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#---------------------------------------------------------------------------------------------------------------------#
# Comfyroll Nodes by RockOfFire and Akatsuzi https://github.com/Suzie1/CR-Animation-Nodes
# for ComfyUI https://github.com/comfyanonymous/ComfyUI
#---------------------------------------------------------------------------------------------------------------------#
import torch
import numpy as np
import folder_paths
from PIL import Image
from ..categories import icons
from .upscale_functions import load_model, upscale_with_model, apply_resize_image
#MAX_RESOLUTION=8192
# PIL to Tensor
def pil2tensor(image):
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
# Tensor to PIL
def tensor2pil(image):
return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
#---------------------------------------------------------------------------------------------------------------------
# NODES
#---------------------------------------------------------------------------------------------------------------------
# These nodes are based on WAS nodes Image Resize and the Comfy Extras upscale with model nodes
#---------------------------------------------------------------------------------------------------------------------
class CR_UpscaleImage:
@classmethod
def INPUT_TYPES(s):
resampling_methods = ["lanczos", "nearest", "bilinear", "bicubic"]
return {"required":
{"image": ("IMAGE",),
"upscale_model": (folder_paths.get_filename_list("upscale_models"), ),
"mode": (["rescale", "resize"],),
"rescale_factor": ("FLOAT", {"default": 2, "min": 0.01, "max": 16.0, "step": 0.01}),
"resize_width": ("INT", {"default": 1024, "min": 1, "max": 48000, "step": 1}),
"resampling_method": (resampling_methods,),
"supersample": (["true", "false"],),
"rounding_modulus": ("INT", {"default": 8, "min": 8, "max": 1024, "step": 8}),
}
}
RETURN_TYPES = ("IMAGE", "STRING", )
RETURN_NAMES = ("IMAGE", "show_help", )
FUNCTION = "upscale"
CATEGORY = icons.get("Comfyroll/Upscale")
def upscale(self, image, upscale_model, rounding_modulus=8, loops=1, mode="rescale", supersample='true', resampling_method="lanczos", rescale_factor=2, resize_width=1024):
# Load upscale model
up_model = load_model(upscale_model)
# Upscale with model
up_image = upscale_with_model(up_model, image)
for img in image:
pil_img = tensor2pil(img)
original_width, original_height = pil_img.size
for img in up_image:
# Get new size
pil_img = tensor2pil(img)
upscaled_width, upscaled_height = pil_img.size
show_help = "https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes/wiki/Upscale-Nodes#cr-upscale-image"
# Return if no rescale needed
if upscaled_width == original_width and rescale_factor == 1:
return (up_image, show_help)
# Image resize
scaled_images = []
for img in up_image:
scaled_images.append(pil2tensor(apply_resize_image(tensor2pil(img), original_width, original_height, rounding_modulus, mode, supersample, rescale_factor, resize_width, resampling_method)))
images_out = torch.cat(scaled_images, dim=0)
return (images_out, show_help, )
#---------------------------------------------------------------------------------------------------------------------
class CR_MultiUpscaleStack:
@classmethod
def INPUT_TYPES(s):
mix_methods = ["Combine", "Average", "Concatenate"]
up_models = ["None"] + folder_paths.get_filename_list("upscale_models")
return {"required":
{
"switch_1": (["On","Off"],),
"upscale_model_1": (up_models, ),
"rescale_factor_1": ("FLOAT", {"default": 2, "min": 0.01, "max": 16.0, "step": 0.01}),
"switch_2": (["On","Off"],),
"upscale_model_2": (up_models, ),
"rescale_factor_2": ("FLOAT", {"default": 2, "min": 0.01, "max": 16.0, "step": 0.01}),
"switch_3": (["On","Off"],),
"upscale_model_3": (up_models, ),
"rescale_factor_3": ("FLOAT", {"default": 2, "min": 0.01, "max": 16.0, "step": 0.01}),
},
"optional": {"upscale_stack": ("UPSCALE_STACK",),
}
}
RETURN_TYPES = ("UPSCALE_STACK", "STRING", )
RETURN_NAMES = ("UPSCALE_STACK", "show_help", )
FUNCTION = "stack"
CATEGORY = icons.get("Comfyroll/Upscale")
def stack(self, switch_1, upscale_model_1, rescale_factor_1, switch_2, upscale_model_2, rescale_factor_2, switch_3, upscale_model_3, rescale_factor_3, upscale_stack=None):
# Initialise the list
upscale_list=list()
if upscale_stack is not None:
upscale_list.extend([l for l in upscale_stack if l[0] != "None"])
if upscale_model_1 != "None" and switch_1 == "On":
upscale_list.extend([(upscale_model_1, rescale_factor_1)]),
if upscale_model_2 != "None" and switch_2 == "On":
upscale_list.extend([(upscale_model_2, rescale_factor_2)]),
if upscale_model_3 != "None" and switch_3 == "On":
upscale_list.extend([(upscale_model_3, rescale_factor_3)]),
show_help = "https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes/wiki/Upscale-Nodes#cr-multi-upscale-stack"
return (upscale_list, show_help, )
#---------------------------------------------------------------------------------------------------------------------
class CR_ApplyMultiUpscale:
@classmethod
def INPUT_TYPES(s):
resampling_methods = ["lanczos", "nearest", "bilinear", "bicubic"]
return {"required": {"image": ("IMAGE",),
"resampling_method": (resampling_methods,),
"supersample": (["true", "false"],),
"rounding_modulus": ("INT", {"default": 8, "min": 8, "max": 1024, "step": 8}),
"upscale_stack": ("UPSCALE_STACK",),
}
}
RETURN_TYPES = ("IMAGE", "STRING", )
RETURN_NAMES = ("IMAGE", "show_help", )
FUNCTION = "apply"
CATEGORY = icons.get("Comfyroll/Upscale")
def apply(self, image, resampling_method, supersample, rounding_modulus, upscale_stack):
# Get original size
pil_img = tensor2pil(image)
original_width, original_height = pil_img.size
# Extend params with upscale-stack items
params = list()
params.extend(upscale_stack)
# Loop through the list
for tup in params:
upscale_model, rescale_factor = tup
print(f"[Info] CR Apply Multi Upscale: Applying {upscale_model} and rescaling by factor {rescale_factor}")
# Load upscale model
up_model = load_model(upscale_model)
# Upscale with model
up_image = upscale_with_model(up_model, image)
# Get new size
pil_img = tensor2pil(up_image)
upscaled_width, upscaled_height = pil_img.size
# Return if no rescale needed
if upscaled_width == original_width and rescale_factor == 1:
image = up_image
else:
# Image resize
scaled_images = []
mode = "rescale"
resize_width = 1024
for img in up_image:
scaled_images.append(pil2tensor(apply_resize_image(tensor2pil(img), original_width, original_height, rounding_modulus, mode, supersample, rescale_factor, resize_width, resampling_method)))
image = torch.cat(scaled_images, dim=0)
show_help = "https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes/wiki/Upscale-Nodes#cr-apply-multi-upscale"
return (image, show_help, )
#---------------------------------------------------------------------------------------------------------------------
# MAPPINGS
#---------------------------------------------------------------------------------------------------------------------#
# For reference only, actual mappings are in __init__.py
# 0 nodes released
'''
NODE_CLASS_MAPPINGS = {
# Conditioning
"CR Multi Upscale Stack":CR_MultiUpscaleStack,
"CR Upscale Image":CR_UpscaleImage,
"CR Apply Multi Upscale":CR_ApplyMultiUpscale,
}
'''
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