mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-07-28 08:46:35 +00:00
V3: primitive nodes; additional ruff rules for V3 nodes
This commit is contained in:
bigcat88
parent
f687f8af7c
commit
c196dd5d0f
@ -65,7 +65,7 @@ class EmptyLatentAudio_V3(io.ComfyNodeV3):
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def execute(cls, seconds, batch_size) -> io.NodeOutput:
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length = round((seconds * 44100 / 2048) / 2) * 2
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latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device())
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return io.NodeOutput({"samples":latent, "type": "audio"})
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return io.NodeOutput({"samples": latent, "type": "audio"})
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class LoadAudio_V3(io.ComfyNodeV3):
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@ -203,7 +203,7 @@ class VAEDecodeAudio_V3(io.ComfyNodeV3):
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@classmethod
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def execute(cls, vae, samples) -> io.NodeOutput:
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audio = vae.decode(samples["samples"]).movedim(-1, 1)
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std = torch.std(audio, dim=[1,2], keepdim=True) * 5.0
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std = torch.std(audio, dim=[1, 2], keepdim=True) * 5.0
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std[std < 1.0] = 1.0
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audio /= std
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return io.NodeOutput({"waveform": audio, "sample_rate": 44100})
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@ -250,7 +250,7 @@ def _save_audio(cls, audio, filename_prefix="ComfyUI", format="flac", quality="1
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OPUS_RATES = [8000, 12000, 16000, 24000, 48000]
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results = []
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for (batch_number, waveform) in enumerate(audio["waveform"].cpu()):
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for batch_number, waveform in enumerate(audio["waveform"].cpu()):
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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}_.{format}"
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output_path = os.path.join(full_output_folder, file)
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@ -277,7 +277,7 @@ def _save_audio(cls, audio, filename_prefix="ComfyUI", format="flac", quality="1
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# Create output with specified format
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output_buffer = BytesIO()
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output_container = av.open(output_buffer, mode='w', format=format)
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output_container = av.open(output_buffer, mode="w", format=format)
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# Set metadata on the container
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for key, value in metadata.items():
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@ -299,7 +299,7 @@ def _save_audio(cls, audio, filename_prefix="ComfyUI", format="flac", quality="1
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elif format == "mp3":
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out_stream = output_container.add_stream("libmp3lame", rate=sample_rate)
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if quality == "V0":
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#TODO i would really love to support V3 and V5 but there doesn't seem to be a way to set the qscale level, the property below is a bool
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# TODO i would really love to support V3 and V5 but there doesn't seem to be a way to set the qscale level, the property below is a bool
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out_stream.codec_context.qscale = 1
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elif quality == "128k":
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out_stream.bit_rate = 128000
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@ -310,8 +310,8 @@ def _save_audio(cls, audio, filename_prefix="ComfyUI", format="flac", quality="1
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frame = av.AudioFrame.from_ndarray(
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waveform.movedim(0, 1).reshape(1, -1).float().numpy(),
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format='flt',
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layout='mono' if waveform.shape[0] == 1 else 'stereo',
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format="flt",
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layout="mono" if waveform.shape[0] == 1 else "stereo",
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)
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frame.sample_rate = sample_rate
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frame.pts = 0
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@ -325,7 +325,7 @@ def _save_audio(cls, audio, filename_prefix="ComfyUI", format="flac", quality="1
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# Write the output to file
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output_buffer.seek(0)
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with open(output_path, 'wb') as f:
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with open(output_path, "wb") as f:
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f.write(output_buffer.getbuffer())
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results.append(ui.SavedResult(file, subfolder, io.FolderType.output))
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@ -1,5 +1,5 @@
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from comfy.cldm.control_types import UNION_CONTROLNET_TYPES
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import comfy.utils
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from comfy.cldm.control_types import UNION_CONTROLNET_TYPES
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from comfy_api.v3 import io
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@ -27,11 +27,13 @@ class ControlNetApplyAdvanced_V3(io.ComfyNodeV3):
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)
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@classmethod
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def execute(cls, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None, extra_concat=[]) -> io.NodeOutput:
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def execute(
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cls, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None, extra_concat=[]
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) -> io.NodeOutput:
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if strength == 0:
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return io.NodeOutput(positive, negative)
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control_hint = image.movedim(-1,1)
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control_hint = image.movedim(-1, 1)
