diff options
Diffstat (limited to 'extensions-builtin/Lora/lora.py')
| -rw-r--r-- | extensions-builtin/Lora/lora.py | 199 |
1 files changed, 199 insertions, 0 deletions
diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py new file mode 100644 index 00000000..da1797dc --- /dev/null +++ b/extensions-builtin/Lora/lora.py @@ -0,0 +1,199 @@ +import glob
+import os
+import re
+import torch
+
+from modules import shared, devices, sd_models
+
+re_digits = re.compile(r"\d+")
+re_unet_down_blocks = re.compile(r"lora_unet_down_blocks_(\d+)_attentions_(\d+)_(.+)")
+re_unet_mid_blocks = re.compile(r"lora_unet_mid_block_attentions_(\d+)_(.+)")
+re_unet_up_blocks = re.compile(r"lora_unet_up_blocks_(\d+)_attentions_(\d+)_(.+)")
+re_text_block = re.compile(r"lora_te_text_model_encoder_layers_(\d+)_(.+)")
+
+
+def convert_diffusers_name_to_compvis(key):
+ def match(match_list, 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, re_unet_down_blocks):
+ return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[1]}_1_{m[2]}"
+
+ if match(m, re_unet_mid_blocks):
+ return f"diffusion_model_middle_block_1_{m[1]}"
+
+ if match(m, re_unet_up_blocks):
+ return f"diffusion_model_output_blocks_{m[0] * 3 + m[1]}_1_{m[2]}"
+
+ if match(m, re_text_block):
+ 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
+
+
+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
+
+
+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 = []
+
+ for key_diffusers, weight in sd.items():
+ fullkey = convert_diffusers_name_to_compvis(key_diffusers)
+ key, lora_key = fullkey.split(".", 1)
+
+ sd_module = shared.sd_model.lora_layer_mapping.get(key, None)
+ if sd_module is None:
+ keys_failed_to_match.append(key_diffusers)
+ 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.Conv2d:
+ module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
+ else:
+ 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.device, dtype=devices.dtype)
+
+ lora_module = lora.modules.get(key, None)
+ if lora_module is None:
+ lora_module = LoraUpDownModule()
+ lora.modules[key] = lora_module
+
+ 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 or lora_down.weight'
+
+ 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_forward(module, input, res):
+ if len(loaded_loras) == 0:
+ return res
+
+ lora_layer_name = getattr(module, 'lora_layer_name', None)
+ for lora in loaded_loras:
+ module = lora.modules.get(lora_layer_name, None)
+ if module is not None:
+ res = res + module.up(module.down(input)) * lora.multiplier
+
+ return res
+
+
+def lora_Linear_forward(self, input):
+ return lora_forward(self, input, torch.nn.Linear_forward_before_lora(self, input))
+
+
+def lora_Conv2d_forward(self, input):
+ return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora(self, input))
+
+
+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):
+ 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()
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