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feat: enhance metadata processing by refining primary sampler selection and adding CLIPTextEncodeFlux extractor. Fixes https://github.com/willmiao/ComfyUI-Lora-Manager/issues/146

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This commit is contained in:
Will Miao
2025-04-29 06:30:48 +08:00
parent 4064980505
commit 4789711910
3 changed files with 53 additions and 333 deletions
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56
py/metadata_collector/metadata_processor.py
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@@ -11,9 +11,10 @@ class MetadataProcessor:
@staticmethod
def find_primary_sampler(metadata):
"""Find the primary KSampler node (with denoise=1)"""
"""Find the primary KSampler node (with highest denoise value)"""
primary_sampler = None
primary_sampler_id = None
max_denoise = -1 # Track the highest denoise value
# First, check for SamplerCustomAdvanced
prompt = metadata.get("current_prompt")
@@ -35,17 +36,17 @@ class MetadataProcessor:
primary_sampler_id = node_id
break
# If no specialized sampler found, fall back to traditional KSampler with denoise=1
# If no specialized sampler found, find the sampler with highest denoise value
if primary_sampler is None:
for node_id, sampler_info in metadata.get(SAMPLING, {}).items():
parameters = sampler_info.get("parameters", {})
denoise = parameters.get("denoise")
# If denoise is 1.0, this is likely the primary sampler
if denoise == 1.0 or denoise == 1:
# If denoise exists and is higher than current max, use this sampler
if denoise is not None and denoise > max_denoise:
max_denoise = denoise
primary_sampler = sampler_info
primary_sampler_id = node_id
break
return primary_sampler_id, primary_sampler
@@ -206,6 +207,17 @@ class MetadataProcessor:
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", "")
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", "")
# Also extract guidance value if present in the sampling data
if positive_flux_node_id in metadata.get(SAMPLING, {}):
flux_params = metadata[SAMPLING][positive_flux_node_id].get("parameters", {})
if "guidance" in flux_params:
params["guidance"] = flux_params.get("guidance")
# 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)
@@ -225,40 +237,6 @@ class MetadataProcessor:
height = metadata[SIZE][primary_sampler_id].get("height")
if width and height:
params["size"] = f"{width}x{height}"
else:
# Fallback to the previous trace method if needed
latent_node_id = MetadataProcessor.trace_node_input(prompt, primary_sampler_id, "latent_image")
if latent_node_id:
# Follow chain to find EmptyLatentImage node
size_found = False
current_node_id = latent_node_id
# Limit depth to avoid infinite loops in complex workflows
max_depth = 10
for _ in range(max_depth):
if current_node_id in metadata.get(SIZE, {}):
width = metadata[SIZE][current_node_id].get("width")
height = metadata[SIZE][current_node_id].get("height")
if width and height:
params["size"] = f"{width}x{height}"
size_found = True
break
# Try to follow the chain
if prompt and prompt.original_prompt and current_node_id in prompt.original_prompt:
node_info = prompt.original_prompt[current_node_id]
if "inputs" in node_info:
# Look for a connection that might lead to size information
for input_name, input_value in node_info["inputs"].items():
if isinstance(input_value, list) and len(input_value) >= 2:
current_node_id = input_value[0]
break
else:
break # No connections to follow
else:
break # No inputs to follow
else:
break # Can't follow further
# Extract LoRAs using the standardized format
lora_parts = []

36
py/metadata_collector/node_extractors.py
View File

@@ -327,6 +327,41 @@ class SamplerCustomAdvancedExtractor(NodeMetadataExtractor):
"node_id": node_id
}
import json
class CLIPTextEncodeFluxExtractor(NodeMetadataExtractor):
@staticmethod
def extract(node_id, inputs, outputs, metadata):
if not inputs or "clip_l" not in inputs or "t5xxl" not in inputs:
return
clip_l_text = inputs.get("clip_l", "")
t5xxl_text = inputs.get("t5xxl", "")
# Create JSON string with T5 content first, then CLIP-L
combined_text = json.dumps({
"T5": t5xxl_text,
"CLIP-L": clip_l_text
})
metadata[PROMPTS][node_id] = {
"text": combined_text,
"node_id": node_id
}
# Extract guidance value if available
if "guidance" in inputs:
guidance_value = inputs.get("guidance")
# Store the guidance value in SAMPLING category
if SAMPLING not in metadata:
metadata[SAMPLING] = {}
if node_id not in metadata[SAMPLING]:
metadata[SAMPLING][node_id] = {"parameters": {}, "node_id": node_id}
metadata[SAMPLING][node_id]["parameters"]["guidance"] = guidance_value
# Registry of node-specific extractors
NODE_EXTRACTORS = {
# Sampling
@@ -343,6 +378,7 @@ NODE_EXTRACTORS = {
"LoraManagerLoader": LoraLoaderManagerExtractor,
# Conditioning
"CLIPTextEncode": CLIPTextEncodeExtractor,
"CLIPTextEncodeFlux": CLIPTextEncodeFluxExtractor, # Add CLIPTextEncodeFlux
# Latent
"EmptyLatentImage": ImageSizeExtractor,
# Flux