mirror of https://github.com/vladmandic/automatic
446 lines
22 KiB
Python
446 lines
22 KiB
Python
from typing import Union, List
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import os
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import re
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import time
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import concurrent
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import torch
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import diffusers.models.lora
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import modules.lora.network as network
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import modules.lora.network_lora as network_lora
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import modules.lora.network_hada as network_hada
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import modules.lora.network_ia3 as network_ia3
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import modules.lora.network_oft as network_oft
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import modules.lora.network_lokr as network_lokr
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import modules.lora.network_full as network_full
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import modules.lora.network_norm as network_norm
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import modules.lora.network_glora as network_glora
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import modules.lora.network_overrides as network_overrides
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import modules.lora.lora_convert as lora_convert
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from modules import shared, devices, sd_models, sd_models_compile, errors, files_cache, model_quant
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debug = os.environ.get('SD_LORA_DEBUG', None) is not None
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extra_network_lora = None
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available_networks = {}
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available_network_aliases = {}
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loaded_networks: List[network.Network] = []
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timer = { 'load': 0, 'apply': 0, 'restore': 0, 'deactivate': 0 }
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lora_cache = {}
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diffuser_loaded = []
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diffuser_scales = []
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available_network_hash_lookup = {}
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forbidden_network_aliases = {}
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re_network_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")
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module_types = [
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network_lora.ModuleTypeLora(),
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network_hada.ModuleTypeHada(),
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network_ia3.ModuleTypeIa3(),
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network_oft.ModuleTypeOFT(),
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network_lokr.ModuleTypeLokr(),
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network_full.ModuleTypeFull(),
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network_norm.ModuleTypeNorm(),
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network_glora.ModuleTypeGLora(),
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]
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def total_time():
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return sum(timer.values())
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def assign_network_names_to_compvis_modules(sd_model):
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if sd_model is None:
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return
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sd_model = getattr(shared.sd_model, "pipe", shared.sd_model) # wrapped model compatiblility
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network_layer_mapping = {}
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if hasattr(sd_model, 'text_encoder') and sd_model.text_encoder is not None:
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for name, module in sd_model.text_encoder.named_modules():
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prefix = "lora_te1_" if hasattr(sd_model, 'text_encoder_2') else "lora_te_"
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network_name = prefix + name.replace(".", "_")
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network_layer_mapping[network_name] = module
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module.network_layer_name = network_name
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if hasattr(sd_model, 'text_encoder_2'):
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for name, module in sd_model.text_encoder_2.named_modules():
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network_name = "lora_te2_" + name.replace(".", "_")
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network_layer_mapping[network_name] = module
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module.network_layer_name = network_name
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if hasattr(sd_model, 'unet'):
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for name, module in sd_model.unet.named_modules():
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network_name = "lora_unet_" + name.replace(".", "_")
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network_layer_mapping[network_name] = module
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module.network_layer_name = network_name
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if hasattr(sd_model, 'transformer'):
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for name, module in sd_model.transformer.named_modules():
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network_name = "lora_transformer_" + name.replace(".", "_")
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network_layer_mapping[network_name] = module
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if "norm" in network_name and "linear" not in network_name and shared.sd_model_type != "sd3":
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continue
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module.network_layer_name = network_name
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shared.sd_model.network_layer_mapping = network_layer_mapping
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def load_diffusers(name, network_on_disk, lora_scale=shared.opts.extra_networks_default_multiplier) -> Union[network.Network, None]:
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name = name.replace(".", "_")
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shared.log.debug(f'Load network: type=LoRA name="{name}" file="{network_on_disk.filename}" detected={network_on_disk.sd_version} method=diffusers scale={lora_scale} fuse={shared.opts.lora_fuse_diffusers}')
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if not shared.native:
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return None
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if not hasattr(shared.sd_model, 'load_lora_weights'):
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shared.log.error(f'Load network: type=LoRA class={shared.sd_model.__class__} does not implement load lora')
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return None
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try:
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shared.sd_model.load_lora_weights(network_on_disk.filename, adapter_name=name)
