support SD3 textual inversion

diffsynth/models/__init__.py

@@ -567,10 +567,22 @@ class ModelManager:
             if component == "sd3_text_encoder_3":
                 if "text_encoders.t5xxl.transformer.encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight" not in state_dict:
                     continue
-            self.model[component] = component_dict[component]()
+            elif component == "sd3_text_encoder_1":
-            self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
+                # Add additional token embeddings to text encoder
-            self.model[component].to(self.torch_dtype).to(self.device)
+                token_embeddings = [state_dict["text_encoders.clip_l.transformer.text_model.embeddings.token_embedding.weight"]]
-            self.model_path[component] = file_path
+                for keyword in self.textual_inversion_dict:
+                    _, embeddings = self.textual_inversion_dict[keyword]
+                    token_embeddings.append(embeddings.to(dtype=token_embeddings[0].dtype))
+                token_embeddings = torch.concat(token_embeddings, dim=0)
+                state_dict["text_encoders.clip_l.transformer.text_model.embeddings.token_embedding.weight"] = token_embeddings
+                self.model[component] = component_dict[component](vocab_size=token_embeddings.shape[0])
+                self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
+                self.model[component].to(self.torch_dtype).to(self.device)
+            else:
+                self.model[component] = component_dict[component]()
+                self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
+                self.model[component].to(self.torch_dtype).to(self.device)
+                self.model_path[component] = file_path
 
     def load_stable_diffusion_3_t5(self, state_dict, file_path=""):
         component = "sd3_text_encoder_3"

diffsynth/models/sd3_text_encoder.py

@@ -5,8 +5,8 @@ from .sdxl_text_encoder import SDXLTextEncoder2, SDXLTextEncoder2StateDictConver
     
 
 class SD3TextEncoder1(SDTextEncoder):
-    def __init__(self):
+    def __init__(self, vocab_size=49408):
-        super().__init__()
+        super().__init__(vocab_size=vocab_size)
 
     def forward(self, input_ids, clip_skip=2):
         embeds = self.token_embedding(input_ids) + self.position_embeds

diffsynth/prompts/sd3_prompter.py

@@ -20,7 +20,7 @@ class SD3Prompter(Prompter):
             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 = CLIPTokenizer.from_pretrained(tokenizer_1_path)
         self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path)
         self.tokenizer_3 = T5TokenizerFast.from_pretrained(tokenizer_3_path)
 
@@ -61,17 +61,17 @@ class SD3Prompter(Prompter):
         positive=True,
         device="cuda"
     ):
-        prompt = self.process_prompt(prompt, positive=positive)
+        prompt, pure_prompt = self.process_prompt(prompt, positive=positive, require_pure_prompt=True)
         
         # 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_1, prompt_emb_1 = self.encode_prompt_using_clip(prompt, text_encoder_1, self.tokenizer, 77, device)
-        pooled_prompt_emb_2, prompt_emb_2 = self.encode_prompt_using_clip(prompt, text_encoder_2, self.tokenizer_2, 77, device)
+        pooled_prompt_emb_2, prompt_emb_2 = self.encode_prompt_using_clip(pure_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 = self.encode_prompt_using_t5(pure_prompt, text_encoder_3, self.tokenizer_3, 256, device)
             prompt_emb_3 = prompt_emb_3.to(prompt_emb_1.dtype) # float32 -> float16
 
         # Merge

diffsynth/prompts/utils.py

@@ -111,14 +111,27 @@ but make sure there is a correlation between the input and output.\n\
         if "beautiful_prompt" in model_manager.model:
             self.load_beautiful_prompt(model_manager.model["beautiful_prompt"], model_manager.model_path["beautiful_prompt"])
 
-    def process_prompt(self, prompt, positive=True):
+    def add_textual_inversion_tokens(self, prompt):
         for keyword in self.keyword_dict:
             if keyword in prompt:
                 prompt = prompt.replace(keyword, self.keyword_dict[keyword])
+        return prompt
+
+    def del_textual_inversion_tokens(self, prompt):
+        for keyword in self.keyword_dict:
+            if keyword in prompt:
+                prompt = prompt.replace(keyword, "")
+        return prompt
+
+    def process_prompt(self, prompt, positive=True, require_pure_prompt=False):
+        prompt, pure_prompt = self.add_textual_inversion_tokens(prompt), self.del_textual_inversion_tokens(prompt)
         if positive and self.translator is not None:
             prompt = self.translator(prompt)
             print(f"Your prompt is translated: \"{prompt}\"")
         if positive and self.beautiful_prompt is not None:
             prompt = self.beautiful_prompt(prompt)
             print(f"Your prompt is refined by BeautifulPrompt: \"{prompt}\"")
-        return prompt
+        if require_pure_prompt:
+            return prompt, pure_prompt
+        else:
+            return prompt

examples/image_synthesis/sd3_text_to_image_textual_inversion.py

@@ -0,0 +1,32 @@
+from diffsynth import ModelManager, SD3ImagePipeline, download_models, load_state_dict
+import torch
+
+
+# Download models (automatically)
+# `models/stable_diffusion_3/sd3_medium_incl_clips.safetensors`: [link](https://huggingface.co/stabilityai/stable-diffusion-3-medium/resolve/main/sd3_medium_incl_clips.safetensors)
+# `models/textual_inversion/verybadimagenegative_v1.3.pt`: [link](https://civitai.com/api/download/models/25820?type=Model&format=PickleTensor&size=full&fp=fp16)
+download_models(["StableDiffusion3_without_T5", "TextualInversion_VeryBadImageNegative_v1.3"])
+model_manager = ModelManager(torch_dtype=torch.float16, device="cuda")
+model_manager.load_textual_inversions("models/textual_inversion")
+model_manager.load_models(["models/stable_diffusion_3/sd3_medium_incl_clips.safetensors"])
+pipe = SD3ImagePipeline.from_model_manager(model_manager)
+
+
+for seed in range(4):
+    torch.manual_seed(seed)
+    image = pipe(
+        prompt="a girl, highly detailed, absurd res, perfect image",
+        negative_prompt="verybadimagenegative_v1.3",
+        cfg_scale=4.5,
+        num_inference_steps=50, width=1024, height=1024,
+    )
+    image.save(f"image_with_textual_inversion_{seed}.jpg")
+
+    torch.manual_seed(seed)
+    image = pipe(
+        prompt="a girl, highly detailed, absurd res, perfect image",
+        negative_prompt="",
+        cfg_scale=4.5,
+        num_inference_steps=50, width=1024, height=1024,
+    )
+    image.save(f"image_without_textual_inversion_{seed}.jpg")