Merge pull request #1371 from d8ahazard/master
Fix LDSR, BSRGAN, recursive SD checkpoint Loading
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commit
f253790b40
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@ -69,7 +69,7 @@ class UpscalerBSRGAN(modules.upscaler.Upscaler):
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if not os.path.exists(filename) or filename is None:
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print(f"BSRGAN: Unable to load model from {filename}", file=sys.stderr)
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return None
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model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=2) # define network
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model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4) # define network
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model.load_state_dict(torch.load(filename), strict=True)
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model.eval()
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for k, v in model.named_parameters():
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@ -22,8 +22,20 @@ class UpscalerLDSR(Upscaler):
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self.scalers = [scaler_data]
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def load_model(self, path: str):
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# Remove incorrect project.yaml file if too big
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yaml_path = os.path.join(self.model_path, "project.yaml")
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old_model_path = os.path.join(self.model_path, "model.pth")
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new_model_path = os.path.join(self.model_path, "model.ckpt")
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if os.path.exists(yaml_path):
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statinfo = os.stat(yaml_path)
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if statinfo.st_size >= 10485760:
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print("Removing invalid LDSR YAML file.")
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os.remove(yaml_path)
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if os.path.exists(old_model_path):
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print("Renaming model from model.pth to model.ckpt")
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os.rename(old_model_path, new_model_path)
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model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
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file_name="model.pth", progress=True)
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file_name="model.ckpt", progress=True)
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yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path,
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file_name="project.yaml", progress=True)
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@ -41,5 +53,4 @@ class UpscalerLDSR(Upscaler):
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print("NO LDSR!")
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return img
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ddim_steps = shared.opts.ldsr_steps
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pre_scale = shared.opts.ldsr_pre_down
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return ldsr.super_resolution(img, ddim_steps, self.scale)
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@ -98,9 +98,7 @@ class LDSR:
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im_og = image
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width_og, height_og = im_og.size
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# If we can adjust the max upscale size, then the 4 below should be our variable
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print("Foo")
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down_sample_rate = target_scale / 4
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print(f"Downsample rate is {down_sample_rate}")
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wd = width_og * down_sample_rate
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hd = height_og * down_sample_rate
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width_downsampled_pre = int(wd)
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@ -111,7 +109,7 @@ class LDSR:
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f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
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im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
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else:
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print(f"Down sample rate is 1 from {target_scale} / 4")
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print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
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logs = self.run(model["model"], im_og, diffusion_steps, eta)
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sample = logs["sample"]
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@ -1,3 +1,4 @@
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import glob
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import os
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import shutil
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import importlib
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@ -41,7 +42,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
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for place in places:
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if os.path.exists(place):
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for file in os.listdir(place):
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for file in glob.iglob(place + '**/**', recursive=True):
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full_path = os.path.join(place, file)
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if os.path.isdir(full_path):
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continue
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