Add MetaMan Load Image node - preserves PNG metadata during loading
- Create MetaManLoadImage node that loads images with metadata preserved - Extracts ComfyUI workflow/prompt data directly from PNG files - Returns both image tensor and metadata JSON - Update MetaManUniversalNodeV2 to accept optional metadata_json input - Prioritize metadata from load image node over PIL extraction - Register new node in both metaman.py and __init__.py Solves metadata stripping issue by reading files before ComfyUI processing.
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PXL8D
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@@ -3,18 +3,20 @@ MetaMan - ComfyUI Custom Node Package
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Universal metadata management for AI image generation platforms
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"""
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from .metaman import MetaManUniversalNodeV2
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from .metaman import MetaManUniversalNodeV2, MetaManLoadImage
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from .specialized_nodes import MetaManWorkflowSaver, MetaManDependencyResolver
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# Combine all node mappings
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NODE_CLASS_MAPPINGS = {
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"MetaManUniversalNodeV2": MetaManUniversalNodeV2,
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"MetaManLoadImage": MetaManLoadImage,
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"MetaManWorkflowSaver": MetaManWorkflowSaver,
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"MetaManDependencyResolver": MetaManDependencyResolver
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"MetaManUniversalNodeV2": "MetaMan Universal V2",
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"MetaManLoadImage": "MetaMan Load Image",
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"MetaManWorkflowSaver": "MetaMan Workflow Saver",
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"MetaManDependencyResolver": "MetaMan Dependency Resolver"
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}
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+295
-11
@@ -18,6 +18,273 @@ from typing import Optional, Any, Union
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from datetime import datetime
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class MetaManLoadImage:
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"""
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Custom Load Image node that preserves PNG metadata
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Designed to work with MetaMan Universal node
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"""
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@classmethod
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def INPUT_TYPES(cls):
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input_dir = folder_paths.get_input_directory()
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files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
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return {
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"required": {
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"image": (sorted(files), {"image_upload": True})
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}
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}
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CATEGORY = "MetaMan"
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RETURN_TYPES = ("IMAGE", "STRING")
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RETURN_NAMES = ("image", "metadata_json")
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FUNCTION = "load_image_with_metadata"
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DESCRIPTION = "Load image while preserving PNG metadata for MetaMan"
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def load_image_with_metadata(self, image):
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"""
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Load image and extract metadata, returning both
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"""
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try:
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input_dir = folder_paths.get_input_directory()
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image_path = folder_paths.get_annotated_filepath(image, input_dir)
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print(f"MetaMan Load Image: Loading {image_path}")
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# Load image with PIL to preserve metadata
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pil_image = Image.open(image_path)
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print(f"MetaMan Load Image: Image size: {pil_image.size}")
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print(f"MetaMan Load Image: Image format: {pil_image.format}")
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# Extract metadata from the original file
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metadata = self._extract_metadata_from_image(pil_image, image_path)
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# Convert PIL image to tensor for ComfyUI
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import numpy as np
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img_array = np.array(pil_image.convert('RGB')).astype(np.float32) / 255.0
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img_tensor = torch.from_numpy(img_array).unsqueeze(0)
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# Create metadata JSON output
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metadata_json = {
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"source_file_path": image_path,
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"image_size": pil_image.size,
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"image_format": pil_image.format,
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"metadata": metadata,
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"extracted_at": datetime.now().isoformat()
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}
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print(f"MetaMan Load Image: Extracted metadata keys: {list(metadata.keys())}")
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return (img_tensor, json.dumps(metadata_json, indent=2))
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except Exception as e:
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print(f"MetaMan Load Image Error: {e}")
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# Return empty tensor and error message
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empty_tensor = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
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error_json = {"error": str(e), "source_file_path": image_path if 'image_path' in locals() else "unknown"}
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return (empty_tensor, json.dumps(error_json, indent=2))
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def _extract_metadata_from_image(self, pil_image, file_path):
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"""Extract all available metadata from PIL image"""
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metadata = {}
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try:
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# PNG text chunks
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if hasattr(pil_image, 'text') and pil_image.text:
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print(f"MetaMan Load Image: Found PNG text chunks: {list(pil_image.text.keys())}")
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for key, value in pil_image.text.items():
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print(f"MetaMan Load Image: Chunk '{key}': {len(value)} characters")
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# Store raw chunk data
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metadata[f"png_chunk_{key}"] = value
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# Parse specific formats
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if key == 'parameters':
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# A1111/Civitai parameters
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try:
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parsed_params = self._parse_a1111_parameters(value)
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metadata.update(parsed_params)
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print(f"MetaMan Load Image: Parsed A1111 parameters: {list(parsed_params.keys())}")
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except Exception as e:
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print(f"MetaMan Load Image: Error parsing A1111 parameters: {e}")
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elif key in ['workflow', 'prompt']:
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# ComfyUI workflow/prompt data
