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https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-05-06 16:36:45 -03:00
fix(metadata): trace conditioning provenance for prompts
This commit is contained in:
Will Miao
@@ -352,50 +352,101 @@ class MetadataProcessor:
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# Check if we have stored conditioning objects for this sampler
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if sampler_id in metadata.get(PROMPTS, {}) and (
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"pos_conditioning" in metadata[PROMPTS][sampler_id] or
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"neg_conditioning" in metadata[PROMPTS][sampler_id]):
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"pos_conditioning" in metadata[PROMPTS][sampler_id] or
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"neg_conditioning" in metadata[PROMPTS][sampler_id]
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):
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pos_conditioning = metadata[PROMPTS][sampler_id].get("pos_conditioning")
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neg_conditioning = metadata[PROMPTS][sampler_id].get("neg_conditioning")
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# Helper function to recursively find prompt text for a conditioning object
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def find_prompt_text_for_conditioning(conditioning_obj, is_positive=True):
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def extend_unique(target, values):
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for value in values:
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if value and value not in target:
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target.append(value)
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# Helper function to recursively find prompt texts for a conditioning object.
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# Transform nodes can map one output conditioning to multiple source conditionings.
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def find_prompt_texts_for_conditioning(
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conditioning_obj, is_positive=True, visited=None
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):
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if conditioning_obj is None:
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return ""
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return []
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if visited is None:
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visited = set()
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conditioning_id = id(conditioning_obj)
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if conditioning_id in visited:
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return []
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visited.add(conditioning_id)
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prompt_texts = []
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# Try to match conditioning objects with those stored by extractors
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for prompt_node_id, prompt_data in metadata[PROMPTS].items():
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# For nodes with single conditioning output
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if "conditioning" in prompt_data:
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if id(prompt_data["conditioning"]) == id(conditioning_obj):
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return prompt_data.get("text", "")
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# For nodes with separate pos_conditioning and neg_conditioning outputs (like TSC_EfficientLoader)
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if is_positive and "positive_encoded" in prompt_data:
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if id(prompt_data["positive_encoded"]) == id(conditioning_obj):
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if "positive_text" in prompt_data:
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return prompt_data["positive_text"]
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else:
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orig_conditioning = prompt_data.get("orig_pos_cond", None)
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if orig_conditioning is not None:
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# Recursively find the prompt text for the original conditioning
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return find_prompt_text_for_conditioning(orig_conditioning, is_positive=True)
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if not is_positive and "negative_encoded" in prompt_data:
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if id(prompt_data["negative_encoded"]) == id(conditioning_obj):
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if "negative_text" in prompt_data:
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return prompt_data["negative_text"]
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else:
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orig_conditioning = prompt_data.get("orig_neg_cond", None)
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if orig_conditioning is not None:
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# Recursively find the prompt text for the original conditioning
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return find_prompt_text_for_conditioning(orig_conditioning, is_positive=False)
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return ""
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if not isinstance(prompt_data, dict):
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continue
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# For CLIP text nodes with a single conditioning output.
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if id(prompt_data.get("conditioning")) == conditioning_id:
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text = prompt_data.get("text", "")
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if text:
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extend_unique(prompt_texts, [text])
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# Generic provenance for passthrough/transform/combine nodes.
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for source in prompt_data.get("conditioning_sources", []):
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if id(source.get("output")) != conditioning_id:
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continue
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for input_conditioning in source.get("inputs", []):
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extend_unique(
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prompt_texts,
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find_prompt_texts_for_conditioning(
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input_conditioning, is_positive, visited
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),
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)
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# For nodes with separate pos_conditioning and neg_conditioning outputs
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# like TSC_EfficientLoader and existing ControlNet-style metadata.
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if (
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is_positive
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and id(prompt_data.get("positive_encoded")) == conditioning_id
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):
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if prompt_data.get("positive_text"):
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extend_unique(prompt_texts, [prompt_data["positive_text"]])
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else:
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extend_unique(
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prompt_texts,
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find_prompt_texts_for_conditioning(
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prompt_data.get("orig_pos_cond"),
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is_positive=True,
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visited=visited,
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),
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)
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if (
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not is_positive
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and id(prompt_data.get("negative_encoded")) == conditioning_id
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):
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if prompt_data.get("negative_text"):
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extend_unique(prompt_texts, [prompt_data["negative_text"]])
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else:
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extend_unique(
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prompt_texts,
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find_prompt_texts_for_conditioning(
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prompt_data.get("orig_neg_cond"),
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is_positive=False,
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visited=visited,
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),
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)
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return prompt_texts
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# Find prompt texts using the helper function
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result["prompt"] = find_prompt_text_for_conditioning(pos_conditioning, is_positive=True)
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result["negative_prompt"] = find_prompt_text_for_conditioning(neg_conditioning, is_positive=False)
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result["prompt"] = ", ".join(
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find_prompt_texts_for_conditioning(pos_conditioning, is_positive=True)
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)
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result["negative_prompt"] = ", ".join(
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find_prompt_texts_for_conditioning(neg_conditioning, is_positive=False)
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)
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return result
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@@ -163,6 +163,193 @@ class CLIPTextEncodeExtractor(NodeMetadataExtractor):
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conditioning = outputs[0][0]
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metadata[PROMPTS][node_id]["conditioning"] = conditioning
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def _ensure_prompt_metadata(metadata, node_id):
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if node_id not in metadata[PROMPTS]:
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metadata[PROMPTS][node_id] = {"node_id": node_id}
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return metadata[PROMPTS][node_id]
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def _first_output_tuple(outputs):
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if not outputs or not isinstance(outputs, list) or len(outputs) == 0:
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return None
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first_output = outputs[0]
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if isinstance(first_output, tuple):
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return first_output
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return None
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def _record_conditioning_source(
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metadata, node_id, output_conditioning, input_conditionings
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):
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if output_conditioning is None:
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return
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sources = [
