add pre_enhance_scale

app_base.py

@@ -10,8 +10,8 @@ from segment_utils import(
 )
 from enhance_utils import enhance_image
 
-DEFAULT_SRC_PROMPT = "a woman, photo"
-DEFAULT_EDIT_PROMPT = "a beautiful woman, photo, hollywood style face, 8k, high quality"
+DEFAULT_SRC_PROMPT = "a person"
+DEFAULT_EDIT_PROMPT = "a person with perfect face"
 
 DEFAULT_CATEGORY = "face"
 
@@ -32,13 +32,14 @@ def create_demo() -> gr.Blocks:
         guidance_scale: float,
         generate_size: int,
         pre_enhance: bool = True,
+        pre_enhance_scale: int = 2,
     ):
         w2 = 1.0
         run_task_time = 0
         time_cost_str = ''
         run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
         if pre_enhance:
-            input_image = enhance_image(input_image, enhance_face=True)
+            input_image = enhance_image(input_image, enhance_face=True, scale=pre_enhance_scale)
             input_image = input_image.resize((generate_size, generate_size))
         run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
         run_model = base_run
@@ -87,6 +88,7 @@ def create_demo() -> gr.Blocks:
                     mask_expansion = gr.Number(label="Mask Expansion", value=50, visible=True)
                     mask_dilation = gr.Slider(minimum=0, maximum=10, value=2, step=1, label="Mask Dilation")
                     pre_enhance = gr.Checkbox(label="Pre Enhance", value=False)
+                    pre_enhance_scale = gr.Number(label="Pre Enhance Scale", value=1)
             with gr.Column():
                 seed = gr.Number(label="Seed", value=8)
                 w1 = gr.Number(label="W1", value=1.5)
@@ -110,7 +112,7 @@ def create_demo() -> gr.Blocks:
             outputs=[origin_area_image, croper],
         ).success(
             fn=image_to_image,
-            inputs=[origin_area_image, input_image_prompt, edit_prompt,seed,w1, num_steps, start_step, guidance_scale, generate_size, pre_enhance],
+            inputs=[origin_area_image, input_image_prompt, edit_prompt,seed,w1, num_steps, start_step, guidance_scale, generate_size, pre_enhance, pre_enhance_scale],
             outputs=[enhanced_image, generated_image, generated_cost],
         ).success(
             fn=restore_result,