mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-24 14:42:11 -03:00
Remove deprecated workflow parameters and associated files
- Deleted the `__init__.py`, `cli.py`, `extension_manager.py`, `integration_example.py`, `README.md`, `simple_test.py`, `test_parser.py`, `verify_workflow.py`, and `workflow_parser.py` files as they are no longer needed. - Updated `.gitignore` to exclude new output files and test scripts. - Cleaned up the node processors directory by removing all processor implementations and their registration logic.
This commit is contained in:
Will Miao
255
py/workflow/mappers.py
Normal file
255
py/workflow/mappers.py
Normal file
@@ -0,0 +1,255 @@
|
||||
"""
|
||||
Node mappers for ComfyUI workflow parsing
|
||||
"""
|
||||
import logging
|
||||
from typing import Dict, List, Any, Optional, Union
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class NodeMapper:
|
||||
"""Base class for node mappers that define how to extract information from a specific node type"""
|
||||
|
||||
def __init__(self, node_type: str, inputs_to_track: List[str]):
|
||||
self.node_type = node_type
|
||||
self.inputs_to_track = inputs_to_track
|
||||
|
||||
def process(self, node_id: str, node_data: Dict, workflow: Dict, parser: 'WorkflowParser') -> Any: # type: ignore
|
||||
"""Process the node and extract relevant information"""
|
||||
result = {}
|
||||
for input_name in self.inputs_to_track:
|
||||
if input_name in node_data.get("inputs", {}):
|
||||
input_value = node_data["inputs"][input_name]
|
||||
# Check if input is a reference to another node's output
|
||||
if isinstance(input_value, list) and len(input_value) == 2:
|
||||
# Format is [node_id, output_slot]
|
||||
ref_node_id, output_slot = input_value
|
||||
# Recursively process the referenced node
|
||||
ref_value = parser.process_node(str(ref_node_id), workflow)
|
||||
result[input_name] = ref_value
|
||||
else:
|
||||
# Direct value
|
||||
result[input_name] = input_value
|
||||
|
||||
# Apply any transformations
|
||||
return self.transform(result)
|
||||
|
||||
def transform(self, inputs: Dict) -> Any:
|
||||
"""Transform the extracted inputs - override in subclasses"""
|
||||
return inputs
|
||||
|
||||
|
||||
class KSamplerMapper(NodeMapper):
|
||||
"""Mapper for KSampler nodes"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
node_type="KSampler",
|
||||
inputs_to_track=["seed", "steps", "cfg", "sampler_name", "scheduler",
|
||||
"denoise", "positive", "negative", "latent_image",
|
||||
"model", "clip_skip"]
|
||||
)
|
||||
|
||||
def transform(self, inputs: Dict) -> Dict:
|
||||
result = {
|
||||
"seed": str(inputs.get("seed", "")),
|
||||
"steps": str(inputs.get("steps", "")),
|
||||
"cfg": str(inputs.get("cfg", "")),
|
||||
"sampler": inputs.get("sampler_name", ""),
|
||||
"scheduler": inputs.get("scheduler", ""),
|
||||
}
|
||||
|
||||
# Process positive prompt
|
||||
if "positive" in inputs:
|
||||
result["prompt"] = inputs["positive"]
|
||||
|
||||
# Process negative prompt
|
||||
if "negative" in inputs:
|
||||
result["negative_prompt"] = inputs["negative"]
|
||||
|
||||
# Get dimensions from latent image
|
||||
if "latent_image" in inputs and isinstance(inputs["latent_image"], dict):
|
||||
width = inputs["latent_image"].get("width", 0)
|
||||
height = inputs["latent_image"].get("height", 0)
|
||||
if width and height:
|
||||
result["size"] = f"{width}x{height}"
|
||||
|
||||
# Add clip_skip if present
|
||||
if "clip_skip" in inputs:
|
||||
result["clip_skip"] = str(inputs.get("clip_skip", ""))
|
||||
|
||||
return result
|
||||
|
||||
|
||||
class EmptyLatentImageMapper(NodeMapper):
|
||||
"""Mapper for EmptyLatentImage nodes"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
node_type="EmptyLatentImage",
|
||||
inputs_to_track=["width", "height", "batch_size"]
|
||||
)
|
||||
|
||||
def transform(self, inputs: Dict) -> Dict:
|
||||
width = inputs.get("width", 0)
|
||||
height = inputs.get("height", 0)
|
||||
return {"width": width, "height": height, "size": f"{width}x{height}"}
|
||||
|
||||
|
||||
class EmptySD3LatentImageMapper(NodeMapper):
|
||||
"""Mapper for EmptySD3LatentImage nodes"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
node_type="EmptySD3LatentImage",
|
||||
inputs_to_track=["width", "height", "batch_size"]
|
||||
)
|
||||
|
||||
def transform(self, inputs: Dict) -> Dict:
|
||||
width = inputs.get("width", 0)
|
||||
height = inputs.get("height", 0)
|
||||
return {"width": width, "height": height, "size": f"{width}x{height}"}
