add enhanceface

app_makeup.py

@@ -34,6 +34,7 @@ def image_to_image(
     canny_scale: float,
     lineart_detect: float,
     canny_detect: float,
+    enhance_face: bool = True,
 ):
     w2 = 1.0
     run_task_time = 0
@@ -57,7 +58,7 @@ def image_to_image(
         canny_detect,
     )
     run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
-    enhanced_image = enhance_image(generated_image, False)
+    enhanced_image = enhance_image(generated_image, enhance_face)
     run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
 
     return enhanced_image, generated_image, time_cost_str
@@ -89,6 +90,7 @@ def create_demo() -> gr.Blocks:
                     generate_size = gr.Number(label="Generate Size", value=1024)
                     mask_expansion = gr.Number(label="Mask Expansion", value=10, visible=True)
                     mask_dilation = gr.Slider(minimum=0, maximum=10, value=2, step=1, label="Mask Dilation")
+                    enhance_face = gr.Checkbox(label="Enhance Face", value=False)
                     lineart_scale = gr.Slider(minimum=0, maximum=5, value=0.8, step=0.1, label="Lineart Weights", visible=True)
                     canny_scale = gr.Slider(minimum=0, maximum=5, value=0.4, step=0.1, label="Canny Weights", visible=True)
                     lineart_detect = gr.Number(label="Lineart Detect", value=0.375, visible=True)
@@ -116,7 +118,7 @@ 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, lineart_scale, canny_scale, lineart_detect, canny_detect],
+            inputs=[origin_area_image, input_image_prompt, edit_prompt,seed,w1, num_steps, start_step, guidance_scale, generate_size, lineart_scale, canny_scale, lineart_detect, canny_detect, enhance_face],
             outputs=[enhanced_image, generated_image, generated_cost],
         ).success(
             fn=restore_result,

enhance_utils.py

@@ -2,19 +2,32 @@ import os
 import torch
 import cv2
 import numpy as np
+import subprocess
 
 from PIL import Image
 from gfpgan.utils import GFPGANer
 from basicsr.archs.srvgg_arch import SRVGGNetCompact
 from realesrgan.utils import RealESRGANer
 
-os.system("pip freeze")
+def runcmd(cmd, verbose = False, *args, **kwargs):
+
+    process = subprocess.Popen(
+        cmd,
+        stdout = subprocess.PIPE,
+        stderr = subprocess.PIPE,
+        text = True,
+        shell = True
+    )
+    std_out, std_err = process.communicate()
+    if verbose:
+        print(std_out.strip(), std_err)
+    pass
+
+runcmd("pip freeze")
 if not os.path.exists('GFPGANv1.4.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
+    runcmd("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
 if not os.path.exists('realesr-general-x4v3.pth'):
-    os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
-
-os.makedirs('output', exist_ok=True)
+    runcmd("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
 
 model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
 model_path = 'realesr-general-x4v3.pth'

segment_utils.py

@@ -30,7 +30,7 @@ def restore_result(croper, category, generated_image):
         extension = 'jpg'
 
     path = f"output/{uuid.uuid4()}.{extension}"
-    restored_image.save(path)
+    restored_image.save(path, quality=100)
 
     return restored_image, path