app.py

app.py

@@ -27,6 +27,12 @@ dl_cn_config(cn_dir)
 dl_tagger_model(tagger_dir)
 dl_lora_model(lora_dir)
 
+def make_line(img_path, sigma, gamma):
+    sigma = float(sigma )
+    gamma = float(gamma)
+    return line_process(img_path, sigma, gamma)
+
+
 def load_model(lora_dir, cn_dir):
     device = "cuda" if torch.cuda.is_available() else "cpu"
     dtype = torch.float16
@@ -44,13 +50,11 @@ def load_model(lora_dir, cn_dir):
 
 
 @spaces.GPU
-def predict(input_image_path, line_image, prompt, negative_prompt, controlnet_scale):
+def predict(input_image_path, prompt, negative_prompt, controlnet_scale):
     pipe = load_model(lora_dir, cn_dir) 
-    input_image_pil = Image.open(input_image_path)
-    base_size = input_image_pil.size
-    resize_image = resize_image_aspect_ratio(input_image_pil)
-    white_base_pil = base_generation(resize_image.size, (255, 255, 255, 255)).convert("RGB")
-    line_image = line_image.resize(resize_image.size, Image.LANCZOS)
+    line_image =make_line(input_image_path, 1.4, 0.98)
+    base_size = line_image.size
+    resize_image = resize_image_aspect_ratio(resize_image)
     generator = torch.manual_seed(0)
     last_time = time.time()
     prompt = "masterpiece, best quality, monochrome, lineart, white background, " + prompt
@@ -61,7 +65,7 @@ def predict(input_image_path, line_image, prompt, negative_prompt, controlnet_sc
     print(prompt)
 
     output_image = pipe(
-        image=line_image,
+        image=resize_image,
         control_image=resize_image,
         strength=1.0,
         prompt=prompt,
@@ -95,10 +99,7 @@ class Img2Img:
             tags_list = remove_color(tags)
         return tags_list
 
-    def _make_line(self, img_path, sigma, gamma):
-        sigma = float(sigma )
-        gamma = float(gamma)
-        return line_process(img_path, sigma, gamma)
+
 
     def layout(self):
         css = """
@@ -113,11 +114,6 @@ class Img2Img:
                 with gr.Column():
                     self.input_image_path = gr.Image(label="input_image", type='filepath')
                     self.line_image = gr.Image(label="line_image", type='pil')
-                    with gr.Row():
-                        line_sigma = gr.Slider(label="sigma", minimum=0.1, value=1.4, maximum=3.0, show_label=False)
-                        line_gamma = gr.Slider(label="gamma", minimum=0.5, value=0.98, maximum=2.0, show_label=False)
-                        line_generate_button = gr.Button("line_generate")
-
                     self.prompt = gr.Textbox(label="prompt", lines=3)
                     self.negative_prompt = gr.Textbox(label="negative_prompt", lines=3, value="lowres, error, extra digit, fewer digits, cropped, worst quality,low quality, normal quality, jpeg artifacts, blurry")
 
@@ -129,13 +125,6 @@ class Img2Img:
                 with gr.Column():
                     self.output_image = gr.Image(type="pil", label="output_image")
 
-            line_generate_button.click(
-                        self._make_line,
-                        inputs=[self.input_image_path, line_sigma, line_gamma],
-                        outputs=self.line_image
-            )
-
-
             prompt_analysis_button.click(
                         self.process_prompt_analysis,
                         inputs=[self.input_image_path],
@@ -148,7 +137,7 @@ class Img2Img:
 
             generate_button.click(
                 fn=predict,
-                inputs=[self.input_image_path, self.line_image, self.prompt, self.negative_prompt, self.controlnet_scale],
+                inputs=[self.input_image_path, self.prompt, self.negative_prompt, self.controlnet_scale],
                 outputs=self.output_image
             )
         return demo