mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-07-27 16:26:39 +00:00
Merge branch 'v3-definition' into v3-definition-wip
This commit is contained in:
kosinkadink1@gmail.com
commit
751c57c853
@ -7,212 +7,353 @@ 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 SaveImage_V3(io.ComfyNodeV3):
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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="SaveImage_V3",
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display_name="Save Image _V3",
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description="Saves the input images to your ComfyUI output directory.",
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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(
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"images",
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tooltip="The images to save.",
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),
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io.String.Input(
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"filename_prefix",
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default="ComfyUI",
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tooltip="The prefix for the file to save. This may include formatting information "
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"such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.",
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),
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io.Image.Input("image"),
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],
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hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
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is_output_node=True,
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)
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@classmethod
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def execute(cls, images, filename_prefix="ComfyUI") -> io.NodeOutput:
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full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
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filename_prefix, folder_paths.get_output_directory(), images[0].shape[1], images[0].shape[0]
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)
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results = []
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for batch_number, image in enumerate(images):
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i = 255.0 * image.cpu().numpy()
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img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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metadata = None
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if not args.disable_metadata:
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metadata = PngInfo()
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if cls.hidden.prompt is not None:
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metadata.add_text("prompt", json.dumps(cls.hidden.prompt))
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if cls.hidden.extra_pnginfo is not None:
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for x in cls.hidden.extra_pnginfo:
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metadata.add_text(x, json.dumps(cls.hidden.extra_pnginfo[x]))
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filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
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file = f"{filename_with_batch_num}_{counter:05}_.png"
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img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4)
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results.append(ui.SavedResult(file, subfolder, io.FolderType.output))
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counter += 1
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return io.NodeOutput(ui={"images": results})
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class SaveAnimatedPNG_V3(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="SaveAnimatedPNG_V3",
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display_name="Save Animated PNG _V3",
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category="image/animation",
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inputs=[
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io.Image.Input("images"),
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io.String.Input("filename_prefix", default="ComfyUI"),
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io.Float.Input("fps", default=6.0, min=0.01, max=1000.0, step=0.01),
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io.Int.Input("compress_level", default=4, min=0, max=9),
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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.prompt, io.Hidden.extra_pnginfo],
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is_output_node=True,
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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, images, fps, compress_level, filename_prefix="ComfyUI") -> io.NodeOutput:
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full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
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filename_prefix, folder_paths.get_output_directory(), images[0].shape[1], images[0].shape[0]
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)
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results = []
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pil_images = []
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for image in images:
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img = Image.fromarray(np.clip(255.0 * image.cpu().numpy(), 0, 255).astype(np.uint8))
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pil_images.append(img)
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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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metadata = None
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if not args.disable_metadata:
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metadata = PngInfo()
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if cls.hidden.prompt is not None:
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metadata.add(
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b"comf",
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"prompt".encode("latin-1", "strict")
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+ b"\0"
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+ json.dumps(cls.hidden.prompt).encode("latin-1", "strict"),
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after_idat=True,
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)
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if cls.hidden.extra_pnginfo is not None:
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for x in cls.hidden.extra_pnginfo:
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metadata.add(
