mirror of
https://github.com/Sygil-Dev/sygil-webui.git
synced 2024-12-15 14:31:44 +03:00
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
# base webui import and utils.
|
|
from webui_streamlit import st
|
|
from sd_utils import *
|
|
|
|
# streamlit imports
|
|
|
|
|
|
#other imports
|
|
#from transformers import CLIPTextModel, CLIPTokenizer
|
|
|
|
# Temp imports
|
|
|
|
|
|
# end of imports
|
|
#---------------------------------------------------------------------------------------------------------------
|
|
|
|
def load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer, token=None):
|
|
loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu")
|
|
|
|
# separate token and the embeds
|
|
trained_token = list(loaded_learned_embeds.keys())[0]
|
|
embeds = loaded_learned_embeds[trained_token]
|
|
|
|
# cast to dtype of text_encoder
|
|
dtype = text_encoder.get_input_embeddings().weight.dtype
|
|
embeds.to(dtype)
|
|
|
|
# add the token in tokenizer
|
|
token = token if token is not None else trained_token
|
|
num_added_tokens = tokenizer.add_tokens(token)
|
|
i = 1
|
|
while(num_added_tokens == 0):
|
|
print(f"The tokenizer already contains the token {token}.")
|
|
token = f"{token[:-1]}-{i}>"
|
|
print(f"Attempting to add the token {token}.")
|
|
num_added_tokens = tokenizer.add_tokens(token)
|
|
i+=1
|
|
|
|
# resize the token embeddings
|
|
text_encoder.resize_token_embeddings(len(tokenizer))
|
|
|
|
# get the id for the token and assign the embeds
|
|
token_id = tokenizer.convert_tokens_to_ids(token)
|
|
text_encoder.get_input_embeddings().weight.data[token_id] = embeds
|
|
return token
|
|
|
|
#def token_loader()
|
|
learned_token = load_learned_embed_in_clip(f"models/custom/embeddings/Custom Ami.pt", pipe.text_encoder, pipe.tokenizer, "*")
|
|
#model_content["token"] = learned_token
|
|
#models.append(model_content)
|
|
|
|
def layout():
|
|
st.write("Textual Inversion") |