feature: add a bunch of upscaling weights converted to safetensors

requirements.txt

@@ -1,3 +1,5 @@
 torch
 safetensors
-numpy
+numpy
+gdown
+spandrel

src/convert_to_safetensors.py

@@ -1,31 +0,0 @@
-import os
-
-import torch
-from safetensors.torch import save_file
-
-project_folder = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-def convert_folder_to_safetensors(input_folder: str, output_folder: str):
-    """Recursively converts all torch weights to safetensors format and saves them in the same folder structure in output_folder."""
-    for root, dirs, files in os.walk(input_folder):
-        for file in files:
-            if file.endswith(('.pt', '.pth')):
-                file_path = os.path.join(root, file)
-                relative_path = os.path.relpath(file_path, input_folder)
-                output_path = os.path.join(output_folder, relative_path)
-                output_path = output_path.rsplit('.', 1)[0] + '.safetensors'
-                print(f'\nConverting {file_path} to {output_path}')
-                # Ensure output directory exists
-                os.makedirs(os.path.dirname(output_path), exist_ok=True)
-
-                data = dict(torch.load(file_path, map_location="cpu"))
-                if "params" in data:
-                    data = data["params"]
-                model_dtype = data[list(data.keys())[-1]].dtype
-                print(f"Model dtype: {model_dtype}")
-
-                save_file(data, output_path)
-
-
-if __name__ == "__main__":
-    convert_folder_to_safetensors(f'{project_folder}/unconverted', f'{project_folder}/weights')

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