| import os
|
| import json
|
| import torch
|
| from deepdiff import DeepDiff
|
| from ..core import CONFIG, logger
|
|
|
|
|
|
|
| def setWidgetValues(value=None, unique_id=None, extra_pnginfo=None) -> None:
|
| if unique_id and extra_pnginfo:
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| workflow = extra_pnginfo["workflow"]
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| node = next((x for x in workflow["nodes"] if str(x["id"]) == unique_id), None)
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|
|
| if node:
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| node["widgets_values"] = value
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|
|
| return None
|
|
|
|
|
|
|
| def findJsonStrDiff(json1, json2):
|
| msgError = "Could not compare jsons"
|
| returnJson = {"error": msgError}
|
| try:
|
|
|
|
|
|
|
|
|
| returnJson = findJsonsDiff(json1, json2)
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|
|
| returnJson = json.dumps(returnJson, indent=CONFIG["indent"])
|
| except Exception as e:
|
| logger.warn(f"{msgError}: {e}")
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|
|
| return returnJson
|
|
|
|
|
| def findJsonsDiff(json1, json2):
|
| msgError = "Could not compare jsons"
|
| returnJson = {"error": msgError}
|
|
|
| try:
|
| diff = DeepDiff(json1, json2, ignore_order=True, verbose_level=2)
|
|
|
| returnJson = {k: v for k, v in diff.items() if
|
| k in ('dictionary_item_added', 'dictionary_item_removed', 'values_changed')}
|
|
|
|
|
| returnJson = dict(reversed(returnJson.items()))
|
|
|
| except Exception as e:
|
| logger.warn(f"{msgError}: {e}")
|
|
|
| return returnJson
|
|
|
|
|
|
|
|
|
|
|
| def get_system_stats():
|
| import psutil
|
|
|
|
|
| ram = psutil.virtual_memory()
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| ram_used = ram.used / (1024 ** 3)
|
| ram_total = ram.total / (1024 ** 3)
|
| ram_stats = f"Used RAM: {ram_used:.2f} GB / Total RAM: {ram_total:.2f} GB"
|
|
|
|
|
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
| vram_used = torch.cuda.memory_allocated(device) / (1024 ** 3)
|
| vram_total = torch.cuda.get_device_properties(device).total_memory / (1024 ** 3)
|
| vram_stats = f"Used VRAM: {vram_used:.2f} GB / Total VRAM: {vram_total:.2f} GB"
|
|
|
|
|
| hard_drive = psutil.disk_usage("/")
|
| used_space = hard_drive.used / (1024 ** 3)
|
| total_space = hard_drive.total / (1024 ** 3)
|
| hard_drive_stats = f"Used Space: {used_space:.2f} GB / Total Space: {total_space:.2f} GB"
|
|
|
| return [ram_stats, vram_stats, hard_drive_stats]
|
|
|
|
|
|
|
| def getResolutionByTensor(image=None) -> dict:
|
| res = {"x": 0, "y": 0}
|
|
|
| if image is not None:
|
| img = image.movedim(-1, 1)
|
|
|
| res["x"] = img.shape[3]
|
| res["y"] = img.shape[2]
|
|
|
| return res
|
|
|
|
|
|
|
| def get_size(path):
|
| size = os.path.getsize(path)
|
| if size < 1024:
|
| return f"{size} bytes"
|
| elif size < pow(1024, 2):
|
| return f"{round(size / 1024, 2)} KB"
|
| elif size < pow(1024, 3):
|
| return f"{round(size / (pow(1024, 2)), 2)} MB"
|
| elif size < pow(1024, 4):
|
| return f"{round(size / (pow(1024, 3)), 2)} GB"
|
|
|
|
|
| def get_nested_value(data, dotted_key, default=None):
|
| keys = dotted_key.split('.')
|
| for key in keys:
|
| if isinstance(data, str):
|
| data = json.loads(data)
|
| if isinstance(data, dict) and key in data:
|
| data = data[key]
|
| else:
|
| return default
|
| return data
|
|
|
