rebuild base modules

diffsynth/prompters/hunyuan_dit_prompter.py

@@ -0,0 +1,69 @@
+from .base_prompter import BasePrompter
+from ..models.model_manager import ModelManager
+from ..models import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder
+from transformers import BertTokenizer, AutoTokenizer
+import warnings, os
+
+
+class HunyuanDiTPrompter(BasePrompter):
+    def __init__(
+        self,
+        tokenizer_path=None,
+        tokenizer_t5_path=None
+    ):
+        if tokenizer_path is None:
+            base_path = os.path.dirname(os.path.dirname(__file__))
+            tokenizer_path = os.path.join(base_path, "tokenizer_configs/hunyuan_dit/tokenizer")
+        if tokenizer_t5_path is None:
+            base_path = os.path.dirname(os.path.dirname(__file__))
+            tokenizer_t5_path = os.path.join(base_path, "tokenizer_configs/hunyuan_dit/tokenizer_t5")
+        super().__init__()
+        self.tokenizer = BertTokenizer.from_pretrained(tokenizer_path)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            self.tokenizer_t5 = AutoTokenizer.from_pretrained(tokenizer_t5_path)
+        self.text_encoder: HunyuanDiTCLIPTextEncoder = None
+        self.text_encoder_t5: HunyuanDiTT5TextEncoder = None
+
+
+    def fetch_models(self, text_encoder: HunyuanDiTCLIPTextEncoder = None, text_encoder_t5: HunyuanDiTT5TextEncoder = None):
+        self.text_encoder = text_encoder
+        self.text_encoder_t5 = text_encoder_t5
+
+
+    def encode_prompt_using_signle_model(self, prompt, text_encoder, tokenizer, max_length, clip_skip, device):
+        text_inputs = tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=max_length,
+            truncation=True,
+            return_attention_mask=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        attention_mask = text_inputs.attention_mask.to(device)
+        prompt_embeds = text_encoder(
+            text_input_ids.to(device),
+            attention_mask=attention_mask,
+            clip_skip=clip_skip
+        )
+        return prompt_embeds, attention_mask
+    
+
+    def encode_prompt(
+        self,
+        prompt,
+        clip_skip=1,
+        clip_skip_2=1,
+        positive=True,
+        device="cuda"
+    ):
+        prompt = self.process_prompt(prompt, positive=positive)
+        
+        # CLIP
+        prompt_emb, attention_mask = self.encode_prompt_using_signle_model(prompt, self.text_encoder, self.tokenizer, self.tokenizer.model_max_length, clip_skip, device)
+
+        # T5
+        prompt_emb_t5, attention_mask_t5 = self.encode_prompt_using_signle_model(prompt, self.text_encoder_t5, self.tokenizer_t5, self.tokenizer_t5.model_max_length, clip_skip_2, device)
+        
+        return prompt_emb, attention_mask, prompt_emb_t5, attention_mask_t5