mirror of https://github.com/vladmandic/automatic
265 lines
11 KiB
Python
265 lines
11 KiB
Python
import os
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import glob
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from copy import deepcopy
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import torch
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from modules import shared, paths, devices, script_callbacks, sd_models
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vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
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vae_dict = {}
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base_vae = None
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loaded_vae_file = None
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checkpoint_info = None
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vae_path = os.path.abspath(os.path.join(paths.models_path, 'VAE'))
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def get_base_vae(model):
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if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model:
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return base_vae
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return None
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def store_base_vae(model):
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global base_vae, checkpoint_info # pylint: disable=global-statement
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if checkpoint_info != model.sd_checkpoint_info:
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assert not loaded_vae_file, "Trying to store non-base VAE!"
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base_vae = deepcopy(model.first_stage_model.state_dict())
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checkpoint_info = model.sd_checkpoint_info
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def delete_base_vae():
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global base_vae, checkpoint_info # pylint: disable=global-statement
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base_vae = None
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checkpoint_info = None
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def restore_base_vae(model):
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global loaded_vae_file # pylint: disable=global-statement
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if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
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shared.log.info("Restoring base VAE")
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_load_vae_dict(model, base_vae)
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loaded_vae_file = None
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delete_base_vae()
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def get_filename(filepath):
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if filepath.endswith(".json"):
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return os.path.basename(os.path.dirname(filepath))
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else:
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return os.path.basename(filepath)
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def refresh_vae_list():
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global vae_path # pylint: disable=global-statement
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vae_path = shared.opts.vae_dir
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vae_dict.clear()
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vae_paths = []
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if shared.backend == shared.Backend.ORIGINAL:
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if sd_models.model_path is not None and os.path.isdir(sd_models.model_path):
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vae_paths += [
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os.path.join(sd_models.model_path, 'VAE', '**/*.vae.ckpt'),
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os.path.join(sd_models.model_path, 'VAE', '**/*.vae.pt'),
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os.path.join(sd_models.model_path, 'VAE', '**/*.vae.safetensors'),
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]
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if shared.opts.ckpt_dir is not None and os.path.isdir(shared.opts.ckpt_dir):
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vae_paths += [
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os.path.join(shared.opts.ckpt_dir, '**/*.vae.ckpt'),
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os.path.join(shared.opts.ckpt_dir, '**/*.vae.pt'),
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os.path.join(shared.opts.ckpt_dir, '**/*.vae.safetensors'),
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]
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if shared.opts.vae_dir is not None and os.path.isdir(shared.opts.vae_dir):
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vae_paths += [
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os.path.join(shared.opts.vae_dir, '**/*.ckpt'),
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os.path.join(shared.opts.vae_dir, '**/*.pt'),
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os.path.join(shared.opts.vae_dir, '**/*.safetensors'),
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]
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elif shared.backend == shared.Backend.DIFFUSERS:
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if sd_models.model_path is not None and os.path.isdir(sd_models.model_path):
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vae_paths += [os.path.join(sd_models.model_path, 'VAE', '**/*.vae.safetensors')]
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if shared.opts.ckpt_dir is not None and os.path.isdir(shared.opts.ckpt_dir):
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vae_paths += [os.path.join(shared.opts.ckpt_dir, '**/*.vae.safetensors')]
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if shared.opts.vae_dir is not None and os.path.isdir(shared.opts.vae_dir):
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vae_paths += [os.path.join(shared.opts.vae_dir, '**/*.safetensors')]
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vae_paths += [
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os.path.join(sd_models.model_path, 'VAE', '**/*.json'),
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os.path.join(shared.opts.vae_dir, '**/*.json'),
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]
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candidates = []
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for path in vae_paths:
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candidates += glob.iglob(path, recursive=True)
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for filepath in candidates:
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name = get_filename(filepath)
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if name == 'VAE':
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continue
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if shared.backend == shared.Backend.ORIGINAL:
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vae_dict[name] = filepath
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else:
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if filepath.endswith(".json"):
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vae_dict[name] = os.path.dirname(filepath)
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else:
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vae_dict[name] = filepath
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shared.log.info(f'Available VAEs: path="{vae_path}" items={len(vae_dict)}')
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return vae_dict
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def find_vae_near_checkpoint(checkpoint_file):
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checkpoint_path = os.path.splitext(checkpoint_file)[0]
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for vae_location in [f"{checkpoint_path}.vae.pt", f"{checkpoint_path}.vae.ckpt", f"{checkpoint_path}.vae.safetensors"]:
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if os.path.isfile(vae_location):
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return vae_location
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return None
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def resolve_vae(checkpoint_file):
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if shared.opts.sd_vae == 'TAESD':
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return None, None
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if shared.cmd_opts.vae is not None: # 1st
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return shared.cmd_opts.vae, 'forced'
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if shared.opts.sd_vae == "None": # 2nd
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return None, None
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vae_near_checkpoint = find_vae_near_checkpoint(checkpoint_file)
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if vae_near_checkpoint is not None: # 3rd
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return vae_near_checkpoint, 'near-checkpoint'
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if shared.opts.sd_vae == "Automatic": # 4th
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basename = os.path.splitext(os.path.basename(checkpoint_file))[0]
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if vae_dict.get(basename, None) is not None:
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return vae_dict[basename], 'automatic'
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else:
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vae_from_options = vae_dict.get(shared.opts.sd_vae, None) # 5th
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if vae_from_options is not None:
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return vae_from_options, 'settings'
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vae_from_options = vae_dict.get(shared.opts.sd_vae + '.safetensors', None) # 6th
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if vae_from_options is not None:
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return vae_from_options, 'settings'
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shared.log.warning(f"VAE not found: {shared.opts.sd_vae}")
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return None, None
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def load_vae_dict(filename):
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vae_ckpt = sd_models.read_state_dict(filename)
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vae_dict_1 = {k: v for k, v in vae_ckpt.items() if k[0:4] != "loss" and k not in vae_ignore_keys}
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return vae_dict_1
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def load_vae(model, vae_file=None, vae_source="unknown-source"):
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global loaded_vae_file # pylint: disable=global-statement
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if vae_file:
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try:
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if not os.path.isfile(vae_file):
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shared.log.error(f"VAE not found: model={vae_file} source={vae_source}")
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return
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store_base_vae(model)
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vae_dict_1 = load_vae_dict(vae_file)
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_load_vae_dict(model, vae_dict_1)
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except Exception as e:
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shared.log.error(f"Loading VAE failed: model={vae_file} source={vae_source} {e}")
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restore_base_vae(model)
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# If vae used is not in dict, update it
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# It will be removed on refresh though
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vae_opt = get_filename(vae_file)
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if vae_opt not in vae_dict:
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vae_dict[vae_opt] = vae_file
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elif loaded_vae_file:
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restore_base_vae(model)
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loaded_vae_file = vae_file
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def load_vae_diffusers(model_file, vae_file=None, vae_source="unknown-source"):
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if vae_file is None:
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return None
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if not os.path.exists(vae_file):
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shared.log.error(f'VAE not found: model{vae_file}')
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return None
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shared.log.info(f"Loading VAE: model={vae_file} source={vae_source}")
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diffusers_load_config = {
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"low_cpu_mem_usage": False,
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"torch_dtype": devices.dtype_vae,
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"use_safetensors": True,
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}
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if shared.opts.diffusers_vae_load_variant == 'default':
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if devices.dtype_vae == torch.float16:
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diffusers_load_config['variant'] = 'fp16'
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elif shared.opts.diffusers_vae_load_variant == 'fp32':
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pass
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else:
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diffusers_load_config['variant'] = shared.opts.diffusers_vae_load_variant
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if shared.opts.diffusers_vae_upcast != 'default':
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diffusers_load_config['force_upcast'] = True if shared.opts.diffusers_vae_upcast == 'true' else False
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shared.log.debug(f'Diffusers VAE load config: {diffusers_load_config}')
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try:
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import diffusers
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if os.path.isfile(vae_file):
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_pipeline, model_type = sd_models.detect_pipeline(model_file, 'vae')
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diffusers_load_config = { "config_file": paths.sd_default_config if model_type != 'Stable Diffusion XL' else os.path.join(paths.sd_configs_path, 'sd_xl_base.yaml')}
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if os.path.getsize(vae_file) > 1310944880:
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vae = diffusers.ConsistencyDecoderVAE.from_pretrained('openai/consistency-decoder', **diffusers_load_config) # consistency decoder does not have from single file, so we'll just download it once more
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else:
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vae = diffusers.AutoencoderKL.from_single_file(vae_file, **diffusers_load_config)
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vae = vae.to(devices.dtype_vae)
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else:
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if 'consistency-decoder' in vae_file:
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vae = diffusers.ConsistencyDecoderVAE.from_pretrained(vae_file, **diffusers_load_config)
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else:
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vae = diffusers.AutoencoderKL.from_pretrained(vae_file, **diffusers_load_config)
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global loaded_vae_file # pylint: disable=global-statement
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loaded_vae_file = os.path.basename(vae_file)
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# shared.log.debug(f'Diffusers VAE config: {vae.config}')
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return vae
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except Exception as e:
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shared.log.error(f"Loading VAE failed: model={vae_file} {e}")
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return None
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# don't call this from outside
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def _load_vae_dict(model, vae_dict_1):
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model.first_stage_model.load_state_dict(vae_dict_1)
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model.first_stage_model.to(devices.dtype_vae)
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def clear_loaded_vae():
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global loaded_vae_file # pylint: disable=global-statement
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loaded_vae_file = None
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unspecified = object()
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def reload_vae_weights(sd_model=None, vae_file=unspecified):
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from modules import lowvram, sd_hijack
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if not sd_model:
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sd_model = shared.sd_model
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if sd_model is None:
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return None
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global checkpoint_info # pylint: disable=global-statement
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checkpoint_info = sd_model.sd_checkpoint_info
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checkpoint_file = checkpoint_info.filename
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if vae_file == unspecified:
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vae_file, vae_source = resolve_vae(checkpoint_file)
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else:
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vae_source = "function-argument"
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if loaded_vae_file == vae_file:
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return None
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if shared.backend == shared.Backend.ORIGINAL and (shared.cmd_opts.lowvram or shared.cmd_opts.medvram):
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lowvram.send_everything_to_cpu()
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# else:
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# sd_models.move_model(sd_model, devices.cpu)
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if shared.backend == shared.Backend.ORIGINAL:
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sd_hijack.model_hijack.undo_hijack(sd_model)
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if shared.cmd_opts.rollback_vae and devices.dtype_vae == torch.bfloat16:
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devices.dtype_vae = torch.float16
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load_vae(sd_model, vae_file, vae_source)
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sd_hijack.model_hijack.hijack(sd_model)
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script_callbacks.model_loaded_callback(sd_model)
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if vae_file is not None:
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shared.log.info(f"VAE weights loaded: {vae_file}")
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else:
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if hasattr(shared.sd_model, "vae") and hasattr(shared.sd_model, "sd_checkpoint_info"):
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vae = load_vae_diffusers(shared.sd_model.sd_checkpoint_info.filename, vae_file, vae_source)
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if vae is not None:
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sd_models.set_diffuser_options(sd_model, vae=vae, op='vae')
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if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
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sd_models.move_model(sd_model, devices.device)
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return sd_model
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