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scripts/mergers/model_util.py

@@ -725,7 +725,10 @@ def filenamecutter(name,model_a = False):
 def load_models_from_stable_diffusion_checkpoint(v2, ckpt_path, dtype=None):
   import diffusers
   print("diffusers version : ",diffusers.__version__)
-  print("version of diffusers must be 0.10.2 to 0.14.0")
+  version = diffusers.__version__.split(".")[1]
+  if int(version) > 14:
+    return None,None,None, f"ERROR: version of diffusers is different, use version 0.10.2 - 0.14.0, your version is {diffusers.__version__}"
+
   state_dict = load_checkpoint_with_text_encoder_conversion(ckpt_path)
   if dtype is not None:
     for k, v in state_dict.items():
@@ -780,7 +783,7 @@ def load_models_from_stable_diffusion_checkpoint(v2, ckpt_path, dtype=None):
     info = text_model.load_state_dict(converted_text_encoder_checkpoint)
   print("loading text encoder:", info)
 
-  return text_model, vae, unet
+  return text_model, vae, unet, info
 
 def usemodelgen(theta_0,model_a,model_name):
   from modules import lowvram, devices, sd_hijack,shared, sd_vae

scripts/mergers/pluslora.py

@@ -156,8 +156,11 @@ def makelora(model_a,model_b,dim,saveto,settings,alpha,beta,precision):
         print(_err_msg)
         return _err_msg
 
-    svd(fullpathfromname(model_a),fullpathfromname(model_b),False,dim,precision,saveto,alpha,beta)
-    return f"saved to {saveto}"
+    result = svd(fullpathfromname(model_a),fullpathfromname(model_b),False,dim,precision,saveto,alpha,beta)
+    if "ERROR" in result:
+        return result
+    else:
+        return f"saved to {saveto}"
 
 def lmerge(loranames,loraratioss,settings,filename,dim,precision):
     import lora
@@ -446,14 +449,16 @@ def svd(model_a,model_b,v2,dim,save_precision,save_to,alpha,beta):
   save_dtype = str_to_dtype(save_precision)
 
   if model_a == model_b:
-    text_encoder_t, _, unet_t = load_models_from_stable_diffusion_checkpoint(v2, model_a)
+    text_encoder_t, _, unet_t, result = load_models_from_stable_diffusion_checkpoint(v2, model_a)
     text_encoder_o, _, unet_o = text_encoder_t, _, unet_t
   else:
     print(f"loading SD model : {model_b}")
-    text_encoder_o, _, unet_o = load_models_from_stable_diffusion_checkpoint(v2, model_b)
+    text_encoder_o, _, unet_o, result  = load_models_from_stable_diffusion_checkpoint(v2, model_b)
     
     print(f"loading SD model : {model_a}")
-    text_encoder_t, _, unet_t = load_models_from_stable_diffusion_checkpoint(v2, model_a)
+    text_encoder_t, _, unet_t, result  = load_models_from_stable_diffusion_checkpoint(v2, model_a)
+   
+  if "ERROR" in result: return result
 
   # create LoRA network to extract weights: Use dim (rank) as alpha
   lora_network_o = create_network(1.0, dim, dim, None, text_encoder_o, unet_o)
@@ -550,7 +555,7 @@ def svd(model_a,model_b,v2,dim,save_precision,save_to,alpha,beta):
 
   lora_network_o.save_weights(save_to, save_dtype, metadata)
   print(f"LoRA weights are saved to: {save_to}")
-  return save_to  
+  return "Finished"  
 
 def load_state_dict(file_name, dtype):
   if os.path.splitext(file_name)[1] == '.safetensors':