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Update
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@ -88,9 +88,16 @@ def torch_gc():
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xpu_specific.torch_xpu_gc()
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xpu_specific.torch_xpu_gc()
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if npu_specific.has_npu:
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if npu_specific.has_npu:
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torch_npu_set_device()
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npu_specific.torch_npu_gc()
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npu_specific.torch_npu_gc()
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def torch_npu_set_device():
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# Work around due to bug in torch_npu, revert me after fixed, @see https://gitee.com/ascend/pytorch/issues/I8KECW?from=project-issue
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if npu_specific.has_npu:
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torch.npu.set_device(0)
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def enable_tf32():
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def enable_tf32():
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if torch.cuda.is_available():
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if torch.cuda.is_available():
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@ -143,10 +143,7 @@ def initialize_rest(*, reload_script_modules=False):
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by that time, so we apply optimization again.
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by that time, so we apply optimization again.
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"""
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"""
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from modules import devices
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from modules import devices
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# Work around due to bug in torch_npu, revert me after fixed, @see https://gitee.com/ascend/pytorch/issues/I8KECW?from=project-issue
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devices.torch_npu_set_device()
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if devices.npu_specific.has_npu:
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import torch
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torch.npu.set_device(0)
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shared.sd_model # noqa: B018
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shared.sd_model # noqa: B018
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@ -338,6 +338,7 @@ def prepare_environment():
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torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://pytorch-extension.intel.com/release-whl/stable/xpu/us/")
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torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://pytorch-extension.intel.com/release-whl/stable/xpu/us/")
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torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.0a0 intel-extension-for-pytorch==2.0.110+gitba7f6c1 --extra-index-url {torch_index_url}")
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torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.0a0 intel-extension-for-pytorch==2.0.110+gitba7f6c1 --extra-index-url {torch_index_url}")
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requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
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requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
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requirements_file_for_npu = os.environ.get('REQS_FILE_FOR_NPU', "requirements_npu.txt")
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xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.23.post1')
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xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.23.post1')
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clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
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clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
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@ -421,6 +422,13 @@ def prepare_environment():
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run_pip(f"install -r \"{requirements_file}\"", "requirements")
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run_pip(f"install -r \"{requirements_file}\"", "requirements")
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startup_timer.record("install requirements")
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startup_timer.record("install requirements")
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if not os.path.isfile(requirements_file_for_npu):
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requirements_file_for_npu = os.path.join(script_path, requirements_file_for_npu)
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if "torch_npu" in torch_command and not requirements_met(requirements_file_for_npu):
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run_pip(f"install -r \"{requirements_file_for_npu}\"", "requirements_for_npu")
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startup_timer.record("install requirements_for_npu")
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if not args.skip_install:
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if not args.skip_install:
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run_extensions_installers(settings_file=args.ui_settings_file)
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run_extensions_installers(settings_file=args.ui_settings_file)
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@ -8,11 +8,10 @@ def check_for_npu():
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if importlib.util.find_spec("torch_npu") is None:
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if importlib.util.find_spec("torch_npu") is None:
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return False
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return False
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import torch_npu
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import torch_npu
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torch_npu.npu.set_device(0)
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try:
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try:
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# Will raise a RuntimeError if no NPU is found
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# Will raise a RuntimeError if no NPU is found
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_ = torch.npu.device_count()
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_ = torch_npu.npu.device_count()
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return torch.npu.is_available()
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return torch.npu.is_available()
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except RuntimeError:
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except RuntimeError:
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return False
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return False
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@ -25,8 +24,6 @@ def get_npu_device_string():
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def torch_npu_gc():
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def torch_npu_gc():
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# Work around due to bug in torch_npu, revert me after fixed, @see https://gitee.com/ascend/pytorch/issues/I8KECW?from=project-issue
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torch.npu.set_device(0)
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with torch.npu.device(get_npu_device_string()):
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with torch.npu.device(get_npu_device_string()):
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torch.npu.empty_cache()
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torch.npu.empty_cache()
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@ -150,10 +150,7 @@ class EmbeddingDatabase:
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return embedding
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return embedding
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def get_expected_shape(self):
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def get_expected_shape(self):
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# workaround
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devices.torch_npu_set_device()
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if devices.npu_specific.has_npu:
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import torch
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torch.npu.set_device(0)
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vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
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vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
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return vec.shape[1]
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return vec.shape[1]
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@ -4,8 +4,6 @@ accelerate
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blendmodes
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blendmodes
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clean-fid
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clean-fid
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cloudpickle
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decorator
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einops
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einops
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facexlib
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facexlib
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fastapi>=0.90.1
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fastapi>=0.90.1
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@ -26,10 +24,8 @@ resize-right
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safetensors
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safetensors
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scikit-image>=0.19
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scikit-image>=0.19
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synr==0.5.0
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tomesd
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tomesd
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torch
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torch
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torchdiffeq
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torchdiffeq
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torchsde
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torchsde
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tornado
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transformers==4.30.2
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transformers==4.30.2
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4
requirements_npu.txt
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4
requirements_npu.txt
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@ -0,0 +1,4 @@
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cloudpickle
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decorator
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synr==0.5.0
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tornado
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@ -3,8 +3,6 @@ Pillow==9.5.0
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accelerate==0.21.0
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accelerate==0.21.0
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blendmodes==2022
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blendmodes==2022
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clean-fid==0.1.35
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clean-fid==0.1.35
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cloudpickle==3.0.0
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decorator==5.1.1
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einops==0.4.1
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einops==0.4.1
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facexlib==0.3.0
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facexlib==0.3.0
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fastapi==0.94.0
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fastapi==0.94.0
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@ -23,12 +21,10 @@ pytorch_lightning==1.9.4
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resize-right==0.0.2
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resize-right==0.0.2
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safetensors==0.4.2
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safetensors==0.4.2
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scikit-image==0.21.0
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scikit-image==0.21.0
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synr==0.5.0
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spandrel==0.1.6
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spandrel==0.1.6
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tomesd==0.1.3
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tomesd==0.1.3
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torch
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torch
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torchdiffeq==0.2.3
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torchdiffeq==0.2.3
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torchsde==0.2.6
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torchsde==0.2.6
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tornado==6.4
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transformers==4.30.2
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transformers==4.30.2
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httpx==0.24.1
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httpx==0.24.1
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