|
||||
|
||||
|
||||
class CLIPTextEncodeMapper(NodeMapper):
|
||||
"""Mapper for CLIPTextEncode nodes"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
node_type="CLIPTextEncode",
|
||||
inputs_to_track=["text", "clip"]
|
||||
)
|
||||
|
||||
def transform(self, inputs: Dict) -> Any:
|
||||
# Simply return the text
|
||||
return inputs.get("text", "")
|
||||
|
||||
|
||||
class LoraLoaderMapper(NodeMapper):
|
||||
"""Mapper for LoraLoader nodes"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
node_type="Lora Loader (LoraManager)",
|
||||
inputs_to_track=["text", "loras", "lora_stack"]
|
||||
)
|
||||
|
||||
def transform(self, inputs: Dict) -> Dict:
|
||||
lora_text = inputs.get("text", "")
|
||||
lora_stack = inputs.get("lora_stack", [])
|
||||
|
||||
# Process lora_stack if it exists
|
||||
stack_text = ""
|
||||
if lora_stack:
|
||||
# Handle the formatted lora_stack info if available
|
||||
stack_loras = []
|
||||
for lora_path, strength, _ in lora_stack:
|
||||
lora_name = lora_path.split(os.sep)[-1].split('.')[0]
|
||||
stack_loras.append(f"<lora:{lora_name}:{strength}>")
|
||||
stack_text = " ".join(stack_loras)
|
||||
|
||||
# Combine lora_text and stack_text
|
||||
combined_text = lora_text
|
||||
if stack_text:
|
||||
combined_text = f"{combined_text} {stack_text}" if combined_text else stack_text
|
||||
|
||||
# Format loras with spaces between them
|
||||
if combined_text:
|
||||
# Replace consecutive closing and opening tags with a space
|
||||
combined_text = combined_text.replace("><", "> <")
|
||||
|
||||
return {"loras": combined_text}
|
||||
|
||||
|
||||
class LoraStackerMapper(NodeMapper):
|
||||
"""Mapper for LoraStacker nodes"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
node_type="Lora Stacker (LoraManager)",
|
||||
inputs_to_track=["loras", "lora_stack"]
|
||||
)
|
||||
|
||||
def transform(self, inputs: Dict) -> Dict:
|
||||
# Return the lora_stack information
|
||||
return inputs.get("lora_stack", [])
|
||||
|
||||
|
||||
class JoinStringsMapper(NodeMapper):
|
||||
"""Mapper for JoinStrings nodes"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
node_type="JoinStrings",
|
||||
inputs_to_track=["string1", "string2", "delimiter"]
|
||||
)
|
||||
|
||||
def transform(self, inputs: Dict) -> str:
|
||||
string1 = inputs.get("string1", "")
|
||||
string2 = inputs.get("string2", "")
|
||||
delimiter = inputs.get("delimiter", "")
|
||||
return f"{string1}{delimiter}{string2}"
|
||||
|
||||
|
||||
class StringConstantMapper(NodeMapper):
|
||||
"""Mapper for StringConstant and StringConstantMultiline nodes"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
node_type="StringConstantMultiline",
|
||||
inputs_to_track=["string"]
|
||||
)
|
||||
|
||||
def transform(self, inputs: Dict) -> str:
|
||||
return inputs.get("string", "")
|
||||
|
||||
|
||||
class TriggerWordToggleMapper(NodeMapper):
|
||||
"""Mapper for TriggerWordToggle nodes"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
node_type="TriggerWord Toggle (LoraManager)",
|
||||
inputs_to_track=["toggle_trigger_words", "orinalMessage", "trigger_words"]
|
||||
)
|
||||
|
||||
def transform(self, inputs: Dict) -> str:
|
||||
# Get the original message or toggled trigger words
|
||||
original_message = inputs.get("orinalMessage", "") or inputs.get("trigger_words", "")
|
||||
|
||||
# Fix double commas to match the reference output format
|
||||
if original_message:
|
||||
# Replace double commas with single commas
|
||||
original_message = original_message.replace(",, ", ", ")
|
||||
|
||||
return original_message
|
||||
|
||||
|
||||
class FluxGuidanceMapper(NodeMapper):
|
||||
"""Mapper for FluxGuidance nodes"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
node_type="FluxGuidance",
|
||||
inputs_to_track=["guidance", "conditioning"]
|
||||
)
|
||||
|
||||
def transform(self, inputs: Dict) -> Dict:
|
||||
result = {}
|
||||
|
||||
# Handle guidance parameter
|
||||
if "guidance" in inputs:
|
||||
result["guidance"] = inputs["guidance"]
|
||||
|
||||
# Handle conditioning (the prompt text)
|
||||
if "conditioning" in inputs:
|
||||
conditioning = inputs["conditioning"]
|
||||
if isinstance(conditioning, str):
|
||||
result["prompt"] = conditioning
|
||||
else:
|
||||
result["prompt"] = "Unknown prompt"
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# Add import os for LoraLoaderMapper to work properly
|
||||
import os
|
||||
Reference in New Issue
Block a user