Description

n8n-nodes-sogni

Enhanced n8n Community Node for Sogni AI Image, Video, Audio, LLM & Creative-Workflow Generation

Generate AI images, videos, audio, LLM responses, and full hosted multi-step creative workflows on the Sogni AI Supernet — directly from your n8n workflows. Highlights:

• Image — text-to-image with full ControlNet (15 types), Qwen Image Edit with multi-reference context images, and a dynamic server-validated size-preset dropdown.
• Video — LTX-2.3, WAN 2.2, and Seedance families with cost estimation, ControlNet, image-to-video, sound-to-video, animate, and v2v workflows.
• Audio — generate music and instrumental audio (ACE-Step) with optional lyrics, BPM/time signature/key, composer mode, and cost estimation. (new in 1.7.0)
• LLM — Sogni Intelligence chat models with optional tool calling and vision input, a one-click toggle to expose Sogni’s hosted creative tool manifest to the model, and pre-flight cost estimates. (new in 1.7.0)
• Creative Workflow — start, list, fetch events for, and cancel hosted multi-step Sogni workflows (storyboard → keyframes → video, etc.) with optional poll-until-terminal mode. (new in 1.7.0)

This node uses your Sogni account credentials. Sign up for free to get 50 free Render credits per day. Under the hood, the project uses @sogni-ai/sogni-intelligence-client, which is built on top of the official @sogni-ai/sogni-client SDK.



—

Features

Resources & Operations

#### Image Resource

• Generate: Create AI images with optional ControlNet guidance and a server-validated Size Preset dropdown that adapts to the chosen model + network.
• Edit: Edit images using Qwen Image Edit models with context images.

#### Video Resource

• Generate: Create AI videos with customizable parameters.
• Estimate Cost: Estimate token/USD cost before generation.

#### Audio Resource (new in 1.7.0)

• Generate: Create music or instrumental audio (ACE-Step) with optional lyrics, BPM, time signature, key/scale, composer mode, and creativity controls.
• Estimate Cost: Estimate token/USD cost for an audio request.

#### LLM Resource

• Generate: Create text responses with Sogni chat models. Supports custom tool calling via Tools JSON, and a one-click Enable Sogni Hosted Tools toggle that injects Sogni’s hosted creative tool manifest (generateimage, generatevideo, generatemusic, editimage, animatephoto, applystyle, etc.) so the model can call Sogni tools without hand-authored tool definitions. (toggle new in 1.7.0)
• Estimate Cost: Pre-flight chat cost estimate for a model + messages + max_tokens combination. (new in 1.7.0)
• Get All: List all available Sogni LLM/chat models.

#### Creative Workflow Resource (new in 1.7.0)

• Start: Run a hosted multi-step workflow from a saved template (Template ID + Inputs JSON) or an inline plan (Inline Workflow JSON with steps[]). Optional Wait Until Terminal polling.
• Get: Fetch a workflow record by ID.
• List: List recent workflows (limit/offset).
• Get Events: Stream the event history for a workflow.
• Cancel: Cancel an in-flight workflow.

#### Model Resource

• Get All: List all available models.
• Get: Get specific model details.
• Get Most Popular: Get the model with the most active workers in a single call. (new in 1.7.0)

#### Account Resource

• Get Balance: Check SOGNI and Spark token balance.

See CHANGELOG.md for the full 1.7.0 release notes.

—

Installation

Option 1: Community Nodes UI (Recommended)

1. In n8n, open Settings ▸ Community Nodes
2. Select Install
3. Enter n8n-nodes-sogni
4. Confirm the installation (restart n8n if prompted)

Option 2: Manual Installation

Run in your n8n installation directory
npm install n8n-nodes-sogni
Restart your n8n instance after installation

—

Configuration

1. Add Credentials

1. In n8n, go to Credentials
2. Click Add Credential
3. Search for “Sogni AI”
4. Enter your credentials:
– Username: Your Sogni account username
– Password: Your Sogni account password
– App ID: (Optional) Leave empty for auto-generation

2. Add Node to Workflow

1. Create or open a workflow
2. Click + to add a node
3. Search for “Sogni AI”
4. Select the node and configure

—

Basic Usage

> 💡 Tip: You can import example workflows directly into n8n! Create a new workflow, click the ⋮ (three dots) in the top right corner, select Import from File…, and choose a sample workflow from the ./examples folder.

