Spaces:
Configuration error
Configuration error
| import glob | |
| import os | |
| import re | |
| import torch | |
| from typing import Union | |
| from modules import shared, devices, sd_models, errors | |
| metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20} | |
| re_digits = re.compile(r"\d+") | |
| re_x_proj = re.compile(r"(.*)_([qkv]_proj)$") | |
| re_compiled = {} | |
| suffix_conversion = { | |
| "attentions": {}, | |
| "resnets": { | |
| "conv1": "in_layers_2", | |
| "conv2": "out_layers_3", | |
| "time_emb_proj": "emb_layers_1", | |
| "conv_shortcut": "skip_connection", | |
| } | |
| } | |
| def convert_diffusers_name_to_compvis(key, is_sd2): | |
| def match(match_list, regex_text): | |
| regex = re_compiled.get(regex_text) | |
| if regex is None: | |
| regex = re.compile(regex_text) | |
| re_compiled[regex_text] = regex | |
| r = re.match(regex, key) | |
| if not r: | |
| return False | |
| match_list.clear() | |
| match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()]) | |
| return True | |
| m = [] | |
| if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"): | |
| suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3]) | |
| return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}" | |
| if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"): | |
| suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2]) | |
| return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}" | |
| if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"): | |
| suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3]) | |
| return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}" | |
| if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"): | |
| return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op" | |
| if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"): | |
| return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv" | |
| if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"): | |
| if is_sd2: | |
| if 'mlp_fc1' in m[1]: | |
| return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}" | |
| elif 'mlp_fc2' in m[1]: | |
| return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}" | |
| else: | |
| return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}" | |
| return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}" | |
| return key | |
| class LoraOnDisk: | |
| def __init__(self, name, filename): | |
| self.name = name | |
| self.filename = filename | |
| self.metadata = {} | |
| _, ext = os.path.splitext(filename) | |
| if ext.lower() == ".safetensors": | |
| try: | |
| self.metadata = sd_models.read_metadata_from_safetensors(filename) | |
| except Exception as e: | |
| errors.display(e, f"reading lora {filename}") | |
| if self.metadata: | |
| m = {} | |
| for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)): | |
| m[k] = v | |
| self.metadata = m | |
| self.ssmd_cover_images = self.metadata.pop('ssmd_cover_images', None) # those are cover images and they are too big to display in UI as text | |
| class LoraModule: | |
| def __init__(self, name): | |
| self.name = name | |
| self.multiplier = 1.0 | |
| self.modules = {} | |
| self.mtime = None | |
| class LoraUpDownModule: | |
| def __init__(self): | |
| self.up = None | |
| self.down = None | |
| self.alpha = None | |
| def assign_lora_names_to_compvis_modules(sd_model): | |
| lora_layer_mapping = {} | |
| for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules(): | |
| lora_name = name.replace(".", "_") | |
| lora_layer_mapping[lora_name] = module | |
| module.lora_layer_name = lora_name | |
| for name, module in shared.sd_model.model.named_modules(): | |
| lora_name = name.replace(".", "_") | |
| lora_layer_mapping[lora_name] = module | |
| module.lora_layer_name = lora_name | |
| sd_model.lora_layer_mapping = lora_layer_mapping | |
| def load_lora(name, filename): | |
| lora = LoraModule(name) | |
| lora.mtime = os.path.getmtime(filename) | |
| sd = sd_models.read_state_dict(filename) | |
| keys_failed_to_match = {} | |
| is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping | |
| for key_diffusers, weight in sd.items(): | |
| key_diffusers_without_lora_parts, lora_key = key_diffusers.split(".", 1) | |
| key = convert_diffusers_name_to_compvis(key_diffusers_without_lora_parts, is_sd2) | |
| sd_module = shared.sd_model.lora_layer_mapping.get(key, None) | |
| if sd_module is None: | |
| m = re_x_proj.match(key) | |
| if m: | |
| sd_module = shared.sd_model.lora_layer_mapping.get(m.group(1), None) | |
| if sd_module is None: | |
| keys_failed_to_match[key_diffusers] = key | |
| continue | |
| lora_module = lora.modules.get(key, None) | |
| if lora_module is None: | |
| lora_module = LoraUpDownModule() | |
| lora.modules[key] = lora_module | |
| if lora_key == "alpha": | |
| lora_module.alpha = weight.item() | |
| continue | |
| if type(sd_module) == torch.nn.Linear: | |
| module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False) | |
| elif type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear: | |
| module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False) | |
| elif type(sd_module) == torch.nn.MultiheadAttention: | |
| module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False) | |
| elif type(sd_module) == torch.nn.Conv2d: | |
| module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False) | |
| else: | |
| print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}') | |
| continue | |
| assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}' | |
| with torch.no_grad(): | |
| module.weight.copy_(weight) | |
| module.to(device=devices.cpu, dtype=devices.dtype) | |
| if lora_key == "lora_up.weight": | |
| lora_module.up = module | |
| elif lora_key == "lora_down.weight": | |
| lora_module.down = module | |
| else: | |
| assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha' | |
