Merge pull request #374 from kohya-ss/dev

block learning rate, block dim(rank) etc.

README.md

@@ -127,58 +127,92 @@ The majority of scripts is licensed under ASL 2.0 (including codes from Diffuser
 
 ## Change History
 
-- 28 Mar. 2023, 2023/3/28:
-  - Fix an issue that the training script crashes when `max_data_loader_n_workers` is 0.
-  - `max_data_loader_n_workers` が0の時に学習スクリプトがエラーとなる不具合を修正しました。
+- 4 Apr. 2023, 2023/4/4:
+  - There may be bugs because I changed a lot. If you cannot revert the script to the previous version when a problem occurs, please wait for the update for a while.
+  - The learning rate and dim (rank) of each block may not work with other modules (LyCORIS, etc.) because the module needs to be changed.
 
-- 27 Mar. 2023, 2023/3/27:
-  - Fix issues when `--persistent_data_loader_workers` is specified.
-    - The batch members of the bucket are not shuffled.
-    - `--caption_dropout_every_n_epochs` does not work.
-    - These issues occurred because the epoch transition was not recognized correctly. Thanks to u-haru for reporting the issue.
-  - Fix an issue that images are loaded twice in Windows environment.
-  - Add Min-SNR Weighting strategy. Details are in [#308](https://github.com/kohya-ss/sd-scripts/pull/308). Thank you to AI-Casanova for this great work!
-    - Add `--min_snr_gamma` option to training scripts, 5 is recommended by paper.
+  - Fix some bugs and add some features.
+    - Fix an issue that `.json` format dataset config files cannot be read.  [issue #351](https://github.com/kohya-ss/sd-scripts/issues/351) Thanks to rockerBOO!
+    - Raise an error when an invalid `--lr_warmup_steps` option is specified (when warmup is not valid for the specified scheduler).  [PR #364](https://github.com/kohya-ss/sd-scripts/pull/364)  Thanks to shirayu!
+    - Add `min_snr_gamma` to metadata in `train_network.py`. [PR #373](https://github.com/kohya-ss/sd-scripts/pull/373) Thanks to rockerBOO!
+    - Fix the data type handling in `fine_tune.py`. This may fix an error that occurs in some environments when using xformers, npz format cache, and mixed_precision.
 
-  - Add tag warmup. Details are in [#322](https://github.com/kohya-ss/sd-scripts/pull/322). Thanks to u-haru!
-    - Add `token_warmup_min` and `token_warmup_step` to dataset settings.
-    - Gradually increase the number of tokens from `token_warmup_min` to `token_warmup_step`.
-    - For example, if `token_warmup_min` is `3` and `token_warmup_step` is `10`, the first step will use the first 3 tokens, and the 10th step will use all tokens.
-  - Fix a bug in `resize_lora.py`. Thanks to mgz-dev! [#328](https://github.com/kohya-ss/sd-scripts/pull/328)  
-  - Add `--debug_dataset` option to step to the next step with `S` key and to the next epoch with `E` key.
-  - Fix other bugs.
+  - Add options to `train_network.py` to specify block weights for learning rates. [PR #355](https://github.com/kohya-ss/sd-scripts/pull/355) Thanks to u-haru for the great contribution!
+    - Specify the weights of 25 blocks for the full model.
+      - No LoRA corresponds to the first block, but 25 blocks are specified for compatibility with 'LoRA block weight' etc. Also, if you do not expand to conv2d3x3, some blocks do not have LoRA, but please specify 25 values ​​for the argument for consistency.
+    - Specify the following arguments with `--network_args`.
+    - `down_lr_weight` : Specify the learning rate weight of the down blocks of U-Net. The following can be specified.
+      - The weight for each block: Specify 12 numbers such as `"down_lr_weight=0,0,0,0,0,0,1,1,1,1,1,1"`.
+      - Specify from preset: Specify such as `"down_lr_weight=sine"` (the weights by sine curve). sine, cosine, linear, reverse_linear, zeros can be specified. Also, if you add `+number` such as `"down_lr_weight=cosine+.25"`, the specified number is added (such as 0.25~1.25).
+    - `mid_lr_weight` : Specify the learning rate weight of the mid block of U-Net. Specify one number such as `"down_lr_weight=0.5"`.
+    - `up_lr_weight` : Specify the learning rate weight of the up blocks of U-Net. The same as down_lr_weight.
+    - If you omit the some arguments, the 1.0 is used. Also, if you set the weight to 0, the LoRA modules of that block are not created.
+    - `block_lr_zero_threshold` : If the weight is not more than this value, the LoRA module is not created. The default is 0.
 