Simple Image Generation

{
  "resource": "image",
  "operation": "generate",
  "modelId": "flux1-schnell-fp8",
  "positivePrompt": "A beautiful sunset over mountains",
  "network": "fast",
  "additionalFields": {
    "negativePrompt": "blurry, low quality",
    "steps": 20,
    "guidance": 7.5,
    "tokenType": "spark",
    "downloadImages": true
  }
}

ControlNet-Guided Generation

{
  "resource": "image",
  "operation": "generate",
  "modelId": "flux1-schnell-fp8",
  "positivePrompt": "A fantasy castle, magical, glowing",
  "network": "fast",
  "additionalFields": {
    "enableControlNet": true,
    "controlNetType": "canny",
    "controlNetImageProperty": "data",
    "controlNetStrength": 0.7,
    "controlNetMode": "balanced",
    "steps": 20,
    "downloadImages": true
  }
}

Video Generation

{
  "resource": "video",
  "operation": "generate",
  "videoModelId": "wanv2.2-14b-fp8t2v_lightx2v",
  "videoPositivePrompt": "A serene waterfall flowing through a lush green forest",
  "videoNetwork": "fast",
  "videoAdditionalFields": {
    "videoSettings": {
      "frames": 81,
      "fps": 16,
      "steps": 4,
      "guidance": 7.5
    },
    "output": {
      "downloadVideos": true,
      "outputFormat": "mp4",
      "width": 640,
      "height": 640
    },
    "advanced": {
      "tokenType": "spark",
      "timeout": 300000
    }
  }
}

Image Edit with Qwen

{
  "resource": "image",
  "operation": "edit",
  "imageEditModelId": "qwenimageedit2511fp8_lightning",
  "imageEditPrompt": "Change the background to a beautiful sunset beach",
  "contextImage1Property": "data",
  "imageEditNetwork": "fast",
  "imageEditAdditionalFields": {
    "generationSettings": {
      "negativePrompt": "blurry, distorted",
      "numberOfMedia": 1
    },
    "output": {
      "downloadImages": true,
      "outputFormat": "png"
    },
    "advanced": {
      "tokenType": "spark"
    }
  }
}

—

ControlNet Types

All 15 ControlNet types are supported:

| Type | Description | Best For |
|——|————-|———-|
| canny | Edge detection | Structure preservation |
| scribble | Hand-drawn sketches | Sketch to image |
| lineart | Line art extraction | Clean line drawings |
| lineartanime | Anime line art | Anime/manga style |
| softedge | Soft edge detection | Artistic control |
| shuffle | Composition transfer | Layout preservation |
| tile | Tiling patterns | Seamless textures |
| inpaint | Masked area filling | Object removal/editing |
| instrp2p | Instruction-based editing | Text-guided edits |
| depth | Depth map | 3D structure |
| normalbae | Normal map | Surface details |
| openpose | Pose detection | Human pose transfer |
| segmentation | Semantic segmentation | Layout control |
| mlsd | Line segment detection | Architecture |
| instantid | Identity preservation | Face consistency |

See ControlNet Guide for detailed usage instructions.

—

Parameters

Required Parameters

| Parameter | Type | Description |
|———–|——|————-|
| Model ID | string | AI model to use (e.g., flux1-schnell-fp8) |
| Positive Prompt | string | What you want to generate |
| Network | options | fast (SOGNI tokens) or relaxed (Spark tokens) |

Optional Parameters (Additional Fields)

| Parameter | Type | Default | Description |
|———–|——|———|————-|
| Negative Prompt | string | “” | What to avoid |
| Style Prompt | string | “” | Style description |
| Number of Images | number | 1 | How many images (1-10) |
| Steps | number | 20 | Inference steps (1-100) |
| Guidance | number | 7.5 | Prompt adherence (0-30) |
| Token Type | options | spark | spark or sogni |
| Output Format | options | png | png or jpg |
| Download Images | boolean | true | Download as binary data |
| Size Preset | string | “” | Size preset ID |
| Width | number | 1024 | Custom width (256-2048) |
| Height | number | 1024 | Custom height (256-2048) |
| Seed | number | random | Reproducibility seed |
| Timeout | number | 600000 | Max wait time (ms) |

