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| from __future__ import annotations | |
| import os | |
| from collections import namedtuple | |
| import enum | |
| from modules import sd_models, cache, errors, hashes, shared | |
| NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module']) | |
| metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20} | |
| class SdVersion(enum.Enum): | |
| Unknown = 1 | |
| SD1 = 2 | |
| SD2 = 3 | |
| SDXL = 4 | |
| class NetworkOnDisk: | |
| def __init__(self, name, filename): | |
| self.name = name | |
| self.filename = filename | |
| self.metadata = {} | |
| self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors" | |
| def read_metadata(): | |
| metadata = sd_models.read_metadata_from_safetensors(filename) | |
| metadata.pop('ssmd_cover_images', None) # those are cover images, and they are too big to display in UI as text | |
| return metadata | |
| if self.is_safetensors: | |
| try: | |
| self.metadata = cache.cached_data_for_file('safetensors-metadata', "lora/" + self.name, filename, read_metadata) | |
| 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.alias = self.metadata.get('ss_output_name', self.name) | |
| self.hash = None | |
| self.shorthash = None | |
| self.set_hash( | |
| self.metadata.get('sshs_model_hash') or | |
| hashes.sha256_from_cache(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or | |
| '' | |
| ) | |
| self.sd_version = self.detect_version() | |
| def detect_version(self): | |
| if str(self.metadata.get('ss_base_model_version', "")).startswith("sdxl_"): | |
| return SdVersion.SDXL | |
| elif str(self.metadata.get('ss_v2', "")) == "True": | |
| return SdVersion.SD2 | |
| elif len(self.metadata): | |
| return SdVersion.SD1 | |
| return SdVersion.Unknown | |
| def set_hash(self, v): | |
| self.hash = v | |
| self.shorthash = self.hash[0:12] | |
| if self.shorthash: | |
| import networks | |
| networks.available_network_hash_lookup[self.shorthash] = self | |
| def read_hash(self): | |
| if not self.hash: | |
| self.set_hash(hashes.sha256(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '') | |
| def get_alias(self): | |
| import networks | |
| if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in networks.forbidden_network_aliases: | |
| return self.name | |
| else: | |
| return self.alias | |
| class Network: # LoraModule | |
| def __init__(self, name, network_on_disk: NetworkOnDisk): | |
| self.name = name | |
| self.network_on_disk = network_on_disk | |
| self.te_multiplier = 1.0 | |
| self.unet_multiplier = 1.0 | |
| self.dyn_dim = None | |
| self.modules = {} | |
| self.mtime = None | |
| self.mentioned_name = None | |
| """the text that was used to add the network to prompt - can be either name or an alias""" | |
| class ModuleType: | |
| def create_module(self, net: Network, weights: NetworkWeights) -> Network | None: | |
| return None | |
| class NetworkModule: | |
| def __init__(self, net: Network, weights: NetworkWeights): | |
| self.network = net | |
| self.network_key = weights.network_key | |
| self.sd_key = weights.sd_key | |
| self.sd_module = weights.sd_module | |
| if hasattr(self.sd_module, 'weight'): | |
| self.shape = self.sd_module.weight.shape | |
| self.dim = None | |
| self.bias = weights.w.get("bias") | |
| self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None | |
| self.scale = weights.w["scale"].item() if "scale" in weights.w else None | |
| def multiplier(self): | |
| if 'transformer' in self.sd_key[:20]: | |
| return self.network.te_multiplier | |
| else: | |
| return self.network.unet_multiplier | |
| def calc_scale(self): | |
| if self.scale is not None: | |
| return self.scale | |
| if self.dim is not None and self.alpha is not None: | |
| return self.alpha / self.dim | |
| return 1.0 | |
| def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None): | |
| if self.bias is not None: | |
| updown = updown.reshape(self.bias.shape) | |
| updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype) | |
| updown = updown.reshape(output_shape) | |
| if len(output_shape) == 4: | |
| updown = updown.reshape(output_shape) | |
| if orig_weight.size().numel() == updown.size().numel(): | |
| updown = updown.reshape(orig_weight.shape) | |
| if ex_bias is not None: | |
| ex_bias = ex_bias * self.multiplier() | |
| return updown * self.calc_scale() * self.multiplier(), ex_bias | |
| def calc_updown(self, target): | |
| raise NotImplementedError() | |
| def forward(self, x, y): | |
| raise NotImplementedError() | |