Update to sd-script dev code base

README.md

@@ -176,6 +176,9 @@ This will store your a backup file with your current locally installed pip packa
 
 ## Change History
 
+* 2023/03/10 (v21.2.1):
+    - Update to latest sd-script code
+    - Add support for SVD based LoRA merge
 * 2023/03/09 (v21.2.0):
     - Fix issue https://github.com/bmaltais/kohya_ss/issues/335
     - Add option to print LoRA trainer command without executing it

library/resize_lora_gui.py

@@ -148,11 +148,11 @@ def gradio_resize_lora_tab():
                 value='fp16',
                 interactive=True,
             )
-            device = gr.Textbox(
+            device = gr.Dropdown(
                 label='Device',
-                placeholder='{Optional) device to use, cuda for GPU. Default: cuda',
+                choices=['cpu', 'cuda',],
-                interactive=True,
                 value='cuda',
+                interactive=True,
             )
 
         convert_button = gr.Button('Resize model')

library/svd_merge_lora_gui.py

@@ -0,0 +1,187 @@
+import gradio as gr
+from easygui import msgbox
+import subprocess
+import os
+from .common_gui import (
+    get_saveasfilename_path,
+    get_any_file_path,
+    get_file_path,
+)
+
+folder_symbol = '\U0001f4c2'  # 📂
+refresh_symbol = '\U0001f504'  # 🔄
+save_style_symbol = '\U0001f4be'  # 💾
+document_symbol = '\U0001F4C4'   # 📄
+PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
+
+
+def svd_merge_lora(
+    lora_a_model,
+    lora_b_model,
+    ratio,
+    save_to,
+    precision,
+    save_precision,
+    new_rank,
+    new_conv_rank,
+    device,
+):
+    # Check for caption_text_input
+    if lora_a_model == '':
+        msgbox('Invalid model A file')
+        return
+
+    if lora_b_model == '':
+        msgbox('Invalid model B file')
+        return
+
+    # Check if source model exist
+    if not os.path.isfile(lora_a_model):
+        msgbox('The provided model A is not a file')
+        return
+
+    if not os.path.isfile(lora_b_model):
+        msgbox('The provided model B is not a file')
+        return
+
+    ratio_a = ratio
+    ratio_b = 1 - ratio
+
+    run_cmd = f'{PYTHON} "{os.path.join("networks","svd_merge_lora.py")}"'
+    run_cmd += f' --save_precision {save_precision}'
+    run_cmd += f' --precision {precision}'
+    run_cmd += f' --save_to "{save_to}"'
+    run_cmd += f' --models "{lora_a_model}" "{lora_b_model}"'
+    run_cmd += f' --ratios {ratio_a} {ratio_b}'
+    run_cmd += f' --device {device}'
+    run_cmd += f' --new_rank "{new_rank}"'
+    run_cmd += f' --new_conv_rank "{new_conv_rank}"'
+
+    print(run_cmd)
+
+    # Run the command
+    if os.name == 'posix':
+        os.system(run_cmd)
+    else:
+        subprocess.run(run_cmd)
+
+
+###
+# Gradio UI
+###
+
+
+def gradio_svd_merge_lora_tab():
+    with gr.Tab('Merge LoRA (SVD)'):
+        gr.Markdown('This utility can merge two LoRA networks together.')
+
+        lora_ext = gr.Textbox(value='*.safetensors *.pt', visible=False)
+        lora_ext_name = gr.Textbox(value='LoRA model types', visible=False)
+
+        with gr.Row():
+            lora_a_model = gr.Textbox(
+                label='LoRA model "A"',
+                placeholder='Path to the LoRA A model',
+                interactive=True,
+            )
+            button_lora_a_model_file = gr.Button(
+                folder_symbol, elem_id='open_folder_small'
+            )
+            button_lora_a_model_file.click(
+                get_file_path,
+                inputs=[lora_a_model, lora_ext, lora_ext_name],
+                outputs=lora_a_model,
+                show_progress=False,
+            )
+
+            lora_b_model = gr.Textbox(
+                label='LoRA model "B"',
+                placeholder='Path to the LoRA B model',
+                interactive=True,
+            )
+            button_lora_b_model_file = gr.Button(
+                folder_symbol, elem_id='open_folder_small'
+            )
+            button_lora_b_model_file.click(
+                get_file_path,
+                inputs=[lora_b_model, lora_ext, lora_ext_name],
+                outputs=lora_b_model,
+                show_progress=False,
+            )
+        with gr.Row():
+            ratio = gr.Slider(
+                label='Merge ratio (eg: 0.7 mean 70% of model A and 30% of model B',
+                minimum=0,
+                maximum=1,
+                step=0.01,
+                value=0.5,
+                interactive=True,
+            )
+            new_rank = gr.Slider(
+                label='New Rank',
+                minimum=1,
+                maximum=1024,
+                step=1,
+                value=128,
+                interactive=True,
+            )
+            new_conv_rank = gr.Slider(
+                label='New Conv Rank',
+                minimum=1,
+                maximum=1024,
+                step=1,
+                value=128,
+                interactive=True,
+            )
+
+        with gr.Row():
+            save_to = gr.Textbox(
+                label='Save to',
+                placeholder='path for the file to save...',
+                interactive=True,
+            )
+            button_save_to = gr.Button(
+                folder_symbol, elem_id='open_folder_small'
+            )
+            button_save_to.click(
+                get_saveasfilename_path,
+                inputs=[save_to, lora_ext, lora_ext_name],
+                outputs=save_to,
+                show_progress=False,
+            )
+            precision = gr.Dropdown(
+                label='Merge precision',
+                choices=['fp16', 'bf16', 'float'],
+                value='float',
+                interactive=True,
+            )
+            save_precision = gr.Dropdown(
+                label='Save precision',
+                choices=['fp16', 'bf16', 'float'],
+                value='float',
+                interactive=True,
+            )
+            device = gr.Dropdown(
+                label='Device',
+                choices=['cpu', 'cuda',],
+                value='cuda',
+                interactive=True,
+            )
+
+        convert_button = gr.Button('Merge model')
+
+        convert_button.click(
+            svd_merge_lora,
+            inputs=[
+                lora_a_model,
+                lora_b_model,
+                ratio,
+                save_to,
+                precision,
+                save_precision,
+                new_rank,
+                new_conv_rank,
+                device,
+            ],
+            show_progress=False,
+        )

