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Zero
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import torch
def load_text_encoders(args, class_one, class_two):
text_encoder_one = class_one.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
)
text_encoder_two = class_two.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
)
return text_encoder_one, text_encoder_two
def tokenize_prompt(tokenizer, prompt, max_sequence_length):
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
return_length=False,
return_overflowing_tokens=False,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
return text_input_ids
def tokenize_prompt_clip(tokenizer, prompt):
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=77,
truncation=True,
return_length=False,
return_overflowing_tokens=False,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
return text_input_ids
def tokenize_prompt_t5(tokenizer, prompt):
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=512,
truncation=True,
return_length=False,
return_overflowing_tokens=False,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
return text_input_ids
def _encode_prompt_with_t5(
text_encoder,
tokenizer,
max_sequence_length=512,
prompt=None,
num_images_per_prompt=1,
device=None,
text_input_ids=None,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
if tokenizer is not None:
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
return_length=False,
return_overflowing_tokens=False,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
else:
if text_input_ids is None:
raise ValueError("text_input_ids must be provided when the tokenizer is not specified")
prompt_embeds = text_encoder(text_input_ids.to(device))[0]
dtype = text_encoder.dtype
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
_, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
return prompt_embeds
def _encode_prompt_with_clip(
text_encoder,
tokenizer,
prompt: str,
device=None,
text_input_ids=None,
num_images_per_prompt: int = 1,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
if tokenizer is not None:
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=77,
truncation=True,
return_overflowing_tokens=False,
return_length=False,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
else:
if text_input_ids is None:
raise ValueError("text_input_ids must be provided when the tokenizer is not specified")
prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=False)
# Use pooled output of CLIPTextModel
prompt_embeds = prompt_embeds.pooler_output
prompt_embeds = prompt_embeds.to(dtype=text_encoder.dtype, device=device)
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, -1)
return prompt_embeds
def encode_prompt(
text_encoders,
tokenizers,
prompt: str,
max_sequence_length,
device=None,
num_images_per_prompt: int = 1,
text_input_ids_list=None,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
dtype = text_encoders[0].dtype
pooled_prompt_embeds = _encode_prompt_with_clip(
text_encoder=text_encoders[0],
tokenizer=tokenizers[0],
prompt=prompt,
device=device if device is not None else text_encoders[0].device,
num_images_per_prompt=num_images_per_prompt,
text_input_ids=text_input_ids_list[0] if text_input_ids_list else None,
)
prompt_embeds = _encode_prompt_with_t5(
text_encoder=text_encoders[1],
tokenizer=tokenizers[1],
max_sequence_length=max_sequence_length,
prompt=prompt,
num_images_per_prompt=num_images_per_prompt,
device=device if device is not None else text_encoders[1].device,
text_input_ids=text_input_ids_list[1] if text_input_ids_list else None,
)
text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
return prompt_embeds, pooled_prompt_embeds, text_ids
def encode_token_ids(text_encoders, tokens, accelerator, num_images_per_prompt=1, device=None):
text_encoder_clip = text_encoders[0]
text_encoder_t5 = text_encoders[1]
tokens_clip, tokens_t5 = tokens[0], tokens[1]
batch_size = tokens_clip.shape[0]
if device == "cpu":
device = "cpu"
else:
device = accelerator.device
# clip
prompt_embeds = text_encoder_clip(tokens_clip.to(device), output_hidden_states=False)
# Use pooled output of CLIPTextModelpreprocess_train
prompt_embeds = prompt_embeds.pooler_output
prompt_embeds = prompt_embeds.to(dtype=text_encoder_clip.dtype, device=accelerator.device)
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
pooled_prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, -1)
pooled_prompt_embeds = pooled_prompt_embeds.to(dtype=text_encoder_clip.dtype, device=accelerator.device)
# t5
prompt_embeds = text_encoder_t5(tokens_t5.to(device))[0]
dtype = text_encoder_t5.dtype
prompt_embeds = prompt_embeds.to(dtype=dtype, device=accelerator.device)
_, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device=accelerator.device, dtype=dtype)
return prompt_embeds, pooled_prompt_embeds, text_ids |