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Will Miao
2025-04-02 06:05:24 +08:00
parent 27db60ce68
commit a8ec5af037
5 changed files with 67 additions and 130 deletions
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68
py/workflow/parser.py
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@@ -166,71 +166,33 @@ class WorkflowParser:
logger.warning("No suitable sampler node found in workflow")
return {}
# Start parsing from the sampler node
result = {
"gen_params": {},
"loras": ""
}
# Process sampler node to extract parameters
sampler_result = self.process_node(sampler_node_id, workflow)
if sampler_result:
# Process the result
for key, value in sampler_result.items():
# Special handling for the positive prompt from FluxGuidance
if key == "positive" and isinstance(value, dict):
# Extract guidance value
if "guidance" in value:
result["gen_params"]["guidance"] = value["guidance"]
# Extract prompt
if "prompt" in value:
result["gen_params"]["prompt"] = value["prompt"]
else:
# Normal handling for other values
result["gen_params"][key] = value
logger.info(f"Sampler result: {sampler_result}")
if not sampler_result:
return {}
# Process the positive prompt node if it exists and we don't have a prompt yet
if "prompt" not in result["gen_params"] and "positive" in sampler_result:
positive_value = sampler_result.get("positive")
if isinstance(positive_value, str):
result["gen_params"]["prompt"] = positive_value
# Manually check for FluxGuidance if we don't have guidance value
if "guidance" not in result["gen_params"]:
flux_node_id = find_node_by_type(workflow, "FluxGuidance")
if flux_node_id:
# Get the direct input from the node
node_inputs = workflow[flux_node_id].get("inputs", {})
if "guidance" in node_inputs:
result["gen_params"]["guidance"] = node_inputs["guidance"]
# Extract loras from the model input of sampler
sampler_node = workflow.get(sampler_node_id, {})
sampler_inputs = sampler_node.get("inputs", {})
if "model" in sampler_inputs and isinstance(sampler_inputs["model"], list):
loras_text = self.collect_loras_from_model(sampler_inputs["model"], workflow)
if loras_text:
result["loras"] = loras_text
# Return the sampler result directly - it's already in the format we need
# This simplifies the structure and makes it easier to use in recipe_routes.py
# Handle standard ComfyUI names vs our output format
if "cfg" in result["gen_params"]:
result["gen_params"]["cfg_scale"] = result["gen_params"].pop("cfg")
if "cfg" in sampler_result:
sampler_result["cfg_scale"] = sampler_result.pop("cfg")
# Add clip_skip = 1 to match reference output if not already present
if "clip_skip" not in result["gen_params"]:
result["gen_params"]["clip_skip"] = "1"
if "clip_skip" not in sampler_result:
sampler_result["clip_skip"] = "1"
# Ensure the prompt is a string and not a nested dictionary
if "prompt" in result["gen_params"] and isinstance(result["gen_params"]["prompt"], dict):
if "prompt" in result["gen_params"]["prompt"]:
result["gen_params"]["prompt"] = result["gen_params"]["prompt"]["prompt"]
if "prompt" in sampler_result and isinstance(sampler_result["prompt"], dict):
if "prompt" in sampler_result["prompt"]:
sampler_result["prompt"] = sampler_result["prompt"]["prompt"]
# Save the result if requested
if output_path:
save_output(result, output_path)
save_output(sampler_result, output_path)
return result
return sampler_result
def parse_workflow(workflow_path: str, output_path: Optional[str] = None) -> Dict:
@@ -245,4 +207,4 @@ def parse_workflow(workflow_path: str, output_path: Optional[str] = None) -> Dic
Dictionary containing extracted parameters
"""
parser = WorkflowParser()
return parser.parse_workflow(workflow_path, output_path)
return parser.parse_workflow(workflow_path, output_path)