Batch Image Generation with AI APIs
Batch image generation with AI APIs on Flixly means sending many generation requests to models such as GPT-Image 2.0 and FLUX Kontext Pro. This guide covers model choice, costs, rate limits and a repeatable workflow.
TL;DR
On Flixly, batch image generation means sending one POST /api/v1/generate request per image and collecting results with GET /api/v1/generations/{id} or webhooks. Pick a model from GET /api/v1/models, test a small subset first, pace requests to your API key's rate limit, and set a spending cap on the key. There is no extra fee for volume: each generation is billed in credits by model and settings.
The real question behind batch image generation
Users searching for batch image generation with AI APIs usually want consistent output across many prompts without clicking through each one. The direct answer is to script many generation requests against a model that fits your style, and to watch credit spend as the run progresses.
Flixly supports this with a public REST API. Create a key in API Keys, read the API docs, and prototype prompts first in the Image Generator.
Model selection for volume work
Call GET /api/v1/models to see the active catalog, and pick an image model, not a video model. Video models such as Kling 3.0 produce clips, not stills.
Comparison table
| Model | Modes | Resolutions | Credit cost |
|---|---|---|---|
| GPT-Image 2.0 | Text to image | 1K, 2K, 4K | quoted in-app |
| FLUX Kontext Pro | Text to image, image to image | Set by aspect ratio | quoted in-app |
| Nano Banana 2 | Text to image, image to image | 0.5K, 1K, 2K, 4K | quoted in-app |
For reference-based edits with the GPT-Image family, use GPT-Image 2.0 Edit.
Handling rate limits and credits
Each API key has its own rate limit and can carry a spending cap, so a runaway loop cannot drain your balance. Credits come from one-time packs on the pricing page.
Send requests with POST /api/v1/generate, then collect results with GET /api/v1/generations/{id} or register webhooks to be notified when each image is ready.
Practical workflow steps
- Prepare a CSV of prompts plus the model and aspect ratio for each row.
- Test a handful of prompts in the Image to Image or Image Generator UI to confirm the look.
- Loop over the CSV, sending one generate request per row, paced to your rate limit.
- Store each generation ID, then download finished images via polling or webhooks.
- Send finished images to Image Tools for upscaling or background removal.
Tradeoffs nobody mentions
Large runs amplify mistakes: one badly worded prompt template or a content-filter trigger repeats across every row. Test a small subset first and log failures so you can retry just those.
Style can drift across a long list when prompts vary a lot. Keep one model per set and reuse a fixed style phrase in every prompt.
Decision rule
Choose the image model whose output meets your quality and resolution target at an acceptable in-app credit quote, then validate a small run before scripting the full list through the developer API.
Frequently Asked Questions
How many images can I generate in one API call?▾
The Flixly API creates one generation per POST /api/v1/generate request. To produce many images, send multiple requests, paced to your API key's rate limit, and track each generation ID.
Does batch mode cost extra credits?▾
No. There is no batch surcharge: each generation costs credits based on the model and settings you choose, so the total is the sum of the individual generations. Credits come from one-time credit packs.
Can I mix models inside one batch?▾
Yes, because every request names its own model. For a consistent look, keep one model per image set; switching models mid-set, for example from GPT-Image 2.0 to FLUX Kontext Pro, changes the style.
What file formats come back from batch jobs?▾
Image models return raster images at the aspect ratio and resolution you request. If you need vector files, run results through the image-to-vector tool in Image Tools.
How do I avoid hitting rate limits?▾
Each API key has a rate limit, so pace requests, retry rate-limited calls with a backoff, and use webhooks instead of constant polling. A spending cap on the key protects your credits if a script misbehaves.
