How to Build a Netflix VOID Video Object Removal and Inpainting Pipeline with CogVideoX, Custom Prompting, and End-to-End Sample Inference

The article discusses the process of building a video object removal and inpainting pipeline using Netflix's VOID (Video Object Removal) model in conjunction with Alibaba Cloud's CogVideoX framework. The tutorial covers several key aspects:

1. Setup Environment:

   • Setting up Google Colab to run Python code.
   • Installing necessary libraries, including transformers, diffusers, and other dependencies.

2. Model and Data Preparation:

   • Downloading the required models and checkpoints from Hugging Face Model Hub.
   • Preparing sample data for inference, which includes input videos and corresponding masks.

3. Pipeline Construction:

   • Initializing tokenizer, text encoder, scheduler, and building the final pipeline using CogVideoX's CogVideoXFunInpaintPipeline.
   • Enabling CPU offloading to manage memory efficiently during computation-intensive tasks.

4. Input Preparation:

   • Loading input videos and masks for object removal.
   • Preparing prompts and negative prompts to guide the model’s generation process.

5. Running Inference:

   • Executing the video inpainting pipeline with specified parameters such as number of inference steps, guidance scale, etc.
   • Saving the generated output videos

Read the full article at MarkTechPost

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