ControlNet Parameters

| Parameter | Type | Default | Description |
|———–|——|———|————-|
| Enable ControlNet | boolean | false | Enable ControlNet |
| ControlNet Type | options | canny | Type of ControlNet |
| Control Image Property | string | data | Binary property name |
| Strength | number | 0.5 | Control strength (0-1) |
| Mode | options | balanced | balanced / promptpriority / cnpriority |
| Guidance Start | number | 0 | When to start (0-1) |
| Guidance End | number | 1 | When to end (0-1) |

Video Generation Parameters

#### Required Parameters
| Parameter | Type | Description |
|———–|——|————-|
| Video Model ID | string | AI model to use for video generation |
| Video Positive Prompt | string | What you want in the video |
| Video Network | options | fast or relaxed |

#### Optional Video Parameters

| Parameter | Type | Default | Description |
|———–|——|———|————-|
| Negative Prompt | string | “” | What to avoid in video |
| Style Prompt | string | “” | Video style description |
| Number of Videos | number | 1 | How many videos (1-4) |
| Frames | number | 30 | Number of frames (10-120). For LTX-2 use 8n+1 frame counts |
| Duration | number | auto | Optional seconds for model-aware frame calculation |
| FPS | number | 30 | Frames per second (10-60) |
| Steps | number | 20 | Inference steps (1-100) |
| Guidance | number | 7.5 | Prompt adherence (0-30) |
| Shift | number | model default | Optional motion intensity control |
| TeaCache Threshold | number | model default | Optional T2V/I2V optimization control |
| Sampler | string | model default | Optional sampler override |
| Scheduler | string | model default | Optional scheduler override |
| Reference Image Property | string | “” | Binary property for i2v/s2v/animate workflows |
| Reference End Image Property | string | “” | Binary property for interpolation end frame |
| Reference Audio Property | string | “” | Binary property for s2v workflows |
| Reference Video Property | string | “” | Binary property for animate/v2v workflows |
| Video Start | number | 0 | Optional source-video offset (seconds) |
| Audio Start | number | 0 | Optional source-audio offset (seconds) |
| Audio Duration | number | server default | Optional source-audio duration (seconds) |
| Trim End Frame | boolean | false | Useful for transition stitching |
| First Frame Strength | number | model default | LTX-2 keyframe interpolation control (0-1) |
| Last Frame Strength | number | model default | LTX-2 keyframe interpolation control (0-1) |
| SAM2 Coordinates (JSON) | string | “” | Animate-replace subject points, e.g. [{"x":0.5,"y":0.5}] |
| Enable LTX-2 Video ControlNet | boolean | false | Enables controlNet for LTX v2v |
| Video ControlNet Type | options | canny | canny, pose, depth, detailer |
| Video ControlNet Strength | number | 0.8 | ControlNet strength for v2v |
| Output Format | options | mp4 | Currently only mp4 is supported |
| Download Videos | boolean | true | Download as binary data |
| Width | number | 512 | Video width (256-1024) |
| Height | number | 512 | Video height (256-1024) |
| Timeout | number | auto | Max wait time (ms) |
| Auto Resize Video Assets | boolean | true | Normalize/resize reference assets for video compatibility |

Image Edit Parameters (Qwen)

#### Required Parameters
| Parameter | Type | Description |
|———–|——|————-|
| Image Edit Model ID | string | Qwen Image Edit model to use |
| Edit Prompt | string | Description of the edit to apply |
| Context Image 1 | string | Binary property name for first context image (required) |
| Network | options | fast or relaxed |

#### Optional Parameters

| Parameter | Type | Default | Description |
|———–|——|———|————-|
| Context Image 2 | string | “” | Binary property for second context image |
| Context Image 3 | string | “” | Binary property for third context image |
| Negative Prompt | string | “” | What to avoid in result |
| Style Prompt | string | “” | Style description |
| Number of Images | number | 1 | How many images (1-10) |
| Steps | number | auto | Inference steps (auto: 20 for standard, 4 for lightning) |
| Guidance | number | auto | Prompt adherence (auto: 4.0 for standard, 1.0 for lightning) |
| Download Images | boolean | true | Download as binary data |
| Output Format | options | png | png or jpg |
| Token Type | options | spark | spark or sogni |
| Timeout | number | auto | Max wait time (ms) |

#### Qwen Image Edit Models

| Model ID | Description | Recommended Steps |
|———-|————-|——————-|
| qwenimageedit2511fp8 | Standard quality model | 20 steps |
| qwenimageedit2511fp8_lightning | Fast lightning model | 4 steps |

