Cyber-BlackCat
357380458e
Add two show functions, you can tensor-show or sth-show through use these nodes.
134 lines
4.6 KiB
Python
134 lines
4.6 KiB
Python
# from nodes import PreviewImage, SaveImage, NODE_CLASS_MAPPINGS as ALL_NODE_CLASS_MAPPINGS
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# import comfy.utils
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# import folder_paths
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import numpy as np
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import json
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# import pprint
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import torch
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from .utility import AlwaysEqualProxy
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all_type = AlwaysEqualProxy("*")
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class SomethingShow:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {},
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"optional": {"something": (all_type, {}), },
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"hidden": {"unique_id": "UNIQUE_ID",
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"extra_pnginfo": "EXTRA_PNGINFO",
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}}
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RETURN_TYPES = (all_type,)
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RETURN_NAMES = ('output',)
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INPUT_IS_LIST = True
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OUTPUT_NODE = True
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IS_OUTPUT = True
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FUNCTION = "someinput"
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CATEGORY = "MoneyMaker😺/show"
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def someinput(self, unique_id=None, extra_pnginfo=None, **kwargs):
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values = []
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if "something" in kwargs:
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for val in kwargs['something']:
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try:
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if type(val) is str:
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values.append(val)
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elif type(val) is list:
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values = val
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elif hasattr(val, 'shape'):
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# Extract shape if val is a tensor
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values.append(str(list(val.shape)))
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else:
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val = json.dumps(val)
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values.append(str(val))
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except Exception as e:
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print(f"Exception occurred while processing 'something': {e}")
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values.append(str(val))
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pass
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if not extra_pnginfo:
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print("Error: extra_pnginfo is empty")
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elif (not isinstance(extra_pnginfo[0], dict) or "workflow" not in extra_pnginfo[0]):
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print("Error: extra_pnginfo[0] is not a dict or missing 'workflow' key")
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else:
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workflow = extra_pnginfo[0]["workflow"]
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# print(f"Workflow: {workflow}")
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node = next((x for x in workflow["nodes"] if str(x["id"]) == unique_id[0]), None)
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if node:
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# print(f"Node found: {node}")
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node["widgets_values"] = [values]
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else:
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print("Node not found")
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if isinstance(values, list) and len(values) == 1:
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# values = ",".join([str(x) for x in values]) #转为字符串
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print(f"values: {values}")
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return { "ui": {"text": values}, "result": (values[0],), }
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else:
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# values = ",".join([str(x) for x in values]) #转为字符串
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print(f"values: {values}")
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return { "ui": {"text": values}, "result": (values,), }
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# {"images": [image_tensor]}
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class TensorShow:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"tensor": (all_type,)},
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"optional": {},
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"hidden": {"unique_id": "UNIQUE_ID",
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"extra_pnginfo": "EXTRA_PNGINFO",
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}}
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RETURN_TYPES = (all_type,)
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RETURN_NAMES = ('tensor',)
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OUTPUT_NODE = True
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IS_OUTPUT = True
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FUNCTION = "showtensor"
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CATEGORY = "MoneyMaker😺/show"
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def showtensor(self, tensor, unique_id=None, extra_pnginfo=None, **kwargs):
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shapes = []
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def tensorShape(tensor):
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if isinstance(tensor, dict):
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for key in tensor:
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tensorShape(tensor[key])
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elif isinstance(tensor, list):
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for i in range(len(tensor)):
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tensorShape(tensor[i])
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elif hasattr(tensor, 'shape'):
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shapes.append(list(tensor.shape))
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print(f"Tensor values: {tensor}") # 修正打印语句
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# check tensor attribute&data type
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if hasattr(tensor, 'shape') or isinstance(tensor, (list, dict)):
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tensorShape(tensor)
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# # 格式化
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# 将张量转换为列表
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# tensor_str = str(tensor)
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tensor_lsstr = [str(tensor)]
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# # shapes已经是一个列表了
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combined_list=["Tensor shape:"]+shapes+["\n"]+tensor_lsstr
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# combined_text = shapes+tensor_str
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# combined_text = pprint.pformat(combined_text)
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# print(f'Tensor shape: {shapes}\n\nTensor: {tensor_str}')
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print(f'Tensor shape: {shapes}')
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return { "ui": {"text": combined_list}}
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else:
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error_msg = f"Not a tensor or not have a shape attribute. Original input is: {tensor}"
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print(error_msg)
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return { "ui": {"text": [error_msg]}} |