mirror of https://github.com/vladmandic/automatic
605 lines
29 KiB
Python
605 lines
29 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 lora_patches
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import network
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import network_lora
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import network_hada
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import network_ia3
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import network_oft
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import network_lokr
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import network_full
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import network_norm
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import network_glora
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import network_overrides
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import lora_convert
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import torch
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import diffusers.models.lora
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from modules import shared, devices, sd_models, sd_models_compile, errors, scripts_manager, files_cache, model_quant
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debug = os.environ.get('SD_LORA_DEBUG', None) is not None
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originals: lora_patches.LoraPatches = 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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# networks_in_memory = {}
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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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convert_diffusers_name_to_compvis = lora_convert.convert_diffusers_name_to_compvis # supermerger compatibility item
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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 shared.native:
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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:
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continue
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module.network_layer_name = network_name
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else:
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if not hasattr(sd_model, 'cond_stage_model'):
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sd_model.network_layer_mapping = {}
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return
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for name, module in sd_model.cond_stage_model.wrapped.named_modules():
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network_name = 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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for name, module in sd_model.model.named_modules():
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network_name = 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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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) -> network.Network:
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t0 = time.time()
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name = name.replace(".", "_")
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#cached = lora_cache.get(name, None)
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shared.log.debug(f'Network load: 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 cached is not None:
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# return cached
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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'Network load: 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'Network load: type=LoRA name="{name}" diffusers unsupported format')
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else:
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shared.log.error(f'Network load: 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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# lora_cache[name] = net
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t1 = time.time()
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timer['load'] += t1 - t0
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return net
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def load_network(name, network_on_disk) -> network.Network:
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if not shared.sd_loaded:
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return None
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t0 = time.time()
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cached = lora_cache.get(name, None)
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if debug:
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shared.log.debug(f'Network load: 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 in ['f1', 'chroma']: # 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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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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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) # Now returns lists
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if sd_module[0] is None:
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if "bundle_emb" not in key_network:
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keys_failed_to_match[key_network] = key
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continue
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for k, module in zip(key, sd_module):
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if k not in matched_networks:
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matched_networks[k] = network.NetworkWeights(network_key=key_network, sd_key=k, w={}, sd_module=module)
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matched_networks[k].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'Network load: type=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'Network load: type=LoRA name="{name}" unmatched={keys_failed_to_match}')
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else:
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shared.log.debug(f'Network load: type=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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t1 = time.time()
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net.bundle_embeddings = bundle_embeddings
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timer['load'] += t1 - t0
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return net
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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 = 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[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
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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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loaded_networks.clear()
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diffuser_loaded.clear()
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diffuser_scales.clear()
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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'Network load: 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.native and (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'Network load: 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'Network load: 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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if shared.native:
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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'Network load: 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'Network load: 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'Network load: 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("Network load: 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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def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention, diffusers.models.lora.LoRACompatibleLinear, diffusers.models.lora.LoRACompatibleConv]):