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conditioning for conditioning in input_conditionings if conditioning is not None
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]
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if not sources:
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return
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prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
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prompt_metadata.setdefault("conditioning_sources", []).append(
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{
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"output": output_conditioning,
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"inputs": sources,
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}
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)
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def _get_variable_name(inputs):
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for key in ("key", "name", "variable_name", "tag", "text"):
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value = inputs.get(key)
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if isinstance(value, str) and value:
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return value
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return None
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def _get_node_variable_name(metadata, node_id, inputs):
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variable_name = _get_variable_name(inputs)
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if variable_name:
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return variable_name
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prompt = metadata.get("current_prompt")
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original_prompt = getattr(prompt, "original_prompt", None)
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if not original_prompt or node_id not in original_prompt:
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return None
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node_data = original_prompt[node_id]
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variable_name = _get_variable_name(node_data.get("inputs", {}))
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if variable_name:
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return variable_name
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widgets_values = node_data.get("widgets_values", [])
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if widgets_values and isinstance(widgets_values[0], str):
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return widgets_values[0]
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return None
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class ControlNetApplyAdvancedExtractor(NodeMetadataExtractor):
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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if not inputs:
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return
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prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
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if inputs.get("positive") is not None:
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prompt_metadata["orig_pos_cond"] = inputs["positive"]
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if inputs.get("negative") is not None:
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prompt_metadata["orig_neg_cond"] = inputs["negative"]
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@staticmethod
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def update(node_id, outputs, metadata):
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output_tuple = _first_output_tuple(outputs)
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if not output_tuple:
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return
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prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
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positive_input = prompt_metadata.get("orig_pos_cond")
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negative_input = prompt_metadata.get("orig_neg_cond")
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if len(output_tuple) >= 1:
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prompt_metadata["positive_encoded"] = output_tuple[0]
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_record_conditioning_source(
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metadata, node_id, output_tuple[0], [positive_input]
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)
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if len(output_tuple) >= 2:
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prompt_metadata["negative_encoded"] = output_tuple[1]
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_record_conditioning_source(
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metadata, node_id, output_tuple[1], [negative_input]
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)
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class ConditioningCombineExtractor(NodeMetadataExtractor):
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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if not inputs:
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return
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input_conditionings = []
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for input_name in inputs:
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if (
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input_name.startswith("conditioning")
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and inputs[input_name] is not None
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):
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input_conditionings.append(inputs[input_name])
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if input_conditionings:
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prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
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prompt_metadata["orig_conditionings"] = input_conditionings
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@staticmethod
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def update(node_id, outputs, metadata):
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output_tuple = _first_output_tuple(outputs)
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if not output_tuple or len(output_tuple) < 1:
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return
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prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
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output_conditioning = output_tuple[0]
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prompt_metadata["conditioning"] = output_conditioning
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_record_conditioning_source(
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metadata,
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node_id,
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output_conditioning,
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prompt_metadata.get("orig_conditionings", []),
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)
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class SetNodeExtractor(NodeMetadataExtractor):
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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if not inputs:
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return
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variable_name = _get_node_variable_name(metadata, node_id, inputs)
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conditioning = inputs.get("CONDITIONING")
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if conditioning is None:
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conditioning = inputs.get("conditioning")
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if conditioning is None:
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return
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prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
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prompt_metadata["conditioning"] = conditioning
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if variable_name:
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prompt_metadata["variable_name"] = variable_name
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metadata[PROMPTS].setdefault("__conditioning_variables__", {})[
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variable_name
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] = conditioning
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class GetNodeExtractor(NodeMetadataExtractor):
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@staticmethod
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def extract(node_id, inputs, outputs, metadata):
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variable_name = _get_node_variable_name(metadata, node_id, inputs or {})
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if variable_name:
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prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
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prompt_metadata["variable_name"] = variable_name
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@staticmethod
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def update(node_id, outputs, metadata):
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output_tuple = _first_output_tuple(outputs)
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if not output_tuple or len(output_tuple) < 1:
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return
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prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
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output_conditioning = output_tuple[0]
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prompt_metadata["conditioning"] = output_conditioning
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variable_name = prompt_metadata.get("variable_name")
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if not variable_name:
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return
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input_conditioning = metadata[PROMPTS].get("__conditioning_variables__", {}).get(
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variable_name
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)
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_record_conditioning_source(
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metadata, node_id, output_conditioning, [input_conditioning]
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)
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# Base Sampler Extractor to reduce code redundancy
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class BaseSamplerExtractor(NodeMetadataExtractor):
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"""Base extractor for sampler nodes with common functionality"""
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@@ -798,6 +985,10 @@ NODE_EXTRACTORS = {
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"smZ_CLIPTextEncode": CLIPTextEncodeExtractor, # From https://github.com/shiimizu/ComfyUI_smZNodes
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"CR_ApplyControlNetStack": CR_ApplyControlNetStackExtractor, # Add CR_ApplyControlNetStack
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"PCTextEncode": CLIPTextEncodeExtractor, # From https://github.com/asagi4/comfyui-prompt-control
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"ControlNetApplyAdvanced": ControlNetApplyAdvancedExtractor,
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"ConditioningCombine": ConditioningCombineExtractor,
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"SetNode": SetNodeExtractor,
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"GetNode": GetNodeExtractor,
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# Latent
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"EmptyLatentImage": ImageSizeExtractor,
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# Flux
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