| | from .base_prompter import BasePrompter |
| | from ..models.flux_text_encoder import FluxTextEncoder2 |
| | from ..models.sd3_text_encoder import SD3TextEncoder1 |
| | from transformers import CLIPTokenizer, T5TokenizerFast |
| | import os, torch |
| |
|
| |
|
| | class FluxPrompter(BasePrompter): |
| | def __init__( |
| | self, |
| | tokenizer_1_path=None, |
| | tokenizer_2_path=None |
| | ): |
| | if tokenizer_1_path is None: |
| | base_path = os.path.dirname(os.path.dirname(__file__)) |
| | tokenizer_1_path = os.path.join(base_path, "tokenizer_configs/flux/tokenizer_1") |
| | if tokenizer_2_path is None: |
| | base_path = os.path.dirname(os.path.dirname(__file__)) |
| | tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/flux/tokenizer_2") |
| | super().__init__() |
| | self.tokenizer_1 = CLIPTokenizer.from_pretrained(tokenizer_1_path) |
| | self.tokenizer_2 = T5TokenizerFast.from_pretrained(tokenizer_2_path) |
| | self.text_encoder_1: SD3TextEncoder1 = None |
| | self.text_encoder_2: FluxTextEncoder2 = None |
| |
|
| |
|
| | def fetch_models(self, text_encoder_1: SD3TextEncoder1 = None, text_encoder_2: FluxTextEncoder2 = None): |
| | self.text_encoder_1 = text_encoder_1 |
| | self.text_encoder_2 = text_encoder_2 |
| |
|
| |
|
| | def encode_prompt_using_clip(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) |
| | pooled_prompt_emb, _ = text_encoder(input_ids) |
| | return pooled_prompt_emb |
| | |
| |
|
| | 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) |
| | return prompt_emb |
| | |
| |
|
| | def encode_prompt( |
| | self, |
| | prompt, |
| | positive=True, |
| | device="cuda", |
| | t5_sequence_length=512, |
| | ): |
| | prompt = self.process_prompt(prompt, positive=positive) |
| | |
| | |
| | pooled_prompt_emb = self.encode_prompt_using_clip(prompt, self.text_encoder_1, self.tokenizer_1, 77, device) |
| | |
| | |
| | prompt_emb = self.encode_prompt_using_t5(prompt, self.text_encoder_2, self.tokenizer_2, t5_sequence_length, device) |
| |
|
| | |
| | text_ids = torch.zeros(prompt_emb.shape[0], prompt_emb.shape[1], 3).to(device=device, dtype=prompt_emb.dtype) |
| |
|
| | return prompt_emb, pooled_prompt_emb, text_ids |
| |
|
