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import base64
import io
import json
from pathlib import Path
from typing import Optional
import folder_paths
import torch
from ..log import log
from ..utils import tensor2pil
# region processors
def process_tensor(tensor):
log.debug(f"Tensor: {tensor.shape}")
image = tensor2pil(tensor)
b64_imgs = []
for im in image:
buffered = io.BytesIO()
im.save(buffered, format="PNG")
b64_imgs.append(
"data:image/png;base64,"
+ base64.b64encode(buffered.getvalue()).decode("utf-8")
)
return {"b64_images": b64_imgs}
def process_list(anything):
text = []
if not anything:
return {"text": []}
first_element = anything[0]
if (
isinstance(first_element, list)
and first_element
and isinstance(first_element[0], torch.Tensor)
):
text.append(
"List of List of Tensors: "
f"{first_element[0].shape} (x{len(anything)})"
)
elif isinstance(first_element, torch.Tensor):
text.append(
f"List of Tensors: {first_element.shape} (x{len(anything)})"
)
else:
text.append(f"Array ({len(anything)}): {anything}")
return {"text": text}
def process_dict(anything):
text = []
if "samples" in anything:
is_empty = (
"(empty)" if torch.count_nonzero(anything["samples"]) == 0 else ""
)
text.append(f"Latent Samples: {anything['samples'].shape} {is_empty}")
else:
text.append(json.dumps(anything, indent=2))
return {"text": text}
def process_bool(anything):
return {"text": ["True" if anything else "False"]}
def process_text(anything):
return {"text": [str(anything)]}
# endregion
class MTB_Debug:
"""Experimental node to debug any Comfy values.
support for more types and widgets is planned.
"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {"output_to_console": ("BOOLEAN", {"default": False})},
}
RETURN_TYPES = ()
FUNCTION = "do_debug"
CATEGORY = "mtb/debug"
OUTPUT_NODE = True
def do_debug(self, output_to_console: bool, **kwargs):
output = {
"ui": {"b64_images": [], "text": []},
# "result": ("A"),
}
processors = {
torch.Tensor: process_tensor,
list: process_list,
dict: process_dict,
bool: process_bool,
}
if output_to_console:
for k, v in kwargs.items():
log.info(f"{k}: {v}")
for anything in kwargs.values():
processor = processors.get(type(anything), process_text)
processed_data = processor(anything)
for ui_key, ui_value in processed_data.items():
output["ui"][ui_key].extend(ui_value)
return output
class MTB_SaveTensors:
"""Save torch tensors (image, mask or latent) to disk.
useful to debug things outside comfy.
"""
def __init__(self):
self.output_dir = folder_paths.get_output_directory()
self.type = "mtb/debug"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"filename_prefix": ("STRING", {"default": "ComfyPickle"}),
},
"optional": {
"image": ("IMAGE",),
"mask": ("MASK",),
"latent": ("LATENT",),
},
}
FUNCTION = "save"
OUTPUT_NODE = True
RETURN_TYPES = ()
CATEGORY = "mtb/debug"
def save(
self,
filename_prefix,
image: Optional[torch.Tensor] = None,
mask: Optional[torch.Tensor] = None,
latent: Optional[torch.Tensor] = None,
):
(
full_output_folder,
filename,
counter,
subfolder,
filename_prefix,
) = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
full_output_folder = Path(full_output_folder)
if image is not None:
image_file = f"{filename}_image_{counter:05}.pt"
torch.save(image, full_output_folder / image_file)
# np.save(full_output_folder/ image_file, image.cpu().numpy())
if mask is not None:
mask_file = f"{filename}_mask_{counter:05}.pt"
torch.save(mask, full_output_folder / mask_file)
# np.save(full_output_folder/ mask_file, mask.cpu().numpy())
if latent is not None:
# for latent we must use pickle
latent_file = f"{filename}_latent_{counter:05}.pt"
torch.save(latent, full_output_folder / latent_file)
# pickle.dump(latent, open(full_output_folder/ latent_file, "wb"))
# np.save(full_output_folder / latent_file,
# latent[""].cpu().numpy())
return f"{filename_prefix}_{counter:05}"
__nodes__ = [MTB_Debug, MTB_SaveTensors]
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