reducing size to 1/4

app.py

@@ -1,3 +1,4 @@
+import logging
 import gradio as gr
 from PIL import Image
 from src.tryon_pipeline import StableDiffusionXLInpaintPipeline as TryonPipeline
@@ -25,7 +26,6 @@ from preprocess.openpose.run_openpose import OpenPose
 from detectron2.data.detection_utils import convert_PIL_to_numpy,_apply_exif_orientation
 from torchvision.transforms.functional import to_pil_image
 
-
 def pil_to_binary_mask(pil_image, threshold=0):
     np_image = np.array(pil_image)
     grayscale_image = Image.fromarray(np_image).convert("L")
@@ -121,10 +121,10 @@ pipe = TryonPipeline.from_pretrained(
 )
 pipe.unet_encoder = UNet_Encoder
 
-WIDTH = int(4160/2) # 768
-HEIGHT = int(6240/2) # 1024
-POSE_WIDTH = int(WIDTH/4)  # int(WIDTH/2)
-POSE_HEIGHT = int(HEIGHT/4)  #int(HEIGHT/2)
+WIDTH = int(4160/4) # 768
+HEIGHT = int(6240/4) # 1024
+POSE_WIDTH = int(WIDTH/2)  # int(WIDTH/2)
+POSE_HEIGHT = int(HEIGHT/2)  #int(HEIGHT/2)
 
 @spaces.GPU
 def start_tryon(dict,garm_img,garment_des,is_checked,is_checked_crop,denoise_steps,seed):
@@ -158,6 +158,7 @@ def start_tryon(dict,garm_img,garment_des,is_checked,is_checked_crop,denoise_ste
         model_parse, _ = parsing_model(human_img.resize((POSE_WIDTH, POSE_HEIGHT)))
         mask, mask_gray = get_mask_location('hd', "upper_body", model_parse, keypoints)
         mask = mask.resize((WIDTH, HEIGHT))
+        logging.info("Mask location on model identified")
     else:
         mask = pil_to_binary_mask(dict['layers'][0].convert("RGB").resize((WIDTH, HEIGHT)))
         # mask = transforms.ToTensor()(mask)