StableDiffusion/WebUI
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Update to support embedding with length greater than 1.

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AUTOMATIC 2022-09-03 01:01:58 +03:00
parent 4cafad66d2
commit 0dca0db7eb
2 changed files with 10 additions and 7 deletions
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2
webui.bat
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@ -7,7 +7,7 @@ set VENV_DIR=venv
mkdir tmp 2>NUL
set TORCH_COMMAND=pip install torch --extra-index-url https://download.pytorch.org/whl/cu113
set TORCH_COMMAND=pip install torch==1.12.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
set REQS_FILE=requirements_versions.txt
%PYTHON% -c "" >tmp/stdout.txt 2>tmp/stderr.txt

15
webui.py
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@ -746,9 +746,9 @@ class StableDiffusionModelHijack:
if hasattr(param_dict, '_parameters'):
param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11
assert len(param_dict) == 1, 'embedding file has multiple terms in it'
emb = next(iter(param_dict.items()))[1].reshape(768)
self.word_embeddings[name] = emb
self.word_embeddings_checksums[name] = f'{const_hash(emb)&0xffff:04x}'
emb = next(iter(param_dict.items()))[1]
self.word_embeddings[name] = emb.detach()
self.word_embeddings_checksums[name] = f'{const_hash(emb.reshape(-1))&0xffff:04x}'
ids = tokenizer([name], add_special_tokens=False)['input_ids'][0]
@ -838,9 +838,10 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
found = False
for ids, word in possible_matches:
if tokens[i:i+len(ids)] == ids:
emb_len = int(self.hijack.word_embeddings[word].shape[0])
fixes.append((len(remade_tokens), word))
remade_tokens.append(777)
multipliers.append(mult)
remade_tokens += [0] * emb_len
multipliers += [mult] * emb_len
i += len(ids) - 1
found = True
used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
@ -903,7 +904,9 @@ class EmbeddingsWithFixes(nn.Module):
if batch_fixes is not None:
for fixes, tensor in zip(batch_fixes, inputs_embeds):
for offset, word in fixes:
tensor[offset] = self.embeddings.word_embeddings[word]
emb = self.embeddings.word_embeddings[word]
emb_len = min(tensor.shape[0]-offset, emb.shape[0])
tensor[offset:offset+emb_len] = self.embeddings.word_embeddings[word][0:emb_len]
return inputs_embeds