fix(api): handle new ComfyUI workflow format with list-based inputs
Fix AttributeError when processing PNG files with newer ComfyUI workflow format where node inputs are stored as lists instead of dictionaries. - Add _get_node_inputs() helper to normalize both dict and list input formats - Add _find_text_in_node() to extract text from nodes using multiple strategies - Update _extract_positive_prompt_from_comfyui_data() to use safe accessors - Maintain full backward compatibility with existing workflow formats Fixes error: "'list' object has no attribute 'get'" when scanning images from workflows using the newer ComfyUI format.
This commit is contained in:
Vito Sansevero
@@ -3234,19 +3234,87 @@ class PromptManagerAPI:
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return None
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def _get_node_inputs(self, node):
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"""
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Safely get inputs from a node, handling both dict and list formats.
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Returns a normalized dict format for consistent access.
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Old format: inputs is a dict with direct key-value pairs
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inputs = {"text": "my prompt", "seed": 123}
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New format: inputs is a list of connection objects
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inputs = [
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{"name": "text", "type": "STRING", "link": null, "widget": {"name": "text"}},
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{"name": "clip", "type": "CLIP", "link": 11}
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]
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"""
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if not isinstance(node, dict):
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return {}
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inputs = node.get("inputs", {})
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# If inputs is already a dict, return it
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if isinstance(inputs, dict):
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return inputs
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# If inputs is a list, convert to dict format
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if isinstance(inputs, list):
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inputs_dict = {}
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for input_item in inputs:
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if isinstance(input_item, dict) and "name" in input_item:
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name = input_item["name"]
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# For now, just mark that this input exists
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# The actual value might be in widgets_values
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inputs_dict[name] = input_item
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return inputs_dict
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# If inputs is neither dict nor list, return empty dict
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return {}
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def _find_text_in_node(self, node):
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"""
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Try to find text content in a node using various strategies.
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Handles both old and new workflow formats.
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"""
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if not isinstance(node, dict):
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return None
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# Strategy 1: Check normalized inputs for 'text' field
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inputs = self._get_node_inputs(node)
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if "text" in inputs and isinstance(inputs["text"], str):
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return inputs["text"]
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# Strategy 2: For text encoder nodes, check widgets_values
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class_type = node.get("class_type", node.get("type", ""))
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text_encoder_types = [
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'CLIPTextEncode', 'CLIPTextEncodeSDXL', 'CLIPTextEncodeSDXLRefiner',
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'CLIPTextEncodeFlux', 'PromptManager', 'PromptManagerText',
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'BNK_CLIPTextEncoder', 'Text Encoder', 'CLIP Text Encode'
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]
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if any(encoder_type.lower() in class_type.lower() for encoder_type in text_encoder_types):
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widgets_values = node.get("widgets_values", [])
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if widgets_values and len(widgets_values) > 0:
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# First widget is usually the text for these nodes
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if isinstance(widgets_values[0], str) and widgets_values[0].strip():
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return widgets_values[0]
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return None
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def _extract_positive_prompt_from_comfyui_data(self, data):
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"""Extract positive prompt from ComfyUI data using the logic from parse-metadata.py."""
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"""Extract positive prompt from ComfyUI data, handling both old and new formats."""
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if not isinstance(data, dict):
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return None
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# Build nodes dictionary similar to parse-metadata.py
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# Build nodes dictionary
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nodes_by_id = {}
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if "nodes" in data:
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# Handle nodes array format
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for node in data["nodes"]:
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nid = node.get("id")
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if nid is not None:
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nodes_by_id[nid] = node
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if isinstance(node, dict):
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nid = node.get("id")
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if nid is not None:
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nodes_by_id[nid] = node
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else:
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# Handle flat dictionary format (node_id -> node_data)
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for nid_str, node in data.items():
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@@ -3265,48 +3333,45 @@ class PromptManagerAPI:
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# First, try to find positive/negative connection pattern
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pos_id = None
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for node in nodes_by_id.values():
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inputs = node.get("inputs", {})
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if "positive" in inputs and "negative" in inputs:
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try:
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pos_id = int(inputs["positive"][0])
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break
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except:
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continue
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if isinstance(node, dict):
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inputs = self._get_node_inputs(node) # Use our safe function
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if "positive" in inputs and "negative" in inputs:
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try:
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# Handle both old format (direct value) and new format (connection object)
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pos_input = inputs["positive"]
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if isinstance(pos_input, list) and len(pos_input) > 0:
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pos_id = int(pos_input[0])
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break
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except:
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continue
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# Get text from the positive node
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if pos_id is not None and pos_id in nodes_by_id:
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text_val = nodes_by_id[pos_id].get("inputs", {}).get("text")
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if isinstance(text_val, str):
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text_val = self._find_text_in_node(nodes_by_id[pos_id])
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if text_val:
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return text_val
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# Fallback: find any text encoder node with text content
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text_encoder_types = [
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'CLIPTextEncode', 'CLIPTextEncodeSDXL', 'CLIPTextEncodeSDXLRefiner',
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'PromptManager', 'BNK_CLIPTextEncoder', 'Text Encoder', 'CLIP Text Encode'
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]
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# Collect all text encoder nodes
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text_nodes = []
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for node in nodes_by_id.values():
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if isinstance(node, dict):
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class_type = node.get('class_type', '')
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inputs = node.get('inputs', {})
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class_type = node.get('class_type', node.get('type', ''))
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# Check if this is a text encoder node
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if any(encoder_type.lower() in class_type.lower() for encoder_type in text_encoder_types):
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if 'text' in inputs and isinstance(inputs['text'], str):
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return inputs['text']
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text_val = self._find_text_in_node(node)
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if text_val:
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# Try to determine if this is positive or negative
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node_title = node.get('title', '').lower()
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if 'neg' not in node_title and 'negative' not in node_title:
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# Prioritize non-negative prompts
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text_nodes.insert(0, text_val)
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else:
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text_nodes.append(text_val)
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# Final fallback: collect all text fields and return the first non-empty one
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text_fields = []
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for node in nodes_by_id.values():
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if isinstance(node, dict):
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inputs = node.get("inputs", {})
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text_val = inputs.get("text")
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if isinstance(text_val, str) and text_val.strip():
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text_fields.append(text_val)
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# Return the first text field (likely positive prompt)
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if text_fields:
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return text_fields[0]
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# Return the first positive-looking prompt
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if text_nodes:
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return text_nodes[0]
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return None
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