Spaces:
Sleeping
Sleeping
| #---------------------------------------------------------------------------------------------------------------------# | |
| # Comfyroll Studio custom nodes by RockOfFire and Akatsuzi https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes | |
| # 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 .functions_upscale import * | |
| #MAX_RESOLUTION=8192 | |
| #---------------------------------------------------------------------------------------------------------------------# | |
| # NODES | |
| #---------------------------------------------------------------------------------------------------------------------# | |
| # These nodes are based on WAS nodes Image Resize and the Comfy Extras upscale with model nodes | |
| class CR_UpscaleImage: | |
| 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: | |
| 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: | |
| 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, | |
| } | |
| ''' | |