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0a2e9f2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | import torch
import torch.nn as nn
import os
from transformers import (
CLIPTextModel,
CLIPTokenizer,
T5EncoderModel,
T5TokenizerFast,
)
from typing import Any, Callable, Dict, List, Optional, Union
from diffusers.models.modeling_utils import ModelMixin
class FluxTextEncoderWithMask(ModelMixin):
def __init__(self, model_path, torch_dtype):
super().__init__()
# CLIP-G
self.tokenizer = CLIPTokenizer.from_pretrained(os.path.join(model_path, 'tokenizer'), torch_dtype=torch_dtype)
self.tokenizer_max_length = (
self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
)
self.text_encoder = CLIPTextModel.from_pretrained(os.path.join(model_path, 'text_encoder'), torch_dtype=torch_dtype)
# T5
self.tokenizer_2 = T5TokenizerFast.from_pretrained(os.path.join(model_path, 'tokenizer_2'))
self.text_encoder_2 = T5EncoderModel.from_pretrained(os.path.join(model_path, 'text_encoder_2'), torch_dtype=torch_dtype)
self._freeze()
def _freeze(self):
for param in self.parameters():
param.requires_grad = False
def _get_t5_prompt_embeds(
self,
prompt: Union[str, List[str]] = None,
num_images_per_prompt: int = 1,
max_sequence_length: int = 256,
device: Optional[torch.device] = None,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
text_inputs = self.tokenizer_2(
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
prompt_attention_mask = text_inputs.attention_mask
prompt_attention_mask = prompt_attention_mask.to(device)
prompt_embeds = self.text_encoder_2(text_input_ids.to(device), attention_mask=prompt_attention_mask, output_hidden_states=False)[0]
dtype = self.text_encoder_2.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)
prompt_attention_mask = prompt_attention_mask.view(batch_size, -1)
prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1)
return prompt_embeds, prompt_attention_mask
def _get_clip_prompt_embeds(
self,
prompt: Union[str, List[str]],
num_images_per_prompt: int = 1,
device: Optional[torch.device] = None,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer_max_length,
truncation=True,
return_overflowing_tokens=False,
return_length=False,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_embeds = self.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=self.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)
prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, -1)
return prompt_embeds
def encode_prompt(self,
prompt,
num_images_per_prompt=1,
device=None,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
pooled_prompt_embeds = self._get_clip_prompt_embeds(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
)
prompt_embeds, prompt_attention_mask = self._get_t5_prompt_embeds(
prompt=prompt,
num_images_per_prompt=num_images_per_prompt,
device=device,
)
return prompt_embeds, prompt_attention_mask, pooled_prompt_embeds
#TODO: 这块需要增加一个max_sequence_length
def forward(self, input_prompts, device, **kwargs):
with torch.no_grad():
prompt_embeds, prompt_attention_mask, pooled_prompt_embeds = self.encode_prompt(input_prompts, 1, device=device)
return prompt_embeds, prompt_attention_mask, pooled_prompt_embeds |