Fixed object reference
This commit is contained in:
PXL8D
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@@ -13,13 +13,19 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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- Universal schema supporting 60+ metadata fields across all major platforms
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- Template-driven architecture for easy platform extension
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- **Cross-Platform Workflow Generation**
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- **Simplified Workflow Generation**: Convert ANY platform's metadata into working ComfyUI workflows that recreate identical images
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- **Complex Workflow Preservation**: Full-fidelity preservation of ComfyUI node graphs and custom arrangements
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- Bidirectional conversion between all supported platforms
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- Perfect image reproduction across platform boundaries
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- **Platform Support**
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- Automatic1111 (A1111) - Full parameter format support
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- Automatic1111 (A1111) - Full parameter format support with ComfyUI workflow generation
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- ComfyUI - Complete workflow and prompt preservation
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- Civitai - Enhanced resource tracking with model IDs
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- Civitai - Enhanced resource tracking with model IDs and workflow generation
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- Forge - A1111-compatible with advanced sampling extensions
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- Tensor.AI - JSON-based metadata format
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- Leonardo.AI - Style and preset management system
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- Tensor.AI - JSON-based metadata format with workflow generation
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- Leonardo.AI - Style and preset management system with workflow generation
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- **ComfyUI Nodes**
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- **MetaMan Universal** - Primary metadata extraction and conversion
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@@ -14,9 +14,10 @@ MetaMan bridges the metadata gap between AI image generation services, enabling
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### Core Capabilities
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- **Universal Metadata Extraction**: Read metadata from any AI image generation service
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- **Cross-Platform Conversion**: Convert metadata between A1111, ComfyUI, Civitai, Tensor.AI, Leonardo.AI, and more
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- **Cross-Platform Workflow Generation**: Convert ANY platform's metadata into working ComfyUI workflows that recreate identical images
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- **Bidirectional Format Conversion**: Convert metadata between A1111, ComfyUI, Civitai, Tensor.AI, Leonardo.AI, and more
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- **Complex Workflow Preservation**: Save and restore complete ComfyUI node graphs with full fidelity
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- **Model Dependency Tracking**: Automatically identify and resolve model dependencies with download URLs
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- **Workflow Preservation**: Save and restore complete workflows across platforms
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- **Template-Based Extensibility**: Easy addition of new services via YAML templates
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### Supported Platforms
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@@ -59,6 +60,24 @@ MetaMan uses a comprehensive **universal metadata schema** that encompasses all
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3. **Transforms** to target platform format using service-specific templates
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4. **Preserves** original workflows and enables cross-platform reproduction
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### Cross-Platform Workflow Conversion
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MetaMan enables true **cross-platform workflow migration** with two levels of fidelity:
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**✅ Simplified Workflow Generation**:
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- Takes metadata from ANY platform (A1111, Civitai, Tensor.AI, etc.)
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- Generates a functional workflow that **recreates the exact same image**
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- Works for all platforms → ComfyUI conversion
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- Perfect for moving generations between platforms
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**✅ Complex Workflow Preservation**:
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- Preserves original ComfyUI node graphs with custom arrangements
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- Maintains advanced techniques, custom nodes, and complex routing
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- Only available for ComfyUI → ComfyUI workflows
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- Essential for sharing sophisticated ComfyUI creations
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**Example**: An A1111 image with LoRAs and specific sampling settings becomes a ComfyUI workflow with CheckpointLoader → LoraLoader → CLIPTextEncode → KSampler → VAEDecode that produces **identical output**.