library/train_util.py

@@ -912,10 +912,14 @@ class FineTuningDataset(BaseDataset):
         if os.path.exists(image_key):
           abs_path = image_key
         else:
-          # わりといい加減だがいい方法が思いつかん
+          npz_path = os.path.join(subset.image_dir, image_key + ".npz")
-          abs_path = glob_images(subset.image_dir, image_key)
+          if os.path.exists(npz_path):
-          assert len(abs_path) >= 1, f"no image / 画像がありません: {image_key}"
+            abs_path = npz_path
-          abs_path = abs_path[0]
+          else:
+            # わりといい加減だがいい方法が思いつかん
+            abs_path = glob_images(subset.image_dir, image_key)
+            assert len(abs_path) >= 1, f"no image / 画像がありません: {image_key}"
+            abs_path = abs_path[0]
 
         caption = img_md.get('caption')
         tags = img_md.get('tags')
@@ -1757,15 +1761,22 @@ def get_optimizer(args, trainable_params):
       raise ImportError("No dadaptation / dadaptation がインストールされていないようです")
     print(f"use D-Adaptation Adam optimizer | {optimizer_kwargs}")
 
-    min_lr = lr
+    actual_lr = lr
+    lr_count = 1
     if type(trainable_params) == list and type(trainable_params[0]) == dict:
+      lrs = set()
+      actual_lr = trainable_params[0].get("lr", actual_lr)
       for group in trainable_params:
-        min_lr = min(min_lr, group.get("lr", lr))
+        lrs.add(group.get("lr", actual_lr))
+      lr_count = len(lrs)
 
-    if min_lr <= 0.1:
+    if actual_lr <= 0.1:
       print(
-          f'learning rate is too low. If using dadaptation, set learning rate around 1.0 / 学習率が低すぎるようです。1.0前後の値を指定してください: {min_lr}')
+          f'learning rate is too low. If using dadaptation, set learning rate around 1.0 / 学習率が低すぎるようです。1.0前後の値を指定してください: lr={actual_lr}')
       print('recommend option: lr=1.0 / 推奨は1.0です')
+    if lr_count > 1:
+      print(
+          f"when multiple learning rates are specified with dadaptation (e.g. for Text Encoder and U-Net), only the first one will take effect / D-Adaptationで複数の学習率を指定した場合（Text EncoderとU-Netなど）、最初の学習率のみが有効になります: lr={actual_lr}")
 
     optimizer_class = dadaptation.DAdaptAdam
     optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
@@ -2296,6 +2307,8 @@ def sample_images(accelerator, args: argparse.Namespace, epoch, steps, device, v
   with torch.no_grad():
     with accelerator.autocast():
       for i, prompt in enumerate(prompts):
+        if not accelerator.is_main_process:
+          continue
         prompt = prompt.strip()
         if len(prompt) == 0 or prompt[0] == '#':
           continue
@@ -2355,6 +2368,12 @@ def sample_images(accelerator, args: argparse.Namespace, epoch, steps, device, v
 
         height = max(64, height - height % 8)                 # round to divisible by 8
         width = max(64, width - width % 8)                 # round to divisible by 8
+        print(f"prompt: {prompt}")
+        print(f"negative_prompt: {negative_prompt}")
+        print(f"height: {height}")
+        print(f"width: {width}")
+        print(f"sample_steps: {sample_steps}")
+        print(f"scale: {scale}")
         image = pipeline(prompt, height, width, sample_steps, scale, negative_prompt).images[0]
 
         ts_str = time.strftime('%Y%m%d%H%M%S', time.localtime())

lora_gui.py

@@ -38,6 +38,7 @@ from library.tensorboard_gui import (
 from library.dataset_balancing_gui import gradio_dataset_balancing_tab
 from library.utilities import utilities_tab
 from library.merge_lora_gui import gradio_merge_lora_tab
+from library.svd_merge_lora_gui import gradio_svd_merge_lora_tab
 from library.verify_lora_gui import gradio_verify_lora_tab
 from library.resize_lora_gui import gradio_resize_lora_tab
 from library.sampler_gui import sample_gradio_config, run_cmd_sample
@@ -879,6 +880,7 @@ def lora_tab(
         )
         gradio_dataset_balancing_tab()
         gradio_merge_lora_tab()
+        gradio_svd_merge_lora_tab()
         gradio_resize_lora_tab()
         gradio_verify_lora_tab()
 

networks/extract_lora_from_models.py

@@ -103,7 +103,8 @@ def svd(args):
 
       if args.device:
         mat = mat.to(args.device)
-      # print(mat.size(), mat.device, rank, in_dim, out_dim)
+
+      # print(lora_name, mat.size(), mat.device, rank, in_dim, out_dim)
       rank = min(rank, in_dim, out_dim)                           # LoRA rank cannot exceed the original dim
 
       if conv2d:
@@ -112,7 +113,7 @@ def svd(args):
         else:
           mat = mat.squeeze()
 
