diff --git a/comfy_extras/v3/nodes_audio.py b/comfy_extras/v3/nodes_audio.py
index fc6d5a3b4..72c4b6c65 100644
--- a/comfy_extras/v3/nodes_audio.py
+++ b/comfy_extras/v3/nodes_audio.py
@@ -65,14 +65,14 @@ class EmptyLatentAudio_V3(io.ComfyNodeV3):
def execute(cls, seconds, batch_size) -> io.NodeOutput:
length = round((seconds * 44100 / 2048) / 2) * 2
latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device())
- return io.NodeOutput({"samples":latent, "type": "audio"})
+ return io.NodeOutput({"samples": latent, "type": "audio"})
class LoadAudio_V3(io.ComfyNodeV3):
@classmethod
def DEFINE_SCHEMA(cls):
return io.SchemaV3(
- node_id="LoadAudio_V3", # frontend expects "LoadAudio" to work
+ node_id="LoadAudio_V3", # frontend expects "LoadAudio" to work
display_name="Load Audio _V3", # frontend ignores "display_name" for this node
category="audio",
inputs=[
@@ -110,7 +110,7 @@ class PreviewAudio_V3(io.ComfyNodeV3):
@classmethod
def DEFINE_SCHEMA(cls):
return io.SchemaV3(
- node_id="PreviewAudio_V3", # frontend expects "PreviewAudio" to work
+ node_id="PreviewAudio_V3", # frontend expects "PreviewAudio" to work
display_name="Preview Audio _V3", # frontend ignores "display_name" for this node
category="audio",
inputs=[
@@ -129,7 +129,7 @@ class SaveAudioMP3_V3(io.ComfyNodeV3):
@classmethod
def DEFINE_SCHEMA(cls):
return io.SchemaV3(
- node_id="SaveAudioMP3_V3", # frontend expects "SaveAudioMP3" to work
+ node_id="SaveAudioMP3_V3", # frontend expects "SaveAudioMP3" to work
display_name="Save Audio(MP3) _V3", # frontend ignores "display_name" for this node
category="audio",
inputs=[
@@ -150,7 +150,7 @@ class SaveAudioOpus_V3(io.ComfyNodeV3):
@classmethod
def DEFINE_SCHEMA(cls):
return io.SchemaV3(
- node_id="SaveAudioOpus_V3", # frontend expects "SaveAudioOpus" to work
+ node_id="SaveAudioOpus_V3", # frontend expects "SaveAudioOpus" to work
display_name="Save Audio(Opus) _V3", # frontend ignores "display_name" for this node
category="audio",
inputs=[
@@ -171,7 +171,7 @@ class SaveAudio_V3(io.ComfyNodeV3):
@classmethod
def DEFINE_SCHEMA(cls):
return io.SchemaV3(
- node_id="SaveAudio_V3", # frontend expects "SaveAudio" to work
+ node_id="SaveAudio_V3", # frontend expects "SaveAudio" to work
display_name="Save Audio _V3", # frontend ignores "display_name" for this node
category="audio",
inputs=[
@@ -203,7 +203,7 @@ class VAEDecodeAudio_V3(io.ComfyNodeV3):
@classmethod
def execute(cls, vae, samples) -> io.NodeOutput:
audio = vae.decode(samples["samples"]).movedim(-1, 1)
- std = torch.std(audio, dim=[1,2], keepdim=True) * 5.0
+ std = torch.std(audio, dim=[1, 2], keepdim=True) * 5.0
std[std < 1.0] = 1.0
audio /= std
return io.NodeOutput({"waveform": audio, "sample_rate": 44100})
@@ -250,7 +250,7 @@ def _save_audio(cls, audio, filename_prefix="ComfyUI", format="flac", quality="1
OPUS_RATES = [8000, 12000, 16000, 24000, 48000]
results = []
- for (batch_number, waveform) in enumerate(audio["waveform"].cpu()):
+ for batch_number, waveform in enumerate(audio["waveform"].cpu()):
filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
file = f"{filename_with_batch_num}_{counter:05}_.{format}"
output_path = os.path.join(full_output_folder, file)
@@ -277,7 +277,7 @@ def _save_audio(cls, audio, filename_prefix="ComfyUI", format="flac", quality="1
# Create output with specified format
output_buffer = BytesIO()
- output_container = av.open(output_buffer, mode='w', format=format)
+ output_container = av.open(output_buffer, mode="w", format=format)
# Set metadata on the container
for key, value in metadata.items():
@@ -299,19 +299,19 @@ def _save_audio(cls, audio, filename_prefix="ComfyUI", format="flac", quality="1
elif format == "mp3":
out_stream = output_container.add_stream("libmp3lame", rate=sample_rate)
if quality == "V0":
- #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
+ # 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
out_stream.codec_context.qscale = 1
elif quality == "128k":
out_stream.bit_rate = 128000
elif quality == "320k":
out_stream.bit_rate = 320000
- else: # format == "flac":
+ else: # format == "flac":
out_stream = output_container.add_stream("flac", rate=sample_rate)
frame = av.AudioFrame.from_ndarray(
waveform.movedim(0, 1).reshape(1, -1).float().numpy(),
- format='flt',
- layout='mono' if waveform.shape[0] == 1 else 'stereo',
+ format="flt",
+ layout="mono" if waveform.shape[0] == 1 else "stereo",
)
frame.sample_rate = sample_rate
frame.pts = 0
@@ -325,7 +325,7 @@ def _save_audio(cls, audio, filename_prefix="ComfyUI", format="flac", quality="1
# Write the output to file
output_buffer.seek(0)
- with open(output_path, 'wb') as f:
+ with open(output_path, "wb") as f:
f.write(output_buffer.getbuffer())
results.append(ui.SavedResult(file, subfolder, io.FolderType.output))
diff --git a/comfy_extras/v3/nodes_controlnet.py b/comfy_extras/v3/nodes_controlnet.py
index 12d91a1ce..528acf0fe 100644
--- a/comfy_extras/v3/nodes_controlnet.py
+++ b/comfy_extras/v3/nodes_controlnet.py
@@ -1,5 +1,5 @@
-from comfy.cldm.control_types import UNION_CONTROLNET_TYPES
import comfy.utils
+from comfy.cldm.control_types import UNION_CONTROLNET_TYPES
from comfy_api.v3 import io
@@ -27,11 +27,13 @@ class ControlNetApplyAdvanced_V3(io.ComfyNodeV3):
)
@classmethod
- def execute(cls, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None, extra_concat=[]) -> io.NodeOutput:
+ def execute(
+ cls, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None, extra_concat=[]
+ ) -> io.NodeOutput:
if strength == 0:
return io.NodeOutput(positive, negative)
- control_hint = image.movedim(-1,1)
+ control_hint = image.movedim(-1, 1)
cnets = {}
out = []
@@ -40,16 +42,18 @@ class ControlNetApplyAdvanced_V3(io.ComfyNodeV3):
for t in conditioning:
d = t[1].copy()
- prev_cnet = d.get('control', None)
+ prev_cnet = d.get("control", None)
if prev_cnet in cnets:
c_net = cnets[prev_cnet]
else:
- c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae=vae, extra_concat=extra_concat)
+ c_net = control_net.copy().set_cond_hint(
+ control_hint, strength, (start_percent, end_percent), vae=vae, extra_concat=extra_concat
+ )
c_net.set_previous_controlnet(prev_cnet)
cnets[prev_cnet] = c_net
- d['control'] = c_net
- d['control_apply_to_uncond'] = False
+ d["control"] = c_net
+ d["control_apply_to_uncond"] = False
n = [t[0], d]
c.append(n)
out.append(c)
@@ -107,7 +111,9 @@ class ControlNetInpaintingAliMamaApply_V3(ControlNetApplyAdvanced_V3):
)
@classmethod
- def execute(cls, positive, negative, control_net, vae, image, mask, strength, start_percent, end_percent) -> io.NodeOutput:
+ def execute(
+ cls, positive, negative, control_net, vae, image, mask, strength, start_percent, end_percent
+ ) -> io.NodeOutput:
extra_concat = []
if control_net.concat_mask:
mask = 1.0 - mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1]))
@@ -115,7 +121,17 @@ class ControlNetInpaintingAliMamaApply_V3(ControlNetApplyAdvanced_V3):
image = image * mask_apply.movedim(1, -1).repeat(1, 1, 1, image.shape[3])
extra_concat = [mask]
- return super().execute(positive, negative, control_net, image, strength, start_percent, end_percent, vae=vae, extra_concat=extra_concat)
+ return super().execute(
+ positive,
+ negative,
+ control_net,
+ image,
+ strength,
+ start_percent,
+ end_percent,
+ vae=vae,
+ extra_concat=extra_concat,
+ )
NODES_LIST: list[type[io.ComfyNodeV3]] = [
diff --git a/comfy_extras/v3/nodes_images.py b/comfy_extras/v3/nodes_images.py
index 81790001e..a13be8a80 100644
--- a/comfy_extras/v3/nodes_images.py
+++ b/comfy_extras/v3/nodes_images.py
@@ -1,16 +1,16 @@
+import hashlib
import json
import os
-import torch
-import hashlib
import numpy as np
+import torch
from PIL import Image, ImageOps, ImageSequence
from PIL.PngImagePlugin import PngInfo
-from comfy_api.v3 import io, ui
-from comfy.cli_args import args
import folder_paths
import node_helpers
+from comfy.cli_args import args
+from comfy_api.v3 import io, ui
class SaveImage_V3(io.ComfyNodeV3):
@@ -29,7 +29,8 @@ class SaveImage_V3(io.ComfyNodeV3):
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.",
+ 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],
@@ -42,8 +43,8 @@ class SaveImage_V3(io.ComfyNodeV3):
filename_prefix, folder_paths.get_output_directory(), images[0].shape[1], images[0].shape[0]
)
results = []
- for (batch_number, image) in enumerate(images):
- i = 255. * image.cpu().numpy()
+ 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:
@@ -82,13 +83,13 @@ class SaveAnimatedPNG_V3(io.ComfyNodeV3):
@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])
+ 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. * image.cpu().numpy(), 0, 255).astype(np.uint8))
+ img = Image.fromarray(np.clip(255.0 * image.cpu().numpy(), 0, 255).astype(np.uint8))
pil_images.append(img)
metadata = None
@@ -96,19 +97,34 @@ class SaveAnimatedPNG_V3(io.ComfyNodeV3):
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
+ 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
+ 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:])
+ 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,) })
+ return io.NodeOutput(ui={"images": results, "animated": (True,)})
class SaveAnimatedWEBP_V3(io.ComfyNodeV3):
@@ -136,11 +152,13 @@ class SaveAnimatedWEBP_V3(io.ComfyNodeV3):
@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])
+ 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. * image.cpu().numpy(), 0, 255).astype(np.uint8))
+ img = Image.fromarray(np.clip(255.0 * image.cpu().numpy(), 0, 255).astype(np.uint8))
pil_images.append(img)
metadata = pil_images[0].getexif()
@@ -148,7 +166,7 @@ class SaveAnimatedWEBP_V3(io.ComfyNodeV3):
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
+ 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
@@ -160,8 +178,9 @@ class SaveAnimatedWEBP_V3(io.ComfyNodeV3):
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],
+ save_all=True,
+ duration=int(1000.0 / fps),
+ append_images=pil_images[i + 1 : i + num_frames],
exif=metadata,
lossless=lossless,
quality=quality,
@@ -228,12 +247,12 @@ class LoadImage_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")
@@ -246,14 +265,14 @@ class LoadImage_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")
+ mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
output_images.append(image)
output_masks.append(mask.unsqueeze(0))
@@ -270,7 +289,7 @@ class LoadImage_V3(io.ComfyNodeV3):
def fingerprint_inputs(s, image):
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()
@@ -288,8 +307,8 @@ class LoadImageOutput_V3(io.ComfyNodeV3):
node_id="LoadImageOutput_V3",
display_name="Load Image (from Outputs) _V3",
description="Load an image from the output folder. "
- "When the refresh button is clicked, the node will update the image list "
- "and automatically select the first image, allowing for easy iteration.",
+ "When the refresh button is clicked, the node will update the image list "
+ "and automatically select the first image, allowing for easy iteration.",
category="image",
inputs=[
io.Combo.Input(
@@ -317,12 +336,12 @@ class LoadImageOutput_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")
@@ -335,12 +354,12 @@ class LoadImageOutput_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)
@@ -359,7 +378,7 @@ class LoadImageOutput_V3(io.ComfyNodeV3):
def fingerprint_inputs(s, image):
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()
diff --git a/comfy_extras/v3/nodes_primitive.py b/comfy_extras/v3/nodes_primitive.py
new file mode 100644
index 000000000..debfa60d5
--- /dev/null
+++ b/comfy_extras/v3/nodes_primitive.py
@@ -0,0 +1,104 @@
+from __future__ import annotations
+
+import sys
+
+from comfy_api.v3 import io
+
+
+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,
+]
diff --git a/comfy_extras/v3/nodes_stable_cascade.py b/comfy_extras/v3/nodes_stable_cascade.py
index 36d7e3321..4693ad9eb 100644
--- a/comfy_extras/v3/nodes_stable_cascade.py
+++ b/comfy_extras/v3/nodes_stable_cascade.py
@@ -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 .
+You should have received a copy of the GNU General Public License
+along with this program. If not, see .
"""
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})
@@ -90,7 +90,7 @@ class StableCascade_StageB_Conditioning_V3(io.ComfyNodeV3):
io.Latent.Input("stage_c"),
],
outputs=[
- io.Conditioning.Output(),
+ io.Conditioning.Output(),
],
)
@@ -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])
diff --git a/comfy_extras/v3/nodes_webcam.py b/comfy_extras/v3/nodes_webcam.py
index 3a4cf8da0..6b65fa7d9 100644
--- a/comfy_extras/v3/nodes_webcam.py
+++ b/comfy_extras/v3/nodes_webcam.py
@@ -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()
diff --git a/nodes.py b/nodes.py
index d38f2e810..11a1b85cb 100644
--- a/nodes.py
+++ b/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",
]
diff --git a/pyproject.toml b/pyproject.toml
index 96ead2157..69e84a997 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -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