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Riverflow 2.0 Pro Edit | Fast Image Editing

sourceful/

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.

image-to-image
Input

Inattivo

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.

$0.135per esecuzione·~74 / $10

Successivo:

EsempiVedi tutto

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.

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.

Modelli correlati

README

Riverflow 2.0 Pro Edit

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.

Why Choose This?

  • 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.

Parameters

ParameterRequiredDescription
promptYesText instruction describing the desired edit
imagesYesReference images (1-10, click "+ Add Item" for multiple)
resolutionNoOutput resolution: 1k (default), 2k, 4k
aspect_ratioNoOutput ratio: auto (default), 1:1, 21:9, 16:9, 3:2, 4:3, 5:4, 4:5, 3:4, 2:3, 9:16
transparencyNoEnable transparent background (default: disabled)

How to Use

  1. Upload your images — add 1-10 reference images by clicking "+ Add Item".
  2. Write your prompt — describe the edit, referencing figures by number (e.g., "Figure 1", "Figure 2").
  3. Select resolution — 1K for speed, 2K for balanced quality, 4K for maximum detail.
  4. Choose aspect ratio — use auto to match source or pick a specific ratio.
  5. Enable transparency (optional) — check if you need a transparent background.
  6. Run — submit and download your edited image.

Pricing

ResolutionCost per image
1K$0.135
2K$0.135
4K$0.297

Best Use Cases

  • Virtual Try-On — Place clothing or accessories from one image onto a person in another.
  • Element Combination — Merge subjects, objects, or backgrounds from multiple images.
  • Product Visualization — Edit product images with style and context changes.
  • Creative Compositing — Combine elements from multiple references into a cohesive scene.
  • Design Iteration — Rapidly explore variations using multiple reference images.

Pro Tips

  • Reference images by figure number in your prompt (e.g., "the woman in Figure 1 wearing the coat from Figure 2").
  • Use auto aspect ratio to preserve the original image proportions.
  • 1K and 2K share the same price — choose 2K for better quality at no extra cost.
  • Enable transparency when creating assets for layered designs.
  • Be specific about what to keep, change, and combine for best results.
  • Use high-quality reference images for more precise edits.

Notes

  • Both prompt and images are required fields.
  • Maximum 10 reference images per edit.
  • Ensure uploaded image URLs are publicly accessible.
  • 4K images take longer to generate but provide maximum detail.

Related Models

Nota:Questo sito web utilizza modelli di intelligenza artificiale forniti da terze parti.

Riverflow 2.0 Pro Edit API — Quick start

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.

HTTP example
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
done
Node.js example
const 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));
}
Python example
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 API — Frequently asked questions

What is the Riverflow 2.0 Pro Edit API?

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.

How do I call the Riverflow 2.0 Pro Edit API?

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.

How much does Riverflow 2.0 Pro Edit cost per run?

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.

What inputs does Riverflow 2.0 Pro Edit accept?

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.

How long does Riverflow 2.0 Pro Edit take to generate?

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.

Can I use Riverflow 2.0 Pro Edit outputs commercially?

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.

Riverflow 2.0 Pro Edit | Fast Image Editing API on WaveSpeedAI