Merge branch 'dev' into gradual_latent_hires_fix

2026-04-08 22:35:09 +00:00 · 2024-01-04 19:53:46 +09:00
parent d61ecb26fd 716bad188b
commit da9b34fa26
19 changed files with 742 additions and 401 deletions
--- a/.github/workflows/typos.yml
+++ b/.github/workflows/typos.yml
@@ -18,4 +18,4 @@ jobs:
      - uses: actions/checkout@v4

      - name: typos-action
-        uses: crate-ci/typos@v1.16.15
+        uses: crate-ci/typos@v1.16.26
--- a/README.md
+++ b/README.md
@@ -281,6 +281,41 @@ ControlNet-LLLite, a novel method for ControlNet with SDXL, is added. See [docum

 ## Change History

+### Dec 24, 2023 / 2023/12/24
+
+- Fixed to work `tools/convert_diffusers20_original_sd.py`. Thanks to Disty0! PR [#1016](https://github.com/kohya-ss/sd-scripts/pull/1016)
+
+- `tools/convert_diffusers20_original_sd.py` が動かなくなっていたのが修正されました。Disty0 氏に感謝します。 PR [#1016](https://github.com/kohya-ss/sd-scripts/pull/1016) 
+
+
+### Dec 21, 2023 / 2023/12/21
+
+- The issues in multi-GPU training are fixed. Thanks to Isotr0py! PR [#989](https://github.com/kohya-ss/sd-scripts/pull/989) and [#1000](https://github.com/kohya-ss/sd-scripts/pull/1000)
+  - `--ddp_gradient_as_bucket_view` and `--ddp_bucket_view`options are added to `sdxl_train.py`. Please specify these options for multi-GPU training.
+- IPEX support is updated. Thanks to Disty0!
+- Fixed the bug that the size of the bucket becomes less than `min_bucket_reso`. Thanks to Cauldrath! PR [#1008](https://github.com/kohya-ss/sd-scripts/pull/1008)
+- `--sample_at_first` option is added to each training script. This option is useful to generate images at the first step, before training. Thanks to shirayu! PR [#907](https://github.com/kohya-ss/sd-scripts/pull/907)
+- `--ss` option is added to the sampling prompt in training. You can specify the scheduler for the sampling like `--ss euler_a`. Thanks to shirayu! PR [#906](https://github.com/kohya-ss/sd-scripts/pull/906)
+- `keep_tokens_separator` is added to the dataset config. This option is useful to keep (prevent from shuffling) the tokens in the captions. See [#975](https://github.com/kohya-ss/sd-scripts/pull/975) for details. Thanks to Linaqruf!
+  - You can specify the separator with an option like `--keep_tokens_separator "|||"` or with `keep_tokens_separator: "|||"` in `.toml`. The tokens before `|||` are not shuffled.
+- Attention processor hook is added. See [#961](https://github.com/kohya-ss/sd-scripts/pull/961) for details. Thanks to rockerBOO!
+- The optimizer `PagedAdamW` is added. Thanks to xzuyn! PR [#955](https://github.com/kohya-ss/sd-scripts/pull/955)
+- NaN replacement in SDXL VAE is sped up. Thanks to liubo0902! PR [#1009](https://github.com/kohya-ss/sd-scripts/pull/1009)
+- Fixed the path error in `finetune/make_captions.py`. Thanks to CjangCjengh! PR [#986](https://github.com/kohya-ss/sd-scripts/pull/986)
+
+- マルチGPUでの学習の不具合を修正しました。Isotr0py 氏に感謝します。 PR [#989](https://github.com/kohya-ss/sd-scripts/pull/989) および [#1000](https://github.com/kohya-ss/sd-scripts/pull/1000)
+  - `sdxl_train.py` に `--ddp_gradient_as_bucket_view` と `--ddp_bucket_view` オプションが追加されました。マルチGPUでの学習時にはこれらのオプションを指定してください。
+- IPEX サポートが更新されました。Disty0 氏に感謝します。
+- Aspect Ratio Bucketing で bucket のサイズが `min_bucket_reso` 未満になる不具合を修正しました。Cauldrath 氏に感謝します。 PR [#1008](https://github.com/kohya-ss/sd-scripts/pull/1008)
+- 各学習スクリプトに `--sample_at_first` オプションが追加されました。学習前に画像を生成することで、学習結果が比較しやすくなります。shirayu 氏に感謝します。 PR [#907](https://github.com/kohya-ss/sd-scripts/pull/907)
+- 学習時のプロンプトに `--ss` オプションが追加されました。`--ss euler_a` のようにスケジューラを指定できます。shirayu 氏に感謝します。 PR [#906](https://github.com/kohya-ss/sd-scripts/pull/906)
+- データセット設定に `keep_tokens_separator` が追加されました。キャプション内のトークンをどの位置までシャッフルしないかを指定できます。詳細は [#975](https://github.com/kohya-ss/sd-scripts/pull/975) を参照してください。Linaqruf 氏に感謝します。
+  - オプションで `--keep_tokens_separator "|||"` のように指定するか、`.toml` で `keep_tokens_separator: "|||"` のように指定します。`|||` の前のトークンはシャッフルされません。
+- Attention processor hook が追加されました。詳細は [#961](https://github.com/kohya-ss/sd-scripts/pull/961) を参照してください。rockerBOO 氏に感謝します。
+- オプティマイザ `PagedAdamW` が追加されました。xzuyn 氏に感謝します。 PR [#955](https://github.com/kohya-ss/sd-scripts/pull/955)
+- 学習時、SDXL VAE で NaN が発生した時の置き換えが高速化されました。liubo0902 氏に感謝します。 PR [#1009](https://github.com/kohya-ss/sd-scripts/pull/1009)
+- `finetune/make_captions.py` で相対パス指定時のエラーが修正されました。CjangCjengh 氏に感謝します。 PR [#986](https://github.com/kohya-ss/sd-scripts/pull/986)
+
 ### Dec 3, 2023 / 2023/12/3

 - `finetune\tag_images_by_wd14_tagger.py` now supports the separator other than `,` with `--caption_separator` option. Thanks to KohakuBlueleaf! PR [#913](https://github.com/kohya-ss/sd-scripts/pull/913)
--- a/finetune/make_captions.py
+++ b/finetune/make_captions.py
@@ -13,7 +13,7 @@ import torch
 from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 sys.path.append(os.path.dirname(__file__))
-from blip.blip import blip_decoder
+from blip.blip import blip_decoder, is_url
 import library.train_util as train_util

 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -76,6 +76,8 @@ def main(args):
        cwd = os.getcwd()
        print("Current Working Directory is: ", cwd)
        os.chdir("finetune")
+        if not is_url(args.caption_weights) and not os.path.isfile(args.caption_weights):
+            args.caption_weights = os.path.join("..", args.caption_weights)

    print(f"load images from {args.train_data_dir}")
    train_data_dir_path = Path(args.train_data_dir)
--- a/library/config_util.py
+++ b/library/config_util.py
@@ -53,6 +53,7 @@ class BaseSubsetParams:
  shuffle_caption: bool = False
  caption_separator: str = ',',
  keep_tokens: int = 0
+  keep_tokens_separator: str = None,
  color_aug: bool = False
  flip_aug: bool = False
  face_crop_aug_range: Optional[Tuple[float, float]] = None
@@ -160,6 +161,7 @@ class ConfigSanitizer:
    "random_crop": bool,
    "shuffle_caption": bool,
    "keep_tokens": int,
+    "keep_tokens_separator": str,
    "token_warmup_min": int,
    "token_warmup_step": Any(float,int),
    "caption_prefix": str,
@@ -461,6 +463,7 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
          num_repeats: {subset.num_repeats}
          shuffle_caption: {subset.shuffle_caption}
          keep_tokens: {subset.keep_tokens}
+          keep_tokens_separator: {subset.keep_tokens_separator}
          caption_dropout_rate: {subset.caption_dropout_rate}
          caption_dropout_every_n_epoches: {subset.caption_dropout_every_n_epochs}
          caption_tag_dropout_rate: {subset.caption_tag_dropout_rate}
--- a/library/ipex/init.py
+++ b/library/ipex/init.py
@@ -140,6 +140,7 @@ 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

@@ -156,20 +157,9 @@ def ipex_init(): # pylint: disable=too-many-statements
        torch.cuda.get_device_properties.minor = 7
        torch.cuda.ipc_collect = lambda *args, **kwargs: None
        torch.cuda.utilization = lambda *args, **kwargs: 0
-        if hasattr(torch.xpu, 'getDeviceIdListForCard'):
-            torch.cuda.getDeviceIdListForCard = torch.xpu.getDeviceIdListForCard
-            torch.cuda.get_device_id_list_per_card = torch.xpu.getDeviceIdListForCard
-        else:
-            torch.cuda.getDeviceIdListForCard = torch.xpu.get_device_id_list_per_card
-            torch.cuda.get_device_id_list_per_card = torch.xpu.get_device_id_list_per_card

        ipex_hijacks()
        if not torch.xpu.has_fp64_dtype():
-            try:
-                from .attention import attention_init
-                attention_init()
-            except Exception: # pylint: disable=broad-exception-caught
-                pass
            try:
                from .diffusers import ipex_diffusers
                ipex_diffusers()
--- a/library/ipex/attention.py
+++ b/library/ipex/attention.py
@@ -1,45 +1,98 @@
+import os
 import torch
 import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
+from functools import cache

 # pylint: disable=protected-access, missing-function-docstring, line-too-long

-original_torch_bmm = torch.bmm
-def torch_bmm(input, mat2, *, out=None):
-    if input.dtype != mat2.dtype:
-        mat2 = mat2.to(input.dtype)
+# ARC GPUs can't allocate more than 4GB to a single block so we slice the attetion layers

