mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-07-27 16:26:39 +00:00
813 lines
30 KiB
Python
813 lines
30 KiB
Python
import hashlib
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import json
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import os
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import numpy as np
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import torch
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from PIL import Image, ImageOps, ImageSequence
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from PIL.PngImagePlugin import PngInfo
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import comfy.utils
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import folder_paths
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import node_helpers
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import nodes
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from comfy.cli_args import args
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from comfy_api.v3 import io, ui
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from server import PromptServer
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class GetImageSize(io.ComfyNodeV3):
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@classmethod
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def define_schema(cls):
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return io.SchemaV3(
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node_id="GetImageSize_V3",
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display_name="Get Image Size _V3",
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description="Returns width and height of the image, and passes it through unchanged.",
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category="image",
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inputs=[
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io.Image.Input("image"),
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],
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outputs=[
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io.Int.Output(display_name="width"),
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io.Int.Output(display_name="height"),
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io.Int.Output(display_name="batch_size"),
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],
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hidden=[io.Hidden.unique_id],
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)
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@classmethod
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def execute(cls, image) -> io.NodeOutput:
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height = image.shape[1]
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width = image.shape[2]
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batch_size = image.shape[0]
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if cls.hidden.unique_id:
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PromptServer.instance.send_progress_text(
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f"width: {width}, height: {height}\n batch size: {batch_size}", cls.hidden.unique_id
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)
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return io.NodeOutput(width, height, batch_size)
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class ImageAddNoise(io.ComfyNodeV3):
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@classmethod
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def define_schema(cls):
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return io.SchemaV3(
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node_id="ImageAddNoise_V3",
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display_name="Image Add Noise _V3",
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category="image",
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inputs=[
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io.Image.Input("image"),
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io.Int.Input(
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"seed",
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default=0,
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min=0,
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max=0xFFFFFFFFFFFFFFFF,
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control_after_generate=True,
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tooltip="The random seed used for creating the noise.",
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),
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io.Float.Input("strength", default=0.5, min=0.0, max=1.0, step=0.01),
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],
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outputs=[io.Image.Output()],
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)
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@classmethod
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def execute(cls, image, seed, strength) -> io.NodeOutput:
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generator = torch.manual_seed(seed)
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s = torch.clip(
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(image + strength * torch.randn(image.size(), generator=generator, device="cpu").to(image)),
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min=0.0,
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max=1.0,
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)
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return io.NodeOutput(s)
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class ImageCrop(io.ComfyNodeV3):
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@classmethod
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def define_schema(cls):
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return io.SchemaV3(
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node_id="ImageCrop_V3",
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display_name="Image Crop _V3",
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category="image/transform",
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inputs=[
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io.Image.Input("image"),
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io.Int.Input("width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
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io.Int.Input("height", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
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io.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
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io.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
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],
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outputs=[io.Image.Output()],
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)
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@classmethod
