Update for alpha value

networks/extract_lora_from_models.py

@@ -44,9 +44,9 @@ def svd(args):
   print(f"loading SD model : {args.model_tuned}")
   text_encoder_t, _, unet_t = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.model_tuned)
 
-  # create LoRA network to extract weights
-  lora_network_o = lora.create_network(1.0, args.dim, None, text_encoder_o, unet_o)
-  lora_network_t = lora.create_network(1.0, args.dim, None, text_encoder_t, unet_t)
+  # create LoRA network to extract weights: Use dim (rank) as alpha
+  lora_network_o = lora.create_network(1.0, args.dim, args.dim, None, text_encoder_o, unet_o)
+  lora_network_t = lora.create_network(1.0, args.dim, args.dim, None, text_encoder_t, unet_t)
   assert len(lora_network_o.text_encoder_loras) == len(
       lora_network_t.text_encoder_loras), f"model version is different (SD1.x vs SD2.x) / それぞれのモデルのバージョンが違います（SD1.xベースとSD2.xベース） "
 
@@ -116,6 +116,9 @@ def svd(args):
   print(f"LoRA has {len(lora_sd)} weights.")
 
   for key in list(lora_sd.keys()):
+    if "alpha" in key:
+      continue
+
     lora_name = key.split('.')[0]
     i = 0 if "lora_up" in key else 1
 
@@ -124,7 +127,7 @@ def svd(args):
     if len(lora_sd[key].size()) == 4:
       weights = weights.unsqueeze(2).unsqueeze(3)
 
-    assert weights.size() == lora_sd[key].size()
+    assert weights.size() == lora_sd[key].size(), f"size unmatch: {key}"
     lora_sd[key] = weights
 
   # load state dict to LoRA and save it
@@ -135,7 +138,10 @@ def svd(args):
   if dir_name and not os.path.exists(dir_name):
     os.makedirs(dir_name, exist_ok=True)
 
-  lora_network_o.save_weights(args.save_to, save_dtype, {})
+  # minimum metadata
+  metadata = {"ss_network_dim": str(args.dim), "ss_network_alpha": str(args.dim)}
+
+  lora_network_o.save_weights(args.save_to, save_dtype, metadata)
   print(f"LoRA weights are saved to: {args.save_to}")
 
 
@@ -151,8 +157,8 @@ if __name__ == '__main__':
                       help="Stable Diffusion tuned model, LoRA is difference of `original to tuned`: ckpt or safetensors file / 派生モデル（生成されるLoRAは元→派生の差分になります）、ckptまたはsafetensors")
   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("--dim", type=int, default=4, help="dimension of LoRA (default 4) / LoRAの次元数（デフォルト4）")
-  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("--dim", type=int, default=4, help="dimension (rank) of LoRA (default 4) / LoRAの次元数（rank）（デフォルト4）")
+  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")
 
   args = parser.parse_args()
   svd(args)

networks/merge_lora.py

@@ -61,6 +61,7 @@ def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
     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"
@@ -73,14 +74,18 @@ def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
         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)
+          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)
+          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)
 
@@ -88,20 +93,35 @@ def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
 def merge_lora_models(models, ratios, merge_dtype):
   merged_sd = {}
 
+  alpha = None
+  dim = None
   for model, ratio in zip(models, ratios):
     print(f"loading: {model}")
     lora_sd = load_state_dict(model, merge_dtype)
 
     print(f"merging...")
     for key in lora_sd.keys():
-      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
+      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:
-        merged_sd[key] = lora_sd[key] * ratio
+        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
 
-  return merged_sd
+  print(f"dim (rank): {dim}, alpha: {alpha}")
+  if alpha is None:
+    alpha = dim
+
+  return merged_sd, dim, alpha
 
 
 def merge(args):
@@ -132,7 +152,7 @@ def merge(args):
     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)
+    state_dict, _, _ = merge_lora_models(args.models, args.ratios, merge_dtype)
 
     print(f"saving model to: {args.save_to}")
     save_to_file(args.save_to, state_dict, state_dict, save_dtype)
@@ -145,7 +165,7 @@ if __name__ == '__main__':
   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 / マージの計算時の精度")
+                      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,