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from typing import * |
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import os, sys |
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import re |
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import glob |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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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_unet_conv_in = re.compile(r"lora_unet_conv_in(.+)") |
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re_unet_conv_out = re.compile(r"lora_unet_conv_out(.+)") |
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re_unet_time_embed = re.compile(r"lora_unet_time_embedding_linear_(\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_unet_down_blocks_res = re.compile(r"lora_unet_down_blocks_(\d+)_resnets_(\d+)_(.+)") |
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re_unet_mid_blocks_res = re.compile(r"lora_unet_mid_block_resnets_(\d+)_(.+)") |
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re_unet_up_blocks_res = re.compile(r"lora_unet_up_blocks_(\d+)_resnets_(\d+)_(.+)") |
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re_unet_downsample = re.compile(r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv(.+)") |
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re_unet_upsample = re.compile(r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv(.+)") |
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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, is_sd2): |
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key = key.replace('text_model_text_model', 'text_model') |
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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_conv_in): |
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return f'diffusion_model_input_blocks_0_0{m[0]}' |
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if match(m, re_unet_conv_out): |
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return f'diffusion_model_out_2{m[0]}' |
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if match(m, re_unet_time_embed): |
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return f"diffusion_model_time_embed_{m[0]*2-2}{m[1]}" |
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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_unet_down_blocks_res): |
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block = f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[1]}_0_" |
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if m[2].startswith('conv1'): |
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return f"{block}in_layers_2{m[2][len('conv1'):]}" |
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elif m[2].startswith('conv2'): |
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return f"{block}out_layers_3{m[2][len('conv2'):]}" |
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elif m[2].startswith('time_emb_proj'): |
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return f"{block}emb_layers_1{m[2][len('time_emb_proj'):]}" |
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elif m[2].startswith('conv_shortcut'): |
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return f"{block}skip_connection{m[2][len('conv_shortcut'):]}" |
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if match(m, re_unet_mid_blocks_res): |
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block = f"diffusion_model_middle_block_{m[0]*2}_" |
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if m[1].startswith('conv1'): |
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return f"{block}in_layers_2{m[1][len('conv1'):]}" |
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elif m[1].startswith('conv2'): |
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return f"{block}out_layers_3{m[1][len('conv2'):]}" |
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elif m[1].startswith('time_emb_proj'): |
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return f"{block}emb_layers_1{m[1][len('time_emb_proj'):]}" |
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elif m[1].startswith('conv_shortcut'): |
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return f"{block}skip_connection{m[1][len('conv_shortcut'):]}" |
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if match(m, re_unet_up_blocks_res): |
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block = f"diffusion_model_output_blocks_{m[0] * 3 + m[1]}_0_" |
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if m[2].startswith('conv1'): |
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return f"{block}in_layers_2{m[2][len('conv1'):]}" |
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elif m[2].startswith('conv2'): |
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return f"{block}out_layers_3{m[2][len('conv2'):]}" |
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elif m[2].startswith('time_emb_proj'): |
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return f"{block}emb_layers_1{m[2][len('time_emb_proj'):]}" |
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elif m[2].startswith('conv_shortcut'): |
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return f"{block}skip_connection{m[2][len('conv_shortcut'):]}" |
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if match(m, re_unet_downsample): |
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return f"diffusion_model_input_blocks_{m[0]*3+3}_0_op{m[1]}" |
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if match(m, re_unet_upsample): |
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return f"diffusion_model_output_blocks_{m[0]*3 + 2}_{1+(m[0]!=0)}_conv{m[1]}" |