-    #ARC GPUs can't allocate more than 4GB to a single block, Slice it:
-    batch_size_attention, input_tokens, mat2_shape = input.shape[0], input.shape[1], mat2.shape[2]
-    block_multiply = input.element_size()
-    slice_block_size = input_tokens * mat2_shape / 1024 / 1024 * block_multiply
+sdpa_slice_trigger_rate = float(os.environ.get('IPEX_SDPA_SLICE_TRIGGER_RATE', 4))
+attention_slice_rate = float(os.environ.get('IPEX_ATTENTION_SLICE_RATE', 4))
+
+# Find something divisible with the input_tokens
+@cache
+def find_slice_size(slice_size, slice_block_size):
+    while (slice_size * slice_block_size) > attention_slice_rate:
+        slice_size = slice_size // 2
+        if slice_size <= 1:
+            slice_size = 1
+            break
+    return slice_size
+
+# Find slice sizes for SDPA
+@cache
+def find_sdpa_slice_sizes(query_shape, query_element_size):
+    if len(query_shape) == 3:
+        batch_size_attention, query_tokens, shape_three = query_shape
+        shape_four = 1
+    else:
+        batch_size_attention, query_tokens, shape_three, shape_four = query_shape
+
+    slice_block_size = query_tokens * shape_three * shape_four / 1024 / 1024 * query_element_size
    block_size = batch_size_attention * slice_block_size

    split_slice_size = batch_size_attention
-    if block_size > 4:
+    split_2_slice_size = query_tokens
+    split_3_slice_size = shape_three
+
+    do_split = False
+    do_split_2 = False
+    do_split_3 = False
+
+    if block_size > sdpa_slice_trigger_rate:
        do_split = True
-        #Find something divisible with the input_tokens
-        while (split_slice_size * slice_block_size) > 4:
-            split_slice_size = split_slice_size // 2
-            if split_slice_size <= 1:
-                split_slice_size = 1
-                break
-    else:
-        do_split = False
+        split_slice_size = find_slice_size(split_slice_size, slice_block_size)
+        if split_slice_size * slice_block_size > attention_slice_rate:
+            slice_2_block_size = split_slice_size * shape_three * shape_four / 1024 / 1024 * query_element_size
+            do_split_2 = True
+            split_2_slice_size = find_slice_size(split_2_slice_size, slice_2_block_size)
+            if split_2_slice_size * slice_2_block_size > attention_slice_rate:
+                slice_3_block_size = split_slice_size * split_2_slice_size * shape_four / 1024 / 1024 * query_element_size
+                do_split_3 = True
+                split_3_slice_size = find_slice_size(split_3_slice_size, slice_3_block_size)

+    return do_split, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size
+
+# Find slice sizes for BMM
+@cache
+def find_bmm_slice_sizes(input_shape, input_element_size, mat2_shape):
+    batch_size_attention, input_tokens, mat2_atten_shape = input_shape[0], input_shape[1], mat2_shape[2]
+    slice_block_size = input_tokens * mat2_atten_shape / 1024 / 1024 * input_element_size
+    block_size = batch_size_attention * slice_block_size
+
+    split_slice_size = batch_size_attention
    split_2_slice_size = input_tokens
-    if split_slice_size * slice_block_size > 4:
-        slice_block_size2 = split_slice_size * mat2_shape / 1024 / 1024 * block_multiply
-        do_split_2 = True
-        #Find something divisible with the input_tokens
-        while (split_2_slice_size * slice_block_size2) > 4:
-            split_2_slice_size = split_2_slice_size // 2
-            if split_2_slice_size <= 1:
-                split_2_slice_size = 1
-                break
-    else:
-        do_split_2 = False
+    split_3_slice_size = mat2_atten_shape

+    do_split = False
+    do_split_2 = False
+    do_split_3 = False
+
+    if block_size > attention_slice_rate:
+        do_split = True
+        split_slice_size = find_slice_size(split_slice_size, slice_block_size)
+        if split_slice_size * slice_block_size > attention_slice_rate:
+            slice_2_block_size = split_slice_size * mat2_atten_shape / 1024 / 1024 * input_element_size
+            do_split_2 = True
+            split_2_slice_size = find_slice_size(split_2_slice_size, slice_2_block_size)
+            if split_2_slice_size * slice_2_block_size > attention_slice_rate:
+                slice_3_block_size = split_slice_size * split_2_slice_size / 1024 / 1024 * input_element_size
+                do_split_3 = True
+                split_3_slice_size = find_slice_size(split_3_slice_size, slice_3_block_size)
+
+    return do_split, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size
+
+
+original_torch_bmm = torch.bmm
+def torch_bmm_32_bit(input, mat2, *, out=None):
+    if input.device.type != "xpu":
+        return original_torch_bmm(input, mat2, out=out)
+    do_split, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size = find_bmm_slice_sizes(input.shape, input.element_size(), mat2.shape)
+
+    # Slice BMM
    if do_split:
+        batch_size_attention, input_tokens, mat2_atten_shape = input.shape[0], input.shape[1], mat2.shape[2]
        hidden_states = torch.zeros(input.shape[0], input.shape[1], mat2.shape[2], device=input.device, dtype=input.dtype)
        for i in range(batch_size_attention // split_slice_size):
            start_idx = i * split_slice_size
@@ -48,11 +101,21 @@ def torch_bmm(input, mat2, *, out=None):
                for i2 in range(input_tokens // split_2_slice_size): # pylint: disable=invalid-name
                    start_idx_2 = i2 * split_2_slice_size
                    end_idx_2 = (i2 + 1) * split_2_slice_size
-                    hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = original_torch_bmm(
-                        input[start_idx:end_idx, start_idx_2:end_idx_2],
-                        mat2[start_idx:end_idx, start_idx_2:end_idx_2],
-                        out=out
-                    )
+                    if do_split_3:
+                        for i3 in range(mat2_atten_shape // split_3_slice_size): # pylint: disable=invalid-name
+                            start_idx_3 = i3 * split_3_slice_size
+                            end_idx_3 = (i3 + 1) * split_3_slice_size
+                            hidden_states[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] = original_torch_bmm(
+                                input[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3],
+                                mat2[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3],
+                                out=out
+                            )
+                    else:
+                        hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = original_torch_bmm(
+                            input[start_idx:end_idx, start_idx_2:end_idx_2],
+                            mat2[start_idx:end_idx, start_idx_2:end_idx_2],
+                            out=out
+                        )
            else:
                hidden_states[start_idx:end_idx] = original_torch_bmm(
                    input[start_idx:end_idx],
@@ -64,51 +127,14 @@ def torch_bmm(input, mat2, *, out=None):
    return hidden_states

 original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
-def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False):
-    #ARC GPUs can't allocate more than 4GB to a single block, Slice it:
-    if len(query.shape) == 3:
-        batch_size_attention, query_tokens, shape_four = query.shape
-        shape_one = 1
-        no_shape_one = True
-    else:
-        shape_one, batch_size_attention, query_tokens, shape_four = query.shape
-        no_shape_one = False
-
-    if query.dtype != key.dtype:
-        key = key.to(dtype=query.dtype)
-    if query.dtype != value.dtype:
-        value = value.to(dtype=query.dtype)
-
-    block_multiply = query.element_size()
-    slice_block_size = shape_one * query_tokens * shape_four / 1024 / 1024 * block_multiply
-    block_size = batch_size_attention * slice_block_size
-
-    split_slice_size = batch_size_attention
-    if block_size > 4:
-        do_split = True
-        #Find something divisible with the shape_one
-        while (split_slice_size * slice_block_size) > 4:
-            split_slice_size = split_slice_size // 2
-            if split_slice_size <= 1:
-                split_slice_size = 1
-                break
-    else:
-        do_split = False
-
-    split_2_slice_size = query_tokens
-    if split_slice_size * slice_block_size > 4:
-        slice_block_size2 = shape_one * split_slice_size * shape_four / 1024 / 1024 * block_multiply
-        do_split_2 = True
-        #Find something divisible with the batch_size_attention
-        while (split_2_slice_size * slice_block_size2) > 4:
-            split_2_slice_size = split_2_slice_size // 2
-            if split_2_slice_size <= 1:
-                split_2_slice_size = 1
-                break
-    else:
-        do_split_2 = False
+def scaled_dot_product_attention_32_bit(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False):
+    if query.device.type != "xpu":
+        return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal)
+    do_split, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size = find_sdpa_slice_sizes(query.shape, query.element_size())

