Upload lora-scripts/sd-scripts/networks/merge_lora_old.py with huggingface_hub

lora-scripts/sd-scripts/networks/merge_lora_old.py

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+
+
+import argparse
+import os
+import torch
+from safetensors.torch import load_file, save_file
+import library.model_util as model_util
+import lora
+from library.utils import setup_logging
+setup_logging()
+import logging
+logger = logging.getLogger(__name__)
+
+def load_state_dict(file_name, dtype):
+  if os.path.splitext(file_name)[1] == '.safetensors':
+    sd = load_file(file_name)
+  else:
+    sd = torch.load(file_name, map_location='cpu')
+  for key in list(sd.keys()):
+    if type(sd[key]) == torch.Tensor:
+      sd[key] = sd[key].to(dtype)
+  return sd
+
+
+def save_to_file(file_name, model, 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)
+  else:
+    torch.save(model, file_name)
+
+
+def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
+  text_encoder.to(merge_dtype)
+  unet.to(merge_dtype)
+
+  # create module map
+  name_to_module = {}
+  for i, root_module in enumerate([text_encoder, unet]):
+    if i == 0:
+      prefix = lora.LoRANetwork.LORA_PREFIX_TEXT_ENCODER
+      target_replace_modules = lora.LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE
+    else:
+      prefix = lora.LoRANetwork.LORA_PREFIX_UNET
+      target_replace_modules = lora.LoRANetwork.UNET_TARGET_REPLACE_MODULE
+
+    for name, module in root_module.named_modules():
+      if module.__class__.__name__ in target_replace_modules:
+        for child_name, child_module in module.named_modules():
+          if child_module.__class__.__name__ == "Linear" or (child_module.__class__.__name__ == "Conv2d" and child_module.kernel_size == (1, 1)):
+            lora_name = prefix + '.' + name + '.' + child_name
+            lora_name = lora_name.replace('.', '_')
+            name_to_module[lora_name] = child_module
+
+  for model, ratio in zip(models, ratios):
+    logger.info(f"loading: {model}")
+    lora_sd = load_state_dict(model, merge_dtype)
+
+    logger.info(f"merging...")
+    for key in lora_sd.keys():
+      if "lora_down" in key:
+        up_key = key.replace("lora_down", "lora_up")
+        alpha_key = key[:key.index("lora_down")] + 'alpha'
+
+        # find original module for this lora
+        module_name = '.'.join(key.split('.')[:-2])               # remove trailing ".lora_down.weight"
+        if module_name not in name_to_module:
+          logger.info(f"no module found for LoRA weight: {key}")
+          continue
+        module = name_to_module[module_name]
+        # logger.info(f"apply {key} to {module}")
+
+        down_weight = lora_sd[key]
+        up_weight = lora_sd[up_key]
+
+        dim = down_weight.size()[0]
+        alpha = lora_sd.get(alpha_key, dim)
+        scale = alpha / dim
+
+        # W <- W + U * D
+        weight = module.weight
+        if len(weight.size()) == 2:
+          # linear
+          weight = weight + ratio * (up_weight @ down_weight) * scale
+        else:
+          # conv2d
+          weight = weight + ratio * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3) * scale
+
+        module.weight = torch.nn.Parameter(weight)
+
+
+def merge_lora_models(models, ratios, merge_dtype):
+  merged_sd = {}
+
+  alpha = None
+  dim = None
+  for model, ratio in zip(models, ratios):
+    logger.info(f"loading: {model}")
+    lora_sd = load_state_dict(model, merge_dtype)
+
+    logger.info(f"merging...")
+    for key in lora_sd.keys():
+      if 'alpha' in key:
+        if key in merged_sd:
+          assert merged_sd[key] == lora_sd[key], f"alpha mismatch / alphaが異なる場合、現時点ではマージできません"
+        else:
+          alpha = lora_sd[key].detach().numpy()
+          merged_sd[key] = lora_sd[key]
+      else:
+        if key in merged_sd:
+          assert merged_sd[key].size() == lora_sd[key].size(
+          ), f"weights shape mismatch merging v1 and v2, different dims? / 重みのサイズが合いません。v1とv2、または次元数の異なるモデルはマージできません"
+          merged_sd[key] = merged_sd[key] + lora_sd[key] * ratio
+        else:
+          if "lora_down" in key:
+            dim = lora_sd[key].size()[0]
+          merged_sd[key] = lora_sd[key] * ratio
+
+  logger.info(f"dim (rank): {dim}, alpha: {alpha}")
+  if alpha is None:
+    alpha = dim
+
+  return merged_sd, dim, alpha
+
+
+def merge(args):
+  assert len(args.models) == len(args.ratios), f"number of models must be equal to number of ratios / モデルの数と重みの数は合わせてください"
+
+  def str_to_dtype(p):
+    if p == 'float':
+      return torch.float
+    if p == 'fp16':
+      return torch.float16
+    if p == 'bf16':
+      return torch.bfloat16
+    return None
+
+  merge_dtype = str_to_dtype(args.precision)
+  save_dtype = str_to_dtype(args.save_precision)
+  if save_dtype is None:
+    save_dtype = merge_dtype
+
+  if args.sd_model is not None:
+    logger.info(f"loading SD model: {args.sd_model}")
+
+    text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.sd_model)
+
+    merge_to_sd_model(text_encoder, unet, args.models, args.ratios, merge_dtype)
+
+    logger.info("")
+    logger.info(f"saving SD model to: {args.save_to}")
+    model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet,
+                                                args.sd_model, 0, 0, save_dtype, vae)
+  else:
+    state_dict, _, _ = merge_lora_models(args.models, args.ratios, merge_dtype)
+
+    logger.info(f"")
+    logger.info(f"saving model to: {args.save_to}")
+    save_to_file(args.save_to, state_dict, state_dict, save_dtype)
+
+
+def setup_parser() -> argparse.ArgumentParser:
+  parser = argparse.ArgumentParser()
+  parser.add_argument("--v2", action='store_true',
+                      help='load Stable Diffusion v2.x model / Stable Diffusion 2.xのモデルを読み込む')
+  parser.add_argument("--save_precision", type=str, default=None,
+                      choices=[None, "float", "fp16", "bf16"], help="precision in saving, same to merging if omitted / 保存時に精度を変更して保存する、省略時はマージ時の精度と同じ")
+  parser.add_argument("--precision", type=str, default="float",
+                      choices=["float", "fp16", "bf16"], help="precision in merging (float is recommended) / マージの計算時の精度（floatを推奨）")
+  parser.add_argument("--sd_model", type=str, default=None,
+                      help="Stable Diffusion model to load: ckpt or safetensors file, merge LoRA models if omitted / 読み込むモデル、ckptまたはsafetensors。省略時はLoRAモデル同士をマージする")
+  parser.add_argument("--save_to", type=str, default=None,
+                      help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors")
+  parser.add_argument("--models", type=str, nargs='*',
+                      help="LoRA models to merge: ckpt or safetensors file / マージするLoRAモデル、ckptまたはsafetensors")
+  parser.add_argument("--ratios", type=float, nargs='*',
+                      help="ratios for each model / それぞれのLoRAモデルの比率")
+
+  return parser
+
+
+if __name__ == '__main__':
+  parser = setup_parser()
+
+  args = parser.parse_args()
+  merge(args)