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from .base_prompter import BasePrompter |
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from ..models.model_manager import ModelManager |
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from ..models import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3 |
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from transformers import CLIPTokenizer, T5TokenizerFast |
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import os, torch |
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class SD3Prompter(BasePrompter): |
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def __init__( |
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self, |
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tokenizer_1_path=None, |
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tokenizer_2_path=None, |
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tokenizer_3_path=None |
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): |
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if tokenizer_1_path is None: |
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base_path = os.path.dirname(os.path.dirname(__file__)) |
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tokenizer_1_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_1") |
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if tokenizer_2_path is None: |
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base_path = os.path.dirname(os.path.dirname(__file__)) |
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tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_2") |
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if tokenizer_3_path is None: |
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base_path = os.path.dirname(os.path.dirname(__file__)) |
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tokenizer_3_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_3") |
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super().__init__() |
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self.tokenizer_1 = CLIPTokenizer.from_pretrained(tokenizer_1_path) |
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self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path) |
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self.tokenizer_3 = T5TokenizerFast.from_pretrained(tokenizer_3_path) |
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self.text_encoder_1: SD3TextEncoder1 = None |
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self.text_encoder_2: SD3TextEncoder2 = None |
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self.text_encoder_3: SD3TextEncoder3 = None |
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def fetch_models(self, text_encoder_1: SD3TextEncoder1 = None, text_encoder_2: SD3TextEncoder2 = None, text_encoder_3: SD3TextEncoder3 = None): |
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self.text_encoder_1 = text_encoder_1 |
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self.text_encoder_2 = text_encoder_2 |
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self.text_encoder_3 = text_encoder_3 |
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def encode_prompt_using_clip(self, prompt, text_encoder, tokenizer, max_length, device): |
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input_ids = tokenizer( |
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prompt, |
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return_tensors="pt", |
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padding="max_length", |
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max_length=max_length, |
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truncation=True |
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).input_ids.to(device) |
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pooled_prompt_emb, prompt_emb = text_encoder(input_ids) |
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return pooled_prompt_emb, prompt_emb |
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def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_length, device): |
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input_ids = tokenizer( |
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prompt, |
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return_tensors="pt", |
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padding="max_length", |
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max_length=max_length, |
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truncation=True, |
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add_special_tokens=True, |
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).input_ids.to(device) |
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prompt_emb = text_encoder(input_ids) |
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prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) |
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return prompt_emb |
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def encode_prompt( |
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self, |
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prompt, |
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positive=True, |
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device="cuda", |
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t5_sequence_length=77, |
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): |
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prompt = self.process_prompt(prompt, positive=positive) |
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pooled_prompt_emb_1, prompt_emb_1 = self.encode_prompt_using_clip(prompt, self.text_encoder_1, self.tokenizer_1, 77, device) |
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pooled_prompt_emb_2, prompt_emb_2 = self.encode_prompt_using_clip(prompt, self.text_encoder_2, self.tokenizer_2, 77, device) |
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if self.text_encoder_3 is None: |
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prompt_emb_3 = torch.zeros((prompt_emb_1.shape[0], t5_sequence_length, 4096), dtype=prompt_emb_1.dtype, device=device) |
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else: |
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prompt_emb_3 = self.encode_prompt_using_t5(prompt, self.text_encoder_3, self.tokenizer_3, t5_sequence_length, device) |
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prompt_emb_3 = prompt_emb_3.to(prompt_emb_1.dtype) |
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prompt_emb = torch.cat([ |
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torch.nn.functional.pad(torch.cat([prompt_emb_1, prompt_emb_2], dim=-1), (0, 4096 - 768 - 1280)), |
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prompt_emb_3 |
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], dim=-2) |
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pooled_prompt_emb = torch.cat([pooled_prompt_emb_1, pooled_prompt_emb_2], dim=-1) |
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return prompt_emb, pooled_prompt_emb |
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