mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-14 22:53:25 +03:00
208 lines
6.6 KiB
Python
208 lines
6.6 KiB
Python
import glob
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import os
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import re
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import torch
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from modules import shared, devices, sd_models
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re_digits = re.compile(r"\d+")
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re_unet_down_blocks = re.compile(r"lora_unet_down_blocks_(\d+)_attentions_(\d+)_(.+)")
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re_unet_mid_blocks = re.compile(r"lora_unet_mid_block_attentions_(\d+)_(.+)")
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re_unet_up_blocks = re.compile(r"lora_unet_up_blocks_(\d+)_attentions_(\d+)_(.+)")
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re_text_block = re.compile(r"lora_te_text_model_encoder_layers_(\d+)_(.+)")
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def convert_diffusers_name_to_compvis(key):
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def match(match_list, 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, re_unet_down_blocks):
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return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[1]}_1_{m[2]}"
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if match(m, re_unet_mid_blocks):
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return f"diffusion_model_middle_block_1_{m[1]}"
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if match(m, re_unet_up_blocks):
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return f"diffusion_model_output_blocks_{m[0] * 3 + m[1]}_1_{m[2]}"
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if match(m, re_text_block):
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return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
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return key
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class LoraOnDisk:
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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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class LoraModule:
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def __init__(self, name):
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self.name = name
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self.multiplier = 1.0
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self.modules = {}
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self.mtime = None
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class LoraUpDownModule:
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def __init__(self):
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self.up = None
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self.down = None
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self.alpha = None
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def assign_lora_names_to_compvis_modules(sd_model):
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lora_layer_mapping = {}
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for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules():
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lora_name = name.replace(".", "_")
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lora_layer_mapping[lora_name] = module
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module.lora_layer_name = lora_name
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for name, module in shared.sd_model.model.named_modules():
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lora_name = name.replace(".", "_")
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lora_layer_mapping[lora_name] = module
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module.lora_layer_name = lora_name
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sd_model.lora_layer_mapping = lora_layer_mapping
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def load_lora(name, filename):
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lora = LoraModule(name)
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lora.mtime = os.path.getmtime(filename)
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sd = sd_models.read_state_dict(filename)
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keys_failed_to_match = []
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for key_diffusers, weight in sd.items():
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fullkey = convert_diffusers_name_to_compvis(key_diffusers)
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key, lora_key = fullkey.split(".", 1)
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sd_module = shared.sd_model.lora_layer_mapping.get(key, None)
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if sd_module is None:
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keys_failed_to_match.append(key_diffusers)
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continue
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lora_module = lora.modules.get(key, None)
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if lora_module is None:
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lora_module = LoraUpDownModule()
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lora.modules[key] = lora_module
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if lora_key == "alpha":
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lora_module.alpha = weight.item()
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continue
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if type(sd_module) == torch.nn.Linear:
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module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
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elif type(sd_module) == torch.nn.Conv2d:
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module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
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else:
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assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'
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with torch.no_grad():
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module.weight.copy_(weight)
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module.to(device=devices.device, dtype=devices.dtype)
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if lora_key == "lora_up.weight":
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lora_module.up = module
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elif lora_key == "lora_down.weight":
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lora_module.down = module
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else:
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assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha'
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if len(keys_failed_to_match) > 0:
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print(f"Failed to match keys when loading Lora {filename}: {keys_failed_to_match}")
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return lora
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def load_loras(names, multipliers=None):
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already_loaded = {}
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for lora in loaded_loras:
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if lora.name in names:
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already_loaded[lora.name] = lora
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loaded_loras.clear()
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loras_on_disk = [available_loras.get(name, None) for name in names]
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if any([x is None for x in loras_on_disk]):
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list_available_loras()
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loras_on_disk = [available_loras.get(name, None) for name in names]
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for i, name in enumerate(names):
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lora = already_loaded.get(name, None)
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lora_on_disk = loras_on_disk[i]
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if lora_on_disk is not None:
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if lora is None or os.path.getmtime(lora_on_disk.filename) > lora.mtime:
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lora = load_lora(name, lora_on_disk.filename)
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if lora is None:
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print(f"Couldn't find Lora with name {name}")
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continue
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lora.multiplier = multipliers[i] if multipliers else 1.0
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loaded_loras.append(lora)
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def lora_forward(module, input, res):
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if len(loaded_loras) == 0:
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return res
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lora_layer_name = getattr(module, 'lora_layer_name', None)
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for lora in loaded_loras:
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module = lora.modules.get(lora_layer_name, None)
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if module is not None:
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if shared.opts.lora_apply_to_outputs and res.shape == input.shape:
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res = res + module.up(module.down(res)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
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else:
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res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
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return res
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def lora_Linear_forward(self, input):
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return lora_forward(self, input, torch.nn.Linear_forward_before_lora(self, input))
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def lora_Conv2d_forward(self, input):
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return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora(self, input))
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def list_available_loras():
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available_loras.clear()
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os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
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candidates = \
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glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.pt'), recursive=True) + \
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glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.safetensors'), recursive=True) + \
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glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.ckpt'), recursive=True)
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for filename in sorted(candidates, key=str.lower):
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if os.path.isdir(filename):
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continue
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name = os.path.splitext(os.path.basename(filename))[0]
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available_loras[name] = LoraOnDisk(name, filename)
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available_loras = {}
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loaded_loras = []
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list_available_loras()
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