—

Example Workflows

See the examples directory for complete workflow JSON files:

1. Basic Image Generation – Simple text-to-image
2. Batch Processing – Generate multiple images
3. Dynamic Model Selection – Auto-select best model
4. Scheduled Generation – Daily automated images
5. Video Generation – AI video creation with customizable parameters
6. Image Edit with Qwen – Edit images using context-aware Qwen models
7. Emotional Slothi Telegram Bot – Dynamic Qwen image-edit + Telegram posting
8. LTX-2 Video-to-Video ControlNet – Advanced v2v workflow with reference video + controls
9. WAN Animate-Replace with SAM2 – Subject-guided video replacement with reference image + source video
10. LTX-2 Text-to-Video – Minimal prompt-only LTX t2v workflow
11. LTX 2.3 Dynamic Text-to-Video – Auto-select an available ltx23-* model before generation
12. Sogni LLM Person Poem Page – Ask for a person’s name in an n8n form, auto-select an available chat model, generate a witty rhyming poem, and show it on n8n’s completion page
13. Sogni LLM Describe Uploaded Image – Upload an image in an n8n form, send it to the documented Qwen3.5 VLM path, and show the generated description on n8n’s completion page

—

Output

Image Generation Output

#### JSON Output

{
  "projectId": "ABC123...",
  "modelId": "flux1-schnell-fp8",
  "prompt": "A beautiful sunset...",
  "imageUrls": [
    "https://complete-images-production.s3-accelerate.amazonaws.com/..."
  ],
  "completed": true,
  "jobs": [
    {
      "id": "JOB123...",
      "status": "completed"
    }
  ]
}

#### Binary Output (when downloadImages = true)

• image: First generated image
• image_1: Second image (if multiple)
• image_2: Third image (if multiple)
• etc.

Video Generation Output

#### JSON Output

{
  "projectId": "VID123...",
  "modelId": "video-model-id",
  "prompt": "A cat playing...",
  "videoUrls": [
    "https://complete-videos-production.s3-accelerate.amazonaws.com/..."
  ],
  "completed": true,
  "jobs": [
    {
      "id": "JOB456...",
      "status": "completed"
    }
  ]
}

#### Binary Output (when downloadVideos = true)

• video: First generated video
• video_1: Second video (if multiple)
• video_2: Third video (if multiple)
• etc.

Binary data includes:

• Proper MIME type (video/mp4)
• Filename: sognivideo[projectId]_[index].[ext]
• Full resolution video data

Image Edit Output

#### JSON Output

{
  "projectId": "EDIT123...",
  "modelId": "qwenimageedit2511fp8_lightning",
  "prompt": "Change the background to a sunset beach",
  "imageUrls": [
    "https://complete-images-production.s3-accelerate.amazonaws.com/..."
  ],
  "completed": true,
  "contextImagesCount": 1,
  "jobs": [
    {
      "id": "JOB789...",
      "status": "completed"
    }
  ]
}

#### Binary Output (when downloadImages = true)

• image: First edited image
• image_1: Second image (if multiple)
• image_2: Third image (if multiple)
• etc.

Binary data includes:

• Proper MIME type (image/png or image/jpeg)
• Filename: sogniedit[projectId]_[index].[ext]
• Full resolution edited image

—

Tips & Best Practices

1. Network Selection

• Fast Network:

– Uses SOGNI tokens
– Faster generation (seconds to minutes)
– Higher cost
– Best for: Time-sensitive applications

• Relaxed Network:

– Uses Spark tokens
– Slower generation (minutes to hours)
– Lower cost
– Best for: Batch processing, scheduled jobs

2. Model Selection

Popular models:

• flux1-schnell-fp8: Fast, high quality, 4 steps recommended
• coreml-sogniartistv1_768: Artistic style
• chroma-v.46-flash_fp8: Fast generation

Use “Get All Models” operation to see all available models.