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def execute(cls, image, width, height, x, y) -> io.NodeOutput:
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x = min(x, image.shape[2] - 1)
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y = min(y, image.shape[1] - 1)
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to_x = width + x
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to_y = height + y
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return io.NodeOutput(image[:, y:to_y, x:to_x, :])
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class ImageFlip(io.ComfyNodeV3):
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@classmethod
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def define_schema(cls):
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return io.SchemaV3(
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node_id="ImageFlip_V3",
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display_name="Image Flip _V3",
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category="image/transform",
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inputs=[
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io.Image.Input("image"),
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io.Combo.Input("flip_method", options=["x-axis: vertically", "y-axis: horizontally"]),
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],
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outputs=[io.Image.Output()],
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)
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@classmethod
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def execute(cls, image, flip_method) -> io.NodeOutput:
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if flip_method.startswith("x"):
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image = torch.flip(image, dims=[1])
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elif flip_method.startswith("y"):
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image = torch.flip(image, dims=[2])
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return io.NodeOutput(image)
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class ImageFromBatch(io.ComfyNodeV3):
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@classmethod
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def define_schema(cls):
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return io.SchemaV3(
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node_id="ImageFromBatch_V3",
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display_name="Image From Batch _V3",
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category="image/batch",
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inputs=[
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io.Image.Input("image"),
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io.Int.Input("batch_index", default=0, min=0, max=4095),
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io.Int.Input("length", default=1, min=1, max=4096),
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],
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outputs=[io.Image.Output()],
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)
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@classmethod
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def execute(cls, image, batch_index, length) -> io.NodeOutput:
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s_in = image
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batch_index = min(s_in.shape[0] - 1, batch_index)
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length = min(s_in.shape[0] - batch_index, length)
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s = s_in[batch_index : batch_index + length].clone()
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return io.NodeOutput(s)
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class ImageRotate(io.ComfyNodeV3):
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@classmethod
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def define_schema(cls):
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return io.SchemaV3(
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node_id="ImageRotate_V3",
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display_name="Image Rotate _V3",
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category="image/transform",
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inputs=[
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io.Image.Input("image"),
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io.Combo.Input("rotation", options=["none", "90 degrees", "180 degrees", "270 degrees"]),
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],
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outputs=[io.Image.Output()],
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)
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@classmethod
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def execute(cls, image, rotation) -> io.NodeOutput:
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rotate_by = 0
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if rotation.startswith("90"):
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rotate_by = 1
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elif rotation.startswith("180"):
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rotate_by = 2
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elif rotation.startswith("270"):
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rotate_by = 3
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return io.NodeOutput(torch.rot90(image, k=rotate_by, dims=[2, 1]))
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class ImageStitch(io.ComfyNodeV3):
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"""Upstreamed from https://github.com/kijai/ComfyUI-KJNodes"""
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@classmethod
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def define_schema(cls):
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return io.SchemaV3(
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node_id="ImageStitch_V3",
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display_name="Image Stitch _V3",
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description="Stitches image2 to image1 in the specified direction. "
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"If image2 is not provided, returns image1 unchanged. "
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"Optional spacing can be added between images.",
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category="image/transform",
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inputs=[
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io.Image.Input("image1"),
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io.Combo.Input("direction", options=["right", "down", "left", "up"], default="right"),
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io.Boolean.Input("match_image_size", default=True),
