Update setup.py

setup.py

@@ -15,14 +15,19 @@ from huggingface_hub import snapshot_download
 # torch.backends.cuda.enable_mem_efficient_sdp(False)
 # torch.backends.cuda.enable_flash_sdp(False)
 
+def get_files(folder_path, extensions=['py', 'png', 'JPEG']):
+    if isinstance(extensions, str):
+        extensions = [extensions]
+    else:
+        extensions = [ex.lower() for ex in extensions]
+    result = [x for x in os.listdir(folder_path) if x.split('.')[-1].lower() in extensions]
+    return result
+
 base_model_path='booksforcharlie/stable-diffusion-inpainting'
 allow_tf32=True
 mixed_precision='bf16'
 resume_path='zhengchong/CatVTON'
-tmp_folder = "/rs"
-
-repo_path = snapshot_download(repo_id=resume_path)
-
+tmp_folder = "/workspace/rs"
 
 automasker = AutoMasker(
     densepose_ckpt=os.path.join(repo_path, "DensePose"),
@@ -125,53 +130,61 @@ def inference(
         new_result_image.paste(result_image, (condition_width + 5, 0))
     return new_result_image
 
-person_path         =  '/content/data/CatVTON__00009_.png'
+person_path         = '/workspace/data/person'
 mask_path           = None
-cloth_path          = "/content/data/Coolmate_Áo thun Smile Together.webp"
+cloth_path          = '/workspace/data/cloth'
+result_path         = '/workspace/data/result'
+
+
+if not os.path.isfile(person_path):
+    os.makedirs(person_path, exist_ok=True)
+    person_files = get_files(person_path, extensions=['png', 'jpeg', 'jpg', 'webp'])
+
+    if mask_path:
+        os.makedirs(mask_path, exist_ok=True)
+        mask_files = [os.path.join(mask_path, f'{os.path.splitext(pf)[0]}.png') for pf in person_files]
+    else:
+        mask_files = [mask_path] * len(person_files)
+    person_files = [os.path.join(person_path, pf) for pf in person_files] if person_files else []
+else:
+    person_files = [person_path]
+    mask_files = [mask_path] * len(person_files)
+
+
+if not os.path.isfile(cloth_path):
+    os.makedirs(cloth_path, exist_ok=True)
+    cloth_files = get_files(cloth_path, extensions=['png', 'jpeg', 'jpg', 'webp'])
+    cloth_files = [os.path.join(cloth_path, cf) for cf in cloth_files] if cloth_files else []
+else:
+    cloth_files = [cloth_path]
+
+
+if not os.path.isdir(result_path):
+    os.makedirs(result_path, exist_ok=True)
+
+repo_path = snapshot_download(repo_id=resume_path)
+
 cloth_type          = "upper"
 image_size          = (1024, 768)
 num_inference_steps = 50
 guidance_scale      = 2.5
 seed                = 42
-show_type           = "result only"
-
-
-person_image = Image.open(person_path).convert("RGB")
-mask_image = Image.open(mask_path).convert("L") if mask_path else None
-cloth_image = Image.open(cloth_path).convert("RGB")
-
-vton_img = inference(person_image,
-                     mask_image,
-                     cloth_image,
-                     cloth_type,
-                     image_size,
-                     num_inference_steps,
-                     guidance_scale,
-                     seed,
-                     show_type)
-
-import atexit, requests, subprocess, time, re, os
-from random import randint
-from threading import Timer
-from queue import Queue
-def cloudflared(port, metrics_port, output_queue):
-    atexit.register(lambda p: p.terminate(), subprocess.Popen(['/workspace/cloudflared-linux-amd64', 'tunnel', '--url', f'http://127.0.0.1:{port}', '--metrics', f'127.0.0.1:{metrics_port}'], stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT))
-    attempts, tunnel_url = 0, None
-    while attempts < 10 and not tunnel_url:
-        attempts += 1
-        time.sleep(3)
-        try:
-            tunnel_url = re.search("(?P<url>https?:\/\/[^\s]+.trycloudflare.com)", requests.get(f'http://127.0.0.1:{metrics_port}/metrics').text).group("url")
-        except:
-            pass
-    if not tunnel_url:
-        raise Exception("Can't connect to Cloudflare Edge")
-    output_queue.put(tunnel_url)
-
-output_queue, metrics_port = Queue(), randint(8100, 9000)
-thread = Timer(2, cloudflared, args=(8188, metrics_port, output_queue))
-thread.start()
-thread.join()
-tunnel_url = output_queue.get()
-os.environ['webui_url'] = tunnel_url
-print(tunnel_url)
+show_type           = "all"
+
+
+for person_file, mask_file in zip(person_files, mask_files):
+    for cloth_file in cloth_files:
+        person_instance = Image.open(person_file).convert("RGB")
+        mask_instance = Image.open(mask_file).convert("L") if mask_file else None
+        cloth_instance = Image.open(cloth_file).convert("RGB")
+
+        vton_img = inference(person_instance,
+                             mask_instance,
+                             cloth_instance,
+                             cloth_type,
+                             image_size,
+                             num_inference_steps,
+                             guidance_scale,
+                             seed,
+                             show_type)
+        vton_img.save(os.path.join(result_path, f'{datetime.now().strftime("%Y%m%d%M%S")}.jpg'))