mirror of https://github.com/vladmandic/automatic
79 lines
3.8 KiB
Python
79 lines
3.8 KiB
Python
import os
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from modules import shared, devices, files_cache, sd_models
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unet_dict = {}
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loaded_unet = None
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failed_unet = []
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debug = os.environ.get('SD_LOAD_DEBUG', None) is not None
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def load_unet(model):
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global loaded_unet # pylint: disable=global-statement
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if shared.opts.sd_unet == 'Default' or shared.opts.sd_unet == 'None':
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return
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if shared.opts.sd_unet not in list(unet_dict):
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shared.log.error(f'Load module: type=UNet not found: {shared.opts.sd_unet}')
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return
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config_file = os.path.splitext(unet_dict[shared.opts.sd_unet])[0] + '.json'
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if os.path.exists(config_file):
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config = shared.readfile(config_file)
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else:
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config = None
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config_file = 'default'
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try:
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if shared.opts.sd_unet == loaded_unet or shared.opts.sd_unet in failed_unet:
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pass
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elif "StableCascade" in model.__class__.__name__:
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from pipelines.model_stablecascade import load_prior
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prior_unet, prior_text_encoder = load_prior(unet_dict[shared.opts.sd_unet], config_file=config_file)
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loaded_unet = shared.opts.sd_unet
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if prior_unet is not None:
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model.prior_pipe.prior = None # Prevent OOM
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model.prior_pipe.prior = prior_unet.to(devices.device, dtype=devices.dtype_unet)
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if prior_text_encoder is not None:
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model.prior_pipe.text_encoder = None # Prevent OOM
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model.prior_pipe.text_encoder = prior_text_encoder.to(devices.device, dtype=devices.dtype)
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elif "Flux" in model.__class__.__name__ or "StableDiffusion3" in model.__class__.__name__ or "HiDream" in model.__class__.__name__ or "Lumina2" in model.__class__.__name__:
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loaded_unet = shared.opts.sd_unet
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sd_models.load_diffuser() # TODO model load: force-reloading entire model as loading transformers only leads to massive memory usage
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"""
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from pipelines.model_flux import load_transformer
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transformer = load_transformer(unet_dict[shared.opts.sd_unet])
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if transformer is not None:
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model.transformer = None
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if shared.opts.diffusers_offload_mode == 'none':
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sd_models.move_model(transformer, devices.device)
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model.transformer = transformer
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loaded_unet = shared.opts.sd_unet
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from modules.sd_models import set_diffuser_offload
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set_diffuser_offload(model, 'model')
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"""
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else:
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if not hasattr(model, 'unet') or model.unet is None:
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shared.log.error('Load module: type=UNET not found in current model')
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return
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shared.log.info(f'Load module: type=UNet name="{shared.opts.sd_unet}" file="{unet_dict[shared.opts.sd_unet]}" config="{config_file}"')
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from diffusers import UNet2DConditionModel
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from safetensors.torch import load_file
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unet = UNet2DConditionModel.from_config(model.unet.config if config is None else config).to(devices.device, devices.dtype)
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state_dict = load_file(unet_dict[shared.opts.sd_unet])
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unet.load_state_dict(state_dict)
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model.unet = unet.to(devices.device, devices.dtype_unet)
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except Exception as e:
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shared.log.error(f'Failed to load UNet model: {e}')
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if debug:
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from modules import errors
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errors.display(e, 'UNet load:')
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return
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devices.torch_gc()
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def refresh_unet_list():
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unet_dict.clear()
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for file in files_cache.list_files(shared.opts.unet_dir, ext_filter=[".safetensors", ".gguf", ".pth"]):
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basename = os.path.basename(file)
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name = os.path.splitext(basename)[0] if ".safetensors" in basename else basename
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unet_dict[name] = file
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shared.log.info(f'Available UNets: path="{shared.opts.unet_dir}" items={len(unet_dict)}')
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