mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
125 lines
3.8 KiB
Python
Executable File
125 lines
3.8 KiB
Python
Executable File
from ldm.modules.encoders.modules import FrozenCLIPEmbedder, BERTEmbedder
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from ldm.modules.embedding_manager import EmbeddingManager
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from ldm.invoke.globals import Globals
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import argparse, os
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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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else:
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new_placeholder = input(f"Placeholder string '{new_placeholder}' maps to more than a single token. Please enter another string: ")
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token = get_bert_token_for_string(embedder.tknz_fn, new_placeholder) if use_bert else get_clip_token_for_string(embedder.tokenizer, new_placeholder)
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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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truncation=True,
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max_length=77,
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return_length=True,
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return_overflowing_tokens=False,
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padding="max_length",
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return_tensors="pt"
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)
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tokens = batch_encoding["input_ids"]
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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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token = tokenizer(string)
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if torch.count_nonzero(token) == 3:
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return token[0, 1]
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return None
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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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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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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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parser.add_argument(
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"--output_path",
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type=str,
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required=True,
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help="Output path for the merged manager",
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)
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parser.add_argument(
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"-sd", "--use_bert",
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action="store_true",
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help="Flag to denote that we are not merging stable diffusion embeddings"
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)
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args = parser.parse_args()
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Globals.root=args.root_dir
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if args.use_bert:
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embedder = BERTEmbedder(n_embed=1280, n_layer=32).cuda()
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else:
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embedder = FrozenCLIPEmbedder().cuda()
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EmbeddingManager = partial(EmbeddingManager, embedder, ["*"])
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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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for manager_ckpt in args.manager_ckpts:
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print(f"Parsing {manager_ckpt}...")
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manager = EmbeddingManager()
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manager.load(manager_ckpt)
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for placeholder_string in manager.string_to_token_dict:
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if not placeholder_string in string_to_token_dict:
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string_to_token_dict[placeholder_string] = manager.string_to_token_dict[placeholder_string]
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string_to_param_dict[placeholder_string] = manager.string_to_param_dict[placeholder_string]
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placeholder_to_src[placeholder_string] = manager_ckpt
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else:
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new_placeholder, new_token = get_placeholder_loop(placeholder_string, embedder, use_bert=args.use_bert)
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string_to_token_dict[new_placeholder] = new_token
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string_to_param_dict[new_placeholder] = manager.string_to_param_dict[placeholder_string]
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placeholder_to_src[new_placeholder] = manager_ckpt
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print("Saving combined manager...")
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merged_manager = EmbeddingManager()
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merged_manager.string_to_param_dict = string_to_param_dict
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merged_manager.string_to_token_dict = string_to_token_dict
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merged_manager.save(args.output_path)
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print("Managers merged. Final list of placeholders: ")
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print(placeholder_to_src)
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