models

Sourceful: Riverflow V2 Pro

Markdown · JSON

Sourceful: Riverflow V2 Pro

Riverflow 2.0 Pro is Sourceful’s Pro image generation and editing profile for brand-sensitive creative. Its family reviews and corrects candidates, supports font guidance and provides reference-based detail repair for product labels and artwork.

About the model

This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.

Riverflow 2.0 Pro is the Pro profile of Sourceful’s Riverflow 2.0 image generation and editing family for product, packaging and marketing creative. It combines image creation or editing with a reasoning model that reviews candidates and makes corrections. Its defining concerns are reliable execution of a visual brief, typography and preservation of the product details that make an asset useful to a brand.

For a quality-sensitive composition, supply the product image or artwork, the exact wording and the intended visual arrangement. A useful scene can place a labelled jar beside its ingredients while preserving the container shape and label hierarchy. Describe the lighting, background and crop, then state which product details should not change. The result is a rendered creative image; deciding how it fits into a wider campaign or an editable layout is a separate application step.

The 2.0 workflow automatically assesses candidates and corrects problems over multiple steps. Give that process a clear target by separating required content from optional style choices. For an edit, name the element to change and the surrounding features to retain. A product colour update should not silently become a new label design, and a background replacement should keep the supplied object recognisable. The finished image still needs comparison with the brief and source references.

Font control uses public or supplied custom fonts to guide brand typography. Riverflow 2.0 supports up to two fonts at a time and up to 300 characters per image in its font-control workflow. Provide the exact text, hierarchy and intended weight or spacing rather than expecting the model to choose the wording. Inspect important glyphs, numerals, ligatures and small supporting text in the resulting asset. Keeping the wording concise also leaves space for the product and visual explanation.

The family’s reference-based super-resolution workflow repairs fine text or artwork that a lower-resolution scene does not contain accurately. Supply high-quality source artwork so the detail-repair stage has the actual label or design to reproduce. The 2.0 release describes up to four replacement instances per request, allowing fixes on one product or several products in a scene. This is a reference-based repair workflow around the generated image, rather than a guarantee that every initial picture has perfect micro-detail.

For a related product set, keep the style, lighting and scene structure consistent while supplying the correct reference for each item. A campaign asset can combine a main product view with practical space for approved copy; a product close-up can prioritise label and material detail. Choose the source and output preparation for the intended display size, and preserve the selected image with the references that establish its identity. A scene-level correction and a detail-repair step address different parts of the artifact.

Pro is distinct from the Fast profile and from Riverflow 2.5. The later generation adds custom scoring criteria and deeper review controls; those later settings should not be assumed to be part of this 2.0 profile. For a useful final review, check the requested change, typography, product identity and surrounding details against the source. Revise the specific instruction or reference responsible for a mismatch while retaining the composition that already fits the task.

Ailoot Ultra · Images POST /v1/images/generations sourceful/riverflow-v2-pro

sourceful/riverflow-v2-pro

Sourceful /sourceful/riverflow-v2-pro

Overview

Riverflow 2.0 Pro is Sourceful’s Pro image generation and editing profile for brand-sensitive creative. Its family reviews and corrects candidates, supports font guidance and provides reference-based detail repair for product labels and artwork.

Accepted inputs
Text · Images
Results
Images

Available in Ailoot · inputs & methods

Ailoot Ultra · Images POST /v1/images/generations
Input types
text, image
Output types
image
FieldTypeRequirementValues & limitsDefault
FieldmodelTypestringRequirementRequiredValues & limits
sourceful/riverflow-v2-pro
Default—
FieldpromptTypestringRequirementRequiredValues & limits
Minimum length: 1
Default—
FieldresolutionTypestringRequirementOptionalValues & limits
1K, 2K, 4K
Default—
Fieldaspect_ratioTypestringRequirementOptionalValues & limits
1:1, 4:3, 3:4, 3:2, 2:3, 16:9, 9:16, 21:9, auto
Default—
FieldnTypeintegerRequirementOptionalValues & limits
Minimum: 1
Maximum: 1
Default—
Fieldinput_referencesTypearrayRequirementOptionalValues & limits
Minimum items: 0
Maximum items: 10
Default—
FieldproviderTypeobjectRequirementOptionalValues & limitsDefault—
Method parameters and lifecycle
ParameterContract
Parametercontent_typeContractapplication/json
ParameterrequiredContractmodel, prompt
Parametermaterial_typesContracttext, image

Lifecycle: synchronous_result_then_owned_library

GET /v1/media/tasks/{task_id} · actual returned task_id or media library record

GET /v1/media/assets/{asset_id}/content

GET /v1/media/assets

GET /v1/media/assets/{asset_id}

Pricing

How the final charge is calculated

The displayed rates estimate the cost before generation. The final charge is calculated from the cost recorded for the original generation. Measured output duration and forecast quantities are not the final billable quantity.

Tariff references do not add request options. Supported parameters are listed for the selected method.

Fast mode
Not specified When fast mode is selected
Flex mode
Not specified When flex mode is selected
Maximum reasoning effort
Not specified When maximum reasoning effort uses the default adjustment

Estimated rates for comparison

Prices by configuration

Match each charge to the conditions shown. Use one rate for each set of alternative configurations.

ConfigurationRate scheduleForecast quantityBilling unitCharge
ConfigurationBase token ratesRate scheduleBase rateForecast quantity—Billing unit—Charge
Input · Input tokens · Estimate for comparison only — / 1K tokens
Output · Output tokens · Estimate for comparison only — / 1K tokens
Input · Cache read · Estimate for comparison only — / 1K tokens
Input · Cache write · Estimate for comparison only — / 1K tokens
Input · Cache write, 1 hour · Estimate for comparison only — / 1K tokens
ConfigurationFont inputRate schedule—Forecast quantity—Billing unitper imageCharge
Input · Font input · Estimate for comparison only $0.06 per image
ConfigurationInput referenceRate schedule—Forecast quantity—Billing unitper imageCharge
Input · Input reference · Estimate for comparison only $0.4 per image
Configurationoutput imageRate schedule—Forecast quantity—Billing unitper output imageCharge
Output · output image · Estimate for comparison only $0.3 per output image
ConfigurationEstimated generation costRate schedule—Forecast quantityone generation estimateBilling unit—Charge
Output · Estimated generation cost · Estimate for comparison only $0.3 estimate before generation

This estimate is not an additional charge or a fixed rate for the completed generation.

Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →

Example requests

Ailoot Ultra · Images

curl
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
export AILOOT_API_KEY
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p={'model': 'sourceful/riverflow-v2-pro', 'prompt': 'a red panda astronaut floating in space, studio lighting'}

Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/images/generations \
  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import base64, json, os, subprocess
from pathlib import Path
from urllib.parse import urlsplit, urljoin

def save_images(items):
    if not items: raise SystemExit("No completed images; retain the full response")
    for index, item in enumerate(items):
        encoded=item.get("b64_json")
        uri=item.get("url", "")
        if not encoded and uri.startswith("data:image/"):
            header, separator, encoded=uri.partition(",")
            if not separator or ";base64" not in header: raise SystemExit("Unsupported image data URI")
        if encoded:
            raw=base64.b64decode(encoded,validate=True)
        elif uri:
            uri=urljoin("https://api.ailoot.ai",uri)
            parsed=urlsplit(uri)
            if parsed.scheme!="https" or not parsed.netloc or parsed.username or parsed.password:
                raise SystemExit("Invalid returned image URL")
            owned=parsed.netloc=="api.ailoot.ai" and parsed.path.startswith("/v1/media/assets/") and parsed.path.endswith("/content")
            args=["curl","--fail-with-body","--silent","--show-error",uri]
            if owned: args += ["-H","Authorization: Bearer "+os.environ["AILOOT_API_KEY"]]
            temporary=Path(f"image-{index}.bin")
            subprocess.run(args+["-o",str(temporary)],check=True)
            raw=temporary.read_bytes()
            temporary.unlink()
        else: raise SystemExit("Image has neither bytes nor URL")
        if not raw: raise SystemExit("Empty image result")
        ext="png" if raw.startswith(b"\x89PNG") else "jpg" if raw.startswith(b"\xff\xd8") else "webp" if raw[:4]==b"RIFF" and raw[8:12]==b"WEBP" else "svg" if raw.lstrip().startswith((b"<svg",b"<?xml")) else "bin"
        Path(f"image-{index}.{ext}").write_bytes(raw)
    print("Every returned image saved; complete response metadata retained")
save_images(json.loads(Path("result.json").read_text())["data"])
PY_RESULT

Receive and save results

Ailoot Ultra · Images

Download each returned image or save its Base64 contents

  • GET /v1/media/tasks/{task_id}
  • GET /v1/media/assets/{asset_id}/content
  • GET /v1/media/assets
  • GET /v1/media/assets/{asset_id}
Other content reads
Read existing owned assets
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/media/assets \
  -H "Authorization: Bearer $AILOOT_API_KEY" -o owned-assets.json
# Preserve every returned asset and follow its exact signed content URL. Do not construct a signed token.
Keep all completed results

Save every completed image; retrieve each returned URL or decode its Base64 contents.

Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again.

If the original response needs checking

Keep the returned X-Client-Request-ID for reconciliation. Read only actual returned task_id/asset_id values or the owned media library; never build a task URL from the operation ID and never resubmit creation.

Response fields
FieldType
FieldcreatedType
Fielddata[].urlType
Fielddata[].b64_jsonType
FieldusageType

Aliases

Model aliases

Ailoot Ultra sourceful/riverflow-v2-pro

Method path aliases

/v1/images

Related caller IDs

sourceful/riverflow-v2-pro

FAQ · Sourceful: Riverflow V2 Pro
What inputs and results are available?

POST /v1/images/generations · sourceful/riverflow-v2-pro: text, image → image

Which fields does the request use?

POST /v1/images/generations · sourceful/riverflow-v2-pro: model, prompt, resolution, aspect_ratio, n, input_references, provider

What limits apply?

POST /v1/images/generations · sourceful/riverflow-v2-pro: n.minimum: 1; n.maximum: 1

More details

Connect Ailoot to Claude Code, OpenCode, Hermes or your own app.

The company and its project portfolio

The company and its project portfolio: millions of customers; monthly sales in the billions.

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Subscription discounts for regular use. Promo codes offer up to 50% off token purchases.

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Spending in your history
The model, tokens and amount are recorded for each request.

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