mirror of
https://github.com/invoke-ai/InvokeAI
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207 lines
10 KiB
Python
207 lines
10 KiB
Python
'''
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This module handles the generation of the conditioning tensors, including management of
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weighted subprompts.
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Useful function exports:
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get_uc_and_c_and_ec() get the conditioned and unconditioned latent, and edited conditioning if we're doing cross-attention control
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split_weighted_subpromopts() split subprompts, normalize and weight them
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log_tokenization() print out colour-coded tokens and warn if truncated
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'''
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import re
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from difflib import SequenceMatcher
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from typing import Union
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import torch
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from .prompt_parser import PromptParser, Blend, FlattenedPrompt, \
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CrossAttentionControlledFragment, CrossAttentionControlSubstitute, CrossAttentionControlAppend, Fragment
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from ..models.diffusion.cross_attention_control import CrossAttentionControl
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from ..models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
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from ..modules.encoders.modules import WeightedFrozenCLIPEmbedder
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def get_uc_and_c_and_ec(prompt_string_uncleaned, model, log_tokens=False, skip_normalize=False):
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# Extract Unconditioned Words From Prompt
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unconditioned_words = ''
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unconditional_regex = r'\[(.*?)\]'
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unconditionals = re.findall(unconditional_regex, prompt_string_uncleaned)
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if len(unconditionals) > 0:
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unconditioned_words = ' '.join(unconditionals)
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# Remove Unconditioned Words From Prompt
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unconditional_regex_compile = re.compile(unconditional_regex)
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clean_prompt = unconditional_regex_compile.sub(' ', prompt_string_uncleaned)
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prompt_string_cleaned = re.sub(' +', ' ', clean_prompt)
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else:
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prompt_string_cleaned = prompt_string_uncleaned
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pp = PromptParser()
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# we don't support conjunctions for now
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parsed_prompt: Union[FlattenedPrompt, Blend] = pp.parse(prompt_string_cleaned).prompts[0]
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parsed_negative_prompt: FlattenedPrompt = pp.parse(unconditioned_words).prompts[0]
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print("parsed prompt to", parsed_prompt)
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conditioning = None
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cac_args:CrossAttentionControl.Arguments = None
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if type(parsed_prompt) is Blend:
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blend: Blend = parsed_prompt
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embeddings_to_blend = None
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for flattened_prompt in blend.prompts:
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this_embedding, _ = build_embeddings_and_tokens_for_flattened_prompt(model, flattened_prompt)
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embeddings_to_blend = this_embedding if embeddings_to_blend is None else torch.cat(
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(embeddings_to_blend, this_embedding))
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conditioning = WeightedFrozenCLIPEmbedder.apply_embedding_weights(embeddings_to_blend.unsqueeze(0),
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blend.weights,
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normalize=blend.normalize_weights)
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else:
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flattened_prompt: FlattenedPrompt = parsed_prompt
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wants_cross_attention_control = type(flattened_prompt) is not Blend \
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and any([issubclass(type(x), CrossAttentionControlledFragment) for x in flattened_prompt.children])
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if wants_cross_attention_control:
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original_prompt = FlattenedPrompt()
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edited_prompt = FlattenedPrompt()
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# for name, a0, a1, b0, b1 in edit_opcodes: only name == 'equal' is currently parsed
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original_token_count = 0
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edited_token_count = 0
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edit_opcodes = []
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edit_options = []
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for fragment in flattened_prompt.children:
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if type(fragment) is CrossAttentionControlSubstitute:
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original_prompt.append(fragment.original)
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edited_prompt.append(fragment.edited)
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to_replace_token_count = get_tokens_length(model, fragment.original)
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replacement_token_count = get_tokens_length(model, fragment.edited)
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edit_opcodes.append(('replace',
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original_token_count, original_token_count + to_replace_token_count,
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edited_token_count, edited_token_count + replacement_token_count
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))
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original_token_count += to_replace_token_count
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edited_token_count += replacement_token_count
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edit_options.append(fragment.options)
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#elif type(fragment) is CrossAttentionControlAppend:
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# edited_prompt.append(fragment.fragment)
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else:
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# regular fragment
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original_prompt.append(fragment)
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edited_prompt.append(fragment)
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count = get_tokens_length(model, [fragment])
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edit_opcodes.append(('equal', original_token_count, original_token_count+count, edited_token_count, edited_token_count+count))
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edit_options.append(None)
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original_token_count += count
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edited_token_count += count
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original_embeddings, original_tokens = build_embeddings_and_tokens_for_flattened_prompt(model, original_prompt)
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# naïvely building a single edited_embeddings like this disregards the effects of changing the absolute location of
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# subsequent tokens when there is >1 edit and earlier edits change the total token count.
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# eg "a cat.swap(smiling dog, s_start=0.5) eating a hotdog.swap(pizza)" - when the 'pizza' edit is active but the
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# 'cat' edit is not, the 'pizza' feature vector will nevertheless be affected by the introduction of the extra
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# token 'smiling' in the inactive 'cat' edit.
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# todo: build multiple edited_embeddings, one for each edit, and pass just the edited fragments through to the CrossAttentionControl functions
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edited_embeddings, edited_tokens = build_embeddings_and_tokens_for_flattened_prompt(model, edited_prompt)
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conditioning = original_embeddings
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edited_conditioning = edited_embeddings
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print('got edit_opcodes', edit_opcodes, 'options', edit_options)
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cac_args = CrossAttentionControl.Arguments(
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edited_conditioning = edited_conditioning,
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edit_opcodes = edit_opcodes,
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edit_options = edit_options
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)
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else:
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conditioning, _ = build_embeddings_and_tokens_for_flattened_prompt(model, flattened_prompt)
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unconditioning, _ = build_embeddings_and_tokens_for_flattened_prompt(model, parsed_negative_prompt)
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return (
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unconditioning, conditioning, InvokeAIDiffuserComponent.ExtraConditioningInfo(
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cross_attention_control_args=cac_args
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)
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)
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def build_token_edit_opcodes(original_tokens, edited_tokens):
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original_tokens = original_tokens.cpu().numpy()[0]
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edited_tokens = edited_tokens.cpu().numpy()[0]
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return SequenceMatcher(None, original_tokens, edited_tokens).get_opcodes()
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def build_embeddings_and_tokens_for_flattened_prompt(model, flattened_prompt: FlattenedPrompt):
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if type(flattened_prompt) is not FlattenedPrompt:
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raise Exception(f"embeddings can only be made from FlattenedPrompts, got {type(flattened_prompt)} instead")
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fragments = [x.text for x in flattened_prompt.children]
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weights = [x.weight for x in flattened_prompt.children]
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embeddings, tokens = model.get_learned_conditioning([fragments], return_tokens=True, fragment_weights=[weights])
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return embeddings, tokens
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def get_tokens_length(model, fragments: list[Fragment]):
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fragment_texts = [x.text for x in fragments]
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tokens = model.cond_stage_model.get_tokens(fragment_texts, include_start_and_end_markers=False)
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return sum([len(x) for x in tokens])
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def split_weighted_subprompts(text, skip_normalize=False)->list:
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"""
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grabs all text up to the first occurrence of ':'
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uses the grabbed text as a sub-prompt, and takes the value following ':' as weight
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if ':' has no value defined, defaults to 1.0
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repeats until no text remaining
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"""
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prompt_parser = re.compile("""
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(?P<prompt> # capture group for 'prompt'
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(?:\\\:|[^:])+ # match one or more non ':' characters or escaped colons '\:'
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) # end 'prompt'
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(?: # non-capture group
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:+ # match one or more ':' characters
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(?P<weight> # capture group for 'weight'
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-?\d+(?:\.\d+)? # match positive or negative integer or decimal number
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)? # end weight capture group, make optional
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\s* # strip spaces after weight
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| # OR
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$ # else, if no ':' then match end of line
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) # end non-capture group
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""", re.VERBOSE)
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parsed_prompts = [(match.group("prompt").replace("\\:", ":"), float(
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match.group("weight") or 1)) for match in re.finditer(prompt_parser, text)]
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if skip_normalize:
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return parsed_prompts
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weight_sum = sum(map(lambda x: x[1], parsed_prompts))
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if weight_sum == 0:
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print(
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"Warning: Subprompt weights add up to zero. Discarding and using even weights instead.")
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equal_weight = 1 / max(len(parsed_prompts), 1)
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return [(x[0], equal_weight) for x in parsed_prompts]
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return [(x[0], x[1] / weight_sum) for x in parsed_prompts]
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# shows how the prompt is tokenized
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# usually tokens have '</w>' to indicate end-of-word,
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# but for readability it has been replaced with ' '
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def log_tokenization(text, model, log=False, weight=1):
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if not log:
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return
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tokens = model.cond_stage_model.tokenizer._tokenize(text)
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tokenized = ""
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discarded = ""
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usedTokens = 0
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totalTokens = len(tokens)
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for i in range(0, totalTokens):
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token = tokens[i].replace('</w>', ' ')
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# alternate color
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s = (usedTokens % 6) + 1
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if i < model.cond_stage_model.max_length:
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tokenized = tokenized + f"\x1b[0;3{s};40m{token}"
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usedTokens += 1
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else: # over max token length
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discarded = discarded + f"\x1b[0;3{s};40m{token}"
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print(f"\n>> Tokens ({usedTokens}), Weight ({weight:.2f}):\n{tokenized}\x1b[0m")
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if discarded != "":
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print(
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f">> Tokens Discarded ({totalTokens-usedTokens}):\n{discarded}\x1b[0m"
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)
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