Unlimited Token Works
Unlimited tokens actually work now. Works with textual inversion too. Replaces the previous not-so-much-working implementation.
This commit is contained in:
hentailord85ez
AUTOMATIC1111
parent
f347ddfd80
commit
b340439586
1 changed files with 46 additions and 23 deletions
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@ -43,10 +43,7 @@ def undo_optimizations():
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def get_target_prompt_token_count(token_count):
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if token_count < 75:
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return 75
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return math.ceil(token_count / 10) * 10
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return math.ceil(max(token_count, 1) / 75) * 75
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class StableDiffusionModelHijack:
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@ -127,7 +124,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
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self.token_mults[ident] = mult
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def tokenize_line(self, line, used_custom_terms, hijack_comments):
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id_start = self.wrapped.tokenizer.bos_token_id
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id_end = self.wrapped.tokenizer.eos_token_id
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if opts.enable_emphasis:
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@ -154,7 +150,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
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i += 1
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else:
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emb_len = int(embedding.vec.shape[0])
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fixes.append((len(remade_tokens), embedding))
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iteration = len(remade_tokens) // 75
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fixes.append((iteration, (len(remade_tokens) % 75, embedding)))
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remade_tokens += [0] * emb_len
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multipliers += [weight] * emb_len
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used_custom_terms.append((embedding.name, embedding.checksum()))
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@ -162,10 +159,10 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
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token_count = len(remade_tokens)
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prompt_target_length = get_target_prompt_token_count(token_count)
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tokens_to_add = prompt_target_length - len(remade_tokens) + 1
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tokens_to_add = prompt_target_length - len(remade_tokens)
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remade_tokens = [id_start] + remade_tokens + [id_end] * tokens_to_add
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multipliers = [1.0] + multipliers + [1.0] * tokens_to_add
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remade_tokens = remade_tokens + [id_end] * tokens_to_add
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multipliers = multipliers + [1.0] * tokens_to_add
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return remade_tokens, fixes, multipliers, token_count
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@ -260,29 +257,55 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
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hijack_fixes.append(fixes)
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batch_multipliers.append(multipliers)
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return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
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def forward(self, text):
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if opts.use_old_emphasis_implementation:
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use_old = opts.use_old_emphasis_implementation
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if use_old:
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batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
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else:
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batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
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self.hijack.fixes = hijack_fixes
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self.hijack.comments += hijack_comments
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if len(used_custom_terms) > 0:
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self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
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if use_old:
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self.hijack.fixes = hijack_fixes
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return self.process_tokens(remade_batch_tokens, batch_multipliers)
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z = None
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i = 0
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while max(map(len, remade_batch_tokens)) != 0:
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rem_tokens = [x[75:] for x in remade_batch_tokens]
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rem_multipliers = [x[75:] for x in batch_multipliers]
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self.hijack.fixes = []
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for unfiltered in hijack_fixes:
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fixes = []
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for fix in unfiltered:
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if fix[0] == i:
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fixes.append(fix[1])
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self.hijack.fixes.append(fixes)
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z1 = self.process_tokens([x[:75] for x in remade_batch_tokens], [x[:75] for x in batch_multipliers])
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z = z1 if z is None else torch.cat((z, z1), axis=-2)
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remade_batch_tokens = rem_tokens
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batch_multipliers = rem_multipliers
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i += 1
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return z
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def process_tokens(self, remade_batch_tokens, batch_multipliers):
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if not opts.use_old_emphasis_implementation:
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remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens]
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batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
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tokens = torch.asarray(remade_batch_tokens).to(device)
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outputs = self.wrapped.transformer(input_ids=tokens)
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target_token_count = get_target_prompt_token_count(token_count) + 2
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position_ids_array = [min(x, 75) for x in range(target_token_count-1)] + [76]
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position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1))
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remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens]
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tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device)
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outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=-opts.CLIP_stop_at_last_layers)
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if opts.CLIP_stop_at_last_layers > 1:
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z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
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z = self.wrapped.transformer.text_model.final_layer_norm(z)
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@ -290,7 +313,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
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z = outputs.last_hidden_state
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# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
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batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers]
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batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
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batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device)
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original_mean = z.mean()
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z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
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