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.
", "aboutText": "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.\n\nFor 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.\n\nThe 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.\n\nFont 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.\n\nThe 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.\n\nFor 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.\n\nPro 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.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "66432705d81517449e8b8fb9a89ec2929933153327014a5d2e10e96cc7f47158", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "sourceful/riverflow-v2-proRiverflow 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.
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.
POST /v1/images/generationstext, imageimage| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits sourceful/riverflow-v2-pro | Default— |
Fieldprompt | Typestring | RequirementRequired | Values & limits Minimum length: 1 | Default— |
Fieldresolution | Typestring | RequirementOptional | Values & limits 1K, 2K, 4K | Default— |
Fieldaspect_ratio | Typestring | RequirementOptional | Values & limits 1:1, 4:3, 3:4, 3:2, 2:3, 16:9, 9:16, 21:9, auto | Default— |
Fieldn | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Maximum: 1 | Default— |
Fieldinput_references | Typearray | RequirementOptional | Values & limits Minimum items: 0 Maximum items: 10 | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits | Default— |
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, prompt |
Parametermaterial_types | Contracttext, 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}
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.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Forecast quantity | Billing unit | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Forecast 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 input | Rate schedule— | Forecast quantity— | Billing unitper image | Charge Input · Font input · Estimate for comparison only $0.06 per image |
| ConfigurationInput reference | Rate schedule— | Forecast quantity— | Billing unitper image | Charge Input · Input reference · Estimate for comparison only $0.4 per image |
| Configurationoutput image | Rate schedule— | Forecast quantity— | Billing unitper output image | Charge Output · output image · Estimate for comparison only $0.3 per output image |
| ConfigurationEstimated generation cost | Rate schedule— | Forecast quantityone generation estimate | Billing unit— | Charge Output · Estimated generation cost · Estimate for comparison only $0.3 estimate before generation This estimate is not an additional charge or a fixed rate for the completed generation. |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np={'model': 'sourceful/riverflow-v2-pro', 'prompt': 'a red panda astronaut floating in space, studio lighting'}\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/images/generations \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport base64, json, os, subprocess\nfrom pathlib import Path\nfrom urllib.parse import urlsplit, urljoin\n\ndef save_images(items):\n if not items: raise SystemExit("No completed images; retain the full response")\n for index, item in enumerate(items):\n encoded=item.get("b64_json")\n uri=item.get("url", "")\n if not encoded and uri.startswith("data:image/"):\n header, separator, encoded=uri.partition(",")\n if not separator or ";base64" not in header: raise SystemExit("Unsupported image data URI")\n if encoded:\n raw=base64.b64decode(encoded,validate=True)\n elif uri:\n uri=urljoin("https://api.ailoot.ai",uri)\n parsed=urlsplit(uri)\n if parsed.scheme!="https" or not parsed.netloc or parsed.username or parsed.password:\n raise SystemExit("Invalid returned image URL")\n owned=parsed.netloc=="api.ailoot.ai" and parsed.path.startswith("/v1/media/assets/") and parsed.path.endswith("/content")\n args=["curl","--fail-with-body","--silent","--show-error",uri]\n if owned: args += ["-H","Authorization: Bearer "+os.environ["AILOOT_API_KEY"]]\n temporary=Path(f"image-{index}.bin")\n subprocess.run(args+["-o",str(temporary)],check=True)\n raw=temporary.read_bytes()\n temporary.unlink()\n else: raise SystemExit("Image has neither bytes nor URL")\n if not raw: raise SystemExit("Empty image result")\n 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"\n Path(f"image-{index}.{ext}").write_bytes(raw)\n print("Every returned image saved; complete response metadata retained")\nsave_images(json.loads(Path("result.json").read_text())["data"])\nPY_RESULT\nDownload each returned image or save its Base64 contents
GET /v1/media/tasks/{task_id}GET /v1/media/assets/{asset_id}/contentGET /v1/media/assetsGET /v1/media/assets/{asset_id}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/media/assets \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -o owned-assets.json\n# Preserve every returned asset and follow its exact signed content URL. Do not construct a signed token.\nSave 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.
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.
| Field | Type |
|---|---|
Fieldcreated | Type |
Fielddata[].url | Type |
Fielddata[].b64_json | Type |
Fieldusage | Type |
Ailoot Ultra sourceful/riverflow-v2-pro
/v1/images