Diffusion Models for Image Generation
Dive into the architecture and application of diffusion models for creating high-quality images from text prompts. Explore models like Stable Diffusion and DALL-E, and learn techniques for image synthesis and editing.
38 courses
Understand the deep learning principles behind generative AI and code your own models for image synthesis and manipulation.
Learn the core mathematical foundations and write clean Python code to build, train, and optimize DDPM and DDIM generative models from scratch.
Learn to build and understand generative AI models by implementing Latent Diffusion Models and Diffusion Transformers using Python and PyTorch.
Build custom text-to-image pipelines and generate tailored digital assets using Python, Hugging Face libraries, and Stable Diffusion.
Learn how to manage model states, save training progress, and resume deep learning workflows seamlessly in PyTorch using industry-standard checkpointing techniques.
Learn how to safely modify model weights and manage the computation graph in PyTorch to build stable, custom training loops.
Understand how PyTorch computes gradients automatically to build, train, and debug neural networks with confidence.
Set up clean, isolated Python environments and install PyTorch computer vision packages using pip, conda, and modern dependency managers to build a reliable workspace.
Learn how to load trained PyTorch models, preprocess input data, and deploy reliable text and image prediction pipelines for production environments.
Discover how diffusion models power modern generative AI, exploring their core mechanics, practical applications in image generation, and real-world implementation challenges.
Learn how to convert, optimize, and export PyTorch image models to ONNX and TorchScript for efficient production deployment.
Learn how to adapt pre-trained PyTorch models for custom image datasets using transfer learning to achieve high accuracy with minimal training time.
Learn to configure isolated Anaconda and Python environments with GPU acceleration to run deep learning models locally.
Learn how to speed up diffusion models using DDIM and DPM-Solver to generate high-quality images in fewer steps without retraining.
Master the installation and configuration of the Diffusers library in Python to generate images and audio using state-of-the-art pretrained models.
Master the foundational concepts of Stable Diffusion and DALL-E to generate, edit, and transform digital images using text-to-image and inpainting techniques.
Learn the underlying mechanics of modern AI image generators, from noise reduction to neural network architectures, through clear written explanations.
Learn to reconstruct textures from 2D images and master differentiable rendering workflows using PyTorch3D for your 3D computer vision projects.
Learn how to leverage the powerful timm library to build, fine-tune, and validate modern computer vision models for image classification using written guides and code.
Learn how diffusion models generate images from noise, understand the underlying neural networks, and evaluate generated samples through clear written guides.
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