+    # Slice SDPA
    if do_split:
+        batch_size_attention, query_tokens, shape_three = query.shape[0], query.shape[1], query.shape[2]
        hidden_states = torch.zeros(query.shape, device=query.device, dtype=query.dtype)
        for i in range(batch_size_attention // split_slice_size):
            start_idx = i * split_slice_size
@@ -117,7 +143,18 @@ def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.
                for i2 in range(query_tokens // split_2_slice_size): # pylint: disable=invalid-name
                    start_idx_2 = i2 * split_2_slice_size
                    end_idx_2 = (i2 + 1) * split_2_slice_size
-                    if no_shape_one:
+                    if do_split_3:
+                        for i3 in range(shape_three // split_3_slice_size): # pylint: disable=invalid-name
+                            start_idx_3 = i3 * split_3_slice_size
+                            end_idx_3 = (i3 + 1) * split_3_slice_size
+                            hidden_states[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] = original_scaled_dot_product_attention(
+                                query[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3],
+                                key[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3],
+                                value[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3],
+                                attn_mask=attn_mask[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] if attn_mask is not None else attn_mask,
+                                dropout_p=dropout_p, is_causal=is_causal
+                            )
+                    else:
                        hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = original_scaled_dot_product_attention(
                            query[start_idx:end_idx, start_idx_2:end_idx_2],
                            key[start_idx:end_idx, start_idx_2:end_idx_2],
@@ -125,38 +162,14 @@ def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.
                            attn_mask=attn_mask[start_idx:end_idx, start_idx_2:end_idx_2] if attn_mask is not None else attn_mask,
                            dropout_p=dropout_p, is_causal=is_causal
                        )
-                    else:
-                        hidden_states[:, start_idx:end_idx, start_idx_2:end_idx_2] = original_scaled_dot_product_attention(
-                            query[:, start_idx:end_idx, start_idx_2:end_idx_2],
-                            key[:, start_idx:end_idx, start_idx_2:end_idx_2],
-                            value[:, start_idx:end_idx, start_idx_2:end_idx_2],
-                            attn_mask=attn_mask[:, start_idx:end_idx, start_idx_2:end_idx_2] if attn_mask is not None else attn_mask,
-                            dropout_p=dropout_p, is_causal=is_causal
-                        )
            else:
-                if no_shape_one:
-                    hidden_states[start_idx:end_idx] = original_scaled_dot_product_attention(
-                        query[start_idx:end_idx],
-                        key[start_idx:end_idx],
-                        value[start_idx:end_idx],
-                        attn_mask=attn_mask[start_idx:end_idx] if attn_mask is not None else attn_mask,
-                        dropout_p=dropout_p, is_causal=is_causal
-                    )
-                else:
-                    hidden_states[:, start_idx:end_idx] = original_scaled_dot_product_attention(
-                        query[:, start_idx:end_idx],
-                        key[:, start_idx:end_idx],
-                        value[:, start_idx:end_idx],
-                        attn_mask=attn_mask[:, start_idx:end_idx] if attn_mask is not None else attn_mask,
-                        dropout_p=dropout_p, is_causal=is_causal
-                    )
+                hidden_states[start_idx:end_idx] = original_scaled_dot_product_attention(
+                    query[start_idx:end_idx],
+                    key[start_idx:end_idx],
+                    value[start_idx:end_idx],
+                    attn_mask=attn_mask[start_idx:end_idx] if attn_mask is not None else attn_mask,
+                    dropout_p=dropout_p, is_causal=is_causal
+                )
    else:
-        return original_scaled_dot_product_attention(
-            query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal
-        )
+        return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal)
    return hidden_states
-
-def attention_init():
-    #ARC GPUs can't allocate more than 4GB to a single block:
-    torch.bmm = torch_bmm
-    torch.nn.functional.scaled_dot_product_attention = scaled_dot_product_attention
--- a/library/ipex/diffusers.py
+++ b/library/ipex/diffusers.py
@@ -1,10 +1,62 @@
+import os
 import torch
 import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
 import diffusers #0.24.0 # pylint: disable=import-error
 from diffusers.models.attention_processor import Attention
+from diffusers.utils import USE_PEFT_BACKEND
+from functools import cache

 # pylint: disable=protected-access, missing-function-docstring, line-too-long

+attention_slice_rate = float(os.environ.get('IPEX_ATTENTION_SLICE_RATE', 4))
+
+@cache
+def find_slice_size(slice_size, slice_block_size):
+    while (slice_size * slice_block_size) > attention_slice_rate:
+        slice_size = slice_size // 2
+        if slice_size <= 1:
+            slice_size = 1
+            break
+    return slice_size
+
+@cache
+def find_attention_slice_sizes(query_shape, query_element_size, query_device_type, slice_size=None):
+    if len(query_shape) == 3:
+        batch_size_attention, query_tokens, shape_three = query_shape
+        shape_four = 1
+    else:
+        batch_size_attention, query_tokens, shape_three, shape_four = query_shape
+    if slice_size is not None:
+        batch_size_attention = slice_size
+
+    slice_block_size = query_tokens * shape_three * shape_four / 1024 / 1024 * query_element_size
+    block_size = batch_size_attention * slice_block_size
+
+    split_slice_size = batch_size_attention
+    split_2_slice_size = query_tokens
+    split_3_slice_size = shape_three
+
+    do_split = False
+    do_split_2 = False
+    do_split_3 = False
+
+    if query_device_type != "xpu":
+        return do_split, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size
+
+    if block_size > attention_slice_rate:
+        do_split = True
+        split_slice_size = find_slice_size(split_slice_size, slice_block_size)
+        if split_slice_size * slice_block_size > attention_slice_rate:
+            slice_2_block_size = split_slice_size * shape_three * shape_four / 1024 / 1024 * query_element_size
+            do_split_2 = True
+            split_2_slice_size = find_slice_size(split_2_slice_size, slice_2_block_size)
+            if split_2_slice_size * slice_2_block_size > attention_slice_rate:
+                slice_3_block_size = split_slice_size * split_2_slice_size * shape_four / 1024 / 1024 * query_element_size
+                do_split_3 = True
+                split_3_slice_size = find_slice_size(split_3_slice_size, slice_3_block_size)
+
+    return do_split, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size
+
 class SlicedAttnProcessor: # pylint: disable=too-few-public-methods
    r"""
    Processor for implementing sliced attention.
@@ -18,7 +70,9 @@ class SlicedAttnProcessor: # pylint: disable=too-few-public-methods
    def __init__(self, slice_size):
        self.slice_size = slice_size

-    def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None): # pylint: disable=too-many-statements, too-many-locals, too-many-branches
+    def __call__(self, attn: Attention, hidden_states: torch.FloatTensor,
+    encoder_hidden_states=None, attention_mask=None) -> torch.FloatTensor: # pylint: disable=too-many-statements, too-many-locals, too-many-branches
+
        residual = hidden_states

        input_ndim = hidden_states.ndim
@@ -54,49 +108,61 @@ class SlicedAttnProcessor: # pylint: disable=too-few-public-methods
            (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype
        )

-        #ARC GPUs can't allocate more than 4GB to a single block, Slice it:
-        block_multiply = query.element_size()
-        slice_block_size = self.slice_size * shape_three / 1024 / 1024 * block_multiply
-        block_size = query_tokens * slice_block_size
-        split_2_slice_size = query_tokens
-        if block_size > 4:
-            do_split_2 = True
-            #Find something divisible with the query_tokens
-            while (split_2_slice_size * slice_block_size) > 4:
-                split_2_slice_size = split_2_slice_size // 2
-                if split_2_slice_size <= 1:
-                    split_2_slice_size = 1
-                    break
-        else:
-            do_split_2 = False
-
-        for i in range(batch_size_attention // self.slice_size):
-            start_idx = i * self.slice_size
-            end_idx = (i + 1) * self.slice_size
+        ####################################################################
+        # ARC GPUs can't allocate more than 4GB to a single block, Slice it:
+        _, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size = find_attention_slice_sizes(query.shape, query.element_size(), query.device.type, slice_size=self.slice_size)

+        for i in range(batch_size_attention // split_slice_size):
+            start_idx = i * split_slice_size
+            end_idx = (i + 1) * split_slice_size
            if do_split_2:
                for i2 in range(query_tokens // split_2_slice_size): # pylint: disable=invalid-name
                    start_idx_2 = i2 * split_2_slice_size
                    end_idx_2 = (i2 + 1) * split_2_slice_size
+                    if do_split_3:
+                        for i3 in range(shape_three // split_3_slice_size): # pylint: disable=invalid-name
+                            start_idx_3 = i3 * split_3_slice_size
+                            end_idx_3 = (i3 + 1) * split_3_slice_size

-                    query_slice = query[start_idx:end_idx, start_idx_2:end_idx_2]
-                    key_slice = key[start_idx:end_idx, start_idx_2:end_idx_2]
-                    attn_mask_slice = attention_mask[start_idx:end_idx, start_idx_2:end_idx_2] if attention_mask is not None else None
+                            query_slice = query[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3]
+                            key_slice = key[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3]
+                            attn_mask_slice = attention_mask[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] if attention_mask is not None else None

-                    attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)
-                    attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx, start_idx_2:end_idx_2])
+                            attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)
+                            del query_slice
+                            del key_slice
+                            del attn_mask_slice
+                            attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3])

-                    hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = attn_slice
+                            hidden_states[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] = attn_slice
+                            del attn_slice
+                    else:
+                        query_slice = query[start_idx:end_idx, start_idx_2:end_idx_2]
+                        key_slice = key[start_idx:end_idx, start_idx_2:end_idx_2]
+                        attn_mask_slice = attention_mask[start_idx:end_idx, start_idx_2:end_idx_2] if attention_mask is not None else None
+
+                        attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)
+                        del query_slice
+                        del key_slice
+                        del attn_mask_slice
+                        attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx, start_idx_2:end_idx_2])
+
+                        hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = attn_slice
+                        del attn_slice
            else:
                query_slice = query[start_idx:end_idx]
                key_slice = key[start_idx:end_idx]
                attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None

                attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)
-
+                del query_slice
+                del key_slice
+                del attn_mask_slice
                attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])

                hidden_states[start_idx:end_idx] = attn_slice
+                del attn_slice
+        ####################################################################

        hidden_states = attn.batch_to_head_dim(hidden_states)