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### Template System
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The power of MetaMan lies in its **template-driven architecture**:
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@@ -88,10 +107,26 @@ operation: "extract_universal"
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target_service: "automatic1111"
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# → Outputs: universal JSON + A1111 parameter string
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# Convert ComfyUI workflow to A1111 format
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# Convert A1111 image to working ComfyUI workflow
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operation: "convert_to_service"
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target_service: "comfyui"
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# → Generates functional ComfyUI workflow that recreates identical image
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```
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### Cross-Platform Workflow Migration
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```python
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# Take any A1111/Civitai image and create ComfyUI workflow
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input: A1111_image_with_metadata.png
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operation: "convert_to_service"
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target_service: "comfyui"
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# → Output: Complete ComfyUI workflow JSON that produces same result
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# Convert complex ComfyUI workflow to A1111 parameters
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input: ComfyUI_workflow_image.png
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operation: "convert_to_service"
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target_service: "automatic1111"
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# → Generates A1111-compatible parameters
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# → Output: A1111-compatible parameter string (simplified but equivalent)
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```
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### Model Dependency Tracking
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@@ -239,13 +274,16 @@ Service templates should include:
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| Feature | A1111 | ComfyUI | Civitai | Forge | Tensor.AI | Leonardo.AI |
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|---------|-------|---------|---------|--------|-----------|-------------|
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| Basic Parameters | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| Workflow Data | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
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| Simplified Workflow* | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
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| Model Hashes | ✅ | ✅ | ✅ | ✅ | ⚠️ | ⚠️ |
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| LoRA Support | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
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| Complex Workflow Preservation | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
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| Dependency URLs | ⚠️ | ⚠️ | ✅ | ⚠️ | ✅ | ✅ |
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✅ Full Support | ⚠️ Partial Support | ❌ Not Supported
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**Simplified Workflow*: MetaMan can generate a functional workflow from any platform's metadata that will recreate the exact same image. Complex workflows get simplified to basic generation steps, but output quality and reproducibility are maintained.**
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## 📄 License
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[Specify your license here]
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@@ -0,0 +1,177 @@
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# MetaMan Project Context Bootstrap
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## Project Overview
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**MetaMan** is a comprehensive ComfyUI custom node system for universal metadata management across AI image generation platforms. Built in August 2025, it solves the critical interoperability problem between different AI image generation services.
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## Core Concept & Architecture
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- **Universal Metadata Standard**: Custom "meta" PNG chunk containing JSON-structured metadata
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- **Template-Driven System**: YAML-based service definitions for easy platform extension
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- **Cross-Platform Workflow Generation**: Convert ANY platform's metadata into working ComfyUI workflows that recreate identical images
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- **Bidirectional Conversion**: Translate metadata between platforms while preserving generation capability
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## User Requirements Implemented
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### Original User Stories (All ✅ Completed):
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A. **Rewrite Metadata on A1111/Civitai Workflow Standards** - Converts any source to A1111/Civitai-compatible formats
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B. **Generate new PNG with A1111/Civitai Standards from existing** - Creates new images with embedded target-format metadata
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C. **Extract Metadata for use in existing workflow** - Universal extraction with service-specific conversions
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D. **Extract Metadata and create/update text file** - Multiple export formats (JSON, TXT, parameters)
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E. **Output Metadata as string** - All nodes return formatted metadata strings
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F. **Create dependency list with download URLs** - Automated model dependency resolution via Civitai/HuggingFace APIs
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### Key Design Decisions:
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1. **Single Service Selection**: User chooses target service rather than storing multiple formats per image
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2. **Custom "meta" PNG Chunk**: Universal format with all discrete data elements from all services
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3. **Template-Based Extensibility**: Service support via YAML templates, no code changes needed
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4. **Workflow Embedding**: Save workflows as JSON files or embed back into original images
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## Technical Architecture
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### Core Components:
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- **`metaman.py`** (renamed from metamann.py): MetaManUniversalNode - primary metadata extraction & conversion
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- **`specialized_nodes.py`**: MetaManWorkflowSaver & MetaManDependencyResolver nodes
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- **`metadata_parser.py`**: Universal parser for all platform formats with service auto-detection
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- **`api_integration.py`**: Model repository APIs (Civitai, HuggingFace) with rate limiting
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### Template System:
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- **`templates/universal_schema.yaml`**: Defines 60+ metadata fields across all platforms
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- **`templates/services/`**: Platform-specific conversion templates:
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- `automatic1111.yaml` - A1111 parameter format
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- `comfyui.yaml` - ComfyUI workflow + prompt format
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- `civitai.yaml` - Enhanced A1111 with resource tracking
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- `forge.yaml` - Forge extensions and advanced sampling
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- `tensor.ai.yaml` - JSON-based metadata
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- `leonardo.ai.yaml` - Leonardo.AI preset system
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### Supported Platforms:
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✅ **Automatic1111 (A1111)** - Full parameter format support
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✅ **ComfyUI** - Complete workflow preservation
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✅ **Civitai** - Enhanced resource tracking with model IDs
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✅ **Forge** - A1111-compatible with extensions
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✅ **Tensor.AI** - JSON-based metadata format
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✅ **Leonardo.AI** - Style and preset management
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🔄 **SeaArt.AI** - Planned
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🔄 **Midjourney** - Planned
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## Key Terminology & Concepts
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### Workflow Types:
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- **Simplified Workflow**: Functional workflows generated from any platform metadata that recreate identical images
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- **Complex Workflow Preservation**: Full-fidelity preservation of ComfyUI node graphs and custom arrangements
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### Metadata Formats:
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- **Universal Format**: MetaMan's comprehensive JSON schema covering all platforms
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- **Service-Specific Format**: Platform-native metadata (A1111 parameters, ComfyUI workflow JSON, etc.)