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try:
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json_data = json.loads(value)
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metadata[f"comfyui_{key}"] = json_data
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print(f"MetaMan Load Image: Parsed ComfyUI {key}: {len(json_data)} nodes")
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# If this is prompt data, extract parameters immediately
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if key == 'prompt':
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extracted_params = self._extract_params_from_comfyui_prompt(json_data)
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metadata.update(extracted_params)
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print(f"MetaMan Load Image: Extracted ComfyUI params: {list(extracted_params.keys())}")
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except Exception as e:
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print(f"MetaMan Load Image: Error parsing ComfyUI {key}: {e}")
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elif key == 'meta':
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# MetaMan universal format
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try:
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meta_data = json.loads(value)
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metadata.update(meta_data)
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print(f"MetaMan Load Image: Parsed MetaMan meta chunk")
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except Exception as e:
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print(f"MetaMan Load Image: Error parsing meta chunk: {e}")
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else:
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print(f"MetaMan Load Image: No PNG text chunks found")
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# Check image.info as fallback
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if hasattr(pil_image, 'info') and pil_image.info:
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print(f"MetaMan Load Image: Found image.info keys: {list(pil_image.info.keys())}")
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for key, value in pil_image.info.items():
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if isinstance(value, str) and len(value) > 50:
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metadata[f"info_{key}"] = value
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print(f"MetaMan Load Image: Stored info['{key}']: {len(value)} characters")
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except Exception as e:
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print(f"MetaMan Load Image: Error extracting metadata: {e}")
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return metadata
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def _parse_a1111_parameters(self, params_text: str) -> dict:
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"""Parse A1111/Civitai parameters format"""
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metadata = {}
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lines = params_text.strip().split('\n')
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if lines:
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# First line is usually the positive prompt
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metadata['prompt'] = lines[0].strip()
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# Look for negative prompt
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for i, line in enumerate(lines[1:], 1):
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if line.startswith('Negative prompt:'):
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metadata['negative_prompt'] = line.replace('Negative prompt:', '').strip()
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continue
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# Parse parameter line (usually the last line)
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if ',' in line and ':' in line:
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params = self._parse_parameter_line(line)
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metadata.update(params)
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return metadata
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def _parse_parameter_line(self, line: str) -> dict:
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"""Parse A1111 parameter line"""
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params = {}
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# Split by comma, but be careful of commas in quoted values
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parts = []
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current = ""
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in_quotes = False
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for char in line:
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if char == '"' and (not current or current[-1] != '\\'):
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in_quotes = not in_quotes
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elif char == ',' and not in_quotes:
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parts.append(current.strip())
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current = ""
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continue
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current += char
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if current.strip():
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parts.append(current.strip())
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# Parse each parameter
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for part in parts:
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if ':' in part:
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key, value = part.split(':', 1)
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key = key.strip().lower().replace(' ', '_')
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value = value.strip()
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# Convert known numeric values
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if key in ['steps', 'width', 'height', 'seed', 'clip_skip']:
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try:
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params[key] = int(value)
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except:
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params[key] = value
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elif key in ['cfg_scale', 'denoising_strength', 'eta']:
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try:
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params[key] = float(value)
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except:
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params[key] = value
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else:
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params[key] = value
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return params
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def _extract_params_from_comfyui_prompt(self, prompt_data: dict) -> dict:
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"""Extract generation parameters from ComfyUI prompt data"""
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params = {}
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try:
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for node_id, node_data in prompt_data.items():
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if not isinstance(node_data, dict):
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continue
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class_type = node_data.get('class_type', '')
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inputs = node_data.get('inputs', {})
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# Extract from KSampler nodes
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if class_type == 'KSampler':
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if 'steps' in inputs:
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params['steps'] = inputs['steps']
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if 'cfg' in inputs:
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params['cfg_scale'] = inputs['cfg']
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if 'sampler_name' in inputs:
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params['sampler'] = inputs['sampler_name']
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if 'scheduler' in inputs:
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params['scheduler'] = inputs['scheduler']
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if 'seed' in inputs:
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params['seed'] = inputs['seed']
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if 'denoise' in inputs:
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params['denoising_strength'] = inputs['denoise']
