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import sys
from os import path
sys.path.insert(0, path.dirname(__file__))
from .ldsrlib.LDSR import LDSR
from folder_paths import get_filename_list, get_full_path
from comfy.model_management import get_torch_device
from comfy.utils import ProgressBar
import torch
class LDSRModelLoader:
@classmethod
def INPUT_TYPES(s):
model_list = get_filename_list("upscale_models")
candidates = [name for name in model_list if 'last.ckpt' in name]
if len(candidates) > 0:
default_path = candidates[0]
else:
default_path = 'last.ckpt'
return {
"required": {
"model": (model_list, {'default': default_path}),
}
}
RETURN_TYPES = ("UPSCALE_MODEL",)
FUNCTION = "load"
CATEGORY = "Flowty LDSR"
def load(self, model):
model_path = get_full_path("upscale_models", model)
model = LDSR.load_model_from_path(model_path)
model['model'].cpu()
return (model, )
class LDSRUpscale:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"upscale_model": ("UPSCALE_MODEL",),
"images": ("IMAGE",),
"steps": (["25", "50", "100", "250", "500", "1000"], {"default": "100"}),
"pre_downscale": (['None', '1/2', '1/4'], {"default": "None"}),
"post_downscale": (['None', 'Original Size', '1/2', '1/4'], {"default": "None"}),
"downsample_method": (['Nearest', 'Lanczos'], {"default": "Lanczos"}),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("images",)
FUNCTION = "upscale"
CATEGORY = "Flowty LDSR"
def upscale(self, upscale_model, images, steps, pre_downscale="None", post_downscale="None", downsample_method="Lanczos"):
pbar = ProgressBar(int(steps))
p = {"prev": 0}
def prog(i):
i = i + 1
if i < p["prev"]:
p["prev"] = 0
pbar.update(i - p["prev"])
p["prev"] = i
ldsr = LDSR(model=upscale_model, on_progress=prog)
outputs = []
for image in images:
outputs.append(ldsr.superResolution(image, int(steps), pre_downscale, post_downscale, downsample_method))
return (torch.stack(outputs),)
class LDSRUpscaler:
@classmethod
def INPUT_TYPES(s):
model_list = get_filename_list("upscale_models")
candidates = [name for name in model_list if 'last.ckpt' in name]
if len(candidates) > 0:
default_path = candidates[0]
else:
default_path = 'last.ckpt'
return {
"required": {
"model": (model_list, {'default': default_path}),
"images": ("IMAGE",),
"steps": (["25", "50", "100", "250", "500", "1000"], {"default": "100"}),
"pre_downscale": (['None', '1/2', '1/4'], {"default": "None"}),
"post_downscale": (['None', 'Original Size', '1/2', '1/4'], {"default": "None"}),
"downsample_method": (['Nearest', 'Lanczos'], {"default": "Lanczos"}),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("images",)
FUNCTION = "upscale"
CATEGORY = "Flowty LDSR"
def upscale(self, model, images, steps, pre_downscale="None", post_downscale="None", downsample_method="Lanczos"):
model_path = get_full_path("upscale_models", model)
pbar = ProgressBar(int(steps))
p = {"prev": 0}
def prog(i):
i = i + 1
if i < p["prev"]:
p["prev"] = 0
pbar.update(i - p["prev"])
p["prev"] = i
ldsr = LDSR(modelPath=model_path, torchdevice=get_torch_device(), on_progress=prog)
outputs = []
for image in images:
outputs.append(ldsr.superResolution(image, int(steps), pre_downscale, post_downscale, downsample_method))
return (torch.stack(outputs),)
NODE_CLASS_MAPPINGS = {
"LDSRUpscaler": LDSRUpscaler,
"LDSRModelLoader": LDSRModelLoader,
"LDSRUpscale": LDSRUpscale
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LDSRUpscaler": "LDSR Upscale (all-in-one)",
"LDSRModelLoader": "Load LDSR Model",
"LDSRUpscale": "LDSR Upscale"
}
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
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