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cnets = {}
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out = []
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@ -40,16 +42,18 @@ class ControlNetApplyAdvanced_V3(io.ComfyNodeV3):
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for t in conditioning:
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d = t[1].copy()
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prev_cnet = d.get('control', None)
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prev_cnet = d.get("control", None)
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if prev_cnet in cnets:
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c_net = cnets[prev_cnet]
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else:
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c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae=vae, extra_concat=extra_concat)
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c_net = control_net.copy().set_cond_hint(
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control_hint, strength, (start_percent, end_percent), vae=vae, extra_concat=extra_concat
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)
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c_net.set_previous_controlnet(prev_cnet)
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cnets[prev_cnet] = c_net
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d['control'] = c_net
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d['control_apply_to_uncond'] = False
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d["control"] = c_net
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d["control_apply_to_uncond"] = False
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n = [t[0], d]
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c.append(n)
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out.append(c)
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@ -107,7 +111,9 @@ class ControlNetInpaintingAliMamaApply_V3(ControlNetApplyAdvanced_V3):
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)
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@classmethod
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def execute(cls, positive, negative, control_net, vae, image, mask, strength, start_percent, end_percent) -> io.NodeOutput:
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def execute(
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cls, positive, negative, control_net, vae, image, mask, strength, start_percent, end_percent
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) -> io.NodeOutput:
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extra_concat = []
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if control_net.concat_mask:
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mask = 1.0 - mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1]))
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@ -115,7 +121,17 @@ class ControlNetInpaintingAliMamaApply_V3(ControlNetApplyAdvanced_V3):
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image = image * mask_apply.movedim(1, -1).repeat(1, 1, 1, image.shape[3])
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extra_concat = [mask]
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return super().execute(positive, negative, control_net, image, strength, start_percent, end_percent, vae=vae, extra_concat=extra_concat)
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return super().execute(
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positive,
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negative,
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control_net,
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image,
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strength,
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start_percent,
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end_percent,
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vae=vae,
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extra_concat=extra_concat,
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)
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NODES_LIST: list[type[io.ComfyNodeV3]] = [
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@ -1,16 +1,16 @@
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import hashlib
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import json
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import os
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import torch
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import hashlib
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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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from comfy_api.v3 import io, ui
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from comfy.cli_args import args
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import folder_paths
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import node_helpers
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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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class SaveImage_V3(io.ComfyNodeV3):
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@ -29,7 +29,8 @@ class SaveImage_V3(io.ComfyNodeV3):
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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 such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.",
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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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],
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hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
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@ -42,8 +43,8 @@ class SaveImage_V3(io.ComfyNodeV3):
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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. * image.cpu().numpy()
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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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@ -82,13 +83,13 @@ class SaveAnimatedPNG_V3(io.ComfyNodeV3):
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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 = (
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folder_paths.get_save_image_path(filename_prefix, folder_paths.get_output_directory(), images[0].shape[1], images[0].shape[0])
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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. * image.cpu().numpy(), 0, 255).astype(np.uint8))
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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 = None
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@ -96,19 +97,34 @@ class SaveAnimatedPNG_V3(io.ComfyNodeV3):
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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", "prompt".encode("latin-1", "strict") + b"\0" + json.dumps(cls.hidden.prompt).encode("latin-1", "strict"), after_idat=True
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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", x.encode("latin-1", "strict") + b"\0" + json.dumps(cls.hidden.extra_pnginfo[x]).encode("latin-1", "strict"), after_idat=True
