468 lines
22 KiB
Python
468 lines
22 KiB
Python
import json
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import torch
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import uuid
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import copy
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import os
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from enum import Enum
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import numpy as np
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import hashlib
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from torchvision import transforms
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import comfy.model_management
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from PIL import Image
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from nodes import SaveImage
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import gc
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import folder_paths
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from server import PromptServer
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from execution import PromptExecutor
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class ExecutionResult(Enum):
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SUCCESS = 0
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FAILURE = 1
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PENDING = 2
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class AnyType(str):
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"""A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
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def __eq__(self, _) -> bool:
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return True
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def __ne__(self, __value: object) -> bool:
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return False
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script_list_path = os.path.join(folder_paths.user_directory, "default", "workflows")
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def recursive_delete(workflow, to_delete):
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# workflow_copy = copy.deepcopy(workflow)
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new_delete = []
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for node_id in to_delete:
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for node_id2, node in workflow.items():
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for input_name, input_value in node["inputs"].items():
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if type(input_value) == list:
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if len(input_value) > 0:
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if input_value[0] == node_id:
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new_delete.append(node_id2)
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if node_id in workflow:
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del workflow[node_id]
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if len(new_delete) > 0:
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workflow = recursive_delete(workflow, new_delete)
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return workflow
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class Workflow(SaveImage):
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def __init__(self):
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self.ws = None
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"workflows": ("COMBO", {"values": []}),
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"workflow": ("STRING", {"default": ""})
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},
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"optional": {}
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}
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RETURN_TYPES = (
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AnyType("*"), AnyType("*"), AnyType("*"), AnyType("*"), AnyType("*"), AnyType("*"), AnyType("*"), AnyType("*"),
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AnyType("*"), AnyType("*"), AnyType("*"), AnyType("*"), AnyType("*"), AnyType("*"), AnyType("*"), AnyType("*"),
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)
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FUNCTION = "generate"
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CATEGORY = "FlowChain ⛓️"
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OUTPUT_NODE = True
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@classmethod
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def IS_CHANGED(s, workflows, workflow, **kwargs):
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m = hashlib.sha256()
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m.update(workflows.encode())
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# Ajouter le contenu du workflow au hash pour détecter les changements de structure
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if workflow:
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workflow_data = json.loads(workflow)
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# Extraire les nœuds de sortie avec leurs positions/types/connexions
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outputs = {}
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for k, v in workflow_data.items():
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if v.get('class_type') == 'WorkflowOutput':
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# Capturer le nom, type et la source de données (connexions entrantes)
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output_info = {
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'name': v['inputs']['Name'],
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'type': v['inputs']['type'],
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'position': v.get('_meta', {}).get('position', [0, 0]),
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}
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# Ajouter les connexions d'entrée pour tracer la provenance des données
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for input_name, input_value in v['inputs'].items():
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if isinstance(input_value, list) and len(input_value) > 0:
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# Stocker les IDs des nœuds connectés à cette sortie
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output_info[input_name + '_source'] = input_value
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outputs[k] = output_info
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# Être sûr de préserver l'ordre des sorties dans le hash
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# en les triant par position verticale
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sorted_outputs = dict(sorted(
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outputs.items(),
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key=lambda item: item[1].get('position', [0, 0])[1]
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))
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# Ajouter l'information des sorties au hash
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m.update(json.dumps(sorted_outputs, sort_keys=True).encode())
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return m.digest().hex()
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def generate(self, workflows, workflow, **kwargs):
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def populate_inputs(workflow, inputs, kwargs_values):
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workflow_inputs = {k: v for k, v in workflow.items() if v["class_type"] == "WorkflowInput"}
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for key, value in workflow_inputs.items():
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if value["inputs"]["Name"] in inputs:
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if type(inputs[value["inputs"]["Name"]]) == list:
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if value["inputs"]["Name"] in kwargs_values:
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workflow[key]["inputs"]["default"] = kwargs_values[value["inputs"]["Name"]]
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else:
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workflow[key]["inputs"]["default"] = inputs[value["inputs"]["Name"]]
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workflow_inputs_images = {k: v for k, v in workflow.items() if
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v["class_type"] == "WorkflowInput" and v["inputs"]["type"] == "IMAGE"}
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for key, value in workflow_inputs_images.items():