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io.Int.Input("spacing_width", default=0, min=0, max=1024, step=2),
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io.Combo.Input("spacing_color", options=["white", "black", "red", "green", "blue"], default="white"),
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io.Image.Input("image2", optional=True),
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],
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outputs=[io.Image.Output()],
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)
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@classmethod
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def execute(cls, image1, direction, match_image_size, spacing_width, spacing_color, image2=None) -> io.NodeOutput:
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if image2 is None:
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return io.NodeOutput(image1)
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# Handle batch size differences
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if image1.shape[0] != image2.shape[0]:
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max_batch = max(image1.shape[0], image2.shape[0])
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if image1.shape[0] < max_batch:
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image1 = torch.cat([image1, image1[-1:].repeat(max_batch - image1.shape[0], 1, 1, 1)])
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if image2.shape[0] < max_batch:
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image2 = torch.cat([image2, image2[-1:].repeat(max_batch - image2.shape[0], 1, 1, 1)])
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# Match image sizes if requested
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if match_image_size:
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h1, w1 = image1.shape[1:3]
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h2, w2 = image2.shape[1:3]
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aspect_ratio = w2 / h2
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if direction in ["left", "right"]:
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target_h, target_w = h1, int(h1 * aspect_ratio)
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else: # up, down
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target_w, target_h = w1, int(w1 / aspect_ratio)
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image2 = comfy.utils.common_upscale(
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image2.movedim(-1, 1), target_w, target_h, "lanczos", "disabled"
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).movedim(1, -1)
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color_map = {
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"white": 1.0,
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"black": 0.0,
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"red": (1.0, 0.0, 0.0),
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"green": (0.0, 1.0, 0.0),
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"blue": (0.0, 0.0, 1.0),
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}
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color_val = color_map[spacing_color]
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# When not matching sizes, pad to align non-concat dimensions
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if not match_image_size:
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h1, w1 = image1.shape[1:3]
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h2, w2 = image2.shape[1:3]
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pad_value = 0.0
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if not isinstance(color_val, tuple):
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pad_value = color_val
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if direction in ["left", "right"]:
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# For horizontal concat, pad heights to match
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if h1 != h2:
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target_h = max(h1, h2)
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if h1 < target_h:
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pad_h = target_h - h1
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pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2
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image1 = torch.nn.functional.pad(
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image1, (0, 0, 0, 0, pad_top, pad_bottom), mode="constant", value=pad_value
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)
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if h2 < target_h:
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pad_h = target_h - h2
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pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2
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image2 = torch.nn.functional.pad(
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image2, (0, 0, 0, 0, pad_top, pad_bottom), mode="constant", value=pad_value
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)
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else: # up, down
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# For vertical concat, pad widths to match
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if w1 != w2:
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target_w = max(w1, w2)
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if w1 < target_w:
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pad_w = target_w - w1
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pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2
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image1 = torch.nn.functional.pad(
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image1, (0, 0, pad_left, pad_right), mode="constant", value=pad_value
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)
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if w2 < target_w:
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pad_w = target_w - w2
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pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2
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image2 = torch.nn.functional.pad(
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image2, (0, 0, pad_left, pad_right), mode="constant", value=pad_value
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)
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# Ensure same number of channels
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if image1.shape[-1] != image2.shape[-1]:
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max_channels = max(image1.shape[-1], image2.shape[-1])
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if image1.shape[-1] < max_channels:
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image1 = torch.cat(
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[
