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https://github.com/invoke-ai/InvokeAI
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Global replace [ \t]+$, add "GB" (#1751)
* "GB" * Replace [ \t]+$ global Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
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@ -24,7 +24,7 @@ for f in filenames:
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except PermissionError:
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sys.stderr.write(f'{f} could not be opened due to inadequate permissions\n')
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continue
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@ -398,7 +398,7 @@ SAMPLER_CHOICES = [
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def create_argv_parser():
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parser = argparse.ArgumentParser(
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description="""Generate images using Stable Diffusion.
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Use --web to launch the web interface.
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Use --web to launch the web interface.
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Use --from_file to load prompts from a file path or standard input ("-").
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Otherwise you will be dropped into an interactive command prompt (type -h for help.)
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Other command-line arguments are defaults that can usually be overridden
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@ -8,9 +8,9 @@ from functools import partial
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import torch
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def get_placeholder_loop(placeholder_string, embedder, use_bert):
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new_placeholder = None
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while True:
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if new_placeholder is None:
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new_placeholder = input(f"Placeholder string {placeholder_string} was already used. Please enter a replacement string: ")
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@ -21,7 +21,7 @@ def get_placeholder_loop(placeholder_string, embedder, use_bert):
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if token is not None:
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return new_placeholder, token
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def get_clip_token_for_string(tokenizer, string):
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batch_encoding = tokenizer(
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string,
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@ -37,7 +37,7 @@ def get_clip_token_for_string(tokenizer, string):
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if torch.count_nonzero(tokens - 49407) == 2:
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return tokens[0, 1]
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return None
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def get_bert_token_for_string(tokenizer, string):
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@ -53,16 +53,16 @@ if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--root_dir",
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type=str,
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"--root_dir",
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type=str,
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default='.',
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help="Path to the InvokeAI install directory containing 'models', 'outputs' and 'configs'."
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)
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parser.add_argument(
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"--manager_ckpts",
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type=str,
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nargs="+",
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"--manager_ckpts",
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type=str,
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nargs="+",
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required=True,
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help="Paths to a set of embedding managers to be merged."
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)
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@ -90,7 +90,7 @@ if __name__ == "__main__":
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EmbeddingManager = partial(EmbeddingManager, embedder, ["*"])
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string_to_token_dict = {}
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string_to_token_dict = {}
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string_to_param_dict = torch.nn.ParameterDict()
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placeholder_to_src = {}
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