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Running
on
Zero
| from .base_prompter import BasePrompter | |
| from ..models.flux_text_encoder import FluxTextEncoder1, FluxTextEncoder2 | |
| 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: FluxTextEncoder1 = None | |
| self.text_encoder_2: FluxTextEncoder2 = None | |
| def fetch_models(self, text_encoder_1: FluxTextEncoder1 = 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) | |
| 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) | |
| # CLIP | |
| pooled_prompt_emb = self.encode_prompt_using_clip(prompt, self.text_encoder_1, self.tokenizer_1, 77, device) | |
| # T5 | |
| prompt_emb = self.encode_prompt_using_t5(prompt, self.text_encoder_2, self.tokenizer_2, 256, device) | |
| # text_ids | |
| 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 | |