| if len(keys_failed_to_match) > 0: | |
| print(f"Failed to match keys when loading Lora {filename}: {keys_failed_to_match}") | |
| return lora | |
| def load_loras(names, multipliers=None): | |
| already_loaded = {} | |
| for lora in loaded_loras: | |
| if lora.name in names: | |
| already_loaded[lora.name] = lora | |
| loaded_loras.clear() | |
| loras_on_disk = [available_loras.get(name, None) for name in names] | |
| if any([x is None for x in loras_on_disk]): | |
| list_available_loras() | |
| loras_on_disk = [available_loras.get(name, None) for name in names] | |
| for i, name in enumerate(names): | |
| lora = already_loaded.get(name, None) | |
| lora_on_disk = loras_on_disk[i] | |
| if lora_on_disk is not None: | |
| if lora is None or os.path.getmtime(lora_on_disk.filename) > lora.mtime: | |
| lora = load_lora(name, lora_on_disk.filename) | |
| if lora is None: | |
| print(f"Couldn't find Lora with name {name}") | |
| continue | |
| lora.multiplier = multipliers[i] if multipliers else 1.0 | |
| loaded_loras.append(lora) | |
| def lora_calc_updown(lora, module, target): | |
| with torch.no_grad(): | |
| up = module.up.weight.to(target.device, dtype=target.dtype) | |
| down = module.down.weight.to(target.device, dtype=target.dtype) | |
| if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1): | |
| updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3) | |
| else: | |
| updown = up @ down | |
| updown = updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0) | |
| return updown | |
| def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]): | |
| """ | |
| Applies the currently selected set of Loras to the weights of torch layer self. | |
| If weights already have this particular set of loras applied, does nothing. | |
| If not, restores orginal weights from backup and alters weights according to loras. | |
| """ | |
| lora_layer_name = getattr(self, 'lora_layer_name', None) | |
| if lora_layer_name is None: | |
| return | |
| current_names = getattr(self, "lora_current_names", ()) | |
| wanted_names = tuple((x.name, x.multiplier) for x in loaded_loras) | |
| weights_backup = getattr(self, "lora_weights_backup", None) | |
| if weights_backup is None: | |
| if isinstance(self, torch.nn.MultiheadAttention): | |
| weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True)) | |
| else: | |
| weights_backup = self.weight.to(devices.cpu, copy=True) | |
| self.lora_weights_backup = weights_backup | |
| if current_names != wanted_names: | |
| if weights_backup is not None: | |
| if isinstance(self, torch.nn.MultiheadAttention): | |
| self.in_proj_weight.copy_(weights_backup[0]) | |
| self.out_proj.weight.copy_(weights_backup[1]) | |
| else: | |
| self.weight.copy_(weights_backup) | |
| for lora in loaded_loras: | |
| module = lora.modules.get(lora_layer_name, None) | |
| if module is not None and hasattr(self, 'weight'): | |
| self.weight += lora_calc_updown(lora, module, self.weight) | |
| continue | |
| module_q = lora.modules.get(lora_layer_name + "_q_proj", None) | |
| module_k = lora.modules.get(lora_layer_name + "_k_proj", None) | |
| module_v = lora.modules.get(lora_layer_name + "_v_proj", None) | |
| module_out = lora.modules.get(lora_layer_name + "_out_proj", None) | |
| if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out: | |
| updown_q = lora_calc_updown(lora, module_q, self.in_proj_weight) | |
| updown_k = lora_calc_updown(lora, module_k, self.in_proj_weight) | |
| updown_v = lora_calc_updown(lora, module_v, self.in_proj_weight) | |
| updown_qkv = torch.vstack([updown_q, updown_k, updown_v]) | |
| self.in_proj_weight += updown_qkv | |
| self.out_proj.weight += lora_calc_updown(lora, module_out, self.out_proj.weight) | |
| continue | |
| if module is None: | |
| continue | |
| print(f'failed to calculate lora weights for layer {lora_layer_name}') | |
| setattr(self, "lora_current_names", wanted_names) | |
| def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]): | |
| setattr(self, "lora_current_names", ()) | |
| setattr(self, "lora_weights_backup", None) | |
| def lora_Linear_forward(self, input): | |
| lora_apply_weights(self) | |
| return torch.nn.Linear_forward_before_lora(self, input) | |
| def lora_Linear_load_state_dict(self, *args, **kwargs): | |
| lora_reset_cached_weight(self) | |
| return torch.nn.Linear_load_state_dict_before_lora(self, *args, **kwargs) | |
| def lora_Conv2d_forward(self, input): | |
| lora_apply_weights(self) | |
| return torch.nn.Conv2d_forward_before_lora(self, input) | |
| def lora_Conv2d_load_state_dict(self, *args, **kwargs): | |
| lora_reset_cached_weight(self) | |
| return torch.nn.Conv2d_load_state_dict_before_lora(self, *args, **kwargs) | |
| def lora_MultiheadAttention_forward(self, *args, **kwargs): | |
| lora_apply_weights(self) | |
| return torch.nn.MultiheadAttention_forward_before_lora(self, *args, **kwargs) | |
| def lora_MultiheadAttention_load_state_dict(self, *args, **kwargs): | |
| lora_reset_cached_weight(self) | |
| return torch.nn.MultiheadAttention_load_state_dict_before_lora(self, *args, **kwargs) | |
| def list_available_loras(): | |
| available_loras.clear() | |
| os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True) | |
| candidates = \ | |
| glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.pt'), recursive=True) + \ | |
| glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.safetensors'), recursive=True) + \ | |
| glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.ckpt'), recursive=True) | |
| for filename in sorted(candidates, key=str.lower): | |
| if os.path.isdir(filename): | |
| continue | |
| name = os.path.splitext(os.path.basename(filename))[0] | |
| available_loras[name] = LoraOnDisk(name, filename) | |
| available_loras = {} | |
| loaded_loras = [] | |
| list_available_loras() | |