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b"comf",
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x.encode("latin-1", "strict")
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+ b"\0"
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+ json.dumps(cls.hidden.extra_pnginfo[x]).encode("latin-1", "strict"),
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after_idat=True,
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)
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file = f"{filename}_{counter:05}_.png"
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pil_images[0].save(
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os.path.join(full_output_folder, file),
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pnginfo=metadata,
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compress_level=compress_level,
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save_all=True,
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duration=int(1000.0 / fps),
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append_images=pil_images[1:],
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)
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results.append(ui.SavedResult(file, subfolder, io.FolderType.output))
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return io.NodeOutput(ui={"images": results, "animated": (True,)})
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class SaveAnimatedWEBP_V3(io.ComfyNodeV3):
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COMPRESS_METHODS = {"default": 4, "fastest": 0, "slowest": 6}
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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="SaveAnimatedWEBP_V3",
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display_name="Save Animated WEBP _V3",
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category="image/animation",
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inputs=[
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io.Image.Input("images"),
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io.String.Input("filename_prefix", default="ComfyUI"),
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io.Float.Input("fps", default=6.0, min=0.01, max=1000.0, step=0.01),
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io.Boolean.Input("lossless", default=True),
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io.Int.Input("quality", default=80, min=0, max=100),
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io.Combo.Input("method", options=list(cls.COMPRESS_METHODS.keys())),
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# "num_frames": ("INT", {"default": 0, "min": 0, "max": 8192}),
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],
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hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
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is_output_node=True,
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)
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@classmethod
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def execute(cls, images, fps, filename_prefix, lossless, quality, method, num_frames=0) -> io.NodeOutput:
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method = cls.COMPRESS_METHODS.get(method)
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full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
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filename_prefix, folder_paths.get_output_directory(), images[0].shape[1], images[0].shape[0]
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)
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results = []
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pil_images = []
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for image in images:
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img = Image.fromarray(np.clip(255.0 * image.cpu().numpy(), 0, 255).astype(np.uint8))
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pil_images.append(img)
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metadata = pil_images[0].getexif()
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if not args.disable_metadata:
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if cls.hidden.prompt is not None:
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metadata[0x0110] = "prompt:{}".format(json.dumps(cls.hidden.prompt))
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if cls.hidden.extra_pnginfo is not None:
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inital_exif = 0x010F
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for x in cls.hidden.extra_pnginfo:
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metadata[inital_exif] = "{}:{}".format(x, json.dumps(cls.hidden.extra_pnginfo[x]))
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inital_exif -= 1
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if num_frames == 0:
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num_frames = len(pil_images)
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for i in range(0, len(pil_images), num_frames):
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file = f"{filename}_{counter:05}_.webp"
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pil_images[i].save(
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os.path.join(full_output_folder, file),
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save_all=True,
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duration=int(1000.0 / fps),
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append_images=pil_images[i + 1 : i + num_frames],
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exif=metadata,
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lossless=lossless,
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quality=quality,
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method=method,
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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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results.append(ui.SavedResult(file, subfolder, io.FolderType.output))
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counter += 1
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return io.NodeOutput(ui={"images": results, "animated": (num_frames != 1,)})
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return io.NodeOutput(width, height, batch_size)
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class PreviewImage_V3(io.ComfyNodeV3):
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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="PreviewImage_V3",
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display_name="Preview Image _V3",
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description="Preview the input images.",
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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("images", tooltip="The images to preview."),