@@ -115,6 +181,130 @@ class SlicedAttnProcessor: # pylint: disable=too-few-public-methods

        return hidden_states

+
+class AttnProcessor:
+    r"""
+    Default processor for performing attention-related computations.
+    """
+
+    def __call__(self, attn: Attention, hidden_states: torch.FloatTensor,
+    encoder_hidden_states=None, attention_mask=None,
+    temb=None, scale: float = 1.0) -> torch.Tensor: # pylint: disable=too-many-statements, too-many-locals, too-many-branches
+
+        residual = hidden_states
+
+        args = () if USE_PEFT_BACKEND else (scale,)
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states, *args)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states, *args)
+        value = attn.to_v(encoder_hidden_states, *args)
+
+        query = attn.head_to_batch_dim(query)
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        ####################################################################
+        # ARC GPUs can't allocate more than 4GB to a single block, Slice it:
+        batch_size_attention, query_tokens, shape_three = query.shape[0], query.shape[1], query.shape[2]
+        hidden_states = torch.zeros(query.shape, device=query.device, dtype=query.dtype)
+        do_split, do_split_2, do_split_3, split_slice_size, split_2_slice_size, split_3_slice_size = find_attention_slice_sizes(query.shape, query.element_size(), query.device.type)
+
+        if do_split:
+            for i in range(batch_size_attention // split_slice_size):
+                start_idx = i * split_slice_size
+                end_idx = (i + 1) * split_slice_size
+                if do_split_2:
+                    for i2 in range(query_tokens // split_2_slice_size): # pylint: disable=invalid-name
+                        start_idx_2 = i2 * split_2_slice_size
+                        end_idx_2 = (i2 + 1) * split_2_slice_size
+                        if do_split_3:
+                            for i3 in range(shape_three // split_3_slice_size): # pylint: disable=invalid-name
+                                start_idx_3 = i3 * split_3_slice_size
+                                end_idx_3 = (i3 + 1) * split_3_slice_size
+
+                                query_slice = query[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3]
+                                key_slice = key[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3]
+                                attn_mask_slice = attention_mask[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] if attention_mask is not None else None
+
+                                attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)
+                                del query_slice
+                                del key_slice
+                                del attn_mask_slice
+                                attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3])
+
+                                hidden_states[start_idx:end_idx, start_idx_2:end_idx_2, start_idx_3:end_idx_3] = attn_slice
+                                del attn_slice
+                        else:
+                            query_slice = query[start_idx:end_idx, start_idx_2:end_idx_2]
+                            key_slice = key[start_idx:end_idx, start_idx_2:end_idx_2]
+                            attn_mask_slice = attention_mask[start_idx:end_idx, start_idx_2:end_idx_2] if attention_mask is not None else None
+
+                            attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)
+                            del query_slice
+                            del key_slice
+                            del attn_mask_slice
+                            attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx, start_idx_2:end_idx_2])
+
+                            hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = attn_slice
+                            del attn_slice
+                else:
+                    query_slice = query[start_idx:end_idx]
+                    key_slice = key[start_idx:end_idx]
+                    attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None
+
+                    attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)
+                    del query_slice
+                    del key_slice
+                    del attn_mask_slice
+                    attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])
+
+                    hidden_states[start_idx:end_idx] = attn_slice
+                    del attn_slice
+        else:
+            attention_probs = attn.get_attention_scores(query, key, attention_mask)
+            hidden_states = torch.bmm(attention_probs, value)
+        ####################################################################
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states, *args)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
 def ipex_diffusers():
    #ARC GPUs can't allocate more than 4GB to a single block:
    diffusers.models.attention_processor.SlicedAttnProcessor = SlicedAttnProcessor
+    diffusers.models.attention_processor.AttnProcessor = AttnProcessor
--- a/library/ipex/hijacks.py
+++ b/library/ipex/hijacks.py
@@ -1,67 +1,9 @@
 import contextlib
-import importlib
 import torch
 import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import

 # pylint: disable=protected-access, missing-function-docstring, line-too-long, unnecessary-lambda, no-else-return

-class CondFunc: # pylint: disable=missing-class-docstring
-    def __new__(cls, orig_func, sub_func, cond_func):
-        self = super(CondFunc, cls).__new__(cls)
-        if isinstance(orig_func, str):
-            func_path = orig_func.split('.')
-            for i in range(len(func_path)-1, -1, -1):
-                try:
-                    resolved_obj = importlib.import_module('.'.join(func_path[:i]))
-                    break
-                except ImportError:
-                    pass
-            for attr_name in func_path[i:-1]:
-                resolved_obj = getattr(resolved_obj, attr_name)
-            orig_func = getattr(resolved_obj, func_path[-1])
-            setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
-        self.__init__(orig_func, sub_func, cond_func)
-        return lambda *args, **kwargs: self(*args, **kwargs)
-    def __init__(self, orig_func, sub_func, cond_func):
-        self.__orig_func = orig_func
-        self.__sub_func = sub_func
-        self.__cond_func = cond_func
-    def __call__(self, *args, **kwargs):
-        if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs):
-            return self.__sub_func(self.__orig_func, *args, **kwargs)
-        else:
-            return self.__orig_func(*args, **kwargs)
-
-_utils = torch.utils.data._utils
-def _shutdown_workers(self):
-    if torch.utils.data._utils is None or torch.utils.data._utils.python_exit_status is True or torch.utils.data._utils.python_exit_status is None:
-        return
-    if hasattr(self, "_shutdown") and not self._shutdown:
-        self._shutdown = True
-        try:
-            if hasattr(self, '_pin_memory_thread'):
-                self._pin_memory_thread_done_event.set()
-                self._worker_result_queue.put((None, None))
-                self._pin_memory_thread.join()
-                self._worker_result_queue.cancel_join_thread()
-                self._worker_result_queue.close()
-            self._workers_done_event.set()
-            for worker_id in range(len(self._workers)):
-                if self._persistent_workers or self._workers_status[worker_id]:
-                    self._mark_worker_as_unavailable(worker_id, shutdown=True)
-            for w in self._workers: # pylint: disable=invalid-name
-                w.join(timeout=torch.utils.data._utils.MP_STATUS_CHECK_INTERVAL)
-            for q in self._index_queues: # pylint: disable=invalid-name
-                q.cancel_join_thread()
-                q.close()
-        finally:
-            if self._worker_pids_set:
-                torch.utils.data._utils.signal_handling._remove_worker_pids(id(self))
-                self._worker_pids_set = False
-            for w in self._workers: # pylint: disable=invalid-name
-                if w.is_alive():
-                    w.terminate()
-
 class DummyDataParallel(torch.nn.Module): # pylint: disable=missing-class-docstring, unused-argument, too-few-public-methods
    def __new__(cls, module, device_ids=None, output_device=None, dim=0): # pylint: disable=unused-argument
        if isinstance(device_ids, list) and len(device_ids) > 1:
@@ -71,17 +13,18 @@ class DummyDataParallel(torch.nn.Module): # pylint: disable=missing-class-docstr
 def return_null_context(*args, **kwargs): # pylint: disable=unused-argument
    return contextlib.nullcontext()

+@property
+def is_cuda(self):
+    return self.device.type == 'xpu' or self.device.type == 'cuda'
+
 def check_device(device):
    return bool((isinstance(device, torch.device) and device.type == "cuda") or (isinstance(device, str) and "cuda" in device) or isinstance(device, int))

 def return_xpu(device):
    return f"xpu:{device.split(':')[-1]}" if isinstance(device, str) and ":" in device else f"xpu:{device}" if isinstance(device, int) else torch.device("xpu") if isinstance(device, torch.device) else "xpu"

-def ipex_no_cuda(orig_func, *args, **kwargs):
-    torch.cuda.is_available = lambda: False
-    orig_func(*args, **kwargs)
-    torch.cuda.is_available = torch.xpu.is_available

+# Autocast
 original_autocast = torch.autocast
 def ipex_autocast(*args, **kwargs):
    if len(args) > 0 and args[0] == "cuda":
@@ -89,15 +32,7 @@ def ipex_autocast(*args, **kwargs):
    else:
        return original_autocast(*args, **kwargs)

-# Embedding BF16
-original_torch_cat = torch.cat
-def torch_cat(tensor, *args, **kwargs):
-    if len(tensor) == 3 and (tensor[0].dtype != tensor[1].dtype or tensor[2].dtype != tensor[1].dtype):
-        return original_torch_cat([tensor[0].to(tensor[1].dtype), tensor[1], tensor[2].to(tensor[1].dtype)], *args, **kwargs)
-    else:
-        return original_torch_cat(tensor, *args, **kwargs)
-
-# Latent antialias:
+# Latent Antialias CPU Offload:
 original_interpolate = torch.nn.functional.interpolate
 def interpolate(tensor, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False): # pylint: disable=too-many-arguments
    if antialias or align_corners is not None:
@@ -109,110 +44,205 @@ def interpolate(tensor, size=None, scale_factor=None, mode='nearest', align_corn
        return original_interpolate(tensor, size=size, scale_factor=scale_factor, mode=mode,
        align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias)

-original_linalg_solve = torch.linalg.solve
-def linalg_solve(A, B, *args, **kwargs): # pylint: disable=invalid-name
-    if A.device != torch.device("cpu") or B.device != torch.device("cpu"):
-        return_device = A.device
-        return original_linalg_solve(A.to("cpu"), B.to("cpu"), *args, **kwargs).to(return_device)
+# Diffusers Float64 (Alchemist GPUs doesn't support 64 bit):
+original_from_numpy = torch.from_numpy
+def from_numpy(ndarray):
+    if ndarray.dtype == float:
+        return original_from_numpy(ndarray.astype('float32'))
    else:
-        return original_linalg_solve(A, B, *args, **kwargs)
+        return original_from_numpy(ndarray)

-@property
-def is_cuda(self):
-    return self.device.type == 'xpu'
+if torch.xpu.has_fp64_dtype():
+    original_torch_bmm = torch.bmm
+    original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
+else:
+    # 32 bit attention workarounds for Alchemist:
+    try:
+        from .attention import torch_bmm_32_bit as original_torch_bmm
+        from .attention import scaled_dot_product_attention_32_bit as original_scaled_dot_product_attention
+    except Exception: # pylint: disable=broad-exception-caught
+        original_torch_bmm = torch.bmm
+        original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention

+
+# Data Type Errors:
+def torch_bmm(input, mat2, *, out=None):
+    if input.dtype != mat2.dtype:
+        mat2 = mat2.to(input.dtype)
+    return original_torch_bmm(input, mat2, out=out)
+
+def scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False):
+    if query.dtype != key.dtype:
+        key = key.to(dtype=query.dtype)
+    if query.dtype != value.dtype:
+        value = value.to(dtype=query.dtype)
+    return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal)
+
+# A1111 FP16
+original_functional_group_norm = torch.nn.functional.group_norm
+def functional_group_norm(input, num_groups, weight=None, bias=None, eps=1e-05):
+    if weight is not None and input.dtype != weight.data.dtype:
+        input = input.to(dtype=weight.data.dtype)
+    if bias is not None and weight is not None and bias.data.dtype != weight.data.dtype:
+        bias.data = bias.data.to(dtype=weight.data.dtype)
+    return original_functional_group_norm(input, num_groups, weight=weight, bias=bias, eps=eps)
+
+# A1111 BF16
+original_functional_layer_norm = torch.nn.functional.layer_norm
+def functional_layer_norm(input, normalized_shape, weight=None, bias=None, eps=1e-05):
+    if weight is not None and input.dtype != weight.data.dtype:
+        input = input.to(dtype=weight.data.dtype)
+    if bias is not None and weight is not None and bias.data.dtype != weight.data.dtype:
+        bias.data = bias.data.to(dtype=weight.data.dtype)
+    return original_functional_layer_norm(input, normalized_shape, weight=weight, bias=bias, eps=eps)
+
+# Training
+original_functional_linear = torch.nn.functional.linear
+def functional_linear(input, weight, bias=None):
+    if input.dtype != weight.data.dtype:
+        input = input.to(dtype=weight.data.dtype)
+    if bias is not None and bias.data.dtype != weight.data.dtype:
+        bias.data = bias.data.to(dtype=weight.data.dtype)
+    return original_functional_linear(input, weight, bias=bias)
+
+original_functional_conv2d = torch.nn.functional.conv2d
+def functional_conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
+    if input.dtype != weight.data.dtype:
+        input = input.to(dtype=weight.data.dtype)
+    if bias is not None and bias.data.dtype != weight.data.dtype:
+        bias.data = bias.data.to(dtype=weight.data.dtype)
+    return original_functional_conv2d(input, weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
+
+# A1111 Embedding BF16
+original_torch_cat = torch.cat
+def torch_cat(tensor, *args, **kwargs):
+    if len(tensor) == 3 and (tensor[0].dtype != tensor[1].dtype or tensor[2].dtype != tensor[1].dtype):
+        return original_torch_cat([tensor[0].to(tensor[1].dtype), tensor[1], tensor[2].to(tensor[1].dtype)], *args, **kwargs)
+    else:
+        return original_torch_cat(tensor, *args, **kwargs)
+
+# SwinIR BF16:
+original_functional_pad = torch.nn.functional.pad
+def functional_pad(input, pad, mode='constant', value=None):
+    if mode == 'reflect' and input.dtype == torch.bfloat16:
+        return original_functional_pad(input.to(torch.float32), pad, mode=mode, value=value).to(dtype=torch.bfloat16)
+    else:
+        return original_functional_pad(input, pad, mode=mode, value=value)
+
+
+original_torch_tensor = torch.tensor
+def torch_tensor(*args, device=None, **kwargs):
+    if check_device(device):
+        return original_torch_tensor(*args, device=return_xpu(device), **kwargs)
+    else:
+        return original_torch_tensor(*args, device=device, **kwargs)
+
+original_Tensor_to = torch.Tensor.to
+def Tensor_to(self, device=None, *args, **kwargs):
+    if check_device(device):
+        return original_Tensor_to(self, return_xpu(device), *args, **kwargs)
+    else:
+        return original_Tensor_to(self, device, *args, **kwargs)
+
+original_Tensor_cuda = torch.Tensor.cuda
+def Tensor_cuda(self, device=None, *args, **kwargs):
+    if check_device(device):
+        return original_Tensor_cuda(self, return_xpu(device), *args, **kwargs)
+    else:
+        return original_Tensor_cuda(self, device, *args, **kwargs)
+
+original_UntypedStorage_init = torch.UntypedStorage.__init__
+def UntypedStorage_init(*args, device=None, **kwargs):
+    if check_device(device):
+        return original_UntypedStorage_init(*args, device=return_xpu(device), **kwargs)
+    else:
+        return original_UntypedStorage_init(*args, device=device, **kwargs)
+
+original_UntypedStorage_cuda = torch.UntypedStorage.cuda
+def UntypedStorage_cuda(self, device=None, *args, **kwargs):
+    if check_device(device):
+        return original_UntypedStorage_cuda(self, return_xpu(device), *args, **kwargs)
+    else:
+        return original_UntypedStorage_cuda(self, device, *args, **kwargs)
+
+original_torch_empty = torch.empty
+def torch_empty(*args, device=None, **kwargs):
+    if check_device(device):
+        return original_torch_empty(*args, device=return_xpu(device), **kwargs)
+    else:
+        return original_torch_empty(*args, device=device, **kwargs)
+
+original_torch_randn = torch.randn
+def torch_randn(*args, device=None, **kwargs):
+    if check_device(device):
+        return original_torch_randn(*args, device=return_xpu(device), **kwargs)
+    else:
+        return original_torch_randn(*args, device=device, **kwargs)
+
+original_torch_ones = torch.ones
+def torch_ones(*args, device=None, **kwargs):
+    if check_device(device):
+        return original_torch_ones(*args, device=return_xpu(device), **kwargs)
+    else:
+        return original_torch_ones(*args, device=device, **kwargs)
+
+original_torch_zeros = torch.zeros
+def torch_zeros(*args, device=None, **kwargs):
+    if check_device(device):
+        return original_torch_zeros(*args, device=return_xpu(device), **kwargs)
+    else:
+        return original_torch_zeros(*args, device=device, **kwargs)
+
+original_torch_linspace = torch.linspace
+def torch_linspace(*args, device=None, **kwargs):
+    if check_device(device):
+        return original_torch_linspace(*args, device=return_xpu(device), **kwargs)
+    else:
+        return original_torch_linspace(*args, device=device, **kwargs)
+
+original_torch_Generator = torch.Generator
+def torch_Generator(device=None):
+    if check_device(device):
+        return original_torch_Generator(return_xpu(device))
+    else:
+        return original_torch_Generator(device)
+
+original_torch_load = torch.load
+def torch_load(f, map_location=None, pickle_module=None, *, weights_only=False, mmap=None, **kwargs):
+    if check_device(map_location):
+        return original_torch_load(f, map_location=return_xpu(map_location), pickle_module=pickle_module, weights_only=weights_only, mmap=mmap, **kwargs)
+    else:
+        return original_torch_load(f, map_location=map_location, pickle_module=pickle_module, weights_only=weights_only, mmap=mmap, **kwargs)
+
+# Hijack Functions:
 def ipex_hijacks():
-    CondFunc('torch.tensor',
-        lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
-        lambda orig_func, *args, device=None, **kwargs: check_device(device))
-    CondFunc('torch.Tensor.to',
-        lambda orig_func, self, device=None, *args, **kwargs: orig_func(self, return_xpu(device), *args, **kwargs),
-        lambda orig_func, self, device=None, *args, **kwargs: check_device(device))
-    CondFunc('torch.Tensor.cuda',
-        lambda orig_func, self, device=None, *args, **kwargs: orig_func(self, return_xpu(device), *args, **kwargs),
-        lambda orig_func, self, device=None, *args, **kwargs: check_device(device))
-    CondFunc('torch.UntypedStorage.__init__',
-        lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
-        lambda orig_func, *args, device=None, **kwargs: check_device(device))
-    CondFunc('torch.UntypedStorage.cuda',
-        lambda orig_func, self, device=None, *args, **kwargs: orig_func(self, return_xpu(device), *args, **kwargs),
-        lambda orig_func, self, device=None, *args, **kwargs: check_device(device))
-    CondFunc('torch.empty',
-        lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
-        lambda orig_func, *args, device=None, **kwargs: check_device(device))
-    CondFunc('torch.randn',
-        lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
-        lambda orig_func, *args, device=None, **kwargs: check_device(device))
-    CondFunc('torch.ones',
-        lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
-        lambda orig_func, *args, device=None, **kwargs: check_device(device))
-    CondFunc('torch.zeros',
-        lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
-        lambda orig_func, *args, device=None, **kwargs: check_device(device))
-    CondFunc('torch.linspace',
-        lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
-        lambda orig_func, *args, device=None, **kwargs: check_device(device))
-    CondFunc('torch.load',
-        lambda orig_func, f, map_location=None, pickle_module=None, *, weights_only=False, mmap=None, **kwargs:
-        orig_func(orig_func, f, map_location=return_xpu(map_location), pickle_module=pickle_module, weights_only=weights_only, mmap=mmap, **kwargs),
-        lambda orig_func, f, map_location=None, pickle_module=None, *, weights_only=False, mmap=None, **kwargs: check_device(map_location))
+    torch.tensor = torch_tensor
+    torch.Tensor.to = Tensor_to
+    torch.Tensor.cuda = Tensor_cuda
+    torch.UntypedStorage.__init__ = UntypedStorage_init
+    torch.UntypedStorage.cuda = UntypedStorage_cuda
+    torch.empty = torch_empty
+    torch.randn = torch_randn
+    torch.ones = torch_ones
+    torch.zeros = torch_zeros
+    torch.linspace = torch_linspace
+    torch.Generator = torch_Generator
+    torch.load = torch_load