-  - `--persistent_data_loader_workers` を指定した時の各種不具合を修正しました。
-    - `--caption_dropout_every_n_epochs` が効かない。
-    - バケットのバッチメンバーがシャッフルされない。
-    - エポックの遷移が正しく認識されないために発生していました。ご指摘いただいたu-haru氏に感謝します。
-  - Windows環境で画像が二重に読み込まれる不具合を修正しました。
-  - Min-SNR Weighting strategyを追加しました。 詳細は [#308](https://github.com/kohya-ss/sd-scripts/pull/308) をご参照ください。AI-Casanova氏の素晴らしい貢献に感謝します。
-    - `--min_snr_gamma` オプションを学習スクリプトに追加しました。論文では5が推奨されています。
-  - タグのウォームアップを追加しました。詳細は [#322](https://github.com/kohya-ss/sd-scripts/pull/322) をご参照ください。u-haru氏に感謝します。
-    - データセット設定に `token_warmup_min` と `token_warmup_step` を追加しました。
-    - `token_warmup_min` で指定した数のトークン（カンマ区切りの文字列）から、`token_warmup_step` で指定したステップまで、段階的にトークンを増やしていきます。
-    - たとえば `token_warmup_min`に `3` を、`token_warmup_step` に `10` を指定すると、最初のステップでは最初から3個のトークンが使われ、10ステップ目では全てのトークンが使われます。
-  - `resize_lora.py` の不具合を修正しました。mgz-dev氏に感謝します。[#328](https://github.com/kohya-ss/sd-scripts/pull/328)  
-  - `--debug_dataset` オプションで、`S`キーで次のステップへ、`E`キーで次のエポックへ進めるようにしました。
-  - その他の不具合を修正しました。
+  - Add options to `train_network.py` to specify block dims (ranks) for variable rank.
+    - Specify 25 values ​​for the full model of 25 blocks. Some blocks do not have LoRA, but specify 25 values ​​always.
+    - Specify the following arguments with `--network_args`.
+    - `block_dims` : Specify the dim (rank) of each block. Specify 25 numbers such as `"block_dims=2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2"`.
+    - `block_alphas` : Specify the alpha of each block. Specify 25 numbers as with block_dims. If omitted, the value of network_alpha is used.
+    - `conv_block_dims` : Expand LoRA to Conv2d 3x3 and specify the dim (rank) of each block.
+    - `conv_block_alphas` : Specify the alpha of each block when expanding LoRA to Conv2d 3x3. If omitted, the value of conv_alpha is used.
+
+  - 大きく変更したため不具合があるかもしれません。問題が起きた時にスクリプトを前のバージョンに戻せない場合は、しばらく更新を控えてください。
+  - 階層別学習率、階層別dim(rank)についてはモジュール側の変更が必要なため、当リポジトリ内のnetworkモジュール以外（LyCORISなど）では現在は動作しないと思われます。
+  
+  - いくつかのバグ修正、機能追加を行いました。
+    - `.json`形式のdataset設定ファイルを読み込めない不具合を修正しました。 [issue #351](https://github.com/kohya-ss/sd-scripts/issues/351) rockerBOO 氏に感謝します。
+    - 無効な`--lr_warmup_steps` オプション（指定したスケジューラでwarmupが無効な場合）を指定している場合にエラーを出すようにしました。 [PR #364](https://github.com/kohya-ss/sd-scripts/pull/364) shirayu 氏に感謝します。
+    - `train_network.py` で `min_snr_gamma` をメタデータに追加しました。 [PR #373](https://github.com/kohya-ss/sd-scripts/pull/373) rockerBOO 氏に感謝します。
