Spaces:
Runtime error
Runtime error
File size: 10,252 Bytes
1ce42b4 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 |
# ComfyUI Node for Ultimate SD Upscale by Coyote-A: https://github.com/Coyote-A/ultimate-upscale-for-automatic1111
import logging
import torch
import comfy
from usdu_patch import usdu
from utils import tensor_to_pil, pil_to_tensor
from modules.processing import StableDiffusionProcessing
import modules.shared as shared
from modules.upscaler import UpscalerData
MAX_RESOLUTION = 8192
# The modes available for Ultimate SD Upscale
MODES = {
"Linear": usdu.USDUMode.LINEAR,
"Chess": usdu.USDUMode.CHESS,
"None": usdu.USDUMode.NONE,
}
# The seam fix modes
SEAM_FIX_MODES = {
"None": usdu.USDUSFMode.NONE,
"Band Pass": usdu.USDUSFMode.BAND_PASS,
"Half Tile": usdu.USDUSFMode.HALF_TILE,
"Half Tile + Intersections": usdu.USDUSFMode.HALF_TILE_PLUS_INTERSECTIONS,
}
def USDU_base_inputs():
required = [
("image", ("IMAGE",)),
# Sampling Params
("model", ("MODEL",)),
("positive", ("CONDITIONING",)),
("negative", ("CONDITIONING",)),
("vae", ("VAE",)),
("upscale_by", ("FLOAT", {"default": 2, "min": 0.05, "max": 4, "step": 0.05})),
("seed", ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff})),
("steps", ("INT", {"default": 20, "min": 1, "max": 10000, "step": 1})),
("cfg", ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0})),
("sampler_name", (comfy.samplers.KSampler.SAMPLERS,)),
("scheduler", (comfy.samplers.KSampler.SCHEDULERS,)),
("denoise", ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01})),
# Upscale Params
("upscale_model", ("UPSCALE_MODEL",)),
("mode_type", (list(MODES.keys()),)),
("tile_width", ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8})),
("tile_height", ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8})),
("mask_blur", ("INT", {"default": 8, "min": 0, "max": 64, "step": 1})),
("tile_padding", ("INT", {"default": 32, "min": 0, "max": MAX_RESOLUTION, "step": 8})),
# Seam fix params
("seam_fix_mode", (list(SEAM_FIX_MODES.keys()),)),
("seam_fix_denoise", ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})),
("seam_fix_width", ("INT", {"default": 64, "min": 0, "max": MAX_RESOLUTION, "step": 8})),
("seam_fix_mask_blur", ("INT", {"default": 8, "min": 0, "max": 64, "step": 1})),
("seam_fix_padding", ("INT", {"default": 16, "min": 0, "max": MAX_RESOLUTION, "step": 8})),
# Misc
("force_uniform_tiles", ("BOOLEAN", {"default": True})),
("tiled_decode", ("BOOLEAN", {"default": False})),
]
optional = []
return required, optional
def prepare_inputs(required: list, optional: list = None):
inputs = {}
if required:
inputs["required"] = {}
for name, type in required:
inputs["required"][name] = type
if optional:
inputs["optional"] = {}
for name, type in optional:
inputs["optional"][name] = type
return inputs
def remove_input(inputs: list, input_name: str):
for i, (n, _) in enumerate(inputs):
if n == input_name:
del inputs[i]
break
def rename_input(inputs: list, old_name: str, new_name: str):
for i, (n, t) in enumerate(inputs):
if n == old_name:
inputs[i] = (new_name, t)
break
class UltimateSDUpscale:
@classmethod
def INPUT_TYPES(s):
required, optional = USDU_base_inputs()
return prepare_inputs(required, optional)
RETURN_TYPES = ("IMAGE",)
FUNCTION = "upscale"
CATEGORY = "image/upscaling"
def upscale(self, image, model, positive, negative, vae, upscale_by, seed,
steps, cfg, sampler_name, scheduler, denoise, upscale_model,
mode_type, tile_width, tile_height, mask_blur, tile_padding,
seam_fix_mode, seam_fix_denoise, seam_fix_mask_blur,
seam_fix_width, seam_fix_padding, force_uniform_tiles, tiled_decode,
custom_sampler=None, custom_sigmas=None):
# Store params
self.tile_width = tile_width
self.tile_height = tile_height
self.mask_blur = mask_blur
self.tile_padding = tile_padding
self.seam_fix_width = seam_fix_width
self.seam_fix_denoise = seam_fix_denoise
self.seam_fix_padding = seam_fix_padding
self.seam_fix_mode = seam_fix_mode
self.mode_type = mode_type
self.upscale_by = upscale_by
self.seam_fix_mask_blur = seam_fix_mask_blur
#
# Set up A1111 patches
#
# Upscaler
# An object that the script works with
shared.sd_upscalers[0] = UpscalerData()
