mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-21 21:22:11 -03:00
Refactor ExifUtils by removing unused methods and imports
- Removed the extract_user_comment and update_user_comment methods to streamline the ExifUtils class. - Cleaned up unnecessary imports and reduced code complexity, focusing on essential functionality for image metadata extraction.
This commit is contained in:
Will Miao
@@ -1,51 +1,16 @@
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import piexif
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import json
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import logging
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from typing import Dict, Optional, Any
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from typing import Optional
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from io import BytesIO
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import os
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from PIL import Image
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import re
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logger = logging.getLogger(__name__)
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class ExifUtils:
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"""Utility functions for working with EXIF data in images"""
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@staticmethod
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def extract_user_comment(image_path: str) -> Optional[str]:
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"""Extract UserComment field from image EXIF data"""
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try:
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# First try to open as image to check format
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with Image.open(image_path) as img:
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if img.format not in ['JPEG', 'TIFF', 'WEBP']:
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# For non-JPEG/TIFF/WEBP images, try to get EXIF through PIL
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exif = img._getexif()
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if exif and piexif.ExifIFD.UserComment in exif:
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user_comment = exif[piexif.ExifIFD.UserComment]
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if isinstance(user_comment, bytes):
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if user_comment.startswith(b'UNICODE\0'):
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return user_comment[8:].decode('utf-16be')
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return user_comment.decode('utf-8', errors='ignore')
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return user_comment
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return None
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# For JPEG/TIFF/WEBP, use piexif
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exif_dict = piexif.load(image_path)
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if piexif.ExifIFD.UserComment in exif_dict.get('Exif', {}):
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user_comment = exif_dict['Exif'][piexif.ExifIFD.UserComment]
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if isinstance(user_comment, bytes):
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if user_comment.startswith(b'UNICODE\0'):
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user_comment = user_comment[8:].decode('utf-16be')
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else:
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user_comment = user_comment.decode('utf-8', errors='ignore')
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return user_comment
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return None
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except Exception as e:
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return None
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@staticmethod
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def extract_image_metadata(image_path: str) -> Optional[str]:
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"""Extract metadata from image including UserComment or parameters field
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@@ -103,53 +68,6 @@ class ExifUtils:
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logger.error(f"Error extracting image metadata: {e}", exc_info=True)
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return None
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@staticmethod
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def update_user_comment(image_path: str, user_comment: str) -> str:
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"""Update UserComment field in image EXIF data"""
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try:
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# Load the image and its EXIF data
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with Image.open(image_path) as img:
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# Get original format
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img_format = img.format
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# For WebP format, we need a different approach
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if img_format == 'WEBP':
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# WebP doesn't support standard EXIF through piexif
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# We'll use PIL's exif parameter directly
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exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + user_comment.encode('utf-16be')}}
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exif_bytes = piexif.dump(exif_dict)
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# Save with the exif data
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img.save(image_path, format='WEBP', exif=exif_bytes, quality=85)
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return image_path
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# For other formats, use the standard approach
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try:
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exif_dict = piexif.load(img.info.get('exif', b''))
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except:
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exif_dict = {'0th':{}, 'Exif':{}, 'GPS':{}, 'Interop':{}, '1st':{}}
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# If no Exif dictionary exists, create one
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if 'Exif' not in exif_dict:
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exif_dict['Exif'] = {}
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# Update the UserComment field - use UNICODE format
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unicode_bytes = user_comment.encode('utf-16be')
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user_comment_bytes = b'UNICODE\0' + unicode_bytes
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exif_dict['Exif'][piexif.ExifIFD.UserComment] = user_comment_bytes
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# Convert EXIF dict back to bytes
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exif_bytes = piexif.dump(exif_dict)
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# Save the image with updated EXIF data
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img.save(image_path, exif=exif_bytes)
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return image_path
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except Exception as e:
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logger.error(f"Error updating EXIF data in {image_path}: {e}")
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return image_path
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@staticmethod
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def update_image_metadata(image_path: str, metadata: str) -> str:
