Generative Media Engineering
Build, orchestrate, and deploy production-grade generative media pipelines
10 phases. 55 lessons. 55 labs. 4 projects. The full generative media stack: diffusion model fundamentals, image generation (FLUX, SDXL, Midjourney), video generation (Sora, Veo, Kling, Runway), audio/music generation (Suno, Stable Audio, MusicGen), 3D generation (Tripo, Rodin, Hunyuan3D), ComfyUI workflow automation, multi-model pipeline orchestration, serverless GPU deployment, quality evaluation, and production operations. You build real pipelines that generate images, video, audio, and 3D as
- Lessons: —
- Labs: —
- Projects: —
- Level: Beginner
Curriculum
- Generative Media Fundamentals — What generative media engineering is, five media modalities, the workflow-not-model insight, pipeline architecture, economics and cost models, API vs self-hosted vs managed platforms, speed-quality-cost trade-off triangle
- Diffusion Model Fundamentals — Forward/reverse diffusion, noise prediction networks, UNet vs DiT architectures, latent diffusion and VAEs, sampling methods (DDPM, DDIM, DPM++), classifier-free guidance, steps vs quality, conditioning (text, image, ControlNet), model file formats
- Image Generation — FLUX.1 (Pro/Dev/Schnell), SDXL and SD 3.5, Midjourney V8 API, DALL-E/GPT Image, LoRA fine-tuning for brand consistency, ControlNet for structural control, inpainting and outpainting, upscaling and face restoration, model selection by use case
- Video Generation — Sora 2, Veo 3/3.1, Kling 3.0, Runway Gen-4, Pika 2.5, Luma Ray3, open-source video models (Wan 2.7, LTX-Video), video generation modes, temporal consistency, motion control, API integration (REST polling, webhooks), multi-provider routing, talking head generation
- Audio & Music Generation — Suno v5.5, Udio v1.5, Stable Audio 3, MusicGen, ElevenLabs Music, Mubert, open-source models (YuE, ACE-Step), text-to-audio vs text-to-music, stem separation, audio post-processing, music structure control, voice cloning, SFX generation, API integration
- 3D Generation — TRELLIS 2, Meshy AI, Tripo AI, Rodin AI, Hunyuan3D, generation modes (text-to-3D, image-to-3D, multi-view), mesh formats and topology, texturing and auto-rigging, 3D model evaluation, game engine integration, API integration
- ComfyUI — Workflow Engine — ComfyUI fundamentals (nodes, graphs, canvas vs API), API integration (/prompt, /history, /view, WebSocket), parameter patching, workflow design patterns (base, refiner, LoRA stacking, ControlNet, upscaling, batch), custom nodes ecosystem, production deployment (self-hosted, serverless, managed), Docker packaging, worker pool management
- Multi-Model Pipeline Orchestration — Orchestration challenge (14+ models, dependency graphs, parallel execution), pipeline patterns (sequential, parallel fan-out, conditional branching, multi-modal), orchestration tools (Python/Celery, ComfyUI, fal.ai, Replicate, Modal, Temporal), model routing and fallback, cost-aware routing, A/B testing models, batch processing at scale
- Serverless GPU Deployment & Infrastructure — fal.ai, Replicate, Modal, RunPod Serverless, deployment patterns (API wrapping, ComfyUI Docker, model hosting), GPU resource management (selection by modality, VRAM, quantization, batch processing, cold starts), storage and delivery (S3, CDN, format conversion), cost optimization, scaling and auto-scaling
- Evaluation, Production & Capstone — Quality metrics (FID, CLIPScore, Aesthetic Score, ImageReward, temporal consistency, MOS, FAD), production QA (generate-N-pick-best, quality gates, human review), monitoring and observability, error handling and resilience, content moderation and safety, legal/compliance (copyright, licensing, C2PA), cost optimization at scale, career and portfolio
Skills You Will Learn
- Diffusion Models
- ComfyUI
- Multi-Model Pipelines
- Serverless GPU
- Quality Evaluation
- Generative Media
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