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Running
on
Zero
File size: 3,127 Bytes
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from transformers import AutoTokenizer
from ..models.model_manager import ModelManager
import torch
class BeautifulPrompt(torch.nn.Module):
def __init__(self, tokenizer_path=None, model=None, template=""):
super().__init__()
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
self.model = model
self.template = template
@staticmethod
def from_model_manager(model_nameger: ModelManager):
model, model_path = model_nameger.fetch_model("beautiful_prompt", require_model_path=True)
template = 'Instruction: Give a simple description of the image to generate a drawing prompt.\nInput: {raw_prompt}\nOutput:'
if model_path.endswith("v2"):
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:"""
beautiful_prompt = BeautifulPrompt(
tokenizer_path=model_path,
model=model,
template=template
)
return beautiful_prompt
def __call__(self, raw_prompt, positive=True, **kwargs):
if positive:
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()
print(f"Your prompt is refined by BeautifulPrompt: {prompt}")
return prompt
else:
return raw_prompt
class Translator(torch.nn.Module):
def __init__(self, tokenizer_path=None, model=None):
super().__init__()
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
self.model = model
@staticmethod
def from_model_manager(model_nameger: ModelManager):
model, model_path = model_nameger.fetch_model("translator", require_model_path=True)
translator = Translator(tokenizer_path=model_path, model=model)
return translator
def __call__(self, prompt, **kwargs):
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]
print(f"Your prompt is translated: {prompt}")
return prompt
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