-      U, S, Vh = torch.linalg.svd(mat)
+      U, S, Vh = torch.linalg.svd(mat.to("cuda"))
 
       U = U[:, :rank]
       S = S[:rank]
@@ -137,27 +138,17 @@ def svd(args):
       lora_weights[lora_name] = (U, Vh)
 
   # make state dict for LoRA
-  lora_network_o.apply_to(text_encoder_o, unet_o, text_encoder_different, True)   # to make state dict
+  lora_sd = {}
-  lora_sd = lora_network_o.state_dict()
+  for lora_name, (up_weight, down_weight) in lora_weights.items():
-  print(f"LoRA has {len(lora_sd)} weights.")
+    lora_sd[lora_name + '.lora_up.weight'] = up_weight
-
+    lora_sd[lora_name + '.lora_down.weight'] = down_weight
-  for key in list(lora_sd.keys()):
+    lora_sd[lora_name + '.alpha'] = torch.tensor(down_weight.size()[0])
-    if "alpha" in key:
-      continue
-
-    lora_name = key.split('.')[0]
-    i = 0 if "lora_up" in key else 1
-
-    weights = lora_weights[lora_name][i]
-    # print(key, i, weights.size(), lora_sd[key].size())
-    # if len(lora_sd[key].size()) == 4:
-    #   weights = weights.unsqueeze(2).unsqueeze(3)
-
-    assert weights.size() == lora_sd[key].size(), f"size unmatch: {key}"
-    lora_sd[key] = weights
 
   # load state dict to LoRA and save it
-  info = lora_network_o.load_state_dict(lora_sd)
+  lora_network_save = lora.create_network_from_weights(1.0, None, None, text_encoder_o, unet_o, weights_sd=lora_sd)
+  lora_network_save.apply_to(text_encoder_o, unet_o)        # create internal module references for state_dict
+
+  info = lora_network_save.load_state_dict(lora_sd)
   print(f"Loading extracted LoRA weights: {info}")
 
   dir_name = os.path.dirname(args.save_to)
@@ -167,7 +158,7 @@ def svd(args):
   # minimum metadata
   metadata = {"ss_network_module": "networks.lora", "ss_network_dim": str(args.dim), "ss_network_alpha": str(args.dim)}
 
-  lora_network_o.save_weights(args.save_to, save_dtype, metadata)
+  lora_network_save.save_weights(args.save_to, save_dtype, metadata)
   print(f"LoRA weights are saved to: {args.save_to}")
 
 

networks/lora.py

@@ -21,30 +21,34 @@ class LoRAModule(torch.nn.Module):
     """ if alpha == 0 or None, alpha is rank (no scaling). """
     super().__init__()
     self.lora_name = lora_name
-    self.lora_dim = lora_dim
 
     if org_module.__class__.__name__ == 'Conv2d':
       in_dim = org_module.in_channels
       out_dim = org_module.out_channels
+    else:
+      in_dim = org_module.in_features
+      out_dim = org_module.out_features
 
-      self.lora_dim = min(self.lora_dim, in_dim, out_dim)
+    # if limit_rank:
-      if self.lora_dim != lora_dim:
+    #   self.lora_dim = min(lora_dim, in_dim, out_dim)
-        print(f"{lora_name} dim (rank) is changed to: {self.lora_dim}")
+    #   if self.lora_dim != lora_dim:
+    #     print(f"{lora_name} dim (rank) is changed to: {self.lora_dim}")
+    # else:
+    self.lora_dim = lora_dim
 
+    if org_module.__class__.__name__ == 'Conv2d':
       kernel_size = org_module.kernel_size
       stride = org_module.stride
       padding = org_module.padding
       self.lora_down = torch.nn.Conv2d(in_dim, self.lora_dim, kernel_size, stride, padding, bias=False)
       self.lora_up = torch.nn.Conv2d(self.lora_dim, out_dim, (1, 1), (1, 1), bias=False)
     else:
-      in_dim = org_module.in_features
+      self.lora_down = torch.nn.Linear(in_dim, self.lora_dim, bias=False)
-      out_dim = org_module.out_features
+      self.lora_up = torch.nn.Linear(self.lora_dim, out_dim, bias=False)
-      self.lora_down = torch.nn.Linear(in_dim, lora_dim, bias=False)
-      self.lora_up = torch.nn.Linear(lora_dim, out_dim, bias=False)
 
     if type(alpha) == torch.Tensor:
       alpha = alpha.detach().float().numpy()                              # without casting, bf16 causes error
-    alpha = lora_dim if alpha is None or alpha == 0 else alpha
+    alpha = self.lora_dim if alpha is None or alpha == 0 else alpha
     self.scale = alpha / self.lora_dim
     self.register_buffer('alpha', torch.tensor(alpha))                    # 定数として扱える
 
