P H3 Image Edit API Documentation

P H3 Image Edit API Documentation

Playground

P-H3-Image by Pruna AI, built on MiniMax H3 open weights, edits images from text instructions with reference-guided consistency, helping preserve faces, products, and important visual details. It is well suited for creative edits, branded assets, product imagery, and production workflows. Ready-to-use REST inference API.

Features

P-H3-Image Edit edits an existing image from a text instruction using the MiniMax H3 open weights. The first input image is treated as the image to edit, while up to 8 additional images can be used as references for people, products, clothing, styles, and other visual details.

Generate edited images from 1k up to 4k with flexible aspect ratios and output formats.

Why Choose This?

• Instruction-based image editing — Describe what should change while using the first image as the main editing target.
• Multi-image references — Use up to 9 images in one request, addressed as <Picture 1>, <Picture 2>, and so on.
• Reference consistency — Use additional images to guide people, products, clothing, hairstyles, and visual details.
• Up to 4K output — Choose 1k, 1.5k, 2k, 3k, or 4k.
• Flexible framing — Keep the first image’s aspect ratio or choose a supported output ratio.
• Multiple output formats — Export as JPEG, PNG, or WebP.

Parameters

How to Use

1. Upload images — Place the image to edit first, followed by any reference images.
2. Write an edit instruction — Describe what should change and what should remain consistent.
3. Reference images clearly — Use <Picture 1>, <Picture 2>, and so on when explaining the role of each image.
4. Choose resolution — Select 1k through 4k depending on the required detail.
5. Choose framing optional — Keep the original aspect ratio or select another supported ratio.
6. Submit — Generate and retrieve the edited image.

Pricing

Pricing is based on the selected resolution.

The first input image is included in the base price. Each additional input image costs $0.005.

Additional Images

Aspect ratio, output format, and seed do not add separate charges.

Best Use Cases

• Portrait editing — Change clothing, environment, lighting, or styling while using the original person as the main subject.
• Product editing — Place products into new scenes or use reference images to guide product details.
• Multi-reference composition — Combine people, objects, styles, and visual elements from multiple sources.
• Advertising creatives — Adapt existing images for campaigns, promotions, and social content.
• Style transformation — Change materials, lighting, colors, or overall visual direction.
• High-resolution editing — Generate detailed final assets up to 4k.

Pro Tips

• Clearly describe what should change and what should remain unchanged.
• Refer to each important image explicitly, such as the jacket from <Picture 2>.
• Use fewer references when several images provide conflicting visual guidance.
• Use 3k or 4k when finer detail is important.
• Use a fixed seed when comparing prompt changes or reference combinations.
• Put short text that should appear in the image inside quotation marks.

Notes

• prompt and images are required.
• images supports between 1 and 9 inputs.
• The first image is the primary image being edited.
• Additional images are treated as references.
• aspect_ratio defaults to the first image’s aspect ratio.
• resolution defaults to 1k.
• output_format defaults to jpeg.
• Use seed=-1 for random seed selection.
• Higher resolutions increase the base generation price.
• Additional reference images add a fixed $0.005 each after the first image.

Related Models

• P-H3-Image Text-to-Image — Generate images directly from text prompts.
• P-H3-Image Text-to-Image LoRA — Generate images with custom LoRA guidance.
• P-H3-Image Edit LoRA — Edit images with custom LoRA guidance.
• MiniMax H3 Text-to-Video — Generate videos with the MiniMax H3 model family.

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result

set -euo pipefail

export WAVESPEED_API_KEY="your-api-key"

REQUEST_BODY=$(cat <<'JSON'
{
  "prompt": "A cinematic ocean wave at sunrise, highly detailed",
  "images": [
    "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
  ],
  "aspect_ratio": "1:1",
  "resolution": "1k",
  "output_format": "jpeg"
}
JSON
)

# 1. Submit the prediction once. Never retry this request automatically:
# a retry after an unclear failure can create a second, billed task.
CHECK='The task may already exist. Check History or the predictions list before submitting again.'
if ! SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  --connect-timeout 10 --max-time 60 \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/p-h3-image/edit" \
  -H "Authorization: Bearer ${WAVESPEED_API_KEY}" \
  -H "Content-Type: application/json" \
  -d "${REQUEST_BODY}"); then
  printf 'Submit failed. %s\n' "${CHECK}" >&2
  exit 1
fi

TASK=$(printf '%s' "${SUBMIT_RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "${TASK}" | jq -r '.id // empty')
if [ -z "${PREDICTION_ID}" ]; then
  printf 'Submission response did not contain a prediction id. %s\n' "${CHECK}" >&2
  exit 1
fi
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/${PREDICTION_ID}/result"

# 2. Poll until the prediction reaches a final status.
BODY_FILE=$(mktemp)
trap 'rm -f "${BODY_FILE}"' EXIT
while true; do
  # Transient errors (429, 5xx) are retried, honoring Retry-After.
  HTTP_CODE=$(curl --silent --show-error --fail-with-body \
    --connect-timeout 10 --max-time 60 --retry 5 --retry-delay 2 \
    --output "${BODY_FILE}" --write-out '%{http_code}' \
    "${RESULT_URL}" \
    -H "Authorization: Bearer ${WAVESPEED_API_KEY}" || true)
  case "${HTTP_CODE}" in
    2??) ;;
    000|429|5??)  # dropped connection or temporary error: keep polling
      printf 'Task %s: HTTP %s, polling again in 10s\n' "${PREDICTION_ID}" "${HTTP_CODE}" >&2
      sleep 10; continue ;;
    *) printf 'Task %s: HTTP %s\n' "${PREDICTION_ID}" "${HTTP_CODE}" >&2
      cat "${BODY_FILE}" >&2; exit 1 ;;
  esac
  RESULT=$(jq 'if type == "object" and has("data") then .data else . end' "${BODY_FILE}")
  STATUS=$(printf '%s' "${RESULT}" | jq -r '.status // empty')

  case "${STATUS}" in
    completed) printf '%s\n' "${RESULT}" | jq '.outputs'; break ;;
    failed|cancelled|timeout|deleted)
      printf 'Task %s ended with status %s\n' "${PREDICTION_ID}" "${STATUS}" >&2
      printf '%s\n' "${RESULT}" | jq . >&2; exit 1 ;;
    *) sleep 2 ;;
  esac
done

Parameters

Task Submission Parameters

Request Parameters

Every model also accepts the global request options enable_sync_mode and enable_base64_output; see their pages for details.

Response Parameters

Result Request Parameters

Result Response Parameters