n8n-nodes-sogni

Enhanced n8n Community Node for Sogni AI Image, Video & LLM Generation

Generate AI images, videos, and LLM responses using Sogni AI Supernet directly in your n8n workflows with full ControlNet support for guided image generation, video generation capabilities, Qwen Image Edit for multi-reference image editing, and Sogni LLM chat generation for text workflows.

This node pulls from your personal Sogni account—sign up for free to get 50 free Render credits per day. Under the hood, the project utilizes the @sogni-ai/sogni-client-wrapper, 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
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

LLM Resource

Generate: Create text responses with Sogni chat models
Get All: List all available Sogni LLM/chat models

Model Resource

Get All: List all available models
Get: Get specific model details

Account Resource

Get Balance: Check SOGNI and Spark token balance

Installation

Option 1: Community Nodes UI (Recommended)

In n8n, open Settings ▸ Community Nodes
Select Install
Enter n8n-nodes-sogni
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

In n8n, go to Credentials
Click Add Credential
Search for "Sogni AI"
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

Create or open a workflow
Click + to add a node
Search for "Sogni AI"
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": "wan_v2.2-14b-fp8_t2v_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": "qwen_image_edit_2511_fp8_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 / prompt_priority / cn_priority
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
`qwen_image_edit_2511_fp8`	Standard quality model	20 steps
`qwen_image_edit_2511_fp8_lightning`	Fast lightning model	4 steps

Example Workflows

See the examples directory for complete workflow JSON files:

Basic Image Generation - Simple text-to-image
Batch Processing - Generate multiple images
Dynamic Model Selection - Auto-select best model
Scheduled Generation - Daily automated images
Video Generation - AI video creation with customizable parameters
Image Edit with Qwen - Edit images using context-aware Qwen models
Emotional Slothi Telegram Bot - Dynamic Qwen image-edit + Telegram posting
LTX-2 Video-to-Video ControlNet - Advanced v2v workflow with reference video + controls
WAN Animate-Replace with SAM2 - Subject-guided video replacement with reference image + source video
LTX-2 Text-to-Video - Minimal prompt-only LTX t2v workflow
LTX 2.3 Dynamic Text-to-Video - Auto-select an available ltx23-* model before generation
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
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: sogni_video_[projectId]_[index].[ext]
Full resolution video data

Image Edit Output

JSON Output

{
  "projectId": "EDIT123...",
  "modelId": "qwen_image_edit_2511_fp8_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: sogni_edit_[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-sogni_artist_v1_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 512x512 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:

Ensure previous node outputs binary data
Check the binary property name
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:

Ensure previous node outputs binary data with the correct property name
Check contextImage1Property matches your binary property (default: data)
Use "View" in n8n to inspect binary data from previous node
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 (qwen_image_edit_2511_fp8) 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-client-wrapper library. For standalone Node.js usage:

import { SogniClientWrapper } from '@sogni-ai/sogni-client-wrapper';

const client = new SogniClientWrapper({
  username: 'your-username',
  password: 'your-password',
  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-client-wrapper for full API documentation.

Version History

v1.5.7 (Current)

📦 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:

Check this README
Review the ControlNet Guide
Check example workflows
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-client-wrapper - Enhanced wrapper library
n8n - Workflow automation platform

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

Name		Name	Last commit message	Last commit date
Latest commit History 52 Commits
credentials		credentials
examples		examples
icons		icons
img		img
nodes/Sogni		nodes/Sogni
test		test
.eslintrc.js		.eslintrc.js
.gitignore		.gitignore
CLAUDE.md		CLAUDE.md
CONTRIBUTING.md		CONTRIBUTING.md
CONTROLNET_GUIDE.md		CONTROLNET_GUIDE.md
LICENSE		LICENSE
README.md		README.md
TROUBLESHOOTING.md		TROUBLESHOOTING.md
index.ts		index.ts
memory.sh		memory.sh
package-lock.json		package-lock.json
package.json		package.json
tsconfig.json		tsconfig.json

Folders and files

Latest commit

History

Repository files navigation

n8n-nodes-sogni

Features

Resources & Operations

Image Resource

Video Resource

LLM Resource

Model Resource

Account Resource

Installation

Option 1: Community Nodes UI (Recommended)

Option 2: Manual Installation

Configuration

1. Add Credentials

2. Add Node to Workflow

Basic Usage

Simple Image Generation

ControlNet-Guided Generation

Video Generation

Image Edit with Qwen

ControlNet Types

Parameters

Required Parameters

Optional Parameters (Additional Fields)

ControlNet Parameters

Video Generation Parameters

Required Parameters

Optional Video Parameters

Image Edit Parameters (Qwen)

Required Parameters

Optional Parameters

Qwen Image Edit Models

Example Workflows

Output

Image Generation Output

JSON Output

Binary Output (when downloadImages = true)

Video Generation Output

JSON Output

Binary Output (when downloadVideos = true)

Image Edit Output

JSON Output

Binary Output (when downloadImages = true)

Tips & Best Practices

1. Network Selection

2. Model Selection

3. Steps Configuration

4. ControlNet Usage

5. Image Download

6. Timeout Configuration

7. Video Generation Tips

8. Image Edit Tips (Qwen)

Troubleshooting

"Insufficient funds" Error

"Model not found" Error

"No binary data found" (ControlNet)

Workflow Times Out

Images Not Downloaded

"No binary data found" (Image Edit)

Image Edit Results Unexpected

Advanced Usage

Combining with Other Nodes

Discord Integration

Google Drive Storage

Social Media Posting

Image Processing Pipeline

Dynamic Prompts

Conditional ControlNet

API Reference

Wrapper Library

Version History

v1.5.7 (Current)

v1.5.5

v1.4.2

v1.4.0

v1.3.1

v1.3.0

Packages