The bug occurs when using torch.compile on StableVideoDiffusionPipeline, and it happens when passing different images for the second time.

### Describe the bug

I created a page using Gradio to generate videos with the `StableVideoDiffusionPipeline`, and I used `torch.compile(pipeline.unet, mode="reduce-overhead", fullgraph=True) `for acceleration. I noticed that after inference with StableVideoDiffusionPipeline, the GPU memory usage increases from 4.8GB to 16GB, sometimes even up to 35GB. The first time I upload an image, it runs normally, but when I upload a different image for the second time, an error occurs. The error is as follows:

```python
Traceback (most recent call last):
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/gradio/queueing.py", line 622, in process_events
response = await route_utils.call_process_api(
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/gradio/route_utils.py", line 323, in call_process_api
output = await app.get_blocks().process_api(
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/gradio/blocks.py", line 2014, in process_api
result = await self.call_function(
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/gradio/blocks.py", line 1567, in call_function
prediction = await anyio.to_thread.run_sync( # type: ignore
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/anyio/to_thread.py", line 33, in run_sync
return await get_asynclib().run_sync_in_worker_thread(
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 877, in run_sync_in_worker_thread
return await future
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 807, in run
result = context.run(func, *args)
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/gradio/utils.py", line 846, in wrapper
response = f(*args, **kwargs)
File "/u01/SdProject/app/gradio_ui/gen_video.py", line 38, in generate_video_from_image
frames = stable_video(image, pipeline, generator)
File "/u01/SdProject/app/service/sd_inference.py", line 226, in stable_video
frames = pipeline(image, decode_chunk_size=8, generator=generator).frames[0]
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py", line 576, in __call__
noise_pred = self.unet(
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
return forward_call(*args, **kwargs)
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 465, in _fn
return fn(*args, **kwargs)
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
return forward_call(*args, **kwargs)
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/diffusers/models/unets/unet_spatio_temporal_condition.py", line 357, in forward
def forward(
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 632, in _fn
return fn(*args, **kwargs)
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py", line 1100, in forward
return compiled_fn(full_args)
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py", line 321, in runtime_wrapper
all_outs = call_func_at_runtime_with_args(
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/utils.py", line 124, in call_func_at_runtime_with_args
out = normalize_as_list(f(args))
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py", line 667, in inner_fn
outs = compiled_fn(args)
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py", line 488, in wrapper
return compiled_fn(runtime_args)
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/torch/_inductor/codecache.py", line 1478, in __call__
return self.current_callable(inputs)
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/torch/_inductor/compile_fx.py", line 1008, in run
return compiled_fn(new_inputs)
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/torch/_inductor/cudagraph_trees.py", line 398, in deferred_cudagraphify
fn, out = cudagraphify(model, inputs, new_static_input_idxs, *args, **kwargs)
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/torch/_inductor/cudagraph_trees.py", line 420, in cudagraphify
manager = get_container(device_index).get_tree_manager()
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/torch/_inductor/cudagraph_trees.py", line 341, in get_container
container_dict = get_obj(local, "tree_manager_containers")
File "/home/self/anaconda3/envs/Diffusers/lib/python3.10/site-packages/torch/_inductor/cudagraph_trees.py", line 336, in get_obj
assert torch._C._is_key_in_tls(attr_name)
AssertionError
```

### Reproduction

```python
def generate_video_path() -> str:
timestamp = int(time.time())

video_dir = settings.VIDEO_DIR
if not os.path.exists(video_dir):
os.makedirs(video_dir)

video_path = os.path.join(video_dir, f"generated_video_{timestamp}.mp4")

return video_path

def generate_random_seed():
"""Generate a random seed."""
return random.randint(0, 2 ** 32 - 1)

def stable_video(image, pipeline, generator):

image = image.resize((1024, 576))
frames = pipeline(image, decode_chunk_size=8, generator=generator).frames[0]
return frames

def generate_video_from_image(image, fps, seed):
# pipeline = model_loader.pipeline
model_path = settings.STABLE_VIDEO_DIFFUSION_DIR
pipeline = StableVideoDiffusionPipeline.from_pretrained(
model_path, torch_dtype=torch.float16, variant="fp16"
).to('cuda')

pipeline.unet = torch.compile(pipeline.unet, mode="reduce-overhead", fullgraph=True)
generator = torch.Generator(device=pipeline.device)
generator.manual_seed(seed)
frames = stable_video(image, pipeline, generator)
video_path = generate_video_path()
video_path = export_to_video(frames, video_path, fps)
return video_path

def make_video():
with gr.Block():
with gr.Row():
with gr.Accordion("stable-video", open=False):
with gr.Row():
with gr.Column():
with gr.Row():
seed_input = gr.Number(value=generate_random_seed(), show_label=False)
generate_seed_button = gr.Button("🎲 Generate Random Seed")
generate_seed_button.click(fn=generate_random_seed, outputs=seed_input)
with gr.Row():
fps = gr.Slider(label="Frames per second", minimum=7, maximum=25, step=1, value=10)
steps_slider = gr.Slider(label="Inference Steps", minimum=0, maximum=100, value=28,
info="The number of steps the denoiser removes image noise")

with gr.Column():
video_output = gr.Video(label='Video Output', width='600px', height='200px') # Video output
img_input = gr.Image(type="pil", width='600px', height='200px') # Image input

generate_button = gr.Button("Generate Video")
generate_button.click(generate_video_from_image,
inputs=[img_input, fps, seed_input],
outputs=video_output)

# Launch the interface
make_video().launch()

```

### Logs

_No response_

### System Info

GPU A5880 48g
python 3.10
diffusers 0.32
torch 2.5.1

### Who can help?

_No response_

Empieza reproduciendo el fallo de la segunda inferencia con StableVideoDiffusionPipeline y torch.compile usando las versiones y el script indicados en el issue. Lee pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py alrededor de la llamada a UNet y models/unets/unet_spatio_temporal_condition.py; después, sigue la interacción de torch.compile mostrada en el traceback. Se considera terminado cuando se pueden procesar imágenes diferentes repetidamente sin que se produzca la assertion ni un crecimiento descontrolado de la memoria de la GPU.

Escrito por el modelo de indexación a partir del texto del issue.