assign devices.dtype early because it's needed before the model is loaded
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@ -314,8 +314,6 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
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timer.record("apply half()")
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timer.record("apply half()")
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devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
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devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
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devices.dtype_unet = model.model.diffusion_model.dtype
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devices.dtype_unet = model.model.diffusion_model.dtype
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devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
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devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
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@ -6,6 +6,7 @@ import threading
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import time
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import time
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import gradio as gr
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import gradio as gr
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import torch
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import tqdm
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import tqdm
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import modules.interrogate
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import modules.interrogate
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@ -63,6 +64,9 @@ cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_op
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devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
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devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
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(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
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(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
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devices.dtype = torch.float32 if cmd_opts.no_half else torch.float16
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devices.dtype_vae = torch.float32 if cmd_opts.no_half or cmd_opts.no_half_vae else torch.float16
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device = devices.device
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device = devices.device
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weight_load_location = None if cmd_opts.lowram else "cpu"
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weight_load_location = None if cmd_opts.lowram else "cpu"
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