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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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def assign_lyco_names_to_compvis_modules(sd_model): |
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lyco_layer_mapping = {} |
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for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules(): |
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lyco_name = name.replace(".", "_") |
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lyco_layer_mapping[lyco_name] = module |
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module.lyco_layer_name = lyco_name |
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for name, module in shared.sd_model.model.named_modules(): |
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lyco_name = name.replace(".", "_") |
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lyco_layer_mapping[lyco_name] = module |
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module.lyco_layer_name = lyco_name |
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sd_model.lyco_layer_mapping = lyco_layer_mapping |
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class LycoOnDisk: |
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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) |
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class LycoModule: |
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def __init__(self, name): |
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self.name = name |
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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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class FullModule: |
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def __init__(self): |
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self.weight = None |
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self.alpha = None |
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self.scale = None |
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self.dim = None |
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self.shape = None |
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class LycoUpDownModule: |
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def __init__(self): |
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self.up_model = None |
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self.mid_model = None |
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self.down_model = None |
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self.alpha = None |
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self.scale = None |
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self.dim = None |
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self.shape = None |
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self.bias = None |
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def make_weight_cp(t, wa, wb): |
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temp = torch.einsum('i j k l, j r -> i r k l', t, wb) |
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return torch.einsum('i j k l, i r -> r j k l', temp, wa) |
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class LycoHadaModule: |
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def __init__(self): |
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self.t1 = None |
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self.w1a = None |
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self.w1b = None |
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self.t2 = None |
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self.w2a = None |
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self.w2b = None |
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self.alpha = None |
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self.scale = None |
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self.dim = None |
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self.shape = None |
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self.bias = None |
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class IA3Module: |
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def __init__(self): |
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self.w = None |
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self.alpha = None |
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self.scale = None |
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self.dim = None |
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self.on_input = None |
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def make_kron(orig_shape, w1, w2): |
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if len(w2.shape) == 4: |
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w1 = w1.unsqueeze(2).unsqueeze(2) |
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w2 = w2.contiguous() |
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return torch.kron(w1, w2).reshape(orig_shape) |
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class LycoKronModule: |
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def __init__(self): |
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self.w1 = None |
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self.w1a = None |
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self.w1b = None |
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self.w2 = None |
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self.t2 = None |
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self.w2a = None |
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self.w2b = None |
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self._alpha = None |
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self.scale = None |
