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refactor: Rename to_comfyui_format method to to_dict and update references in save_image.py

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Will Miao
2025-04-16 21:42:54 +08:00
parent 181f78421b
commit d4b2dd0ec1
3 changed files with 45 additions and 49 deletions
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4
py/metadata_collector/metadata_processor.py
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@@ -157,7 +157,7 @@ class MetadataProcessor:
return params return params
@staticmethod @staticmethod
def to_comfyui_format(metadata): def to_dict(metadata):
"""Convert extracted metadata to the ComfyUI output.json format""" """Convert extracted metadata to the ComfyUI output.json format"""
params = MetadataProcessor.extract_generation_params(metadata) params = MetadataProcessor.extract_generation_params(metadata)
@@ -171,5 +171,5 @@ class MetadataProcessor:
@staticmethod @staticmethod
def to_json(metadata): def to_json(metadata):
"""Convert metadata to JSON string""" """Convert metadata to JSON string"""
params = MetadataProcessor.to_comfyui_format(metadata) params = MetadataProcessor.to_dict(metadata)
return json.dumps(params, indent=4) return json.dumps(params, indent=4)

2
py/metadata_collector/node_extractors.py
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@@ -167,8 +167,6 @@ class LoraLoaderManagerExtractor(NodeMetadataExtractor):
"node_id": node_id "node_id": node_id
} }
print(f"Active LoRAs for node {node_id}: {active_loras}")
# Registry of node-specific extractors # Registry of node-specific extractors
NODE_EXTRACTORS = { NODE_EXTRACTORS = {
"CheckpointLoaderSimple": CheckpointLoaderExtractor, "CheckpointLoaderSimple": CheckpointLoaderExtractor,

