SDtoHD_video_Conversion

                                               Approach

Conversion of Videos From SD to HD Resolution Using Diffusion My approach to convert an SD video to HD using a pre-trained Diffusion model from Hugging Face Hub. It details the steps involved, and libraries used, and provides explanations for each step.

1. Understanding the Approach: • Stable Diffusion is a powerful image generation model. In this case, we leverage its ability to enhance image details to upscale individual frames from an SD video. • The pre-trained model resides on Hugging Face Hub, a platform for sharing and accessing various machine learning models. But before this firstly to generate API token that are used where I used this model .
2. Prerequisites: • A Google Colab account or a local machine with Python, libraries installed, and an appropriate GPU. • An API token from Hugging Face Hub to access the pre-trained model
3. Libraries Used: • OpenCV: For video frame extraction and assembly. • Transformers: Library for loading and interacting with Stable Diffusion pipeline. • Diffusers: Provides the Stable Diffusion pipeline specifically. • PIL (Pillow): Used for image processing tasks like format conversion.
4. Steps Involved: a. Setting Up the Environment:
5. Create a New Notebook on Google Colab or use a Python environment locally.
6. Install Required Libraries: !pip install opencv-python transformers diffusers b. Setup and Authentication • Sign Up and Generate API Token: Create an account on Hugging Face, navigate to your settings, and generate an API token. This token will be used to authenticate and access the pre-trained models c. Load Pre-Trained Diffusion Model • Use the Hugging Face transformers library to load the pre-trained diffusion model. d. Upload SD Video and Set Paths • Upload SD Video: Upload your SD video to the working environment. • Set Paths: Define the input video path, frames extraction folder, upscaled frames folder, and output video path e. Extract Frames from SD Video • Extract frames from the input SD video and save them as images in Frames folder in colab directory

f. Upscale Frames Using the Diffusion Model • Use the pre-trained diffusion model to upscale each extracted frame. g. Combine Upscaled Frames into HD Video • Combine the upscaled frames into a single HD video.

Whole Summary

1. Authentication: Sign up on Hugging Face and generate an API token then use it where I use diffusion model.
2. Library Installation: Install required libraries : transformers, torch, pillow, opencv-python.
3. Model Loading: Load the pre-trained Stable Diffusion model using the API token.
4. Video Processing: o Extract frames from the SD video. o Upscale each frame using the diffusion model. o Combine the upscaled frames into a single HD video.