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108 lines
3.8 KiB
Python
108 lines
3.8 KiB
Python
from transformers import CLIPTokenizer
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from ..models import SDTextEncoder, SDXLTextEncoder, SDXLTextEncoder2, load_state_dict
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import torch, os
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def tokenize_long_prompt(tokenizer, prompt):
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# Get model_max_length from self.tokenizer
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length = tokenizer.model_max_length
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# To avoid the warning. set self.tokenizer.model_max_length to +oo.
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tokenizer.model_max_length = 99999999
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# Tokenize it!
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids
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# Determine the real length.
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max_length = (input_ids.shape[1] + length - 1) // length * length
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# Restore tokenizer.model_max_length
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tokenizer.model_max_length = length
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# Tokenize it again with fixed length.
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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
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# Reshape input_ids to fit the text encoder.
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num_sentence = input_ids.shape[1] // length
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input_ids = input_ids.reshape((num_sentence, length))
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return input_ids
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def search_for_embeddings(state_dict):
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embeddings = []
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for k in state_dict:
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if isinstance(state_dict[k], torch.Tensor):
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embeddings.append(state_dict[k])
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elif isinstance(state_dict[k], dict):
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embeddings += search_for_embeddings(state_dict[k])
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return embeddings
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class SDPrompter:
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def __init__(self, tokenizer_path="configs/stable_diffusion/tokenizer"):
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# We use the tokenizer implemented by transformers
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self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
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self.keyword_dict = {}
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def encode_prompt(self, text_encoder: SDTextEncoder, prompt, clip_skip=1, device="cuda"):
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for keyword in self.keyword_dict:
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if keyword in prompt:
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prompt = prompt.replace(keyword, self.keyword_dict[keyword])
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input_ids = tokenize_long_prompt(self.tokenizer, prompt).to(device)
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prompt_emb = text_encoder(input_ids, clip_skip=clip_skip)
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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 load_textual_inversion(self, textual_inversion_dict):
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self.keyword_dict = {}
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additional_tokens = []
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for keyword in textual_inversion_dict:
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tokens, _ = textual_inversion_dict[keyword]
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additional_tokens += tokens
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self.keyword_dict[keyword] = " " + " ".join(tokens) + " "
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self.tokenizer.add_tokens(additional_tokens)
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class SDXLPrompter:
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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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# We use the tokenizer implemented by transformers
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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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device="cuda"
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):
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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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