Update app.py

app.py

@@ -20,6 +20,7 @@ from multiprocessing import Process, Queue
 
 # external
 
+import spaces
 import torch
 import gradio as gr
 from numpy import asarray as array
@@ -200,48 +201,50 @@ def generate_random_string(length):
     characters = str(ascii_letters + digits)
     return ''.join(random.choice(characters) for _ in range(length))
 
+@spaces.GPU(duration=140)
+def pipe_generate(img,p1,p2,motion):
+    global last_motion
+    global pipe
+
+    if last_motion != motion:
+        if last_motion != "":
+            pipe.unload_lora_weights()
+        if motion != "":
+            pipe.load_lora_weights(motion, adapter_name="motion")
+            pipe.fuse_lora()
+            pipe.set_adapters("motion", [0.7])
+        last_motion = motion
+
+    pipe.to(device,dtype=dtype)
+
+    if not img:
+        img = pipe(
+            prompt=p1,
+            height=height,
+            width=width,
+            guidance_scale=accu,
+            num_inference_steps=step,
+            max_sequence_length=seq,
+            generator=torch.Generator("cuda").manual_seed(0)
+        ).images[0]
+
+    return pipe(
+        prompt=p1,
+        negative_prompt=p2,
+        height=height,
+        width=width,
+        ip_adapter_image=img.convert("RGB"),
+        num_inference_steps=step,
+        guidance_scale=accu,
+        num_frames=(fps*time)
+    )
+
 def handle_generate(*inp):
 
     calc_out = []
     
-    def calc(img,p1,p2,motion):
-        global last_motion
-        global pipe
-    
-        if last_motion != motion:
-            if last_motion != "":
-                pipe.unload_lora_weights()
-            if motion != "":
-                pipe.load_lora_weights(motion, adapter_name="motion")
-                pipe.fuse_lora()
-                pipe.set_adapters("motion", [0.7])
-            last_motion = motion
-    
-        pipe.to(device,dtype=dtype)
-
-        if not img:
-            img = pipe(
-                prompt=p1,
-                height=height,
-                width=width,
-                guidance_scale=accu,
-                num_inference_steps=step,
-                max_sequence_length=seq,
-                generator=torch.Generator("cuda").manual_seed(0)
-            ).images[0]
-
-        calc_out.append(
-            pipe(
-                prompt=p1,
-                negative_prompt=p2,
-                height=height,
-                width=width,
-                ip_adapter_image=img.convert("RGB"),
-                num_inference_steps=step,
-                guidance_scale=accu,
-                num_frames=(fps*time)
-            )
-        )
+    def calc(*args):
+        calc_out.append(pipe_generate(*args))
 
     inp = list(inp)
     
@@ -319,7 +322,6 @@ def ui():
                 run_button = gr.Button("START",elem_classes="btn",scale=0)
             with gr.Row():
                 result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False))
-                result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False))
 
         gr.on(
             triggers=[