Update app.py

app.py

@@ -63,10 +63,6 @@ os.environ["NUMEXPR_NUM_THREADS"] = NUM_THREADS
 if os.environ.get("CACHE_DIR"):
     os.environ["TORCH_HOME"] = os.environ["CACHE_DIR"]
 
-os.environ["TORCH_HOME"] = './'
-
-BUILD_DIR = os.environ.get("LAMA_CLEANER_BUILD_DIR", "app/build")
-
 from share_btn import community_icon_html, loading_icon_html, share_js
 
 HF_TOKEN_SD = os.environ.get('HF_TOKEN_SD')
@@ -74,60 +70,12 @@ HF_TOKEN_SD = os.environ.get('HF_TOKEN_SD')
 device = "cuda" if torch.cuda.is_available() else "cpu"
 print(f'device = {device}')
 
-def get_image_ext(img_bytes):
-    w = imghdr.what("", img_bytes)
-    if w is None:
-        w = "jpeg"
-    return w
-    
-def diffuser_callback(i, t, latents):
-    pass
-
-def preprocess_image(image):
-    w, h = image.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
-    image = image.resize((w, h), resample=PIL.Image.LANCZOS)
-    image = np.array(image).astype(np.float32) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    image = torch.from_numpy(image)
-    return 2.0 * image - 1.0
-
-def preprocess_mask(mask):
-    mask = mask.convert("L")
-    w, h = mask.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
-    mask = mask.resize((w // 8, h // 8), resample=PIL.Image.NEAREST)
-    mask = np.array(mask).astype(np.float32) / 255.0
-    mask = np.tile(mask, (4, 1, 1))
-    mask = mask[None].transpose(0, 1, 2, 3)  # what does this step do?
-    mask = 1 - mask  # repaint white, keep black
-    mask = torch.from_numpy(mask)
-    return mask
-    
-def load_img_1_(nparr, gray: bool = False):
-    # alpha_channel = None
-    # nparr = np.frombuffer(img_bytes, np.uint8)
-    if gray:
-        np_img = cv2.imdecode(nparr, cv2.IMREAD_GRAYSCALE)
-    else:
-        np_img = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED)
-        if len(np_img.shape) == 3 and np_img.shape[2] == 4:
-            alpha_channel = np_img[:, :, -1]
-            np_img = cv2.cvtColor(np_img, cv2.COLOR_BGRA2RGB)
-        else:
-            np_img = cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB)
-
-    return np_img, alpha_channel
-    
 model = None
 
 def model_process(image, mask, alpha_channel, ext):
     global model
     original_shape = image.shape
     interpolation = cv2.INTER_CUBIC
-
-    # image_pil = Image.fromarray(image)
-    # mask_pil = Image.fromarray(mask).convert("L")
     
     size_limit = "Original"
     print(f'size_limit_2_ =  {size_limit}')
@@ -269,15 +217,8 @@ def predict(input):
         ext = 'png'
         
         output = model_process(image, mask, alpha_channel, ext)
-    
-    # output = mask #output.images[0]
-    # output = pipe(prompt = prompt, image=init_image, mask_image=mask,guidance_scale=7.5)
-    # output = input["mask"]
-    # output = None
     return output #, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
 
-print(f'liuyz_500_here_')
-
 css = '''
 .container {max-width: 1150px;margin: auto;padding-top: 1.5rem}
 #image_upload{min-height:512px}
@@ -375,7 +316,7 @@ with image_blocks as demo:
                             full_width=True,
                         )                
                 
-                with gr.Column():
+                # with gr.Column():
                     image_out = gr.Image(label="Output").style(height=512)
                     '''
                     with gr.Group(elem_id="share-btn-container"):