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| from .utils import Prompter, tokenize_long_prompt | |
| from transformers import CLIPTokenizer | |
| from ..models import SDXLTextEncoder, SDXLTextEncoder2 | |
| import torch, os | |
| class SDXLPrompter(Prompter): | |
| def __init__( | |
| self, | |
| tokenizer_path=None, | |
| tokenizer_2_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/stable_diffusion/tokenizer") | |
| 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/stable_diffusion_xl/tokenizer_2") | |
| super().__init__() | |
| self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path) | |
| self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path) | |
| def encode_prompt( | |
| self, | |
| text_encoder: SDXLTextEncoder, | |
| text_encoder_2: SDXLTextEncoder2, | |
| prompt, | |
| clip_skip=1, | |
| clip_skip_2=2, | |
| positive=True, | |
| device="cuda" | |
| ): | |
| prompt = self.process_prompt(prompt, positive=positive) | |
| # 1 | |
| input_ids = tokenize_long_prompt(self.tokenizer, prompt).to(device) | |
| prompt_emb_1 = text_encoder(input_ids, clip_skip=clip_skip) | |
| # 2 | |
| input_ids_2 = tokenize_long_prompt(self.tokenizer_2, prompt).to(device) | |
| add_text_embeds, prompt_emb_2 = text_encoder_2(input_ids_2, clip_skip=clip_skip_2) | |
| # Merge | |
| prompt_emb = torch.concatenate([prompt_emb_1, prompt_emb_2], dim=-1) | |
| # For very long prompt, we only use the first 77 tokens to compute `add_text_embeds`. | |
| add_text_embeds = add_text_embeds[0:1] | |
| prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) | |
| return add_text_embeds, prompt_emb | |