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import torch
from transformers import CLIPTextModel, CLIPTextModelWithProjection
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, file_path_or_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.dtype = torch_dtype
self.text_encoder = CLIPTextModel.from_pretrained(file_path_or_name, subfolder='text_encoder', torch_dtype=torch_dtype)
self.text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(file_path_or_name,
subfolder='text_encoder_2',
torch_dtype=torch_dtype)
@classmethod
def from_pretrained(cls, file_path_or_name='stabilityai/stable-diffusion-xl-base-1.0',
encode_latents_in_fp16=True, torch_dtype=None, **kwargs):
"""
Create a new instance of SDXLTextEncoder with specified pretrained model parameters.
Args:
file_path_or_name (str): Name or path of the model's text encoders to load.
encode_latents_in_fp16 (bool): Whether to encode latents in fp16.
torch_dtype (torch.dtype): Data type for model parameters.
**kwargs: Additional keyword arguments.
Returns:
SDXLTextEncoder: A new instance of SDXLTextEncoder.
"""
# Update arguments with any additional kwargs
init_args = {'file_path_or_name': file_path_or_name,
'encode_latents_in_fp16': encode_latents_in_fp16,
'torch_dtype': torch_dtype}
init_args.update(kwargs)
# Create and return a new instance of SDXLTextEncoder
return cls(**init_args)
@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
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