Number of Generations
How generation count works in Qolaba's Image Generation workspace, when to use batch generation, and how it affects credit cost.
Number of Generations controls how many image outputs Qolaba produces from a single prompt and settings configuration in one run. Generating multiple outputs at once gives you variations to compare and choose from — without re-entering your settings each time.
How It Works
Each generation produces one unique image output from the same prompt, keywords, reference images, and output settings. Increasing the count produces multiple variations simultaneously.
Credit cost scales linearly:
Total credits = credits per image × number of generations
Example: Using Nano Banana 2 at 25 credits per image:
1
25
2
50
4
100
8
200
The total credit cost for your current configuration is displayed on the Generate button before you confirm — so you always know what will be consumed before committing.
Single Generation vs. Batch
Run a batch when:
Your prompt, model, and settings are already validated from a previous single run
You want variations to compare and select from before finalizing
You are preparing multiple options for a client or creative brief
You are generating social media content where variety matters
Generate one at a time when:
You are working with a new or untested prompt
You are trying a model or quality setting for the first time
You want to validate output direction before spending credits on a full batch
You are making incremental prompt or keyword refinements between runs
Always run a single generation first when testing a new prompt or model. Evaluate the output, refine what needs adjusting, then scale to a batch once the direction is confirmed. This single habit saves the most credits in an image generation workflow.
A/B Generation
Setting generations to 2 is useful for quick A/B comparison — two variations from the same prompt are produced simultaneously, giving you a direct quality and style comparison without running separate sessions. Use this when your settings are confirmed and you want to see natural variation between outputs before selecting a final image.
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