@@ -149,12 +153,13 @@ def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, un
   return network
 
 
-def create_network_from_weights(multiplier, file, vae, text_encoder, unet, **kwargs):
+def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weights_sd=None, **kwargs):
-  if os.path.splitext(file)[1] == '.safetensors':
+  if weights_sd is None:
-    from safetensors.torch import load_file, safe_open
+    if os.path.splitext(file)[1] == '.safetensors':
-    weights_sd = load_file(file)
+      from safetensors.torch import load_file, safe_open
-  else:
+      weights_sd = load_file(file)
-    weights_sd = torch.load(file, map_location='cpu')
+    else:
+      weights_sd = torch.load(file, map_location='cpu')
 
   # get dim/alpha mapping
   modules_dim = {}
@@ -174,7 +179,7 @@ def create_network_from_weights(multiplier, file, vae, text_encoder, unet, **kwa
   # support old LoRA without alpha
   for key in modules_dim.keys():
     if key not in modules_alpha:
-      modules_alpha = modules_dim[key]                      
+      modules_alpha = modules_dim[key]
 
   network = LoRANetwork(text_encoder, unet, multiplier=multiplier, modules_dim=modules_dim, modules_alpha=modules_alpha)
   network.weights_sd = weights_sd
@@ -183,7 +188,8 @@ def create_network_from_weights(multiplier, file, vae, text_encoder, unet, **kwa
 
 class LoRANetwork(torch.nn.Module):
   # is it possible to apply conv_in and conv_out?
-  UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel", "Attention", "ResnetBlock2D", "Downsample2D", "Upsample2D"]
+  UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel", "Attention"]
+  UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
   TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
   LORA_PREFIX_UNET = 'lora_unet'
   LORA_PREFIX_TEXT_ENCODER = 'lora_te'
@@ -245,7 +251,12 @@ class LoRANetwork(torch.nn.Module):
                                              text_encoder, LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
     print(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.")
 
-    self.unet_loras = create_modules(LoRANetwork.LORA_PREFIX_UNET, unet, LoRANetwork.UNET_TARGET_REPLACE_MODULE)
+    # extend U-Net target modules if conv2d 3x3 is enabled, or load from weights
+    target_modules = LoRANetwork.UNET_TARGET_REPLACE_MODULE
+    if modules_dim is not None or self.conv_lora_dim is not None:
+      target_modules += LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
+
+    self.unet_loras = create_modules(LoRANetwork.LORA_PREFIX_UNET, unet, target_modules)
     print(f"create LoRA for U-Net: {len(self.unet_loras)} modules.")
 
     self.weights_sd = None
@@ -371,7 +382,7 @@ class LoRANetwork(torch.nn.Module):
     else:
       torch.save(state_dict, file)
 
-  @staticmethod
+  @ staticmethod
   def set_regions(networks, image):
     image = image.astype(np.float32) / 255.0
     for i, network in enumerate(networks[:3]):

networks/resize_lora.py

@@ -1,14 +1,15 @@
 # Convert LoRA to different rank approximation (should only be used to go to lower rank)
 # This code is based off the extract_lora_from_models.py file which is based on https://github.com/cloneofsimo/lora/blob/develop/lora_diffusion/cli_svd.py
-# Thanks to cloneofsimo and kohya
+# Thanks to cloneofsimo
 
 import argparse
-import os
 import torch
 from safetensors.torch import load_file, save_file, safe_open
 from tqdm import tqdm
 from library import train_util, model_util
+import numpy as np
 
+MIN_SV = 1e-6
 
 def load_state_dict(file_name, dtype):
   if model_util.is_safetensors(file_name):
@@ -38,12 +39,149 @@ def save_to_file(file_name, model, state_dict, dtype, metadata):
     torch.save(model, file_name)
 
