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

[ZHJ19970917]

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:

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

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

[hlky]

pipeline is recreated each run of generate_video_from_image, can you try something like a global pipeline = None initially, then

def generate_video_from_image(image, fps, seed):
    global pipeline
    if pipeline is None:
        ...

[ZHJ19970917]

I used the global pipeline. The first time I uploaded an image, it worked normally, but the second time I uploaded a different image, it recognized self.unet as a function.

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 45, 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 475, in __call__
    num_frames = num_frames if num_frames is not None else self.unet.config.num_frames
AttributeError: 'function' object has no attribute 'config'