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self.dim = None |
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self.shape = None |
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self.bias = None |
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@property |
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def alpha(self): |
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if self.w1a is None and self.w2a is None: |
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return None |
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else: |
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return self._alpha |
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@alpha.setter |
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def alpha(self, x): |
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self._alpha = x |
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CON_KEY = { |
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"lora_up.weight", "dyn_up", |
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"lora_down.weight", "dyn_down", |
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"lora_mid.weight" |
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} |
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HADA_KEY = { |
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"hada_t1", |
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"hada_w1_a", |
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"hada_w1_b", |
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"hada_t2", |
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"hada_w2_a", |
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"hada_w2_b", |
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} |
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IA3_KEY = { |
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"weight", |
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"on_input" |
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} |
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KRON_KEY = { |
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"lokr_w1", |
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"lokr_w1_a", |
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"lokr_w1_b", |
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"lokr_t2", |
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"lokr_w2", |
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"lokr_w2_a", |
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"lokr_w2_b", |
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} |
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def load_lyco(name, filename): |
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lyco = LycoModule(name) |
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lyco.mtime = os.path.getmtime(filename) |
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sd = sd_models.read_state_dict(filename) |
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is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lyco_layer_mapping |
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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, is_sd2) |
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key, lyco_key = fullkey.split(".", 1) |
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sd_module = shared.sd_model.lyco_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.lyco_layer_mapping.get(m.group(1), None) |
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if sd_module is None: |
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print(key) |
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keys_failed_to_match.append(key_diffusers) |
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continue |
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lyco_module = lyco.modules.get(key, None) |
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if lyco_module is None: |
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lyco_module = LycoUpDownModule() |
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lyco.modules[key] = lyco_module |
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if lyco_key == "alpha": |
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lyco_module.alpha = weight.item() |
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continue |
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if lyco_key == "scale": |
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lyco_module.scale = weight.item() |
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continue |
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if lyco_key == "diff": |
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weight = weight.to(device=devices.cpu, dtype=devices.dtype) |
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weight.requires_grad_(False) |
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lyco_module = FullModule() |
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lyco.modules[key] = lyco_module |
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lyco_module.weight = weight |
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continue |
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if 'bias_' in lyco_key: |
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if lyco_module.bias is None: |
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lyco_module.bias = [None, None, None] |
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if 'bias_indices' == lyco_key: |
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lyco_module.bias[0] = weight |
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elif 'bias_values' == lyco_key: |
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lyco_module.bias[1] = weight |
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elif 'bias_size' == lyco_key: |
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lyco_module.bias[2] = weight |
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if all((i is not None) for i in lyco_module.bias): |
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print('build bias') |
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lyco_module.bias = torch.sparse_coo_tensor( |
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lyco_module.bias[0], |
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lyco_module.bias[1], |
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tuple(lyco_module.bias[2]), |
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).to(device=devices.cpu, dtype=devices.dtype) |
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lyco_module.bias.requires_grad_(False) |
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continue |
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if lyco_key in CON_KEY: |
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if (type(sd_module) == torch.nn.Linear |
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or type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear |
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or type(sd_module) == torch.nn.MultiheadAttention): |
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weight = weight.reshape(weight.shape[0], -1) |
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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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if lyco_key == "lora_down.weight" or lyco_key == "dyn_up": |
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if len(weight.shape) == 2: |
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weight = weight.reshape(weight.shape[0], -1, 1, 1) |
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if weight.shape[2] != 1 or weight.shape[3] != 1: |
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module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], sd_module.kernel_size, sd_module.stride, sd_module.padding, bias=False) |
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else: |
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module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False) |
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elif lyco_key == "lora_mid.weight": |
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module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], sd_module.kernel_size, sd_module.stride, sd_module.padding, bias=False) |
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elif lyco_key == "lora_up.weight" or lyco_key == "dyn_down": |
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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'Lyco layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}' |
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if hasattr(sd_module, 'weight'): |
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lyco_module.shape = sd_module.weight.shape |
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with torch.no_grad(): |
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if weight.shape != module.weight.shape: |
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weight = weight.reshape(module.weight.shape) |
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module.weight.copy_(weight) |
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module.to(device=devices.cpu, dtype=devices.dtype) |
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module.requires_grad_(False) |
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if lyco_key == "lora_up.weight" or lyco_key == "dyn_up": |
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lyco_module.up_model = module |
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elif lyco_key == "lora_mid.weight": |
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lyco_module.mid_model = module |
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elif lyco_key == "lora_down.weight" or lyco_key == "dyn_down": |
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lyco_module.down_model = module |
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lyco_module.dim = weight.shape[0] |
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else: |
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print(lyco_key) |
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elif lyco_key in HADA_KEY: |
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if type(lyco_module) != LycoHadaModule: |
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alpha = lyco_module.alpha |
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bias = lyco_module.bias |
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lyco_module = LycoHadaModule() |
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lyco_module.alpha = alpha |
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lyco_module.bias = bias |
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lyco.modules[key] = lyco_module |
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if hasattr(sd_module, 'weight'): |
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lyco_module.shape = sd_module.weight.shape |
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weight = weight.to(device=devices.cpu, dtype=devices.dtype) |
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weight.requires_grad_(False) |
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if lyco_key == 'hada_w1_a': |
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lyco_module.w1a = weight |
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elif lyco_key == 'hada_w1_b': |
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lyco_module.w1b = weight |
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lyco_module.dim = weight.shape[0] |
