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Revert "V3 nodes: stable cascade" (#8873)
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"""
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This file is part of ComfyUI.
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Copyright (C) 2024 Stability AI
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <https://www.gnu.org/licenses/>.
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"""
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import torch
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import nodes
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import comfy.utils
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from comfy_api.v3 import io
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class StableCascade_EmptyLatentImage_V3(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="StableCascade_EmptyLatentImage_V3",
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category="latent/stable_cascade",
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inputs=[
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io.Int.Input(
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"width",
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default=1024,
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min=256,
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max=nodes.MAX_RESOLUTION, step=8,
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),
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io.Int.Input(
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"height",
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default=1024,
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min=256,
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max=nodes.MAX_RESOLUTION,
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step=8,
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),
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io.Int.Input(
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"compression",
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default=42,
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min=4,
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max=128,
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step=1,
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),
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io.Int.Input(
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"batch_size",
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default=1,
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min=1,
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max=4096,
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),
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],
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outputs=[
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io.Latent.Output(
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"stage_c",
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display_name="stage_c",
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),
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io.Latent.Output(
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"stage_b",
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display_name="stage_b",
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),
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],
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)
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@classmethod
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def execute(cls, width, height, compression, batch_size=1):
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c_latent = torch.zeros([batch_size, 16, height // compression, width // compression])
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b_latent = torch.zeros([batch_size, 4, height // 4, width // 4])
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return ({
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"samples": c_latent,
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}, {
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"samples": b_latent,
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})
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class StableCascade_StageC_VAEEncode_V3(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="StableCascade_StageC_VAEEncode_V3",
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category="latent/stable_cascade",
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inputs=[
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io.Image.Input(
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"image",
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),
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io.Vae.Input(
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"vae",
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),
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io.Int.Input(
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"compression",
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default=42,
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min=4,
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max=128,
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step=1,
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),
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],
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outputs=[
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io.Latent.Output(
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"stage_c",
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display_name="stage_c",
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),
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io.Latent.Output(
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"stage_b",
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display_name="stage_b",
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),
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],
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)
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@classmethod
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def execute(cls, image, vae, compression):
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width = image.shape[-2]
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height = image.shape[-3]
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out_width = (width // compression) * vae.downscale_ratio
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out_height = (height // compression) * vae.downscale_ratio
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s = comfy.utils.common_upscale(image.movedim(-1,1), out_width, out_height, "bicubic", "center").movedim(1,-1)
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c_latent = vae.encode(s[:,:,:,:3])
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b_latent = torch.zeros([c_latent.shape[0], 4, (height // 8) * 2, (width // 8) * 2])
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return ({
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"samples": c_latent,
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}, {
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"samples": b_latent,
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})
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class StableCascade_StageB_Conditioning_V3(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="StableCascade_StageB_Conditioning_V3",
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category="conditioning/stable_cascade",
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inputs=[
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io.Conditioning.Input(
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"conditioning",
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),
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io.Latent.Input(
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"stage_c",
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),
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],
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outputs=[
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io.Conditioning.Output(
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"CONDITIONING",
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),
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],
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)
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@classmethod
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def execute(cls, conditioning, stage_c):
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c = []
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for t in conditioning:
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d = t[1].copy()
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d['stable_cascade_prior'] = stage_c['samples']
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n = [t[0], d]
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c.append(n)
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return (c, )
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class StableCascade_SuperResolutionControlnet_V3(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="StableCascade_SuperResolutionControlnet_V3",
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category="_for_testing/stable_cascade",
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is_experimental=True,
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inputs=[
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io.Image.Input(
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"image",
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),
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io.Vae.Input(
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"vae",
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),
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],
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outputs=[
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io.Image.Output(
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"controlnet_input",
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display_name="controlnet_input",
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),
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io.Latent.Output(
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"stage_c",
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display_name="stage_c",
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),
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io.Latent.Output(
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"stage_b",
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display_name="stage_b",
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),
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],
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)
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@classmethod
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def execute(cls, image, vae):
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width = image.shape[-2]
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height = image.shape[-3]
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batch_size = image.shape[0]
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controlnet_input = vae.encode(image[:,:,:,:3]).movedim(1, -1)
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c_latent = torch.zeros([batch_size, 16, height // 16, width // 16])
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b_latent = torch.zeros([batch_size, 4, height // 2, width // 2])
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return (controlnet_input, {
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"samples": c_latent,
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}, {
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"samples": b_latent,
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})
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NODES_LIST: list[type[io.ComfyNodeV3]] = [
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StableCascade_EmptyLatentImage_V3,
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StableCascade_StageB_Conditioning_V3,
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StableCascade_StageC_VAEEncode_V3,
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StableCascade_SuperResolutionControlnet_V3,
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]
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