 
-def resize_lora_model(lora_sd, new_rank, save_dtype, device, verbose):
+def index_sv_cumulative(S, target):
+  original_sum = float(torch.sum(S))
+  cumulative_sums = torch.cumsum(S, dim=0)/original_sum
+  index = int(torch.searchsorted(cumulative_sums, target)) + 1
+  if index >= len(S):
+    index = len(S) - 1
+
+  return index
+
+
+def index_sv_fro(S, target):
+  S_squared = S.pow(2)
+  s_fro_sq = float(torch.sum(S_squared))
+  sum_S_squared = torch.cumsum(S_squared, dim=0)/s_fro_sq
+  index = int(torch.searchsorted(sum_S_squared, target**2)) + 1
+  if index >= len(S):
+    index = len(S) - 1
+
+  return index
+
+
+# Modified from Kohaku-blueleaf's extract/merge functions
+def extract_conv(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1):
+    out_size, in_size, kernel_size, _ = weight.size()
+    U, S, Vh = torch.linalg.svd(weight.reshape(out_size, -1).to(device))
+    
+    param_dict = rank_resize(S, lora_rank, dynamic_method, dynamic_param, scale)
+    lora_rank = param_dict["new_rank"]
+
+    U = U[:, :lora_rank]
+    S = S[:lora_rank]
+    U = U @ torch.diag(S)
+    Vh = Vh[:lora_rank, :]
+
+    param_dict["lora_down"] = Vh.reshape(lora_rank, in_size, kernel_size, kernel_size).cpu()
+    param_dict["lora_up"] = U.reshape(out_size, lora_rank, 1, 1).cpu()
+    del U, S, Vh, weight
+    return param_dict
+
+
+def extract_linear(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1):
+    out_size, in_size = weight.size()
+    
+    U, S, Vh = torch.linalg.svd(weight.to(device))
+    
+    param_dict = rank_resize(S, lora_rank, dynamic_method, dynamic_param, scale)
+    lora_rank = param_dict["new_rank"]
+    
+    U = U[:, :lora_rank]
+    S = S[:lora_rank]
+    U = U @ torch.diag(S)
+    Vh = Vh[:lora_rank, :]
+    
+    param_dict["lora_down"] = Vh.reshape(lora_rank, in_size).cpu()
+    param_dict["lora_up"] = U.reshape(out_size, lora_rank).cpu()
+    del U, S, Vh, weight
+    return param_dict
+
+
+def merge_conv(lora_down, lora_up, device):
+    in_rank, in_size, kernel_size, k_ = lora_down.shape
+    out_size, out_rank, _, _ = lora_up.shape
+    assert in_rank == out_rank and kernel_size == k_, f"rank {in_rank} {out_rank} or kernel {kernel_size} {k_} mismatch"
+    
+    lora_down = lora_down.to(device)
+    lora_up = lora_up.to(device)
+
+    merged = lora_up.reshape(out_size, -1) @ lora_down.reshape(in_rank, -1)
+    weight = merged.reshape(out_size, in_size, kernel_size, kernel_size)
+    del lora_up, lora_down
+    return weight
+
+
+def merge_linear(lora_down, lora_up, device):
+    in_rank, in_size = lora_down.shape
+    out_size, out_rank = lora_up.shape
+    assert in_rank == out_rank, f"rank {in_rank} {out_rank} mismatch"
+    
+    lora_down = lora_down.to(device)
+    lora_up = lora_up.to(device)
+    
+    weight = lora_up @ lora_down
+    del lora_up, lora_down
+    return weight
+  
+
+def rank_resize(S, rank, dynamic_method, dynamic_param, scale=1):
+    param_dict = {}
+
+    if dynamic_method=="sv_ratio":
+        # Calculate new dim and alpha based off ratio
+        max_sv = S[0]
+        min_sv = max_sv/dynamic_param
+        new_rank = max(torch.sum(S > min_sv).item(),1)
+        new_alpha = float(scale*new_rank)
+
+    elif dynamic_method=="sv_cumulative":
+        # Calculate new dim and alpha based off cumulative sum
+        new_rank = index_sv_cumulative(S, dynamic_param)
+        new_rank = max(new_rank, 1)
+        new_alpha = float(scale*new_rank)
+
+    elif dynamic_method=="sv_fro":
+        # Calculate new dim and alpha based off sqrt sum of squares
+        new_rank = index_sv_fro(S, dynamic_param)
+        new_rank = min(max(new_rank, 1), len(S)-1)
+        new_alpha = float(scale*new_rank)
+    else:
+        new_rank = rank
+        new_alpha = float(scale*new_rank)
+
+    
+    if S[0] <= MIN_SV: # Zero matrix, set dim to 1
+        new_rank = 1
+        new_alpha = float(scale*new_rank)
+    elif new_rank > rank: # cap max rank at rank
+        new_rank = rank
+        new_alpha = float(scale*new_rank)
+
+
+    # Calculate resize info
+    s_sum = torch.sum(torch.abs(S))
+    s_rank = torch.sum(torch.abs(S[:new_rank]))
+    
+    S_squared = S.pow(2)
+    s_fro = torch.sqrt(torch.sum(S_squared))
+    s_red_fro = torch.sqrt(torch.sum(S_squared[:new_rank]))
+    fro_percent = float(s_red_fro/s_fro)
+
+    param_dict["new_rank"] = new_rank
+    param_dict["new_alpha"] = new_alpha
+    param_dict["sum_retained"] = (s_rank)/s_sum
+    param_dict["fro_retained"] = fro_percent
+    param_dict["max_ratio"] = S[0]/S[new_rank]
+
+    return param_dict
+
+
+def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dynamic_param, verbose):
   network_alpha = None
   network_dim = None
   verbose_str = "\n"
-
+  fro_list = []
-  CLAMP_QUANTILE = 0.99
 
   # Extract loaded lora dim and alpha
   for key, value in lora_sd.items():
@@ -57,9 +195,9 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, verbose):
       network_alpha = network_dim
 
   scale = network_alpha/network_dim
-  new_alpha = float(scale*new_rank)  # calculate new alpha from scale
 
-  print(f"old dimension: {network_dim}, old alpha: {network_alpha}, new alpha: {new_alpha}")
+  if dynamic_method:
+    print(f"Dynamically determining new alphas and dims based off {dynamic_method}: {dynamic_param}, max rank is {new_rank}")
 
   lora_down_weight = None
   lora_up_weight = None
@@ -68,7 +206,6 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, verbose):
   block_down_name = None
   block_up_name = None
 