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elif lyco_key == 'hada_w2_a': |
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lyco_module.w2a = weight |
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elif lyco_key == 'hada_w2_b': |
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lyco_module.w2b = weight |
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lyco_module.dim = weight.shape[0] |
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elif lyco_key == 'hada_t1': |
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lyco_module.t1 = weight |
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elif lyco_key == 'hada_t2': |
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lyco_module.t2 = weight |
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elif lyco_key in IA3_KEY: |
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if type(lyco_module) != IA3Module: |
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lyco_module = IA3Module() |
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lyco.modules[key] = lyco_module |
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if lyco_key == "weight": |
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lyco_module.w = weight.to(devices.device, dtype=devices.dtype) |
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elif lyco_key == "on_input": |
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lyco_module.on_input = weight |
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elif lyco_key in KRON_KEY: |
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if not isinstance(lyco_module, LycoKronModule): |
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alpha = lyco_module.alpha |
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bias = lyco_module.bias |
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lyco_module = LycoKronModule() |
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lyco_module.alpha = alpha |
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lyco_module.bias = bias |
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lyco.modules[key] = lyco_module |
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if hasattr(sd_module, 'weight'): |
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lyco_module.shape = sd_module.weight.shape |
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weight = weight.to(device=devices.cpu, dtype=devices.dtype) |
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weight.requires_grad_(False) |
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|
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if lyco_key == 'lokr_w1': |
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lyco_module.w1 = weight |
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elif lyco_key == 'lokr_w1_a': |
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lyco_module.w1a = weight |
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elif lyco_key == 'lokr_w1_b': |
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lyco_module.w1b = weight |
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lyco_module.dim = weight.shape[0] |
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elif lyco_key == 'lokr_w2': |
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lyco_module.w2 = weight |
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elif lyco_key == 'lokr_w2_a': |
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lyco_module.w2a = weight |
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elif lyco_key == 'lokr_w2_b': |
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lyco_module.w2b = weight |
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lyco_module.dim = weight.shape[0] |
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elif lyco_key == 'lokr_t2': |
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lyco_module.t2 = weight |
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else: |
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assert False, f'Bad Lyco layer name: {key_diffusers} - must end in lyco_up.weight, lyco_down.weight or alpha' |
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|
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if len(keys_failed_to_match) > 0: |
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print(shared.sd_model.lyco_layer_mapping) |
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print(f"Failed to match keys when loading Lyco {filename}: {keys_failed_to_match}") |
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return lyco |
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def load_lycos(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None): |
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already_loaded = {} |
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|
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for lyco in loaded_lycos: |
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if lyco.name in names: |
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already_loaded[lyco.name] = lyco |
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|
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loaded_lycos.clear() |
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|
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lycos_on_disk = [available_lycos.get(name, None) for name in names] |
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if any([x is None for x in lycos_on_disk]): |
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list_available_lycos() |
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|
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lycos_on_disk = [available_lycos.get(name, None) for name in names] |
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|
