Developing Multi-modal Agents Introduction
Overview
Developing Multi-modal Agents Introduction provides a hands-on exploration of modern generative image and video modeling, with a focus on diffusion-based architectures that power today’s state-of-the-art text-to-image and text-to-video systems. Participants begin by grounding themselves in the evolution of generative modeling—from VAEs and GANs to autoregressive and diffusion approaches—before learning how diffusion models are trained, conditioned, sampled, and evaluated. From there, the course moves into latent diffusion
and large-scale system design, covering real-world data pipelines, scaling strategies, and fine-tuning workflows for domain-specific creativity or brand alignment. Finally, the course extends these methods into the temporal domain to construct text-to-video pipelines, addressing temporal consistency, model architecture variants, and runtime performance considerations. Through progressive labs, students will implement, train, fine-tune, and deploy practical T2I and T2V systems.
Duration
1 day
Who Should Take This Course
Audience
ML/AI Developers, Software & Data Engineers, IT & QA Staff, Technical Managers.
Prerequisites
Participants should have basic skills in Python, experience with machine learning and an understanding of agent development.
Why You Should Take This Course
Developing Multi-modal Agents Introduction ensures participants are able to:
- Explain principles of generative image and video modeling
- Construct and train a text-to-image diffusion model
- Apply latent diffusion techniques to reduce compute cost and enable large-scale training and finetuning workflows
- Design and implement scalable data pipelines for image and video datasets
- Build and deploy a text-to-video generation pipeline
Course Outline
Developing Multi-modal Agents Introduction
Module 1: Image and Video Generation Overview
- Overview of generative modeling: images vs. video
- Variational Autoencoders (VAE) and latent variable models
- Generative Adversarial Networks (GANs) and adversarial training dynamics
- Auto-regressive generation and sequential modeling for pixels/frames
- Diffusion models as the modern dominant paradigm
- LAB: Building a basic multi-modal agent
Module 2: Diffusion Models for Images
- Text-to-Image (T2I) system components and conditioning
- Diffusion model training: forward (noise) and backward (denoising) processes
- Sampling strategies (DDIM, ancestral sampling, classifier-free guidance)
- Diffusion architectures: U-Net and DiT (Diffusion Transformers)
- Evaluation metrics: image quality, diversity, alignment (IS, FID, CLIP score)
- LAB: Implementing a text-to-image diffusion model
Module 3: Latent Diffusion and Model Scaling
- Latent Diffusion Models (LDMs) and compression networks for efficiency
- Using VAEs to encode images/video into latent space
- Data preparation for large-scale training: filtering & standardization
- Large-scale training challenges (compute, dataset scale, stability)
- System design considerations for scalable T2I pipelines
- LAB: Training and fine-tuning a latent diffusion model
Module 4: Text to Video Systems
- Text-to-Video extensions of latent diffusion
- Data preparation for video: temporal consistency, video latent caching
- Diffusion architectures for video (3D U-Net, temporal DiT variants)
- Sampling strategies for temporally coherent motion
- Overall system integration: model + scheduler + encoder/decoder + runtime scaling
- LAB: Building a basic text-to-video solution