change to controlnet-p2p app

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 
-from app_haircolor_pix2pix import create_demo as create_demo_haircolor
+from app_haircolor_img2img import create_demo as create_demo_haircolor
 
 with gr.Blocks(css="style.css") as demo:
     with gr.Tabs():

app_haircolor_img2img.py

@@ -29,8 +29,7 @@ BASE_MODEL = "stable-diffusion-v1-5/stable-diffusion-v1-5"
 
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
-DEFAULT_EDIT_PROMPT = "change to blue 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_EDIT_PROMPT = "change hair to blue"
 
 DEFAULT_CATEGORY = "hair"
 
@@ -75,6 +74,7 @@ def image_to_image(
     seed: int,
     num_steps: int,
     guidance_scale: float,
+    image_guidance_scale: float,
     generate_size: int,
     cond_scale1: float = 1.2,
     cond_scale2: float = 1.2,
@@ -82,19 +82,19 @@ def image_to_image(
     run_task_time = 0
     time_cost_str = ''
     run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
-    canny_image = canny_detector(input_image)
+    lineart_image = lineart_detector(input_image, 384, generate_size)
 
-    cond_image = [input_image, canny_image]
+    cond_image = [input_image, lineart_image]
 
     generator = torch.Generator(device=DEVICE).manual_seed(seed)
     generated_image = basepipeline(
         generator=generator,
         prompt=edit_prompt,
-        negative_prompt=DEFAULT_NEGATIVE_PROMPT,
         image=cond_image,
         height=generate_size,
         width=generate_size,
         guidance_scale=guidance_scale,
+        image_guidance_scale=image_guidance_scale,
         num_inference_steps=num_steps,
         controlnet_conditioning_scale=[cond_scale1, cond_scale2],
     ).images[0]
@@ -134,6 +134,7 @@ def create_demo() -> gr.Blocks:
             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")
+                image_guidance_scale = gr.Slider(minimum=0, maximum=30, value=1.5, step=0.1, label="Image Guidance Scale")
             with gr.Column():
                 with gr.Accordion("Advanced Options", open=False):
                     mask_expansion = gr.Number(label="Mask Expansion", value=50, visible=True)
@@ -160,7 +161,7 @@ def create_demo() -> gr.Blocks:
             outputs=[origin_area_image, croper],
         ).success(
             fn=image_to_image,
-            inputs=[origin_area_image, edit_prompt,seed, num_steps, guidance_scale, generate_size, cond_scale1, cond_scale2],
+            inputs=[origin_area_image, edit_prompt,seed, num_steps, guidance_scale, image_guidance_scale, generate_size, cond_scale1, cond_scale2],
             outputs=[generated_image, generated_cost],
         ).success(
             fn=restore_result,