change to app_haircolor_inpaint_15

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 
-from app_haircolor_pix2pix_sdxl 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

@@ -24,13 +24,13 @@ from controlnet_aux import (
     HEDdetector,
 )
 
-# BASE_MODEL = "stable-diffusion-v1-5/stable-diffusion-v1-5"
+BASE_MODEL = "stable-diffusion-v1-5/stable-diffusion-v1-5"
 # BASE_MODEL = "stable-diffusion-v1-5/stable-diffusion-inpainting"
-BASE_MODEL = "SG161222/Realistic_Vision_V2.0"
+# BASE_MODEL = "SG161222/Realistic_Vision_V2.0"
 
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
-DEFAULT_EDIT_PROMPT = "a woman, blue hair, high detailed"
+DEFAULT_EDIT_PROMPT = "change hair to linen blonde"
 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"
@@ -47,11 +47,11 @@ hed_detector = hed_detector.to(DEVICE)
 
 controlnet = [
     ControlNetModel.from_pretrained(
-        "lllyasviel/control_v11p_sd15_lineart", 
+        "lllyasviel/control_v11e_sd15_ip2p",
         torch_dtype=torch.float16,
     ),
     ControlNetModel.from_pretrained(
-        "lllyasviel/control_v11p_sd15_softedge",
+        "lllyasviel/control_v11p_sd15_lineart", 
         torch_dtype=torch.float16,
     ),
 ]
@@ -59,7 +59,7 @@ controlnet = [
 basepipeline = StableDiffusionControlNetInpaintPipeline.from_pretrained(
     BASE_MODEL,
     torch_dtype=torch.float16,
-    # use_safetensors=True,
+    use_safetensors=True,
     controlnet=controlnet,
 )
 # basepipeline.scheduler = DDIMScheduler.from_config(basepipeline.scheduler.config)
@@ -78,8 +78,8 @@ def image_to_image(
     num_steps: int,
     guidance_scale: float,
     generate_size: int,
-    cond_scale1: float = 1.2,
-    cond_scale2: float = 1.2,
+    cond_scale1: float = 1.0,
+    cond_scale2: float = 0.6,
 ):
     run_task_time = 0
     time_cost_str = ''
@@ -87,8 +87,8 @@ def image_to_image(
     # canny_image = canny_detector(input_image, int(generate_size*1), generate_size)
     lineart_image = lineart_detector(input_image, 384, 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)
-    control_image = [lineart_image, pidiNet_image]
+    # pidiNet_image = pidiNet_detector(input_image, 512, generate_size)
+    control_image = [lineart_image, input_image]
 
     generator = torch.Generator(device=DEVICE).manual_seed(seed)
     generated_image = basepipeline(
@@ -138,12 +138,12 @@ def create_demo() -> gr.Blocks:
                 edit_prompt = gr.Textbox(lines=1, label="Edit Prompt", value=DEFAULT_EDIT_PROMPT)
                 generate_size = gr.Number(label="Generate Size", value=512)
             with gr.Column():
-                num_steps = gr.Slider(minimum=1, maximum=100, value=25, step=1, label="Num Steps")
+                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():
                 with gr.Accordion("Advanced Options", open=False):
-                    cond_scale1 = gr.Slider(minimum=0, maximum=3, value=1.2, step=0.1, label="Lineart Scale")
-                    cond_scale2 = gr.Slider(minimum=0, maximum=3, value=1.2, step=0.1, label="PidiNet Scale")
+                    cond_scale1 = gr.Slider(minimum=0, maximum=3, value=1, step=0.1, label="Cond Scale1")
+                    cond_scale2 = gr.Slider(minimum=0, maximum=3, value=0.6, step=0.1, label="Cond Scale2")
                     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")
                     seed = gr.Number(label="Seed", value=8)