569 lines
21 KiB
Python
569 lines
21 KiB
Python
import logging
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import os
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import re
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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_lokr
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import network_full
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import network_norm
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import torch
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from typing import Union
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from modules import shared, devices, sd_models, errors, scripts, sd_hijack
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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_lokr.ModuleTypeLokr(),
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network_full.ModuleTypeFull(),
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network_norm.ModuleTypeNorm(),
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]
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re_digits = re.compile(r"\d+")
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re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
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re_compiled = {}
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suffix_conversion = {
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"attentions": {},
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"resnets": {
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"conv1": "in_layers_2",
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"conv2": "out_layers_3",
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"norm1": "in_layers_0",
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"norm2": "out_layers_0",
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"time_emb_proj": "emb_layers_1",
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"conv_shortcut": "skip_connection",
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}
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}
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def convert_diffusers_name_to_compvis(key, is_sd2):
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def match(match_list, regex_text):
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regex = re_compiled.get(regex_text)
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if regex is None:
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regex = re.compile(regex_text)
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re_compiled[regex_text] = regex
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r = re.match(regex, key)
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if not r:
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return False
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match_list.clear()
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match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()])
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return True
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m = []
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if match(m, r"lora_unet_conv_in(.*)"):
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return f'diffusion_model_input_blocks_0_0{m[0]}'
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if match(m, r"lora_unet_conv_out(.*)"):
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return f'diffusion_model_out_2{m[0]}'
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if match(m, r"lora_unet_time_embedding_linear_(\d+)(.*)"):
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return f"diffusion_model_time_embed_{m[0] * 2 - 2}{m[1]}"
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if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
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suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
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return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
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if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
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suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
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return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
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if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
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suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
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return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
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if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
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return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
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if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
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return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
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if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
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if is_sd2:
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if 'mlp_fc1' in m[1]:
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return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
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elif 'mlp_fc2' in m[1]:
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return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
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else:
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return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
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return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
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if match(m, r"lora_te2_text_model_encoder_layers_(\d+)_(.+)"):
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if 'mlp_fc1' in m[1]:
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return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
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elif 'mlp_fc2' in m[1]:
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return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
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else:
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return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
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return key
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def assign_network_names_to_compvis_modules(sd_model):
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network_layer_mapping = {}
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if shared.sd_model.is_sdxl:
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for i, embedder in enumerate(shared.sd_model.conditioner.embedders):
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if not hasattr(embedder, 'wrapped'):
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continue
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for name, module in embedder.wrapped.named_modules():
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network_name = f'{i}_{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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else:
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for name, module in shared.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 shared.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_network(name, network_on_disk):
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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)
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# this should not be needed but is here as an emergency fix for an unknown error people are experiencing in 1.2.0
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if not hasattr(shared.sd_model, 'network_layer_mapping'):
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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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is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping
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matched_networks = {}
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for key_network, weight in sd.items():
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key_network_without_network_parts, network_part = key_network.split(".", 1)
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key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
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sd_module = shared.sd_model.network_layer_mapping.get(key, None)
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if sd_module is None:
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m = re_x_proj.match(key)
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if m:
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sd_module = shared.sd_model.network_layer_mapping.get(m.group(1), None)
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# SDXL loras seem to already have correct compvis keys, so only need to replace "lora_unet" with "diffusion_model"
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if sd_module is None and "lora_unet" in key_network_without_network_parts:
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key = key_network_without_network_parts.replace("lora_unet", "diffusion_model")
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sd_module = shared.sd_model.network_layer_mapping.get(key, None)
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elif sd_module is None and "lora_te1_text_model" in key_network_without_network_parts:
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key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")
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sd_module = shared.sd_model.network_layer_mapping.get(key, None)
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# some SD1 Loras also have correct compvis keys
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if sd_module is None:
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key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model")
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sd_module = shared.sd_model.network_layer_mapping.get(key, None)
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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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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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break
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if net_module is None:
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raise AssertionError(f"Could not find a module type (out of {', '.join([x.__class__.__name__ for x in module_types])}) that would accept those keys: {', '.join(weights.w)}")
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net.modules[key] = net_module
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if keys_failed_to_match:
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logging.debug(f"Network {network_on_disk.filename} didn't match keys: {keys_failed_to_match}")
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return net
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def purge_networks_from_memory():
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while len(networks_in_memory) > shared.opts.lora_in_memory_limit and len(networks_in_memory) > 0:
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name = next(iter(networks_in_memory))
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networks_in_memory.pop(name, None)
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devices.torch_gc()
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def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
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already_loaded = {}
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for net in loaded_networks:
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if net.name in names:
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already_loaded[net.name] = net
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loaded_networks.clear()
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networks_on_disk = [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 = [available_network_aliases.get(name, None) for name in names]
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failed_to_load_networks = []
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for i, (network_on_disk, name) in enumerate(zip(networks_on_disk, names)):
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net = already_loaded.get(name, None)
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if network_on_disk is not None:
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if net is None:
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net = networks_in_memory.get(name)
