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Running
on
Zero
| from .base_prompter import BasePrompter | |
| from ..models.model_manager import ModelManager | |
| from ..models import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3 | |
| from transformers import CLIPTokenizer, T5TokenizerFast | |
| import os, torch | |
| class SD3Prompter(BasePrompter): | |
| def __init__( | |
| self, | |
| tokenizer_1_path=None, | |
| tokenizer_2_path=None, | |
| tokenizer_3_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/stable_diffusion_3/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/stable_diffusion_3/tokenizer_2") | |
| if tokenizer_3_path is None: | |
| base_path = os.path.dirname(os.path.dirname(__file__)) | |
| tokenizer_3_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_3") | |
| super().__init__() | |
| self.tokenizer_1 = CLIPTokenizer.from_pretrained(tokenizer_1_path) | |
| self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path) | |
| self.tokenizer_3 = T5TokenizerFast.from_pretrained(tokenizer_3_path) | |
| self.text_encoder_1: SD3TextEncoder1 = None | |
| self.text_encoder_2: SD3TextEncoder2 = None | |
| self.text_encoder_3: SD3TextEncoder3 = None | |
| def fetch_models(self, text_encoder_1: SD3TextEncoder1 = None, text_encoder_2: SD3TextEncoder2 = None, text_encoder_3: SD3TextEncoder3 = None): | |
| self.text_encoder_1 = text_encoder_1 | |
| self.text_encoder_2 = text_encoder_2 | |
| self.text_encoder_3 = text_encoder_3 | |
| 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, prompt_emb = text_encoder(input_ids) | |
| return pooled_prompt_emb, 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, | |
| add_special_tokens=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_1, prompt_emb_1 = self.encode_prompt_using_clip(prompt, self.text_encoder_1, self.tokenizer_1, 77, device) | |
| pooled_prompt_emb_2, prompt_emb_2 = self.encode_prompt_using_clip(prompt, self.text_encoder_2, self.tokenizer_2, 77, device) | |
| # T5 | |
| if self.text_encoder_3 is None: | |
| prompt_emb_3 = torch.zeros((prompt_emb_1.shape[0], 256, 4096), dtype=prompt_emb_1.dtype, device=device) | |
| else: | |
| prompt_emb_3 = self.encode_prompt_using_t5(prompt, self.text_encoder_3, self.tokenizer_3, 256, device) | |
| prompt_emb_3 = prompt_emb_3.to(prompt_emb_1.dtype) # float32 -> float16 | |
| # Merge | |
| prompt_emb = torch.cat([ | |
| torch.nn.functional.pad(torch.cat([prompt_emb_1, prompt_emb_2], dim=-1), (0, 4096 - 768 - 1280)), | |
| prompt_emb_3 | |
| ], dim=-2) | |
| pooled_prompt_emb = torch.cat([pooled_prompt_emb_1, pooled_prompt_emb_2], dim=-1) | |
| return prompt_emb, pooled_prompt_emb | |