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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(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:])
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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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return io.NodeOutput(ui={"images": results, "animated": (True,)})
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class SaveAnimatedWEBP_V3(io.ComfyNodeV3):
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@ -136,11 +152,13 @@ class SaveAnimatedWEBP_V3(io.ComfyNodeV3):
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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(filename_prefix, folder_paths.get_output_directory(), images[0].shape[1], images[0].shape[0])
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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. * image.cpu().numpy(), 0, 255).astype(np.uint8))
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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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@ -148,7 +166,7 @@ class SaveAnimatedWEBP_V3(io.ComfyNodeV3):
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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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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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@ -160,8 +178,9 @@ class SaveAnimatedWEBP_V3(io.ComfyNodeV3):
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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, duration=int(1000.0/fps),
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append_images=pil_images[i + 1:i + num_frames],
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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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@ -228,12 +247,12 @@ class LoadImage_V3(io.ComfyNodeV3):
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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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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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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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@ -246,14 +265,14 @@ class LoadImage_V3(io.ComfyNodeV3):
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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. - 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. - torch.from_numpy(mask)
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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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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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@ -270,7 +289,7 @@ class LoadImage_V3(io.ComfyNodeV3):
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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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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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@ -317,12 +336,12 @@ class LoadImageOutput_V3(io.ComfyNodeV3):
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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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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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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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@ -335,12 +354,12 @@ class LoadImageOutput_V3(io.ComfyNodeV3):
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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. - 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. - torch.from_numpy(mask)
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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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@ -359,7 +378,7 @@ class LoadImageOutput_V3(io.ComfyNodeV3):
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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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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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104
comfy_extras/v3/nodes_primitive.py
Normal file
104
comfy_extras/v3/nodes_primitive.py
Normal file
@ -0,0 +1,104 @@
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from __future__ import annotations
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import sys
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from comfy_api.v3 import io
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||||
|
||||
class String_V3(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def DEFINE_SCHEMA(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="PrimitiveString_V3",
|
||||
display_name="String _V3",
|
||||
category="utils/primitive",
|
||||
inputs=[
|
||||
io.String.Input("value"),
|
||||
],
|
||||
outputs=[io.String.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, value: str) -> io.NodeOutput:
|
||||
return io.NodeOutput(value)
|
||||
|
||||
|
||||
class StringMultiline_V3(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def DEFINE_SCHEMA(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="PrimitiveStringMultiline_V3",
|
||||
display_name="String (Multiline) _V3",
|
||||
category="utils/primitive",
|
||||
inputs=[
|
||||
io.String.Input("value", multiline=True),
|
||||
],
|
||||
outputs=[io.String.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, value: str) -> io.NodeOutput:
|
||||
return io.NodeOutput(value)
|
||||
|
||||
|
||||
class Int_V3(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def DEFINE_SCHEMA(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="PrimitiveInt_V3",
|
||||
display_name="Int _V3",
|
||||
category="utils/primitive",
|
||||
inputs=[
|
||||
io.Int.Input("value", min=-sys.maxsize, max=sys.maxsize, control_after_generate=True),
|
||||
],
|
||||
outputs=[io.Int.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, value: int) -> io.NodeOutput:
|
||||
return io.NodeOutput(value)
|
||||
|
||||
|
||||
class Float_V3(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def DEFINE_SCHEMA(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="PrimitiveFloat_V3",
|
||||
display_name="Float _V3",
|
||||
category="utils/primitive",
|
||||
inputs=[
|
||||
io.Float.Input("value", min=-sys.maxsize, max=sys.maxsize),
|
||||
],
|
||||
outputs=[io.Float.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, value: float) -> io.NodeOutput:
|
||||
return io.NodeOutput(value)
|
||||
|
||||
|
||||
class Boolean_V3(io.ComfyNodeV3):
|
||||
@classmethod
|
||||
def DEFINE_SCHEMA(cls):
|
||||
return io.SchemaV3(
|
||||
node_id="PrimitiveBoolean_V3",
|
||||
display_name="Boolean _V3",
|
||||
category="utils/primitive",
|
||||
inputs=[
|
||||
io.Boolean.Input("value"),
|
||||
],
|
||||
outputs=[io.Boolean.Output()],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, value: bool) -> io.NodeOutput:
|
||||
return io.NodeOutput(value)
|
||||
|
||||
|
||||
NODES_LIST: list[type[io.ComfyNodeV3]] = [
|
||||
String_V3,
|
||||
StringMultiline_V3,
|
||||
Int_V3,
|
||||
Float_V3,
|
||||
Boolean_V3,
|
||||
]
|
||||
@ -1,25 +1,25 @@
|
||||
"""
|
||||
This file is part of ComfyUI.
