update

app.py

@@ -24,31 +24,6 @@ from controlnet_aux import OpenposeDetector
 from transformers import DPTImageProcessor, DPTForDepthEstimation
 import gradio as gr
 
-def get_depth_map(image):
-    image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda")
-    with torch.no_grad(), torch.autocast("cuda"):
-        depth_map = depth_estimator(image).predicted_depth
-
-    depth_map = torch.nn.functional.interpolate(
-        depth_map.unsqueeze(1),
-        size=(1024, 1024),
-        mode="bicubic",
-        align_corners=False,
-    )
-    depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
-    depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
-    depth_map = (depth_map - depth_min) / (depth_max - depth_min)
-    image = torch.cat([depth_map] * 3, dim=1)
-
-    image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
-    image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
-    return image
-
-def get_canny_image(image, t1=100, t2=200):
-    image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
-    edges = cv2.Canny(image, t1, t2)
-    return Image.fromarray(edges, "L")
-
 # global variable
 MAX_SEED = np.iinfo(np.int32).max
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -104,6 +79,31 @@ controlnet_depth = ControlNetModel.from_pretrained(
     controlnet_depth_model, torch_dtype=dtype
 ).to(device)
 
+def get_depth_map(image):
+    image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda")
+    with torch.no_grad(), torch.autocast("cuda"):
+        depth_map = depth_estimator(image).predicted_depth
+
+    depth_map = torch.nn.functional.interpolate(
+        depth_map.unsqueeze(1),
+        size=(1024, 1024),
+        mode="bicubic",
+        align_corners=False,
+    )
+    depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
+    depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
+    depth_map = (depth_map - depth_min) / (depth_max - depth_min)
+    image = torch.cat([depth_map] * 3, dim=1)
+
+    image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
+    image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
+    return image
+
+def get_canny_image(image, t1=100, t2=200):
+    image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    edges = cv2.Canny(image, t1, t2)
+    return Image.fromarray(edges, "L")
+
 controlnet_map = {
     "pose": controlnet_pose,
     "canny": controlnet_canny,