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except Exception as e:
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if 'already in use' in str(e):
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pass
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else:
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if 'The following keys have not been correctly renamed' in str(e):
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shared.log.error(f'Load network: type=LoRA name="{name}" diffusers unsupported format')
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else:
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shared.log.error(f'Load network: type=LoRA name="{name}" {e}')
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if debug:
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errors.display(e, "LoRA")
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return None
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if name not in diffuser_loaded:
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diffuser_loaded.append(name)
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diffuser_scales.append(lora_scale)
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net = network.Network(name, network_on_disk)
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net.mtime = os.path.getmtime(network_on_disk.filename)
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return net
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def load_network(name, network_on_disk) -> Union[network.Network, None]:
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if not shared.sd_loaded:
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return None
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cached = lora_cache.get(name, None)
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if debug:
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shared.log.debug(f'Load network: type=LoRA name="{name}" file="{network_on_disk.filename}" type=lora {"cached" if cached else ""}')
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if cached is not None:
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return cached
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net = network.Network(name, network_on_disk)
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net.mtime = os.path.getmtime(network_on_disk.filename)
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sd = sd_models.read_state_dict(network_on_disk.filename, what='network')
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if shared.sd_model_type == 'f1': # if kohya flux lora, convert state_dict
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sd = lora_convert._convert_kohya_flux_lora_to_diffusers(sd) or sd # pylint: disable=protected-access
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if shared.sd_model_type == 'sd3': # if kohya flux lora, convert state_dict
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try:
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sd = lora_convert._convert_kohya_sd3_lora_to_diffusers(sd) or sd # pylint: disable=protected-access
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except ValueError: # EAFP for diffusers PEFT keys
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pass
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assign_network_names_to_compvis_modules(shared.sd_model)
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keys_failed_to_match = {}
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matched_networks = {}
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bundle_embeddings = {}
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convert = lora_convert.KeyConvert()
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for key_network, weight in sd.items():
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parts = key_network.split('.')
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if parts[0] == "bundle_emb":
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emb_name, vec_name = parts[1], key_network.split(".", 2)[-1]
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emb_dict = bundle_embeddings.get(emb_name, {})
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emb_dict[vec_name] = weight
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bundle_embeddings[emb_name] = emb_dict
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continue
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if len(parts) > 5: # messy handler for diffusers peft lora
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key_network_without_network_parts = '_'.join(parts[:-2])
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if not key_network_without_network_parts.startswith('lora_'):
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key_network_without_network_parts = 'lora_' + key_network_without_network_parts
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network_part = '.'.join(parts[-2:]).replace('lora_A', 'lora_down').replace('lora_B', 'lora_up')
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else:
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key_network_without_network_parts, network_part = key_network.split(".", 1)
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key, sd_module = convert(key_network_without_network_parts)
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if sd_module is None:
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keys_failed_to_match[key_network] = key
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continue
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if key not in matched_networks:
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matched_networks[key] = network.NetworkWeights(network_key=key_network, sd_key=key, w={}, sd_module=sd_module)
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matched_networks[key].w[network_part] = weight
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network_types = []
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for key, weights in matched_networks.items():
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net_module = None
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for nettype in module_types:
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net_module = nettype.create_module(net, weights)
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if net_module is not None:
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network_types.append(nettype.__class__.__name__)
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break
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if net_module is None:
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shared.log.error(f'LoRA unhandled: name={name} key={key} weights={weights.w.keys()}')
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else:
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net.modules[key] = net_module
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if len(keys_failed_to_match) > 0:
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shared.log.warning(f'LoRA name="{name}" type={set(network_types)} unmatched={len(keys_failed_to_match)} matched={len(matched_networks)}')
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if debug:
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shared.log.debug(f'LoRA name="{name}" unmatched={keys_failed_to_match}')
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else:
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shared.log.debug(f'LoRA name="{name}" type={set(network_types)} keys={len(matched_networks)}')
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if len(matched_networks) == 0:
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return None
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lora_cache[name] = net
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net.bundle_embeddings = bundle_embeddings
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return net
