b731bb860c
i Update initialization.py initialization initialization Update initialization.py i i Update sd_samplers_common.py Update sd_hijack.py i Update sd_models.py Update sd_models.py Update forge_loader.py Update sd_models.py i Update sd_model.py i Update sd_models.py Create sd_model.py i i Update sd_models.py i Update sd_models.py Update sd_models.py i i Update sd_samplers_common.py i Update sd_models.py Update sd_models.py Update sd_samplers_common.py Update sd_models.py Update sd_models.py Update sd_models.py Update sd_models.py Update sd_samplers_common.py i Update shared_options.py Update prompt_parser.py Update sd_hijack_unet.py i Update sd_models.py Update sd_models.py Update sd_models.py Update devices.py i Update sd_vae.py Update sd_models.py Update processing.py Update ui_settings.py Update sd_models_xl.py i i Update sd_samplers_kdiffusion.py Update sd_samplers_timesteps.py Update ui_settings.py Update cmd_args.py Update cmd_args.py Update initialization.py Update shared_options.py Update initialization.py Update shared_options.py i Update cmd_args.py Update initialization.py Update initialization.py Update initialization.py Update cmd_args.py Update cmd_args.py Update sd_hijack.py
43 lines
1.8 KiB
Python
43 lines
1.8 KiB
Python
import torch
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from ldm_patched.modules import model_management
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from ldm_patched.modules import model_detection
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from ldm_patched.modules.sd import VAE
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import ldm_patched.modules.model_patcher
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import ldm_patched.modules.utils
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def load_unet_and_vae(sd):
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parameters = ldm_patched.modules.utils.calculate_parameters(sd, "model.diffusion_model.")
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unet_dtype = model_management.unet_dtype(model_params=parameters)
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load_device = model_management.get_torch_device()
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manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device)
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model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", unet_dtype)
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model_config.set_manual_cast(manual_cast_dtype)
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if model_config is None:
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raise RuntimeError("ERROR: Could not detect model type of")
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initial_load_device = model_management.unet_inital_load_device(parameters, unet_dtype)
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model = model_config.get_model(sd, "model.diffusion_model.", device=initial_load_device)
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model.load_model_weights(sd, "model.diffusion_model.")
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model_patcher = ldm_patched.modules.model_patcher.ModelPatcher(model,
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load_device=load_device,
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offload_device=model_management.unet_offload_device(),
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current_device=initial_load_device)
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vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True)
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vae_sd = model_config.process_vae_state_dict(vae_sd)
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vae_patcher = VAE(sd=vae_sd)
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return model_patcher, vae_patcher
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class FakeObject(torch.nn.Module):
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def __init__(self, *args, **kwargs):
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super().__init__()
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return
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