remove parsing command line from devices.py
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@ -15,14 +15,10 @@ def extract_device_id(args, name):
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def get_optimal_device():
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if torch.cuda.is_available():
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# CUDA device selection support:
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if "shared" not in sys.modules:
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commandline_args = os.environ.get('COMMANDLINE_ARGS', "") #re-parse the commandline arguments because using the shared.py module creates an import loop.
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sys.argv += shlex.split(commandline_args)
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device_id = extract_device_id(sys.argv, '--device-id')
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else:
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device_id = shared.cmd_opts.device_id
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from modules import shared
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device_id = shared.cmd_opts.device_id
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if device_id is not None:
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cuda_device = f"cuda:{device_id}"
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return torch.device(cuda_device)
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@ -49,7 +45,7 @@ def enable_tf32():
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errors.run(enable_tf32, "Enabling TF32")
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device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
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device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = None
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dtype = torch.float16
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dtype_vae = torch.float16
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@ -1,9 +1,8 @@
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import torch
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from modules.devices import get_optimal_device
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from modules import devices
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module_in_gpu = None
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cpu = torch.device("cpu")
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device = gpu = get_optimal_device()
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def send_everything_to_cpu():
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@ -33,7 +32,7 @@ def setup_for_low_vram(sd_model, use_medvram):
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if module_in_gpu is not None:
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module_in_gpu.to(cpu)
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module.to(gpu)
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module.to(devices.device)
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module_in_gpu = module
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# see below for register_forward_pre_hook;
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@ -51,7 +50,7 @@ def setup_for_low_vram(sd_model, use_medvram):
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# send the model to GPU. Then put modules back. the modules will be in CPU.
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stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model
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sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = None, None, None
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sd_model.to(device)
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sd_model.to(devices.device)
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sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = stored
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# register hooks for those the first two models
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@ -70,7 +69,7 @@ def setup_for_low_vram(sd_model, use_medvram):
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# so that only one of them is in GPU at a time
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stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed
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diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None
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sd_model.model.to(device)
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sd_model.model.to(devices.device)
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diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored
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# install hooks for bits of third model
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