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def maybe_recompile_model(names, te_multipliers):
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recompile_model = False
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if shared.compiled_model_state is not None and shared.compiled_model_state.is_compiled:
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if len(names) == len(shared.compiled_model_state.lora_model):
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for i, name in enumerate(names):
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if shared.compiled_model_state.lora_model[
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i] != f"{name}:{te_multipliers[i] if te_multipliers else shared.opts.extra_networks_default_multiplier}":
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recompile_model = True
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shared.compiled_model_state.lora_model = []
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break
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if not recompile_model:
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if len(loaded_networks) > 0 and debug:
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shared.log.debug('Model Compile: Skipping LoRa loading')
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return recompile_model
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else:
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recompile_model = True
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shared.compiled_model_state.lora_model = []
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if recompile_model:
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backup_cuda_compile = shared.opts.cuda_compile
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sd_models.unload_model_weights(op='model')
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shared.opts.cuda_compile = []
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sd_models.reload_model_weights(op='model')
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shared.opts.cuda_compile = backup_cuda_compile
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return recompile_model
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def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
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networks_on_disk: list[network.NetworkOnDisk] = [available_network_aliases.get(name, None) for name in names]
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if any(x is None for x in networks_on_disk):
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list_available_networks()
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networks_on_disk: list[network.NetworkOnDisk] = [available_network_aliases.get(name, None) for name in names]
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failed_to_load_networks = []
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recompile_model = maybe_recompile_model(names, te_multipliers)
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loaded_networks.clear()
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diffuser_loaded.clear()
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diffuser_scales.clear()
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timer['load'] = 0
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t0 = time.time()
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for i, (network_on_disk, name) in enumerate(zip(networks_on_disk, names)):
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net = None
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if network_on_disk is not None:
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shorthash = getattr(network_on_disk, 'shorthash', '').lower()
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if debug:
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shared.log.debug(f'Load network: type=LoRA name="{name}" file="{network_on_disk.filename}" hash="{shorthash}"')
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try:
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if recompile_model:
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shared.compiled_model_state.lora_model.append(f"{name}:{te_multipliers[i] if te_multipliers else shared.opts.extra_networks_default_multiplier}")
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if shared.opts.lora_force_diffusers or network_overrides.check_override(shorthash): # OpenVINO only works with Diffusers LoRa loading
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net = load_diffusers(name, network_on_disk, lora_scale=te_multipliers[i] if te_multipliers else shared.opts.extra_networks_default_multiplier)
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else:
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net = load_network(name, network_on_disk)
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if net is not None:
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net.mentioned_name = name
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network_on_disk.read_hash()
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except Exception as e:
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shared.log.error(f'Load network: type=LoRA file="{network_on_disk.filename}" {e}')
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if debug:
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errors.display(e, 'LoRA')
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continue
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if net is None:
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failed_to_load_networks.append(name)
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shared.log.error(f'Load network: type=LoRA name="{name}" detected={network_on_disk.sd_version if network_on_disk is not None else None} failed')
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continue
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shared.sd_model.embedding_db.load_diffusers_embedding(None, net.bundle_embeddings)
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net.te_multiplier = te_multipliers[i] if te_multipliers else shared.opts.extra_networks_default_multiplier
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net.unet_multiplier = unet_multipliers[i] if unet_multipliers else shared.opts.extra_networks_default_multiplier
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net.dyn_dim = dyn_dims[i] if dyn_dims else shared.opts.extra_networks_default_multiplier
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loaded_networks.append(net)
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while len(lora_cache) > shared.opts.lora_in_memory_limit:
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name = next(iter(lora_cache))
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lora_cache.pop(name, None)
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if len(diffuser_loaded) > 0:
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shared.log.debug(f'Load network: type=LoRA loaded={diffuser_loaded} available={shared.sd_model.get_list_adapters()} active={shared.sd_model.get_active_adapters()} scales={diffuser_scales}')
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try:
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shared.sd_model.set_adapters(adapter_names=diffuser_loaded, adapter_weights=diffuser_scales)
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if shared.opts.lora_fuse_diffusers:
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shared.sd_model.fuse_lora(adapter_names=diffuser_loaded, lora_scale=1.0, fuse_unet=True, fuse_text_encoder=True) # fuse uses fixed scale since later apply does the scaling
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shared.sd_model.unload_lora_weights()
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except Exception as e:
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shared.log.error(f'Load network: type=LoRA {e}')
