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44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
from .utils import Prompter, tokenize_long_prompt
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from transformers import CLIPTokenizer
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from ..models import SDXLTextEncoder, SDXLTextEncoder2
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import torch
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class SDXLPrompter(Prompter):
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def __init__(
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self,
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tokenizer_path="configs/stable_diffusion/tokenizer",
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tokenizer_2_path="configs/stable_diffusion_xl/tokenizer_2"
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):
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super().__init__()
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self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
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self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path)
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def encode_prompt(
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self,
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text_encoder: SDXLTextEncoder,
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text_encoder_2: SDXLTextEncoder2,
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prompt,
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clip_skip=1,
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clip_skip_2=2,
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positive=True,
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device="cuda"
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):
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prompt = self.process_prompt(prompt, positive=positive)
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# 1
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input_ids = tokenize_long_prompt(self.tokenizer, prompt).to(device)
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prompt_emb_1 = text_encoder(input_ids, clip_skip=clip_skip)
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# 2
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input_ids_2 = tokenize_long_prompt(self.tokenizer_2, prompt).to(device)
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add_text_embeds, prompt_emb_2 = text_encoder_2(input_ids_2, clip_skip=clip_skip_2)
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# Merge
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prompt_emb = torch.concatenate([prompt_emb_1, prompt_emb_2], dim=-1)
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# For very long prompt, we only use the first 77 tokens to compute `add_text_embeds`.
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add_text_embeds = add_text_embeds[0:1]
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prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1))
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return add_text_embeds, prompt_emb
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