88
py/nodes/save_image.py
View File

@@ -5,7 +5,8 @@ import re
import numpy as np import numpy as np
import folder_paths # type: ignore import folder_paths # type: ignore
from ..services.lora_scanner import LoraScanner from ..services.lora_scanner import LoraScanner
from ..workflow.parser import WorkflowParser from ..metadata_collector.metadata_processor import MetadataProcessor
from ..metadata_collector import get_metadata
from PIL import Image, PngImagePlugin from PIL import Image, PngImagePlugin
import piexif import piexif
from io import BytesIO from io import BytesIO
@@ -61,21 +62,21 @@ class SaveImage:
return item.get('sha256') return item.get('sha256')
return None return None
async def format_metadata(self, parsed_workflow, custom_prompt=None): async def format_metadata(self, metadata_dict, custom_prompt=None):
"""Format metadata in the requested format similar to userComment example""" """Format metadata in the requested format similar to userComment example"""
if not parsed_workflow: if not metadata_dict:
return "" return ""
# Extract the prompt and negative prompt # Extract the prompt and negative prompt
prompt = parsed_workflow.get('prompt', '') prompt = metadata_dict.get('prompt', '')
negative_prompt = parsed_workflow.get('negative_prompt', '') negative_prompt = metadata_dict.get('negative_prompt', '')
# Override prompt with custom_prompt if provided # Override prompt with custom_prompt if provided
if custom_prompt: if custom_prompt:
prompt = custom_prompt prompt = custom_prompt
# Extract loras from the prompt if present # Extract loras from the prompt if present
loras_text = parsed_workflow.get('loras', '') loras_text = metadata_dict.get('loras', '')
lora_hashes = {} lora_hashes = {}
# If loras are found, add them on a new line after the prompt # If loras are found, add them on a new line after the prompt
@@ -104,11 +105,11 @@ class SaveImage:
params = [] params = []
# Add standard parameters in the correct order # Add standard parameters in the correct order
if 'steps' in parsed_workflow: if 'steps' in metadata_dict:
params.append(f"Steps: {parsed_workflow.get('steps')}") params.append(f"Steps: {metadata_dict.get('steps')}")
if 'sampler' in parsed_workflow: if 'sampler' in metadata_dict:
sampler = parsed_workflow.get('sampler') sampler = metadata_dict.get('sampler')
# Convert ComfyUI sampler names to user-friendly names # Convert ComfyUI sampler names to user-friendly names
sampler_mapping = { sampler_mapping = {
'euler': 'Euler', 'euler': 'Euler',
@@ -130,8 +131,8 @@ class SaveImage:
sampler_name = sampler_mapping.get(sampler, sampler) sampler_name = sampler_mapping.get(sampler, sampler)
params.append(f"Sampler: {sampler_name}") params.append(f"Sampler: {sampler_name}")
if 'scheduler' in parsed_workflow: if 'scheduler' in metadata_dict:
scheduler = parsed_workflow.get('scheduler') scheduler = metadata_dict.get('scheduler')
scheduler_mapping = { scheduler_mapping = {
'normal': 'Simple', 'normal': 'Simple',
'karras': 'Karras', 'karras': 'Karras',
@@ -142,24 +143,24 @@ class SaveImage:
scheduler_name = scheduler_mapping.get(scheduler, scheduler) scheduler_name = scheduler_mapping.get(scheduler, scheduler)
params.append(f"Schedule type: {scheduler_name}") params.append(f"Schedule type: {scheduler_name}")
# CFG scale (cfg in parsed_workflow) # CFG scale (cfg_scale in metadata_dict)
if 'cfg_scale' in parsed_workflow: if 'cfg_scale' in metadata_dict:
params.append(f"CFG scale: {parsed_workflow.get('cfg_scale')}") params.append(f"CFG scale: {metadata_dict.get('cfg_scale')}")
elif 'cfg' in parsed_workflow: elif 'cfg' in metadata_dict:
params.append(f"CFG scale: {parsed_workflow.get('cfg')}") params.append(f"CFG scale: {metadata_dict.get('cfg')}")
# Seed # Seed
if 'seed' in parsed_workflow: if 'seed' in metadata_dict:
params.append(f"Seed: {parsed_workflow.get('seed')}") params.append(f"Seed: {metadata_dict.get('seed')}")
# Size # Size
if 'size' in parsed_workflow: if 'size' in metadata_dict:
params.append(f"Size: {parsed_workflow.get('size')}") params.append(f"Size: {metadata_dict.get('size')}")
# Model info # Model info
if 'checkpoint' in parsed_workflow: if 'checkpoint' in metadata_dict:
# Extract basename without path # Extract basename without path
checkpoint = os.path.basename(parsed_workflow.get('checkpoint', '')) checkpoint = os.path.basename(metadata_dict.get('checkpoint', ''))
# Remove extension if present # Remove extension if present
checkpoint = os.path.splitext(checkpoint)[0] checkpoint = os.path.splitext(checkpoint)[0]
params.append(f"Model: {checkpoint}") params.append(f"Model: {checkpoint}")
@@ -181,9 +182,9 @@ class SaveImage:
# credit to nkchocoai # credit to nkchocoai
# Add format_filename method to handle pattern substitution # Add format_filename method to handle pattern substitution
def format_filename(self, filename, parsed_workflow): def format_filename(self, filename, metadata_dict):
"""Format filename with metadata values""" """Format filename with metadata values"""
if not parsed_workflow: if not metadata_dict:
return filename return filename
result = re.findall(self.pattern_format, filename) result = re.findall(self.pattern_format, filename)
@@ -191,30 +192,30 @@ class SaveImage:
parts = segment.replace("%", "").split(":") parts = segment.replace("%", "").split(":")
key = parts[0] key = parts[0]
if key == "seed" and 'seed' in parsed_workflow: if key == "seed" and 'seed' in metadata_dict:
filename = filename.replace(segment, str(parsed_workflow.get('seed', ''))) filename = filename.replace(segment, str(metadata_dict.get('seed', '')))
elif key == "width" and 'size' in parsed_workflow: elif key == "width" and 'size' in metadata_dict:
size = parsed_workflow.get('size', 'x') size = metadata_dict.get('size', 'x')
w = size.split('x')[0] if isinstance(size, str) else size[0] w = size.split('x')[0] if isinstance(size, str) else size[0]
filename = filename.replace(segment, str(w)) filename = filename.replace(segment, str(w))
elif key == "height" and 'size' in parsed_workflow: elif key == "height" and 'size' in metadata_dict:
size = parsed_workflow.get('size', 'x') size = metadata_dict.get('size', 'x')
h = size.split('x')[1] if isinstance(size, str) else size[1] h = size.split('x')[1] if isinstance(size, str) else size[1]
filename = filename.replace(segment, str(h)) filename = filename.replace(segment, str(h))
elif key == "pprompt" and 'prompt' in parsed_workflow: elif key == "pprompt" and 'prompt' in metadata_dict:
prompt = parsed_workflow.get('prompt', '').replace("\n", " ") prompt = metadata_dict.get('prompt', '').replace("\n", " ")
if len(parts) >= 2: if len(parts) >= 2:
length = int(parts[1]) length = int(parts[1])
prompt = prompt[:length] prompt = prompt[:length]
filename = filename.replace(segment, prompt.strip()) filename = filename.replace(segment, prompt.strip())
elif key == "nprompt" and 'negative_prompt' in parsed_workflow: elif key == "nprompt" and 'negative_prompt' in metadata_dict:
prompt = parsed_workflow.get('negative_prompt', '').replace("\n", " ") prompt = metadata_dict.get('negative_prompt', '').replace("\n", " ")
if len(parts) >= 2: if len(parts) >= 2:
length = int(parts[1]) length = int(parts[1])
prompt = prompt[:length] prompt = prompt[:length]
filename = filename.replace(segment, prompt.strip()) filename = filename.replace(segment, prompt.strip())
elif key == "model" and 'checkpoint' in parsed_workflow: elif key == "model" and 'checkpoint' in metadata_dict:
model = parsed_workflow.get('checkpoint', '') model = metadata_dict.get('checkpoint', '')
model = os.path.splitext(os.path.basename(model))[0] model = os.path.splitext(os.path.basename(model))[0]
if len(parts) >= 2: if len(parts) >= 2:
length = int(parts[1]) length = int(parts[1])
@@ -251,18 +252,15 @@ class SaveImage:
"""Save images with metadata""" """Save images with metadata"""
results = [] results = []
# Parse the workflow using the WorkflowParser # Get metadata using the metadata collector
parser = WorkflowParser() raw_metadata = get_metadata()
if prompt: metadata_dict = MetadataProcessor.to_dict(raw_metadata)
parsed_workflow = parser.parse_workflow(prompt)
else:
parsed_workflow = {}
# Get or create metadata asynchronously # Get or create metadata asynchronously
metadata = asyncio.run(self.format_metadata(parsed_workflow, custom_prompt)) metadata = asyncio.run(self.format_metadata(metadata_dict, custom_prompt))
# Process filename_prefix with pattern substitution # Process filename_prefix with pattern substitution
filename_prefix = self.format_filename(filename_prefix, parsed_workflow) filename_prefix = self.format_filename(filename_prefix, metadata_dict)
# Get initial save path info once for the batch # Get initial save path info once for the batch
full_output_folder, filename, counter, subfolder, processed_prefix = folder_paths.get_save_image_path( full_output_folder, filename, counter, subfolder, processed_prefix = folder_paths.get_save_image_path(