From 8deae077004f0332ca607fc3a5d568b1a4705bec Mon Sep 17 00:00:00 2001 From: d8ahazard Date: Fri, 30 Sep 2022 15:28:37 -0500 Subject: Add ScuNET DeNoiser/Upscaler Q&D Implementation of ScuNET, thanks to our handy model loader. :P https://github.com/cszn/SCUNet --- modules/scunet_model.py | 90 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 90 insertions(+) create mode 100644 modules/scunet_model.py (limited to 'modules/scunet_model.py') diff --git a/modules/scunet_model.py b/modules/scunet_model.py new file mode 100644 index 00000000..7987ac14 --- /dev/null +++ b/modules/scunet_model.py @@ -0,0 +1,90 @@ +import os.path +import sys +import traceback + +import PIL.Image +import numpy as np +import torch +from basicsr.utils.download_util import load_file_from_url + +import modules.upscaler +from modules import shared, modelloader +from modules.paths import models_path +from modules.scunet_model_arch import SCUNet as net + + +class UpscalerScuNET(modules.upscaler.Upscaler): + def __init__(self, dirname): + self.name = "ScuNET" + self.model_path = os.path.join(models_path, self.name) + self.model_name = "ScuNET GAN" + self.model_name2 = "ScuNET PSNR" + self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth" + self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth" + self.user_path = dirname + super().__init__() + model_paths = self.find_models(ext_filter=[".pth"]) + scalers = [] + add_model2 = True + for file in model_paths: + if "http" in file: + name = self.model_name + else: + name = modelloader.friendly_name(file) + if name == self.model_name2 or file == self.model_url2: + add_model2 = False + try: + scaler_data = modules.upscaler.UpscalerData(name, file, self, 4) + scalers.append(scaler_data) + except Exception: + print(f"Error loading ScuNET model: {file}", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + if add_model2: + scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self) + scalers.append(scaler_data2) + self.scalers = scalers + + def do_upscale(self, img: PIL.Image, selected_file): + torch.cuda.empty_cache() + + model = self.load_model(selected_file) + if model is None: + return img + + device = shared.device + img = np.array(img) + img = img[:, :, ::-1] + img = np.moveaxis(img, 2, 0) / 255 + img = torch.from_numpy(img).float() + img = img.unsqueeze(0).to(shared.device) + + img = img.to(device) + with torch.no_grad(): + output = model(img) + output = output.squeeze().float().cpu().clamp_(0, 1).numpy() + output = 255. * np.moveaxis(output, 0, 2) + output = output.astype(np.uint8) + output = output[:, :, ::-1] + torch.cuda.empty_cache() + return PIL.Image.fromarray(output, 'RGB') + + def load_model(self, path: str): + device = shared.device + if "http" in path: + filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name, + progress=True) + 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.load_state_dict(torch.load(filename), strict=True) + model.eval() + for k, v in model.named_parameters(): + v.requires_grad = False + model = model.to(device) + + return model + -- cgit v1.2.3 From eeab7aedf532680a6ae9058ee272450bb07e41eb Mon Sep 17 00:00:00 2001 From: brkirch Date: Tue, 4 Oct 2022 04:24:35 -0400 Subject: Add --use-cpu command line option Remove MPS detection to use CPU for GFPGAN / CodeFormer and add a --use-cpu command line option. --- modules/devices.py | 5 ++--- modules/esrgan_model.py | 9 ++++----- modules/scunet_model.py | 8 ++++---- modules/shared.py | 9 +++++++-- 4 files changed, 17 insertions(+), 14 deletions(-) (limited to 'modules/scunet_model.py') diff --git a/modules/devices.py b/modules/devices.py index 5d9c7a07..b5a0cd29 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -1,8 +1,8 @@ import torch -# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility from modules import errors +# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility has_mps = getattr(torch, 'has_mps', False) cpu = torch.device("cpu") @@ -32,8 +32,7 @@ def enable_tf32(): errors.run(enable_tf32, "Enabling TF32") -device = get_optimal_device() -device_gfpgan = device_codeformer = cpu if device.type == 'mps' else device +device = device_gfpgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device() dtype = torch.float16 def randn(seed, shape): diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index 4aed9283..d17e730f 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -6,8 +6,7 @@ from PIL import Image from basicsr.utils.download_util import load_file_from_url import modules.esrgam_model_arch as arch -from modules import shared, modelloader, images -from modules.devices import has_mps +from modules import shared, modelloader, images, devices from modules.paths import models_path from modules.upscaler import Upscaler, UpscalerData from modules.shared import opts @@ -97,7 +96,7 @@ class UpscalerESRGAN(Upscaler): model = self.load_model(selected_model) if model is None: return img - model.to(shared.device) + model.to(devices.device_esrgan) img = esrgan_upscale(model, img) return img @@ -112,7 +111,7 @@ class UpscalerESRGAN(Upscaler): print("Unable to load %s from %s" % (self.model_path, filename)) return None - pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None) + pretrained_net = torch.load(filename, map_location='cpu' if shared.device.type == 'mps' else None) crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32) pretrained_net = fix_model_layers(crt_model, pretrained_net) @@ -127,7 +126,7 @@ def upscale_without_tiling(model, img): img = img[:, :, ::-1] img = np.moveaxis(img, 2, 0) / 255 img = torch.from_numpy(img).float() - img = img.unsqueeze(0).to(shared.device) + img = img.unsqueeze(0).to(devices.device_esrgan) with torch.no_grad(): output = model(img) output = output.squeeze().float().cpu().clamp_(0, 1).numpy() diff --git a/modules/scunet_model.py b/modules/scunet_model.py index 7987ac14..fb64b740 100644 --- a/modules/scunet_model.py +++ b/modules/scunet_model.py @@ -8,7 +8,7 @@ import torch from basicsr.utils.download_util import load_file_from_url import modules.upscaler -from modules import shared, modelloader +from modules import devices, modelloader from modules.paths import models_path from modules.scunet_model_arch import SCUNet as net @@ -51,12 +51,12 @@ class UpscalerScuNET(modules.upscaler.Upscaler): if model is None: return img - device = shared.device + device = devices.device_scunet img = np.array(img) img = img[:, :, ::-1] img = np.moveaxis(img, 2, 0) / 255 img = torch.from_numpy(img).float() - img = img.unsqueeze(0).to(shared.device) + img = img.unsqueeze(0).to(device) img = img.to(device) with torch.no_grad(): @@ -69,7 +69,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler): return PIL.Image.fromarray(output, 'RGB') def load_model(self, path: str): - device = shared.device + device = devices.device_scunet if "http" in path: filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name, progress=True) diff --git a/modules/shared.py b/modules/shared.py index 2a599e9c..7899ab8d 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -12,7 +12,7 @@ import modules.interrogate import modules.memmon import modules.sd_models import modules.styles -from modules.devices import get_optimal_device +import modules.devices as devices from modules.paths import script_path, sd_path sd_model_file = os.path.join(script_path, 'model.ckpt') @@ -46,6 +46,7 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.") parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization") parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find") +parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU for specified modules", default=[]) parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests") parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None) parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False) @@ -63,7 +64,11 @@ parser.add_argument("--enable-console-prompts", action='store_true', help="print cmd_opts = parser.parse_args() -device = get_optimal_device() + +devices.device, devices.device_gfpgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \ +(devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'ESRGAN', 'SCUNet', 'CodeFormer']) + +device = devices.device batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram) parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram -- cgit v1.2.3