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