Merge pull request #1371 from d8ahazard/master

Fix LDSR, BSRGAN, recursive SD checkpoint Loading
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AUTOMATIC1111 2022-09-30 23:09:18 +03:00 committed by GitHub
commit f253790b40
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4 changed files with 17 additions and 7 deletions

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@ -69,7 +69,7 @@ class UpscalerBSRGAN(modules.upscaler.Upscaler):
if not os.path.exists(filename) or filename is None:
print(f"BSRGAN: Unable to load model from {filename}", file=sys.stderr)
return None
model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=2) # define network
model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4) # define network
model.load_state_dict(torch.load(filename), strict=True)
model.eval()
for k, v in model.named_parameters():

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@ -22,8 +22,20 @@ class UpscalerLDSR(Upscaler):
self.scalers = [scaler_data]
def load_model(self, path: str):
# Remove incorrect project.yaml file if too big
yaml_path = os.path.join(self.model_path, "project.yaml")
old_model_path = os.path.join(self.model_path, "model.pth")
new_model_path = os.path.join(self.model_path, "model.ckpt")
if os.path.exists(yaml_path):
statinfo = os.stat(yaml_path)
if statinfo.st_size >= 10485760:
print("Removing invalid LDSR YAML file.")
os.remove(yaml_path)
if os.path.exists(old_model_path):
print("Renaming model from model.pth to model.ckpt")
os.rename(old_model_path, new_model_path)
model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
file_name="model.pth", progress=True)
file_name="model.ckpt", progress=True)
yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path,
file_name="project.yaml", progress=True)
@ -41,5 +53,4 @@ class UpscalerLDSR(Upscaler):
print("NO LDSR!")
return img
ddim_steps = shared.opts.ldsr_steps
pre_scale = shared.opts.ldsr_pre_down
return ldsr.super_resolution(img, ddim_steps, self.scale)

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@ -98,9 +98,7 @@ class LDSR:
im_og = image
width_og, height_og = im_og.size
# If we can adjust the max upscale size, then the 4 below should be our variable
print("Foo")
down_sample_rate = target_scale / 4
print(f"Downsample rate is {down_sample_rate}")
wd = width_og * down_sample_rate
hd = height_og * down_sample_rate
width_downsampled_pre = int(wd)
@ -111,7 +109,7 @@ class LDSR:
f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
else:
print(f"Down sample rate is 1 from {target_scale} / 4")
print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
logs = self.run(model["model"], im_og, diffusion_steps, eta)
sample = logs["sample"]

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@ -1,3 +1,4 @@
import glob
import os
import shutil
import importlib
@ -41,7 +42,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
for place in places:
if os.path.exists(place):
for file in os.listdir(place):
for file in glob.iglob(place + '**/**', recursive=True):
full_path = os.path.join(place, file)
if os.path.isdir(full_path):
continue