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if "default" not in value["inputs"]:
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workflow[key]["inputs"]["default"] = torch.tensor([])
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else:
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if value["inputs"]["default"].numel() == 0:
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workflow[key]["inputs"]["default"] = torch.tensor([])
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return workflow
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def treat_switch(workflow):
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to_delete = []
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#do_net_delete = []
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switch_to_delete = [-1]
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while len(switch_to_delete) > 0:
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switch_nodes = {k: v for k, v in workflow.items() if
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v["class_type"].startswith("Switch") and v["class_type"].endswith("[Crystools]")}
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# order switch nodes by inputs.boolean value
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switch_to_delete = []
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switch_nodes_copy = copy.deepcopy(switch_nodes)
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for switch_id, switch_node in switch_nodes.items():
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# create list of inputs who have switch in their inputs
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inputs_from_switch = []
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for node_ids, node in workflow.items():
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for input_name, input_value in node["inputs"].items():
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if type(input_value) == list:
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if len(input_value) > 0:
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if input_value[0] == switch_id:
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inputs_from_switch.append({node_ids: input_name})
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# convert to dictionary
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inputs_from_switch = {k: v for d in inputs_from_switch for k, v in d.items()}
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switch = switch_nodes_copy[switch_id]
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for node_id, input_name in inputs_from_switch.items():
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if type(switch["inputs"]["boolean"]) == list:
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switch_boolean_value = workflow[switch["inputs"]["boolean"][0]]["inputs"]
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other_input_name = None
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if "default" in switch_boolean_value:
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other_input_name = "default"
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elif "boolean" in switch_boolean_value:
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other_input_name = "boolean"
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if other_input_name is not None:
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if switch_boolean_value[other_input_name] == True:
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if type(switch["inputs"]["on_true"]) == list:
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workflow[node_id]["inputs"][input_name] = switch["inputs"]["on_true"]
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if node_id in switch_nodes_copy:
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switch_nodes_copy[node_id]["inputs"][input_name] = switch["inputs"]["on_true"]
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else:
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to_delete.append(node_id)
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else:
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if type(switch["inputs"]["on_false"]) == list:
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workflow[node_id]["inputs"][input_name] = switch["inputs"]["on_false"]
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if node_id in switch_nodes_copy:
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switch_nodes_copy[node_id]["inputs"][input_name] = switch["inputs"]["on_false"]
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else:
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to_delete.append(node_id)
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switch_to_delete.append(switch_id)
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else:
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if switch["inputs"]["boolean"] == True:
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if type(switch["inputs"]["on_true"]) == list:
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workflow[node_id]["inputs"][input_name] = switch["inputs"]["on_true"]
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if node_id in switch_nodes_copy:
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switch_nodes_copy[node_id]["inputs"][input_name] = switch["inputs"]["on_true"]
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else:
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to_delete.append(node_id)
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else:
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if type(switch["inputs"]["on_false"]) == list:
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workflow[node_id]["inputs"][input_name] = switch["inputs"]["on_false"]
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if node_id in switch_nodes_copy:
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switch_nodes_copy[node_id]["inputs"][input_name] = switch["inputs"]["on_false"]
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else:
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to_delete.append(node_id)
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switch_to_delete.append(switch_id)
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print(switch_to_delete)
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workflow = {k: v for k, v in workflow.items() if
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not (v["class_type"].startswith("Switch") and v["class_type"].endswith(
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"[Crystools]") and k in switch_to_delete)}
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return workflow, to_delete
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def treat_continue(workflow):
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to_delete = []
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continue_nodes = {k: v for k, v in workflow.items() if
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v["class_type"].startswith("WorkflowContinue")}
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do_net_delete = []
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for continue_node_id, continue_node in continue_nodes.items():
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for node_id, node in workflow.items():
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for input_name, input_value in node["inputs"].items():
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if type(input_value) == list:
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if len(input_value) > 0:
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if input_value[0] == continue_node_id:
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if type(continue_node["inputs"]["continue_workflow"]) == list:
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input_other_node = \
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workflow[continue_node["inputs"]["continue_workflow"][0]][
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"inputs"]
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other_input_name = None
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if "default" in input_other_node:
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other_input_name = "default"
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elif "boolean" in input_other_node:
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other_input_name = "boolean"
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if other_input_name is not None:
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if input_other_node[other_input_name]:
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workflow[node_id]["inputs"][input_name] = continue_node["inputs"]["input"]
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else:
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to_delete.append(node_id)
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else:
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do_net_delete.append(continue_node_id)
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else:
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if continue_node["inputs"]["continue_workflow"]:
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workflow[node_id]["inputs"][input_name] = continue_node["inputs"]["input"]
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else:
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to_delete.append(node_id)
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workflow = {k: v for k, v in workflow.items() if
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not (v["class_type"].startswith("WorkflowContinue") and k not in do_net_delete)}
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return workflow, to_delete
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def redefine_id(subworkflow, max_id):
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new_sub_workflow = {}
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for k, v in subworkflow.items():
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max_id += 1
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new_sub_workflow[str(max_id)] = v
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# replace old id by new id items in inputs of workflow
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for node_id, node in subworkflow.items():
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for input_name, input_value in node["inputs"].items():
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if type(input_value) == list:
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if len(input_value) > 0:
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if input_value[0] == k:
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subworkflow[node_id]["inputs"][input_name][0] = str(max_id)
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for node_id, node in new_sub_workflow.items():
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for input_name, input_value in node["inputs"].items():
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if type(input_value) == list:
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if len(input_value) > 0:
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if input_value[0] == k:
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new_sub_workflow[node_id]["inputs"][input_name][0] = str(max_id)
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return new_sub_workflow, max_id
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def change_subnode(subworkflow, node_id_to_find, value):
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for node_id, node in subworkflow.items():
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for input_name, input_value in node["inputs"].items():
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if type(input_value) == list:
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if len(input_value) > 0:
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if input_value[0] == node_id_to_find:
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subworkflow[node_id]["inputs"][input_name] = value
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return subworkflow
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def merge_inputs_outputs(workflow, workflow_name, subworkflow, workflow_outputs):
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# get max workflow id
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# coinvert workflow_outputs to list
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workflow_outputs = list(workflow_outputs.values())
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# prendre le premier workflow
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workflow_node = [{"id":k, **v} for k, v in workflow.items() if v["class_type"] == "Workflow" and v["inputs"]["workflows"] == workflow_name][0]
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sub_input_nodes = {k: v for k, v in subworkflow.items() if v["class_type"] == "WorkflowInput"}
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do_not_delete = []
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for sub_id, sub_node in sub_input_nodes.items():
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if sub_node["inputs"]["Name"] in workflow_node["inputs"]:
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value = workflow_node["inputs"][sub_node["inputs"]["Name"]]
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if type(value) == list:
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subworkflow = change_subnode(subworkflow, sub_id, value)
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else:
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subworkflow[sub_id]["inputs"]["default"] = value
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do_not_delete.append(sub_id)
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# remove input node
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subworkflow = {k: v for k, v in subworkflow.items() if not (v["class_type"] == "WorkflowInput" and k not in do_not_delete)}
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sub_output_nodes = {k: v for k, v in subworkflow.items() if v["class_type"] == "WorkflowOutput"}
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workflow_copy = copy.deepcopy(workflow)
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for node_id, node in workflow_copy.items():
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for input_name, input_value in node["inputs"].items():
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if type(input_value) == list:
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if len(input_value) > 0:
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if input_value[0] == workflow_node["id"]:
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for sub_output_id, sub_output_node in sub_output_nodes.items():
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if sub_output_node["inputs"]["Name"] == workflow_outputs[input_value[1]]["inputs"]["Name"]:
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workflow[node_id]["inputs"][input_name] = sub_output_node["inputs"]["default"]
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# remove output node
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subworkflow = {k: v for k, v in subworkflow.items() if not (v["class_type"] == "WorkflowOutput")}
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return workflow, subworkflow
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def clean_workflow(workflow, inputs=None, kwargs_values=None):
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if kwargs_values is None:
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kwargs_values = {}
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if inputs is None:
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inputs = {}
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if inputs is not None:
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workflow = populate_inputs(workflow, inputs, kwargs_values)
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workflow_outputs = {k: v for k, v in workflow.items() if v["class_type"] == "WorkflowOutput"}
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for output_id, output_node in workflow_outputs.items():
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workflow[output_id]["inputs"]["ui"] = False
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workflow, switch_to_delete = treat_switch(workflow)
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workflow, continue_to_delete = treat_continue(workflow)
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workflow = recursive_delete(workflow, switch_to_delete + continue_to_delete)
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return workflow, workflow_outputs
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def get_recursive_workflow(workflow_name, workflows, max_id=0):
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# if workflows[-5:] == ".json":
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# workflow = get_workflow(workflows)
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# else:
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try:
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if workflows == "{}":
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raise ValueError("Empty workflow.")
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workflow = json.loads(workflows)
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except:
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raise RuntimeError(f"Error while loading workflow: {workflow_name}, See <a href='https://github.com/numz/Comfyui-FlowChain'> for more information.")
