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from transformers import BertModel, BertConfig, T5EncoderModel, T5Config
import torch
class HunyuanDiTCLIPTextEncoder(BertModel):
def __init__(self):
config = BertConfig(
_name_or_path = "",
architectures = ["BertModel"],
attention_probs_dropout_prob = 0.1,
bos_token_id = 0,
classifier_dropout = None,
directionality = "bidi",
eos_token_id = 2,
hidden_act = "gelu",
hidden_dropout_prob = 0.1,
hidden_size = 1024,
initializer_range = 0.02,
intermediate_size = 4096,
layer_norm_eps = 1e-12,
max_position_embeddings = 512,
model_type = "bert",
num_attention_heads = 16,
num_hidden_layers = 24,
output_past = True,
pad_token_id = 0,
pooler_fc_size = 768,
pooler_num_attention_heads = 12,
pooler_num_fc_layers = 3,
pooler_size_per_head = 128,
pooler_type = "first_token_transform",
position_embedding_type = "absolute",
torch_dtype = "float32",
transformers_version = "4.37.2",
type_vocab_size = 2,
use_cache = True,
vocab_size = 47020
)
super().__init__(config, add_pooling_layer=False)
self.eval()
def forward(self, input_ids, attention_mask, clip_skip=1):
input_shape = input_ids.size()
batch_size, seq_length = input_shape
device = input_ids.device
past_key_values_length = 0
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=None,
token_type_ids=None,
inputs_embeds=None,
past_key_values_length=0,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=False,
output_attentions=False,
output_hidden_states=True,
return_dict=True,
)
all_hidden_states = encoder_outputs.hidden_states
prompt_emb = all_hidden_states[-clip_skip]
if clip_skip > 1:
mean, std = all_hidden_states[-1].mean(), all_hidden_states[-1].std()
prompt_emb = (prompt_emb - prompt_emb.mean()) / prompt_emb.std() * std + mean
return prompt_emb
def state_dict_converter(self):
return HunyuanDiTCLIPTextEncoderStateDictConverter()
class HunyuanDiTT5TextEncoder(T5EncoderModel):
def __init__(self):
config = T5Config(
_name_or_path = "../HunyuanDiT/t2i/mt5",
architectures = ["MT5ForConditionalGeneration"],
classifier_dropout = 0.0,
d_ff = 5120,
d_kv = 64,
d_model = 2048,
decoder_start_token_id = 0,
dense_act_fn = "gelu_new",
dropout_rate = 0.1,
eos_token_id = 1,
feed_forward_proj = "gated-gelu",
initializer_factor = 1.0,
is_encoder_decoder = True,
is_gated_act = True,
layer_norm_epsilon = 1e-06,
model_type = "t5",
num_decoder_layers = 24,
num_heads = 32,
num_layers = 24,
output_past = True,
pad_token_id = 0,
relative_attention_max_distance = 128,
relative_attention_num_buckets = 32,
tie_word_embeddings = False,
tokenizer_class = "T5Tokenizer",
transformers_version = "4.37.2",
use_cache = True,
vocab_size = 250112
)
super().__init__(config)
self.eval()
def forward(self, input_ids, attention_mask, clip_skip=1):
outputs = super().forward(
input_ids=input_ids,
attention_mask=attention_mask,
output_hidden_states=True,
)
prompt_emb = outputs.hidden_states[-clip_skip]
if clip_skip > 1:
mean, std = outputs.hidden_states[-1].mean(), outputs.hidden_states[-1].std()
prompt_emb = (prompt_emb - prompt_emb.mean()) / prompt_emb.std() * std + mean
return prompt_emb
def state_dict_converter(self):
return HunyuanDiTT5TextEncoderStateDictConverter()
class HunyuanDiTCLIPTextEncoderStateDictConverter():
def __init__(self):
pass
def from_diffusers(self, state_dict):
state_dict_ = {name[5:]: param for name, param in state_dict.items() if name.startswith("bert.")}
return state_dict_
def from_civitai(self, state_dict):
return self.from_diffusers(state_dict)
class HunyuanDiTT5TextEncoderStateDictConverter():
def __init__(self):
pass
def from_diffusers(self, state_dict):
state_dict_ = {name: param for name, param in state_dict.items() if name.startswith("encoder.")}
state_dict_["shared.weight"] = state_dict["shared.weight"]
return state_dict_
def from_civitai(self, state_dict):
return self.from_diffusers(state_dict)
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