hf cuda issue

app.py

@@ -663,7 +663,7 @@ def ready_sample(img_cropped, img_original, ex_mask, inpaint_mask, keypts, keypt
         img = cv2.resize(img_cropped["background"][..., :3], opts.image_size, interpolation=cv2.INTER_AREA)
     else:
         img = cv2.resize(img_original[..., :3], opts.image_size, interpolation=cv2.INTER_AREA)
-    sam_predictor.set_image(img)
+    sam_predictor.to("cuda").set_image(img)
     if keypts is None and keypts_np is not None:
         keypts = keypts_np
     else:
@@ -724,7 +724,7 @@ def ready_sample(img_cropped, img_original, ex_mask, inpaint_mask, keypts, keypt
         img,
         keypts,
         hand_mask,
-        device="cuda",
+        device,
         target_size=(256, 256),
         latent_size=(32, 32),
     ):
@@ -760,11 +760,11 @@ def ready_sample(img_cropped, img_original, ex_mask, inpaint_mask, keypts, keypt
         img,
         keypts,
         hand_mask * (1 - inpaint_mask),
-        # device=pre_device,
+        device=pre_device,
         target_size=opts.image_size,
         latent_size=opts.latent_size,
     )
-    latent = opts.latent_scaling_factor * autoencoder.encode(image).sample()
+    latent = opts.latent_scaling_factor * autoencoder.encode(image.cuda()).sample()
     target_cond = torch.cat([heatmaps, torch.zeros_like(mask)], 1)
     ref_cond = torch.cat([latent, heatmaps, mask], 1)
     ref_cond = torch.zeros_like(ref_cond)