Topaz Image Upscale Generative API Documentation
Playground
Try it on WaveSpeedAI!Topaz Image Upscale Generative upscales and restores images with Topaz Wonder, Recover, and Redefine models, improving resolution, reconstructing detail, and repairing low-quality imagery for creative assets, photography, design, and production workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Features
Topaz Image Upscale Generative enhances and upscales images with a choice of generative models.
Why Choose This?
- Generative image upscaling — Add generated detail while enlarging the input.
- Model selection — Choose from the available processing models.
- Resolution options — Select an output resolution tier.
Parameters
| Parameter | Required | Description |
|---|---|---|
| image | Yes | The image to process (JPEG, PNG, TIFF or WebP). |
| model | No | Processing model. Options: Wonder 4, Wonder 3.5, Recover 3, Standard MAX, Redefine, Detail. Default: Wonder 4. |
| target_resolution | No | The target resolution of the output: 2k (about 4 MP), 4k (about 16.8 MP) or 8k (about 67 MP). The source aspect ratio is preserved. Options: 2k, 4k, 8k. Default: 4k. |
| output_format | No | The format of the output image. Options: jpeg, png. Default: jpeg. |
How to Use
- Upload your image — Provide a file or URL.
- Choose a model — Select the processing option for your input.
- Select a resolution — Choose the desired output tier.
- Submit — Process and download the result.
Pricing
Pricing is per image, based on the selected resolution tier and model.
| Model | 2k | 4k | 8k |
|---|---|---|---|
| Wonder 4 / Wonder 3.5 | $0.10 | $0.30 | $0.90 |
| Recover 3 / Standard MAX / Redefine | $0.20 | $0.50 | $1.70 |
| Detail | $0.10 | $0.10 | $0.30 |
Output format does not add a separate charge.
Notes
- Resolution tiers are square-equivalent:
2kis about 2048 x 2048 pixels,4kabout 4096 x 4096, and8kabout 8192 x 8192. - Actual width and height vary with the source aspect ratio.
- Generative processing can change fine details; review the result for your intended use.
Authentication
For authentication details, please refer to the Authentication Guide.
API Endpoints
Submit Task & Query Result
set -euo pipefail
export WAVESPEED_API_KEY="your-api-key"
REQUEST_BODY=$(cat <<'JSON'
{
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"model": "Wonder 4",
"target_resolution": "4k",
"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/topaz/image/upscale-generative" \
-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
doneRetry temporary errors only while polling, never the submit request. Errors, retries and timeouts
Parameters
Task Submission Parameters
Request Parameters
| Parameter | Type | Required | Default | Range | Description |
|---|---|---|---|---|---|
| image | string | Yes | - | The image to process (JPEG, PNG, TIFF or WebP). | |
| model | string | No | Wonder 4 | Wonder 4, Wonder 3.5, Recover 3, Standard MAX, Redefine, Detail | The Topaz model to use. Wonder 4: The most advanced all-purpose generative enhancement and upscaling (default). Wonder 3.5: High-quality generative upscaling with great realism. Recover 3: Recovers rich detail in heavily degraded images. Standard MAX: High-quality upscaling with generative detail. Redefine: Adds new generative detail, guided by an automatic description. Detail: Improves realism in AI-generated faces. |
| target_resolution | string | No | 4k | 2k, 4k, 8k | The target resolution of the output: 2k (about 4 MP), 4k (about 16.8 MP) or 8k (about 67 MP). The source aspect ratio is preserved. |
| output_format | string | No | jpeg | jpeg, png | The format of the output image. |
Every model also accepts the global request options enable_sync_mode and enable_base64_output; see their pages for details.
Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data.id | string | Unique identifier for the prediction, Task Id |
| data.model | string | Model ID used for the prediction |
| data.outputs | array | Output values, usually URL strings; some models return text strings or structured result objects (empty when status is not completed) |
| data.urls | object | Object containing related API endpoints |
| data.status | string | Task status. completed is successful; failed, cancelled, timeout, and deleted are failure terminal statuses. |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |
Result Request Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| id | string | Yes | - | Task ID |
Result Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data | object | The prediction data object containing all details |
| data.id | string | Unique identifier for the prediction |
| data.model | string | Model ID used for the prediction |
| data.outputs | array<string | object> | Array of generated outputs (empty when status is not completed). Items are usually URL strings, but may be text strings or structured result objects, depending on the model. |
| data.urls | object | Object containing related API endpoints |
| data.status | string | Status: completed is successful; failed, cancelled, timeout, and deleted are failure terminal statuses |
| data.created_at | string | ISO timestamp of when the request was created |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |