159 lines
5.2 KiB
Python
159 lines
5.2 KiB
Python
from __future__ import annotations
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import os
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from collections import namedtuple
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import enum
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from modules import sd_models, cache, errors, hashes, shared
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NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module'])
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metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
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class SdVersion(enum.Enum):
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Unknown = 1
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SD1 = 2
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SD2 = 3
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SDXL = 4
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class NetworkOnDisk:
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def __init__(self, name, filename):
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self.name = name
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self.filename = filename
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self.metadata = {}
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self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
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def read_metadata():
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metadata = sd_models.read_metadata_from_safetensors(filename)
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metadata.pop('ssmd_cover_images', None) # those are cover images, and they are too big to display in UI as text
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return metadata
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if self.is_safetensors:
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try:
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self.metadata = cache.cached_data_for_file('safetensors-metadata', "lora/" + self.name, filename, read_metadata)
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except Exception as e:
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errors.display(e, f"reading lora {filename}")
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if self.metadata:
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m = {}
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for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)):
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m[k] = v
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self.metadata = m
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self.alias = self.metadata.get('ss_output_name', self.name)
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self.hash = None
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self.shorthash = None
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self.set_hash(
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self.metadata.get('sshs_model_hash') or
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hashes.sha256_from_cache(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or
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''
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)
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self.sd_version = self.detect_version()
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def detect_version(self):
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if str(self.metadata.get('ss_base_model_version', "")).startswith("sdxl_"):
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return SdVersion.SDXL
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elif str(self.metadata.get('ss_v2', "")) == "True":
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return SdVersion.SD2
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elif len(self.metadata):
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return SdVersion.SD1
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return SdVersion.Unknown
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def set_hash(self, v):
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self.hash = v
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self.shorthash = self.hash[0:12]
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if self.shorthash:
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import networks
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networks.available_network_hash_lookup[self.shorthash] = self
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def read_hash(self):
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if not self.hash:
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self.set_hash(hashes.sha256(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '')
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def get_alias(self):
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import networks
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if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in networks.forbidden_network_aliases:
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return self.name
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else:
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return self.alias
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class Network: # LoraModule
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def __init__(self, name, network_on_disk: NetworkOnDisk):
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self.name = name
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self.network_on_disk = network_on_disk
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self.te_multiplier = 1.0
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self.unet_multiplier = 1.0
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self.dyn_dim = None
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self.modules = {}
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self.mtime = None
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self.mentioned_name = None
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"""the text that was used to add the network to prompt - can be either name or an alias"""
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class ModuleType:
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def create_module(self, net: Network, weights: NetworkWeights) -> Network | None:
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return None
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class NetworkModule:
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def __init__(self, net: Network, weights: NetworkWeights):
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self.network = net
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self.network_key = weights.network_key
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self.sd_key = weights.sd_key
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self.sd_module = weights.sd_module
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if hasattr(self.sd_module, 'weight'):
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self.shape = self.sd_module.weight.shape
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self.dim = None
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self.bias = weights.w.get("bias")
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self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None
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self.scale = weights.w["scale"].item() if "scale" in weights.w else None
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def multiplier(self):
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if 'transformer' in self.sd_key[:20]:
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return self.network.te_multiplier
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else:
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return self.network.unet_multiplier
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def calc_scale(self):
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if self.scale is not None:
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return self.scale
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if self.dim is not None and self.alpha is not None:
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return self.alpha / self.dim
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return 1.0
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def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
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if self.bias is not None:
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updown = updown.reshape(self.bias.shape)
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updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
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updown = updown.reshape(output_shape)
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if len(output_shape) == 4:
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updown = updown.reshape(output_shape)
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if orig_weight.size().numel() == updown.size().numel():
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updown = updown.reshape(orig_weight.shape)
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if ex_bias is not None:
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ex_bias = ex_bias * self.multiplier()
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return updown * self.calc_scale() * self.multiplier(), ex_bias
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def calc_updown(self, target):
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raise NotImplementedError()
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def forward(self, x, y):
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raise NotImplementedError()
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