Update gradio_app.py

gradio_app.py

@@ -65,11 +65,11 @@ video_pipe = LatentVideoDiffusionPipeline.from_pretrained(
     'lllyasviel/paints_undo_multi_frame',
     fp16=True
 )
-
-memory_management.unload_all_models([
-    video_pipe.unet, video_pipe.vae, video_pipe.text_encoder, video_pipe.image_projection, video_pipe.image_encoder,
-    unet, vae, text_encoder
-])
+if is_gpu_associated:
+    memory_management.unload_all_models([
+        video_pipe.unet, video_pipe.vae, video_pipe.text_encoder, video_pipe.image_projection, video_pipe.image_encoder,
+        unet, vae, text_encoder
+    ])
 
 k_sampler = KDiffusionSampler(
     unet=unet,
@@ -132,6 +132,9 @@ def interrogator_process(x):
     if is_shared_ui:
         raise gr.Error("This Space only works in duplicated instances")
 
+    if not is_gpu_associated:
+        raise gr.Error("Please associate a T4 or A10G GPU for this Space")
+
     return wd14tagger.default_interrogator(x)
 
 
@@ -142,6 +145,9 @@ def process(input_fg, prompt, input_undo_steps, image_width, image_height, seed,
     if is_shared_ui:
         raise gr.Error("This Space only works in duplicated instances")
 
+    if not is_gpu_associated:
+        raise gr.Error("Please associate a T4 or A10G GPU for this Space")
+
     rng = torch.Generator(device=memory_management.gpu).manual_seed(int(seed))
 
     memory_management.load_models_to_gpu(vae)
@@ -240,6 +246,9 @@ def process_video(keyframes, prompt, steps, cfg, fps, seed, progress=gr.Progress
     if is_shared_ui:
         raise gr.Error("This Space only works in duplicated instances")
 
+    if not is_gpu_associated:
+        raise gr.Error("Please associate a T4 or A10G GPU for this Space")
+
     result_frames = []
     cropped_images = []