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import torch
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
class SDXLTextEncoder(torch.nn.Module):
"""Wrapper around HuggingFace text encoders for SDXL.
Creates two text encoders (a CLIPTextModel and CLIPTextModelWithProjection) that behave like one.
Args:
model_name (str): Name of the model's text encoders to load. Defaults to 'stabilityai/stable-diffusion-xl-base-1.0'.
encode_latents_in_fp16 (bool): Whether to encode latents in fp16. Defaults to True.
"""
def __init__(self, model_name='stabilityai/stable-diffusion-xl-base-1.0', encode_latents_in_fp16=True, torch_dtype=None):
super().__init__()
if torch_dtype is None:
torch_dtype = torch.float16 if encode_latents_in_fp16 else None
self.text_encoder = CLIPTextModel.from_pretrained(model_name, subfolder='text_encoder', torch_dtype=torch_dtype)
self.text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(model_name,
subfolder='text_encoder_2',
torch_dtype=torch_dtype)
@property
def device(self):
return self.text_encoder.device
def forward(self, tokenized_text):
# first text encoder
conditioning = self.text_encoder(tokenized_text[0], output_hidden_states=True).hidden_states[-2]
# second text encoder
text_encoder_2_out = self.text_encoder_2(tokenized_text[1], output_hidden_states=True)
pooled_conditioning = text_encoder_2_out[0] # (batch_size, 1280)
conditioning_2 = text_encoder_2_out.hidden_states[-2] # (batch_size, 77, 1280)
conditioning = torch.concat([conditioning, conditioning_2], dim=-1)
return conditioning, pooled_conditioning
class SDXLTokenizer:
"""Wrapper around HuggingFace tokenizers for SDXL.
Tokenizes prompt with two tokenizers and returns the joined output.
Args:
model_name (str): Name of the model's text encoders to load. Defaults to 'stabilityai/stable-diffusion-xl-base-1.0'.
"""
def __init__(self, model_name='stabilityai/stable-diffusion-xl-base-1.0'):
self.tokenizer = CLIPTokenizer.from_pretrained(model_name, subfolder='tokenizer')
self.tokenizer_2 = CLIPTokenizer.from_pretrained(model_name, subfolder='tokenizer_2')
def __call__(self, prompt, padding, truncation, return_tensors, max_length=None):
tokenized_output = self.tokenizer(
prompt,
padding=padding,
max_length=self.tokenizer.model_max_length if max_length is None else max_length,
truncation=truncation,
return_tensors=return_tensors)
tokenized_output_2 = self.tokenizer_2(
prompt,
padding=padding,
max_length=self.tokenizer_2.model_max_length if max_length is None else max_length,
truncation=truncation,
return_tensors=return_tensors)
# Add second tokenizer output to first tokenizer
for key in tokenized_output.keys():
tokenized_output[key] = [tokenized_output[key], tokenized_output_2[key]]
return tokenized_output
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