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from ..utils import tensor2pil
from ..log import log
import io, base64
import torch
import folder_paths
from typing import Optional
from pathlib import Path
class Debug:
"""Experimental node to debug any Comfy values, support for more types and widgets is planned"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {"anything_1": ("*")},
}
RETURN_TYPES = ("STRING",)
FUNCTION = "do_debug"
CATEGORY = "mtb/debug"
OUTPUT_NODE = True
def do_debug(self, **kwargs):
output = {
"ui": {"b64_images": [], "text": []},
"result": ("A"),
}
for k, v in kwargs.items():
anything = v
text = ""
if isinstance(anything, torch.Tensor):
log.debug(f"Tensor: {anything.shape}")
# write the images to temp
image = tensor2pil(anything)
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")
)
output["ui"]["b64_images"] += b64_imgs
log.debug(f"Input {k} contains {len(b64_imgs)} images")
elif isinstance(anything, bool):
log.debug(f"Input {k} contains boolean: {anything}")
output["ui"]["text"] += ["True" if anything else "False"]
else:
text = str(anything)
log.debug(f"Input {k} contains text: {text}")
output["ui"]["text"] += [text]
return output
class 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__ = [Debug, SaveTensors]
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