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75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
from .base_prompter import BasePrompter
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from ..models.flux_text_encoder import FluxTextEncoder2
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from ..models.sd3_text_encoder import SD3TextEncoder1
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from transformers import CLIPTokenizer, T5TokenizerFast
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import os, torch
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class FluxPrompter(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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):
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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/flux/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/flux/tokenizer_2")
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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 = T5TokenizerFast.from_pretrained(tokenizer_2_path)
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self.text_encoder_1: SD3TextEncoder1 = None
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self.text_encoder_2: FluxTextEncoder2 = None
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def fetch_models(self, text_encoder_1: SD3TextEncoder1 = None, text_encoder_2: FluxTextEncoder2 = 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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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, _ = text_encoder(input_ids)
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return pooled_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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).input_ids.to(device)
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prompt_emb = text_encoder(input_ids)
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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=512,
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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 = self.encode_prompt_using_clip(prompt, self.text_encoder_1, self.tokenizer_1, 77, device)
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# T5
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prompt_emb = self.encode_prompt_using_t5(prompt, self.text_encoder_2, self.tokenizer_2, t5_sequence_length, device)
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# text_ids
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text_ids = torch.zeros(prompt_emb.shape[0], prompt_emb.shape[1], 3).to(device=device, dtype=prompt_emb.dtype)
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return prompt_emb, pooled_prompt_emb, text_ids
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