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# Extract from CheckpointLoaderSimple
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elif class_type == 'CheckpointLoaderSimple':
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if 'ckpt_name' in inputs:
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params['model_name'] = inputs['ckpt_name']
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# Extract from CLIPTextEncode (prompts)
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elif class_type == 'CLIPTextEncode':
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if 'text' in inputs:
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# First positive prompt we find
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if 'prompt' not in params and inputs['text'].strip():
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params['prompt'] = inputs['text']
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# If we already have a prompt, this might be negative
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elif 'negative_prompt' not in params and inputs['text'].strip():
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# Simple heuristic: if it contains common negative words
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negative_indicators = ['worst', 'low quality', 'blurry', 'bad', 'ugly', 'deformed']
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if any(indicator in inputs['text'].lower() for indicator in negative_indicators):
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params['negative_prompt'] = inputs['text']
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# Extract from EmptyLatentImage (dimensions)
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elif class_type == 'EmptyLatentImage':
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if 'width' in inputs:
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params['width'] = inputs['width']
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if 'height' in inputs:
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params['height'] = inputs['height']
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# Extract from LoraLoader nodes
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elif class_type == 'LoraLoader':
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if 'lora_name' in inputs and 'strength_model' in inputs:
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if 'loras' not in params:
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params['loras'] = []
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lora_info = {
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'name': inputs['lora_name'],
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'weight': inputs['strength_model']
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}
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if 'strength_clip' in inputs:
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lora_info['clip_weight'] = inputs['strength_clip']
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params['loras'].append(lora_info)
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except Exception as e:
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print(f"MetaMan Load Image: Error extracting ComfyUI params: {e}")
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return params
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class MetaManUniversalNodeV2:
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"""
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Universal metadata management node for ComfyUI
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@@ -36,6 +303,9 @@ class MetaManUniversalNodeV2:
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"required": {
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"image": ("IMAGE",),
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"target_service": (cls.SUPPORTED_SERVICES, {"default": "automatic1111"}),
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},
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"optional": {
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"metadata_json": ("STRING", {"default": "", "multiline": True}),
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}
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}
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@@ -254,7 +524,7 @@ class MetaManUniversalNodeV2:
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return templates
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def process_metadata(self, image, target_service):
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def process_metadata(self, image, target_service, metadata_json=""):
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"""
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Main processing function: extract metadata, convert to target service, and embed in image
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"""
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@@ -267,16 +537,28 @@ class MetaManUniversalNodeV2:
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else:
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pil_image = image
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# Check if we can extract metadata from the PIL image
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source_metadata = self._extract_source_metadata(pil_image)
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# Check if metadata was provided from MetaManLoadImage node
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source_metadata = {}
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if metadata_json and metadata_json.strip():
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try:
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metadata_data = json.loads(metadata_json)
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source_metadata = metadata_data.get("metadata", {})
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print(f"MetaMan Debug: Using metadata from MetaManLoadImage: {list(source_metadata.keys())}")
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print(f"MetaMan Debug: Source file: {metadata_data.get('source_file_path', 'unknown')}")
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except Exception as e:
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print(f"MetaMan Debug: Error parsing metadata_json: {e}")
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# If no metadata found, try to find and read the original file
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# If no metadata from load image node, try extracting from PIL image
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if not source_metadata:
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print(f"MetaMan Debug: No metadata in PIL image, attempting direct file reading...")
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original_file_metadata = self._try_read_original_file(pil_image)
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if original_file_metadata:
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source_metadata = original_file_metadata
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print(f"MetaMan Debug: Successfully read metadata from original file")
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source_metadata = self._extract_source_metadata(pil_image)
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# If still no metadata found, try to find and read the original file
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if not source_metadata:
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print(f"MetaMan Debug: No metadata in PIL image, attempting direct file reading...")
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original_file_metadata = self._try_read_original_file(pil_image)
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if original_file_metadata:
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source_metadata = original_file_metadata
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print(f"MetaMan Debug: Successfully read metadata from original file")
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source_service = self._detect_source_service(source_metadata)
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@@ -1099,9 +1381,11 @@ class MetaManUniversalNodeV2:
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# Node mappings for ComfyUI
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NODE_CLASS_MAPPINGS = {
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"MetaManUniversalNodeV2": MetaManUniversalNodeV2
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"MetaManUniversalNodeV2": MetaManUniversalNodeV2,
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"MetaManLoadImage": MetaManLoadImage
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"MetaManUniversalNodeV2": "MetaMan Universal V2"
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"MetaManUniversalNodeV2": "MetaMan Universal V2",
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"MetaManLoadImage": "MetaMan Load Image"
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}
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