Skip to content

LuqP2/ImageMetaHub-ComfyUI-Save

BranchesTags

Repository files navigation

MetaHub Save Image - ComfyUI Custom Node

Official Companion Comfy Registry

Published on Comfy Registry
https://registry.comfy.org/publishers/image-metahub/nodes/imagemetahub-comfyui-save

Official companion node for Image MetaHub.

Advanced image saving node for ComfyUI with dual metadata support.

Features

  • Auto-Extraction - Detects sampler params, prompts, model/VAE, and LoRAs directly from your workflow
  • Performance Metrics - Auto-tracks GPU usage, VRAM peak, generation time, and software versions
  • Multi-Format - PNG, JPEG, and WebP with metadata injection
  • Filename Patterns - Placeholder-based filenames with sanitization
  • A1111/Civitai Compatible - Saves metadata in tEXt chunk ("parameters") recognized by Automatic1111, Civitai, and most SD tools
  • Image MetaHub Compatible - Saves extended metadata in iTXt chunk ("imagemetahub_data") with full workflow JSON
  • Model Hashes - Calculates SHA256 hashes (AutoV2 format) for models and LoRAs
  • IMH Pro Fields - Support for user tags, notes, and project names
  • Performance - Hash caching and graceful degradation ensure fast generation
  • Fail-Safe Saving - Errors never interrupt generation; image saving gracefully degrades

Installation

Method 1: ComfyUI Manager (Recommended)

  1. Open ComfyUI Manager
  2. Search for ImageMetaHub Save
  3. Install the node
  4. Restart ComfyUI

Published on the official Comfy Registry: https://registry.comfy.org/publishers/image-metahub/nodes/imagemetahub-comfyui-save

Method 2: Comfy CLI

comfy node install imagemetahub-comfyui-save

Restart ComfyUI after installation.

Method 3: Clone Repository (Manual)

  1. Navigate to your ComfyUI custom nodes directory:

    cd ComfyUI/custom_nodes

  2. Clone this repository:

    git clone https://github.com/LuqP2/ImageMetaHub-ComfyUI-Save.git

  3. Install dependencies:

    cd ImageMetaHub-ComfyUI-Save
pip install -r requirements.txt

  4. Restart ComfyUI

Usage

Quick Start

  1. Add "MetaHub Save Image" node to your workflow
  2. Connect the images output from your decoder/sampler
  3. Generate! The node auto-detects and saves metadata.

Auto-detected fields:

  • seed, steps, cfg
  • sampler and scheduler
  • model name + hash
  • positive/negative prompts
  • LoRAs + weights
  • VAE name

Override (Optional)

Generation Parameters: Connect any override input to replace auto-detected values:

  • seed, steps, cfg
  • sampler_name, scheduler
  • model_name, vae_name
  • positive, negative
  • denoise

Performance Metrics:

  • Automatic: VRAM peak and GPU device are auto-detected
  • Generation Time: Use the MetaHub Timer node for accurate workflow timing

MetaHub Timer Node (Recommended for accurate timing):

  1. Add "MetaHub Timer" node to your workflow
  2. Connect one of these inputs (Timer will record a timestamp when it executes):
    • clip - From CLIP loader or CLIPTextEncode (for total workflow time)
    • latent - From KSampler (for sampling + post-processing time)
    • image - From VAEDecode or image loader (for post-processing only)
    • conditioning - From CLIPTextEncode
  3. Connect the corresponding output to continue your workflow (e.g., clip → CLIPTextEncode)
  4. Connect elapsed_time output to Save Node's generation_time_override
    • The Timer records a timestamp when it executes
    • The Save Node automatically calculates elapsed time from this timestamp

Multiple Timers (Advanced profiling): You can add multiple Timer nodes to measure different workflow stages:

  • Timer at CLIP → total workflow time (from CLIP load to save)
  • Timer after KSampler → sampling + decode time
  • Timer after VAEDecode → save/post-processing time only

Performance Overrides (Advanced): For custom benchmarking, connect these hidden inputs:

  • vram_peak_mb - Override VRAM peak measurement
  • gpu_device_override - Override GPU device name
  • generation_time_override - Manual generation time (or from Timer node)

All overrides use forceInput: True (hidden from UI, connection-only).