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t0 = time.time()
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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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t1 = time.time()
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timer['restore'] += t1 - t0
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return
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# if debug:
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# shared.log.debug('LoRA restore weights')
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if weights_backup is not None:
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if isinstance(self, torch.nn.MultiheadAttention):
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self.in_proj_weight.copy_(weights_backup[0])
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self.out_proj.weight.copy_(weights_backup[1])
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elif hasattr(self, "qweight") and hasattr(self, "freeze"):
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self.weight = torch.nn.Parameter(weights_backup.to(self.weight.device, copy=True))
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self.freeze()
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elif getattr(self, "quant_type", None) in ['nf4', 'fp4']:
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bnb = model_quant.load_bnb('Network load: type=LoRA', silent=True)
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if bnb is not None:
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device = self.weight.device
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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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self.weight.to(device)
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else:
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self.weight.copy_(weights_backup)
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else:
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self.weight.copy_(weights_backup)
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if bias_backup is not None:
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if isinstance(self, torch.nn.MultiheadAttention):
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self.out_proj.bias.copy_(bias_backup)
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else:
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self.bias.copy_(bias_backup)
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else:
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if isinstance(self, torch.nn.MultiheadAttention):
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self.out_proj.bias = None
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else:
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self.bias = None
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t1 = time.time()
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timer['restore'] += t1 - t0
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def maybe_backup_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention, diffusers.models.lora.LoRACompatibleLinear, diffusers.models.lora.LoRACompatibleConv], wanted_names, current_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 isinstance(self, torch.nn.MultiheadAttention):
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weights_backup = (self.in_proj_weight.clone().to(devices.cpu), self.out_proj.weight.clone().to(devices.cpu))
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elif getattr(self.weight, "quant_type", None) in ['nf4', 'fp4']:
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bnb = model_quant.load_bnb('Network load: 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,).to(devices.cpu)
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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().to(devices.cpu)
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else:
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weights_backup = self.weight.clone().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 isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None:
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bias_backup = self.out_proj.bias.clone().to(devices.cpu)
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elif getattr(self, 'bias', None) is not None:
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bias_backup = self.bias.clone().to(devices.cpu)
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else:
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bias_backup = None
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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, torch.nn.MultiheadAttention, 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, current_names)
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if current_names != wanted_names:
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network_restore_weights_from_backup(self)
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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)
|
|
if module is not None and hasattr(self, 'weight'):
|
|
try:
|
|
with devices.inference_context():
|
|
weight = self.weight # calculate quant weights once
|
|
updown, ex_bias = module.calc_updown(weight)
|
|
if len(weight.shape) == 4 and weight.shape[1] == 9:
|
|
# inpainting model. zero pad updown to make channel[1] 4 to 9
|
|
updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5)) # pylint: disable=not-callable
|
|
if getattr(self.weight, "quant_type", None) in ['nf4', 'fp4']: # or self.weight.numel() != updown.numel():
|
|
bnb = model_quant.load_bnb('Network load: type=LoRA', silent=True)
|
|
if bnb is not None:
|
|
device = self.weight.device
|
|
weight = bnb.functional.dequantize_4bit(self.weight, quant_state=self.weight.quant_state, quant_type=self.weight.quant_type, blocksize=self.weight.blocksize)
|
|
self.weight = bnb.nn.Params4bit(weight + updown, quant_state=self.quant_state, quant_type=shared.opts.lora_quant.lower(), blocksize=self.blocksize)
|
|
self.weight.to(device)
|
|
else:
|
|
self.weight = torch.nn.Parameter(weight + updown)
|
|
else:
|
|
self.weight = torch.nn.Parameter(weight + updown)
|
|
if hasattr(self, "qweight") and hasattr(self, "freeze"):
|
|
self.freeze()
|
|
if ex_bias is not None and hasattr(self, 'bias'):
|
|
if self.bias is None:
|
|
self.bias = torch.nn.Parameter(ex_bias)
|
|
else:
|
|
self.bias += ex_bias
|
|
except RuntimeError as e:
|
|
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
|
|
# alternative workflow looking at _*_proj layers
|
|
module_q = net.modules.get(network_layer_name + "_q_proj", None)
|
|
module_k = net.modules.get(network_layer_name + "_k_proj", None)
|
|
module_v = net.modules.get(network_layer_name + "_v_proj", None)
|
|
module_out = net.modules.get(network_layer_name + "_out_proj", None)
|
|
if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
|
|
try:
|
|
with devices.inference_context():
|
|
updown_q, _ = module_q.calc_updown(self.in_proj_weight)
|
|
updown_k, _ = module_k.calc_updown(self.in_proj_weight)
|
|
updown_v, _ = module_v.calc_updown(self.in_proj_weight)
|
|
updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
|
|
updown_out, ex_bias = module_out.calc_updown(self.out_proj.weight)
|
|
self.in_proj_weight += updown_qkv
|
|
self.out_proj.weight += updown_out
|
|
if ex_bias is not None:
|
|
if self.out_proj.bias is None:
|
|
self.out_proj.bias = torch.nn.Parameter(ex_bias)
|
|
else:
|
|
self.out_proj.bias += ex_bias
|
|
except RuntimeError as e:
|
|
if debug:
|
|
shared.log.debug(f'LoRA network="{net.name}" layer="{network_layer_name}" {e}')
|
|
extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
|
|
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
|
|
self.network_current_names = wanted_names
|
|
t1 = time.time()
|
|
timer['apply'] += t1 - t0
|
|
|
|
|
|
def network_forward(module, input, original_forward): # pylint: disable=W0622
|
|
"""
|
|
Old way of applying Lora by executing operations during layer's forward.
|
|
Stacking many loras this way results in big performance degradation.
