Use Kaggle GPU resources to experience the Stable Diffusion open source project for free

foreword

• Due to my limited level, it is inevitable that there will be mistakes and omissions, please criticize and correct.
• For more exciting content, click to enter the YOLO series column or my personal homepage to view
• YOLOv5: Add SE, CBAM, CoordAtt, ECA attention mechanism
• YOLOv5: Interpretation of yolov5s.yaml configuration file, adding small target detection layer
• YOLOv5：IoU、GIoU、DIoU、CIoU、EIoU
• YOLOv7 trains its own data set (mask detection)
• YOLOv8 trains its own data set (football detection)
• Playing with Jetson Nano (5): TensorRT accelerates YOLOv5 target detection

related introduction

• Python is a cross-platform computer programming language. It is a high-level scripting language that combines interpretability, compilation, interactivity and object-oriented. Originally designed for writing automation scripts (shell), as the version is continuously updated and new language features are added, it is more and more used for the development of independent and large-scale projects.
• PyTorch is a deep learning framework, which encapsulates many network and deep learning related tools for us to call, instead of writing them one by one. It is divided into CPU and GPU versions, and other frameworks include TensorFlow, Caffe, etc. PyTorch is launched by Facebook Artificial Intelligence Research Institute (FAIR) based on Torch. It is a Python-based sustainable computing package that provides two advanced features: 1. Tensor computing with powerful GPU acceleration (such as NumPy); 2. , Automatic differentiation mechanism when constructing deep neural network.
• Kaggle is a popular online community and data science competition platform that connects data scientists, machine learning engineers, and data analysts around the world. It provides a platform for users to share, explore, compete, and collaborate on various data science problems.
• AIGC (Artificial Intelligence Generated Content) refers to content created or generated by artificial intelligence systems. It involves the use of artificial intelligence techniques, such as machine learning, natural language processing, and computer vision, to generate various forms of content, including text, images, video, music, and more.
• Stable Diffusion is a method used in probabilistic modeling and image processing. It is based on the theory of diffusion processes and aims to smooth and denoise images while preserving important image structure and details.
  The stable diffusion method achieves smoothing and denoising by applying a nonlinear diffusion operator on the image. Different from traditional linear diffusion methods, stable diffusion introduces non-linear terms to better preserve the edges and details of images.
  The core idea of ​​stable diffusion is to consider the gradient information during the diffusion process, and adjust the diffusion speed according to the gradient size and direction. This effectively suppresses blurring of edges and loss of detail while smoothing the image.
  The stable diffusion method has a wide range of applications in image denoising, edge preservation, texture enhancement and so on. It provides a way to balance smoothing and preserving image structure, which can be applied in fields such as computer vision, image processing and pattern recognition.

Stable Diffusion Kaggle Open Source Project

edit and copy project

• Address: https://www.kaggle.com/code/camenduru/stable-diffusion-webui-kaggle

run project

• Since this open source project is very resource-intensive and time-consuming to run, please be patient.

After running successfully, a URL will appear.

Open the URL to experience

• There are still many functions in the website, and interested friends can explore by themselves!

reference

[1] https://github.com/camenduru/stable-diffusion-webui
[2] https://www.kaggle.com/code/camenduru/stable-diffusion-webui-kaggle

• Due to my limited level, it is inevitable that there will be mistakes and omissions, please criticize and correct.
• For more exciting content, click to enter the YOLO series column or my personal homepage to view
• YOLOv5: Add SE, CBAM, CoordAtt, ECA attention mechanism
• YOLOv5: Interpretation of yolov5s.yaml configuration file, adding small target detection layer
• YOLOv5：IoU、GIoU、DIoU、CIoU、EIoU
• YOLOv7 trains its own data set (mask detection)
• YOLOv8 trains its own data set (football detection)
• Playing with Jetson Nano (5): TensorRT accelerates YOLOv5 target detection