mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-07-27 16:26:39 +00:00
279 lines
9.5 KiB
Python
279 lines
9.5 KiB
Python
import json
|
|
import os
|
|
import torch
|
|
import hashlib
|
|
|
|
import numpy as np
|
|
from PIL import Image, ImageOps, ImageSequence
|
|
from PIL.PngImagePlugin import PngInfo
|
|
|
|
from comfy_api.v3 import io, ui
|
|
from comfy.cli_args import args
|
|
import folder_paths
|
|
import node_helpers
|
|
|
|
|
|
class SaveImage_V3(io.ComfyNodeV3):
|
|
@classmethod
|
|
def DEFINE_SCHEMA(cls):
|
|
return io.SchemaV3(
|
|
node_id="SaveImage_V3",
|
|
display_name="Save Image _V3",
|
|
description="Saves the input images to your ComfyUI output directory.",
|
|
category="image",
|
|
inputs=[
|
|
io.Image.Input(
|
|
"images",
|
|
tooltip="The images to save.",
|
|
),
|
|
io.String.Input(
|
|
"filename_prefix",
|
|
default="ComfyUI",
|
|
tooltip="The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.",
|
|
),
|
|
],
|
|
hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
|
|
is_output_node=True,
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, images, filename_prefix="ComfyUI"):
|
|
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
|
|
filename_prefix, folder_paths.get_output_directory(), images[0].shape[1], images[0].shape[0]
|
|
)
|
|
results = []
|
|
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 cls.hidden.prompt is not None:
|
|
metadata.add_text("prompt", json.dumps(cls.hidden.prompt))
|
|
if cls.hidden.extra_pnginfo is not None:
|
|
for x in cls.hidden.extra_pnginfo:
|
|
metadata.add_text(x, json.dumps(cls.hidden.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=4)
|
|
results.append({
|
|
"filename": file,
|
|
"subfolder": subfolder,
|
|
"type": "output",
|
|
})
|
|
counter += 1
|
|
|
|
return io.NodeOutput(ui={"images": results})
|
|
|
|
|
|
class PreviewImage_V3(io.ComfyNodeV3):
|
|
@classmethod
|
|
def DEFINE_SCHEMA(cls):
|
|
return io.SchemaV3(
|
|
node_id="PreviewImage_V3",
|
|
display_name="Preview Image _V3",
|
|
description="Preview the input images.",
|
|
category="image",
|
|
inputs=[
|
|
io.Image.Input(
|
|
"images",
|
|
tooltip="The images to preview.",
|
|
),
|
|
],
|
|
hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
|
|
is_output_node=True,
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, images):
|
|
return io.NodeOutput(ui=ui.PreviewImage(images, cls=cls))
|
|
|
|
|
|
class LoadImage_V3(io.ComfyNodeV3):
|
|
@classmethod
|
|
def DEFINE_SCHEMA(cls):
|
|
return io.SchemaV3(
|
|
node_id="LoadImage_V3",
|
|
display_name="Load Image _V3",
|
|
category="image",
|
|
inputs=[
|
|
io.Combo.Input(
|
|
"image",
|
|
image_upload=True,
|
|
image_folder=io.FolderType.input,
|
|
content_types=["image"],
|
|
options=cls.get_files_options(),
|
|
),
|
|
],
|
|
outputs=[
|
|
io.Image.Output(),
|
|
io.Mask.Output(),
|
|
],
|
|
)
|
|
|
|
@classmethod
|
|
def get_files_options(cls) -> list[str]:
|
|
target_dir = folder_paths.get_input_directory()
|
|
files = [f for f in os.listdir(target_dir) if os.path.isfile(os.path.join(target_dir, f))]
|
|
return sorted(folder_paths.filter_files_content_types(files, ["image"]))
|
|
|
|
@classmethod
|
|
def execute(cls, image) -> io.NodeOutput:
|
|
img = node_helpers.pillow(Image.open, folder_paths.get_annotated_filepath(image))
|
|
|
|
output_images = []
|
|
output_masks = []
|
|
w, h = None, None
|
|
|
|
excluded_formats = ['MPO']
|
|
|
|
for i in ImageSequence.Iterator(img):
|
|
i = node_helpers.pillow(ImageOps.exif_transpose, i)
|
|
|
|
if i.mode == 'I':
|
|
i = i.point(lambda i: i * (1 / 255))
|
|
image = i.convert("RGB")
|
|
|
|
if len(output_images) == 0:
|
|
w = image.size[0]
|
|
h = image.size[1]
|
|
|
|
if image.size[0] != w or image.size[1] != h:
|
|
continue
|
|
|
|
image = np.array(image).astype(np.float32) / 255.0
|
|
image = torch.from_numpy(image)[None,]
|
|
if 'A' in i.getbands():
|
|