+    - `fine_tune.py` でデータ型の取り扱いが誤っていたのを修正しました。一部の環境でxformersを使い、npz形式のキャッシュ、mixed_precisionで学習した時にエラーとなる不具合が解消されるかもしれません。
+  
+  - 階層別学習率を `train_network.py` で指定できるようになりました。[PR #355](https://github.com/kohya-ss/sd-scripts/pull/355) u-haru 氏の多大な貢献に感謝します。
+    - フルモデルの25個のブロックの重みを指定できます。
+      - 最初のブロックに該当するLoRAは存在しませんが、階層別LoRA適用等との互換性のために25個としています。またconv2d3x3に拡張しない場合も一部のブロックにはLoRAが存在しませんが、記述を統一するため常に25個の値を指定してください。
+    -`--network_args` で以下の引数を指定してください。
+    - `down_lr_weight` : U-Netのdown blocksの学習率の重みを指定します。以下が指定可能です。
+      - ブロックごとの重み : `"down_lr_weight=0,0,0,0,0,0,1,1,1,1,1,1"` のように12個の数値を指定します。
+      - プリセットからの指定 : `"down_lr_weight=sine"` のように指定します（サインカーブで重みを指定します）。sine, cosine, linear, reverse_linear, zeros が指定可能です。また `"down_lr_weight=cosine+.25"` のように `+数値` を追加すると、指定した数値を加算します（0.25~1.25になります）。
+    - `mid_lr_weight` : U-Netのmid blockの学習率の重みを指定します。`"down_lr_weight=0.5"` のように数値を一つだけ指定します。
+    - `up_lr_weight` : U-Netのup blocksの学習率の重みを指定します。down_lr_weightと同様です。
+    - 指定を省略した部分は1.0として扱われます。また重みを0にするとそのブロックのLoRAモジュールは作成されません。
+    - `block_lr_zero_threshold` : 重みがこの値以下の場合、LoRAモジュールを作成しません。デフォルトは0です。
+
+  - 階層別dim (rank)を `train_network.py` で指定できるようになりました。
+    - フルモデルの25個のブロックのdim (rank)を指定できます。階層別学習率と同様に一部のブロックにはLoRAが存在しない場合がありますが、常に25個の値を指定してください。
+    - `--network_args` で以下の引数を指定してください。
+    - `block_dims` : 各ブロックのdim (rank)を指定します。`"block_dims=2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2"` のように25個の数値を指定します。
+    - `block_alphas` : 各ブロックのalphaを指定します。block_dimsと同様に25個の数値を指定します。省略時はnetwork_alphaの値が使用されます。
+    - `conv_block_dims` : LoRAをConv2d 3x3に拡張し、各ブロックのdim (rank)を指定します。
+    - `conv_block_alphas` : LoRAをConv2d 3x3に拡張したときの各ブロックのalphaを指定します。省略時はconv_alphaの値が使用されます。
+
+  - 階層別学習率コマンドライン指定例 / Examples of block learning rate command line specification:
+
+    ` --network_args "down_lr_weight=0.5,0.5,0.5,0.5,1.0,1.0,1.0,1.0,1.5,1.5,1.5,1.5" "mid_lr_weight=2.0" "up_lr_weight=1.5,1.5,1.5,1.5,1.0,1.0,1.0,1.0,0.5,0.5,0.5,0.5"`
+  
+    ` --network_args "block_lr_zero_threshold=0.1" "down_lr_weight=sine+.5" "mid_lr_weight=1.5" "up_lr_weight=cosine+.5"`
+
+  - 階層別学習率tomlファイル指定例 / Examples of block learning rate toml file specification
+
+    `network_args = [ "down_lr_weight=0.5,0.5,0.5,0.5,1.0,1.0,1.0,1.0,1.5,1.5,1.5,1.5", "mid_lr_weight=2.0", "up_lr_weight=1.5,1.5,1.5,1.5,1.0,1.0,1.0,1.0,0.5,0.5,0.5,0.5",]`
+
+    `network_args = [ "block_lr_zero_threshold=0.1", "down_lr_weight=sine+.5", "mid_lr_weight=1.5", "up_lr_weight=cosine+.5", ]`
 
