mirrors/ComfyUI
1
0
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2025-07-28 16:56:38 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #8919 from bigcat88/v3/nodes/primitive

Browse Source 
[V3] primitive nodes
This commit is contained in:
Jedrzej Kosinski 2025-07-15 12:23:32 -07:00 committed by GitHub
commit 8d9e4c76dd
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
8 changed files with 242 additions and 96 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

28
comfy_extras/v3/nodes_audio.py
View File

@ -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))

34
comfy_extras/v3/nodes_controlnet.py
View File

@ -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]] = [

99
comfy_extras/v3/nodes_images.py
View File

@ -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()

104
comfy_extras/v3/nodes_primitive.py Normal file
View File

@ -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,
]

40
comfy_extras/v3/nodes_stable_cascade.py
View File

@ -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})
@ -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])

25
comfy_extras/v3/nodes_webcam.py
View File

@ -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
View File

@ -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",
]

7
pyproject.toml
View File

@ -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