change to inpaint 15

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 
-from app_haircolor_img2img import create_demo as create_demo_haircolor
+from app_haircolor_inpaint_15 import create_demo as create_demo_haircolor
 
 with gr.Blocks(css="style.css") as demo:
     with gr.Tabs():

app_haircolor_inpaint_15.py

@@ -30,7 +30,7 @@ BASE_MODEL = "stable-diffusion-v1-5/stable-diffusion-inpainting"
 
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
-DEFAULT_EDIT_PROMPT = "change hair to linen blonde"
+DEFAULT_EDIT_PROMPT = "RAW photo, Fujifilm XT3,  sharp hair, high resolution hair, hair tones, natural hair, magazine hair, white color hair"
 DEFAULT_NEGATIVE_PROMPT = "worst quality, normal quality, low quality, low res, blurry, text, watermark, logo, banner, extra digits, cropped, jpeg artifacts, signature, username, error, sketch ,duplicate, ugly, monochrome, horror, geometry, mutation, disgusting, poorly drawn face, bad face, fused face, ugly face, worst face, asymmetrical, unrealistic skin texture, bad proportions, out of frame, poorly drawn hands, cloned face, double face"
 
 DEFAULT_CATEGORY = "hair"
@@ -77,6 +77,7 @@ def image_to_image(
     seed: int,
     num_steps: int,
     guidance_scale: float,
+    strength: float,
     generate_size: int,
     cond_scale1: float = 1.0,
     cond_scale2: float = 0.6,
@@ -85,9 +86,9 @@ def image_to_image(
     time_cost_str = ''
     run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
     # canny_image = canny_detector(input_image, int(generate_size*1), generate_size)
-    lineart_image = lineart_detector(input_image, 384, generate_size)
+    lineart_image = lineart_detector(input_image, 768, generate_size)
     run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
-    pidiNet_image = pidiNet_detector(input_image, 512, generate_size)
+    pidiNet_image = pidiNet_detector(input_image, 768, generate_size)
     control_image = [lineart_image, pidiNet_image]
 
     generator = torch.Generator(device=DEVICE).manual_seed(seed)
@@ -101,6 +102,7 @@ def image_to_image(
         height=generate_size,
         width=generate_size,
         guidance_scale=guidance_scale,
+        strength=strength,
         num_inference_steps=num_steps,
         controlnet_conditioning_scale=[cond_scale1, cond_scale2],
     ).images[0]
@@ -136,11 +138,12 @@ def create_demo() -> gr.Blocks:
         with gr.Row():
             with gr.Column():
                 edit_prompt = gr.Textbox(lines=1, label="Edit Prompt", value=DEFAULT_EDIT_PROMPT)
-                generate_size = gr.Number(label="Generate Size", value=512)
+                generate_size = gr.Number(label="Generate Size", value=768)
             with gr.Column():
                 num_steps = gr.Slider(minimum=1, maximum=100, value=20, step=1, label="Num Steps")
                 guidance_scale = gr.Slider(minimum=0, maximum=30, value=5, step=0.5, label="Guidance Scale")
             with gr.Column():
+                strength = gr.Slider(minimum=0, maximum=2, value=0.2, step=0.1, label="Strength")
                 with gr.Accordion("Advanced Options", open=False):
                     cond_scale1 = gr.Slider(minimum=0, maximum=3, value=1.2, step=0.1, label="Cond Scale1")
                     cond_scale2 = gr.Slider(minimum=0, maximum=3, value=1.2, step=0.1, label="Cond Scale2")
@@ -167,7 +170,7 @@ def create_demo() -> gr.Blocks:
             outputs=[origin_area_image, mask_image, croper],
         ).success(
             fn=image_to_image,
-            inputs=[origin_area_image, mask_image, edit_prompt,seed, num_steps, guidance_scale, generate_size, cond_scale1, cond_scale2],
+            inputs=[origin_area_image, mask_image, edit_prompt,seed, num_steps, guidance_scale, strength, generate_size, cond_scale1, cond_scale2],
             outputs=[generated_image, generated_cost],
         ).success(
             fn=restore_result,