support SD3

diffsynth/prompts/__init__.py

@@ -1,3 +1,4 @@
 from .sd_prompter import SDPrompter
 from .sdxl_prompter import SDXLPrompter
+from .sd3_prompter import SD3Prompter
 from .hunyuan_dit_prompter import HunyuanDiTPrompter

diffsynth/prompts/sd3_prompter.py

@@ -0,0 +1,84 @@
+from .utils import Prompter
+from transformers import CLIPTokenizer, T5TokenizerFast
+import os, torch
+
+
+class SD3Prompter(Prompter):
+    def __init__(
+        self,
+        tokenizer_1_path=None,
+        tokenizer_2_path=None,
+        tokenizer_3_path=None
+    ):
+        if tokenizer_1_path is None:
+            base_path = os.path.dirname(os.path.dirname(__file__))
+            tokenizer_1_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_1")
+        if tokenizer_2_path is None:
+            base_path = os.path.dirname(os.path.dirname(__file__))
+            tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_2")
+        if tokenizer_3_path is None:
+            base_path = os.path.dirname(os.path.dirname(__file__))
+            tokenizer_3_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_3")
+        super().__init__()
+        self.tokenizer_1 = CLIPTokenizer.from_pretrained(tokenizer_1_path)
+        self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path)
+        self.tokenizer_3 = T5TokenizerFast.from_pretrained(tokenizer_3_path)
+
+
+    def encode_prompt_using_clip(self, prompt, text_encoder, tokenizer, max_length, device):
+        input_ids = tokenizer(
+            prompt,
+            return_tensors="pt",
+            padding="max_length",
+            max_length=max_length,
+            truncation=True
+        ).input_ids.to(device)
+        pooled_prompt_emb, prompt_emb = text_encoder(input_ids)
+        return pooled_prompt_emb, prompt_emb
+    
+
+    def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_length, device):
+        input_ids = tokenizer(
+            prompt,
+            return_tensors="pt",
+            padding="max_length",
+            max_length=max_length,
+            truncation=True,
+            add_special_tokens=True,
+        ).input_ids.to(device)
+        prompt_emb = text_encoder(input_ids)
+        prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1))
+
+        return prompt_emb
+    
+
+    def encode_prompt(
+        self,
+        text_encoder_1,
+        text_encoder_2,
+        text_encoder_3,
+        prompt,
+        positive=True,
+        device="cuda"
+    ):
+        prompt = self.process_prompt(prompt, positive=positive)
+        
+        # CLIP
+        pooled_prompt_emb_1, prompt_emb_1 = self.encode_prompt_using_clip(prompt, text_encoder_1, self.tokenizer_1, 77, device)
+        pooled_prompt_emb_2, prompt_emb_2 = self.encode_prompt_using_clip(prompt, text_encoder_2, self.tokenizer_2, 77, device)
+
+        # T5
+        if text_encoder_3 is None:
+            prompt_emb_3 = torch.zeros((1, 256, 4096), dtype=prompt_emb_1.dtype, device=device)
+        else:
+            prompt_emb_3 = self.encode_prompt_using_t5(prompt, text_encoder_3, self.tokenizer_3, 256, device)
+            prompt_emb_3 = prompt_emb_3.to(prompt_emb_1.dtype) # float32 -> float16
+
+        # Merge
+        prompt_emb = torch.cat([
+            torch.nn.functional.pad(torch.cat([prompt_emb_1, prompt_emb_2], dim=-1), (0, 4096 - 768 - 1280)),
+            prompt_emb_3
+        ], dim=-2)
+        pooled_prompt_emb = torch.cat([pooled_prompt_emb_1, pooled_prompt_emb_2], dim=-1)
+
+        return prompt_emb, pooled_prompt_emb

diffsynth/prompts/utils.py

@@ -87,7 +87,8 @@ class Prompter:
             tokens, _ = textual_inversion_dict[keyword]
             additional_tokens += tokens
             self.keyword_dict[keyword] = " " + " ".join(tokens) + " "
-        self.tokenizer.add_tokens(additional_tokens)
+        if self.tokenizer is not None:
+            self.tokenizer.add_tokens(additional_tokens)
 
     def load_beautiful_prompt(self, model, model_path):
         model_folder = os.path.dirname(model_path)