testing jit

Dockerfile

@@ -33,13 +33,13 @@ ENV HOME=/home/user \
 
 RUN pip3 install --no-cache-dir --upgrade -r /code/requirements.txt
 
+RUN apt-get install -y nvidia-340
+RUN nvidia-smi
+
 # Set the working directory to the user's home directory
 WORKDIR $HOME/app
 
 # Copy the current directory contents into the container at $HOME/app setting the owner to the user
 COPY --chown=user . $HOME/app
 
-CMD ["python3", "anime_app.py"]
-
-RUN apt-get install -y nvidia-340
-RUN nvidia-smi
+CMD ["python3", "anime_app.py"]

controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/activations/activations_me.py

@@ -18,7 +18,7 @@ __all__ = ['swish_me', 'SwishMe', 'mish_me', 'MishMe',
            'hard_sigmoid_me', 'HardSigmoidMe', 'hard_swish_me', 'HardSwishMe']
 
 
-# @torch.jit.script
+@torch.jit.script
 def swish_jit_fwd(x):
     return x.mul(torch.sigmoid(x))
 

test

@@ -1,90 +0,0 @@
-Spaces
-Hugging Face's logo
-
-broyang
-/
-anime-ai
-
-
-like
-4
-
-Logs
-App
-Files
-Community
-Settings
-
-
-Logs
-
-Build
-Container
-Lock scroll
-
-Clear
-
-
-===== Application Startup at 2024-05-21 21:14:15 =====
-
-The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.
-
-0it [00:00, ?it/s]
-0it [00:00, ?it/s]
-CUDA version: 12.1
-loading pipe
-initializing controlnet
-Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
- ['text_model.embeddings.position_ids']
-You have disabled the safety checker for <class 'diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline'> by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .
-Traceback (most recent call last):
-  File "/home/user/app/anime_app.py", line 79, in <module>
-    preprocessor.load("NormalBae")
-  File "/home/user/app/preprocess_anime.py", line 22, in load
-    self.model = NormalBaeDetector.from_pretrained(self.MODEL_ID).to("cuda")
-  File "/home/user/app/controlnet_aux/normalbae/__init__.py", line 52, in from_pretrained
-    model = NNET(args)
-  File "/home/user/app/controlnet_aux/normalbae/nets/NNET.py", line 12, in __init__
-    self.encoder = Encoder()
-  File "/home/user/app/controlnet_aux/normalbae/nets/submodules/encoder.py", line 14, in __init__
-    basemodel = torch.hub.load(repo_path, basemodel_name, pretrained=False, source='local')
-  File "/usr/local/lib/python3.10/site-packages/torch/hub.py", line 566, in load
-    model = _load_local(repo_or_dir, model, *args, **kwargs)
-  File "/usr/local/lib/python3.10/site-packages/torch/hub.py", line 592, in _load_local
-    hub_module = _import_module(MODULE_HUBCONF, hubconf_path)
-  File "/usr/local/lib/python3.10/site-packages/torch/hub.py", line 106, in _import_module
-    spec.loader.exec_module(module)
-  File "<frozen importlib._bootstrap_external>", line 883, in exec_module
-  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
-  File "/home/user/app/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/hubconf.py", line 3, in <module>
-    from geffnet import efficientnet_b0
-  File "/home/user/app/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/__init__.py", line 1, in <module>
-    from .gen_efficientnet import *
-  File "/home/user/app/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/gen_efficientnet.py", line 35, in <module>
-    from .efficientnet_builder import *
-  File "/home/user/app/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/efficientnet_builder.py", line 9, in <module>
-    from geffnet.activations import *
-  File "/home/user/app/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/activations/__init__.py", line 2, in <module>
-    from geffnet.activations.activations_me import *
-  File "/home/user/app/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/activations/activations_me.py", line 146, in <module>
-    def hard_swish_jit_bwd(x, grad_output):
-  File "/usr/local/lib/python3.10/site-packages/torch/jit/_script.py", line 1395, in script
-    fn = torch._C._jit_script_compile(
-  File "/usr/local/lib/python3.10/site-packages/torch/jit/annotations.py", line 115, in get_signature
-    signature = try_real_annotations(fn, loc)
-  File "/usr/local/lib/python3.10/site-packages/torch/jit/annotations.py", line 345, in try_real_annotations
-    arg_types = [ann_to_type(p.annotation, loc) for p in sig.parameters.values()]
-  File "/usr/local/lib/python3.10/site-packages/torch/jit/annotations.py", line 345, in <listcomp>
-    arg_types = [ann_to_type(p.annotation, loc) for p in sig.parameters.values()]
-  File "/usr/local/lib/python3.10/site-packages/torch/jit/annotations.py", line 513, in ann_to_type
-    raise ValueError(f"Unknown type annotation: '{ann}' at {loc.highlight()}")
-ValueError: Unknown type annotation: 'Any' at   File "/home/user/app/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/activations/activations_me.py", line 147
-@torch.jit.script
-def hard_swish_jit_bwd(x, grad_output):
-    m = torch.ones_like(x) * (x >= 3.)
-        ~~~~~~~~~~~~~~~ <--- HERE
-    m = torch.where((x >= -3.) & (x <= 3.),  x / 3. + .5, m)
-    return grad_output * m
-
-Loading base model ()...
-Building...