-  print("resizing lora...")
   with torch.no_grad():
     for key, value in tqdm(lora_sd.items()):
       if 'lora_down' in key:
@@ -85,57 +222,43 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, verbose):
         conv2d = (len(lora_down_weight.size()) == 4)
 
         if conv2d:
-          lora_down_weight = lora_down_weight.squeeze()
+          full_weight_matrix = merge_conv(lora_down_weight, lora_up_weight, device)
-          lora_up_weight = lora_up_weight.squeeze()
+          param_dict = extract_conv(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale)
-
+        else:
-        if device:
+          full_weight_matrix = merge_linear(lora_down_weight, lora_up_weight, device)
-          org_device = lora_up_weight.device
+          param_dict = extract_linear(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale)
-          lora_up_weight = lora_up_weight.to(args.device)
-          lora_down_weight = lora_down_weight.to(args.device)
-
-        full_weight_matrix = torch.matmul(lora_up_weight, lora_down_weight)
-
-        U, S, Vh = torch.linalg.svd(full_weight_matrix)
 
         if verbose:
-          s_sum = torch.sum(torch.abs(S))
+          max_ratio = param_dict['max_ratio']
-          s_rank = torch.sum(torch.abs(S[:new_rank]))
+          sum_retained = param_dict['sum_retained']
-          verbose_str+=f"{block_down_name:76} | "
+          fro_retained = param_dict['fro_retained']
-          verbose_str+=f"sum(S) retained: {(s_rank)/s_sum:.1%}, max(S) ratio: {S[0]/S[new_rank]:0.1f}\n"
+          if not np.isnan(fro_retained):
+            fro_list.append(float(fro_retained))
 
-        U = U[:, :new_rank]
+          verbose_str+=f"{block_down_name:75} | "
-        S = S[:new_rank]
+          verbose_str+=f"sum(S) retained: {sum_retained:.1%}, fro retained: {fro_retained:.1%}, max(S) ratio: {max_ratio:0.1f}"
-        U = U @ torch.diag(S)
 
-        Vh = Vh[:new_rank, :]
+        if verbose and dynamic_method:
+          verbose_str+=f", dynamic | dim: {param_dict['new_rank']}, alpha: {param_dict['new_alpha']}\n"
+        else:
+          verbose_str+=f"\n"
 
-        dist = torch.cat([U.flatten(), Vh.flatten()])
+        new_alpha = param_dict['new_alpha']
-        hi_val = torch.quantile(dist, CLAMP_QUANTILE)
+        o_lora_sd[block_down_name + "." + "lora_down.weight"] = param_dict["lora_down"].to(save_dtype).contiguous()
-        low_val = -hi_val
+        o_lora_sd[block_up_name + "." + "lora_up.weight"] = param_dict["lora_up"].to(save_dtype).contiguous()
-
+        o_lora_sd[block_up_name + "." "alpha"] = torch.tensor(param_dict['new_alpha']).to(save_dtype)
-        U = U.clamp(low_val, hi_val)
-        Vh = Vh.clamp(low_val, hi_val)
-
-        if conv2d:
-          U = U.unsqueeze(2).unsqueeze(3)
-          Vh = Vh.unsqueeze(2).unsqueeze(3)
-
-        if device:
-          U = U.to(org_device)
-          Vh = Vh.to(org_device)
-
-        o_lora_sd[block_down_name + "." + "lora_down.weight"] = Vh.to(save_dtype).contiguous()
-        o_lora_sd[block_up_name + "." + "lora_up.weight"] = U.to(save_dtype).contiguous()
-        o_lora_sd[block_up_name + "." "alpha"] = torch.tensor(new_alpha).to(save_dtype)
 
         block_down_name = None
         block_up_name = None
         lora_down_weight = None
         lora_up_weight = None
         weights_loaded = False
+        del param_dict
 
   if verbose:
     print(verbose_str)
+
+    print(f"Average Frobenius norm retention: {np.mean(fro_list):.2%} | std: {np.std(fro_list):0.3f}")
   print("resizing complete")
   return o_lora_sd, network_dim, new_alpha
 
@@ -151,6 +274,9 @@ def resize(args):
       return torch.bfloat16
     return None
 
+  if args.dynamic_method and not args.dynamic_param:
+    raise Exception("If using dynamic_method, then dynamic_param is required")
+
   merge_dtype = str_to_dtype('float')  # matmul method above only seems to work in float32
   save_dtype = str_to_dtype(args.save_precision)
   if save_dtype is None:
@@ -159,17 +285,23 @@ def resize(args):
   print("loading Model...")
   lora_sd, metadata = load_state_dict(args.model, merge_dtype)
 
-  print("resizing rank...")
+  print("Resizing Lora...")
-  state_dict, old_dim, new_alpha = resize_lora_model(lora_sd, args.new_rank, save_dtype, args.device, args.verbose)
+  state_dict, old_dim, new_alpha = resize_lora_model(lora_sd, args.new_rank, save_dtype, args.device, args.dynamic_method, args.dynamic_param, args.verbose)
 
