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Unlock the full potential of generative AI with our advanced course module focused on state-of-the-art multimodal models. This course is designed for learners eager to bridge the gap between images and text, and to master the latest techniques in AI-driven content generation. You’ll begin by exploring the foundational concepts behind multimodal models, learning how contrastive language-image pre-training enables seamless integration of visual and textual data. Discover how these models power innovative applications like semantic image search, allowing you to query image content without manual labeling. Dive deeper into the mechanics of latent diffusion models and unravel the inner workings of stable diffusion, gaining the skills to transform text prompts into entirely new, never-before-seen images. The course also covers essential strategies for evaluating generative models and introduces efficient methods for fine-tuning and adapting pre-trained models to new styles and subjects. By the end, you’ll be equipped to build, adapt, and optimize cutting-edge text-to-image systems—ready to innovate in creative, research, or commercial settings.
This module delves into multimodal generative AI, focusing on models that connect images and text. Learners explore contrastive language-image pre-training for semantic image search and uncover the workings of latent diffusion and stable diffusion for text-to-image generation. The module then covers evaluation of generative models, parameter-efficient fine-tuning, and techniques to teach pre-trained models new styles and subjects. It concludes with methods to optimize diffusion models for faster, near real-time image generation, equipping students with both conceptual understanding and practical skills in advanced multimodal AI systems.
Inclus
44 vidéos3 devoirs
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44 vidéos•Total 408 minutes
Topics•1 minute
Components of a Multimodal Model•5 minutes
Vision-Language Understanding•10 minutes
Contrastive Language-Image Pretraining•6 minutes
Embedding Text and Images with CLIP•14 minutes
Zero-Shot Image Classification with CLIP•4 minutes
Semantic Image Search with CLIP•11 minutes
Conditional Generative Models•5 minutes
Introduction to Latent Diffusion Models•9 minutes
The Latent Diffusion Model Architecture•6 minutes
Failure Modes and Additional Tools•7 minutes
Stable Diffusion Deconstructed•12 minutes
Writing Our Own Stable Diffusion Pipeline•11 minutes
Decoding Images from the Stable Diffusion Latent Space•5 minutes
Improving Generation with Guidance•9 minutes
Playing with Prompts•30 minutes
Topics•1 minute
Methods and Metrics for Evaluating Generative AI•7 minutes
Manual Evaluation of Stable Diffusion with DrawBench•14 minutes
Quantitative Evaluation of Diffusion Models with Human Preference Predictors•20 minutes
Overview of Methods for Fine-Tuning Diffusion Models•10 minutes
Sourcing and Preparing Image Datasets for Fine-Tuning•8 minutes
Generating Automatic Captions with BLIP-2•8 minutes
Parameter Efficient Fine-Tuning with LoRA•12 minutes
Inspecting the Results of Fine-Tuning•5 minutes
Inference with LoRAs for Style-Specific Generation•12 minutes
Conceptual Overview of Textual Inversion•8 minutes
Subject-Specific Personalization with Dreambooth•8 minutes
Dreambooth versus LoRA Fine-Tuning•6 minutes
Dreambooth Fine-Tuning with Hugging Face•14 minutes
Inference with Dreambooth to Create Personalized AI Avatars•14 minutes
Adding Conditional Control to Text-to-Image Diffusion Models•4 minutes
Creating Edge and Depth Maps for Conditioning•16 minutes
Depth and Edge-Guided Stable Diffusion with ControlNet•17 minutes
Understanding and Experimenting with ControlNet Parameters•9 minutes
Generative Text Effects with Font Depth Maps•3 minutes
Few Step Generation with Adversarial Diffusion Distillation (ADD)•7 minutes
Reasons to Distill•6 minutes
Comparing SDXL and SDXL Turbo•12 minutes
Text-Guided Image-to-Image Translation•17 minutes
Video-Driven Frame-by-Frame Generation with SDXL Turbo•13 minutes
Near Real-Time Inference with PyTorch Performance Optimizations•11 minutes
Programming Generative AI: Summary•1 minute
Course Summary•1 minute
3 devoirs•Total 90 minutes
Connecting Text and Images Quiz•30 minutes
Post-Training Procedures for Diffusion Models Quiz•30 minutes
End of Assessment Quiz•30 minutes
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