mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
362 lines
13 KiB
Python
362 lines
13 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, errors
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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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re_digits = re.compile(r"\d+")
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re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
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re_compiled = {}
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suffix_conversion = {
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"attentions": {},
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"resnets": {
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"conv1": "in_layers_2",
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"conv2": "out_layers_3",
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"time_emb_proj": "emb_layers_1",
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"conv_shortcut": "skip_connection",
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}
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}
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def convert_diffusers_name_to_compvis(key, is_sd2):
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def match(match_list, regex_text):
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regex = re_compiled.get(regex_text)
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if regex is None:
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regex = re.compile(regex_text)
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re_compiled[regex_text] = 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, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
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suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
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return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
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if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
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suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
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return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
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if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
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suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
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return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
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if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
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return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
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if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
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return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
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if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
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if is_sd2:
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if 'mlp_fc1' in m[1]:
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return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
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elif 'mlp_fc2' in m[1]:
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return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
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else:
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return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
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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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self.metadata = {}
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_, ext = os.path.splitext(filename)
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if ext.lower() == ".safetensors":
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try:
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self.metadata = sd_models.read_metadata_from_safetensors(filename)
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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.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
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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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is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping
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for key_diffusers, weight in sd.items():
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key_diffusers_without_lora_parts, lora_key = key_diffusers.split(".", 1)
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key = convert_diffusers_name_to_compvis(key_diffusers_without_lora_parts, is_sd2)
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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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m = re_x_proj.match(key)
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if m:
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sd_module = shared.sd_model.lora_layer_mapping.get(m.group(1), None)
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if sd_module is None:
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keys_failed_to_match[key_diffusers] = key
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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.modules.linear.NonDynamicallyQuantizableLinear:
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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.MultiheadAttention:
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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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print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
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continue
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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.cpu, 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_calc_updown(lora, module, target):
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with torch.no_grad():
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up = module.up.weight.to(target.device, dtype=target.dtype)
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down = module.down.weight.to(target.device, dtype=target.dtype)
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if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
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updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
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else:
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updown = up @ down
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updown = updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
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return updown
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def lora_apply_weights(self: torch.nn.Conv2d | torch.nn.Linear | torch.nn.MultiheadAttention):
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"""
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Applies the currently selected set of Loras to the weights of torch layer self.
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If weights already have this particular set of loras applied, does nothing.
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If not, restores orginal weights from backup and alters weights according to loras.
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"""
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lora_layer_name = getattr(self, 'lora_layer_name', None)
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if lora_layer_name is None:
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return
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current_names = getattr(self, "lora_current_names", ())
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wanted_names = tuple((x.name, x.multiplier) for x in loaded_loras)
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weights_backup = getattr(self, "lora_weights_backup", None)
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if weights_backup is None:
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if isinstance(self, torch.nn.MultiheadAttention):
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weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
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else:
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weights_backup = self.weight.to(devices.cpu, copy=True)
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self.lora_weights_backup = weights_backup
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if current_names != wanted_names:
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if weights_backup is not None:
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if isinstance(self, torch.nn.MultiheadAttention):
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self.in_proj_weight.copy_(weights_backup[0])
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self.out_proj.weight.copy_(weights_backup[1])
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else:
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self.weight.copy_(weights_backup)
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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 and hasattr(self, 'weight'):
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self.weight += lora_calc_updown(lora, module, self.weight)
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continue
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module_q = lora.modules.get(lora_layer_name + "_q_proj", None)
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module_k = lora.modules.get(lora_layer_name + "_k_proj", None)
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module_v = lora.modules.get(lora_layer_name + "_v_proj", None)
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module_out = lora.modules.get(lora_layer_name + "_out_proj", None)
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if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
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updown_q = lora_calc_updown(lora, module_q, self.in_proj_weight)
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updown_k = lora_calc_updown(lora, module_k, self.in_proj_weight)
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updown_v = lora_calc_updown(lora, module_v, self.in_proj_weight)
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updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
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self.in_proj_weight += updown_qkv
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self.out_proj.weight += lora_calc_updown(lora, module_out, self.out_proj.weight)
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continue
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if module is None:
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continue
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print(f'failed to calculate lora weights for layer {lora_layer_name}')
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setattr(self, "lora_current_names", wanted_names)
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def lora_reset_cached_weight(self: torch.nn.Conv2d | torch.nn.Linear):
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setattr(self, "lora_current_names", ())
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setattr(self, "lora_weights_backup", None)
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def lora_Linear_forward(self, input):
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lora_apply_weights(self)
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return torch.nn.Linear_forward_before_lora(self, input)
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def lora_Linear_load_state_dict(self, *args, **kwargs):
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lora_reset_cached_weight(self)
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return torch.nn.Linear_load_state_dict_before_lora(self, *args, **kwargs)
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def lora_Conv2d_forward(self, input):
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lora_apply_weights(self)
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return torch.nn.Conv2d_forward_before_lora(self, input)
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def lora_Conv2d_load_state_dict(self, *args, **kwargs):
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lora_reset_cached_weight(self)
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return torch.nn.Conv2d_load_state_dict_before_lora(self, *args, **kwargs)
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def lora_MultiheadAttention_forward(self, *args, **kwargs):
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lora_apply_weights(self)
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return torch.nn.MultiheadAttention_forward_before_lora(self, *args, **kwargs)
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def lora_MultiheadAttention_load_state_dict(self, *args, **kwargs):
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lora_reset_cached_weight(self)
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return torch.nn.MultiheadAttention_load_state_dict_before_lora(self, *args, **kwargs)
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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):
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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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