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"""Update metadata in image's EXIF data or parameters fields
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@@ -394,210 +312,4 @@ class ExifUtils:
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if isinstance(image_data, str) and os.path.exists(image_data):
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with open(image_data, 'rb') as f:
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return f.read(), os.path.splitext(image_data)[1]
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return image_data, '.jpg'
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@staticmethod
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def _parse_comfyui_workflow(workflow_data: Any) -> Dict[str, Any]:
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"""
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Parse ComfyUI workflow data and extract relevant generation parameters
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Args:
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workflow_data: Raw workflow data (string or dict)
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Returns:
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Formatted generation parameters dictionary
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"""
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try:
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# If workflow_data is a string, try to parse it as JSON
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if isinstance(workflow_data, str):
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try:
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workflow_data = json.loads(workflow_data)
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except json.JSONDecodeError:
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logger.error("Failed to parse workflow data as JSON")
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return {}
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# Now workflow_data should be a dictionary
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if not isinstance(workflow_data, dict):
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logger.error(f"Workflow data is not a dictionary: {type(workflow_data)}")
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return {}
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# Initialize parameters dictionary with only the required fields
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gen_params = {
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"prompt": "",
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"negative_prompt": "",
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"steps": "",
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"sampler": "",
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"cfg_scale": "",
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"seed": "",
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"size": "",
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"clip_skip": ""
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}
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# First pass: find the KSampler node to get basic parameters and node references
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# Store node references to follow for prompts
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positive_ref = None
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negative_ref = None
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for node_id, node_data in workflow_data.items():
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if not isinstance(node_data, dict):
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continue
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# Extract node inputs if available
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inputs = node_data.get("inputs", {})
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if not inputs:
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continue
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# KSampler nodes contain most generation parameters and references to prompt nodes
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if "KSampler" in node_data.get("class_type", ""):
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# Extract basic sampling parameters
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gen_params["steps"] = inputs.get("steps", "")
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gen_params["cfg_scale"] = inputs.get("cfg", "")
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gen_params["sampler"] = inputs.get("sampler_name", "")
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gen_params["seed"] = inputs.get("seed", "")
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if isinstance(gen_params["seed"], list) and len(gen_params["seed"]) > 1:
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gen_params["seed"] = gen_params["seed"][1] # Use the actual value if it's a list
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# Get references to positive and negative prompt nodes
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positive_ref = inputs.get("positive", "")
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negative_ref = inputs.get("negative", "")
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# CLIPSetLastLayer contains clip_skip information
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elif "CLIPSetLastLayer" in node_data.get("class_type", ""):
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gen_params["clip_skip"] = inputs.get("stop_at_clip_layer", "")
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if isinstance(gen_params["clip_skip"], int) and gen_params["clip_skip"] < 0:
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# Convert negative layer index to positive clip skip value
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gen_params["clip_skip"] = abs(gen_params["clip_skip"])
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# Look for resolution information
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elif "LatentImage" in node_data.get("class_type", "") or "Empty" in node_data.get("class_type", ""):
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width = inputs.get("width", 0)
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height = inputs.get("height", 0)
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if width and height:
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gen_params["size"] = f"{width}x{height}"
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# Some nodes have resolution as a string like "832x1216 (0.68)"
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resolution = inputs.get("resolution", "")
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if isinstance(resolution, str) and "x" in resolution:
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gen_params["size"] = resolution.split(" ")[0] # Extract just the dimensions
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# Helper function to follow node references and extract text content
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def get_text_from_node_ref(node_ref, workflow_data):
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if not node_ref or not isinstance(node_ref, list) or len(node_ref) < 2:
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return ""
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node_id, slot_idx = node_ref
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# If we can't find the node, return empty string
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if node_id not in workflow_data:
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return ""
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node = workflow_data[node_id]
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inputs = node.get("inputs", {})
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# Direct text input in CLIP Text Encode nodes
