initial version

diffsynth/prompts/__init__.py

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+from transformers import CLIPTokenizer
+from ..models import SDTextEncoder, SDXLTextEncoder, SDXLTextEncoder2
+import torch, os
+from safetensors import safe_open
+
+
+def tokenize_long_prompt(tokenizer, prompt):
+    # Get model_max_length from self.tokenizer
+    length = tokenizer.model_max_length
+
+    # To avoid the warning. set self.tokenizer.model_max_length to +oo.
+    tokenizer.model_max_length = 99999999
+
+    # Tokenize it!
+    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+
+    # Determine the real length.
+    max_length = (input_ids.shape[1] + length - 1) // length * length
+
+    # Restore tokenizer.model_max_length
+    tokenizer.model_max_length = length
+    
+    # Tokenize it again with fixed length.
+    input_ids = tokenizer(
+        prompt,
+        return_tensors="pt",
+        padding="max_length",
+        max_length=max_length,
+        truncation=True
+    ).input_ids
+
+    # Reshape input_ids to fit the text encoder.
+    num_sentence = input_ids.shape[1] // length
+    input_ids = input_ids.reshape((num_sentence, length))
+    
+    return input_ids
+
+
+def load_textual_inversion(prompt):
+    # TODO: This module is not enabled now.
+    textual_inversion_files = os.listdir("models/textual_inversion")
+    embeddings_768 = []
+    embeddings_1280 = []
+    for file_name in textual_inversion_files:
+        if not file_name.endswith(".safetensors"):
+            continue
+        keyword = file_name[:-len(".safetensors")]
+        if keyword in prompt:
+            prompt = prompt.replace(keyword, "")
+            with safe_open(f"models/textual_inversion/{file_name}", framework="pt", device="cpu") as f:
+                for k in f.keys():
+                    embedding = f.get_tensor(k).to(torch.float32)
+                if embedding.shape[-1] == 768:
+                    embeddings_768.append(embedding)
+                elif embedding.shape[-1] == 1280:
+                    embeddings_1280.append(embedding)
+
+    if len(embeddings_768)==0:
+        embeddings_768 = torch.zeros((0, 768))
+    else:
+        embeddings_768 = torch.concat(embeddings_768, dim=0)
+
+    if len(embeddings_1280)==0:
+        embeddings_1280 = torch.zeros((0, 1280))
+    else:
+        embeddings_1280 = torch.concat(embeddings_1280, dim=0)
+
+    return prompt, embeddings_768, embeddings_1280
+
+
+class SDPrompter:
+    def __init__(self, tokenizer_path="configs/stable_diffusion/tokenizer"):
+        # We use the tokenizer implemented by transformers
+        self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
+    
+    def encode_prompt(self, text_encoder: SDTextEncoder, prompt, clip_skip=1, device="cuda"):
+        input_ids = tokenize_long_prompt(self.tokenizer, prompt).to(device)
+        prompt_emb = text_encoder(input_ids, clip_skip=clip_skip)
+        prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1))
+
+        return prompt_emb
+
+
+class SDXLPrompter:
+    def __init__(
+        self,
+        tokenizer_path="configs/stable_diffusion/tokenizer",
+        tokenizer_2_path="configs/stable_diffusion_xl/tokenizer_2"
+    ):
+        # We use the tokenizer implemented by transformers
+        self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
+        self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path)
+    
+    def encode_prompt(
+        self,
+        text_encoder: SDXLTextEncoder,
+        text_encoder_2: SDXLTextEncoder2,
+        prompt,
+        clip_skip=1,
+        clip_skip_2=2,
+        device="cuda"
+    ):
+        # 1
+        input_ids = tokenize_long_prompt(self.tokenizer, prompt).to(device)
+        prompt_emb_1 = text_encoder(input_ids, clip_skip=clip_skip)
+
+        # 2
+        input_ids_2 = tokenize_long_prompt(self.tokenizer_2, prompt).to(device)
+        add_text_embeds, prompt_emb_2 = text_encoder_2(input_ids_2, clip_skip=clip_skip_2)
+
+        # Merge
+        prompt_emb = torch.concatenate([prompt_emb_1, prompt_emb_2], dim=-1)
+
+        # For very long prompt, we only use the first 77 tokens to compute `add_text_embeds`.
+        add_text_embeds = add_text_embeds[0:1]
+        prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1))
+        return add_text_embeds, prompt_emb