fix: 去除多种子的业务逻辑

app.py

@@ -91,6 +91,7 @@ class Demo:
         
         self.guidance_scale = 1
         self.num_inference_steps = 3
+        self.seed = 42753  # 默认种子值
         
         with gr.Blocks() as demo:
             self.layout()
@@ -124,18 +125,18 @@ class Demo:
             for model_name in model_map.keys():
                 with gr.Row():
                     model_checkbox = gr.Checkbox(label=model_name, value=False)
-                    seed_infr = gr.Number(label="种子值", value=42753)
                     slider_scale_infr = gr.Slider(-4, 4, label="滑块刻度", value=3, info="较大的滑块刻度会导致更强的编辑效果")
-                    self.model_sections.append(((model_checkbox.label, model_checkbox.value), seed_infr.value, slider_scale_infr.value))
+                    self.model_sections.append(((model_checkbox.label, model_checkbox.value), slider_scale_infr.value))
                     
                     # 添加复选框的change事件处理程序
                     model_checkbox.change(
                         fn=self.update_model_sections,
-                        inputs=[gr.Text(value=f"{model_checkbox.label}"), model_checkbox, seed_infr, slider_scale_infr],
+                        inputs=[gr.Text(value=f"{model_checkbox.label}"), model_checkbox, slider_scale_infr],
                         outputs=[]
                     )
 
             with gr.Row():
+                self.seed_infr = gr.Number(label="种子值", value=self.seed)
                 self.start_noise_infr = gr.Slider(
                     600, 900, 
                     value=750, 
@@ -249,7 +250,8 @@ class Demo:
         self.infr_button.click(self.inference, inputs=[
             self.prompt_input_infr,
             self.start_noise_infr,
-            self.model_type
+            self.model_type,
+            self.seed_infr
             ],
             outputs=[
                 self.image_new,
@@ -270,10 +272,10 @@ class Demo:
         outputs=[self.train_button,  self.train_status, self.download, self.model_dropdown]
         )
 
-    def update_model_sections(self, label, checkbox, seed, scale):
+    def update_model_sections(self, label, checkbox, scale):
         for i, section in enumerate(self.model_sections):
             if section[0][0] == label:
-                self.model_sections[i] = ((label, checkbox), seed, scale)
+                self.model_sections[i] = ((label, checkbox), scale)
                 break
 
     def train(self, target_concept,positive_prompt, negative_prompt, rank, iterations_input, lr_input, attributes_input, is_person, train_method_input, pbar = gr.Progress(track_tqdm=True)):
@@ -309,23 +311,24 @@ class Demo:
     def get_selected_models(self):
         # 过滤出被选中的模型数据
         selected = [
-            (section[0][0], section[1], section[2])  # (label, seed, scale)
+            (section[0][0], section[1])  # (label, scale)
             for section in self.model_sections 
             if section[0][1]  # 检查 checkbox value 是否为 True
         ]
         
         if selected:
-            # 解包成三个数组
-            labels, seeds, scales = zip(*selected)
-            return list(labels), list(seeds), list(scales)
+            # 解包成两个数组
+            labels, scales = zip(*selected)
+            return list(labels), list(scales)
         else:
-            return [], [], []
+            return [], []
     
-    def inference(self, prompt, start_noise, model, pbar=gr.Progress(track_tqdm=True)):
+    def inference(self, prompt, start_noise, model, seed, pbar=gr.Progress(track_tqdm=True)):
+        self.seed = seed  # 更新种子值
         result = self.get_selected_models()
         print(111, self.model_sections)
-        model_names, seed_list, scale_list = result
-        print(222, model_names, seed_list, scale_list)
+        model_names, scale_list = result
+        print(222, model_names, scale_list)
 
         if self.current_model != model:
             if model=='SDXL Turbo':
@@ -347,7 +350,7 @@ class Demo:
                 self.current_model = 'SDXL'
         
         networks = []
-        for i, model_name in enumerate(model_names):
+        for model_name in model_names:
             model_path = model_map[model_name]
             unet = self.pipe.unet
             network_type = "c3lier"
@@ -380,17 +383,32 @@ class Demo:
                     train_method=train_method,
                 ).to(self.device, dtype=self.weight_dtype)
             network.load_state_dict(torch.load(model_path))
-            networks.append((network, seed_list[i], scale_list[i]))
-
-        generator = torch.manual_seed(seed_list[0])
-        edited_image = self.pipe(prompt, num_images_per_prompt=1, num_inference_steps=self.num_inference_steps, generator=generator, network=networks[0][0], start_noise=int(start_noise), scale=networks[0][2], unet=unet, guidance_scale=self.guidance_scale).images[0]
-        
-        generator = torch.manual_seed(seed_list[0])
-        original_image = self.pipe(prompt, num_images_per_prompt=1, num_inference_steps=self.num_inference_steps, generator=generator, network=networks[0][0], start_noise=start_noise, scale=0, unet=unet, guidance_scale=self.guidance_scale).images[0]
-        
-        for network, seed, scale in networks[1:]:
-            generator = torch.manual_seed(seed)
-            edited_image = self.pipe(prompt, num_images_per_prompt=1, num_inference_steps=self.num_inference_steps, generator=generator, network=network, start_noise=int(start_noise), scale=scale, unet=unet, guidance_scale=self.guidance_scale).images[0]
+            networks.append(network)
+
+        # 设置种子
+        generator = torch.manual_seed(self.seed)
+        # 生成编辑后的图像（应用多权重）
+        edited_image = self.pipe(
+            prompt,
+            num_images_per_prompt=1,
+            num_inference_steps=self.num_inference_steps,
+            generator=generator,
+            networks=networks,  # 加载多个 LoRA 模型
+            scales=scale_list,   # 设置每个 LoRA 的权重
+            guidance_scale=self.guidance_scale
+        ).images[0]
+
+        # 生成原始图像（不应用权重）
+        generator = torch.manual_seed(self.seed)
+        original_image = self.pipe(
+            prompt,
+            num_images_per_prompt=1,
+            num_inference_steps=self.num_inference_steps,
+            generator=generator,
+            networks=[],  # 不加载任何 LoRA 模型
+            scales=[],    # 不设置任何权重
+            guidance_scale=self.guidance_scale
+        ).images[0]
         
         del unet, networks
         unet = None