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Remove BSRGAN from --use-cpu, add SwinIR

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brkirch 2022-10-24 23:04:50 -04:00 committed by AUTOMATIC1111
parent f53ca51638
commit 4c24347e45
3 changed files with 10 additions and 10 deletions
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2
modules/devices.py
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@ -45,7 +45,7 @@ def enable_tf32():
errors.run(enable_tf32, "Enabling TF32") errors.run(enable_tf32, "Enabling TF32")
device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = None device = device_interrogate = device_gfpgan = device_swinir = device_esrgan = device_scunet = device_codeformer = None
dtype = torch.float16 dtype = torch.float16
dtype_vae = torch.float16 dtype_vae = torch.float16

6
modules/shared.py
View file

@ -58,7 +58,7 @@ parser.add_argument("--opt-split-attention", action='store_true', help="force-en
parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.") parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
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("--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("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization") parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
parser.add_argument("--use-cpu", nargs='+',choices=['all', 'sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'], help="use CPU as torch device for specified modules", default=[], type=str.lower) parser.add_argument("--use-cpu", nargs='+',choices=['all', 'sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'], help="use CPU as torch device for specified modules", default=[], type=str.lower)
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("--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("--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) parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
@ -96,8 +96,8 @@ restricted_opts = [
"outdir_save", "outdir_save",
] ]
devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \ devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer']) (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'])
device = devices.device device = devices.device
weight_load_location = None if cmd_opts.lowram else "cpu" weight_load_location = None if cmd_opts.lowram else "cpu"

12
modules/swinir_model.py
View file

@ -7,8 +7,8 @@ from PIL import Image
from basicsr.utils.download_util import load_file_from_url from basicsr.utils.download_util import load_file_from_url
from tqdm import tqdm from tqdm import tqdm
from modules import modelloader from modules import modelloader, devices
from modules.shared import cmd_opts, opts, device from modules.shared import cmd_opts, opts
from modules.swinir_model_arch import SwinIR as net from modules.swinir_model_arch import SwinIR as net
from modules.swinir_model_arch_v2 import Swin2SR as net2 from modules.swinir_model_arch_v2 import Swin2SR as net2
from modules.upscaler import Upscaler, UpscalerData from modules.upscaler import Upscaler, UpscalerData
@ -42,7 +42,7 @@ class UpscalerSwinIR(Upscaler):
model = self.load_model(model_file) model = self.load_model(model_file)
if model is None: if model is None:
return img return img
model = model.to(device) model = model.to(devices.device_swinir)
img = upscale(img, model) img = upscale(img, model)
try: try:
torch.cuda.empty_cache() torch.cuda.empty_cache()
@ -111,7 +111,7 @@ def upscale(
img = img[:, :, ::-1] img = img[:, :, ::-1]
img = np.moveaxis(img, 2, 0) / 255 img = np.moveaxis(img, 2, 0) / 255
img = torch.from_numpy(img).float() img = torch.from_numpy(img).float()
img = img.unsqueeze(0).to(device) img = img.unsqueeze(0).to(devices.device_swinir)
with torch.no_grad(), precision_scope("cuda"): with torch.no_grad(), precision_scope("cuda"):
_, _, h_old, w_old = img.size() _, _, h_old, w_old = img.size()
h_pad = (h_old // window_size + 1) * window_size - h_old h_pad = (h_old // window_size + 1) * window_size - h_old
@ -139,8 +139,8 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
stride = tile - tile_overlap stride = tile - tile_overlap
h_idx_list = list(range(0, h - tile, stride)) + [h - tile] h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
w_idx_list = list(range(0, w - tile, stride)) + [w - tile] w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img) E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=devices.device_swinir).type_as(img)
W = torch.zeros_like(E, dtype=torch.half, device=device) W = torch.zeros_like(E, dtype=torch.half, device=devices.device_swinir)
with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar: with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
for h_idx in h_idx_list: for h_idx in h_idx_list: