Merge branch 'kohya-ss:dev' into dev

finetune/tag_images_by_wd14_tagger.py

@@ -130,10 +130,10 @@ def main(args):
         input_name = model.graph.input[0].name
         try:
             batch_size = model.graph.input[0].type.tensor_type.shape.dim[0].dim_value
-        except:
+        except Exception:
             batch_size = model.graph.input[0].type.tensor_type.shape.dim[0].dim_param
 
-        if args.batch_size != batch_size and type(batch_size) != str and batch_size > 0:
+        if args.batch_size != batch_size and not isinstance(batch_size, str) and batch_size > 0:
             # some rebatch model may use 'N' as dynamic axes
             logger.warning(
                 f"Batch size {args.batch_size} doesn't match onnx model batch size {batch_size}, use model batch size {batch_size}"
@@ -169,13 +169,14 @@ def main(args):
 
     with open(os.path.join(model_location, CSV_FILE), "r", encoding="utf-8") as f:
         reader = csv.reader(f)
-        l = [row for row in reader]
-        header = l[0]  # tag_id,name,category,count
-        rows = l[1:]
+        line = [row for row in reader]
+        header = line[0]  # tag_id,name,category,count
+        rows = line[1:]
     assert header[0] == "tag_id" and header[1] == "name" and header[2] == "category", f"unexpected csv format: {header}"
 
-    general_tags = [row[1] for row in rows[1:] if row[2] == "0"]
-    character_tags = [row[1] for row in rows[1:] if row[2] == "4"]
+    rating_tags = [row[1] for row in rows[0:] if row[2] == "9"]
+    general_tags = [row[1] for row in rows[0:] if row[2] == "0"]
+    character_tags = [row[1] for row in rows[0:] if row[2] == "4"]
 
     # 画像を読み込む
 
@@ -202,17 +203,13 @@ def run_batch(path_imgs):
             probs = probs.numpy()
 
         for (image_path, _), prob in zip(path_imgs, probs):
-            # 最初の4つはratingなので無視する
-            # # First 4 labels are actually ratings: pick one with argmax
-            # ratings_names = label_names[:4]
-            # rating_index = ratings_names["probs"].argmax()
-            # found_rating = ratings_names[rating_index: rating_index + 1][["name", "probs"]]
+            combined_tags = []
+            rating_tag_text = ""
+            character_tag_text = ""
+            general_tag_text = ""
 
             # それ以降はタグなのでconfidenceがthresholdより高いものを追加する
             # Everything else is tags: pick any where prediction confidence > threshold
-            combined_tags = []
-            general_tag_text = ""
-            character_tag_text = ""
             for i, p in enumerate(prob[4:]):
                 if i < len(general_tags) and p >= args.general_threshold:
                     tag_name = general_tags[i]
@@ -231,7 +228,24 @@ def run_batch(path_imgs):
                     if tag_name not in undesired_tags:
                         tag_freq[tag_name] = tag_freq.get(tag_name, 0) + 1
                         character_tag_text += caption_separator + tag_name
-                        combined_tags.append(tag_name)
+                        if args.character_tags_first: # insert to the beginning
+                            combined_tags.insert(0,tag_name)
+                        else:
+                            combined_tags.append(tag_name)
+
+            #最初の4つはratingなので無視する
+            # First 4 labels are actually ratings: pick one with argmax
+            if args.use_rating_tags:
+                ratings_names = prob[:4]
+                rating_index = ratings_names.argmax()
+                found_rating = rating_tags[rating_index]
+                if args.remove_underscore and len(found_rating) > 3:
+                    found_rating = found_rating.replace("_", " ")
+
+                if found_rating not in undesired_tags:
+                    tag_freq[found_rating] = tag_freq.get(found_rating, 0) + 1
+                    rating_tag_text = found_rating
+                    combined_tags.insert(0,found_rating) # insert to the beginning
 
             # 先頭のカンマを取る
             if len(general_tag_text) > 0:
@@ -264,6 +278,7 @@ def run_batch(path_imgs):
                 if args.debug:
                     logger.info("")
                     logger.info(f"{image_path}:")
+                    logger.info(f"\tRating tags: {rating_tag_text}")
                     logger.info(f"\tCharacter tags: {character_tag_text}")
                     logger.info(f"\tGeneral tags: {general_tag_text}")
 
@@ -321,7 +336,9 @@ def run_batch(path_imgs):
 