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image1,
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torch.ones(
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*image1.shape[:-1],
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max_channels - image1.shape[-1],
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device=image1.device,
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),
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],
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dim=-1,
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)
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if image2.shape[-1] < max_channels:
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image2 = torch.cat(
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[
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image2,
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torch.ones(
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*image2.shape[:-1],
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max_channels - image2.shape[-1],
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device=image2.device,
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),
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],
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dim=-1,
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)
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# Add spacing if specified
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if spacing_width > 0:
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spacing_width = spacing_width + (spacing_width % 2) # Ensure even
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if direction in ["left", "right"]:
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spacing_shape = (
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image1.shape[0],
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max(image1.shape[1], image2.shape[1]),
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spacing_width,
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image1.shape[-1],
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)
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else:
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spacing_shape = (
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image1.shape[0],
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spacing_width,
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max(image1.shape[2], image2.shape[2]),
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image1.shape[-1],
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)
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spacing = torch.full(spacing_shape, 0.0, device=image1.device)
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if isinstance(color_val, tuple):
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for i, c in enumerate(color_val):
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if i < spacing.shape[-1]:
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spacing[..., i] = c
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if spacing.shape[-1] == 4: # Add alpha
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spacing[..., 3] = 1.0
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else:
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spacing[..., : min(3, spacing.shape[-1])] = color_val
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if spacing.shape[-1] == 4:
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spacing[..., 3] = 1.0
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# Concatenate images
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images = [image2, image1] if direction in ["left", "up"] else [image1, image2]
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if spacing_width > 0:
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images.insert(1, spacing)
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concat_dim = 2 if direction in ["left", "right"] else 1
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return io.NodeOutput(torch.cat(images, dim=concat_dim))
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class LoadImage(io.ComfyNodeV3):
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@classmethod
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def define_schema(cls):
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return io.SchemaV3(
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node_id="LoadImage_V3",
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display_name="Load Image _V3",
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category="image",
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inputs=[
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io.Combo.Input(
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"image",
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upload=io.UploadType.image,
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image_folder=io.FolderType.input,
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options=cls.get_files_options(),
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),
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],
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outputs=[
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io.Image.Output(),
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io.Mask.Output(),
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],
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)
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@classmethod
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def get_files_options(cls) -> list[str]:
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target_dir = folder_paths.get_input_directory()
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files = [f for f in os.listdir(target_dir) if os.path.isfile(os.path.join(target_dir, f))]
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return sorted(folder_paths.filter_files_content_types(files, ["image"]))
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@classmethod
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def execute(cls, image) -> io.NodeOutput:
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img = node_helpers.pillow(Image.open, folder_paths.get_annotated_filepath(image))
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output_images = []
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output_masks = []
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w, h = None, None
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excluded_formats = ["MPO"]
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for i in ImageSequence.Iterator(img):
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i = node_helpers.pillow(ImageOps.exif_transpose, i)
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if i.mode == "I":
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i = i.point(lambda i: i * (1 / 255))
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image = i.convert("RGB")