   # update metadata
   if metadata is None:
     metadata = {}
 
   comment = metadata.get("ss_training_comment", "")
-  metadata["ss_training_comment"] = f"dimension is resized from {old_dim} to {args.new_rank}; {comment}"
+
-  metadata["ss_network_dim"] = str(args.new_rank)
+  if not args.dynamic_method:
-  metadata["ss_network_alpha"] = str(new_alpha)
+    metadata["ss_training_comment"] = f"dimension is resized from {old_dim} to {args.new_rank}; {comment}"
+    metadata["ss_network_dim"] = str(args.new_rank)
+    metadata["ss_network_alpha"] = str(new_alpha)
+  else:
+    metadata["ss_training_comment"] = f"Dynamic resize with {args.dynamic_method}: {args.dynamic_param} from {old_dim}; {comment}"
+    metadata["ss_network_dim"] = 'Dynamic'
+    metadata["ss_network_alpha"] = 'Dynamic'
 
   model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
   metadata["sshs_model_hash"] = model_hash
@@ -193,6 +325,11 @@ if __name__ == '__main__':
   parser.add_argument("--device", type=str, default=None, help="device to use, cuda for GPU / 計算を行うデバイス、cuda でGPUを使う")
   parser.add_argument("--verbose", action="store_true", 
                       help="Display verbose resizing information / rank変更時の詳細情報を出力する")
+  parser.add_argument("--dynamic_method", type=str, default=None, choices=[None, "sv_ratio", "sv_fro", "sv_cumulative"],
+                      help="Specify dynamic resizing method, --new_rank is used as a hard limit for max rank")
+  parser.add_argument("--dynamic_param", type=float, default=None,
+                      help="Specify target for dynamic reduction")
+                                           
 
   args = parser.parse_args()
   resize(args)

networks/svd_merge_lora.py

@@ -23,16 +23,16 @@ def load_state_dict(file_name, dtype):
   return sd
 
 
-def save_to_file(file_name, model, state_dict, dtype):
+def save_to_file(file_name, state_dict, dtype):
   if dtype is not None:
     for key in list(state_dict.keys()):
       if type(state_dict[key]) == torch.Tensor:
         state_dict[key] = state_dict[key].to(dtype)
 
   if os.path.splitext(file_name)[1] == '.safetensors':
-    save_file(model, file_name)
+    save_file(state_dict, file_name)
   else:
-    torch.save(model, file_name)
+    torch.save(state_dict, file_name)
 
 
 def merge_lora_models(models, ratios, new_rank, new_conv_rank, device, merge_dtype):
@@ -76,7 +76,7 @@ def merge_lora_models(models, ratios, new_rank, new_conv_rank, device, merge_dty
         down_weight = down_weight.to(device)
 
       # W <- W + U * D
-      scale = (alpha / network_dim)
+      scale = (alpha / network_dim).to(device)
       if not conv2d:        # linear
         weight = weight + ratio * (up_weight @ down_weight) * scale
       elif kernel_size == (1, 1):
@@ -105,6 +105,7 @@ def merge_lora_models(models, ratios, new_rank, new_conv_rank, device, merge_dty
           mat = mat.squeeze()
 
       module_new_rank = new_conv_rank if conv2d_3x3 else new_rank
+      module_new_rank = min(module_new_rank, in_dim, out_dim)                           # LoRA rank cannot exceed the original dim
 
       U, S, Vh = torch.linalg.svd(mat)
 
@@ -114,12 +115,12 @@ def merge_lora_models(models, ratios, new_rank, new_conv_rank, device, merge_dty
 
       Vh = Vh[:module_new_rank, :]
 
-      dist = torch.cat([U.flatten(), Vh.flatten()])
+      # dist = torch.cat([U.flatten(), Vh.flatten()])
-      hi_val = torch.quantile(dist, CLAMP_QUANTILE)
+      # hi_val = torch.quantile(dist, CLAMP_QUANTILE)
-      low_val = -hi_val
+      # low_val = -hi_val
 
-      U = U.clamp(low_val, hi_val)
+      # U = U.clamp(low_val, hi_val)
-      Vh = Vh.clamp(low_val, hi_val)
+      # Vh = Vh.clamp(low_val, hi_val)
 
       if conv2d:
         U = U.reshape(out_dim, module_new_rank, 1, 1)
@@ -156,7 +157,7 @@ def merge(args):
   state_dict = merge_lora_models(args.models, args.ratios, args.new_rank, new_conv_rank, args.device, merge_dtype)
 
   print(f"saving model to: {args.save_to}")
-  save_to_file(args.save_to, state_dict, state_dict, save_dtype)
+  save_to_file(args.save_to, state_dict, save_dtype)
 
 
 if __name__ == '__main__':

train_README-ja.md

@@ -502,6 +502,14 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
 
     clip_skipと同様に、モデルの学習状態と異なる長さで学習するには、ある程度の教師データ枚数、長めの学習時間が必要になると思われます。
 
+- `--persistent_data_loader_workers`
+
+    Windows環境で指定するとエポック間の待ち時間が大幅に短縮されます。
+
+- `--max_data_loader_n_workers`
+
+    データ読み込みのプロセス数を指定します。プロセス数が多いとデータ読み込みが速くなりGPUを効率的に利用できますが、メインメモリを消費します。デフォルトは「`8` または `CPU同時実行スレッド数-1` の小さいほう」なので、メインメモリに余裕がない場合や、GPU使用率が90%程度以上なら、それらの数値を見ながら `2` または `1` 程度まで下げてください。
+
 - `--logging_dir` / `--log_prefix`
 