-    CondFunc('torch.Generator',
-        lambda orig_func, device=None: torch.xpu.Generator(return_xpu(device)),
-        lambda orig_func, device=None: device is not None and device != torch.device("cpu") and device != "cpu")
-
-    # TiledVAE and ControlNet:
-    CondFunc('torch.batch_norm',
-        lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input,
-        weight if weight is not None else torch.ones(input.size()[1], device=input.device),
-        bias if bias is not None else torch.zeros(input.size()[1], device=input.device), *args, **kwargs),
-        lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu"))
-    CondFunc('torch.instance_norm',
-        lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input,
-        weight if weight is not None else torch.ones(input.size()[1], device=input.device),
-        bias if bias is not None else torch.zeros(input.size()[1], device=input.device), *args, **kwargs),
-        lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu"))
-
-    # Functions with dtype errors:
-    CondFunc('torch.nn.modules.GroupNorm.forward',
-        lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
-        lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
-    # Training:
-    CondFunc('torch.nn.modules.linear.Linear.forward',
-        lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
-        lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
-    CondFunc('torch.nn.modules.conv.Conv2d.forward',
-        lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
-        lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
-    # BF16:
-    CondFunc('torch.nn.functional.layer_norm',
-        lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
-        orig_func(input.to(weight.data.dtype), normalized_shape, weight, *args, **kwargs),
-        lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
-        weight is not None and input.dtype != weight.data.dtype)
-    # SwinIR BF16:
-    CondFunc('torch.nn.functional.pad',
-        lambda orig_func, input, pad, mode='constant', value=None: orig_func(input.to(torch.float32), pad, mode=mode, value=value).to(dtype=torch.bfloat16),
-        lambda orig_func, input, pad, mode='constant', value=None: mode == 'reflect' and input.dtype == torch.bfloat16)
-
-    # Diffusers Float64 (Alchemist GPUs doesn't support 64 bit):
-    if not torch.xpu.has_fp64_dtype():
-        CondFunc('torch.from_numpy',
-        lambda orig_func, ndarray: orig_func(ndarray.astype('float32')),
-        lambda orig_func, ndarray: ndarray.dtype == float)
-
-    # Broken functions when torch.cuda.is_available is True:
-    # Pin Memory:
-    CondFunc('torch.utils.data.dataloader._BaseDataLoaderIter.__init__',
-        lambda orig_func, *args, **kwargs: ipex_no_cuda(orig_func, *args, **kwargs),
-        lambda orig_func, *args, **kwargs: True)
-
-    # Functions that make compile mad with CondFunc:
-    torch.utils.data.dataloader._MultiProcessingDataLoaderIter._shutdown_workers = _shutdown_workers
-    torch.nn.DataParallel = DummyDataParallel
-    torch.autocast = ipex_autocast
-    torch.cat = torch_cat
-    torch.linalg.solve = linalg_solve
-    torch.UntypedStorage.is_cuda = is_cuda
-    torch.nn.functional.interpolate = interpolate
    torch.backends.cuda.sdp_kernel = return_null_context
+    torch.nn.DataParallel = DummyDataParallel
+    torch.UntypedStorage.is_cuda = is_cuda
+    torch.autocast = ipex_autocast
+
+    torch.nn.functional.scaled_dot_product_attention = scaled_dot_product_attention
+    torch.nn.functional.group_norm = functional_group_norm
+    torch.nn.functional.layer_norm = functional_layer_norm
+    torch.nn.functional.linear = functional_linear
+    torch.nn.functional.conv2d = functional_conv2d
+    torch.nn.functional.interpolate = interpolate
+    torch.nn.functional.pad = functional_pad
+
+    torch.bmm = torch_bmm
+    torch.cat = torch_cat
+    if not torch.xpu.has_fp64_dtype():
+        torch.from_numpy = from_numpy
--- a/library/lpw_stable_diffusion.py
+++ b/library/lpw_stable_diffusion.py
@@ -9,7 +9,7 @@ import numpy as np
 import PIL.Image
 import torch
 from packaging import version
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection

 import diffusers
 from diffusers import SchedulerMixin, StableDiffusionPipeline
@@ -520,6 +520,7 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
        safety_checker: StableDiffusionSafetyChecker,
        feature_extractor: CLIPFeatureExtractor,
        requires_safety_checker: bool = True,
+        image_encoder: CLIPVisionModelWithProjection = None,
        clip_skip: int = 1,
    ):
        super().__init__(
@@ -531,32 +532,11 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
            requires_safety_checker=requires_safety_checker,
+            image_encoder=image_encoder,
        )
-        self.clip_skip = clip_skip
+        self.custom_clip_skip = clip_skip
        self.__init__additional__()

-    # else:
-    #     def __init__(
-    #         self,
-    #         vae: AutoencoderKL,
-    #         text_encoder: CLIPTextModel,
-    #         tokenizer: CLIPTokenizer,
-    #         unet: UNet2DConditionModel,
-    #         scheduler: SchedulerMixin,
-    #         safety_checker: StableDiffusionSafetyChecker,
-    #         feature_extractor: CLIPFeatureExtractor,
-    #     ):
-    #         super().__init__(
-    #             vae=vae,
-    #             text_encoder=text_encoder,
-    #             tokenizer=tokenizer,
-    #             unet=unet,
-    #             scheduler=scheduler,
-    #             safety_checker=safety_checker,
-    #             feature_extractor=feature_extractor,
-    #         )
-    #         self.__init__additional__()
-
    def __init__additional__(self):
        if not hasattr(self, "vae_scale_factor"):
            setattr(self, "vae_scale_factor", 2 ** (len(self.vae.config.block_out_channels) - 1))
@@ -624,7 +604,7 @@ class StableDiffusionLongPromptWeightingPipeline(StableDiffusionPipeline):
            prompt=prompt,
            uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
            max_embeddings_multiples=max_embeddings_multiples,
-            clip_skip=self.clip_skip,
+            clip_skip=self.custom_clip_skip,
        )
        bs_embed, seq_len, _ = text_embeddings.shape
        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
--- a/library/model_util.py
+++ b/library/model_util.py
@@ -4,10 +4,13 @@
 import math
 import os
 import torch
+
 try:
    import intel_extension_for_pytorch as ipex
+
    if torch.xpu.is_available():
        from library.ipex import ipex_init
+
        ipex_init()
 except Exception:
    pass
@@ -571,9 +574,9 @@ def convert_ldm_clip_checkpoint_v1(checkpoint):
        if key.startswith("cond_stage_model.transformer"):
            text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key]

-    # support checkpoint without position_ids (invalid checkpoint)
-    if "text_model.embeddings.position_ids" not in text_model_dict:
-        text_model_dict["text_model.embeddings.position_ids"] = torch.arange(77).unsqueeze(0)  # 77 is the max length of the text
+    # remove position_ids for newer transformer, which causes error :(
+    if "text_model.embeddings.position_ids" in text_model_dict:
+        text_model_dict.pop("text_model.embeddings.position_ids")

    return text_model_dict

@@ -1307,19 +1310,19 @@ def load_vae(vae_id, dtype):

 def make_bucket_resolutions(max_reso, min_size=256, max_size=1024, divisible=64):
    max_width, max_height = max_reso
-    max_area = (max_width // divisible) * (max_height // divisible)
+    max_area = max_width * max_height

    resos = set()

-    size = int(math.sqrt(max_area)) * divisible
-    resos.add((size, size))
+    width = int(math.sqrt(max_area) // divisible) * divisible
+    resos.add((width, width))

-    size = min_size
-    while size <= max_size:
-        width = size
-        height = min(max_size, (max_area // (width // divisible)) * divisible)
-        resos.add((width, height))
-        resos.add((height, width))
+    width = min_size
+    while width <= max_size:
+        height = min(max_size, int((max_area // width) // divisible) * divisible)
+        if height >= min_size:
+            resos.add((width, height))
+            resos.add((height, width))

        # # make additional resos
        # if width >= height and width - divisible >= min_size:
@@ -1329,7 +1332,7 @@ def make_bucket_resolutions(max_reso, min_size=256, max_size=1024, divisible=64)
        #   resos.add((width, height - divisible))
        #   resos.add((height - divisible, width))

-        size += divisible
+        width += divisible

    resos = list(resos)
    resos.sort()
--- a/library/sdxl_model_util.py
+++ b/library/sdxl_model_util.py
@@ -100,7 +100,7 @@ def convert_sdxl_text_encoder_2_checkpoint(checkpoint, max_length):
            key = key.replace(".ln_final", ".final_layer_norm")
        # ckpt from comfy has this key: text_model.encoder.text_model.embeddings.position_ids
        elif ".embeddings.position_ids" in key:
-            key = None  # remove this key: make position_ids by ourselves
+            key = None  # remove this key: position_ids is not used in newer transformers
        return key

    keys = list(checkpoint.keys())
@@ -126,10 +126,6 @@ def convert_sdxl_text_encoder_2_checkpoint(checkpoint, max_length):
            new_sd[key_pfx + "k_proj" + key_suffix] = values[1]
            new_sd[key_pfx + "v_proj" + key_suffix] = values[2]

-    # original SD にはないので、position_idsを追加
-    position_ids = torch.Tensor([list(range(max_length))]).to(torch.int64)
-    new_sd["text_model.embeddings.position_ids"] = position_ids
-
    # logit_scale はDiffusersには含まれないが、保存時に戻したいので別途返す
    logit_scale = checkpoint.get(SDXL_KEY_PREFIX + "logit_scale", None)

@@ -265,9 +261,9 @@ def load_models_from_sdxl_checkpoint(model_version, ckpt_path, map_location, dty
        elif k.startswith("conditioner.embedders.1.model."):
            te2_sd[k] = state_dict.pop(k)

