AssertionError: Torch not compiled with CUDA enabled

[EightiesPower]

I get the following error when running app.py:
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ G:\stable diffusion stuff\Smooth Diffusion\Smooth-Diffusion\app.py:939 in │
│ │
│ 936 │ # css = css_empty │
│ 937 │ css = css_version_4_11_0 │
│ 938 │ │
│ ❱ 939 │ wrapper_obj = wrapper( │
│ 940 │ │ fp16=False, │
│ 941 │ │ tag_diffuser=default.diffuser, │
│ 942 │ │ tag_lora=default.lora, │
│ │
│ G:\stable diffusion stuff\Smooth Diffusion\Smooth-Diffusion\app.py:263 in init │
│ │
│ 260 │ │ │ self.torch_dtype = torch.float16 │
│ 261 │ │ else: │
│ 262 │ │ │ self.torch_dtype = torch.float32 │
│ ❱ 263 │ │ self.load_all(tag_diffuser, tag_lora, tag_scheduler) │
│ 264 │ │ │
│ 265 │ │ self.image_latent_dim = 4 │
│ 266 │ │ self.batchsize = 8 │
│ │
│ G:\stable diffusion stuff\Smooth Diffusion\Smooth-Diffusion\app.py:277 in load_all │
│ │
│ 274 │ │ self.cache_inverse_maxn = 500 │
│ 275 │ │
│ 276 │ def load_all(self, tag_diffuser, tag_lora, tag_scheduler): │
│ ❱ 277 │ │ self.load_diffuser_lora(tag_diffuser, tag_lora) │
│ 278 │ │ self.load_scheduler(tag_scheduler) │
│ 279 │ │ return tag_diffuser, tag_lora, tag_scheduler │
│ 280 │
│ │
│ G:\stable diffusion stuff\Smooth Diffusion\Smooth-Diffusion\app.py:282 in load_diffuser_lora │
│ │
│ 279 │ │ return tag_diffuser, tag_lora, tag_scheduler │
│ 280 │ │
│ 281 │ def load_diffuser_lora(self, tag_diffuser, tag_lora): │
│ ❱ 282 │ │ self.net = StableDiffusionPipeline.from_pretrained( │
│ 283 │ │ │ choices.diffuser[tag_diffuser], torch_dtype=self.torch_dtype).to(self.device │
│ 284 │ │ self.net.safety_checker = None │
│ 285 │ │ if tag_lora != 'empty': │
│ │
│ G:\miniconda3\envs\dev\lib\site-packages\diffusers\pipelines\pipeline_utils.py:727 in to │
│ │
│ 724 │ │ │ │ │ f"The module '{module.class.name}' has been loaded in 8bit a │
│ 725 │ │ │ │ ) │
│ 726 │ │ │ else: │
│ ❱ 727 │ │ │ │ module.to(torch_device, torch_dtype) │
│ 728 │ │ │ │
│ 729 │ │ │ if ( │
│ 730 │ │ │ │ module.dtype == torch.float16 │
│ │
│ G:\miniconda3\envs\dev\lib\site-packages\transformers\modeling_utils.py:1902 in to │
│ │
│ 1899 │ │ │ │ " model has already been set to the correct devices and casted to the co │
│ 1900 │ │ │ ) │
│ 1901 │ │ else: │
│ ❱ 1902 │ │ │ return super().to(*args, **kwargs) │
│ 1903 │ │
│ 1904 │ def half(self, *args): │
│ 1905 │ │ # Checks if the model has been loaded in 8-bit │
│ │
│ G:\miniconda3\envs\dev\lib\site-packages\torch\nn\modules\module.py:1145 in to │
│ │
│ 1142 │ │ │ │ │ │ │ non_blocking, memory_format=convert_to_format) │
│ 1143 │ │ │ return t.to(device, dtype if t.is_floating_point() or t.is_complex() else No │
│ 1144 │ │ │
│ ❱ 1145 │ │ return self._apply(convert) │
│ 1146 │ │
│ 1147 │ def register_full_backward_pre_hook( │
│ 1148 │ │ self, │
│ │
│ G:\miniconda3\envs\dev\lib\site-packages\torch\nn\modules\module.py:797 in _apply │
│ │
│ 794 │ │
│ 795 │ def _apply(self, fn): │
│ 796 │ │ for module in self.children(): │
│ ❱ 797 │ │ │ module._apply(fn) │
│ 798 │ │ │
│ 799 │ │ def compute_should_use_set_data(tensor, tensor_applied): │
│ 800 │ │ │ if torch._has_compatible_shallow_copy_type(tensor, tensor_applied): │
│ │
│ G:\miniconda3\envs\dev\lib\site-packages\torch\nn\modules\module.py:797 in _apply │
│ │
│ 794 │ │
│ 795 │ def _apply(self, fn): │
│ 796 │ │ for module in self.children(): │
│ ❱ 797 │ │ │ module._apply(fn) │
│ 798 │ │ │
│ 799 │ │ def compute_should_use_set_data(tensor, tensor_applied): │