|
|
"""
|
|
if len(loaded_networks) == 0:
|
|
return original_forward(module, input)
|
|
input = devices.cond_cast_unet(input)
|
|
network_restore_weights_from_backup(module)
|
|
network_reset_cached_weight(module)
|
|
y = original_forward(module, input)
|
|
network_layer_name = getattr(module, 'network_layer_name', None)
|
|
for lora in loaded_networks:
|
|
module = lora.modules.get(network_layer_name, None)
|
|
if module is None:
|
|
continue
|
|
y = module.forward(input, y)
|
|
return y
|
|
|
|
|
|
def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
|
|
self.network_current_names = ()
|
|
self.network_weights_backup = None
|
|
|
|
|
|
def network_Linear_forward(self, input): # pylint: disable=W0622
|
|
network_apply_weights(self)
|
|
return originals.Linear_forward(self, input)
|
|
|
|
|
|
def network_QLinear_forward(self, input): # pylint: disable=W0622
|
|
network_apply_weights(self)
|
|
return torch.nn.functional.linear(input, self.qweight, bias=self.bias)
|
|
|
|
|
|
def network_Linear_load_state_dict(self, *args, **kwargs):
|
|
network_reset_cached_weight(self)
|
|
return originals.Linear_load_state_dict(self, *args, **kwargs)
|
|
|
|
|
|
def network_Linear4bit_forward(self, input): # pylint: disable=W0622
|
|
network_apply_weights(self)
|
|
return originals.Linear4bit_forward(self, input)
|
|
#
|
|
# def network_Linear4bit_load_state_dict(self, *args, **kwargs):
|
|
# network_reset_cached_weight(self)
|
|
# return originals.Linear4bit_load_state_dict(self, *args, **kwargs)
|
|
|
|
def network_Conv2d_forward(self, input): # pylint: disable=W0622
|
|
network_apply_weights(self)
|
|
return originals.Conv2d_forward(self, input)
|
|
|
|
|
|
def network_QConv2d_forward(self, input): # pylint: disable=W0622
|
|
network_apply_weights(self)
|
|
return self._conv_forward(input, self.qweight, self.bias) # pylint: disable=protected-access
|
|
|
|
|
|
def network_Conv2d_load_state_dict(self, *args, **kwargs):
|
|
network_reset_cached_weight(self)
|
|
return originals.Conv2d_load_state_dict(self, *args, **kwargs)
|
|
|
|
|
|
def network_GroupNorm_forward(self, input): # pylint: disable=W0622
|
|
network_apply_weights(self)
|
|
return originals.GroupNorm_forward(self, input)
|
|
|
|
|
|
def network_GroupNorm_load_state_dict(self, *args, **kwargs):
|
|
network_reset_cached_weight(self)
|
|
return originals.GroupNorm_load_state_dict(self, *args, **kwargs)
|
|
|
|
|
|
def network_LayerNorm_forward(self, input): # pylint: disable=W0622
|
|
network_apply_weights(self)
|
|
return originals.LayerNorm_forward(self, input)
|
|
|
|
|
|
def network_LayerNorm_load_state_dict(self, *args, **kwargs):
|
|
network_reset_cached_weight(self)
|
|
return originals.LayerNorm_load_state_dict(self, *args, **kwargs)
|
|
|
|
|
|
def network_MultiheadAttention_forward(self, *args, **kwargs):
|
|
network_apply_weights(self)
|
|
return originals.MultiheadAttention_forward(self, *args, **kwargs)
|
|
|
|
|
|
def network_MultiheadAttention_load_state_dict(self, *args, **kwargs):
|
|
network_reset_cached_weight(self)
|
|
return originals.MultiheadAttention_load_state_dict(self, *args, **kwargs)
|
|
|
|
|
|
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}')
|
|
|
|
|
|
def infotext_pasted(infotext, params): # pylint: disable=W0613
|
|
if "AddNet Module 1" in [x[1] for x in scripts_manager.scripts_txt2img.infotext_fields]:
|
|
return # if the other extension is active, it will handle those fields, no need to do anything
|
|
added = []
|
|
for k in params:
|
|
if not k.startswith("AddNet Model "):
|
|
continue
|
|
num = k[13:]
|
|
if params.get("AddNet Module " + num) != "LoRA":
|
|
continue
|
|
name = params.get("AddNet Model " + num)
|
|
if name is None:
|
|
continue
|
|
m = re_network_name.match(name)
|
|
if m:
|
|
name = m.group(1)
|
|
multiplier = params.get("AddNet Weight A " + num, "1.0")
|
|
added.append(f"<lora:{name}:{multiplier}>")
|
|
if added:
|
|
params["Prompt"] += "\n" + "".join(added)
|
|
|
|
|
|
list_available_networks()
|