3. Steps Configuration

• Flux models: 4-8 steps (optimized for speed)
• SD models: 15-30 steps (better quality)
• ControlNet: 20-30 steps (more control)

4. ControlNet Usage

• Start with strength 0.5 and adjust
• Use balanced mode for most cases
• Match ControlNet type to your control image
• See ControlNet Guide for details

5. Image Download

• Enable downloadImages to prevent URL expiry
• URLs expire after 24 hours
• Binary data is permanent in n8n
• Recommended for production workflows

6. Timeout Configuration

• Image – Fast network: 60,000ms (1 minute) usually enough
• Image – Relaxed network: 600,000ms (10 minutes) recommended
• Video – Fast network: 120,000ms (2 minutes) minimum
• Video – Relaxed network: 1,200,000ms (20 minutes) recommended
• Adjust based on complexity and model

7. Video Generation Tips

• Frame Count: Start with 30 frames for quick tests, increase for longer videos
• FPS: Use 30 fps for smooth motion, 10-15 fps for stylized/animated look
• Resolution: Start with 512×512 for faster generation, increase as needed
• Format: Currently only MP4 format is supported
• Models: Look for models with “video”, “animation”, or “motion” in their names

8. Image Edit Tips (Qwen)

• Model Selection: Use lightning variant for fast results (4 steps), standard for quality (20 steps)
• Context Images: Provide 1-3 reference images that inform the edit
• Edit Prompts: Be specific about what to change (e.g., “change background to beach” vs “make it better”)
• Multiple References: Use 2-3 context images for complex edits like style transfer or object compositing
• Steps: Leave empty for auto-detection based on model, or override for fine control

—

Troubleshooting

“Insufficient funds” Error

Solution: Add more Spark or SOGNI tokens to your account

“Model not found” Error

Solution: Use “Get All Models” to see available models

“No binary data found” (ControlNet)

Solution:
1. Ensure previous node outputs binary data
2. Check the binary property name
3. Use “View” in n8n to inspect data

Workflow Times Out

Solution:

• Use relaxed network for slower but more reliable generation
• Increase timeout in Additional Fields
• Split large batches into smaller chunks

Images Not Downloaded

Solution:

• Check downloadImages is enabled
• Verify network connectivity
• Check n8n logs for download errors

“No binary data found” (Image Edit)

Solution:
1. Ensure previous node outputs binary data with the correct property name
2. Check contextImage1Property matches your binary property (default: data)
3. Use “View” in n8n to inspect binary data from previous node
4. For multiple context images, verify each property name is correct

Image Edit Results Unexpected

Solution:

• Use more specific edit prompts describing exactly what to change
• Try the standard model (qwenimageedit2511fp8) for better quality
• Adjust guidance value (higher = more adherence to prompt)
• Provide additional context images for complex edits

—

Advanced Usage

Combining with Other Nodes

#### Discord Integration

Sogni Generate → HTTP Request (Discord Webhook)

#### Google Drive Storage

Sogni Generate → Google Drive (Upload File)

#### Social Media Posting

Sogni Generate → Twitter/Instagram API

#### Image Processing Pipeline

Load Image → Sogni ControlNet → Post-Processing → Save

Dynamic Prompts

Use expressions to generate dynamic prompts:

{{ "A " + $json.style + " image of " + $json.subject }}

Conditional ControlNet

Enable ControlNet based on conditions:

{{ $json.hasControlImage ? true : false }}

—

API Reference

Wrapper Library

This node uses the @sogni-ai/sogni-intelligence-client library. For standalone Node.js usage:

import { SogniClientWrapper } from '@sogni-ai/sogni-intelligence-client';
const client = new SogniClientWrapper({
  username: 'your-username',
  password: 'your-password',
  appSource: 'n8n-nodes-sogni',
  autoConnect: true,
});
const result = await client.createProject({
  modelId: 'flux1-schnell-fp8',
  positivePrompt: 'A beautiful sunset',
  network: 'fast',
  tokenType: 'spark',
  waitForCompletion: true,
});

See @sogni-ai/sogni-intelligence-client for full API documentation.

—

Version History

v1.7.0 (Current)

• Updated @sogni-ai/sogni-intelligence-client to ^2.3.0, including

@sogni-ai/sogni-client@^5.0.0-alpha.11.

