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In diesem Kurs gibt es 1 Modul
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.
Das ist alles enthalten
44 Videos3 Aufgaben
Infos zu Modulinhalt anzeigen
44 Videos•Insgesamt 408 Minuten
Topics•1 Minute
Components of a Multimodal Model•5 Minuten
Vision-Language Understanding•10 Minuten
Contrastive Language-Image Pretraining•6 Minuten
Embedding Text and Images with CLIP•14 Minuten
Zero-Shot Image Classification with CLIP•4 Minuten
Semantic Image Search with CLIP•11 Minuten
Conditional Generative Models•5 Minuten
Introduction to Latent Diffusion Models•9 Minuten
The Latent Diffusion Model Architecture•6 Minuten
Failure Modes and Additional Tools•7 Minuten
Stable Diffusion Deconstructed•12 Minuten
Writing Our Own Stable Diffusion Pipeline•11 Minuten
Decoding Images from the Stable Diffusion Latent Space•5 Minuten
Improving Generation with Guidance•9 Minuten
Playing with Prompts•30 Minuten
Topics•1 Minute
Methods and Metrics for Evaluating Generative AI•7 Minuten
Manual Evaluation of Stable Diffusion with DrawBench•14 Minuten
Quantitative Evaluation of Diffusion Models with Human Preference Predictors•20 Minuten
Overview of Methods for Fine-Tuning Diffusion Models•10 Minuten
Sourcing and Preparing Image Datasets for Fine-Tuning•8 Minuten
Generating Automatic Captions with BLIP-2•8 Minuten
Parameter Efficient Fine-Tuning with LoRA•12 Minuten
Inspecting the Results of Fine-Tuning•5 Minuten
Inference with LoRAs for Style-Specific Generation•12 Minuten
Conceptual Overview of Textual Inversion•8 Minuten
Subject-Specific Personalization with Dreambooth•8 Minuten
Dreambooth versus LoRA Fine-Tuning•6 Minuten
Dreambooth Fine-Tuning with Hugging Face•14 Minuten
Inference with Dreambooth to Create Personalized AI Avatars•14 Minuten
Adding Conditional Control to Text-to-Image Diffusion Models•4 Minuten
Creating Edge and Depth Maps for Conditioning•16 Minuten
Depth and Edge-Guided Stable Diffusion with ControlNet•17 Minuten
Understanding and Experimenting with ControlNet Parameters•9 Minuten
Generative Text Effects with Font Depth Maps•3 Minuten
Few Step Generation with Adversarial Diffusion Distillation (ADD)•7 Minuten
Reasons to Distill•6 Minuten
Comparing SDXL and SDXL Turbo•12 Minuten
Text-Guided Image-to-Image Translation•17 Minuten
Video-Driven Frame-by-Frame Generation with SDXL Turbo•13 Minuten
Near Real-Time Inference with PyTorch Performance Optimizations•11 Minuten
Programming Generative AI: Summary•1 Minute
Course Summary•1 Minute
3 Aufgaben•Insgesamt 90 Minuten
Connecting Text and Images Quiz•30 Minuten
Post-Training Procedures for Diffusion Models Quiz•30 Minuten
End of Assessment Quiz•30 Minuten
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