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if debug:
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errors.display(e, 'LoRA')
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if len(loaded_networks) > 0 and debug:
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shared.log.debug(f'Load network: type=LoRA loaded={len(loaded_networks)} cache={list(lora_cache)}')
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devices.torch_gc()
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if recompile_model:
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shared.log.info("Load network: type=LoRA recompiling model")
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backup_lora_model = shared.compiled_model_state.lora_model
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if 'Model' in shared.opts.cuda_compile:
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shared.sd_model = sd_models_compile.compile_diffusers(shared.sd_model)
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shared.compiled_model_state.lora_model = backup_lora_model
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if shared.opts.diffusers_offload_mode == "balanced":
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sd_models.apply_balanced_offload(shared.sd_model)
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t1 = time.time()
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timer['load'] += t1 - t0
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def set_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, diffusers.models.lora.LoRACompatibleLinear, diffusers.models.lora.LoRACompatibleConv], updown, ex_bias):
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weights_backup = getattr(self, "network_weights_backup", None)
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bias_backup = getattr(self, "network_bias_backup", None)
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if weights_backup is None and bias_backup is None:
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return
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device = self.weight.device
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with devices.inference_context():
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if weights_backup is not None:
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if updown is not None:
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if len(weights_backup.shape) == 4 and weights_backup.shape[1] == 9:
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# inpainting model. zero pad updown to make channel[1] 4 to 9
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updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5)) # pylint: disable=not-callable
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weights_backup = weights_backup.clone().to(device)
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weights_backup += updown.to(weights_backup)
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if getattr(self, "quant_type", None) in ['nf4', 'fp4']:
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bnb = model_quant.load_bnb('Load network: type=LoRA', silent=True)
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if bnb is not None:
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self.weight = bnb.nn.Params4bit(weights_backup, quant_state=self.quant_state, quant_type=self.quant_type, blocksize=self.blocksize)
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else:
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self.weight.copy_(weights_backup, non_blocking=True)
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else:
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self.weight.copy_(weights_backup, non_blocking=True)
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if hasattr(self, "qweight") and hasattr(self, "freeze"):
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self.freeze()
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if bias_backup is not None:
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if ex_bias is not None:
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bias_backup = bias_backup.clone() + ex_bias.to(weights_backup)
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self.bias.copy_(bias_backup)
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else:
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self.bias = None
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self.to(device)
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def maybe_backup_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, diffusers.models.lora.LoRACompatibleLinear, diffusers.models.lora.LoRACompatibleConv], wanted_names): # pylint: disable=W0613
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weights_backup = getattr(self, "network_weights_backup", None)
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if weights_backup is None and wanted_names != (): # pylint: disable=C1803
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if getattr(self.weight, "quant_type", None) in ['nf4', 'fp4']:
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bnb = model_quant.load_bnb('Load network: type=LoRA', silent=True)
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if bnb is not None:
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with devices.inference_context():
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weights_backup = bnb.functional.dequantize_4bit(self.weight, quant_state=self.weight.quant_state, quant_type=self.weight.quant_type, blocksize=self.weight.blocksize,)
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self.quant_state = self.weight.quant_state
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self.quant_type = self.weight.quant_type
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self.blocksize = self.weight.blocksize
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else:
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weights_backup = self.weight.clone()
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else:
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weights_backup = self.weight.clone()
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if shared.opts.lora_offload_backup and weights_backup is not None:
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weights_backup = weights_backup.to(devices.cpu)
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self.network_weights_backup = weights_backup
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bias_backup = getattr(self, "network_bias_backup", None)
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if bias_backup is None:
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if getattr(self, 'bias', None) is not None:
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bias_backup = self.bias.clone()
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else:
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bias_backup = None
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if shared.opts.lora_offload_backup and bias_backup is not None:
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bias_backup = bias_backup.to(devices.cpu)
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self.network_bias_backup = bias_backup
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def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, diffusers.models.lora.LoRACompatibleLinear, diffusers.models.lora.LoRACompatibleConv]):
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"""
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Applies the currently selected set of networks to the weights of torch layer self.
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If weights already have this particular set of networks applied, does nothing.
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If not, restores orginal weights from backup and alters weights according to networks.