 def setup_parser() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser()
-    parser.add_argument("train_data_dir", type=str, help="directory for train images / 学習画像データのディレクトリ")
+    parser.add_argument(
+        "train_data_dir", type=str, help="directory for train images / 学習画像データのディレクトリ"
+    )
     parser.add_argument(
         "--repo_id",
         type=str,
@@ -339,7 +356,9 @@ def setup_parser() -> argparse.ArgumentParser:
         action="store_true",
         help="force downloading wd14 tagger models / wd14 taggerのモデルを再ダウンロードします",
     )
-    parser.add_argument("--batch_size", type=int, default=1, help="batch size in inference / 推論時のバッチサイズ")
+    parser.add_argument(
+        "--batch_size", type=int, default=1, help="batch size in inference / 推論時のバッチサイズ"
+    )
     parser.add_argument(
         "--max_data_loader_n_workers",
         type=int,
@@ -378,7 +397,9 @@ def setup_parser() -> argparse.ArgumentParser:
         action="store_true",
         help="replace underscores with spaces in the output tags / 出力されるタグのアンダースコアをスペースに置き換える",
     )
-    parser.add_argument("--debug", action="store_true", help="debug mode")
+    parser.add_argument(
+        "--debug", action="store_true", help="debug mode"
+    )
     parser.add_argument(
         "--undesired_tags",
         type=str,
@@ -388,10 +409,18 @@ def setup_parser() -> argparse.ArgumentParser:
     parser.add_argument(
         "--frequency_tags", action="store_true", help="Show frequency of tags for images / 画像ごとのタグの出現頻度を表示する"
     )
-    parser.add_argument("--onnx", action="store_true", help="use onnx model for inference / onnxモデルを推論に使用する")
+    parser.add_argument(
+        "--onnx", action="store_true", help="use onnx model for inference / onnxモデルを推論に使用する"
+    )
     parser.add_argument(
         "--append_tags", action="store_true", help="Append captions instead of overwriting / 上書きではなくキャプションを追記する"
     )
+    parser.add_argument(
+        "--use_rating_tags", action="store_true", help="Adds rating tags as the first tag",
+    )
+    parser.add_argument(
+        "--character_tags_first", action="store_true", help="Always inserts character tags before the general tags",
+    )
     parser.add_argument(
         "--caption_separator",
         type=str,

library/ipex/__init__.py

@@ -32,6 +32,7 @@ def ipex_init(): # pylint: disable=too-many-statements
             torch.cuda.FloatTensor = torch.xpu.FloatTensor
             torch.Tensor.cuda = torch.Tensor.xpu
             torch.Tensor.is_cuda = torch.Tensor.is_xpu
+            torch.nn.Module.cuda = torch.nn.Module.xpu
             torch.UntypedStorage.cuda = torch.UntypedStorage.xpu
             torch.cuda._initialization_lock = torch.xpu.lazy_init._initialization_lock
             torch.cuda._initialized = torch.xpu.lazy_init._initialized
@@ -147,9 +148,9 @@ def ipex_init(): # pylint: disable=too-many-statements
 
             # C
             torch._C._cuda_getCurrentRawStream = ipex._C._getCurrentStream
-            ipex._C._DeviceProperties.multi_processor_count = ipex._C._DeviceProperties.gpu_eu_count
-            ipex._C._DeviceProperties.major = 2023
-            ipex._C._DeviceProperties.minor = 2
+            ipex._C._DeviceProperties.multi_processor_count = ipex._C._DeviceProperties.gpu_subslice_count
+            ipex._C._DeviceProperties.major = 2024
+            ipex._C._DeviceProperties.minor = 0
 
             # Fix functions with ipex:
             torch.cuda.mem_get_info = lambda device=None: [(torch.xpu.get_device_properties(device).total_memory - torch.xpu.memory_reserved(device)), torch.xpu.get_device_properties(device).total_memory]

library/ipex/hijacks.py

@@ -190,6 +190,16 @@ def Tensor_cuda(self, device=None, *args, **kwargs):
     else:
         return original_Tensor_cuda(self, device, *args, **kwargs)
 
+original_Tensor_pin_memory = torch.Tensor.pin_memory
+@wraps(torch.Tensor.pin_memory)
+def Tensor_pin_memory(self, device=None, *args, **kwargs):
+    if device is None:
+        device = "xpu"
+    if check_device(device):
+        return original_Tensor_pin_memory(self, return_xpu(device), *args, **kwargs)
+    else:
+        return original_Tensor_pin_memory(self, device, *args, **kwargs)
+
 original_UntypedStorage_init = torch.UntypedStorage.__init__
 @wraps(torch.UntypedStorage.__init__)
 def UntypedStorage_init(*args, device=None, **kwargs):
@@ -259,17 +269,20 @@ def torch_Generator(device=None):
 original_torch_load = torch.load
 @wraps(torch.load)
 def torch_load(f, map_location=None, *args, **kwargs):
+    if map_location is None:
+        map_location = "xpu"
     if check_device(map_location):
-        return original_torch_load(f, map_location=return_xpu(map_location), *args, **kwargs)
+        return original_torch_load(f, *args, map_location=return_xpu(map_location), **kwargs)
     else:
-        return original_torch_load(f, map_location=map_location, *args, **kwargs)
+        return original_torch_load(f, *args, map_location=map_location, **kwargs)
 
 
 # Hijack Functions:
 def ipex_hijacks():
     torch.tensor = torch_tensor
     torch.Tensor.to = Tensor_to
     torch.Tensor.cuda = Tensor_cuda
+    torch.Tensor.pin_memory = Tensor_pin_memory
     torch.UntypedStorage.__init__ = UntypedStorage_init
     torch.UntypedStorage.cuda = UntypedStorage_cuda
     torch.empty = torch_empty