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feat(metadata_processor): enhance primary sampler selection logic

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- Add pre-processing step to populate missing parameters for candidate samplers, especially for SamplerCustomAdvanced requiring tracing
- Change sampler selection from most recent (closest to downstream) to first in execution order to prioritize base samplers over refine samplers
- Improve parameter handling by updating sampler parameters with traced values before ranking
- Maintain backward compatibility with fallback to first sampler if no criteria match
This commit is contained in:
Will Miao
2025-12-19 01:30:08 +08:00
parent c8a179488a
commit 154ae82519
1 changed files with 55 additions and 12 deletions
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67
py/metadata_collector/metadata_processor.py
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@@ -39,8 +39,39 @@ class MetadataProcessor:
if node_id in metadata.get(SAMPLING, {}) and metadata[SAMPLING][node_id].get(IS_SAMPLER, False):
candidate_samplers[node_id] = metadata[SAMPLING][node_id]
# If we found candidate samplers, apply primary sampler logic to these candidates only
if candidate_samplers:
# If we found candidate samplers, apply primary sampler logic to these candidates only
# PRE-PROCESS: Ensure all candidate samplers have their parameters populated
# This is especially important for SamplerCustomAdvanced which needs tracing
prompt = metadata.get("current_prompt")
for node_id in candidate_samplers:
# If a sampler is missing common parameters like steps or denoise,
# try to populate them using tracing before ranking
sampler_info = candidate_samplers[node_id]
params = sampler_info.get("parameters", {})
if prompt and (params.get("steps") is None or params.get("denoise") is None):
# Create a temporary params dict to use the handler
temp_params = {
"steps": params.get("steps"),
"denoise": params.get("denoise"),
"sampler": params.get("sampler_name"),
"scheduler": params.get("scheduler")
}
# Check if it's SamplerCustomAdvanced
if prompt.original_prompt and node_id in prompt.original_prompt:
if prompt.original_prompt[node_id].get("class_type") == "SamplerCustomAdvanced":
MetadataProcessor.handle_custom_advanced_sampler(metadata, prompt, node_id, temp_params)
# Update the actual parameters with found values
params["steps"] = temp_params.get("steps")
params["denoise"] = temp_params.get("denoise")
if temp_params.get("sampler"):
params["sampler_name"] = temp_params.get("sampler")
if temp_params.get("scheduler"):
params["scheduler"] = temp_params.get("scheduler")
# Collect potential primary samplers based on different criteria
custom_advanced_samplers = []
advanced_add_noise_samplers = []
@@ -49,7 +80,6 @@ class MetadataProcessor:
high_denoise_id = None
# First, check for SamplerCustomAdvanced among candidates
prompt = metadata.get("current_prompt")
if prompt and prompt.original_prompt:
for node_id in candidate_samplers:
node_info = prompt.original_prompt.get(node_id, {})
@@ -77,15 +107,16 @@ class MetadataProcessor:
# Combine all potential primary samplers
potential_samplers = custom_advanced_samplers + advanced_add_noise_samplers + high_denoise_samplers
# Find the most recent potential primary sampler (closest to downstream node)
for i in range(downstream_index - 1, -1, -1):
# Find the first potential primary sampler (prefer base sampler over refine)
# Use forward search to prioritize the first one in execution order
for i in range(downstream_index):
node_id = execution_order[i]
if node_id in potential_samplers:
return node_id, candidate_samplers[node_id]
# If no potential sampler found from our criteria, return the most recent sampler
# If no potential sampler found from our criteria, return the first sampler
if candidate_samplers:
for i in range(downstream_index - 1, -1, -1):
for i in range(downstream_index):
node_id = execution_order[i]
if node_id in candidate_samplers:
return node_id, candidate_samplers[node_id]
@@ -176,8 +207,11 @@ class MetadataProcessor:
found_node_id = input_value[0] # Connected node_id
# If we're looking for a specific node class
if target_class and prompt.original_prompt[found_node_id].get("class_type") == target_class:
return found_node_id
if target_class:
if found_node_id not in prompt.original_prompt:
return None
if prompt.original_prompt[found_node_id].get("class_type") == target_class:
return found_node_id
# If we're not looking for a specific class, update the last valid node
if not target_class:
@@ -185,11 +219,19 @@ class MetadataProcessor:
# Continue tracing through intermediate nodes
current_node_id = found_node_id
# For most conditioning nodes, the input we want to follow is named "conditioning"
if "conditioning" in prompt.original_prompt[current_node_id].get("inputs", {}):
# Check if current source node exists
if current_node_id not in prompt.original_prompt:
return found_node_id if not target_class else None
# Determine which input to follow next on the source node
source_node_inputs = prompt.original_prompt[current_node_id].get("inputs", {})
if input_name in source_node_inputs:
current_input = input_name
elif "conditioning" in source_node_inputs:
current_input = "conditioning"
else:
# If there's no "conditioning" input, return the current node
# If there's no suitable input to follow, return the current node
# if we're not looking for a specific target_class
return found_node_id if not target_class else None
else:
@@ -523,6 +565,7 @@ class MetadataProcessor:
scheduler_params = metadata[SAMPLING][scheduler_node_id].get("parameters", {})
params["steps"] = scheduler_params.get("steps")
params["scheduler"] = scheduler_params.get("scheduler")
params["denoise"] = scheduler_params.get("denoise")
# 2. Trace sampler input to find KSamplerSelect (only if sampler input exists)
if "sampler" in sampler_inputs: