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Merge branch 'v3-definition' into v3-definition-wip

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kosinkadink1@gmail.com 2025-07-14 02:55:43 -05:00
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125
comfy_extras/v3/nodes_controlnet.py Normal file
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from comfy.cldm.control_types import UNION_CONTROLNET_TYPES
import comfy.utils
from comfy_api.v3 import io
class ControlNetApplyAdvanced_V3(io.ComfyNodeV3):
@classmethod
def DEFINE_SCHEMA(cls):
return io.SchemaV3(
node_id="ControlNetApplyAdvanced_V3",
display_name="Apply ControlNet _V3",
category="conditioning/controlnet",
inputs=[
io.Conditioning.Input("positive"),
io.Conditioning.Input("negative"),
io.ControlNet.Input("control_net"),
io.Image.Input("image"),
io.Float.Input("strength", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001),
io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001),
io.Vae.Input("vae", optional=True),
],
outputs=[
io.Conditioning.Output("positive_out", display_name="positive"),
io.Conditioning.Output("negative_out", display_name="negative"),
],
)
@classmethod
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)
cnets = {}
out = []
for conditioning in [positive, negative]:
c = []
for t in conditioning:
d = t[1].copy()
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.set_previous_controlnet(prev_cnet)
cnets[prev_cnet] = c_net
d['control'] = c_net
d['control_apply_to_uncond'] = False
n = [t[0], d]
c.append(n)
out.append(c)
return io.NodeOutput(out[0], out[1])
class SetUnionControlNetType_V3(io.ComfyNodeV3):
@classmethod
def DEFINE_SCHEMA(cls):
return io.SchemaV3(
node_id="SetUnionControlNetType_V3",
category="conditioning/controlnet",
inputs=[
io.ControlNet.Input("control_net"),
io.Combo.Input("type", options=["auto"] + list(UNION_CONTROLNET_TYPES.keys())),
],
outputs=[
io.ControlNet.Output("control_net_out"),
],
)
@classmethod
def execute(cls, control_net, type) -> io.NodeOutput:
control_net = control_net.copy()
type_number = UNION_CONTROLNET_TYPES.get(type, -1)
if type_number >= 0:
control_net.set_extra_arg("control_type", [type_number])
else:
control_net.set_extra_arg("control_type", [])
return io.NodeOutput(control_net)
class ControlNetInpaintingAliMamaApply_V3(ControlNetApplyAdvanced_V3):
@classmethod
def DEFINE_SCHEMA(cls):
return io.SchemaV3(
node_id="ControlNetInpaintingAliMamaApply_V3",
category="conditioning/controlnet",
inputs=[
io.Conditioning.Input("positive"),
io.Conditioning.Input("negative"),
io.ControlNet.Input("control_net"),
io.Vae.Input("vae"),
io.Image.Input("image"),
io.Mask.Input("mask"),
io.Float.Input("strength", default=1.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001),
io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001),
],
outputs=[
io.Conditioning.Output("positive_out", display_name="positive"),
io.Conditioning.Output("negative_out", display_name="negative"),
],
)
@classmethod
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]))
mask_apply = comfy.utils.common_upscale(mask, image.shape[2], image.shape[1], "bilinear", "center").round()
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)
NODES_LIST: list[type[io.ComfyNodeV3]] = [
ControlNetApplyAdvanced_V3,
SetUnionControlNetType_V3,
ControlNetInpaintingAliMamaApply_V3,
]

143
comfy_extras/v3/nodes_stable_cascade.py Normal file
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"""
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 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/>.
"""
import torch
import nodes
import comfy.utils
from comfy_api.v3 import io
class StableCascade_EmptyLatentImage_V3(io.ComfyNodeV3):
@classmethod
def DEFINE_SCHEMA(cls):
return io.SchemaV3(
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("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),
],
outputs=[
io.Latent.Output("stage_c", display_name="stage_c"),
io.Latent.Output("stage_b", display_name="stage_b"),
],
)
@classmethod
def execute(cls, width, height, compression, batch_size=1):
c_latent = torch.zeros([batch_size, 16, height // compression, width // compression])
b_latent = torch.zeros([batch_size, 4, height // 4, width // 4])
return io.NodeOutput({"samples": c_latent}, {"samples": b_latent})
class StableCascade_StageC_VAEEncode_V3(io.ComfyNodeV3):
@classmethod
def DEFINE_SCHEMA(cls):
return io.SchemaV3(
node_id="StableCascade_StageC_VAEEncode_V3",
category="latent/stable_cascade",
inputs=[
io.Image.Input("image"),
io.Vae.Input("vae"),
io.Int.Input("compression", default=42, min=4, max=128, step=1),
],
outputs=[
io.Latent.Output("stage_c", display_name="stage_c"),
io.Latent.Output("stage_b", display_name="stage_b"),
],
)
@classmethod
def execute(cls, image, vae, compression):
width = image.shape[-2]
height = image.shape[-3]
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)
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})
class StableCascade_StageB_Conditioning_V3(io.ComfyNodeV3):
@classmethod
def DEFINE_SCHEMA(cls):
return io.SchemaV3(
node_id="StableCascade_StageB_Conditioning_V3",
category="conditioning/stable_cascade",
inputs=[
io.Conditioning.Input("conditioning"),
io.Latent.Input("stage_c"),
],
outputs=[
io.Conditioning.Output(),
],
)
@classmethod
def execute(cls, conditioning, stage_c):
c = []
for t in conditioning:
d = t[1].copy()
d['stable_cascade_prior'] = stage_c['samples']
n = [t[0], d]
c.append(n)
return io.NodeOutput(c)
class StableCascade_SuperResolutionControlnet_V3(io.ComfyNodeV3):
@classmethod
def DEFINE_SCHEMA(cls):
return io.SchemaV3(
node_id="StableCascade_SuperResolutionControlnet_V3",
category="_for_testing/stable_cascade",
is_experimental=True,
inputs=[
io.Image.Input("image"),
io.Vae.Input("vae"),
],
outputs=[
io.Image.Output("controlnet_input", display_name="controlnet_input"),
io.Latent.Output("stage_c", display_name="stage_c"),
io.Latent.Output("stage_b", display_name="stage_b"),
],
)
@classmethod
def execute(cls, image, vae):
width = image.shape[-2]
height = image.shape[-3]
batch_size = image.shape[0]
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])
return io.NodeOutput(controlnet_input, {"samples": c_latent}, {"samples": b_latent})
NODES_LIST: list[type[io.ComfyNodeV3]] = [
StableCascade_EmptyLatentImage_V3,
StableCascade_StageB_Conditioning_V3,
StableCascade_StageC_VAEEncode_V3,
StableCascade_SuperResolutionControlnet_V3,
]

2
nodes.py
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@ -2299,9 +2299,11 @@ def init_builtin_extra_nodes():
"nodes_tcfg.py",
"nodes_v3_test.py",
"nodes_v1_test.py",
"v3/nodes_controlnet.py",
"v3/nodes_images.py",
"v3/nodes_mask.py",
"v3/nodes_webcam.py",
"v3/nodes_stable_cascade.py",
]
import_failed = []