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"""
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network_layer_name = getattr(self, 'network_layer_name', None)
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if network_layer_name is None:
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return
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t0 = time.time()
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current_names = getattr(self, "network_current_names", ())
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wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks)
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if any([net.modules.get(network_layer_name, None) for net in loaded_networks]): # noqa: C419 # pylint: disable=R1729
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maybe_backup_weights(self, wanted_names)
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if current_names != wanted_names:
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batch_updown = None
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batch_ex_bias = None
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for net in loaded_networks:
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# default workflow where module is known and has weights
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module = net.modules.get(network_layer_name, None)
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if module is not None and hasattr(self, 'weight'):
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try:
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with devices.inference_context():
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weight = self.weight # calculate quant weights once
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updown, ex_bias = module.calc_updown(weight)
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if batch_updown is not None and updown is not None:
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batch_updown += updown
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else:
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batch_updown = updown
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if batch_ex_bias is not None and ex_bias is not None:
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batch_ex_bias += ex_bias
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else:
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batch_ex_bias = ex_bias
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except RuntimeError as e:
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extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
|
|
if debug:
|
|
module_name = net.modules.get(network_layer_name, None)
|
|
shared.log.error(f'LoRA apply weight name="{net.name}" module="{module_name}" layer="{network_layer_name}" {e}')
|
|
errors.display(e, 'LoRA')
|
|
raise RuntimeError('LoRA apply weight') from e
|
|
continue
|
|
if module is None:
|
|
continue
|
|
shared.log.warning(f'LoRA network="{net.name}" layer="{network_layer_name}" unsupported operation')
|
|
extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
|
|
set_weights(self, batch_updown, batch_ex_bias) # Set or restore weights from backup
|
|
self.network_current_names = wanted_names
|
|
t1 = time.time()
|
|
timer['apply'] += t1 - t0
|
|
|
|
|
|
def network_load():
|
|
for k in timer.keys():
|
|
timer[k] = 0
|
|
sd_model = getattr(shared.sd_model, "pipe", shared.sd_model) # wrapped model compatiblility
|
|
for component_name in ['text_encoder','text_encoder_2', 'unet', 'transformer']:
|
|
component = getattr(sd_model, component_name, None)
|
|
if component is not None:
|
|
for _, module in component.named_modules():
|
|
network_apply_weights(module)
|
|
|
|
|
|
def list_available_networks():
|
|
t0 = time.time()
|
|
available_networks.clear()
|
|
available_network_aliases.clear()
|
|
forbidden_network_aliases.clear()
|
|
available_network_hash_lookup.clear()
|
|
forbidden_network_aliases.update({"none": 1, "Addams": 1})
|
|
if not os.path.exists(shared.cmd_opts.lora_dir):
|
|
shared.log.warning(f'LoRA directory not found: path="{shared.cmd_opts.lora_dir}"')
|
|
|
|
def add_network(filename):
|
|
if not os.path.isfile(filename):
|
|
return
|
|
name = os.path.splitext(os.path.basename(filename))[0]
|
|
name = name.replace('.', '_')
|
|
try:
|
|
entry = network.NetworkOnDisk(name, filename)
|
|
available_networks[entry.name] = entry
|
|
if entry.alias in available_network_aliases:
|
|
forbidden_network_aliases[entry.alias.lower()] = 1
|
|
if shared.opts.lora_preferred_name == 'filename':
|
|
available_network_aliases[entry.name] = entry
|
|
else:
|
|
available_network_aliases[entry.alias] = entry
|
|
if entry.shorthash:
|
|
available_network_hash_lookup[entry.shorthash] = entry
|
|
except OSError as e: # should catch FileNotFoundError and PermissionError etc.
|
|
shared.log.error(f'LoRA: filename="{filename}" {e}')
|
|
|
|
candidates = list(files_cache.list_files(shared.cmd_opts.lora_dir, ext_filter=[".pt", ".ckpt", ".safetensors"]))
|
|
with concurrent.futures.ThreadPoolExecutor(max_workers=shared.max_workers) as executor:
|
|
for fn in candidates:
|
|
executor.submit(add_network, fn)
|
|
t1 = time.time()
|
|
shared.log.info(f'Available LoRAs: path="{shared.cmd_opts.lora_dir}" items={len(available_networks)} folders={len(forbidden_network_aliases)} time={t1 - t0:.2f}')
|