# How to Generate Images in Bulk | FlyMy.AI

> Plan a CSV-to-images workflow with a tested generation step, bounded concurrency and per-row execution records.

Page: https://flymy.ai/use-cases/bulk-image-generation/

A CSV of prompts to a traceable set of image results

# Bulk image generation API

Generate images in bulk by testing one image workflow, freezing it and supplying one row of variables per execution. For a 2,000-row CSV, process bounded batches and record each execution before moving on. Choose the available model and inspect its current price first. Reconcile uncertain runs instead of silently repeating paid generation.

[Build this agent](https://app.flymy.ai/signup) [Read the recipe](https://flymy.ai/use-cases/bulk-image-generation/#recipe)

## How to generate images in bulk

1. ### Prepare and sample the CSV Give each row a stable item ID and the inputs your image workflow requires. Test a small representative sample before submitting the full file. The 2,000 rows are a workload example, not a platform limit or throughput promise.
2. ### Choose and price the image step Discover the currently available Nano Banana endpoint or another image model and read its input schema and price. Complete and review one generation before freezing the workflow with a matching input schema.
3. ### Use a bounded worker batch The documented worker excerpt below calls one frozen compilation and returns its status, result and run ID. Initialize AgentClient and set your tested COMPILATION_ID as in the source. Adapt CSV fields to the actual frozen schema. Keep only a small batch in memory.
4. ### Record outputs and reconcile failures Write each item ID, execution ID, status and output reference to your own durable manifest as it completes. Distinguish a confirmed failure from an unknown dispatch. Review paid retries explicitly; do not resubmit the whole CSV because a response was lost.

Documented one-row worker excerpt - [source documentation](https://docs.flymy.ai/agents/guides/parallel-runs)

```
def process(row: dict) -> dict:
    result = client.compilations.run_instruction_and_wait(
        COMPILATION_ID,
        variables=row,
        timeout=600,
    )
    return {"row": row, "status": result.status, "output": result.output,
            "error": result.error, "run_id": result.id}
```

This is the worker function from the parallel-runs guide, not a complete CSV program. Its client and COMPILATION_ID are initialized in that guide. The example worker count is not an account concurrency guarantee.

## What you get

Illustrative output - example data

```
item_id,execution_id,status,output_reference
image-0001,example-run-1,completed,returned-image-reference
image-0002,example-run-2,needs-review,no-automatic-resubmission
```

### Where the result goes

Persist output references and execution IDs in a manifest you control. Download or send completed images only through a configured destination. Avoid keeping thousands of image payloads in one request or agent response.

## Cost and operating limits

Total usage depends on the chosen model, inputs and accepted generation attempts. Verify price before the sample and review the sample cost before scaling. [[FACT NEEDED: verified Nano Banana input schema, current batch concurrency allowance and throughput for the target account.]]

[Check current pricing](https://flymy.ai/pricing/)

## Choose the right approach

**Implementation choices for this job**

| Approach | What to evaluate |
|---|---|
| A frozen image agent | Useful when each row needs validated preparation, generation and an explicit output step. |
| Direct model API calls | Useful when each row is already a complete model request and you operate concurrency, idempotency and storage yourself. |
| One giant agent request | A large combined response is harder to reconcile and can hold too much data in memory. Prefer a bounded manifest-driven job. |

## Frequently asked questions

**Can I submit 2,000 rows at once?**

This guide does not establish an account limit or one-request batch endpoint. Use bounded batches after checking your available concurrency and model contract.

**Are failed or repeated generations free?**

Do not assume that. Check the documented outcome and billing for the selected model; reconcile unknown executions before repeating a paid operation.

**Can I use Nano Banana?**

Discover the currently available endpoint and its schema and price. The operating guide lists Nano Banana, but a concrete bulk workflow still needs its own verified sample.

## Related jobs

Use case

### [Create AI agent by describing it](https://flymy.ai/use-cases/create-ai-agent-by-describing-it/)

Turn a plain-English job into a cloud agent, review a real run, and freeze the accepted workflow for reuse.

[Read the guide →](https://flymy.ai/use-cases/create-ai-agent-by-describing-it/)

Use case

### [Deploy your own model for an AI agent](https://flymy.ai/use-cases/deploy-custom-model/)

Package a custom Python model, deploy its endpoint and verify the request contract before adding it to an agent workflow.

[Read the guide →](https://flymy.ai/use-cases/deploy-custom-model/)

## Sources

[Parallel execution recipe](https://docs.flymy.ai/agents/guides/parallel-runs), [Model discovery and execution](https://flymy.ai/skill.md), [Pricing](https://flymy.ai/pricing.md)

Sources checked 2026-10-23. Examples are documentation excerpts, not a claim that a paid run has been performed.

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For agents: the FlyMyAI operating guide (sign up, connect, run models, tools and agents) is https://flymy.ai/skill.md, and https://flymy.ai/llms.txt lists the other pages and the documentation.