Filename Pattern

Use filename_pattern to customize names (default ComfyUI_%counter%). Invalid filename characters are sanitized and missing values become unknown. filename_prefix is deprecated but still supported.

Placeholder Description Example
%counter% Auto-increment (00001, 00002...) 00042
%seed% Seed used 1234567890
%date% Date YYYY-MM-DD 2025-01-15
%time% Time HH-MM-SS 14-30-22
%datetime% Date + time 2025-01-15_14-30-22
%model% Model name (no extension) dreamshaper_v8
%sampler% Sampler name euler_ancestral
%steps% Steps 20
%cfg% CFG scale 7.5
%width% Image width 1024
%height% Image height 768

File Formats

  • file_format: PNG, JPEG, or WebP
  • quality: JPEG/WebP quality (1-100)
  • output_path: custom output directory (empty = ComfyUI default)
  • PNG uses tEXt "parameters" and iTXt "imagemetahub_data"
  • JPEG/WebP use EXIF UserComment (A1111) and ImageDescription (IMH JSON)

If structured metadata cannot be written, the image is still saved.

Multi-Sampler Heuristic

When multiple samplers exist, the node prefers the sampler that feeds the latent used by the VAEDecode connected to the saved images; it falls back to the first sampler found.

IMH Pro Fields (Optional)

Organize your generations with Image MetaHub Pro features:

  • user_tags: Tag your images (e.g., "portrait, fantasy, character")
  • notes: Add generation notes or experiments
  • project_name: Group images by project

Performance Metrics (Auto-Tracked)

The node automatically collects GPU and performance metrics for every generation:

Tier 1 - Critical Metrics:

  • VRAM Peak - Peak VRAM usage in MB (CUDA GPUs only)
  • GPU Device - GPU name (e.g., "NVIDIA GeForce RTX 3090")
  • Generation Time - Total generation time in milliseconds

Tier 2 - Very Useful:

  • Steps per Second - Performance benchmark (steps / generation_time)
  • ComfyUI Version - Your ComfyUI version

Tier 3 - Nice-to-Have:

  • PyTorch Version - PyTorch version with CUDA info
  • Python Version - Python runtime version

Auto-Detection:

  • ✅ CUDA (NVIDIA) - Full metrics including VRAM tracking
  • ✅ MPS (Apple Metal) - GPU device name (VRAM not available)
  • ✅ CPU - Marks as "CPU (CUDA not available)"

Advanced Overrides (Optional): For custom benchmarking workflows, connect these inputs:

  • vram_peak_mb - Override VRAM peak (FLOAT)
  • gpu_device_override - Override GPU device name (STRING)
  • generation_time_override - Override generation time in seconds (FLOAT)

All metrics are stored in the analytics field of the Image MetaHub metadata chunk and displayed in the Performance section of Image MetaHub App.

Configuration (Optional)

If your models are in non-standard locations, set environment variables:

# Windows (PowerShell)
$env:COMFYUI_CHECKPOINT_PATH="D:\MyModels\Checkpoints"
$env:COMFYUI_LORA_PATH="D:\MyModels\Loras"
$env:COMFYUI_VAE_PATH="D:\MyModels\VAE"

# Linux/macOS
export COMFYUI_CHECKPOINT_PATH="/path/to/checkpoints"
export COMFYUI_LORA_PATH="/path/to/loras"
export COMFYUI_VAE_PATH="/path/to/vae"

Metadata Formats

A1111/Civitai Format (PNG: tEXt "parameters", JPEG/WebP: EXIF UserComment)

masterpiece, best quality, 1girl, portrait
Negative prompt: ugly, blurry
Steps: 20, Sampler: euler, CFG scale: 7.0, Seed: 12345, Size: 512x768, Model: mymodel, Model hash: abc1234567, Lora hashes: "detail: def9876543"

