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feat: Add CR_ApplyControlNetStack extractor and enhance prompt conditioning handling in metadata processing. Fixes #277

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Will Miao
2025-07-10 09:26:53 +08:00
parent 1bd2635864
commit 849b97afba
2 changed files with 72 additions and 17 deletions
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54
py/metadata_collector/metadata_processor.py
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@@ -238,25 +238,45 @@ class MetadataProcessor:
pos_conditioning = metadata[PROMPTS][sampler_id].get("pos_conditioning")
neg_conditioning = metadata[PROMPTS][sampler_id].get("neg_conditioning")
# Try to match conditioning objects with those stored by CLIPTextEncodeExtractor
for prompt_node_id, prompt_data in metadata[PROMPTS].items():
# For nodes with single conditioning output
if "conditioning" in prompt_data:
if pos_conditioning is not None and id(prompt_data["conditioning"]) == id(pos_conditioning):
result["prompt"] = prompt_data.get("text", "")
# Helper function to recursively find prompt text for a conditioning object
def find_prompt_text_for_conditioning(conditioning_obj, is_positive=True):
if conditioning_obj is None:
return ""
if neg_conditioning is not None and id(prompt_data["conditioning"]) == id(neg_conditioning):
result["negative_prompt"] = prompt_data.get("text", "")
# Try to match conditioning objects with those stored by extractors
for prompt_node_id, prompt_data in metadata[PROMPTS].items():
# For nodes with single conditioning output
if "conditioning" in prompt_data:
if id(prompt_data["conditioning"]) == id(conditioning_obj):
return prompt_data.get("text", "")
# For nodes with separate pos_conditioning and neg_conditioning outputs (like TSC_EfficientLoader)
if is_positive and "positive_encoded" in prompt_data:
if id(prompt_data["positive_encoded"]) == id(conditioning_obj):
if "positive_text" in prompt_data:
return prompt_data["positive_text"]
else:
orig_conditioning = prompt_data.get("orig_pos_cond", None)
if orig_conditioning is not None:
# Recursively find the prompt text for the original conditioning
return find_prompt_text_for_conditioning(orig_conditioning, is_positive=True)
if not is_positive and "negative_encoded" in prompt_data:
if id(prompt_data["negative_encoded"]) == id(conditioning_obj):
if "negative_text" in prompt_data:
return prompt_data["negative_text"]
else:
orig_conditioning = prompt_data.get("orig_neg_cond", None)
if orig_conditioning is not None:
# Recursively find the prompt text for the original conditioning
return find_prompt_text_for_conditioning(orig_conditioning, is_positive=False)
# For nodes with separate pos_conditioning and neg_conditioning outputs (like TSC_EfficientLoader)
if "positive_encoded" in prompt_data:
if pos_conditioning is not None and id(prompt_data["positive_encoded"]) == id(pos_conditioning):
result["prompt"] = prompt_data.get("positive_text", "")
if "negative_encoded" in prompt_data:
if neg_conditioning is not None and id(prompt_data["negative_encoded"]) == id(neg_conditioning):
result["negative_prompt"] = prompt_data.get("negative_text", "")
return ""
# Find prompt texts using the helper function
result["prompt"] = find_prompt_text_for_conditioning(pos_conditioning, is_positive=True)
result["negative_prompt"] = find_prompt_text_for_conditioning(neg_conditioning, is_positive=False)
return result

35
py/metadata_collector/node_extractors.py
View File

@@ -569,6 +569,40 @@ class CFGGuiderExtractor(NodeMetadataExtractor):
metadata[SAMPLING][node_id]["parameters"]["cfg"] = cfg_value
class CR_ApplyControlNetStackExtractor(NodeMetadataExtractor):
@staticmethod
def extract(node_id, inputs, outputs, metadata):
if not inputs:
return
# Save the original conditioning inputs
base_positive = inputs.get("base_positive")
base_negative = inputs.get("base_negative")
if base_positive is not None or base_negative is not None:
if node_id not in metadata[PROMPTS]:
metadata[PROMPTS][node_id] = {"node_id": node_id}
metadata[PROMPTS][node_id]["orig_pos_cond"] = base_positive
metadata[PROMPTS][node_id]["orig_neg_cond"] = base_negative
@staticmethod
def update(node_id, outputs, metadata):
# Extract transformed conditionings from outputs
# outputs structure: [(base_positive, base_negative, show_help, )]
if outputs and isinstance(outputs, list) and len(outputs) > 0:
first_output = outputs[0]
if isinstance(first_output, tuple) and len(first_output) >= 2:
transformed_positive = first_output[0]
transformed_negative = first_output[1]
# Save transformed conditioning objects in metadata
if node_id not in metadata[PROMPTS]:
metadata[PROMPTS][node_id] = {"node_id": node_id}
metadata[PROMPTS][node_id]["positive_encoded"] = transformed_positive
metadata[PROMPTS][node_id]["negative_encoded"] = transformed_negative
# Registry of node-specific extractors
# Keys are node class names
NODE_EXTRACTORS = {
@@ -595,6 +629,7 @@ NODE_EXTRACTORS = {
"WAS_Text_to_Conditioning": CLIPTextEncodeExtractor,
"AdvancedCLIPTextEncode": CLIPTextEncodeExtractor, # From https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb
"smZ_CLIPTextEncode": CLIPTextEncodeExtractor, # From https://github.com/shiimizu/ComfyUI_smZNodes
"CR_ApplyControlNetStack": CR_ApplyControlNetStackExtractor, # Add CR_ApplyControlNetStack
# Latent
"EmptyLatentImage": ImageSizeExtractor,
# Flux