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workflow, max_id = redefine_id(workflow, max_id)
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sub_workflows = {k: v for k, v in workflow.items() if v["class_type"] == "Workflow"}
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for key, sub_workflow_node in sub_workflows.items():
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workflow_json = sub_workflow_node["inputs"]["workflow"]
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workflow_name = sub_workflow_node["inputs"]["workflows"]
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subworkflow, max_id = get_recursive_workflow(workflow_name, workflow_json, max_id)
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workflow_outputs_sub = {k: v for k, v in subworkflow.items() if v["class_type"] == "WorkflowOutput"}
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workflow, subworkflow = merge_inputs_outputs(workflow, workflow_name, subworkflow, workflow_outputs_sub)
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workflow = {k: v for k, v in workflow.items() if k != key}
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# add subworkflow to workflow
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workflow.update(subworkflow)
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return workflow, max_id
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server_instance = PromptServer.instance
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client_id = server_instance.client_id
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if server_instance and hasattr(server_instance, 'prompt_queue'):
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current_queue = server_instance.prompt_queue.get_current_queue()
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queue_info = {
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'queue_running': current_queue[0],
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'queue_pending': current_queue[1]
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}
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# Now you can access the original inputs as before
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queue_to_use = queue_info["queue_running"]
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original_inputs = [v["inputs"] for k, v in queue_to_use[0][2].items() if
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"workflows" in v["inputs"] and v["inputs"]["workflows"] == workflows][0]
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else:
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# Fallback to empty inputs if server instance not available
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original_inputs = {}
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workflow, _ = get_recursive_workflow(workflows, workflow, 5000)
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workflow, workflow_outputs = clean_workflow(workflow, original_inputs, kwargs)
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# Accéder au fichier JSON original pour obtenir les positions correctes
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workflow_file_path = os.path.join(folder_paths.user_directory, "default", "workflows", workflows)
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original_positions = {}
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# Récupérer les positions des noeuds de sortie depuis le fichier original
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if os.path.exists(workflow_file_path):
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try:
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with open(workflow_file_path, "r", encoding="utf-8") as f:
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original_workflow = json.load(f)
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# Créer un mapping node_id -> position pour les noeuds WorkflowOutput
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if "nodes" in original_workflow:
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for node in original_workflow["nodes"]:
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if node.get("type") == "WorkflowOutput":
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node_id = str(node.get("id", "unknown"))
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pos_y = node.get("pos", [0, 0])[1]
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node_name = node.get("widgets_values", "")["Name"]["value"]
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original_positions[node_name] = pos_y
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except Exception as e:
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print(f"Erreur lors de la lecture du fichier workflow original: {str(e)}")
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# Récupérer les nœuds de sortie et les trier par position Y
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workflow_outputs_with_position = []
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for k, v in workflow_outputs.items():
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output_name = v["inputs"]["Name"]
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# Utiliser la position du fichier original si disponible, sinon utiliser une position par défaut
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y_position = original_positions.get(output_name, 999999)
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workflow_outputs_with_position.append((k, y_position))
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# Trier par position Y croissante
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workflow_outputs_with_position.sort(key=lambda x: x[1])
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# Extraire seulement les IDs dans l'ordre trié
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workflow_outputs_id = [k for k, _ in workflow_outputs_with_position]
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prompt_id = str(uuid.uuid4())
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class SimpleServer:
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def __init__(self):
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self.client_id = client_id
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self.last_node_id = None
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self.last_prompt_id = prompt_id
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def send_sync(self, *args, **kwargs):
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pass # No-op implementation
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simple_server = SimpleServer()
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executor = PromptExecutor(simple_server)
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executor.execute(workflow, prompt_id, {"client_id": client_id}, workflow_outputs_id)
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history_result = executor.history_result
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comfy.model_management.unload_all_models()
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gc.collect()
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# Remplacer la boucle de génération d'output qui ne respecte pas l'ordre
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output = []
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for id_node in workflow_outputs_id: # Utiliser l'ordre trié des IDs
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if id_node in history_result["outputs"]:
|
|
result_value = history_result["outputs"][id_node]["default"]
|
|
# Apply formatting based on the expected output type
|
|
output.append(result_value[0])
|
|
else:
|
|
node = workflow_outputs[id_node] # Récupérer le nœud correspondant à l'ID
|
|
if node["inputs"]["type"] == "IMAGE" or node["inputs"]["type"] == "MASK":
|
|
black_image_np = np.zeros((255, 255, 3), dtype=np.uint8)
|
|
black_image_pil = Image.fromarray(black_image_np)
|
|
transform = transforms.ToTensor()
|
|
image_tensor = transform(black_image_pil)
|
|
image_tensor = image_tensor.permute(1, 2, 0)
|
|
image_tensor = image_tensor.unsqueeze(0)
|
|
output.append(image_tensor)
|
|
else:
|
|
output.append(None)
|
|
|
|
return tuple(output)
|
|
# return tuple(queue[uid]["outputs"])
|
|
|
|
|
|
NODE_CLASS_MAPPINGS_WORKFLOW = {
|
|
"Workflow": Workflow,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS_WORKFLOW = {
|
|
"Workflow": "Workflow (FlowChain ⛓️)",
|
|
}
|