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if net is None or os.path.getmtime(network_on_disk.filename) > net.mtime:
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try:
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net = load_network(name, network_on_disk)
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networks_in_memory.pop(name, None)
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networks_in_memory[name] = net
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except Exception as e:
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errors.display(e, f"loading network {network_on_disk.filename}")
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continue
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net.mentioned_name = name
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network_on_disk.read_hash()
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if net is None:
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failed_to_load_networks.append(name)
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logging.info(f"Couldn't find network with name {name}")
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continue
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net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0
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net.unet_multiplier = unet_multipliers[i] if unet_multipliers else 1.0
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net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0
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loaded_networks.append(net)
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if failed_to_load_networks:
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sd_hijack.model_hijack.comments.append("Networks not found: " + ", ".join(failed_to_load_networks))
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purge_networks_from_memory()
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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]):
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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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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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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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def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
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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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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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weights_backup = getattr(self, "network_weights_backup", None)
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if weights_backup is None:
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if isinstance(self, torch.nn.MultiheadAttention):
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weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
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else:
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weights_backup = self.weight.to(devices.cpu, copy=True)
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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.to(devices.cpu, copy=True)
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elif getattr(self, 'bias', None) is not None:
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bias_backup = self.bias.to(devices.cpu, copy=True)
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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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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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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 torch.no_grad():
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updown, ex_bias = module.calc_updown(self.weight)
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if len(self.weight.shape) == 4 and self.weight.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))
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self.weight += updown
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if ex_bias is not None and hasattr(self, 'bias'):
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if self.bias is None:
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self.bias = torch.nn.Parameter(ex_bias)
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else:
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self.bias += ex_bias
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except RuntimeError as e:
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logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
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extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
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continue
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module_q = net.modules.get(network_layer_name + "_q_proj", None)
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module_k = net.modules.get(network_layer_name + "_k_proj", None)
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module_v = net.modules.get(network_layer_name + "_v_proj", None)
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module_out = net.modules.get(network_layer_name + "_out_proj", None)
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if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
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try:
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with torch.no_grad():
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updown_q, _ = module_q.calc_updown(self.in_proj_weight)
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updown_k, _ = module_k.calc_updown(self.in_proj_weight)
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updown_v, _ = module_v.calc_updown(self.in_proj_weight)
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updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
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updown_out, ex_bias = module_out.calc_updown(self.out_proj.weight)
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self.in_proj_weight += updown_qkv
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self.out_proj.weight += updown_out
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if ex_bias is not None:
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if self.out_proj.bias is None:
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self.out_proj.bias = torch.nn.Parameter(ex_bias)
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else:
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self.out_proj.bias += ex_bias
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except RuntimeError as e:
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logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
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extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
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continue
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if module is None:
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continue
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logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation")
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extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
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self.network_current_names = wanted_names
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def network_forward(module, input, original_forward):
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"""
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Old way of applying Lora by executing operations during layer's forward.
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Stacking many loras this way results in big performance degradation.
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"""
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if len(loaded_networks) == 0:
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return original_forward(module, input)
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input = devices.cond_cast_unet(input)
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network_restore_weights_from_backup(module)
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network_reset_cached_weight(module)
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y = original_forward(module, input)
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network_layer_name = getattr(module, 'network_layer_name', None)
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for lora in loaded_networks:
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module = lora.modules.get(network_layer_name, None)
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if module is None:
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continue
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y = module.forward(input, y)
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return y
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def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
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self.network_current_names = ()
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self.network_weights_backup = None
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def network_Linear_forward(self, input):
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if shared.opts.lora_functional:
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return network_forward(self, input, originals.Linear_forward)
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network_apply_weights(self)
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return originals.Linear_forward(self, input)
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def network_Linear_load_state_dict(self, *args, **kwargs):
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network_reset_cached_weight(self)
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return originals.Linear_load_state_dict(self, *args, **kwargs)
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def network_Conv2d_forward(self, input):
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if shared.opts.lora_functional:
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return network_forward(self, input, originals.Conv2d_forward)
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network_apply_weights(self)
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return originals.Conv2d_forward(self, input)
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def network_Conv2d_load_state_dict(self, *args, **kwargs):
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network_reset_cached_weight(self)
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return originals.Conv2d_load_state_dict(self, *args, **kwargs)
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def network_GroupNorm_forward(self, input):
|
|
if shared.opts.lora_functional:
|
|
return network_forward(self, input, originals.GroupNorm_forward)
|
|
|
|
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):
|
|
if shared.opts.lora_functional:
|
|
return network_forward(self, input, originals.LayerNorm_forward)
|
|
|
|
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():
|
|
available_networks.clear()
|
|
available_network_aliases.clear()
|
|
forbidden_network_aliases.clear()
|
|
available_network_hash_lookup.clear()
|
|
forbidden_network_aliases.update({"none": 1, "Addams": 1})
|
|
|
|
os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
|
|
|
|
candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
|
|
candidates += list(shared.walk_files(shared.cmd_opts.lyco_dir_backcompat, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
|
|
for filename in candidates:
|
|
if os.path.isdir(filename):
|
|
continue
|
|
|
|
name = os.path.splitext(os.path.basename(filename))[0]
|
|
try:
|
|
entry = network.NetworkOnDisk(name, filename)
|
|
except OSError: # should catch FileNotFoundError and PermissionError etc.
|
|
errors.report(f"Failed to load network {name} from {filename}", exc_info=True)
|
|
continue
|
|
|
|
available_networks[name] = entry
|
|
|
|
if entry.alias in available_network_aliases:
|
|
forbidden_network_aliases[entry.alias.lower()] = 1
|
|
|
|
available_network_aliases[name] = entry
|
|
available_network_aliases[entry.alias] = entry
|
|
|
|
|
|
re_network_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")
|
|
|
|
|
|
def infotext_pasted(infotext, params):
|
|
if "AddNet Module 1" in [x[1] for x in scripts.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)
|
|
|
|
|
|
originals: lora_patches.LoraPatches = None
|
|
|
|
extra_network_lora = None
|
|
|
|
available_networks = {}
|
|
available_network_aliases = {}
|
|
loaded_networks = []
|
|
networks_in_memory = {}
|
|
available_network_hash_lookup = {}
|
|
forbidden_network_aliases = {}
|
|
|
|
list_available_networks()
|