of updating your Comfy UI and custom nodes, and Show you how to Create a text-to-video workflow using stable video diffusion.

Updating Comfy UI and Custom Nodes

To begin, download the latest stable video diffusion model files from Hugging Face. We will provide the download links in the video description for your convenience. Start by downloading the custom node called "Was Node Suite" and install it. This custom node is essential for our workflow. Next, update the other custom nodes and the Comfy UI itself. By updating everything together, you can save a significant amount of time compared to updating each component individually. Be sure to check for any error messages during the update process, as you may need to reinstall some custom nodes.

Text-to-Image Workflow

In the Comfy UI, create a new workflow and clear the workflow Diagram. We will start with a simple text-to-image workflow. Use a text prompt to generate an image using the SDXL model. The VA decode from this text-to-image workflow will then be passed as the input image to the SVD conditioning node.

Stable Video Diffusion (SVD)

The stable video diffusion (SVD) nodes are the Core of our text-to-video workflow. Let's dive into each of the new SVD nodes and their functionalities.

Loading SVD Checkpoint Models

The "Image Only Checkpoint Loader" node is used for loading the new SVD checkpoint models. This node follows the same concept as the normal Stable Diffusion AI image loading process.

SVD Image to Video Conditioning

The "SVD Image to Video Conditioning" node is where You can adjust the width and Height of the video frames, motion bucket ID, FPS, and augmentation level. These parameters allow you to manipulate the animation effects and control the outcome of your video.

Augmentation Level in SVD

The "Augmentation Level" parameter in SVD plays a significant role in determining the animation effects. Experiment with different levels to achieve the desired outcome. Keep in mind that each image has a unique structure and style, so there is no one-size-fits-all approach.

Augmentation Level and Animation Effects

By adjusting the motion bucket ID, FPS, and augmentation level, you can create different animation effects in your videos. Play around with these parameters to explore the range of possibilities.

Image to Video Conditioning for Different Animations

The image-to-video conditioning process is crucial for achieving different animation effects or motions. Remember to customize the width and height of the conditioning section to match the text-to-image section.

Results and Examples

Once the rendering process is complete, you can view the results of your text-to-video workflow. The SVD is capable of detecting and animating objects with simple motions. However, for images of complex characters or animals, maintaining facial details can be challenging for the SVD.

Example 1: Walking Motion

In the first example, we created a text-to-video workflow of a girl walking with a tiger. The SVD was able to detect the motion of the two objects but struggled with the Clarity of the tiger's face.

Example 2: F1 Racing Car

In this example, we prompted the SVD to detect the motion of an F1 racing car and the spinning of its tire wheels. We adjusted the sampler method, sampling steps, image width and height, and set the output video format as MP4. The resulting video showcased the realistic animation, including the movement of the car and the smoke from the tires.

Example 3: Drifting Animation

We attempted to create a drifting animation similar to Initial D, a popular Japanese cartoon. While the SVD successfully twisted the image of the car, there were some issues with the appearance of the front tire. This example demonstrates the limitations of the SVD when rendering complex animations.

In conclusion, the text-to-video workflow using stable video diffusion in Comfy UI provides an innovative way to create animations. By combining text and image processing, you can achieve impressive results. Remember to experiment with different parameters and have fun exploring the possibilities of this workflow.

Highlights

• Learn how to use KY UI for stable video diffusion
• Incorporate text into the video workflow using text-to-image and stable video diffusion nodes
• Update Comfy UI and custom nodes to ensure the latest versions are installed
• Adjust parameters such as motion bucket ID, FPS, and augmentation level to control animation effects
• Explore examples of walking motions, F1 racing car animations, and drifting animations
• Understand the limitations of the SVD when rendering complex animations

FAQ

Q: Can the SVD maintain facial details in complex images? A: The SVD may struggle to maintain facial details in complex images, such as those of characters or animals. It is more suitable for simple motions and objects.

Q: How can I improve the clarity of object faces in the SVD output? A: Using newer models and refining the parameters, such as motion bucket ID, FPS, and augmentation level, may improve the clarity of object faces in the SVD output.

Q: Are there any limitations to the SVD when rendering animations? A: The SVD is more effective at rendering simple motions or easy-to-move objects. Complex animations and detailed facial features may be challenging for the SVD to recreate accurately.