Spaces:
Running
Running
File size: 8,126 Bytes
613c9ab |
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 |
# ComfyUI Node for Ultimate SD Upscale by Coyote-A: https://github.com/Coyote-A/ultimate-upscale-for-automatic1111
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():
return [
("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})),
]
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):
return prepare_inputs(USDU_base_inputs())
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):
#
# 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
sdprocessing = StableDiffusionProcessing(
tensor_to_pil(image), model, positive, negative, vae,
seed, steps, cfg, sampler_name, scheduler, denoise, upscale_by, force_uniform_tiles, tiled_decode
)
#
# Running the script
#
script = usdu.Script()
processed = script.run(p=sdprocessing, _=None, tile_width=tile_width, tile_height=tile_height,
mask_blur=mask_blur, padding=tile_padding, seams_fix_width=seam_fix_width,
seams_fix_denoise=seam_fix_denoise, seams_fix_padding=seam_fix_padding,
upscaler_index=0, save_upscaled_image=False, redraw_mode=MODES[mode_type],
save_seams_fix_image=False, seams_fix_mask_blur=seam_fix_mask_blur,
seams_fix_type=SEAM_FIX_MODES[seam_fix_mode], target_size_type=2,
custom_width=None, custom_height=None, custom_scale=upscale_by)
# Return the resulting images
images = [pil_to_tensor(img) for img in shared.batch]
tensor = torch.cat(images, dim=0)
return (tensor,)
class UltimateSDUpscaleNoUpscale:
@classmethod
def INPUT_TYPES(s):
required = USDU_base_inputs()
remove_input(required, "upscale_model")
remove_input(required, "upscale_by")
rename_input(required, "image", "upscaled_image")
return prepare_inputs(required)
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):
shared.sd_upscalers[0] = UpscalerData()
shared.actual_upscaler = None
shared.batch = [tensor_to_pil(upscaled_image, i) for i in range(len(upscaled_image))]
sdprocessing = StableDiffusionProcessing(
tensor_to_pil(upscaled_image), model, positive, negative, vae,
seed, steps, cfg, sampler_name, scheduler, denoise, 1, force_uniform_tiles, tiled_decode
)
script = usdu.Script()
processed = script.run(p=sdprocessing, _=None, tile_width=tile_width, tile_height=tile_height,
mask_blur=mask_blur, padding=tile_padding, seams_fix_width=seam_fix_width,
seams_fix_denoise=seam_fix_denoise, seams_fix_padding=seam_fix_padding,
upscaler_index=0, save_upscaled_image=False, redraw_mode=MODES[mode_type],
save_seams_fix_image=False, seams_fix_mask_blur=seam_fix_mask_blur,
seams_fix_type=SEAM_FIX_MODES[seam_fix_mode], target_size_type=2,
custom_width=None, custom_height=None, custom_scale=1)
images = [pil_to_tensor(img) for img in shared.batch]
tensor = torch.cat(images, dim=0)
return (tensor,)
# 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
}
# 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)"
}
|