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Unlimited Token Works

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Unlimited tokens actually work now. Works with textual inversion too. Replaces the previous not-so-much-working implementation.
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hentailord85ez 2022-10-10 05:28:06 +01:00 committed by AUTOMATIC1111
parent f347ddfd80
commit b340439586
1 changed files with 46 additions and 23 deletions
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61
modules/sd_hijack.py
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@ -43,10 +43,7 @@ def undo_optimizations():
def get_target_prompt_token_count(token_count):
if token_count < 75:
return 75
return math.ceil(token_count / 10) * 10
return math.ceil(max(token_count, 1) / 75) * 75
class StableDiffusionModelHijack:
@ -127,7 +124,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
self.token_mults[ident] = mult
def tokenize_line(self, line, used_custom_terms, hijack_comments):
id_start = self.wrapped.tokenizer.bos_token_id
id_end = self.wrapped.tokenizer.eos_token_id
if opts.enable_emphasis:
@ -154,7 +150,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
i += 1
else:
emb_len = int(embedding.vec.shape[0])
fixes.append((len(remade_tokens), embedding))
iteration = len(remade_tokens) // 75
fixes.append((iteration, (len(remade_tokens) % 75, embedding)))
remade_tokens += [0] * emb_len
multipliers += [weight] * emb_len
used_custom_terms.append((embedding.name, embedding.checksum()))
@ -162,10 +159,10 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
token_count = len(remade_tokens)
prompt_target_length = get_target_prompt_token_count(token_count)
tokens_to_add = prompt_target_length - len(remade_tokens) + 1
tokens_to_add = prompt_target_length - len(remade_tokens)
remade_tokens = [id_start] + remade_tokens + [id_end] * tokens_to_add
multipliers = [1.0] + multipliers + [1.0] * tokens_to_add
remade_tokens = remade_tokens + [id_end] * tokens_to_add
multipliers = multipliers + [1.0] * tokens_to_add
return remade_tokens, fixes, multipliers, token_count
@ -262,27 +259,53 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
def forward(self, text):
if opts.use_old_emphasis_implementation:
use_old = opts.use_old_emphasis_implementation
if use_old:
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
else:
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
self.hijack.fixes = hijack_fixes
self.hijack.comments += hijack_comments
if len(used_custom_terms) > 0:
self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
target_token_count = get_target_prompt_token_count(token_count) + 2
if use_old:
self.hijack.fixes = hijack_fixes
return self.process_tokens(remade_batch_tokens, batch_multipliers)
position_ids_array = [min(x, 75) for x in range(target_token_count-1)] + [76]
position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1))
z = None
i = 0
while max(map(len, remade_batch_tokens)) != 0:
rem_tokens = [x[75:] for x in remade_batch_tokens]
rem_multipliers = [x[75:] for x in batch_multipliers]
remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens]
tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device)
self.hijack.fixes = []
for unfiltered in hijack_fixes:
fixes = []
for fix in unfiltered:
if fix[0] == i:
fixes.append(fix[1])
self.hijack.fixes.append(fixes)
z1 = self.process_tokens([x[:75] for x in remade_batch_tokens], [x[:75] for x in batch_multipliers])
z = z1 if z is None else torch.cat((z, z1), axis=-2)
remade_batch_tokens = rem_tokens
batch_multipliers = rem_multipliers
i += 1
return z
def process_tokens(self, remade_batch_tokens, batch_multipliers):
if not opts.use_old_emphasis_implementation:
remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens]
batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
tokens = torch.asarray(remade_batch_tokens).to(device)
outputs = self.wrapped.transformer(input_ids=tokens)
outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=-opts.CLIP_stop_at_last_layers)
if opts.CLIP_stop_at_last_layers > 1:
z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
z = self.wrapped.transformer.text_model.final_layer_norm(z)
@ -290,7 +313,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
z = outputs.last_hidden_state
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers]
batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device)
original_mean = z.mean()
z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)