-    # 一部のposition_idsがないモデルへの対応 / add position_ids for some models
-    if "text_model.embeddings.position_ids" not in te1_sd:
-        te1_sd["text_model.embeddings.position_ids"] = torch.arange(77).unsqueeze(0)
+    # 最新の transformers では position_ids を含むとエラーになるので削除 / remove position_ids for latest transformers
+    if "text_model.embeddings.position_ids" in te1_sd:
+        te1_sd.pop("text_model.embeddings.position_ids")

    info1 = _load_state_dict_on_device(text_model1, te1_sd, device=map_location)  # remain fp32
    print("text encoder 1:", info1)
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -19,7 +19,7 @@ from typing import (
    Tuple,
    Union,
 )
-from accelerate import Accelerator, InitProcessGroupKwargs
+from accelerate import Accelerator, InitProcessGroupKwargs, DistributedDataParallelKwargs
 import gc
 import glob
 import math
@@ -351,6 +351,7 @@ class BaseSubset:
        shuffle_caption: bool,
        caption_separator: str,
        keep_tokens: int,
+        keep_tokens_separator: str,
        color_aug: bool,
        flip_aug: bool,
        face_crop_aug_range: Optional[Tuple[float, float]],
@@ -368,6 +369,7 @@ class BaseSubset:
        self.shuffle_caption = shuffle_caption
        self.caption_separator = caption_separator
        self.keep_tokens = keep_tokens
+        self.keep_tokens_separator = keep_tokens_separator
        self.color_aug = color_aug
        self.flip_aug = flip_aug
        self.face_crop_aug_range = face_crop_aug_range
@@ -395,6 +397,7 @@ class DreamBoothSubset(BaseSubset):
        shuffle_caption,
        caption_separator: str,
        keep_tokens,
+        keep_tokens_separator,
        color_aug,
        flip_aug,
        face_crop_aug_range,
@@ -415,6 +418,7 @@ class DreamBoothSubset(BaseSubset):
            shuffle_caption,
            caption_separator,
            keep_tokens,
+            keep_tokens_separator,
            color_aug,
            flip_aug,
            face_crop_aug_range,
@@ -449,6 +453,7 @@ class FineTuningSubset(BaseSubset):
        shuffle_caption,
        caption_separator,
        keep_tokens,
+        keep_tokens_separator,
        color_aug,
        flip_aug,
        face_crop_aug_range,
@@ -469,6 +474,7 @@ class FineTuningSubset(BaseSubset):
            shuffle_caption,
            caption_separator,
            keep_tokens,
+            keep_tokens_separator,
            color_aug,
            flip_aug,
            face_crop_aug_range,
@@ -500,6 +506,7 @@ class ControlNetSubset(BaseSubset):
        shuffle_caption,
        caption_separator,
        keep_tokens,
+        keep_tokens_separator,
        color_aug,
        flip_aug,
        face_crop_aug_range,
@@ -520,6 +527,7 @@ class ControlNetSubset(BaseSubset):
            shuffle_caption,
            caption_separator,
            keep_tokens,
+            keep_tokens_separator,
            color_aug,
            flip_aug,
            face_crop_aug_range,
@@ -654,15 +662,33 @@ class BaseDataset(torch.utils.data.Dataset):
            caption = ""
        else:
            if subset.shuffle_caption or subset.token_warmup_step > 0 or subset.caption_tag_dropout_rate > 0:
-                tokens = [t.strip() for t in caption.strip().split(subset.caption_separator)]
+                fixed_tokens = []
+                flex_tokens = []
+                if (
+                    hasattr(subset, "keep_tokens_separator")
+                    and subset.keep_tokens_separator
+                    and subset.keep_tokens_separator in caption
+                ):
+                    fixed_part, flex_part = caption.split(subset.keep_tokens_separator, 1)
+                    fixed_tokens = [t.strip() for t in fixed_part.split(subset.caption_separator) if t.strip()]
+                    flex_tokens = [t.strip() for t in flex_part.split(subset.caption_separator) if t.strip()]
+                else:
+                    tokens = [t.strip() for t in caption.strip().split(subset.caption_separator)]
+                    flex_tokens = tokens[:]
+                    if subset.keep_tokens > 0:
+                        fixed_tokens = flex_tokens[: subset.keep_tokens]
+                        flex_tokens = tokens[subset.keep_tokens :]
+
                if subset.token_warmup_step < 1:  # 初回に上書きする
                    subset.token_warmup_step = math.floor(subset.token_warmup_step * self.max_train_steps)
                if subset.token_warmup_step and self.current_step < subset.token_warmup_step:
                    tokens_len = (
-                        math.floor((self.current_step) * ((len(tokens) - subset.token_warmup_min) / (subset.token_warmup_step)))
+                        math.floor(
+                            (self.current_step) * ((len(flex_tokens) - subset.token_warmup_min) / (subset.token_warmup_step))
+                        )
                        + subset.token_warmup_min
                    )
-                    tokens = tokens[:tokens_len]
+                    flex_tokens = flex_tokens[:tokens_len]

                def dropout_tags(tokens):
                    if subset.caption_tag_dropout_rate <= 0:
@@ -673,12 +699,6 @@ class BaseDataset(torch.utils.data.Dataset):
                            l.append(token)
                    return l

-                fixed_tokens = []
-                flex_tokens = tokens[:]
-                if subset.keep_tokens > 0:
-                    fixed_tokens = flex_tokens[: subset.keep_tokens]
-                    flex_tokens = tokens[subset.keep_tokens :]
-
                if subset.shuffle_caption:
                    random.shuffle(flex_tokens)

@@ -1724,6 +1744,7 @@ class ControlNetDataset(BaseDataset):
                subset.shuffle_caption,
                subset.caption_separator,
                subset.keep_tokens,
+                subset.keep_tokens_separator,
                subset.color_aug,
                subset.flip_aug,
                subset.face_crop_aug_range,
@@ -2827,6 +2848,17 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
        action="store_true",
        help="use memory efficient attention for CrossAttention / CrossAttentionに省メモリ版attentionを使う",
    )
+    parser.add_argument("--torch_compile", action="store_true", help="use torch.compile (requires PyTorch 2.0) / torch.compile を使う")
+    parser.add_argument(
+        "--dynamo_backend", 
+        type=str, 
+        default="inductor", 
+        # available backends:
+        # https://github.com/huggingface/accelerate/blob/d1abd59114ada8ba673e1214218cb2878c13b82d/src/accelerate/utils/dataclasses.py#L376-L388C5
+        # https://pytorch.org/docs/stable/torch.compiler.html
+        choices=["eager", "aot_eager", "inductor", "aot_ts_nvfuser", "nvprims_nvfuser", "cudagraphs", "ofi", "fx2trt", "onnxrt"], 
+        help="dynamo backend type (default is inductor) / dynamoのbackendの種類（デフォルトは inductor）"
+    )
    parser.add_argument("--xformers", action="store_true", help="use xformers for CrossAttention / CrossAttentionにxformersを使う")
    parser.add_argument(
        "--sdpa",
@@ -2878,6 +2910,16 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
        default=None,
        help="DDP timeout (min, None for default of accelerate) / DDPのタイムアウト（分、Noneでaccelerateのデフォルト）",
    )
+    parser.add_argument(
+        "--ddp_gradient_as_bucket_view",
+        action="store_true",
+        help="enable gradient_as_bucket_view for DDP / DDPでgradient_as_bucket_viewを有効にする",
+    )
+    parser.add_argument(
+        "--ddp_static_graph",
+        action="store_true",
+        help="enable static_graph for DDP / DDPでstatic_graphを有効にする",
+    )
    parser.add_argument(
        "--clip_skip",
        type=int,
@@ -3131,6 +3173,13 @@ def add_dataset_arguments(
        default=0,
        help="keep heading N tokens when shuffling caption tokens (token means comma separated strings) / captionのシャッフル時に、先頭からこの個数のトークンをシャッフルしないで残す（トークンはカンマ区切りの各部分を意味する）",
    )
+    parser.add_argument(
+        "--keep_tokens_separator",
+        type=str,
+        default="",
+        help="A custom separator to divide the caption into fixed and flexible parts. Tokens before this separator will not be shuffled. If not specified, '--keep_tokens' will be used to determine the fixed number of tokens."
+        + " / captionを固定部分と可変部分に分けるためのカスタム区切り文字。この区切り文字より前のトークンはシャッフルされない。指定しない場合、'--keep_tokens'が固定部分のトークン数として使用される。",
+    )
    parser.add_argument(
        "--caption_prefix",
        type=str,
@@ -3832,15 +3881,25 @@ def prepare_accelerator(args: argparse.Namespace):
            if args.wandb_api_key is not None:
                wandb.login(key=args.wandb_api_key)
    
+    # torch.compile のオプション。 NO の場合は torch.compile は使わない
+    dynamo_backend = "NO"
+    if args.torch_compile:
+        dynamo_backend = args.dynamo_backend
+
    kwargs_handlers = (
-        None if args.ddp_timeout is None else [InitProcessGroupKwargs(timeout=datetime.timedelta(minutes=args.ddp_timeout))]
+        InitProcessGroupKwargs(timeout=datetime.timedelta(minutes=args.ddp_timeout)) if args.ddp_timeout else None,
+        DistributedDataParallelKwargs(gradient_as_bucket_view=args.ddp_gradient_as_bucket_view, static_graph=args.ddp_static_graph)
+        if args.ddp_gradient_as_bucket_view or args.ddp_static_graph
+        else None,
    )
+    kwargs_handlers = list(filter(lambda x: x is not None, kwargs_handlers))
    accelerator = Accelerator(
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        mixed_precision=args.mixed_precision,
        log_with=log_with,
        project_dir=logging_dir,
        kwargs_handlers=kwargs_handlers,
+        dynamo_backend=dynamo_backend,
    )
    return accelerator

@@ -4561,7 +4620,7 @@ def line_to_prompt_dict(line: str) -> dict:

 def sample_images_common(
    pipe_class,
-    accelerator,
+    accelerator: Accelerator,
    args: argparse.Namespace,
    epoch,
    steps,
@@ -4598,6 +4657,13 @@ def sample_images_common(
    org_vae_device = vae.device  # CPUにいるはず
    vae.to(device)

+    # unwrap unet and text_encoder(s)
+    unet = accelerator.unwrap_model(unet)
+    if isinstance(text_encoder, (list, tuple)):
+        text_encoder = [accelerator.unwrap_model(te) for te in text_encoder]
+    else:
+        text_encoder = accelerator.unwrap_model(text_encoder)
+
    # read prompts

