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from transformers import CLIPTokenizer, AutoTokenizer
from ..models import ModelManager
import os
def tokenize_long_prompt(tokenizer, prompt):
# Get model_max_length from self.tokenizer
length = tokenizer.model_max_length
# To avoid the warning. set self.tokenizer.model_max_length to +oo.
tokenizer.model_max_length = 99999999
# Tokenize it!
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
# Determine the real length.
max_length = (input_ids.shape[1] + length - 1) // length * length
# Restore tokenizer.model_max_length
tokenizer.model_max_length = length
# Tokenize it again with fixed length.
input_ids = tokenizer(
prompt,
return_tensors="pt",
padding="max_length",
max_length=max_length,
truncation=True
).input_ids
# Reshape input_ids to fit the text encoder.
num_sentence = input_ids.shape[1] // length
input_ids = input_ids.reshape((num_sentence, length))
return input_ids
class BeautifulPrompt:
def __init__(self, tokenizer_path=None, model=None):
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
self.model = model
self.template = 'Instruction: Give a simple description of the image to generate a drawing prompt.\nInput: {raw_prompt}\nOutput:'
def __call__(self, raw_prompt):
model_input = self.template.format(raw_prompt=raw_prompt)
input_ids = self.tokenizer.encode(model_input, return_tensors='pt').to(self.model.device)
outputs = self.model.generate(
input_ids,
max_new_tokens=384,
do_sample=True,
temperature=0.9,
top_k=50,
top_p=0.95,
repetition_penalty=1.1,
num_return_sequences=1
)
prompt = raw_prompt + ", " + self.tokenizer.batch_decode(
outputs[:, input_ids.size(1):],
skip_special_tokens=True
)[0].strip()
return prompt
class Translator:
def __init__(self, tokenizer_path=None, model=None):
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
self.model = model
def __call__(self, prompt):
input_ids = self.tokenizer.encode(prompt, return_tensors='pt').to(self.model.device)
output_ids = self.model.generate(input_ids)
prompt = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
return prompt
class Prompter:
def __init__(self):
self.tokenizer: CLIPTokenizer = None
self.keyword_dict = {}
self.translator: Translator = None
self.beautiful_prompt: BeautifulPrompt = None
def load_textual_inversion(self, textual_inversion_dict):
self.keyword_dict = {}
additional_tokens = []
for keyword in textual_inversion_dict:
tokens, _ = textual_inversion_dict[keyword]
additional_tokens += tokens
self.keyword_dict[keyword] = " " + " ".join(tokens) + " "
self.tokenizer.add_tokens(additional_tokens)
def load_beautiful_prompt(self, model, model_path):
model_folder = os.path.dirname(model_path)
self.beautiful_prompt = BeautifulPrompt(tokenizer_path=model_folder, model=model)
if model_folder.endswith("v2"):
self.beautiful_prompt.template = """Converts a simple image description into a prompt. \
Prompts are formatted as multiple related tags separated by commas, plus you can use () to increase the weight, [] to decrease the weight, \
or use a number to specify the weight. You should add appropriate words to make the images described in the prompt more aesthetically pleasing, \
but make sure there is a correlation between the input and output.\n\
### Input: {raw_prompt}\n### Output:"""
def load_translator(self, model, model_path):
model_folder = os.path.dirname(model_path)
self.translator = Translator(tokenizer_path=model_folder, model=model)
def load_from_model_manager(self, model_manager: ModelManager):
self.load_textual_inversion(model_manager.textual_inversion_dict)
if "translator" in model_manager.model:
self.load_translator(model_manager.model["translator"], model_manager.model_path["translator"])
if "beautiful_prompt" in model_manager.model:
self.load_beautiful_prompt(model_manager.model["beautiful_prompt"], model_manager.model_path["beautiful_prompt"])
def process_prompt(self, prompt, positive=True):
for keyword in self.keyword_dict:
if keyword in prompt:
prompt = prompt.replace(keyword, self.keyword_dict[keyword])
if positive and self.translator is not None:
prompt = self.translator(prompt)
print(f"Your prompt is translated: \"{prompt}\"")
if positive and self.beautiful_prompt is not None:
prompt = self.beautiful_prompt(prompt)
print(f"Your prompt is refined by BeautifulPrompt: \"{prompt}\"")
return prompt
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