Image MetaHub Format (PNG: iTXt "imagemetahub_data", JPEG/WebP: EXIF ImageDescription)

{
  "generator": "ComfyUI",
  "prompt": "masterpiece, best quality, 1girl, portrait",
  "negativePrompt": "ugly, blurry",
  "seed": 12345,
  "steps": 20,
  "cfg": 7.0,
  "sampler_name": "euler",
  "model": "mymodel.safetensors",
  "model_hash": "abc1234567",
  "loras": [{"name": "detail.safetensors", "weight": 0.8}],
  "imh_pro": {
    "user_tags": "portrait, fantasy",
    "notes": "Experimental composition",
    "project_name": "Character Design"
  },
  "analytics": {
    "vram_peak_mb": 8234.5,
    "gpu_device": "NVIDIA GeForce RTX 3090",
    "generation_time_ms": 1523,
    "steps_per_second": 13.2,
    "comfyui_version": "0.1.0",
    "torch_version": "2.0.1+cu118",
    "python_version": "3.10.12"
  },
  "workflow": { }
}

Integration with Image MetaHub

This node is the official companion for Image MetaHub.

Images saved with this node are fully compatible with Image MetaHub's:

  • Instant parsing - MetaHub chunk extraction (no graph traversal needed)
  • Performance section - Displays GPU metrics, VRAM, generation time, and versions
  • Search and filtering - Full metadata indexing
  • LoRA detection - Automatic LoRA weight extraction
  • Analytics dashboard - Performance benchmarking
  • IMH Pro features - Tags, notes, and project organization

The Performance section in Image MetaHub App displays:

  • Generation time with smart formatting (ms/s/m+s)
  • VRAM usage with GPU percentage (e.g., "8.0 GB / 24 GB (33%)")
  • GPU device name
  • Steps per second benchmark
  • Software versions (ComfyUI, PyTorch, Python)

Generating Variations: Image MetaHub App includes a "Generate with ComfyUI" feature that creates simple txt2img workflows from saved metadata and sends them to your ComfyUI instance via API. See the Image MetaHub documentation for details on workflow generation and ComfyUI integration features.

Troubleshooting

"Model hash is 0000000000"

  • Your model file wasn't found in standard paths
  • Set COMFYUI_CHECKPOINT_PATH environment variable
  • Or ensure models are in ComfyUI/models/checkpoints/

"LoRAs not detected"

  • Make sure you're using standard LoRA Loader nodes
  • Check that LoRAs are connected in the workflow
  • Custom LoRA nodes might not be detected

"Cannot create output directory"

  • Check folder permissions
  • Try using default output path (leave output_path empty)

"Performance metrics not showing in Image MetaHub"

  • Ensure you're using the latest version of both the node and Image MetaHub App
  • Performance section only appears for images saved with MetaHub Save Node
  • Legacy images saved with default ComfyUI Save Image won't have performance data

"VRAM peak is None/null"

  • VRAM tracking requires CUDA (NVIDIA GPUs)
  • MPS (Apple Metal) and CPU modes don't support VRAM tracking
  • This is expected behavior and not an error

Performance

  • First image: ~200ms overhead (hash calculation)
  • Subsequent images: ~60ms overhead (hashes cached)
  • Batch processing: Minimal overhead per image
  • GPU metrics: <1ms overhead (PyTorch API calls)
  • Version detection: <5ms overhead (cached after first call)

Hash calculation is cached in memory, so repeated generations with the same models are very fast. Performance metrics collection has negligible impact on generation speed.

License

MIT License

Contributing

Issues and pull requests welcome at https://github.com/LuqP2/ImageMetaHub-ComfyUI-Save

Credits

Developed for the Image MetaHub ecosystem.

About

No description, website, or topics provided.

Resources

Readme
Activity

Stars

11 stars

Watchers

1 watching

Forks

1 fork
Report repository

Releases

No releases published

Packages

 
 
 

Contributors

Languages