mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-07-28 08:46:35 +00:00
Merge branch 'v3-definition' into v3-definition-wip
This commit is contained in:
kosinkadink1@gmail.com
commit
039a64be76
125
comfy_extras/v3/nodes_controlnet.py
Normal file
125
comfy_extras/v3/nodes_controlnet.py
Normal file
@ -0,0 +1,125 @@
|
|||||||
|
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
143
comfy_extras/v3/nodes_stable_cascade.py
Normal file
@ -0,0 +1,143 @@
|
|||||||
|
"""
|
||||||
|
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
2
nodes.py
@ -2299,9 +2299,11 @@ def init_builtin_extra_nodes():
|
|||||||
"nodes_tcfg.py",
|
"nodes_tcfg.py",
|
||||||
"nodes_v3_test.py",
|
"nodes_v3_test.py",
|
||||||
"nodes_v1_test.py",
|
"nodes_v1_test.py",
|
||||||
|
"v3/nodes_controlnet.py",
|
||||||
"v3/nodes_images.py",
|
"v3/nodes_images.py",
|
||||||
"v3/nodes_mask.py",
|
"v3/nodes_mask.py",
|
||||||
"v3/nodes_webcam.py",
|
"v3/nodes_webcam.py",
|
||||||
|
"v3/nodes_stable_cascade.py",
|
||||||
]
|
]
|
||||||
|
|
||||||
import_failed = []
|
import_failed = []
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user