|
||||
Copyright (C) 2024 Stability AI
|
||||
This file is part of ComfyUI.
|
||||
Copyright (C) 2024 Stability AI
|
||||
|
||||
This program is free software: you can redistribute it and/or modify
|
||||
it under the terms of the GNU General Public License as published by
|
||||
the Free Software Foundation, either version 3 of the License, or
|
||||
(at your option) any later version.
|
||||
This program is free software: you can redistribute it and/or modify
|
||||
it under the terms of the GNU General Public License as published by
|
||||
the Free Software Foundation, either version 3 of the License, or
|
||||
(at your option) any later version.
|
||||
|
||||
This program is distributed in the hope that it will be useful,
|
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
GNU General Public License for more details.
|
||||
This program is distributed in the hope that it will be useful,
|
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
GNU General Public License for more details.
|
||||
|
||||
You should have received a copy of the GNU General Public License
|
||||
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
You should have received a copy of the GNU General Public License
|
||||
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
||||
"""
|
||||
|
||||
import torch
|
||||
import nodes
|
||||
import comfy.utils
|
||||
|
||||
import comfy.utils
|
||||
import nodes
|
||||
from comfy_api.v3 import io
|
||||
|
||||
|
||||
@ -30,7 +30,7 @@ class StableCascade_EmptyLatentImage_V3(io.ComfyNodeV3):
|
||||
node_id="StableCascade_EmptyLatentImage_V3",
|
||||
category="latent/stable_cascade",
|
||||
inputs=[
|
||||
io.Int.Input("width", default=1024,min=256,max=nodes.MAX_RESOLUTION, step=8),
|
||||
io.Int.Input("width", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8),
|
||||
io.Int.Input("height", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8),
|
||||
io.Int.Input("compression", default=42, min=4, max=128, step=1),
|
||||
io.Int.Input("batch_size", default=1, min=1, max=4096),
|
||||
@ -72,9 +72,9 @@ class StableCascade_StageC_VAEEncode_V3(io.ComfyNodeV3):
|
||||
out_width = (width // compression) * vae.downscale_ratio
|
||||
out_height = (height // compression) * vae.downscale_ratio
|
||||
|
||||
s = comfy.utils.common_upscale(image.movedim(-1,1), out_width, out_height, "bicubic", "center").movedim(1,-1)
|
||||
s = comfy.utils.common_upscale(image.movedim(-1, 1), out_width, out_height, "bicubic", "center").movedim(1, -1)
|
||||
|
||||
c_latent = vae.encode(s[:,:,:,:3])
|
||||
c_latent = vae.encode(s[:, :, :, :3])
|
||||
b_latent = torch.zeros([c_latent.shape[0], 4, (height // 8) * 2, (width // 8) * 2])
|
||||
return io.NodeOutput({"samples": c_latent}, {"samples": b_latent})
|
||||
|
||||
@ -99,7 +99,7 @@ class StableCascade_StageB_Conditioning_V3(io.ComfyNodeV3):
|
||||
c = []
|
||||
for t in conditioning:
|
||||
d = t[1].copy()
|
||||
d['stable_cascade_prior'] = stage_c['samples']
|