 
-- 21 Mar. 2023, 2023/3/21:
-  - Add `--vae_batch_size` for faster latents caching to each training script. This  batches VAE calls.
-    - Please start with`2` or `4` depending on the size of VRAM.
-  - Fix a number of training steps with `--gradient_accumulation_steps` and `--max_train_epochs`. Thanks to tsukimiya!
-  - Extract parser setup to external scripts. Thanks to robertsmieja!
-  - Fix an issue without `.npz` and with `--full_path` in training.
-  - Support extensions with upper cases for images for not Windows environment.
-  - Fix `resize_lora.py` to work with LoRA with dynamic rank (including `conv_dim != network_dim`). Thanks to toshiaki!
-  - latentsのキャッシュを高速化する`--vae_batch_size` オプションを各学習スクリプトに追加しました。VAE呼び出しをバッチ化します。
-    -VRAMサイズに応じて、`2` か `4` 程度から試してください。
-  - `--gradient_accumulation_steps` と `--max_train_epochs` を指定した時、当該のepochで学習が止まらない不具合を修正しました。tsukimiya氏に感謝します。
-  - 外部のスクリプト用に引数parserの構築が関数化されました。robertsmieja氏に感謝します。
-  - 学習時、`--full_path` 指定時に `.npz` が存在しない場合の不具合を解消しました。
-  - Windows以外の環境向けに、画像ファイルの大文字の拡張子をサポートしました。
-  - `resize_lora.py` を dynamic rank （rankが各LoRAモジュールで異なる場合、`conv_dim` が `network_dim` と異なる場合も含む）の時に正しく動作しない不具合を修正しました。toshiaki氏に感謝します。
+  - 階層別dim (rank)コマンドライン指定例 / Examples of block dim (rank) command line specification:
+
+    ` --network_args "block_dims=2,4,4,4,8,8,8,8,12,12,12,12,16,12,12,12,12,8,8,8,8,4,4,4,2"`
+  
+    ` --network_args "block_dims=2,4,4,4,8,8,8,8,12,12,12,12,16,12,12,12,12,8,8,8,8,4,4,4,2" "conv_block_dims=2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2"`
+
+    ` --network_args "block_dims=2,4,4,4,8,8,8,8,12,12,12,12,16,12,12,12,12,8,8,8,8,4,4,4,2" "block_alphas=2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2"`
+
+  - 階層別dim (rank)tomlファイル指定例 / Examples of block dim (rank) toml file specification
+
+    `network_args = [ "block_dims=2,4,4,4,8,8,8,8,12,12,12,12,16,12,12,12,12,8,8,8,8,4,4,4,2",]`
+    
+    `network_args = [ "block_dims=2,4,4,4,8,8,8,8,12,12,12,12,16,12,12,12,12,8,8,8,8,4,4,4,2", "block_alphas=2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2",]`
+
 