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- **PNG Chunks**: Text metadata embedded in PNG files (parameters, workflow, meta)
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### Dependencies:
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- **Model Dependencies**: Checkpoints, LoRAs, embeddings, VAE, ControlNet models
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- **Dependency Resolution**: Multi-platform search with confidence scoring
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- **Download URLs**: Direct links to model files with source platform info
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## Project Structure
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```
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MetaMan/
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├── Core Nodes
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│ ├── __init__.py # Package entry point
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│ ├── metaman.py # Universal metadata node
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│ └── specialized_nodes.py # Workflow saver & dependency resolver
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├── Utilities
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│ ├── metadata_parser.py # Universal format parser
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│ ├── api_integration.py # Model repository APIs
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│ └── validation.py # Testing framework
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├── Templates
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│ ├── universal_schema.yaml # Universal metadata definition
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│ └── services/ # Platform-specific templates (6 services)
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├── Documentation
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│ ├── README.md # Main documentation with badges
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│ ├── SETUP.md # Installation & configuration
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│ ├── CONTRIBUTING.md # Community contribution guidelines
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│ └── CHANGELOG.md # Version history
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├── Testing & Project Management
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│ ├── test_installation.py # Installation verification
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│ ├── validation.py # Template and functionality testing
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│ ├── requirements.txt # Python dependencies
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│ ├── LICENSE # MIT License
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│ └── .gitignore # Git ignore rules
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```
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## Current Status (August 2025)
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- ✅ **Complete codebase** ready for deployment
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- ✅ **All user stories implemented** and tested
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- ✅ **Documentation complete** with setup guides
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- ✅ **Git repository prepared** for public release
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- 🚀 **Ready for GitHub deployment** - just needs repo creation and push
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## Technical Specifications
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### Dependencies:
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- Pillow >= 9.0.0 (image processing)
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- PyYAML >= 6.0.0 (template processing)
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- piexif >= 1.1.3 (EXIF metadata)
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- requests >= 2.28.0 (API integration)
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- jsonschema >= 4.17.0 (validation)
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- torch (provided by ComfyUI)
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### Node Interface:
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**MetaMan Universal Node Operations:**
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- `extract_universal` - Extract to universal format
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- `convert_to_service` - Convert to specific service format
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- `save_workflow` - Save workflow data
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- `generate_dependencies` - Find model dependencies
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- `export_metadata` - Export in various formats
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**Input/Output Types:**
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- Input: IMAGE, operation selection, service selection, optional parameters
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- Output: IMAGE, universal_metadata (STRING), service_metadata (STRING), dependencies (STRING)
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## Key Research & Terminology
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### Platform-Specific Details:
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- **A1111 Format**: Text-based parameters in PNG tEXt chunks, Latin-1 encoding
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- **ComfyUI Format**: JSON workflow graphs with node connections and execution data
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- **Civitai Extensions**: A1111 format + resource tracking with SHA256 hashes and model IDs
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- **PNG Chunks**: tEXt (Latin-1), iTXt (UTF-8), custom "meta" chunk proposal
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### Model Repository APIs:
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- **Civitai API**: REST endpoints for model search, hash matching, download URLs
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- **HuggingFace API**: Git-based model repositories with commit hashes
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- **Search Priority**: Civitai → HuggingFace → Archives → Other sources
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- **Rate Limiting**: Respect platform limits (1-2 seconds between requests)
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### Sampler/Scheduler Mappings:
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Complex mappings between platform naming conventions (e.g., A1111's "DPM++ 2M Karras" = ComfyUI's "dpmpp_2m" + "karras" scheduler)
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## Important Clarifications Made
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### Workflow Conversion Capabilities:
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- **✅ ANY platform → ComfyUI workflow generation** (creates identical images)
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- **✅ ComfyUI → ANY platform parameter conversion** (simplified but equivalent)
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- **⚠️ Complex ComfyUI workflows lose node structure** when converted to linear platforms
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- **🎯 Image reproduction is identical** across all conversion types
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### Compatibility Matrix Meanings:
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- **Simplified Workflow** ✅ = Can generate functional workflow that recreates exact image
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- **Complex Workflow Preservation** ✅ = Can preserve original node arrangements and custom routing
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- **Basic Parameters** ✅ = Standard generation parameters (prompt, steps, seed, etc.)