# Where the actual upscaler is stored, will be used when the script upscales using the Upscaler in UpscalerData
shared.actual_upscaler = upscale_model
# Set the batch of images
shared.batch = [tensor_to_pil(image, i) for i in range(len(image))]
# Processing
self.sdprocessing = StableDiffusionProcessing(
tensor_to_pil(image), model, positive, negative, vae,
seed, steps, cfg, sampler_name, scheduler, denoise, upscale_by, force_uniform_tiles, tiled_decode,
custom_sampler, custom_sigmas
)
# Disable logging
logger = logging.getLogger()
old_level = logger.getEffectiveLevel()
logger.setLevel(logging.CRITICAL + 1)
try:
#
# Running the script
#
script = usdu.Script()
processed = script.run(p=self.sdprocessing, _=None, tile_width=self.tile_width, tile_height=self.tile_height,
mask_blur=self.mask_blur, padding=self.tile_padding, seams_fix_width=self.seam_fix_width,
seams_fix_denoise=self.seam_fix_denoise, seams_fix_padding=self.seam_fix_padding,
upscaler_index=0, save_upscaled_image=False, redraw_mode=MODES[self.mode_type],
save_seams_fix_image=False, seams_fix_mask_blur=self.seam_fix_mask_blur,
seams_fix_type=SEAM_FIX_MODES[self.seam_fix_mode], target_size_type=2,
custom_width=None, custom_height=None, custom_scale=self.upscale_by)
# Return the resulting images
images = [pil_to_tensor(img) for img in shared.batch]
tensor = torch.cat(images, dim=0)
return (tensor,)
finally:
# Restore the original logging level
logger.setLevel(old_level)
class UltimateSDUpscaleNoUpscale(UltimateSDUpscale):
@classmethod
def INPUT_TYPES(s):
required, optional = USDU_base_inputs()
remove_input(required, "upscale_model")
remove_input(required, "upscale_by")
rename_input(required, "image", "upscaled_image")
return prepare_inputs(required, optional)
RETURN_TYPES = ("IMAGE",)
FUNCTION = "upscale"
CATEGORY = "image/upscaling"
def upscale(self, upscaled_image, model, positive, negative, vae, seed,
steps, cfg, sampler_name, scheduler, denoise,
mode_type, tile_width, tile_height, mask_blur, tile_padding,
seam_fix_mode, seam_fix_denoise, seam_fix_mask_blur,
seam_fix_width, seam_fix_padding, force_uniform_tiles, tiled_decode):
upscale_by = 1.0
return super().upscale(upscaled_image, model, positive, negative, vae, upscale_by, seed,
steps, cfg, sampler_name, scheduler, denoise, None,
mode_type, tile_width, tile_height, mask_blur, tile_padding,
seam_fix_mode, seam_fix_denoise, seam_fix_mask_blur,
seam_fix_width, seam_fix_padding, force_uniform_tiles, tiled_decode)
class UltimateSDUpscaleCustomSample(UltimateSDUpscale):
@classmethod
def INPUT_TYPES(s):
required, optional = USDU_base_inputs()
remove_input(required, "upscale_model")
optional.append(("upscale_model", ("UPSCALE_MODEL",)))
optional.append(("custom_sampler", ("SAMPLER",)))
optional.append(("custom_sigmas", ("SIGMAS",)))
return prepare_inputs(required, optional)
RETURN_TYPES = ("IMAGE",)
FUNCTION = "upscale"
CATEGORY = "image/upscaling"
def upscale(self, image, model, positive, negative, vae, upscale_by, seed,
steps, cfg, sampler_name, scheduler, denoise,
mode_type, tile_width, tile_height, mask_blur, tile_padding,
seam_fix_mode, seam_fix_denoise, seam_fix_mask_blur,
seam_fix_width, seam_fix_padding, force_uniform_tiles, tiled_decode,
upscale_model=None,
custom_sampler=None, custom_sigmas=None):
return super().upscale(image, model, positive, negative, vae, upscale_by, seed,
steps, cfg, sampler_name, scheduler, denoise, upscale_model,
mode_type, tile_width, tile_height, mask_blur, tile_padding,
seam_fix_mode, seam_fix_denoise, seam_fix_mask_blur,
seam_fix_width, seam_fix_padding, force_uniform_tiles, tiled_decode,
custom_sampler, custom_sigmas)
# A dictionary that contains all nodes you want to export with their names
# NOTE: names should be globally unique
NODE_CLASS_MAPPINGS = {
"UltimateSDUpscale": UltimateSDUpscale,
"UltimateSDUpscaleNoUpscale": UltimateSDUpscaleNoUpscale,
"UltimateSDUpscaleCustomSample": UltimateSDUpscaleCustomSample
}
# A dictionary that contains the friendly/humanly readable titles for the nodes
NODE_DISPLAY_NAME_MAPPINGS = {
"UltimateSDUpscale": "Ultimate SD Upscale",
"UltimateSDUpscaleNoUpscale": "Ultimate SD Upscale (No Upscale)",
"UltimateSDUpscaleCustomSample": "Ultimate SD Upscale (Custom Sample)"
}
|