Lora support for SD2
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AUTOMATIC
parent
b705c9b72b
commit
650ddc9dd3
@ -8,14 +8,27 @@ 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_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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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):
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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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@ -26,16 +39,25 @@ def convert_diffusers_name_to_compvis(key, is_sd2):
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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, 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, re_unet_mid_blocks):
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return f"diffusion_model_middle_block_1_{m[1]}"
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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, 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, 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, re_text_block):
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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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@ -109,16 +131,22 @@ def load_lora(name, filename):
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sd = sd_models.read_state_dict(filename)
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keys_failed_to_match = []
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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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fullkey = convert_diffusers_name_to_compvis(key_diffusers, is_sd2)
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key, lora_key = fullkey.split(".", 1)
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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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keys_failed_to_match.append(key_diffusers)
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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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@ -133,7 +161,9 @@ def load_lora(name, filename):
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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.modules.linear.NonDynamicallyQuantizableLinear(weight.shape[1], weight.shape[0], bias=False)
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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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@ -190,54 +220,94 @@ def load_loras(names, multipliers=None):
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loaded_loras.append(lora)
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def lora_apply_weights(self: torch.nn.Conv2d | torch.nn.Linear):
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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 weight of torch layer self.
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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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weights_backup = self.weight.to(devices.cpu, copy=True)
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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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self.weight.copy_(weights_backup)
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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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lora_layer_name = getattr(self, '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 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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with torch.no_grad():
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up = module.up.weight.to(self.weight.device, dtype=self.weight.dtype)
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down = module.down.weight.to(self.weight.device, dtype=self.weight.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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self.weight += updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
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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: torch.nn.Linear, *args, **kwargs):
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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_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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@ -248,15 +318,22 @@ def lora_Conv2d_forward(self, input):
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return torch.nn.Conv2d_forward_before_lora(self, input)
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def lora_Conv2d_load_state_dict(self: torch.nn.Conv2d, *args, **kwargs):
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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_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_NonDynamicallyQuantizableLinear_forward(self, input):
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return lora_forward(self, input, torch.nn.NonDynamicallyQuantizableLinear_forward_before_lora(self, input))
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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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@ -12,6 +12,8 @@ def unload():
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torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
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torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora
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torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_lora
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torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_lora
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torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_lora
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def before_ui():
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@ -31,10 +33,18 @@ if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
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if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_lora'):
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torch.nn.Conv2d_load_state_dict_before_lora = torch.nn.Conv2d._load_from_state_dict
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if not hasattr(torch.nn, 'MultiheadAttention_forward_before_lora'):
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torch.nn.MultiheadAttention_forward_before_lora = torch.nn.MultiheadAttention.forward
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if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_lora'):
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torch.nn.MultiheadAttention_load_state_dict_before_lora = torch.nn.MultiheadAttention._load_from_state_dict
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torch.nn.Linear.forward = lora.lora_Linear_forward
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torch.nn.Linear._load_from_state_dict = lora.lora_Linear_load_state_dict
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torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
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torch.nn.Conv2d._load_from_state_dict = lora.lora_Conv2d_load_state_dict
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torch.nn.MultiheadAttention.forward = lora.lora_MultiheadAttention_forward
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torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention_load_state_dict
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script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
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script_callbacks.on_script_unloaded(unload)
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