File size: 4,913 Bytes
932ae62 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
"""
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,
}
|