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Add support for SamplerCustomAdvanced node in metadata extraction

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Will Miao
2025-04-26 09:40:44 +08:00
parent b4e22cd375
commit b0a9d44b0c
2 changed files with 137 additions and 17 deletions
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77
py/metadata_collector/metadata_processor.py
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@@ -11,7 +11,16 @@ class MetadataProcessor:
primary_sampler = None
primary_sampler_id = None
# First, check for KSamplerAdvanced with add_noise="enable"
# First, check for SamplerCustomAdvanced
prompt = metadata.get("current_prompt")
if prompt and prompt.original_prompt:
for node_id, node_info in prompt.original_prompt.items():
if node_info.get("class_type") == "SamplerCustomAdvanced":
# Found a SamplerCustomAdvanced node
if node_id in metadata.get(SAMPLING, {}):
return node_id, metadata[SAMPLING][node_id]
# Next, check for KSamplerAdvanced with add_noise="enable"
for node_id, sampler_info in metadata.get(SAMPLING, {}).items():
parameters = sampler_info.get("parameters", {})
add_noise = parameters.get("add_noise")
@@ -22,7 +31,7 @@ class MetadataProcessor:
primary_sampler_id = node_id
break
# If no KSamplerAdvanced found, fall back to traditional KSampler with denoise=1
# If no specialized sampler found, fall back to traditional KSampler with denoise=1
if primary_sampler is None:
for node_id, sampler_info in metadata.get(SAMPLING, {}).items():
parameters = sampler_info.get("parameters", {})
@@ -152,22 +161,60 @@ class MetadataProcessor:
# Trace connections from the primary sampler
if prompt and primary_sampler_id:
# Trace positive prompt - look specifically for CLIPTextEncode
positive_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", "CLIPTextEncode", max_depth=10)
if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
# Check if this is a SamplerCustomAdvanced node
is_custom_advanced = False
if prompt.original_prompt and primary_sampler_id in prompt.original_prompt:
is_custom_advanced = prompt.original_prompt[primary_sampler_id].get("class_type") == "SamplerCustomAdvanced"
# Find any FluxGuidance nodes in the positive conditioning path
flux_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", "FluxGuidance", max_depth=5)
if flux_node_id and flux_node_id in metadata.get(SAMPLING, {}):
flux_params = metadata[SAMPLING][flux_node_id].get("parameters", {})
params["guidance"] = flux_params.get("guidance")
if is_custom_advanced:
# For SamplerCustomAdvanced, trace specific inputs
# 1. Trace sigmas input to find BasicScheduler
scheduler_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "sigmas", "BasicScheduler", max_depth=5)
if scheduler_node_id and scheduler_node_id in metadata.get(SAMPLING, {}):
scheduler_params = metadata[SAMPLING][scheduler_node_id].get("parameters", {})
params["steps"] = scheduler_params.get("steps")
params["scheduler"] = scheduler_params.get("scheduler")
# 2. Trace sampler input to find KSamplerSelect
sampler_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "sampler", "KSamplerSelect", max_depth=5)
if sampler_node_id and sampler_node_id in metadata.get(SAMPLING, {}):
sampler_params = metadata[SAMPLING][sampler_node_id].get("parameters", {})
params["sampler"] = sampler_params.get("sampler_name")
# 3. Trace guider input for FluxGuidance and CLIPTextEncode
guider_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "guider", max_depth=5)
if guider_node_id:
# Look for FluxGuidance along the guider path
flux_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", "FluxGuidance", max_depth=5)
if flux_node_id and flux_node_id in metadata.get(SAMPLING, {}):
flux_params = metadata[SAMPLING][flux_node_id].get("parameters", {})
params["guidance"] = flux_params.get("guidance")
# Find CLIPTextEncode for positive prompt (through conditioning)
positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", "CLIPTextEncode", max_depth=10)
if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
# Trace negative prompt - look specifically for CLIPTextEncode
negative_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "negative", "CLIPTextEncode", 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", "")
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", "CLIPTextEncode", max_depth=10)
if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
# Find any FluxGuidance nodes in the positive conditioning path
flux_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "positive", "FluxGuidance", max_depth=5)
if flux_node_id and flux_node_id in metadata.get(SAMPLING, {}):
flux_params = metadata[SAMPLING][flux_node_id].get("parameters", {})
params["guidance"] = flux_params.get("guidance")
# Trace negative prompt - look specifically for CLIPTextEncode
negative_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "negative", "CLIPTextEncode", 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")

77
py/metadata_collector/node_extractors.py
View File

@@ -257,12 +257,85 @@ class VAEDecodeExtractor(NodeMetadataExtractor):
if "first_decode" not in metadata[IMAGES]:
metadata[IMAGES]["first_decode"] = metadata[IMAGES][node_id]
class KSamplerSelectExtractor(NodeMetadataExtractor):
@staticmethod
def extract(node_id, inputs, outputs, metadata):
if not inputs or "sampler_name" not in inputs:
return
sampling_params = {}
if "sampler_name" in inputs:
sampling_params["sampler_name"] = inputs["sampler_name"]
metadata[SAMPLING][node_id] = {
"parameters": sampling_params,
"node_id": node_id
}
class BasicSchedulerExtractor(NodeMetadataExtractor):
@staticmethod
def extract(node_id, inputs, outputs, metadata):
if not inputs:
return
sampling_params = {}
for key in ["scheduler", "steps", "denoise"]:
if key in inputs:
sampling_params[key] = inputs[key]
metadata[SAMPLING][node_id] = {
"parameters": sampling_params,
"node_id": node_id
}
class SamplerCustomAdvancedExtractor(NodeMetadataExtractor):
@staticmethod
def extract(node_id, inputs, outputs, metadata):
if not inputs:
return
sampling_params = {}
# Handle noise.seed as seed
if "noise" in inputs and inputs["noise"] is not None and hasattr(inputs["noise"], "seed"):
noise = inputs["noise"]
sampling_params["seed"] = noise.seed
metadata[SAMPLING][node_id] = {
"parameters": sampling_params,
"node_id": node_id
}
# Extract latent image dimensions if available
if "latent_image" in inputs and inputs["latent_image"] is not None:
latent = inputs["latent_image"]
if isinstance(latent, dict) and "samples" in latent:
# Extract dimensions from latent tensor
samples = latent["samples"]
if hasattr(samples, "shape") and len(samples.shape) >= 3:
# Correct shape interpretation: [batch_size, channels, height/8, width/8]
# Multiply by 8 to get actual pixel dimensions
height = int(samples.shape[2] * 8)
width = int(samples.shape[3] * 8)
if SIZE not in metadata:
metadata[SIZE] = {}
metadata[SIZE][node_id] = {
"width": width,
"height": height,
"node_id": node_id
}
# Registry of node-specific extractors
NODE_EXTRACTORS = {
# Sampling
"KSampler": SamplerExtractor,
"KSamplerAdvanced": KSamplerAdvancedExtractor, # Add KSamplerAdvanced
"SamplerCustomAdvanced": SamplerExtractor, # Add SamplerCustomAdvanced
"KSamplerAdvanced": KSamplerAdvancedExtractor,
"SamplerCustomAdvanced": SamplerCustomAdvancedExtractor, # Updated to use dedicated extractor
# Sampling Selectors
"KSamplerSelect": KSamplerSelectExtractor, # Add KSamplerSelect
"BasicScheduler": BasicSchedulerExtractor, # Add BasicScheduler
# Loaders
"CheckpointLoaderSimple": CheckpointLoaderExtractor,
"UNETLoader": UNETLoaderExtractor, # Updated to use dedicated extractor