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Revert "V3 nodes: stable cascade" (#8873)

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218
comfy_extras/v3/nodes_stable_cascade.py
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@ -1,218 +0,0 @@
"""
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 ({
"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 ({
"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(
"CONDITIONING",
),
],
)
@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 (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 (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,
]

1
nodes.py
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@ -2302,7 +2302,6 @@ def init_builtin_extra_nodes():
"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 = []