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for i, name in enumerate(names): |
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lyco = already_loaded.get(name, None) |
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|
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lyco_on_disk = lycos_on_disk[i] |
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if lyco_on_disk is not None: |
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if lyco is None or os.path.getmtime(lyco_on_disk.filename) > lyco.mtime: |
|
lyco = load_lyco(name, lyco_on_disk.filename) |
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|
|
if lyco is None: |
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print(f"Couldn't find Lora with name {name}") |
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continue |
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|
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lyco.te_multiplier = te_multipliers[i] if te_multipliers else 1.0 |
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lyco.unet_multiplier = unet_multipliers[i] if unet_multipliers else lyco.te_multiplier |
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lyco.dyn_dim = dyn_dims[i] if dyn_dims else None |
|
loaded_lycos.append(lyco) |
|
|
|
|
|
def _rebuild_conventional(up, down, shape, dyn_dim=None): |
|
up = up.reshape(up.size(0), -1) |
|
down = down.reshape(down.size(0), -1) |
|
if dyn_dim is not None: |
|
up = up[:, :dyn_dim] |
|
down = down[:dyn_dim, :] |
|
return (up @ down).reshape(shape) |
|
|
|
|
|
def _rebuild_cp_decomposition(up, down, mid): |
|
up = up.reshape(up.size(0), -1) |
|
down = down.reshape(down.size(0), -1) |
|
return torch.einsum('n m k l, i n, m j -> i j k l', mid, up, down) |
|
|
|
|
|
def rebuild_weight(module, orig_weight: torch.Tensor, dyn_dim: int=None) -> torch.Tensor: |
|
output_shape: Sized |
|
if module.__class__.__name__ == 'LycoUpDownModule': |
|
up = module.up_model.weight.to(orig_weight.device, dtype=orig_weight.dtype) |
|
down = module.down_model.weight.to(orig_weight.device, dtype=orig_weight.dtype) |
|
|
|
output_shape = [up.size(0), down.size(1)] |
|
if (mid:=module.mid_model) is not None: |
|
|
|
mid = mid.weight.to(orig_weight.device, dtype=orig_weight.dtype) |
|
updown = _rebuild_cp_decomposition(up, down, mid) |
|
output_shape += mid.shape[2:] |
|
else: |
|
if len(down.shape) == 4: |
|
output_shape += down.shape[2:] |
|
updown = _rebuild_conventional(up, down, output_shape, dyn_dim) |
|
|
|
elif module.__class__.__name__ == 'LycoHadaModule': |
|
w1a = module.w1a.to(orig_weight.device, dtype=orig_weight.dtype) |
|
w1b = module.w1b.to(orig_weight.device, dtype=orig_weight.dtype) |
|
w2a = module.w2a.to(orig_weight.device, dtype=orig_weight.dtype) |
|
w2b = module.w2b.to(orig_weight.device, dtype=orig_weight.dtype) |
|
|
|
output_shape = [w1a.size(0), w1b.size(1)] |
|
|
|
if module.t1 is not None: |
|
output_shape = [w1a.size(1), w1b.size(1)] |
|
t1 = module.t1.to(orig_weight.device, dtype=orig_weight.dtype) |
|
updown1 = make_weight_cp(t1, w1a, w1b) |
|
output_shape += t1.shape[2:] |
|
else: |
|
if len(w1b.shape) == 4: |
|
output_shape += w1b.shape[2:] |
|
updown1 = _rebuild_conventional(w1a, w1b, output_shape) |
|
|
|
if module.t2 is not None: |
|
t2 = module.t2.to(orig_weight.device, dtype=orig_weight.dtype) |
|
updown2 = make_weight_cp(t2, w2a, w2b) |
|
else: |
|
updown2 = _rebuild_conventional(w2a, w2b, output_shape) |
|
|
|
updown = updown1 * updown2 |
|
|
|
elif module.__class__.__name__ == 'FullModule': |
|
output_shape = module.weight.shape |
|
updown = module.weight.to(orig_weight.device, dtype=orig_weight.dtype) |
|
|
|
elif module.__class__.__name__ == 'IA3Module': |
|
output_shape = [module.w.size(0), orig_weight.size(1)] |
|
if module.on_input: |
|
output_shape.reverse() |
|
else: |
|
module.w = module.w.reshape(-1, 1) |
|
updown = orig_weight * module.w |
|
|
|
elif module.__class__.__name__ == 'LycoKronModule': |
|
if module.w1 is not None: |
|
w1 = module.w1.to(orig_weight.device, dtype=orig_weight.dtype) |
|
else: |
|
w1a = module.w1a.to(orig_weight.device, dtype=orig_weight.dtype) |
|
w1b = module.w1b.to(orig_weight.device, dtype=orig_weight.dtype) |
|
w1 = w1a @ w1b |
|
|
|
if module.w2 is not None: |
|
w2 = module.w2.to(orig_weight.device, dtype=orig_weight.dtype) |
|
elif module.t2 is None: |
|
w2a = module.w2a.to(orig_weight.device, dtype=orig_weight.dtype) |
|
w2b = module.w2b.to(orig_weight.device, dtype=orig_weight.dtype) |
|
w2 = w2a @ w2b |
|
else: |
|
t2 = module.t2.to(orig_weight.device, dtype=orig_weight.dtype) |
|
w2a = module.w2a.to(orig_weight.device, dtype=orig_weight.dtype) |
|
w2b = module.w2b.to(orig_weight.device, dtype=orig_weight.dtype) |
|
w2 = make_weight_cp(t2, w2a, w2b) |
|
|
|
output_shape = [w1.size(0)*w2.size(0), w1.size(1)*w2.size(1)] |
|
if len(orig_weight.shape) == 4: |
|
output_shape = orig_weight.shape |
|
|
|
updown = make_kron( |
|
output_shape, w1, w2 |
|
) |
|
|
|
else: |
|
raise NotImplementedError( |
|
f"Unknown module type: {module.__class__.__name__}\n" |
|
"If the type is one of " |
|
"'LycoUpDownModule', 'LycoHadaModule', 'FullModule', 'IA3Module', 'LycoKronModule'" |
|
"You may have other lyco extension that conflict with locon extension." |
|
) |
|
|
|
if hasattr(module, 'bias') and module.bias != None: |
|
updown = updown.reshape(module.bias.shape) |
|
updown += module.bias.to(orig_weight.device, dtype=orig_weight.dtype) |
|
updown = updown.reshape(output_shape) |
|
|
|
if len(output_shape) == 4: |
|
updown = updown.reshape(output_shape) |
|
|
|
if orig_weight.size().numel() == updown.size().numel(): |
|
updown = updown.reshape(orig_weight.shape) |
|
|
|
return updown |
|
|
|
|
|
def lyco_calc_updown(lyco, module, target): |
|
with torch.no_grad(): |
|
updown = rebuild_weight(module, target, lyco.dyn_dim) |
|
if lyco.dyn_dim and module.dim: |
