| from .base_prompter import BasePrompter |
| from ..models.flux_text_encoder import FluxTextEncoder2 |
| from transformers import T5TokenizerFast |
| import os |
|
|
|
|
| class CogPrompter(BasePrompter): |
| def __init__( |
| self, |
| tokenizer_path=None |
| ): |
| if tokenizer_path is None: |
| base_path = os.path.dirname(os.path.dirname(__file__)) |
| tokenizer_path = os.path.join(base_path, "tokenizer_configs/cog/tokenizer") |
| super().__init__() |
| self.tokenizer = T5TokenizerFast.from_pretrained(tokenizer_path) |
| self.text_encoder: FluxTextEncoder2 = None |
|
|
|
|
| def fetch_models(self, text_encoder: FluxTextEncoder2 = None): |
| self.text_encoder = text_encoder |
|
|
|
|
| def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_length, device): |
| input_ids = tokenizer( |
| prompt, |
| return_tensors="pt", |
| padding="max_length", |
| max_length=max_length, |
| truncation=True, |
| ).input_ids.to(device) |
| prompt_emb = text_encoder(input_ids) |
| prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) |
|
|
| return prompt_emb |
| |
|
|
| def encode_prompt( |
| self, |
| prompt, |
| positive=True, |
| device="cuda" |
| ): |
| prompt = self.process_prompt(prompt, positive=positive) |
| prompt_emb = self.encode_prompt_using_t5(prompt, self.text_encoder, self.tokenizer, 226, device) |
| return prompt_emb |
|
|
