this generative AI model is capable of performing various image processing and manipulation tasks, such as generating images from text descriptions, editing and modifying existing images, improving image quality and resolution, applying artistic styles and visual effects, and automatically segmenting and annotating elements in images;
To achieve these objectives, this model focuses on the use of state-of-the-art generative AI models, such as diffusion models, generative adversarial networks (GANs) and computer vision models, which will be integrated into a modular and scalable system, allowing testing and interfacing with image generation and processing capabilities through a simple and intuitive interface, with key features such as photorealistic image generation, editing and enhancement of existing images, resolution enhancement, segmentation and automatic annotation of elements in images, resolution enhancement, automatic segmentation and annotation, and a user-friendly interface, which will allow this generative AI project to be used for image processing in a wide variety of applications, such as creating visual content, assisting in image and digital art production, improving image quality in medical and scientific applications, automatic image annotation, and generating customized images from text descriptions.
GAN's/CNN/OpenCV/Yolo/DifussionModels.
On-Premise/AWS
TensorFlow, Pytorch, Skilearn, Keras.
Process
Optimizers/GPU Capacity.