AI media generation workflows - Trigger

import UseCasesCards from "/snippets/use-cases-cards.mdx";

Overview

Build AI media generation pipelines that handle unpredictable API latencies and long-running operations. Generate images, videos, audio, and multi-modal content with automatic retries, progress tracking, and no timeout limits.

Featured examples

<CardGroup cols={3}> <Card title="Product image generator" icon="book" href="/guides/example-projects/product-image-generator" > Transform product photos into professional marketing images using Replicate. </Card> <Card title="Meme generator (human-in-the-loop)" icon="book" href="/guides/example-projects/meme-generator-human-in-the-loop" > Generate memes with DALL·E 3 and add human approval steps. </Card> <Card title="Vercel AI SDK image generation" icon="book" href="/guides/example-projects/vercel-ai-sdk-image-generator" > Generate images from text prompts using the Vercel AI SDK. </Card> </CardGroup>

Benefits of using Trigger.dev for AI media generation workflows

Pay only for active compute, not AI inference time: Checkpoint-resume pauses during AI API calls. Generate content that takes minutes or hours without paying for idle inference time.

No timeout limits for long generations: Handle generations that take minutes or hours without execution limits. Perfect for high-quality video synthesis and complex multi-modal workflows.

Human approval gates for brand safety: Add review steps before publishing AI-generated content. Pause workflows for human approval using waitpoint tokens.

Production use cases

Read how Icon uses Trigger.dev to process and generate thousands of videos per month for their AI-driven video creation platform.

</Card> <Card title="Papermark customer story" href="https://trigger.dev/customers/papermark-customer-story">

Read how Papermark process thousands of documents per month using Trigger.dev.

</Card> </CardGroup>

Example workflow patterns

<Tabs> <Tab title="AI content with approval"> **Supervisor pattern with approval gate**. Generates AI content, pauses execution with wait.forToken to allow human review, applies feedback if needed, publishes approved content. <div align="center">

mermaid

graph TB
    A[generateContent] --> B[createWithAI]
    B --> C[wait.forToken approval]
    C --> D{Approved?}

    D -->|Yes| E[publishContent]
    D -->|Needs revision| F[applyFeedback]
    F --> B

</div> </Tab> <Tab title="AI image generation"> Simple AI image generation. Receives prompt and parameters, calls OpenAI DALL·E 3, post-processes result, uploads to storage. <div align="center">

mermaid

graph TB
    A[generateImage] --> B[optimizeImage]
    B --> C[uploadToStorage]
    C --> D[updateDatabase]

</div> </Tab> <Tab title="Batch image generation"> **Coordinator pattern with rate limiting**. Receives batch of generation requests, coordinates parallel processing with configurable concurrency to respect API rate limits, validates outputs, stores results. <div align="center">

mermaid

graph TB
    A[processBatch] --> B[coordinateGeneration]
    B --> C[batchTriggerAndWait]

    C --> D[generateImage1]
    C --> E[generateImage2]
    C --> F[generateImageN]

    D --> G[validateResults]
    E --> G
    F --> G

    G --> H[storeResults]
    H --> I[notifyCompletion]

</div> </Tab> <Tab title="Multi-step image enhancement"> **Coordinator pattern with sequential processing**. Generates initial content with AI, applies style transfer or enhancement, upscales resolution, optimizes and compresses for delivery. <div align="center">

mermaid

graph TB
    A[processCreative] --> B[generateWithAI]
    B --> C[applyStyleTransfer]
    C --> D[upscaleResolution]
    D --> E[optimizeAndCompress]
    E --> F[uploadToStorage]

</div> </Tab> </Tabs> <UseCasesCards />