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
|
|
mask = 1. - torch.from_numpy(mask)
|
|
elif i.mode == 'P' and 'transparency' in i.info:
|
|
mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
|
|
mask = 1. - torch.from_numpy(mask)
|
|
else:
|
|
mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
|
|
output_images.append(image)
|
|
output_masks.append(mask.unsqueeze(0))
|
|
|
|
if len(output_images) > 1 and img.format not in excluded_formats:
|
|
output_image = torch.cat(output_images, dim=0)
|
|
output_mask = torch.cat(output_masks, dim=0)
|
|
else:
|
|
output_image = output_images[0]
|
|
output_mask = output_masks[0]
|
|
|
|
return io.NodeOutput(output_image, output_mask)
|
|
|
|
@classmethod
|
|
def fingerprint_inputs(s, image):
|
|
image_path = folder_paths.get_annotated_filepath(image)
|
|
m = hashlib.sha256()
|
|
with open(image_path, 'rb') as f:
|
|
m.update(f.read())
|
|
return m.digest().hex()
|
|
|
|
@classmethod
|
|
def validate_inputs(s, image):
|
|
if not folder_paths.exists_annotated_filepath(image):
|
|
return "Invalid image file: {}".format(image)
|
|
return True
|
|
|
|
|
|
class LoadImageOutput_V3(io.ComfyNodeV3):
|
|
@classmethod
|
|
def DEFINE_SCHEMA(cls):
|
|
return io.SchemaV3(
|
|
node_id="LoadImageOutput_V3",
|
|
display_name="Load Image (from Outputs) _V3",
|
|
description="Load an image from the output folder. "
|
|
"When the refresh button is clicked, the node will update the image list "
|
|
"and automatically select the first image, allowing for easy iteration.",
|
|
category="image",
|
|
inputs=[
|
|
io.Combo.Input(
|
|
"image",
|
|
image_upload=True,
|
|
image_folder=io.FolderType.output,
|
|
content_types=["image"],
|
|
remote=io.RemoteOptions(
|
|
route="/internal/files/output",
|
|
refresh_button=True,
|
|
control_after_refresh="first",
|
|
),
|
|
),
|
|
],
|
|
outputs=[
|
|
io.Image.Output(),
|
|
io.Mask.Output(),
|
|
],
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, image) -> io.NodeOutput:
|
|
img = node_helpers.pillow(Image.open, folder_paths.get_annotated_filepath(image))
|
|
|
|
output_images = []
|
|
output_masks = []
|
|
w, h = None, None
|
|
|
|
excluded_formats = ['MPO']
|
|
|
|
for i in ImageSequence.Iterator(img):
|
|
i = node_helpers.pillow(ImageOps.exif_transpose, i)
|
|
|
|
if i.mode == 'I':
|
|
i = i.point(lambda i: i * (1 / 255))
|
|
image = i.convert("RGB")
|
|
|
|
if len(output_images) == 0:
|
|
w = image.size[0]
|
|
h = image.size[1]
|
|
|
|
if image.size[0] != w or image.size[1] != h:
|
|
continue
|
|
|
|
image = np.array(image).astype(np.float32) / 255.0
|
|
image = torch.from_numpy(image)[None,]
|
|
if 'A' in i.getbands():
|
|
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
|
|
mask = 1. - torch.from_numpy(mask)
|
|
elif i.mode == 'P' and 'transparency' in i.info:
|
|
mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
|
|
mask = 1. - torch.from_numpy(mask)
|
|
else:
|
|
mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
|
|
output_images.append(image)
|
|
output_masks.append(mask.unsqueeze(0))
|
|
|
|
if len(output_images) > 1 and img.format not in excluded_formats:
|
|
output_image = torch.cat(output_images, dim=0)
|
|
output_mask = torch.cat(output_masks, dim=0)
|
|
else:
|
|
output_image = output_images[0]
|
|
output_mask = output_masks[0]
|
|
|
|
return io.NodeOutput(output_image, output_mask)
|
|
|
|
@classmethod
|
|
def fingerprint_inputs(s, image):
|
|
image_path = folder_paths.get_annotated_filepath(image)
|
|
m = hashlib.sha256()
|
|
with open(image_path, 'rb') as f:
|
|
m.update(f.read())
|
|
return m.digest().hex()
|
|
|
|
@classmethod
|
|
def validate_inputs(s, image):
|
|
if not folder_paths.exists_annotated_filepath(image):
|
|
return "Invalid image file: {}".format(image)
|
|
return True
|
|
|
|
|
|
|
|
NODES_LIST: list[type[io.ComfyNodeV3]] = [
|
|
SaveImage_V3,
|
|
PreviewImage_V3,
|
|
LoadImage_V3,
|
|
LoadImageOutput_V3,
|
|
]
|