ControlNet Analysis
Published:
ControlNet Analysis
Description: This project investigates the accuracy of image generation using ControlNet, a model designed to incorporate conditional inputs into text-to-image diffusion processes. The analysis emphasizes the Canny edge detection model within ControlNet, applying it to a dataset of horse images to assess performance.
- Language/Frameworks: Python, PyTorch, OpenCV
- Features:
- Implementation of ControlNet with Canny edge detection.
- Conversion of datasets into Canny edge images for analysis.
- Evaluation of image generation fidelity using ControlNet.
- Detailed reporting on the model’s performance metrics.
Additional Information:
Developed as part of a research initiative to enhance understanding of conditional diffusion models in image generation.