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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
class StableCascade_EmptyLatentImage:
def __init__(self, device="cpu"):
self.device = device
@classmethod
def INPUT_TYPES(s):
return {"required": {
"width": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}),
"compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})
}}
RETURN_TYPES = ("LATENT", "LATENT")
RETURN_NAMES = ("stage_c", "stage_b")
FUNCTION = "generate"
CATEGORY = "latent/stable_cascade"
def generate(self, 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:
def __init__(self, device="cpu"):
self.device = device
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image": ("IMAGE",),
"vae": ("VAE", ),
"compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}),
}}
RETURN_TYPES = ("LATENT", "LATENT")
RETURN_NAMES = ("stage_c", "stage_b")
FUNCTION = "generate"
CATEGORY = "latent/stable_cascade"
def generate(self, 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:
@classmethod
def INPUT_TYPES(s):
return {"required": { "conditioning": ("CONDITIONING",),
"stage_c": ("LATENT",),
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "set_prior"
CATEGORY = "conditioning/stable_cascade"
def set_prior(self, 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:
def __init__(self, device="cpu"):
self.device = device
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image": ("IMAGE",),
"vae": ("VAE", ),
}}
RETURN_TYPES = ("IMAGE", "LATENT", "LATENT")
RETURN_NAMES = ("controlnet_input", "stage_c", "stage_b")
FUNCTION = "generate"
CATEGORY = "_for_testing/stable_cascade"
def generate(self, 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,
})
NODE_CLASS_MAPPINGS = {
"StableCascade_EmptyLatentImage": StableCascade_EmptyLatentImage,
"StableCascade_StageB_Conditioning": StableCascade_StageB_Conditioning,
"StableCascade_StageC_VAEEncode": StableCascade_StageC_VAEEncode,
"StableCascade_SuperResolutionControlnet": StableCascade_SuperResolutionControlnet,
}
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