Phase 7: Multi-Model Pipeline Orchestration · 55 min · Python · asyncio · Celery

Pipeline Patterns

Sequential, parallel fan-out, conditional branching, multi-modal — four patterns that cover every production pipeline.

What you will learn

Implement sequential pipeline: image → bg removal → upscale → style LoRA → delivery
Implement parallel fan-out: generate 10 variants simultaneously, score them, pick the best
Implement conditional branching: if quality < threshold → regenerate with different parameters
Implement multi-modal pipeline: LLM → image → video → audio → music → post-production → final output

Objective

Compose media stages with explicit state, routing, retry, cost, and quality boundaries. In the paired studio lab, implement all four pipeline patterns: (1) sequential: generate image → upscale → deliver, (2) parallel fan-out: generate 4 variants, pick best, (3) conditional: if quality score < 0.7, regenerate with different seed, (4) multi-modal: text → image → video. Use asyncio for parallel execution.

The Problem

A team needs to build different types of generative media pipelines:

Product images: generate → bg removal → upscale → style → deliver (sequential)
A/B testing: generate 10 variants simultaneously, score, pick best (parallel fan-out)
Quality control: generate → check quality → if low, regenerate (conditional)
Full campaign: LLM → image → video → audio → music → post → deliver (multi-modal)

Each requires a different pipeline pattern. Knowing which pattern to use when is a core architecture skill.

What you'll build
Implement four pipeline patterns in Python with asyncio: sequential, parallel fan-out, conditional branching, and multi-modal. These cover every production generative media pipeline.

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Key terms in this lesson

LoRA (Low-Rank Adaptation)