pip-tools or poetry which offer more robust dependency resolution mechanisms than pip's default resolver.

 

To help with resolving complex dependency graphs, you can use tools like pip-tools or poetry which offer more robust dependency resolution mechanisms than pip's default resolver.

Pip-tools:
pip install pip-tools
pip-compile --output-file requirements.txt
pip-sync requirements.txt

Poetry:
pip install poetry
poetry init
poetry add <package-name>

Thank you

🙇🏻‍♂️

git tip, delete all history (commit) information

 

.

# Warning: This will destroy your current history.

# Ensure that you understand the consequences before running these commands.

# Go to your repository directory

cd path/to/your/repository

# Create a new initial commit

git checkout --orphan newBranch

git add -A

git commit -m "Initial commit"

# Delete the main branch

git branch -D main

# Rename the current branch to main

git branch -m main

# Force push to overwrite the history on the remote repository

git push --force origin main

..

install dlib on ubuntu

 

1.

sudo apt update

sudo apt install cmake

2.

pip install cmake

3.

pip install dlib

Check amout of size with consider .gitignore files.

Put this cmd. 

git ls-files \

  | xargs du -ch \

  | grep total$

Thank you!

comfyui custom node developing v2

 add one more class 

save image to png 

.

# from .imagetransfer import ImageTransfer

# __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

from PIL import Image

import torch

from torchvision.transforms.functional import to_pil_image, to_tensor

import os

import folder_paths

from comfy.cli_args import args

from PIL.PngImagePlugin import PngInfo

import json

import numpy as np

class ImageTransfer:

@classmethod

def INPUT_TYPES(cls):

return {

"required": {

"image": ("IMAGE",),

},

}

RETURN_TYPES = ("IMAGE",)

FUNCTION = "transfer"

CATEGORY = "Utility"

def transfer(self, image):

# Ensure we're not tracking gradients for this operation

print(f"Type of the image tensor: {image.dtype}")

print(f"Dimensions of the image tensor: {image.shape}")

with torch.no_grad():

# Check if image tensor is in the expected format [1, H, W, C]

if image.dim() == 4 and image.shape[-1] in [1, 3]:

# Permute the tensor to match [C, H, W] format expected by to_pil_image

image_permuted = image.squeeze(0).permute(2, 0, 1)

# Convert the PyTorch tensor to a PIL Image

image_pil = to_pil_image(image_permuted)

# Resize the PIL Image

resized_image_pil = image_pil.resize((256, 256), Image.LANCZOS)

# Convert the PIL Image back to a PyTorch tensor

resized_image_tensor = to_tensor(resized_image_pil)

# Permute dimensions back to [1, H, W, C] and add the batch dimension

resized_image_tensor = resized_image_tensor.permute(1, 2, 0).unsqueeze(0)

print(f"Type of the resized image tensor: {resized_image_tensor.dtype}")

print(f"Dimensions of the resized image tensor: {resized_image_tensor.shape}")

return (resized_image_tensor,)

else:

raise ValueError("Image tensor format not recognized. Expected format: [1, H, W, C].")

class Save_png2svg:

def __init__(self):

self.output_dir = folder_paths.get_output_directory()

self.type = "output"

self.prefix_append = ""

self.compress_level = 4

@classmethod

def INPUT_TYPES(s):

return {"required":

{"images": ("IMAGE", ),

"filename_prefix": ("STRING", {"default": "ComfyUI"})},

"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},

}

RETURN_TYPES = ()

FUNCTION = "Save_png2svg"

OUTPUT_NODE = True

CATEGORY = "image"

def Save_png2svg(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):

filename_prefix += self.prefix_append

full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])

results = list()

for (batch_number, image) in enumerate(images):

i = 255. * image.cpu().numpy()

img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))

metadata = None

if not args.disable_metadata:

metadata = PngInfo()

if prompt is not None:

metadata.add_text("prompt", json.dumps(prompt))

if extra_pnginfo is not None:

for x in extra_pnginfo:

metadata.add_text(x, json.dumps(extra_pnginfo[x]))

filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))

file = f"{filename_with_batch_num}_{counter:05}_.png"

img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)

results.append({

"filename": file,

"subfolder": subfolder,

"type": self.type

})

counter += 1

return { "ui": { "images": results } }

NODE_CLASS_MAPPINGS = {

"ImageTransfer": ImageTransfer,

"Save_png2svg":Save_png2svg

}

NODE_DISPLAY_NAME_MAPPINGS = {

"ImageTransfer": "Image Transfer",

"Save_png2svg": "Save png2svg"

}

..

comfyui image resize simple custom node code

 refer to code for your scratch 

:

# from .imagetransfer import ImageTransfer

# __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

from PIL import Image

import torch

from torchvision.transforms.functional import to_pil_image, to_tensor

class ImageTransfer:

@classmethod

def INPUT_TYPES(cls):

return {

"required": {

"image": ("IMAGE",),

},

}

RETURN_TYPES = ("IMAGE",)

FUNCTION = "transfer"

CATEGORY = "Utility"

def transfer(self, image):

# Ensure we're not tracking gradients for this operation

print(f"Type of the image tensor: {image.dtype}")

print(f"Dimensions of the image tensor: {image.shape}")

with torch.no_grad():

# Check if image tensor is in the expected format [1, H, W, C]

if image.dim() == 4 and image.shape[-1] in [1, 3]:

# Permute the tensor to match [C, H, W] format expected by to_pil_image

image_permuted = image.squeeze(0).permute(2, 0, 1)

# Convert the PyTorch tensor to a PIL Image

image_pil = to_pil_image(image_permuted)

# Resize the PIL Image

resized_image_pil = image_pil.resize((256, 256), Image.LANCZOS)

# Convert the PIL Image back to a PyTorch tensor

resized_image_tensor = to_tensor(resized_image_pil)

# Permute dimensions back to [1, H, W, C] and add the batch dimension

resized_image_tensor = resized_image_tensor.permute(1, 2, 0).unsqueeze(0)

print(f"Type of the resized image tensor: {resized_image_tensor.dtype}")

print(f"Dimensions of the resized image tensor: {resized_image_tensor.shape}")

return (resized_image_tensor,)

else:

raise ValueError("Image tensor format not recognized. Expected format: [1, H, W, C].")

NODE_CLASS_MAPPINGS = {

"ImageTransfer": ImageTransfer

}

NODE_DISPLAY_NAME_MAPPINGS = {

"ImageTransfer": "Image Transfer"

}