Sourceful Riverflow 2.0 Pro Edit is an agentic image model optimized for robust, high-precision image editing and transformation. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.
Boşta

$0.135çalıştırma başına·~74 / $10

Change to a natural, realistic style, the woman depicted in Figure 1 is now wearing the coat from Figure 2 — a detailed, well-fitted garment with visible texture, stitching, and realistic fabric folds. The coat’s design, color, and material should match the original image precisely, with no alterations to its structure or appearance. The woman’s posture, expression, and surroundings remain unchanged, ensuring the transformation is seamless and visually coherent. The lighting, background, and overall environment should stay consistent with the original scene to preserve narrative continuity. This is a natural, high-resolution, photorealistic rendering.
Riverflow 2.0 Pro Edit is a premium image editing model that transforms existing images based on text instructions. Upload up to 10 reference images and describe your edits — the model intelligently combines and modifies elements with photorealistic quality at resolutions up to 4K.
Multi-image reference Support up to 10 reference images for complex editing and element combination.
Ultra-high resolution Output at 1K, 2K, or 4K for professional-grade results.
Flexible aspect ratios Auto-detect from source or choose from 10 preset options.
Transparent background Optional transparency support for compositing workflows.
Prompt Enhancer Built-in tool to automatically improve your editing instructions.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text instruction describing the desired edit |
| images | Yes | Reference images (1-10, click "+ Add Item" for multiple) |
| resolution | No | Output resolution: 1k (default), 2k, 4k |
| aspect_ratio | No | Output ratio: auto (default), 1:1, 21:9, 16:9, 3:2, 4:3, 5:4, 4:5, 3:4, 2:3, 9:16 |
| transparency | No | Enable transparent background (default: disabled) |
| Resolution | Cost per image |
|---|---|
| 1K | $0.135 |
| 2K | $0.135 |
| 4K | $0.297 |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/sourceful/riverflow-2.0-pro/edit with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Riverflow 2.0 Pro Edit below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"resolution": "1k",
"aspect_ratio": "auto",
"transparency": false
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/sourceful/riverflow-2.0-pro/edit" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d "$REQUEST_BODY")
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
-H "Authorization: Bearer $WAVESPEED_API_KEY")
RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
case "$STATUS" in
completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
created|processing) sleep 2 ;;
*) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/sourceful/riverflow-2.0-pro/edit";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
async function requestJson(url, options = {}) {
const response = await fetch(url, options);
if (!response.ok) throw new Error(await response.text());
return response.json();
}
// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"resolution": "1k",
"aspect_ratio": "auto",
"transparency": false
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;
// 2. Poll until the prediction finishes.
while (true) {
const resultBody = await requestJson(resultUrl, {
headers: { "Authorization": `Bearer ${apiKey}` },
});
const result = resultBody.data ?? resultBody;
if (result.status === "completed") {
console.log(result.outputs);
break;
}
if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
await new Promise(resolve => setTimeout(resolve, 2000));
}import json
import os
import time
from urllib.request import Request, urlopen
api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"resolution": "1k",
"aspect_ratio": "auto",
"transparency": False
}
def request_json(url, data=None):
request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
with urlopen(request) as response:
return json.load(response)
# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/sourceful/riverflow-2.0-pro/edit", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"
# 2. Poll until the prediction finishes.
while True:
result_body = request_json(result_url)
result = result_body.get("data", result_body)
status = result.get("status")
if status == "completed":
print(result.get("outputs", []))
break
if status in {"failed", "cancelled", "timeout"}:
raise RuntimeError(result)
if status not in {"created", "processing"}:
raise RuntimeError(f"Unexpected status: {status}")
time.sleep(2)Riverflow 2.0 Pro Edit is a Sourceful model for image editing, exposed as a REST API on WaveSpeedAI. Sourceful Riverflow 2.0 Pro Edit is an agentic image model optimized for robust, high-precision image editing and transformation. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/sourceful/sourceful-riverflow-2.0-pro-edit.
Riverflow 2.0 Pro Edit starts at $0.14 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `prompt`, `images`, `aspect_ratio`, `resolution`, `transparency`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/sourceful/sourceful-riverflow-2.0-pro-edit.
Median end-to-end generation time on WaveSpeedAI is around 68 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (Sourceful). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.