• Added wrapper-level appSource attribution for all n8n Sogni connections.
• Added Audio, Creative Workflow, LLM cost estimation, hosted tools, server-validated

image size presets, and Model Get Most Popular. See CHANGELOG.md
for the full notes.

v1.5.7

• 📦 Updated @sogni-ai/sogni-client-wrapper to v1.6.1
• 🔄 Pulled in wrapper-side upgrades from @sogni-ai/sogni-client@4.1.1
• ✅ Revalidated the n8n node against the latest wrapper release

v1.5.5

• 🤖 Added support for Sogni Intelligence with Sogni LLM models like Qwen3.5, including Generate and Get All chat model operations
• 🧠 Added advanced chat inputs:

– Messages JSON
– Tools JSON
– Tool Choice JSON

• 🧩 Preserved incoming item JSON in LLM -> Generate outputs so looped/stateful workflows can carry execution state forward
• ⏱️ Extended chat-model lookup timeouts for more reliable LLM workflow startup
• 🧪 Added bundled LLM example workflows for:

– person poem generation with n8n forms and completion pages
– uploaded-image description using the documented Qwen3.5 VLM path

• 🖼️ Added a bundled sample upload image (examples/duck.jpg) for quick vision workflow testing
• 📚 Refreshed README and example docs around the current LLM and vision workflows
• 📦 Updated @sogni-ai/sogni-client-wrapper to v1.6.0

v1.4.2

• 🧪 Added dedicated LTX-2 text-to-video example workflow (examples/10-ltx2-text-to-video.json)
• 📦 Updated @sogni-ai/sogni-client-wrapper to v1.5.2
• 🎬 Added ltx23- / ltx2.3- video model detection and LTX frame normalization coverage
• 🧪 Added dynamic LTX 2.3 example workflow (examples/11-ltx23-dynamic-text-to-video.json)

v1.4.0

• 📦 Updated @sogni-ai/sogni-client-wrapper to v1.4.3
• 🎬 Added Video → Estimate Cost operation (wrapper estimateVideoCost)
• 🧠 Improved video model detection to include ltx2-, ltx23-, and wan_* model families
• 🧩 Added advanced video workflow inputs/controls for LTX/WAN (referenceVideo, referenceAudio, SAM2, keyframe strengths, video ControlNet)
• 🖼️ Aligned Qwen image-edit guidance defaults with wrapper (4.0 standard, 1.0 lightning)
• 🎥 Added Auto Resize Video Assets toggle for video generation

v1.3.1

• 📚 Enhanced README documentation for Image Edit feature
• 📝 Added Image Edit output section, tips, and troubleshooting

v1.3.0

• 🖼️ Added Qwen Image Edit support with multi-reference context images
• 📦 Updated @sogni-ai/sogni-client-wrapper to v1.4.0
• ⚡ Auto-detection of optimal steps based on model (20 for standard, 4 for lightning)
• 🎯 Up to 3 context images for sophisticated multi-reference editing

v1.2.0

• 🎬 Added full video generation support
• 📦 Updated @sogni-ai/sogni-client-wrapper to v1.2.0
• 🎥 MP4 video format support
• ⚙️ Configurable video parameters (frames, FPS, resolution)
• 📥 Automatic video download as binary data
• 🔍 Dedicated video model selection and filtering

v1.1.9

• 📝 Updated Sogni signup copy and highlighted ControlNet positioning

v1.1.8

• 🆕 Refreshed installation instructions and Sogni account links
• 📚 Added references to Sogni platform, docs, and SDK packages

v1.1.6

• ⚡ Changed default network from “relaxed” to “fast” for quicker generation
• 📝 Documentation updates

v1.1.5

• 🔧 Minor bug fixes and improvements
• 📝 Documentation updates

v1.1.0-1.1.4

• ✨ Added full ControlNet support (15 types)
• 📥 Added automatic image download
• 🔑 Enhanced appId auto-generation
• ⚙️ Improved default values
• 📚 Added ControlNet guide

v1.0.0

• Initial release
• Basic image generation
• Model and account operations
• Size presets support

—

Resources

• ControlNet Guide – Complete ControlNet usage guide
• Example Workflows – Ready-to-use workflow examples
• Sogni AI – Platform overview and product updates
• Sogni SDK Docs – Official SDK documentation
• Sogni Docs – Platform guides and API references
• Integration Guide – Complete integration guide

—

Support

For issues or questions:
1. Check this README
2. Review the ControlNet Guide
3. Check example workflows
4. Submit an issue on GitHub

—

License

MIT License – See LICENSE file for details

—

Credits

Built with:

• @sogni-ai/sogni-client – Official Sogni SDK
• @sogni-ai/sogni-intelligence-client – Enhanced wrapper library
• n8n – Workflow automation platform

—

Ready to generate and edit amazing AI images in your n8n workflows! 🎨✨