support qwen prompt refiner

diffsynth/configs/model_config.py

@@ -78,6 +78,7 @@ huggingface_model_loader_configs = [
     ("ChatGLMModel", "diffsynth.models.kolors_text_encoder", "kolors_text_encoder", None),
     ("MarianMTModel", "transformers.models.marian.modeling_marian", "translator", None),
     ("BloomForCausalLM", "transformers.models.bloom.modeling_bloom", "beautiful_prompt", None),
+    ("Qwen2ForCausalLM", "transformers.models.qwen2.modeling_qwen2", "qwen_prompt", None),
     ("T5EncoderModel", "diffsynth.models.flux_text_encoder", "flux_text_encoder_2", "FluxTextEncoder2"),
 ]
 patch_model_loader_configs = [
@@ -307,4 +308,5 @@ Preset_model_id: TypeAlias = Literal[
     "ControlNet_union_sdxl_promax",
     "FLUX.1-dev",
     "SDXL_lora_zyd232_ChineseInkStyle_SDXL_v1_0",
+    "QwenPrompt",
 ]

diffsynth/prompters/__init__.py

@@ -1,4 +1,4 @@
-from .prompt_refiners import Translator, BeautifulPrompt
+from .prompt_refiners import Translator, BeautifulPrompt, QwenPrompt
 from .sd_prompter import SDPrompter
 from .sdxl_prompter import SDXLPrompter
 from .sd3_prompter import SD3Prompter

diffsynth/prompters/prompt_refiners.py

@@ -3,19 +3,18 @@ from ..models.model_manager import ModelManager
 import torch
 
 
-class QwenPrompt(torch.nn.Modile):
-    # This class leverages the open-source Qwen model to translate Chinese prompts into English, 
-    #    with an integrated optimization mechanism for enhanced translation quality.
+
+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("qwen_prompt", require_model_path=True)
 
+    @staticmethod
+    def from_model_manager(model_manager: ModelManager):
+        model, model_path = model_manager.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. \
@@ -23,12 +22,13 @@ Prompts are formatted as multiple related tags separated by commas, plus you can
 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 = QwenPrompt(
+        beautiful_prompt = BeautifulPrompt(
             tokenizer_path=model_path,
             model=model,
             template=template
         )
-        return qwen_prompt
+        return beautiful_prompt
+    
 
     def __call__(self, raw_prompt, positive=True, **kwargs):
         if positive:
@@ -48,49 +48,50 @@ but make sure there is a correlation between the input and output.\n\
                 outputs[:, input_ids.size(1):],
                 skip_special_tokens=True
             )[0].strip()
-            print(f"Your prompt is refined by Qwen : {prompt}")
+            print(f"Your prompt is refined by BeautifulPrompt: {prompt}")
             return prompt
         else:
             return raw_prompt
 
 
-class BeautifulPrompt(torch.nn.Module):
-    def __init__(self, tokenizer_path=None, model=None, template=""):
+
+class QwenPrompt(torch.nn.Module):
+    # This class leverages the open-source Qwen model to translate Chinese prompts into English, 
+    #    with an integrated optimization mechanism for enhanced translation quality.
+    def __init__(self, tokenizer_path=None, model=None, system_prompt=""):
         super().__init__()
         self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
         self.model = model
-        self.template = template
+        self.system_prompt = system_prompt
 
 
     @staticmethod
     def from_model_manager(model_nameger: ModelManager):
-        model, model_path = model_nameger.fetch_model("beautiful_prompt", require_model_path=True)
+        model, model_path = model_nameger.fetch_model("qwen_prompt", require_model_path=True)
         system_prompt = """你是一个英文图片描述家，你看到一段中文图片描述后，尽可能用精简准确的英文，将中文的图片描述的意境用英文短句展示出来，并附带图片风格描述，如果中文描述中没有明确的风格，你需要根据中文意境额外添加一些风格描述，确保图片中的内容丰富生动。\n\n你有如下几种不同的风格描述示例进行参考：\n\n特写风格: Extreme close-up by Oliver Dum, magnified view of a dewdrop on a spider web occupying the frame, the camera focuses closely on the object with the background blurred. The image is lit with natural sunlight, enhancing the vivid textures and contrasting colors.\n\n复古风格: Photograph of women working, Daguerreotype, calotype, tintype, collodion, ambrotype, carte-de-visite, gelatin silver, dry plate, wet plate, stereoscope, albumen print, cyanotype, glass, lantern slide, camera  \n\n动漫风格： a happy dairy cow just finished grazing, in the style of cartoon realism, disney animation, hyper-realistic portraits, 32k uhd, cute cartoonish designs, wallpaper, luminous brushwork  \n\n普通人物场景风格： A candid shot of young best friends dirty, at the skatepark, natural afternoon light, Canon EOS R5, 100mm, F 1.2 aperture setting capturing a moment, cinematic \n\n景观风格: bright beautiful sunrise over the sea and rocky mountains, photorealistic, \n\n设计风格: lionface circle tshirt design, in the style of detailed botanical illustrations, colorful cartoon, exotic atmosphere, 2d game art, white background, contour \n\n动漫风格: Futuristic mecha robot walking through a neon cityscape, with lens flares, dramatic lighting, illustrated like a Gundam anime poster \n\n都市风格: warmly lit room with large monitors on the clean desk, overlooking the city, ultrareal and photorealistic,  \n\n\n请根据上述图片风格，以及中文描述生成对应的英文图片描述  \n\n 请注意：\n\n 如果中文为成语或古诗，不能只根据表层含义来进行描述，而要描述其中的意境！例如：“胸有成竹”的图片场景中并没有竹子，而是描述一个人非常自信的场景，请在英文翻译中不要提到bamboo，以此类推\n\n字数不超过100字"""
-        messages = [{
-            'role': 'system',
-            'content': system_prompt
-        }, {
-            'role': 'user',
-            'content': "{raw_prompt}"
-        }]
-
         qwen_prompt = QwenPrompt(
             tokenizer_path=model_path,
             model=model,
-            template=template
+            system_prompt=system_prompt
         )
         return qwen_prompt
-    
+
 
     def __call__(self, raw_prompt, positive=True, **kwargs):
         if positive:
-            model_input = self.template.format(raw_prompt=raw_prompt)
+            messages = [{
+                'role': 'system',
+                'content': self.system_prompt
+            }, {
+                'role': 'user',
+                'content': raw_prompt
+            }]
             text = self.tokenizer.apply_chat_template(
                 messages,
                 tokenize=False,
                 add_generation_prompt=True
             )
-            model_inputs = self.tokenizer([text], return_tensors="pt").to(device)
+            model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device)
 
             generated_ids = self.model.generate(
                 model_inputs.input_ids,
@@ -100,12 +101,12 @@ class BeautifulPrompt(torch.nn.Module):
                 output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
             ]
 
-            prompt = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-            print(f"Your prompt is refined by Qwen: {prompt}, RawPrompt: {raw_prompt}")
+            prompt = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+            print(f"Your prompt is refined by Qwen: {prompt}")
             return prompt
         else:
             return raw_prompt
-    
+
 
 
 class Translator(torch.nn.Module):