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if "CLIPTextEncode" in node.get("class_type", ""):
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text = inputs.get("text", "")
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if isinstance(text, str):
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return text
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elif isinstance(text, list) and len(text) >= 2:
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# If text is a reference to another node, follow it
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return get_text_from_node_ref(text, workflow_data)
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# Other nodes might have text input with different field names
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for field_name, field_value in inputs.items():
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if field_name == "text" and isinstance(field_value, str):
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return field_value
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elif isinstance(field_value, list) and len(field_value) >= 2 and field_name in ["text"]:
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# If it's a reference to another node, follow it
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return get_text_from_node_ref(field_value, workflow_data)
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return ""
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# Extract prompts by following references from KSampler node
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if positive_ref:
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gen_params["prompt"] = get_text_from_node_ref(positive_ref, workflow_data)
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if negative_ref:
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gen_params["negative_prompt"] = get_text_from_node_ref(negative_ref, workflow_data)
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# Fallback: if we couldn't extract prompts via references, use the traditional method
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if not gen_params["prompt"] or not gen_params["negative_prompt"]:
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for node_id, node_data in workflow_data.items():
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if not isinstance(node_data, dict):
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continue
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inputs = node_data.get("inputs", {})
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if not inputs:
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continue
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if "CLIPTextEncode" in node_data.get("class_type", ""):
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# Check for negative prompt nodes
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title = node_data.get("_meta", {}).get("title", "").lower()
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prompt_text = inputs.get("text", "")
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if isinstance(prompt_text, str):
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if "negative" in title and not gen_params["negative_prompt"]:
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gen_params["negative_prompt"] = prompt_text
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elif prompt_text and not "negative" in title and not gen_params["prompt"]:
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gen_params["prompt"] = prompt_text
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return gen_params
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except Exception as e:
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logger.error(f"Error parsing ComfyUI workflow: {e}", exc_info=True)
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return {}
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@staticmethod
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def extract_comfyui_gen_params(image_path: str) -> Dict[str, Any]:
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"""
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Extract ComfyUI workflow data from PNG images and format for recipe data
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Only extracts the specific generation parameters needed for recipes.
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Args:
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image_path: Path to the ComfyUI-generated PNG image
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Returns:
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Dictionary containing formatted generation parameters
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"""
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try:
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# Check if the file exists and is accessible
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if not os.path.exists(image_path):
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logger.error(f"Image file not found: {image_path}")
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return {}
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# Open the image to extract embedded workflow data
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with Image.open(image_path) as img:
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workflow_data = None
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# For PNG images, look for the ComfyUI workflow data in PNG chunks
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if img.format == 'PNG':
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# Check standard metadata fields that might contain workflow
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if 'parameters' in img.info:
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workflow_data = img.info['parameters']
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elif 'prompt' in img.info:
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workflow_data = img.info['prompt']
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else:
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# Look for other potential field names that might contain workflow data
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for key in img.info:
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if isinstance(key, str) and ('workflow' in key.lower() or 'comfy' in key.lower()):
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workflow_data = img.info[key]
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break
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# If no workflow data found in PNG chunks, try extract_image_metadata as fallback
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if not workflow_data:
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metadata = ExifUtils.extract_image_metadata(image_path)
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if metadata and '{' in metadata and '}' in metadata:
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# Try to extract JSON part
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json_start = metadata.find('{')
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json_end = metadata.rfind('}') + 1
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workflow_data = metadata[json_start:json_end]
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# Parse workflow data if found
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if workflow_data:
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return ExifUtils._parse_comfyui_workflow(workflow_data)
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return {}
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except Exception as e:
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logger.error(f"Error extracting ComfyUI gen params from {image_path}: {e}", exc_info=True)
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return {}
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return image_data, '.jpg'
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