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refactor: streamline prompt matching logic in MetadataProcessor

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Will Miao
2025-06-20 17:00:23 +08:00
parent 32d12bb334
commit aa34c4c84c
1 changed files with 30 additions and 30 deletions
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60
py/metadata_collector/metadata_processor.py
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@@ -299,14 +299,7 @@ class MetadataProcessor:
params["sampler"] = sampling_params.get("sampler_name")
params["scheduler"] = sampling_params.get("scheduler")
# First try to match conditioning objects to prompts (new method)
if primary_sampler_id:
prompt_results = MetadataProcessor.match_conditioning_to_prompts(metadata, primary_sampler_id)
params["prompt"] = prompt_results["prompt"]
params["negative_prompt"] = prompt_results["negative_prompt"]
# If prompts were not found by object matching, fall back to tracing connections
if not params["prompt"] and prompt and primary_sampler_id:
if prompt and primary_sampler_id:
# Check if this is a SamplerCustomAdvanced node
is_custom_advanced = False
if prompt.original_prompt and primary_sampler_id in prompt.original_prompt:
@@ -353,29 +346,36 @@ class MetadataProcessor:
params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
else:
# Original tracing for standard samplers
# Trace positive prompt - look specifically for CLIPTextEncode
positive_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", max_depth=10)
if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
else:
# If CLIPTextEncode is not found, try to find CLIPTextEncodeFlux
positive_flux_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", "CLIPTextEncodeFlux", max_depth=10)
if positive_flux_node_id and positive_flux_node_id in metadata.get(PROMPTS, {}):
params["prompt"] = metadata[PROMPTS][positive_flux_node_id].get("text", "")
# Trace negative prompt - look specifically for CLIPTextEncode
negative_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "negative", max_depth=10)
if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
# For standard samplers, match conditioning objects to prompts
prompt_results = MetadataProcessor.match_conditioning_to_prompts(metadata, primary_sampler_id)
params["prompt"] = prompt_results["prompt"]
params["negative_prompt"] = prompt_results["negative_prompt"]
# If prompts were still not found, fall back to tracing connections
if not params["prompt"]:
# Original tracing for standard samplers
# Trace positive prompt - look specifically for CLIPTextEncode
positive_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", max_depth=10)
if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
else:
# If CLIPTextEncode is not found, try to find CLIPTextEncodeFlux
positive_flux_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", "CLIPTextEncodeFlux", max_depth=10)
if positive_flux_node_id and positive_flux_node_id in metadata.get(PROMPTS, {}):
params["prompt"] = metadata[PROMPTS][positive_flux_node_id].get("text", "")
# Trace negative prompt - look specifically for CLIPTextEncode
negative_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "negative", max_depth=10)
if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
# Size extraction is same for all sampler types
# Check if the sampler itself has size information (from latent_image)
if primary_sampler_id in metadata.get(SIZE, {}):
width = metadata[SIZE][primary_sampler_id].get("width")
height = metadata[SIZE][primary_sampler_id].get("height")
if width and height:
params["size"] = f"{width}x{height}"
# Size extraction is same for all sampler types
# Check if the sampler itself has size information (from latent_image)
if primary_sampler_id in metadata.get(SIZE, {}):
width = metadata[SIZE][primary_sampler_id].get("width")
height = metadata[SIZE][primary_sampler_id].get("height")
if width and height:
params["size"] = f"{width}x{height}"
# Extract LoRAs using the standardized format
lora_parts = []