 ## Sample image generation during training
   A prompt file might look like this, for example

XTI_hijack.py

@@ -0,0 +1,209 @@
+import torch
+from typing import Union, List, Optional, Dict, Any, Tuple
+from diffusers.models.unet_2d_condition import UNet2DConditionOutput
+
+def unet_forward_XTI(self,
+        sample: torch.FloatTensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        class_labels: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ) -> Union[UNet2DConditionOutput, Tuple]:
+        r"""
+        Args:
+            sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
+            timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
+            encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            returning a tuple, the first element is the sample tensor.
+        """
+        # By default samples have to be AT least a multiple of the overall upsampling factor.
+        # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
+        # However, the upsampling interpolation output size can be forced to fit any upsampling size
+        # on the fly if necessary.
+        default_overall_up_factor = 2**self.num_upsamplers
+
+        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
+        forward_upsample_size = False
+        upsample_size = None
+
+        if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
+            logger.info("Forward upsample size to force interpolation output size.")
+            forward_upsample_size = True
+
+        # 0. center input if necessary
+        if self.config.center_input_sample:
+            sample = 2 * sample - 1.0
+
+        # 1. time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=self.dtype)
+        emb = self.time_embedding(t_emb)
+
+        if self.config.num_class_embeds is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
+            emb = emb + class_emb
+
+        # 2. pre-process
+        sample = self.conv_in(sample)
+
+        # 3. down
+        down_block_res_samples = (sample,)
+        down_i = 0
+        for downsample_block in self.down_blocks:
+            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states[down_i:down_i+2],
+                )
+                down_i += 2
+            else:
+                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
+
+            down_block_res_samples += res_samples
+
+        # 4. mid
+        sample = self.mid_block(sample, emb, encoder_hidden_states=encoder_hidden_states[6])
+
+        # 5. up
+        up_i = 7
+        for i, upsample_block in enumerate(self.up_blocks):
+            is_final_block = i == len(self.up_blocks) - 1
+
+            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+
+            # if we have not reached the final block and need to forward the
+            # upsample size, we do it here
+            if not is_final_block and forward_upsample_size:
+                upsample_size = down_block_res_samples[-1].shape[2:]
+
+            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    encoder_hidden_states=encoder_hidden_states[up_i:up_i+3],
+                    upsample_size=upsample_size,
+                )
+                up_i += 3
+            else:
+                sample = upsample_block(
+                    hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
+                )
+        # 6. post-process
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+
+        if not return_dict:
+            return (sample,)
+
+        return UNet2DConditionOutput(sample=sample)
+
+def downblock_forward_XTI(
+    self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None
+):
+    output_states = ()
+    i = 0
+
+    for resnet, attn in zip(self.resnets, self.attentions):
+        if self.training and self.gradient_checkpointing:
+
+            def create_custom_forward(module, return_dict=None):
+                def custom_forward(*inputs):
+                    if return_dict is not None:
+                        return module(*inputs, return_dict=return_dict)
+                    else:
+                        return module(*inputs)
+
+                return custom_forward
+
+            hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
+            hidden_states = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states[i]
+            )[0]
+        else:
+            hidden_states = resnet(hidden_states, temb)
+            hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states[i]).sample
+
+        output_states += (hidden_states,)
+        i += 1
+
+    if self.downsamplers is not None:
+        for downsampler in self.downsamplers:
+            hidden_states = downsampler(hidden_states)
+
+        output_states += (hidden_states,)
+
+    return hidden_states, output_states
+
+def upblock_forward_XTI(
+    self,
+    hidden_states,
+    res_hidden_states_tuple,
+    temb=None,
+    encoder_hidden_states=None,
+    upsample_size=None,
+):
+    i = 0
+    for resnet, attn in zip(self.resnets, self.attentions):
+        # pop res hidden states
+        res_hidden_states = res_hidden_states_tuple[-1]
+        res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+        hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+        if self.training and self.gradient_checkpointing:
+
+            def create_custom_forward(module, return_dict=None):
+                def custom_forward(*inputs):
+                    if return_dict is not None:
+                        return module(*inputs, return_dict=return_dict)
+                    else:
+                        return module(*inputs)
+
+                return custom_forward
+
+            hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
+            hidden_states = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states[i]
+            )[0]
+        else:
+            hidden_states = resnet(hidden_states, temb)
+            hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states[i]).sample
+        
+        i += 1
+
+    if self.upsamplers is not None:
+        for upsampler in self.upsamplers:
+            hidden_states = upsampler(hidden_states, upsample_size)
+
+    return hidden_states

fine_tune.py

@@ -275,7 +275,7 @@ def train(args):
             with accelerator.accumulate(training_models[0]):  # 複数モデルに対応していない模様だがとりあえずこうしておく
                 with torch.no_grad():
                     if "latents" in batch and batch["latents"] is not None:
-                        latents = batch["latents"].to(accelerator.device)
+                        latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype)
                     else:
                         # latentに変換
                         latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()

gen_img_diffusers.py

@@ -95,6 +95,8 @@ import library.train_util as train_util
 import tools.original_control_net as original_control_net
 from tools.original_control_net import ControlNetInfo
 
+from XTI_hijack import unet_forward_XTI, downblock_forward_XTI, upblock_forward_XTI
+
 # Tokenizer: checkpointから読み込むのではなくあらかじめ提供されているものを使う
 TOKENIZER_PATH = "openai/clip-vit-large-patch14"
 V2_STABLE_DIFFUSION_PATH = "stabilityai/stable-diffusion-2"  # ここからtokenizerだけ使う
@@ -491,6 +493,9 @@ class PipelineLike:
         # Textual Inversion
         self.token_replacements = {}
 
+        # XTI
+        self.token_replacements_XTI = {}
+
         # CLIP guidance
         self.clip_guidance_scale = clip_guidance_scale
         self.clip_image_guidance_scale = clip_image_guidance_scale
@@ -514,15 +519,26 @@ class PipelineLike:
     def add_token_replacement(self, target_token_id, rep_token_ids):
         self.token_replacements[target_token_id] = rep_token_ids
 
