commit
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@ -39,6 +39,10 @@ def torch_gc():
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model_management.soft_empty_cache()
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def torch_npu_set_device():
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return
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def enable_tf32():
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return
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@ -96,4 +100,3 @@ def test_for_nans(x, where):
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def first_time_calculation():
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return
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@ -146,13 +146,14 @@ def initialize_rest(*, reload_script_modules=False):
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its optimization may be None because the list of optimizaers has neet been filled
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by that time, so we apply optimization again.
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"""
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from modules import devices
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devices.torch_npu_set_device()
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shared.sd_model # noqa: B018
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if sd_hijack.current_optimizer is None:
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sd_hijack.apply_optimizations()
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from modules import devices
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devices.first_time_calculation()
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if not shared.cmd_opts.skip_load_model_at_start:
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Thread(target=load_model).start()
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@ -379,6 +379,7 @@ 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_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_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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clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
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@ -462,6 +463,13 @@ def prepare_environment():
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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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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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run_extensions_installers(settings_file=args.ui_settings_file)
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31
modules/npu_specific.py
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31
modules/npu_specific.py
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@ -0,0 +1,31 @@
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import importlib
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import torch
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from modules import shared
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def check_for_npu():
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if importlib.util.find_spec("torch_npu") is None:
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return False
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import torch_npu
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try:
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# Will raise a RuntimeError if no NPU is found
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_ = torch_npu.npu.device_count()
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return torch.npu.is_available()
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except RuntimeError:
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return False
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def get_npu_device_string():
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if shared.cmd_opts.device_id is not None:
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return f"npu:{shared.cmd_opts.device_id}"
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return "npu:0"
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def torch_npu_gc():
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with torch.npu.device(get_npu_device_string()):
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torch.npu.empty_cache()
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has_npu = check_for_npu()
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@ -95,7 +95,7 @@ class CFGDenoiser(torch.nn.Module):
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def pad_cond_uncond(self, cond, uncond):
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empty = shared.sd_model.cond_stage_model_empty_prompt
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num_repeats = (cond.shape[1] - cond.shape[1]) // empty.shape[1]
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num_repeats = (cond.shape[1] - uncond.shape[1]) // empty.shape[1]
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if num_repeats < 0:
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cond = pad_cond(cond, -num_repeats, empty)
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@ -150,6 +150,7 @@ class EmbeddingDatabase:
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return embedding
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def get_expected_shape(self):
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devices.torch_npu_set_device()
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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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@ -96,9 +96,9 @@ class Toprow:
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with gr.Row(elem_id=f"{self.id_part}_generate_box", elem_classes=["generate-box"] + (["generate-box-compact"] if self.is_compact else []), render=not self.is_compact) as submit_box:
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self.submit_box = submit_box
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self.interrupt = gr.Button('Interrupt', elem_id=f"{self.id_part}_interrupt", elem_classes="generate-box-interrupt")
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self.skip = gr.Button('Skip', elem_id=f"{self.id_part}_skip", elem_classes="generate-box-skip")
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self.submit = gr.Button('Generate', elem_id=f"{self.id_part}_generate", variant='primary')
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self.interrupt = gr.Button('Interrupt', elem_id=f"{self.id_part}_interrupt", elem_classes="generate-box-interrupt", tooltip="End generation immediately or after completing current batch")
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self.skip = gr.Button('Skip', elem_id=f"{self.id_part}_skip", elem_classes="generate-box-skip", tooltip="Stop generation of current batch and continues onto next batch")
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self.submit = gr.Button('Generate', elem_id=f"{self.id_part}_generate", variant='primary', tooltip="Right click generate forever menu")
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self.skip.click(
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fn=lambda: shared.state.skip(),
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@ -6,7 +6,7 @@ import torch
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import tqdm
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from PIL import Image
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from modules import images, shared, torch_utils
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from modules import devices, images, shared, torch_utils
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logger = logging.getLogger(__name__)
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@ -44,7 +44,8 @@ def upscale_pil_patch(model, img: Image.Image) -> Image.Image:
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with torch.no_grad():
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tensor = pil_image_to_torch_bgr(img).unsqueeze(0) # add batch dimension
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tensor = tensor.to(device=param.device, dtype=param.dtype)
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return torch_bgr_to_pil_image(model(tensor))
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with devices.without_autocast():
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return torch_bgr_to_pil_image(model(tensor))
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def upscale_with_model(
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4
requirements_npu.txt
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4
requirements_npu.txt
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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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psutil==5.9.5
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pytorch_lightning==1.9.4
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resize-right==0.0.2
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safetensors==0.3.1
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safetensors==0.4.2
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scikit-image==0.21.0
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spandrel==0.1.6
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tomesd==0.1.3
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4
webui.sh
4
webui.sh
@ -158,6 +158,10 @@ then
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if echo "$gpu_info" | grep -q "AMD" && [[ -z "${TORCH_COMMAND}" ]]
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then
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export TORCH_COMMAND="pip install torch==2.0.1+rocm5.4.2 torchvision==0.15.2+rocm5.4.2 --index-url https://download.pytorch.org/whl/rocm5.4.2"
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elif echo "$gpu_info" | grep -q "Huawei" && [[ -z "${TORCH_COMMAND}" ]]
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then
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export TORCH_COMMAND="pip install torch==2.1.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu; pip install torch_npu"
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fi
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fi
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Loading…
Reference in New Issue
Block a user