Pruna Ai P Image Text To Image Trainer

Pruna Ai P Image Text To Image Trainer

Playground

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.

Features

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

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.

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

• Pruna AI P-Image Text-to-Image LoRA — Generate images from prompts using a trained LoRA.
• Pruna AI P-Image Text-to-Image — Generate images directly from prompts without a LoRA.
• Pruna AI P-Image Edit Trainer — Train LoRAs for downstream image editing workflows.
• Pruna AI P-Image Edit LoRA — Edit images with LoRA-guided control.

# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/pruna-ai/p-image/text-to-image-trainer" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "steps": 101
}'

# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"