Upscaler.load_model: don't return None, just use exceptions (bf67a5dc) · Commits · github_fork / Stable Diffusion Webui

extensions-builtin/LDSR/scripts/ldsr_model.py

+5 −8

Original line number	Diff line number	Diff line
		@@ -46,16 +46,13 @@ class UpscalerLDSR(Upscaler):

		yaml = local_yaml_path or load_file_from_url(self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml")

		try:
		return LDSR(model, yaml)
		except Exception:
		errors.report("Error importing LDSR", exc_info=True)
		return None

		def do_upscale(self, img, path):
		try:
		ldsr = self.load_model(path)
		if ldsr is None:
		print("NO LDSR!")
		except Exception:
		errors.report(f"Failed loading LDSR model {path}", exc_info=True)
		return img
		ddim_steps = shared.opts.ldsr_steps
		return ldsr.super_resolution(img, ddim_steps, self.scale)

extensions-builtin/ScuNET/scripts/scunet_model.py

+6 −10

Original line number	Diff line number	Diff line
		import os.path
		import sys

		import PIL.Image
		@@ -8,7 +7,7 @@ from tqdm import tqdm

		import modules.upscaler
		from modules import devices, modelloader, script_callbacks, errors
		from scunet_model_arch import SCUNet as net
		from scunet_model_arch import SCUNet

		from modules.modelloader import load_file_from_url
		from modules.shared import opts
		@@ -88,9 +87,10 @@ class UpscalerScuNET(modules.upscaler.Upscaler):

		torch.cuda.empty_cache()

		try:
		model = self.load_model(selected_file)
		if model is None:
		print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr)
		except Exception as e:
		print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr)
		return img

		device = devices.get_device_for('scunet')
		@@ -123,11 +123,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
		filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
		else:
		filename = path
		if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
		print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
		return None

		model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
		model = SCUNet(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
		model.load_state_dict(torch.load(filename), strict=True)
		model.eval()
		for _, v in model.named_parameters():

extensions-builtin/SwinIR/scripts/swinir_model.py

+20 −20

Original line number	Diff line number	Diff line
		import os
		import sys

		import numpy as np
		import torch
		@@ -7,8 +7,8 @@ from tqdm import tqdm

		from modules import modelloader, devices, script_callbacks, shared
		from modules.shared import opts, state
		from swinir_model_arch import SwinIR as net
		from swinir_model_arch_v2 import Swin2SR as net2
		from swinir_model_arch import SwinIR
		from swinir_model_arch_v2 import Swin2SR
		from modules.upscaler import Upscaler, UpscalerData


		@@ -36,8 +36,10 @@ class UpscalerSwinIR(Upscaler):
		self.scalers = scalers

		def do_upscale(self, img, model_file):
		try:
		model = self.load_model(model_file)
		if model is None:
		except Exception as e:
		print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr)
		return img
		model = model.to(device_swinir, dtype=devices.dtype)
		img = upscale(img, model)
		@@ -56,10 +58,8 @@ class UpscalerSwinIR(Upscaler):
		)
		else:
		filename = path
		if filename is None or not os.path.exists(filename):
		return None
		if filename.endswith(".v2.pth"):
		model = net2(
		model = Swin2SR(
		upscale=scale,
		in_chans=3,
		img_size=64,
		@@ -74,7 +74,7 @@ class UpscalerSwinIR(Upscaler):
		)
		params = None
		else:
		model = net(
		model = SwinIR(
		upscale=scale,
		in_chans=3,
		img_size=64,

modules/esrgan_model.py

+6 −8

Original line number	Diff line number	Diff line
		import os
		import sys

		import numpy as np
		import torch
		@@ -6,9 +6,8 @@ from PIL import Image

		import modules.esrgan_model_arch as arch
		from modules import modelloader, images, devices
		from modules.upscaler import Upscaler, UpscalerData
		from modules.shared import opts

		from modules.upscaler import Upscaler, UpscalerData


		def mod2normal(state_dict):
		@@ -142,8 +141,10 @@ class UpscalerESRGAN(Upscaler):
		self.scalers.append(scaler_data)

		def do_upscale(self, img, selected_model):
		try:
		model = self.load_model(selected_model)
		if model is None:
		except Exception as e:
		print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr)
		return img
		model.to(devices.device_esrgan)
		img = esrgan_upscale(model, img)
		@@ -159,9 +160,6 @@ class UpscalerESRGAN(Upscaler):
		)
		else:
		filename = path
		if not os.path.exists(filename) or filename is None:
		print(f"Unable to load {self.model_path} from {filename}")
		return None

		state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)

modules/realesrgan_model.py

+15 −18

Original line number	Diff line number	Diff line
		@@ -9,7 +9,6 @@ from modules.shared import cmd_opts, opts
		from modules import modelloader, errors



		class UpscalerRealESRGAN(Upscaler):
		def __init__(self, path):
		self.name = "RealESRGAN"
		@@ -43,9 +42,10 @@ class UpscalerRealESRGAN(Upscaler):
		if not self.enable:
		return img

		try:
		info = self.load_model(path)
		if not os.path.exists(info.local_data_path):
		print(f"Unable to load RealESRGAN model: {info.name}")
		except Exception:
		errors.report(f"Unable to load RealESRGAN model {path}", exc_info=True)
		return img

		upsampler = RealESRGANer(
		@@ -63,20 +63,17 @@ class UpscalerRealESRGAN(Upscaler):
		return image

		def load_model(self, path):
		try:
		info = next(iter([scaler for scaler in self.scalers if scaler.data_path == path]), None)

		if info is None:
		print(f"Unable to find model info: {path}")
		return None

		if info.local_data_path.startswith("http"):
		info.local_data_path = modelloader.load_file_from_url(info.data_path, model_dir=self.model_download_path)

		return info
		except Exception:
		errors.report("Error making Real-ESRGAN models list", exc_info=True)
		return None
		for scaler in self.scalers:
		if scaler.data_path == path:
		if scaler.local_data_path.startswith("http"):
		scaler.local_data_path = modelloader.load_file_from_url(
		scaler.data_path,
		model_dir=self.model_download_path,
		)
		if not os.path.exists(scaler.local_data_path):
		raise FileNotFoundError(f"RealESRGAN data missing: {scaler.local_data_path}")
		return scaler
		raise ValueError(f"Unable to find model info: {path}")

		def load_models(self, _):
		return get_realesrgan_models(self)

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