move spaces.GPU

app.py

@@ -162,12 +162,14 @@ def pipeline_callback(output_queue: SimpleQueue, pipe: Any, step_index: int, tim
     image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0]  # type: ignore[attr-defined]
     image = pipe.image_processor.postprocess(image, output_type="np").squeeze()  # type: ignore[attr-defined]
 
+    # rr.log("mvs/image", rr.Image(image))
+    # rr.log("mvs/latents", rr.Tensor(latents.squeeze()))
+
     output_queue.put(("log", "mvs/image", rr.Image(image)))
     output_queue.put(("log", "mvs/latents", rr.Tensor(latents.squeeze())))
 
     return callback_kwargs
 
-@spaces.GPU
 def generate_mvs(input_image, sample_steps, sample_seed):
 
     seed_everything(sample_seed)
@@ -200,7 +202,6 @@ def generate_mvs(input_image, sample_steps, sample_seed):
     z123_thread.join()
 
 
-@spaces.GPU
 def make3d(images: Image.Image):
     output_queue = SimpleQueue()
     handle = threading.Thread(target=_make3d, args=[output_queue, images])
@@ -314,6 +315,8 @@ def _make3d(output_queue: SimpleQueue, images: Image.Image):
     
     output_queue.put(("mesh", mesh_out))
 
+
+@spaces.GPU
 @rr.thread_local_stream("InstantMesh")
 def log_to_rr(input_image, do_remove_background, sample_steps, sample_seed):
     preprocessed_image = preprocess(input_image, do_remove_background)
@@ -341,6 +344,10 @@ def log_to_rr(input_image, do_remove_background, sample_steps, sample_seed):
         if msg[0] == "log":
             rr.log(msg[1], msg[2])
             yield stream.read()
+        if msg[0] == "mesh":
+            mesh = msg[1]
+
+    # return mesh
 
 _HEADER_ = '''
 <h2><b>Official 🤗 Gradio Demo</b></h2><h2><a href='https://github.com/TencentARC/InstantMesh' target='_blank'><b>InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models</b></a></h2>