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Pruna AI P-Image Text to Image Trainer API

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Pruna AI P-Image Text to Image Trainer is a fast AI model training workflow for customizing text-to-image generation models with user-provided data. Ready-to-use REST inference API for training custom styles, brand-specific visuals, character concepts, product image generation, marketing creatives, and personalized AI image workflows with simple integration, no coldstarts, and affordable pricing.

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Input

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Inattivo

$1.8per esecuzione

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README

Pruna AI P-Image Text-to-Image Trainer

Pruna AI P-Image Text-to-Image Trainer is a fast training workflow for creating custom LoRAs for the Pruna text-to-image ecosystem. Upload your training image data, optionally provide a default caption, choose the number of training steps, and generate a LoRA that can be used to steer future image generation toward your style, subject, or brand look.

Why Choose This?

  • Fast custom LoRA training Train a text-to-image LoRA for specialized styles, subjects, or branded visuals.

  • Simple training interface Provide your training image data, optional default caption, and training steps without a complex setup process.

  • Optional caption guidance Use default_caption to provide consistent text conditioning across the training dataset.

  • Flexible training depth Use steps to balance speed, cost, and how strongly the LoRA learns your dataset.

  • Built for the Pruna image stack Trained outputs are intended for downstream use with Pruna text-to-image LoRA workflows.

  • Production-ready API Suitable for custom style pipelines, branded asset generation, and repeatable image workflow customization.

Parameters

ParameterRequiredDescription
image_dataYesTraining image data used to create the LoRA.
default_captionNoOptional default caption applied across the training workflow for more consistent conditioning.
stepsNoNumber of training steps. Higher values generally increase training time and cost. Default: 101.

How to Use

  1. Upload your training data — provide the image dataset you want to use for training.
  2. Add a default caption (optional) — use a short caption if you want more consistent text conditioning across the dataset.
  3. Set training steps — choose how many steps to run based on your desired balance of speed and training strength.
  4. Submit — start the training job.
  5. Use the trained LoRA — apply the resulting LoRA in downstream Pruna text-to-image LoRA workflows.

Example Workflow

Train a custom style LoRA from a curated image set, optionally using a shared default caption, then use the resulting weights in Pruna AI P-Image Text-to-Image LoRA for generation.

Pricing

Pricing is based on the selected steps value.

StepsCost
100$0.18
101$0.1818
250$0.45
500$0.90
1000$1.80
2000$3.60

Billing Rules

  • Pricing scales linearly with steps
  • Cost is $1.80 per 1,000 steps
  • Higher steps values increase total training cost proportionally
  • default_caption does not affect pricing

Best Use Cases

  • Custom style training — Create LoRAs for a distinct visual style or art direction.
  • Brand consistency — Train reusable LoRAs for campaigns, products, or branded aesthetics.
  • Subject-focused generation — Teach the model a recurring character, fashion look, or visual concept.
  • Creative workflow personalization — Build specialized LoRAs for repeatable prompt-driven generation.
  • Marketing asset pipelines — Create tailored generation tools for ongoing content production.

Pro Tips

  • Use a clean, consistent training dataset for better LoRA quality.
  • Add a default_caption when your dataset shares a common concept, subject, or style cue.
  • Start with a moderate number of steps before pushing to larger training runs.
  • Increase steps gradually if the initial LoRA is too weak or underfit.
  • Keep your dataset focused on the style or subject you want the LoRA to learn.
  • Test the trained LoRA in downstream generation workflows before scaling up training volume.

Notes

  • image_data is required.
  • default_caption is optional.
  • steps defaults to 101.
  • Pricing depends only on the selected steps value.
  • LoRAs trained here are intended for Pruna text-to-image LoRA usage.

Related Models

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