rebuild base modules

diffsynth/prompters/prompt_refiners.py

@@ -0,0 +1,77 @@
+from transformers import AutoTokenizer
+from ..models.model_manager import ModelManager
+import torch
+
+
+
+class BeautifulPrompt(torch.nn.Module):
+    def __init__(self, tokenizer_path=None, model=None, template=""):
+        super().__init__()
+        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+        self.model = model
+        self.template = template
+
+
+    @staticmethod
+    def from_model_manager(model_nameger: ModelManager):
+        model, model_path = model_nameger.fetch_model("beautiful_prompt", require_model_path=True)
+        template = 'Instruction: Give a simple description of the image to generate a drawing prompt.\nInput: {raw_prompt}\nOutput:'
+        if model_path.endswith("v2"):
+            template = """Converts a simple image description into a prompt. \
+Prompts are formatted as multiple related tags separated by commas, plus you can use () to increase the weight, [] to decrease the weight, \
+or use a number to specify the weight. You should add appropriate words to make the images described in the prompt more aesthetically pleasing, \
+but make sure there is a correlation between the input and output.\n\
+### Input: {raw_prompt}\n### Output:"""
+        beautiful_prompt = BeautifulPrompt(
+            tokenizer_path=model_path,
+            model=model,
+            template=template
+        )
+        return beautiful_prompt
+    
+
+    def __call__(self, raw_prompt, positive=True, **kwargs):
+        if positive:
+            model_input = self.template.format(raw_prompt=raw_prompt)
+            input_ids = self.tokenizer.encode(model_input, return_tensors='pt').to(self.model.device)
+            outputs = self.model.generate(
+                input_ids,
+                max_new_tokens=384,
+                do_sample=True,
+                temperature=0.9,
+                top_k=50,
+                top_p=0.95,
+                repetition_penalty=1.1,
+                num_return_sequences=1
+            )
+            prompt = raw_prompt + ", " + self.tokenizer.batch_decode(
+                outputs[:, input_ids.size(1):],
+                skip_special_tokens=True
+            )[0].strip()
+            print(f"Your prompt is refined by BeautifulPrompt: {prompt}")
+            return prompt
+        else:
+            return raw_prompt
+    
+
+
+class Translator(torch.nn.Module):
+    def __init__(self, tokenizer_path=None, model=None):
+        super().__init__()
+        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+        self.model = model
+
+
+    @staticmethod
+    def from_model_manager(model_nameger: ModelManager):
+        model, model_path = model_nameger.fetch_model("translator", require_model_path=True)
+        translator = Translator(tokenizer_path=model_path, model=model)
+        return translator
+    
+
+    def __call__(self, prompt, **kwargs):
+        input_ids = self.tokenizer.encode(prompt, return_tensors='pt').to(self.model.device)
+        output_ids = self.model.generate(input_ids)
+        prompt = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
+        print(f"Your prompt is translated: {prompt}")
+        return prompt