    # with open(args.sample_prompts, "rt", encoding="utf-8") as f:
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,10 +1,10 @@
-accelerate==0.23.0
-transformers==4.30.2
-diffusers[torch]==0.21.2
+accelerate==0.25.0
+transformers==4.36.2
+diffusers[torch]==0.25.0
 ftfy==6.1.1
 # albumentations==1.3.0
 opencv-python==4.7.0.68
-einops==0.6.0
+einops==0.6.1
 pytorch-lightning==1.9.0
 # bitsandbytes==0.39.1
 tensorboard==2.10.1
@@ -14,7 +14,7 @@ altair==4.2.2
 easygui==0.98.3
 toml==0.10.2
 voluptuous==0.13.1
-huggingface-hub==0.15.1
+huggingface-hub==0.20.1
 # for BLIP captioning
 # requests==2.28.2
 # timm==0.6.12
--- a/sdxl_train.py
+++ b/sdxl_train.py
@@ -398,6 +398,9 @@ def train(args):
    if train_unet:
        unet = accelerator.prepare(unet)
    if train_text_encoder1:
+        # freeze last layer and final_layer_norm in te1 since we use the output of the penultimate layer
+        text_encoder1.text_model.encoder.layers[-1].requires_grad_(False)
+        text_encoder1.text_model.final_layer_norm.requires_grad_(False)
        text_encoder1 = accelerator.prepare(text_encoder1)
    if train_text_encoder2:
        text_encoder2 = accelerator.prepare(text_encoder2)
@@ -484,7 +487,7 @@ def train(args):
                        # NaNが含まれていれば警告を表示し0に置き換える
                        if torch.any(torch.isnan(latents)):
                            accelerator.print("NaN found in latents, replacing with zeros")
-                            latents = torch.where(torch.isnan(latents), torch.zeros_like(latents), latents)
+                            latents = torch.nan_to_num(latents, 0, out=latents)
                latents = latents * sdxl_model_util.VAE_SCALE_FACTOR

                if "text_encoder_outputs1_list" not in batch or batch["text_encoder_outputs1_list"] is None:
--- a/sdxl_train_control_net_lllite.py
+++ b/sdxl_train_control_net_lllite.py
@@ -394,7 +394,7 @@ def train(args):
                        # NaNが含まれていれば警告を表示し0に置き換える
                        if torch.any(torch.isnan(latents)):
                            accelerator.print("NaN found in latents, replacing with zeros")
-                            latents = torch.where(torch.isnan(latents), torch.zeros_like(latents), latents)
+                            latents = torch.nan_to_num(latents, 0, out=latents)
                    latents = latents * sdxl_model_util.VAE_SCALE_FACTOR

                if "text_encoder_outputs1_list" not in batch or batch["text_encoder_outputs1_list"] is None:
--- a/sdxl_train_control_net_lllite_old.py
+++ b/sdxl_train_control_net_lllite_old.py
@@ -363,7 +363,7 @@ def train(args):
                        # NaNが含まれていれば警告を表示し0に置き換える
                        if torch.any(torch.isnan(latents)):
                            accelerator.print("NaN found in latents, replacing with zeros")
-                            latents = torch.where(torch.isnan(latents), torch.zeros_like(latents), latents)
+                            latents = torch.nan_to_num(latents, 0, out=latents)
                    latents = latents * sdxl_model_util.VAE_SCALE_FACTOR

                if "text_encoder_outputs1_list" not in batch or batch["text_encoder_outputs1_list"] is None:
--- a/tools/convert_diffusers20_original_sd.py
+++ b/tools/convert_diffusers20_original_sd.py
@@ -23,7 +23,7 @@ def convert(args):
    is_load_ckpt = os.path.isfile(args.model_to_load)
    is_save_ckpt = len(os.path.splitext(args.model_to_save)[1]) > 0

-    assert not is_load_ckpt or args.v1 != args.v2, f"v1 or v2 is required to load checkpoint / checkpointの読み込みにはv1/v2指定が必要です"
+    assert not is_load_ckpt or args.v1 != args.v2, "v1 or v2 is required to load checkpoint / checkpointの読み込みにはv1/v2指定が必要です"
    # assert (
    #     is_save_ckpt or args.reference_model is not None
    # ), f"reference model is required to save as Diffusers / Diffusers形式での保存には参照モデルが必要です"
@@ -34,10 +34,12 @@ def convert(args):

    if is_load_ckpt:
        v2_model = args.v2
-        text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(v2_model, args.model_to_load, unet_use_linear_projection_in_v2=args.unet_use_linear_projection)
+        text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(
+            v2_model, args.model_to_load, unet_use_linear_projection_in_v2=args.unet_use_linear_projection
+        )
    else:
        pipe = StableDiffusionPipeline.from_pretrained(
-            args.model_to_load, torch_dtype=load_dtype, tokenizer=None, safety_checker=None
+            args.model_to_load, torch_dtype=load_dtype, tokenizer=None, safety_checker=None, variant=args.variant
        )
        text_encoder = pipe.text_encoder
        vae = pipe.vae
@@ -57,15 +59,26 @@ def convert(args):
    if is_save_ckpt:
        original_model = args.model_to_load if is_load_ckpt else None
        key_count = model_util.save_stable_diffusion_checkpoint(
-            v2_model, args.model_to_save, text_encoder, unet, original_model, args.epoch, args.global_step, save_dtype, vae
+            v2_model,
+            args.model_to_save,
+            text_encoder,
+            unet,
+            original_model,
+            args.epoch,
+            args.global_step,
+            None if args.metadata is None else eval(args.metadata),
+            save_dtype=save_dtype,
+            vae=vae,
        )
        print(f"model saved. total converted state_dict keys: {key_count}")
    else:
-        print(f"copy scheduler/tokenizer config from: {args.reference_model if args.reference_model is not None else 'default model'}")
+        print(
+            f"copy scheduler/tokenizer config from: {args.reference_model if args.reference_model is not None else 'default model'}"
+        )
        model_util.save_diffusers_checkpoint(
            v2_model, args.model_to_save, text_encoder, unet, args.reference_model, vae, args.use_safetensors
        )
-        print(f"model saved.")
+        print("model saved.")


 def setup_parser() -> argparse.ArgumentParser:
@@ -77,7 +90,9 @@ def setup_parser() -> argparse.ArgumentParser:
        "--v2", action="store_true", help="load v2.0 model (v1 or v2 is required to load checkpoint) / 2.0のモデルを読み込む"
    )
    parser.add_argument(
-        "--unet_use_linear_projection", action="store_true", help="When saving v2 model as Diffusers, set U-Net config to `use_linear_projection=true` (to match stabilityai's model) / Diffusers形式でv2モデルを保存するときにU-Netの設定を`use_linear_projection=true`にする（stabilityaiのモデルと合わせる）"
+        "--unet_use_linear_projection",
+        action="store_true",
+        help="When saving v2 model as Diffusers, set U-Net config to `use_linear_projection=true` (to match stabilityai's model) / Diffusers形式でv2モデルを保存するときにU-Netの設定を`use_linear_projection=true`にする（stabilityaiのモデルと合わせる）",
    )
    parser.add_argument(
        "--fp16",
@@ -99,6 +114,18 @@ def setup_parser() -> argparse.ArgumentParser:
    parser.add_argument(
        "--global_step", type=int, default=0, help="global_step to write to checkpoint / checkpointに記録するglobal_stepの値"
    )
+    parser.add_argument(
+        "--metadata",
+        type=str,
+        default=None,
+        help='モデルに保存されるメタデータ、Pythonの辞書形式で指定 / metadata: metadata written in to the model in Python Dictionary. Example metadata: \'{"name": "model_name", "resolution": "512x512"}\'',
+    )
+    parser.add_argument(
+        "--variant",
+        type=str,
+        default=None,
+        help="読む込むDiffusersのvariantを指定する、例: fp16 / variant: Diffusers variant to load. Example: fp16",
+    )
    parser.add_argument(
        "--reference_model",
        type=str,
--- a/train_network.py
+++ b/train_network.py
@@ -750,7 +750,7 @@ class NetworkTrainer:
                            # NaNが含まれていれば警告を表示し0に置き換える
                            if torch.any(torch.isnan(latents)):
                                accelerator.print("NaN found in latents, replacing with zeros")
-                                latents = torch.where(torch.isnan(latents), torch.zeros_like(latents), latents)
+                                latents = torch.nan_to_num(latents, 0, out=latents)
                        latents = latents * self.vae_scale_factor
                    b_size = latents.shape[0]

--- a/train_textual_inversion.py
+++ b/train_textual_inversion.py
@@ -441,9 +441,10 @@ class TextualInversionTrainer:

            # Freeze all parameters except for the token embeddings in text encoder
            text_encoder.requires_grad_(True)
-            text_encoder.text_model.encoder.requires_grad_(False)
-            text_encoder.text_model.final_layer_norm.requires_grad_(False)
-            text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
+            unwrapped_text_encoder = accelerator.unwrap_model(text_encoder)
+            unwrapped_text_encoder.text_model.encoder.requires_grad_(False)
+            unwrapped_text_encoder.text_model.final_layer_norm.requires_grad_(False)
+            unwrapped_text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
            # text_encoder.text_model.embeddings.token_embedding.requires_grad_(True)

        unet.requires_grad_(False)
@@ -603,7 +604,7 @@ class TextualInversionTrainer:

                    accelerator.backward(loss)
                    if accelerator.sync_gradients and args.max_grad_norm != 0.0:
-                        params_to_clip = text_encoder.get_input_embeddings().parameters()
+                        params_to_clip = accelerator.unwrap_model(text_encoder).get_input_embeddings().parameters()
                        accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)

                    optimizer.step()
@@ -615,9 +616,11 @@ class TextualInversionTrainer:
                        for text_encoder, orig_embeds_params, index_no_updates in zip(
                            text_encoders, orig_embeds_params_list, index_no_updates_list
                        ):
-                            accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
+                            # if full_fp16/bf16, input_embeddings_weight is fp16/bf16, orig_embeds_params is fp32
+                            input_embeddings_weight = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight
+                            input_embeddings_weight[index_no_updates] = orig_embeds_params.to(input_embeddings_weight.dtype)[
                                index_no_updates
-                            ] = orig_embeds_params[index_no_updates]
+                            ]

                # Checks if the accelerator has performed an optimization step behind the scenes
                if accelerator.sync_gradients: