| from .base_prompter import BasePrompter |
| from ..models.model_manager import ModelManager |
| from ..models import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder |
| from transformers import BertTokenizer, AutoTokenizer |
| import warnings, os |
|
|
|
|
| class HunyuanDiTPrompter(BasePrompter): |
| def __init__( |
| self, |
| tokenizer_path=None, |
| tokenizer_t5_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/hunyuan_dit/tokenizer") |
| if tokenizer_t5_path is None: |
| base_path = os.path.dirname(os.path.dirname(__file__)) |
| tokenizer_t5_path = os.path.join(base_path, "tokenizer_configs/hunyuan_dit/tokenizer_t5") |
| super().__init__() |
| self.tokenizer = BertTokenizer.from_pretrained(tokenizer_path) |
| with warnings.catch_warnings(): |
| warnings.simplefilter("ignore") |
| self.tokenizer_t5 = AutoTokenizer.from_pretrained(tokenizer_t5_path) |
| self.text_encoder: HunyuanDiTCLIPTextEncoder = None |
| self.text_encoder_t5: HunyuanDiTT5TextEncoder = None |
|
|
|
|
| def fetch_models(self, text_encoder: HunyuanDiTCLIPTextEncoder = None, text_encoder_t5: HunyuanDiTT5TextEncoder = None): |
| self.text_encoder = text_encoder |
| self.text_encoder_t5 = text_encoder_t5 |
|
|
|
|
| def encode_prompt_using_signle_model(self, prompt, text_encoder, tokenizer, max_length, clip_skip, device): |
| text_inputs = tokenizer( |
| prompt, |
| padding="max_length", |
| max_length=max_length, |
| truncation=True, |
| return_attention_mask=True, |
| return_tensors="pt", |
| ) |
| text_input_ids = text_inputs.input_ids |
| attention_mask = text_inputs.attention_mask.to(device) |
| prompt_embeds = text_encoder( |
| text_input_ids.to(device), |
| attention_mask=attention_mask, |
| clip_skip=clip_skip |
| ) |
| return prompt_embeds, attention_mask |
| |
|
|
| def encode_prompt( |
| self, |
| prompt, |
| clip_skip=1, |
| clip_skip_2=1, |
| positive=True, |
| device="cuda" |
| ): |
| prompt = self.process_prompt(prompt, positive=positive) |
| |
| |
| prompt_emb, attention_mask = self.encode_prompt_using_signle_model(prompt, self.text_encoder, self.tokenizer, self.tokenizer.model_max_length, clip_skip, device) |
|
|
| |
| prompt_emb_t5, attention_mask_t5 = self.encode_prompt_using_signle_model(prompt, self.text_encoder_t5, self.tokenizer_t5, self.tokenizer_t5.model_max_length, clip_skip_2, device) |
| |
| return prompt_emb, attention_mask, prompt_emb_t5, attention_mask_t5 |
|
|
