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# 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():
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
)
# 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=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,)
finally:
# Restore the original logging level
print("Restoring logging level to", old_level)
logger.setLevel(old_level)
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
)
# Disable logging
logger = logging.getLogger()
old_level = logger.getEffectiveLevel()
logger.setLevel(logging.CRITICAL + 1)
try:
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,)
finally:
# Restore the original logging level
print("Restoring logging level to", old_level)
logger.setLevel(old_level)
# 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)"
}
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