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if len(output_images) == 0:
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w = image.size[0]
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h = image.size[1]
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if image.size[0] != w or image.size[1] != h:
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continue
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image = np.array(image).astype(np.float32) / 255.0
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image = torch.from_numpy(image)[None,]
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if "A" in i.getbands():
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mask = np.array(i.getchannel("A")).astype(np.float32) / 255.0
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mask = 1.0 - torch.from_numpy(mask)
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elif i.mode == "P" and "transparency" in i.info:
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mask = np.array(i.convert("RGBA").getchannel("A")).astype(np.float32) / 255.0
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mask = 1.0 - torch.from_numpy(mask)
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else:
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mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
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output_images.append(image)
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output_masks.append(mask.unsqueeze(0))
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if len(output_images) > 1 and img.format not in excluded_formats:
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output_image = torch.cat(output_images, dim=0)
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output_mask = torch.cat(output_masks, dim=0)
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else:
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output_image = output_images[0]
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output_mask = output_masks[0]
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return io.NodeOutput(output_image, output_mask)
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@classmethod
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def fingerprint_inputs(s, image):
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image_path = folder_paths.get_annotated_filepath(image)
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m = hashlib.sha256()
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with open(image_path, "rb") as f:
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m.update(f.read())
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return m.digest().hex()
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@classmethod
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def validate_inputs(s, image):
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if not folder_paths.exists_annotated_filepath(image):
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return "Invalid image file: {}".format(image)
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return True
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class LoadImageOutput(io.ComfyNodeV3):
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@classmethod
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def define_schema(cls):
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return io.SchemaV3(
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node_id="LoadImageOutput_V3",
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display_name="Load Image (from Outputs) _V3",
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description="Load an image from the output folder. "
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"When the refresh button is clicked, the node will update the image list "
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"and automatically select the first image, allowing for easy iteration.",
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category="image",
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inputs=[
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io.Combo.Input(
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"image",
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upload=io.UploadType.image,
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image_folder=io.FolderType.output,
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remote=io.RemoteOptions(
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route="/internal/files/output",
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refresh_button=True,
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control_after_refresh="first",
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),
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),
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],
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outputs=[
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io.Image.Output(),
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io.Mask.Output(),
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],
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)
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@classmethod
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def execute(cls, image) -> io.NodeOutput:
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img = node_helpers.pillow(Image.open, folder_paths.get_annotated_filepath(image))
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output_images = []
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output_masks = []
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w, h = None, None
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excluded_formats = ["MPO"]
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for i in ImageSequence.Iterator(img):
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i = node_helpers.pillow(ImageOps.exif_transpose, i)
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if i.mode == "I":
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i = i.point(lambda i: i * (1 / 255))
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image = i.convert("RGB")
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if len(output_images) == 0:
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w = image.size[0]
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h = image.size[1]
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if image.size[0] != w or image.size[1] != h:
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continue
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image = np.array(image).astype(np.float32) / 255.0
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image = torch.from_numpy(image)[None,]
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if "A" in i.getbands():
|
|
mask = np.array(i.getchannel("A")).astype(np.float32) / 255.0
|
|
mask = 1.0 - 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 = 1.0 - 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 PreviewImage(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) -> io.NodeOutput:
