Fix.

Fix mean image aspect ratio error calculation to avoid NaN values

.github/FUNDING.yml

@@ -0,0 +1,3 @@
+# These are supported funding model platforms
+
+github: kohya-ss

library/train_util.py

@@ -957,8 +957,11 @@ class BaseDataset(torch.utils.data.Dataset):
                     self.bucket_info["buckets"][i] = {"resolution": reso, "count": len(bucket)}
                     logger.info(f"bucket {i}: resolution {reso}, count: {len(bucket)}")
 
-            img_ar_errors = np.array(img_ar_errors)
-            mean_img_ar_error = np.mean(np.abs(img_ar_errors))
+            if len(img_ar_errors) == 0:
+                mean_img_ar_error = 0  # avoid NaN
+            else:
+                img_ar_errors = np.array(img_ar_errors)
+                mean_img_ar_error = np.mean(np.abs(img_ar_errors))
             self.bucket_info["mean_img_ar_error"] = mean_img_ar_error
             logger.info(f"mean ar error (without repeats): {mean_img_ar_error}")
 

networks/resize_lora.py

@@ -240,7 +240,7 @@ def resize_lora_model(lora_sd, new_rank, new_conv_rank, save_dtype, device, dyna
         for key, value in tqdm(lora_sd.items()):
             weight_name = None
             if LORAFMT[0] in key:
-                block_down_name = key.rsplit(f".LORAFMT[0]", 1)[0]
+                block_down_name = key.rsplit(f".{LORAFMT[0]}", 1)[0]
                 weight_name = key.rsplit(".", 1)[-1]
                 lora_down_weight = value
             else:
@@ -248,7 +248,7 @@ def resize_lora_model(lora_sd, new_rank, new_conv_rank, save_dtype, device, dyna
 
             # find corresponding lora_up and alpha
             block_up_name = block_down_name
-            lora_up_weight = lora_sd.get(block_up_name + f".LORAFMT[1]." + weight_name, None)
+            lora_up_weight = lora_sd.get(block_up_name + f".{LORAFMT[1]}." + weight_name, None)
             lora_alpha = lora_sd.get(block_down_name + ".alpha", None)
 
             weights_loaded = lora_down_weight is not None and lora_up_weight is not None
@@ -286,8 +286,8 @@ def resize_lora_model(lora_sd, new_rank, new_conv_rank, save_dtype, device, dyna
                     verbose_str += "\n"
 
                 new_alpha = param_dict["new_alpha"]
-                o_lora_sd[block_down_name + f".LORAFMT[0].weight"] = param_dict[LORAFMT[0]].to(save_dtype).contiguous()
-                o_lora_sd[block_up_name + f".LORAFMT[1].weight"] = param_dict[LORAFMT[1]].to(save_dtype).contiguous()
+                o_lora_sd[block_down_name + f".{LORAFMT[0]}.weight"] = param_dict[LORAFMT[0]].to(save_dtype).contiguous()
+                o_lora_sd[block_up_name + f".{LORAFMT[1]}.weight"] = param_dict[LORAFMT[1]].to(save_dtype).contiguous()
                 o_lora_sd[block_up_name + ".alpha"] = torch.tensor(param_dict["new_alpha"]).to(save_dtype)
 
                 block_down_name = None