mirror of
https://github.com/modelscope/DiffSynth-Studio.git
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94 lines
3.8 KiB
Python
94 lines
3.8 KiB
Python
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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# CLIP
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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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# T5
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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) # float32 -> float16
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# Merge
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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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