|
|
return io.NodeOutput(ui=ui.PreviewImage(images, cls=cls))
|
|
|
|
|
|
class RepeatImageBatch(io.ComfyNodeV3):
|
|
@classmethod
|
|
def define_schema(cls):
|
|
return io.SchemaV3(
|
|
node_id="RepeatImageBatch_V3",
|
|
display_name="Repeat Image Batch _V3",
|
|
category="image/batch",
|
|
inputs=[
|
|
io.Image.Input("image"),
|
|
io.Int.Input("amount", default=1, min=1, max=4096),
|
|
],
|
|
outputs=[io.Image.Output()],
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, image, amount) -> io.NodeOutput:
|
|
return io.NodeOutput(image.repeat((amount, 1, 1, 1)))
|
|
|
|
|
|
class ResizeAndPadImage(io.ComfyNodeV3):
|
|
@classmethod
|
|
def define_schema(cls):
|
|
return io.SchemaV3(
|
|
node_id="ResizeAndPadImage_V3",
|
|
display_name="Resize and Pad Image _V3",
|
|
category="image/transform",
|
|
inputs=[
|
|
io.Image.Input("image"),
|
|
io.Int.Input("target_width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
|
|
io.Int.Input("target_height", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
|
|
io.Combo.Input("padding_color", options=["white", "black"]),
|
|
io.Combo.Input("interpolation", options=["area", "bicubic", "nearest-exact", "bilinear", "lanczos"]),
|
|
],
|
|
outputs=[io.Image.Output()],
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, image, target_width, target_height, padding_color, interpolation) -> io.NodeOutput:
|
|
batch_size, orig_height, orig_width, channels = image.shape
|
|
|
|
scale_w = target_width / orig_width
|
|
scale_h = target_height / orig_height
|
|
scale = min(scale_w, scale_h)
|
|
|
|
new_width = int(orig_width * scale)
|
|
new_height = int(orig_height * scale)
|
|
|
|
image_permuted = image.permute(0, 3, 1, 2)
|
|
|
|
resized = comfy.utils.common_upscale(image_permuted, new_width, new_height, interpolation, "disabled")
|
|
|
|
pad_value = 0.0 if padding_color == "black" else 1.0
|
|
padded = torch.full(
|
|
(batch_size, channels, target_height, target_width), pad_value, dtype=image.dtype, device=image.device
|
|
)
|
|
|
|
y_offset = (target_height - new_height) // 2
|
|
x_offset = (target_width - new_width) // 2
|
|
|
|
padded[:, :, y_offset : y_offset + new_height, x_offset : x_offset + new_width] = resized
|
|
|
|
return io.NodeOutput(padded.permute(0, 2, 3, 1))
|
|
|
|
|
|
class SaveAnimatedPNG(io.ComfyNodeV3):
|
|
@classmethod
|
|
def define_schema(cls):
|
|
return io.SchemaV3(
|
|
node_id="SaveAnimatedPNG_V3",
|
|
display_name="Save Animated PNG _V3",
|
|
category="image/animation",
|
|
inputs=[
|
|
io.Image.Input("images"),
|
|
io.String.Input("filename_prefix", default="ComfyUI"),
|
|
io.Float.Input("fps", default=6.0, min=0.01, max=1000.0, step=0.01),
|
|
io.Int.Input("compress_level", default=4, min=0, max=9),
|
|
],
|
|
hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
|
|
is_output_node=True,
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, images, fps, compress_level, filename_prefix="ComfyUI") -> io.NodeOutput:
|
|
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 = []
|
|
pil_images = []
|
|
for image in images:
|
|
img = Image.fromarray(np.clip(255.0 * image.cpu().numpy(), 0, 255).astype(np.uint8))
|
|
pil_images.append(img)
|
|
|
|
metadata = None
|
|
if not args.disable_metadata:
|
|
metadata = PngInfo()
|
|
if cls.hidden.prompt is not None:
|
|
metadata.add(
|
|
b"comf",
|
|
"prompt".encode("latin-1", "strict")
|
|
+ b"\0"
|
|
+ json.dumps(cls.hidden.prompt).encode("latin-1", "strict"),
|
|
after_idat=True,
|
|
)
|
|
if cls.hidden.extra_pnginfo is not None:
|
|
for x in cls.hidden.extra_pnginfo:
|
|
metadata.add(
|
|
b"comf",
|
|
x.encode("latin-1", "strict")
|
|
+ b"\0"
|
|
+ json.dumps(cls.hidden.extra_pnginfo[x]).encode("latin-1", "strict"),
|
|
after_idat=True,
|
|
)
|
|
|
|
file = f"{filename}_{counter:05}_.png"
|
|
pil_images[0].save(
|
|
os.path.join(full_output_folder, file),
|
|
pnginfo=metadata,
|
|
compress_level=compress_level,
|
|
save_all=True,
|
|
duration=int(1000.0 / fps),
|
|
append_images=pil_images[1:],
|
|
)
|
|
results.append(ui.SavedResult(file, subfolder, io.FolderType.output))
|
|
|
|
return io.NodeOutput(ui={"images": results, "animated": (True,)})
|
|
|
|
|
|
class SaveAnimatedWEBP(io.ComfyNodeV3):
|
|
COMPRESS_METHODS = {"default": 4, "fastest": 0, "slowest": 6}
|
|
|
|
@classmethod
|
|
def define_schema(cls):
|
|
return io.SchemaV3(
|
|
node_id="SaveAnimatedWEBP_V3",
|
|
display_name="Save Animated WEBP _V3",
|
|
category="image/animation",
|
|
inputs=[
|
|
io.Image.Input("images"),
|
|
io.String.Input("filename_prefix", default="ComfyUI"),
|
|
io.Float.Input("fps", default=6.0, min=0.01, max=1000.0, step=0.01),
|
|
io.Boolean.Input("lossless", default=True),
|
|
io.Int.Input("quality", default=80, min=0, max=100),
|
|
io.Combo.Input("method", options=list(cls.COMPRESS_METHODS.keys())),
|
|
# "num_frames": ("INT", {"default": 0, "min": 0, "max": 8192}),
|
|
],
|
|
hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
|
|
is_output_node=True,
|
|
)
|
|
|
|
@classmethod
|
|
def execute(cls, images, fps, filename_prefix, lossless, quality, method, num_frames=0) -> io.NodeOutput:
|
|
method = cls.COMPRESS_METHODS.get(method)
|
|
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 = []
|
|
pil_images = []
|
|
for image in images:
|
|
img = Image.fromarray(np.clip(255.0 * image.cpu().numpy(), 0, 255).astype(np.uint8))
|
|
pil_images.append(img)
|
|
|
|
metadata = pil_images[0].getexif()
|
|
if not args.disable_metadata:
|
|
if cls.hidden.prompt is not None:
|
|
metadata[0x0110] = "prompt:{}".format(json.dumps(cls.hidden.prompt))
|
|
if cls.hidden.extra_pnginfo is not None:
|
|
inital_exif = 0x010F
|
|
for x in cls.hidden.extra_pnginfo:
|
|
metadata[inital_exif] = "{}:{}".format(x, json.dumps(cls.hidden.extra_pnginfo[x]))
|
|
inital_exif -= 1
|
|
|
|
if num_frames == 0:
|
|
num_frames = len(pil_images)
|
|
|
|
for i in range(0, len(pil_images), num_frames):
|
|
file = f"{filename}_{counter:05}_.webp"
|
|
pil_images[i].save(
|
|
os.path.join(full_output_folder, file),
|
|
save_all=True,
|
|
duration=int(1000.0 / fps),
|
|
append_images=pil_images[i + 1 : i + num_frames],
|
|
exif=metadata,
|
|
lossless=lossless,
|
|
quality=quality,
|
|
method=method,
|
|
)
|
|
results.append(ui.SavedResult(file, subfolder, io.FolderType.output))
|
|
counter += 1
|
|
|
|
return io.NodeOutput(ui={"images": results, "animated": (num_frames != 1,)})
|
|
|
|
|
|
class SaveImage(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") -> io.NodeOutput:
|
|
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 = 255.0 * 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(ui.SavedResult(file, subfolder, io.FolderType.output))
|
|
counter += 1
|
|
|
|
return io.NodeOutput(ui={"images": results})
|
|
|
|
|
|
NODES_LIST: list[type[io.ComfyNodeV3]] = [
|
|
GetImageSize,
|
|
ImageAddNoise,
|
|
ImageCrop,
|
|
ImageFlip,
|
|
ImageFromBatch,
|
|
ImageRotate,
|
|
ImageStitch,
|
|
LoadImage,
|
|
LoadImageOutput,
|
|
PreviewImage,
|
|
RepeatImageBatch,
|
|
ResizeAndPadImage,
|
|
SaveAnimatedPNG,
|
|
SaveAnimatedWEBP,
|
|
SaveImage,
|
|
]
|