Sourceful's state-of-the-art image editing model using a vision language model with chain-of-thought reasoning combined with open weights diffusion models for design-grade precision
Example outputs coming soon
Details
riverflow-1
Starting from
Prices shown are in USD
Full pricing detailsProviders & Pricing (1)
Riverflow 1 is available exclusively through Runware, starting at $0.039/image.
runware/riverflow-1
riverflow-1-standard API OpenAI-compatible
Integrate Riverflow 1 into your applications via Lumenfall’s OpenAI-compatible API to perform complex image editing and high-fidelity image generation. Modern diffusion workflows allow for precise visual manipulations by leveraging this model’s chain-of-thought reasoning capabilities.
https://api.lumenfall.ai/openai/v1
riverflow-1
Code Examples
Image Edit
/v1/images/editscurl -X POST \ https://api.lumenfall.ai/openai/v1/images/edits \ -H "Authorization: Bearer $LUMENFALL_API_KEY" \ -F "model=riverflow-1" \ -F "[email protected]" \ -F "prompt=Add a starry night sky to this image" \ -F "size=1024x1024"# Response:# { "created": 1234567890, "data": [{ "url": "https://...", "revised_prompt": "..." }] }import OpenAI from 'openai';import fs from 'fs';const client = new OpenAI({ apiKey: 'YOUR_API_KEY', baseURL: 'https://api.lumenfall.ai/openai/v1'});const response = await client.images.edit({ model: 'riverflow-1', image: fs.createReadStream('source.png'), prompt: 'Add a starry night sky to this image', size: '1024x1024'});// { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }console.log(response.data[0].url);from openai import OpenAIclient = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.lumenfall.ai/openai/v1")response = client.images.edit( model="riverflow-1", image=open("source.png", "rb"), prompt="Add a starry night sky to this image", size="1024x1024")# { created: 1234567890, data: [{ url: "https://...", revised_prompt: "..." }] }print(response.data[0].url)Parameter Reference
Size & Layout
| Parameter | Type | Description | Modes |
|---|---|---|---|
size
|
string |
Image dimensions as WxH pixels (e.g. "1024x1024") or aspect ratio (e.g. "16:9")
WxH determines both shape and scale (aspect_ratio and resolution are ignored when size is provided). W:H format is equivalent to aspect_ratio.
|
Edit
|
aspect_ratio
|
string |
Aspect ratio of the output image (e.g. "16:9", "1:1")
Controls shape independently of scale. Use with resolution to control both. If size is also provided, size takes precedence. Any ratio is accepted and mapped to the nearest supported value.
|
Edit
|
resolution
|
string |
Output resolution tier (e.g. "1K", "4K")
Controls scale independently of shape. Higher tiers produce larger images and cost more. If size is also provided, size takes precedence for scale. Any tier is accepted and mapped to the nearest supported value.
|
Edit
|
size
Exact pixel dimensions
"1920x1080"
aspect_ratio
Shape only, default scale
"16:9"
resolution
Scale tier, preserves shape
"1K"
Priority when combined
size is most specific and always wins. aspect_ratio and resolution control shape and scale independently.
How matching works
7:1 on a model with
4:1 and 8:1,
you get 8:1.
0.5K 1K 2K 4K)
or megapixel tiers (0.25 1).
If the exact tier isn't available, you get the nearest one.
Media Inputs
| Parameter | Type | Description | Modes |
|---|---|---|---|
image
|
file |
Required.
Input image(s) to edit
Supports PNG, JPEG, WebP.
|
Edit
|
Output & Format
| Parameter | Type | Description | Modes |
|---|---|---|---|
response_format
|
string |
How to return the image
url
b64_json
Default:
"url" |
Edit
|
output_format
|
string |
Output image format
png
jpeg
gif
webp
avif
Gateway converts to requested format if provider doesn't support it natively.
|
Edit
|
output_compression
|
integer | Compression level for lossy formats (JPEG, WebP, AVIF) |
Edit
|
n
|
integer |
Number of images to generate
Default:
1Gateway generates multiple images in parallel even if provider only supports 1.
|
Edit
|
Parameter Normalization
How we handle parameters across different providers
Not every provider speaks the same language. When you send a parameter, we handle it in one of four ways depending on what the model supports:
| Behavior | What happens | Example |
|---|---|---|
passthrough |
Sent as-is to the provider | style, quality |
renamed |
Same value, mapped to the field name the provider expects | prompt |
converted |
Transformed to the provider's native format | size |
emulated |
Works even if the provider has no concept of it | n, response_format |
Parameters we don't recognize pass straight through to the upstream API, so provider-specific options still work.
Gallery
View all 1 imagesRiverflow 1 FAQ
How much does Riverflow 1 cost?
Riverflow 1 starts at $0.039 per image through Lumenfall. Pricing varies by provider. Lumenfall does not add any markup to provider pricing.
How do I use Riverflow 1 via API?
You can use Riverflow 1 through Lumenfall's OpenAI-compatible API. Send requests to the unified endpoint with model ID "riverflow-1". Code examples are available in Python, JavaScript, and cURL.
Which providers offer Riverflow 1?
Riverflow 1 is available through Runware on Lumenfall. Lumenfall automatically routes requests to the best available provider.
What is the maximum resolution for Riverflow 1?
Riverflow 1 supports images up to 1024x1024 resolution.
Overview
Riverflow 1 is a multimodal image editing model developed by Sourceful that focuses on high-precision design tasks. It differentiates itself from standard diffusion models by integrating a vision language model (VLM) that utilizes chain-of-thought reasoning to interpret complex editing instructions. This architecture allows the model to better understand spatial relationships and specific design constraints before executing pixel-level changes.
Strengths
- Instruction Adherence: The integration of chain-of-thought reasoning helps the model follow multi-step or nuanced natural language instructions more accurately than models that rely on simple CLIP embeddings.
- Design-Grade Precision: Optimized for professional workflows where maintaining the structural integrity of the original image—such as perspective, lighting, and object proportions—is critical during the editing process.
- Spatial Awareness: The vision-language component excels at identifying specific regions for modification, reducing the need for manual masking or complex in-painting coordinates.
- Multimodal Input Flexibility: Seamlessly processes both text prompts and reference images to perform contextual edits, such as style transfers or object replacements that match the surrounding environment.
Limitations
- Processing Latency: Because the model performs cognitive reasoning steps (chain-of-thought) before generating the output, it may have higher inference times compared to single-pass diffusion models.
- Stylistic Range: While highly effective for realistic and design-oriented modifications, it may not exhibit the same level of abstract creativity as specialized artistic models when given highly open-ended or vague prompts.
Technical Background
Riverflow 1 is built on a hybrid architecture that bridges vision-language modeling with open-weights diffusion frameworks. The core innovation involves using the VLM to generate an internal reasoning path that guides the diffusion process, effectively acting as an intelligent controller for the image generation backbone. This approach mimics a designer’s logic by first analyzing the “what” and “where” of an edit before committing to the final visual output.
Best For
Riverflow 1 is best suited for professional product photography editing, architectural visualization updates, and marketing asset iteration where precise control over existing imagery is required. It is an excellent choice for developers building tools that require “smart” image manipulation without forcing users to learn complex prompt engineering.
You can experiment with Riverflow 1 and integrate it into your applications through Lumenfall’s unified API and interactive playground.
Try Riverflow 1 in Playground
Generate images with custom prompts — no API key needed.