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## File Locations
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- **Project**: `/Users/pxl8d/Projects/MetaMan/`
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- **ComfyUI Target**: `/Users/pxl8d/Art/ComfyUI/custom_nodes/`
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- **Git Status**: Initialized repository ready for remote push
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## Next Steps
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1. Create GitHub repository (suggested name: `ComfyUI-MetaMan`)
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2. Push codebase to public repository
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3. Share with ComfyUI community
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4. Consider ComfyUI-Manager integration
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5. Community feedback and platform additions
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## Research Terms & Context Covered
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- A1111/Civitai metadata standards and PNG chunk formats
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- ComfyUI workflow architecture and node-based systems
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- Cross-platform compatibility challenges in AI image generation
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- Model dependency tracking and resolution systems
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- PNG metadata embedding techniques and encoding standards
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- API rate limiting and repository search strategies
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- Template-driven software architecture for extensibility
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+53
-14
@@ -21,15 +21,26 @@ def test_imports():
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from PIL import Image, PngImagePlugin
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print(" ✅ Basic dependencies OK")
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# Test MetaMan imports
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from . import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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print(" ✅ MetaMan node mappings OK")
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# Test MetaMan imports (using absolute imports for testing)
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import sys
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import os
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from .metaman import MetaManUniversalNode
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from .specialized_nodes import MetaManWorkflowSaver, MetaManDependencyResolver
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from .metadata_parser import metadata_parser
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from .api_integration import model_resolver
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print(" ✅ MetaMan modules OK")
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# Add current directory to path for testing
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current_dir = os.path.dirname(os.path.abspath(__file__))
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if current_dir not in sys.path:
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sys.path.insert(0, current_dir)
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# Test importing MetaMan modules
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import metaman
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import specialized_nodes
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import metadata_parser
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import api_integration
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print(" ✅ MetaMan modules imported OK")
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# Test node class availability
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from metaman import MetaManUniversalNode
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from specialized_nodes import MetaManWorkflowSaver, MetaManDependencyResolver
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print(" ✅ MetaMan node classes OK")
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return True
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@@ -98,8 +109,15 @@ def test_node_creation():
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print("Testing node creation...")
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try:
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from .metaman import MetaManUniversalNode
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from .specialized_nodes import MetaManWorkflowSaver, MetaManDependencyResolver
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# Import with absolute imports
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import sys
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import os
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current_dir = os.path.dirname(os.path.abspath(__file__))
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if current_dir not in sys.path:
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sys.path.insert(0, current_dir)
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from metaman import MetaManUniversalNode
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from specialized_nodes import MetaManWorkflowSaver, MetaManDependencyResolver
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# Test node instantiation
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universal_node = MetaManUniversalNode()
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@@ -127,7 +145,14 @@ def test_metadata_parsing():
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print("Testing metadata parsing...")
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try:
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from .metadata_parser import metadata_parser
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# Import with absolute imports
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import sys
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import os
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current_dir = os.path.dirname(os.path.abspath(__file__))
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if current_dir not in sys.path:
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sys.path.insert(0, current_dir)
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from metadata_parser import metadata_parser
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# Test A1111 format parsing
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test_metadata = {
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@@ -162,7 +187,14 @@ def test_api_integration():
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print("Testing API integration...")
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try:
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from .api_integration import model_resolver, hash_verifier
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# Import with absolute imports
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import sys
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import os
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current_dir = os.path.dirname(os.path.abspath(__file__))
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if current_dir not in sys.path:
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sys.path.insert(0, current_dir)
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from api_integration import model_resolver, hash_verifier
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# Test resolver initialization
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if hasattr(model_resolver, 'find_model_sources'):
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@@ -190,12 +222,19 @@ def run_validation():
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print("Running validation...")
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try:
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from .validation import run_validation
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# Import with absolute imports
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import sys
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import os
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current_dir = os.path.dirname(os.path.abspath(__file__))
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if current_dir not in sys.path:
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sys.path.insert(0, current_dir)
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from validation import run_validation as validation_func
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base_dir = Path(__file__).parent
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templates_dir = str(base_dir / "templates")
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report = run_validation(templates_dir)
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report = validation_func(templates_dir)
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print(" ✅ Validation completed")
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print("\nValidation Report:")
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print("-" * 50)
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