     学習ログの保存に関するオプションです。logging_dirオプションにログ保存先フォルダを指定してください。TensorBoard形式のログが保存されます。

train_network.py

@@ -106,6 +106,7 @@ def train(args):
   # acceleratorを準備する
   print("prepare accelerator")
   accelerator, unwrap_model = train_util.prepare_accelerator(args)
+  is_main_process = accelerator.is_main_process
 
   # mixed precisionに対応した型を用意しておき適宜castする
   weight_dtype, save_dtype = train_util.prepare_dtype(args)
@@ -134,6 +135,8 @@ def train(args):
     gc.collect()
 
   # prepare network
+  import sys
+  sys.path.append(os.path.dirname(__file__))
   print("import network module:", args.network_module)
   network_module = importlib.import_module(args.network_module)
 
@@ -175,12 +178,13 @@ def train(args):
 
   # 学習ステップ数を計算する
   if args.max_train_epochs is not None:
-    args.max_train_steps = args.max_train_epochs * len(train_dataloader)
+    args.max_train_steps = args.max_train_epochs * math.ceil(len(train_dataloader) / accelerator.num_processes)
-    print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
+    if is_main_process:
+      print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
 
   # lr schedulerを用意する
   lr_scheduler = train_util.get_scheduler_fix(args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps,
-                                              num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+                                              num_training_steps=args.max_train_steps * accelerator.num_processes * args.gradient_accumulation_steps,
                                               num_cycles=args.lr_scheduler_num_cycles, power=args.lr_scheduler_power)
 
   # 実験的機能：勾配も含めたfp16学習を行う　モデル全体をfp16にする
@@ -251,15 +255,17 @@ def train(args):
   # 学習する
   # TODO: find a way to handle total batch size when there are multiple datasets
   total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-  print("running training / 学習開始")
+  
-  print(f"  num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
+  if is_main_process:
-  print(f"  num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
+    print("running training / 学習開始")
-  print(f"  num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
+    print(f"  num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
-  print(f"  num epochs / epoch数: {num_train_epochs}")
+    print(f"  num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
-  print(f"  batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}")
+    print(f"  num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
-  # print(f"  total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
+    print(f"  num epochs / epoch数: {num_train_epochs}")
-  print(f"  gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
+    print(f"  batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}")
-  print(f"  total optimization steps / 学習ステップ数: {args.max_train_steps}")
+    # print(f"  total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
+    print(f"  gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
+    print(f"  total optimization steps / 学習ステップ数: {args.max_train_steps}")
 
   # TODO refactor metadata creation and move to util
   metadata = {
@@ -471,7 +477,8 @@ def train(args):
   loss_list = []
   loss_total = 0.0
   for epoch in range(num_train_epochs):
-    print(f"epoch {epoch+1}/{num_train_epochs}")
+    if is_main_process:
+      print(f"epoch {epoch+1}/{num_train_epochs}")
     train_dataset_group.set_current_epoch(epoch + 1)
 
     metadata["ss_epoch"] = str(epoch+1)
@@ -583,9 +590,10 @@ def train(args):
           print(f"removing old checkpoint: {old_ckpt_file}")
           os.remove(old_ckpt_file)
 
-      saving = train_util.save_on_epoch_end(args, save_func, remove_old_func, epoch + 1, num_train_epochs)
+      if is_main_process:
-      if saving and args.save_state:
+        saving = train_util.save_on_epoch_end(args, save_func, remove_old_func, epoch + 1, num_train_epochs)
-        train_util.save_state_on_epoch_end(args, accelerator, model_name, epoch + 1)
+        if saving and args.save_state:
+          train_util.save_state_on_epoch_end(args, accelerator, model_name, epoch + 1)
 
     train_util.sample_images(accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
 
@@ -594,7 +602,6 @@ def train(args):
   metadata["ss_epoch"] = str(num_train_epochs)
   metadata["ss_training_finished_at"] = str(time.time())
 
-  is_main_process = accelerator.is_main_process
   if is_main_process:
     network = unwrap_model(network)
 

train_network_README-ja.md

@@ -64,6 +64,10 @@ accelerate launch --num_cpu_threads_per_process 1 train_network.py
   * LoRAのRANKを指定します（``--networkdim=4``など）。省略時は4になります。数が多いほど表現力は増しますが、学習に必要なメモリ、時間は増えます。また闇雲に増やしても良くないようです。
 * `--network_alpha`
   *  アンダーフローを防ぎ安定して学習するための ``alpha`` 値を指定します。デフォルトは1です。``network_dim``と同じ値を指定すると以前のバージョンと同じ動作になります。
+* `--persistent_data_loader_workers`
+  * Windows環境で指定するとエポック間の待ち時間が大幅に短縮されます。
+* `--max_data_loader_n_workers`
+  * データ読み込みのプロセス数を指定します。プロセス数が多いとデータ読み込みが速くなりGPUを効率的に利用できますが、メインメモリを消費します。デフォルトは「`8` または `CPU同時実行スレッド数-1` の小さいほう」なので、メインメモリに余裕がない場合や、GPU使用率が90%程度以上なら、それらの数値を見ながら `2` または `1` 程度まで下げてください。
 * `--network_weights`
   * 学習前に学習済みのLoRAの重みを読み込み、そこから追加で学習します。
 * `--network_train_unet_only`