│ 800 │ │ │ if torch._has_compatible_shallow_copy_type(tensor, tensor_applied): │
│ │
│ G:\miniconda3\envs\dev\lib\site-packages\torch\nn\modules\module.py:797 in _apply │
│ │
│ 794 │ │
│ 795 │ def _apply(self, fn): │
│ 796 │ │ for module in self.children(): │
│ ❱ 797 │ │ │ module._apply(fn) │
│ 798 │ │ │
│ 799 │ │ def compute_should_use_set_data(tensor, tensor_applied): │
│ 800 │ │ │ if torch._has_compatible_shallow_copy_type(tensor, tensor_applied): │
│ │
│ G:\miniconda3\envs\dev\lib\site-packages\torch\nn\modules\module.py:797 in _apply │
│ │
│ 794 │ │
│ 795 │ def _apply(self, fn): │
│ 796 │ │ for module in self.children(): │
│ ❱ 797 │ │ │ module._apply(fn) │
│ 798 │ │ │
│ 799 │ │ def compute_should_use_set_data(tensor, tensor_applied): │
│ 800 │ │ │ if torch._has_compatible_shallow_copy_type(tensor, tensor_applied): │
│ │
│ G:\miniconda3\envs\dev\lib\site-packages\torch\nn\modules\module.py:820 in _apply │
│ │
│ 817 │ │ │ # track autograd history of param_applied, so we have to use │
│ 818 │ │ │ # with torch.no_grad(): │
│ 819 │ │ │ with torch.no_grad(): │
│ ❱ 820 │ │ │ │ param_applied = fn(param) │
│ 821 │ │ │ should_use_set_data = compute_should_use_set_data(param, param_applied) │
│ 822 │ │ │ if should_use_set_data: │
│ 823 │ │ │ │ param.data = param_applied │
│ │
│ G:\miniconda3\envs\dev\lib\site-packages\torch\nn\modules\module.py:1143 in convert │
│ │
│ 1140 │ │ │ if convert_to_format is not None and t.dim() in (4, 5): │
│ 1141 │ │ │ │ return t.to(device, dtype if t.is_floating_point() or t.is_complex() els │
│ 1142 │ │ │ │ │ │ │ non_blocking, memory_format=convert_to_format) │
│ ❱ 1143 │ │ │ return t.to(device, dtype if t.is_floating_point() or t.is_complex() else No │
│ 1144 │ │ │
│ 1145 │ │ return self.apply(convert) │
│ 1146 │
│ │
│ G:\miniconda3\envs\dev\lib\site-packages\torch\cuda_init.py:239 in _lazy_init │
│ │
│ 236 │ │ │ │ "Cannot re-initialize CUDA in forked subprocess. To use CUDA with " │
│ 237 │ │ │ │ "multiprocessing, you must use the 'spawn' start method") │
│ 238 │ │ if not hasattr(torch._C, '_cuda_getDeviceCount'): │
│ ❱ 239 │ │ │ raise AssertionError("Torch not compiled with CUDA enabled") │
│ 240 │ │ if _cudart is None: │
│ 241 │ │ │ raise AssertionError( │
│ 242 │ │ │ │ "libcudart functions unavailable. It looks like you have a broken build? │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
AssertionError: Torch not compiled with CUDA enabled

I tried uninstalling Torch 2.0.0 and installing 2.2.1. Afterwards it seems to run, but when opening the provided link I just get "The site can't be reached" error.

[JiayiGuo821]

Hi @EightiesPower,

The required torch version can be varied due to different device/CUDA settings.
For the "The site can't be reached" error, please try to comment out the last several lines in app.py.

# port = args.port if args.port is not None else default.port
# ipaddr = os.popen("curl icanhazip.com").read().strip()
# print('LINK: http://{}:{}'.format(ipaddr, port))
# demo.launch(server_name="0.0.0.0", server_port=port)
demo.launch()

Let me know if there are still some errors. I will also update the code soon.

[EightiesPower]

Alright, after editing app.py I got the WebUI to launch. Thank you!
One more question though, is it possible to do drag editing using this WebUI? If so, how?

[JiayiGuo821]

It's not implemented in this WebUI. We integrate our smooth LoRA into the DragDiffusion framework for drag editing. You could make that by just replacing the model.