|
dim = min(lyco.dyn_dim, module.dim) |
|
elif lyco.dyn_dim: |
|
dim = lyco.dyn_dim |
|
elif module.dim: |
|
dim = module.dim |
|
else: |
|
dim = None |
|
scale = ( |
|
module.scale if module.scale is not None |
|
else module.alpha / dim if dim is not None and module.alpha is not None |
|
else 1.0 |
|
) |
|
|
|
updown = updown * scale |
|
return updown |
|
|
|
|
|
def lyco_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]): |
|
""" |
|
Applies the currently selected set of Lycos to the weights of torch layer self. |
|
If weights already have this particular set of lycos applied, does nothing. |
|
If not, restores orginal weights from backup and alters weights according to lycos. |
|
""" |
|
|
|
lyco_layer_name = getattr(self, 'lyco_layer_name', None) |
|
if lyco_layer_name is None: |
|
return |
|
|
|
current_names = getattr(self, "lyco_current_names", ()) |
|
lora_prev_names = getattr(self, "lora_prev_names", ()) |
|
lora_names = getattr(self, "lora_current_names", ()) |
|
wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_lycos) |
|
|
|
weights_backup = getattr(self, "lyco_weights_backup", None) |
|
lora_weights_backup = getattr(self, "lora_weights_backup", None) |
|
if weights_backup is None and len(loaded_lycos): |
|
|
|
if isinstance(self, torch.nn.MultiheadAttention): |
|
weights_backup = ( |
|
self.in_proj_weight.to(devices.cpu, copy=True), |
|
self.out_proj.weight.to(devices.cpu, copy=True) |
|
) |
|
else: |
|
weights_backup = self.weight.to(devices.cpu, copy=True) |
|
self.lyco_weights_backup = weights_backup |
|
elif lora_prev_names != lora_names: |
|
|
|
self.lyco_weights_backup = None |
|
lora_weights_backup = None |
|
elif len(loaded_lycos) == 0: |
|
self.lyco_weights_backup = None |
|
|
|
if current_names != wanted_names or lora_prev_names != lora_names: |
|
if weights_backup is not None and lora_names == lora_prev_names: |
|
|
|
if isinstance(self, torch.nn.MultiheadAttention): |
|
self.in_proj_weight.copy_(weights_backup[0]) |
|
self.out_proj.weight.copy_(weights_backup[1]) |
|
else: |
|
self.weight.copy_(weights_backup) |
|
elif lora_weights_backup is not None and lora_names == (): |
|
|
|
if isinstance(self, torch.nn.MultiheadAttention): |
|
self.in_proj_weight.copy_(lora_weights_backup[0]) |
|
self.out_proj.weight.copy_(lora_weights_backup[1]) |
|
else: |
|
self.weight.copy_(lora_weights_backup) |
|
|
|
for lyco in loaded_lycos: |
|
module = lyco.modules.get(lyco_layer_name, None) |
|
multiplier = ( |
|
lyco.te_multiplier if 'transformer' in lyco_layer_name[:20] |
|
else lyco.unet_multiplier |
|
) |
|
if module is not None and hasattr(self, 'weight'): |
|
|
|
updown = lyco_calc_updown(lyco, module, self.weight) |
|
if len(self.weight.shape) == 4 and self.weight.shape[1] == 9: |
|
|
|
updown = F.pad(updown, (0, 0, 0, 0, 0, 5)) |
|
self.weight += updown * multiplier |
|
continue |
|
|
|
module_q = lyco.modules.get(lyco_layer_name + "_q_proj", None) |
|
module_k = lyco.modules.get(lyco_layer_name + "_k_proj", None) |
|
module_v = lyco.modules.get(lyco_layer_name + "_v_proj", None) |
|
module_out = lyco.modules.get(lyco_layer_name + "_out_proj", None) |
|
|
|
if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out: |
|
updown_q = lyco_calc_updown(lyco, module_q, self.in_proj_weight) |
|
updown_k = lyco_calc_updown(lyco, module_k, self.in_proj_weight) |
|
updown_v = lyco_calc_updown(lyco, module_v, self.in_proj_weight) |
|
updown_qkv = torch.vstack([updown_q, updown_k, updown_v]) |
|
|
|
self.in_proj_weight += updown_qkv |
|
self.out_proj.weight += lyco_calc_updown(lyco, module_out, self.out_proj.weight) |
|
continue |
|
|
|
if module is None: |
|
continue |
|
|
|
print(3, f'failed to calculate lyco weights for layer {lyco_layer_name}') |
|
|
|
|
|
setattr(self, "lora_prev_names", lora_names) |
|
setattr(self, "lyco_current_names", wanted_names) |
|
|
|
|
|
def lyco_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]): |
|
setattr(self, "lyco_current_names", ()) |
|
setattr(self, "lyco_weights_backup", None) |
|
|
|
|
|
def lyco_Linear_forward(self, input): |
|
lyco_apply_weights(self) |
|
|
|
return torch.nn.Linear_forward_before_lyco(self, input) |
|
|
|
|
|
def lyco_Linear_load_state_dict(self, *args, **kwargs): |
|
lyco_reset_cached_weight(self) |
|
|
|
return torch.nn.Linear_load_state_dict_before_lyco(self, *args, **kwargs) |
|
|
|
|
|
def lyco_Conv2d_forward(self, input): |
|
lyco_apply_weights(self) |
|
|
|
return torch.nn.Conv2d_forward_before_lyco(self, input) |
|
|
|
|
|
def lyco_Conv2d_load_state_dict(self, *args, **kwargs): |
|
lyco_reset_cached_weight(self) |
|
|
|
return torch.nn.Conv2d_load_state_dict_before_lyco(self, *args, **kwargs) |
|
|
|
|
|
def lyco_MultiheadAttention_forward(self, *args, **kwargs): |
|
lyco_apply_weights(self) |
|
|
|
return torch.nn.MultiheadAttention_forward_before_lyco(self, *args, **kwargs) |
|
|
|
|
|
def lyco_MultiheadAttention_load_state_dict(self, *args, **kwargs): |
|
lyco_reset_cached_weight(self) |
|
|
|
return torch.nn.MultiheadAttention_load_state_dict_before_lyco(self, *args, **kwargs) |
|
|
|
|
|
def list_available_lycos(): |
|
available_lycos.clear() |
|
|
|
os.makedirs(shared.cmd_opts.lyco_dir, exist_ok=True) |
|
|
|
candidates = \ |
|
glob.glob(os.path.join(shared.cmd_opts.lyco_dir, '**/*.pt'), recursive=True) + \ |
|
glob.glob(os.path.join(shared.cmd_opts.lyco_dir, '**/*.safetensors'), recursive=True) + \ |
|
glob.glob(os.path.join(shared.cmd_opts.lyco_dir, '**/*.ckpt'), recursive=True) |
|
|
|
for filename in sorted(candidates, key=str.lower): |
|
if os.path.isdir(filename): |
|
continue |
|
|
|
name = os.path.splitext(os.path.basename(filename))[0] |
|
|
|
available_lycos[name] = LycoOnDisk(name, filename) |
|
|
|
|
|
available_lycos: Dict[str, LycoOnDisk] = {} |
|
loaded_lycos: List[LycoModule] = [] |
|
|
|
list_available_lycos() |
|
|