||||
d["stable_cascade_prior"] = stage_c["samples"]
|
||||
n = [t[0], d]
|
||||
c.append(n)
|
||||
return io.NodeOutput(c)
|
||||
@ -128,7 +128,7 @@ class StableCascade_SuperResolutionControlnet_V3(io.ComfyNodeV3):
|
||||
width = image.shape[-2]
|
||||
height = image.shape[-3]
|
||||
batch_size = image.shape[0]
|
||||
controlnet_input = vae.encode(image[:,:,:,:3]).movedim(1, -1)
|
||||
controlnet_input = vae.encode(image[:, :, :, :3]).movedim(1, -1)
|
||||
|
||||
c_latent = torch.zeros([batch_size, 16, height // 16, width // 16])
|
||||
b_latent = torch.zeros([batch_size, 4, height // 2, width // 2])
|
||||
|
||||
@ -1,14 +1,13 @@
|
||||
import hashlib
|
||||
import torch
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from PIL import Image, ImageOps, ImageSequence
|
||||
|
||||
from comfy_api.v3 import io
|
||||
import nodes
|
||||
import folder_paths
|
||||
import node_helpers
|
||||
|
||||
import nodes
|
||||
from comfy_api.v3 import io
|
||||
|
||||
MAX_RESOLUTION = nodes.MAX_RESOLUTION
|
||||
|
||||
@ -51,12 +50,12 @@ class WebcamCapture_V3(io.ComfyNodeV3):
|
||||
output_masks = []
|
||||
w, h = None, None
|
||||
|
||||
excluded_formats = ['MPO']
|
||||
excluded_formats = ["MPO"]
|
||||
|
||||
for i in ImageSequence.Iterator(img):
|
||||
i = node_helpers.pillow(ImageOps.exif_transpose, i)
|
||||
|
||||
if i.mode == 'I':
|
||||
if i.mode == "I":
|
||||
i = i.point(lambda i: i * (1 / 255))
|
||||
image = i.convert("RGB")
|
||||
|
||||
@ -69,12 +68,12 @@ class WebcamCapture_V3(io.ComfyNodeV3):
|
||||
|
||||
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)
|
||||
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)
|
||||
@ -93,7 +92,7 @@ class WebcamCapture_V3(io.ComfyNodeV3):
|
||||
def fingerprint_inputs(s, image, width, height, capture_on_queue):
|
||||
image_path = folder_paths.get_annotated_filepath(image)
|
||||
m = hashlib.sha256()
|
||||
with open(image_path, 'rb') as f:
|
||||
with open(image_path, "rb") as f:
|
||||
m.update(f.read())
|
||||
return m.digest().hex()
|
||||
|
||||
|
||||
1
nodes.py
1
nodes.py
@ -2303,6 +2303,7 @@ def init_builtin_extra_nodes():
|
||||
"v3/nodes_controlnet.py",
|
||||
"v3/nodes_images.py",
|
||||
"v3/nodes_mask.py",
|
||||
"v3/nodes_primitive.py",
|
||||
"v3/nodes_webcam.py",
|
||||
"v3/nodes_stable_cascade.py",
|
||||
]
|
||||
|
||||
@ -12,6 +12,8 @@ documentation = "https://docs.comfy.org/"
|
||||
|
||||
[tool.ruff]
|
||||
lint.select = [
|
||||
"E", # pycodestyle errors
|
||||
"I", # isort
|
||||
"N805", # invalid-first-argument-name-for-method
|
||||
"S307", # suspicious-eval-usage
|
||||
"S102", # exec
|
||||
@ -22,3 +24,8 @@ lint.select = [
|
||||
"F",
|
||||
]
|
||||
exclude = ["*.ipynb"]
|
||||
line-length = 120
|
||||
lint.pycodestyle.ignore-overlong-task-comments = true
|
||||
|
||||
[tool.ruff.lint.per-file-ignores]
|
||||
"!comfy_extras/v3/*" = ["E", "I"] # enable these rules only for V3 nodes
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user