Skip to main content
Pruna AIFastStandard

Pruna P-Image

Pruna AI's distilled text-to-image model optimized for extremely low-cost, high-throughput generation. One of the most-run community models on Replicate (15.6M+ runs) thanks to its speed and price.

12 credits
per image
Distilled architecture tuned for extremely low-cost, high-throughput generation
One of the most-run community models on Replicate (15.6M+ runs)
Custom aspect_ratio=custom mode with explicit width/height (multiples of 16, up to 1440px)
Optional prompt upsampling via an LLM for richer prompts
LoRA weight loading from HuggingFace URLs for style customization
Seed control for reproducible generation

Run it right now

Test this model instantly in the Console Playground โ€” no code required

Sign in to try

Use with AI Assistant

Copy usage instructions for Claude, ChatGPT, or other AI

Quick Start

curl -X POST "https://api.core.today/v1/predictions" \
  -H "Content-Type: application/json" \
  -H "X-API-Key: cdt_your_api_key" \
  -d '{
  "model": "prunaai/p-image",
  "input": {
    "prompt": "A minimalist logo concept for a coffee roastery, flat vector style, warm tones",
    "aspect_ratio": "16:9"
  }
}'

Parameters

ParameterTypeRequiredDefaultDescription
promptstringYes-Text prompt for image generation.
aspect_ratiostringNo16:9Aspect ratio for the generated image.
1:116:99:164:33:43:22:3custom
widthintegerNo-Width of the generated image. Only used when aspect_ratio=custom. Must be a multiple of 16.
heightintegerNo-Height of the generated image. Only used when aspect_ratio=custom. Must be a multiple of 16.
prompt_upsamplingbooleanNofalseUpsample the prompt with an LLM.
seedintegerNo-Random seed. Set for reproducible generation.
disable_safety_checkerbooleanNofalseDisable safety checker for generated images.
lora_weightsstringNo-Load LoRA weights. Supports HuggingFace URLs in the format huggingface.co/<owner>/<model-name>[/<lora-weights-file.safetensors>]. HuggingFace LoRAs may require an API token to access, which you can provide in the `hf_api_token` input.
lora_scalenumberNo0.5Determines how strongly the main LoRA should be applied. 0.5 usually works well for most LoRAs.
hf_api_tokenstringNo-HuggingFace API token. If you're using a HuggingFace LoRAs that needs authentication, you'll need to provide an API token.

Common Parameters

Common parameters used when calling POST /v1/predictions.

ParameterTypeRequiredDefaultDescription
modelstringYes-Model identifier
inputobjectYes-Object containing the model-specific parameters from the table above
output_folderstringNo-Folder path for output files (max 256 chars, '..' not allowed)
webhook_urlstringNo-Webhook URL to call on completion
is_publicbooleanNofalseIf true, output files are also available via permanent public URLs

Examples

Quick concept variation

A low-cost, fast draft at the default 16:9 aspect ratio.

curl -X POST "https://api.core.today/v1/predictions" \
  -H "Content-Type: application/json" \
  -H "X-API-Key: cdt_your_api_key" \
  -d '{
  "model": "prunaai/p-image",
  "input": {
    "prompt": "A minimalist logo concept for a coffee roastery, flat vector style, warm tones",
    "aspect_ratio": "16:9"
  }
}'

Custom pixel dimensions

Generates at an exact width/height using aspect_ratio=custom.

curl -X POST "https://api.core.today/v1/predictions" \
  -H "Content-Type: application/json" \
  -H "X-API-Key: cdt_your_api_key" \
  -d '{
  "model": "prunaai/p-image",
  "input": {
    "prompt": "A product photo of running shoes on a plain white background, studio lighting",
    "aspect_ratio": "custom",
    "width": 1024,
    "height": 768
  }
}'

Tips & Best Practices

1Use aspect_ratio=custom with width/height for exact pixel dimensions (must be multiples of 16, up to 1440)
2This is a low-cost, high-throughput model โ€” better suited to generating many variations quickly than to a single maximally polished hero image
3prompt_upsampling can help expand short prompts into more descriptive ones via an LLM
4hf_api_token is only needed for gated/private HuggingFace LoRA repos โ€” leave it unset for public LoRAs

Use Cases

High-volume batch generation where per-image cost matters more than peak fidelity
Fast prototyping and iterative creative exploration
Apps/tools where many users generate images on-demand (chat apps, utilities)
Custom-styled output using community LoRA weights