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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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hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
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is_output_node=True,
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outputs=[io.Image.Output()],
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)
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@classmethod
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def execute(cls, images) -> io.NodeOutput:
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return io.NodeOutput(ui=ui.PreviewImage(images, cls=cls))
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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 LoadImage_V3(io.ComfyNodeV3):
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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=[
|
||||
io.Image.Input("image1"),
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io.Combo.Input("direction", options=["right", "down", "left", "up"], default="right"),
|
||||
io.Boolean.Input("match_image_size", default=True),
|
||||
io.Int.Input("spacing_width", default=0, min=0, max=1024, step=2),
|
||||
io.Combo.Input("spacing_color", options=["white", "black", "red", "green", "blue"], default="white"),
|
||||
io.Image.Input("image2", optional=True),
|
||||
],
|
||||
outputs=[io.Image.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, image1, direction, match_image_size, spacing_width, spacing_color, image2=None) -> io.NodeOutput:
|
||||
if image2 is None:
|
||||
return io.NodeOutput(image1)
|
||||
|
||||
# Handle batch size differences
|
||||
if image1.shape[0] != image2.shape[0]:
|
||||
max_batch = max(image1.shape[0], image2.shape[0])
|
||||
if image1.shape[0] < max_batch:
|
||||
image1 = torch.cat([image1, image1[-1:].repeat(max_batch - image1.shape[0], 1, 1, 1)])
|
||||
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
|
||||
if match_image_size:
|
||||
h1, w1 = image1.shape[1:3]
|
||||
h2, w2 = image2.shape[1:3]
|
||||
aspect_ratio = w2 / h2
|
||||
|
||||
if direction in ["left", "right"]:
|
||||
target_h, target_w = h1, int(h1 * aspect_ratio)
|
||||
else: # up, down
|
||||
target_w, target_h = w1, int(w1 / aspect_ratio)
|
||||
|
||||
image2 = comfy.utils.common_upscale(
|
||||
image2.movedim(-1, 1), target_w, target_h, "lanczos", "disabled"
|
||||
).movedim(1, -1)
|
||||
|
||||
color_map = {
|
||||
"white": 1.0,
|
||||
"black": 0.0,
|
||||
"red": (1.0, 0.0, 0.0),
|
||||
"green": (0.0, 1.0, 0.0),
|
||||
"blue": (0.0, 0.0, 1.0),
|
||||
}
|
||||
|
||||
color_val = color_map[spacing_color]
|
||||
|
||||
# When not matching sizes, pad to align non-concat dimensions
|
||||
if not match_image_size:
|
||||
h1, w1 = image1.shape[1:3]
|
||||
h2, w2 = image2.shape[1:3]
|
||||
pad_value = 0.0
|
||||
if not isinstance(color_val, tuple):
|
||||
pad_value = color_val
|
||||
|
||||
if direction in ["left", "right"]:
|
||||
# For horizontal concat, pad heights to match
|
||||
if h1 != h2:
|
||||
target_h = max(h1, h2)
|
||||
if h1 < target_h:
|
||||
pad_h = target_h - h1
|
||||
pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2
|
||||
image1 = torch.nn.functional.pad(
|
||||
image1, (0, 0, 0, 0, pad_top, pad_bottom), mode="constant", value=pad_value
|
||||
)
|
||||
if h2 < target_h:
|
||||
pad_h = target_h - h2
|
||||
pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2
|
||||
image2 = torch.nn.functional.pad(
|
||||
image2, (0, 0, 0, 0, pad_top, pad_bottom), mode="constant", value=pad_value
|
||||
)
|
||||
else: # up, down
|
||||
# For vertical concat, pad widths to match
|
||||
if w1 != w2:
|
||||
target_w = max(w1, w2)
|
||||
if w1 < target_w:
|
||||
pad_w = target_w - w1
|
||||
pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2
|
||||
image1 = torch.nn.functional.pad(
|
||||
image1, (0, 0, pad_left, pad_right), mode="constant", value=pad_value
|
||||
)
|
||||
if w2 < target_w:
|
||||
pad_w = target_w - w2
|
||||
pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2
|
||||
image2 = torch.nn.functional.pad(
|
||||
image2, (0, 0, pad_left, pad_right), mode="constant", value=pad_value
|
||||
)
|
||||
|
||||
# Ensure same number of channels
|
||||
if image1.shape[-1] != image2.shape[-1]:
|
||||
max_channels = max(image1.shape[-1], image2.shape[-1])
|
||||
if image1.shape[-1] < max_channels:
|
||||
image1 = torch.cat(
|
||||
[
|
||||
image1,
|
||||
torch.ones(
|
||||
*image1.shape[:-1],
|
||||
max_channels - image1.shape[-1],
|
||||
device=image1.device,
|
||||
),
|
||||
],
|
||||
dim=-1,
|
||||
)
|
||||
if image2.shape[-1] < max_channels:
|
||||
image2 = torch.cat(
|
||||
[
|
||||
image2,
|
||||
torch.ones(
|
||||
*image2.shape[:-1],
|
||||
max_channels - image2.shape[-1],
|
||||
device=image2.device,
|
||||
),
|
||||
],
|
||||
dim=-1,
|
||||
)
|
||||
|
||||
# Add spacing if specified
|
||||
if spacing_width > 0:
|
||||
spacing_width = spacing_width + (spacing_width % 2) # Ensure even
|
||||
|
||||
if direction in ["left", "right"]:
|
||||
spacing_shape = (
|
||||
image1.shape[0],
|
||||
max(image1.shape[1], image2.shape[1]),
|
||||
spacing_width,
|
||||
image1.shape[-1],
|
||||
)
|
||||
else:
|
||||
spacing_shape = (
|
||||
image1.shape[0],
|
||||
spacing_width,
|
||||
max(image1.shape[2], image2.shape[2]),
|
||||
image1.shape[-1],
|
||||
)
|
||||
|
||||
spacing = torch.full(spacing_shape, 0.0, device=image1.device)
|
||||
if isinstance(color_val, tuple):
|
||||
for i, c in enumerate(color_val):
|
||||
if i < spacing.shape[-1]:
|
||||
spacing[..., i] = c
|
||||
if spacing.shape[-1] == 4: # Add alpha
|
||||
spacing[..., 3] = 1.0
|
||||
else:
|
||||
spacing[..., : min(3, spacing.shape[-1])] = color_val
|
||||
if spacing.shape[-1] == 4:
|
||||
spacing[..., 3] = 1.0
|
||||
|
||||
# Concatenate images
|
||||
images = [image2, image1] if direction in ["left", "up"] else [image1, image2]
|
||||
if spacing_width > 0:
|
||||
images.insert(1, spacing)
|
||||
|
||||
concat_dim = 2 if direction in ["left", "right"] else 1
|
||||
return io.NodeOutput(torch.cat(images, dim=concat_dim))
|
||||
|
||||
|
||||
class LoadImage(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def DEFINE_SCHEMA(cls):
|
||||
return io.SchemaV3(
|
||||
@ -300,7 +441,7 @@ class LoadImage_V3(io.ComfyNodeV3):
|
||||
return True
|
||||
|
||||
|
||||
class LoadImageOutput_V3(io.ComfyNodeV3):
|
||||
class LoadImageOutput(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def DEFINE_SCHEMA(cls):
|
||||
return io.SchemaV3(
|
||||
@ -389,11 +530,283 @@ class LoadImageOutput_V3(io.ComfyNodeV3):
|
||||
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]] = [
|
||||
SaveAnimatedPNG_V3,
|
||||
SaveAnimatedWEBP_V3,
|
||||
SaveImage_V3,
|
||||
PreviewImage_V3,
|
||||
LoadImage_V3,
|
||||
LoadImageOutput_V3,
|
||||
GetImageSize,
|
||||
ImageAddNoise,
|
||||
ImageCrop,
|
||||
ImageFlip,
|
||||
ImageFromBatch,
|
||||
ImageRotate,
|
||||
ImageStitch,
|
||||
LoadImage,
|
||||
LoadImageOutput,
|
||||
PreviewImage,
|
||||
RepeatImageBatch,
|
||||
ResizeAndPadImage,
|
||||
SaveAnimatedPNG,
|
||||
SaveAnimatedWEBP,
|
||||
SaveImage,
|
||||
]
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user