Creating an AI Workflow for Image-to-Video Conversion AI TOOL: A Breakdown of 10 Nodes

In this article, we will explore a workflow designed in Tensor Art for converting images into dynamic video sequences. The process involves ten interconnected nodes, each serving a distinct purpose in the pipeline. Let’s break down the workflow step-by-step, starting with the Load Image node and ending with the Video Combine node.

Image-to-Video Workflow in Tensor Art

The diagram below illustrates the complete workflow with labeled nodes:

Node Explanations

Load Image

This is the entry point of the workflow, where the image file is uploaded to be used as the base for video generation.

Resize Image

Resizes the uploaded image to match the desired dimensions (e.g., 720x480 pixels). This ensures the output video maintains consistent proportions and compatibility with subsequent processing steps.

Load CLIP

Loads a CLIP model for text-to-image or text-to-video encoding. This allows the workflow to interpret text-based prompts, influencing how the visual content evolves during video generation.

CogVideo TextEncode (Prompt)

Encodes the primary textual prompt (e.g., describing what the flames or animations should look like) into a format understandable by the video model.

CogVideo TextEncode (Secondary)

Encodes an additional textual prompt to modify or enhance the animation, such as describing changes in intensity or movement within the scene.

Download CogVideo Model

Downloads and initializes the CogVideo model, which is the primary AI tool responsible for generating video frames from textual and visual inputs.

CogVideo ImageEncode

Converts the resized image into a format compatible with the CogVideo system, acting as a bridge between the static image and the dynamic video generation.

CogVideo Sampler

The core of the workflow, this node generates video frames based on the encoded prompts, image data, and model configuration. Parameters such as the number of frames, steps, and noise strength are set here to control the quality and length of the video.

CogVideo Decode

Decodes the sampled video data into a usable video format. This includes applying settings for tile sizing, overlap, and resolution to ensure the final video meets the desired specifications.

Video Combine

Combines the generated video frames into a cohesive video file. This node allows additional settings such as frame rate, loop count, and output format (e.g., MP4).

Summary of Workflow

This workflow efficiently converts an image into a video through a seamless integration of ten nodes. Starting from image uploading (Load Image) and resizing (Resize Image), the process uses text prompts (CLIP and CogVideo TextEncode) and AI models (CogVideo Sampler and Decode) to generate dynamic animations. The final product is rendered and saved through the Video Combine node. This modular approach ensures flexibility and precision in creating high-quality AI-generated videos. You can use this workflow to create AI TOOL for image to video!