-    def replace_token(self, tokens):
+    def replace_token(self, tokens, layer=None):
         new_tokens = []
         for token in tokens:
             if token in self.token_replacements:
-                new_tokens.extend(self.token_replacements[token])
+                replacer_ = self.token_replacements[token]
+                if layer:
+                    replacer = []
+                for r in replacer_:
+                    if r in self.token_replacements_XTI:
+                        replacer.append(self.token_replacements_XTI[r][layer])
+                    else:
+                        replacer = replacer_
+                new_tokens.extend(replacer)
             else:
                 new_tokens.append(token)
         return new_tokens
 
+    def add_token_replacement_XTI(self, target_token_id, rep_token_ids):
+        self.token_replacements_XTI[target_token_id] = rep_token_ids
+
     def set_control_nets(self, ctrl_nets):
         self.control_nets = ctrl_nets
 
@@ -744,14 +760,15 @@ class PipelineLike:
                 " the batch size of `prompt`."
             )
 
-        text_embeddings, uncond_embeddings, prompt_tokens = get_weighted_text_embeddings(
-            pipe=self,
-            prompt=prompt,
-            uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
-            max_embeddings_multiples=max_embeddings_multiples,
-            clip_skip=self.clip_skip,
-            **kwargs,
-        )
+        if not self.token_replacements_XTI:
+            text_embeddings, uncond_embeddings, prompt_tokens = get_weighted_text_embeddings(
+                pipe=self,
+                prompt=prompt,
+                uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
+                max_embeddings_multiples=max_embeddings_multiples,
+                clip_skip=self.clip_skip,
+                **kwargs,
+            )
 
         if negative_scale is not None:
             _, real_uncond_embeddings, _ = get_weighted_text_embeddings(
@@ -763,11 +780,47 @@ class PipelineLike:
                 **kwargs,
             )
 
-        if do_classifier_free_guidance:
-            if negative_scale is None:
-                text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-            else:
-                text_embeddings = torch.cat([uncond_embeddings, text_embeddings, real_uncond_embeddings])
+        if self.token_replacements_XTI:
+            text_embeddings_concat = []
+            for layer in [
+                "IN01",
+                "IN02",
+                "IN04",
+                "IN05",
+                "IN07",
+                "IN08",
+                "MID",
+                "OUT03",
+                "OUT04",
+                "OUT05",
+                "OUT06",
+                "OUT07",
+                "OUT08",
+                "OUT09",
+                "OUT10",
+                "OUT11",
+            ]:
+                text_embeddings, uncond_embeddings, prompt_tokens = get_weighted_text_embeddings(
+                    pipe=self,
+                    prompt=prompt,
+                    uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
+                    max_embeddings_multiples=max_embeddings_multiples,
+                    clip_skip=self.clip_skip,
+                    layer=layer,
+                    **kwargs,
+                )
+                if do_classifier_free_guidance:
+                    if negative_scale is None:
+                        text_embeddings_concat.append(torch.cat([uncond_embeddings, text_embeddings]))
+                    else:
+                        text_embeddings_concat.append(torch.cat([uncond_embeddings, text_embeddings, real_uncond_embeddings]))
+                text_embeddings = torch.stack(text_embeddings_concat)
+        else:
+            if do_classifier_free_guidance:
+                if negative_scale is None:
+                    text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+                else:
+                    text_embeddings = torch.cat([uncond_embeddings, text_embeddings, real_uncond_embeddings])
 
         # CLIP guidanceで使用するembeddingsを取得する
         if self.clip_guidance_scale > 0:
@@ -1675,7 +1728,7 @@ def parse_prompt_attention(text):
     return res
 
 
-def get_prompts_with_weights(pipe: PipelineLike, prompt: List[str], max_length: int):
+def get_prompts_with_weights(pipe: PipelineLike, prompt: List[str], max_length: int, layer=None):
     r"""
     Tokenize a list of prompts and return its tokens with weights of each token.
     No padding, starting or ending token is included.
@@ -1691,7 +1744,7 @@ def get_prompts_with_weights(pipe: PipelineLike, prompt: List[str], max_length:
             # tokenize and discard the starting and the ending token
             token = pipe.tokenizer(word).input_ids[1:-1]
 
-            token = pipe.replace_token(token)
+            token = pipe.replace_token(token, layer=layer)
 
             text_token += token
             # copy the weight by length of token
@@ -1807,6 +1860,7 @@ def get_weighted_text_embeddings(
     skip_parsing: Optional[bool] = False,
     skip_weighting: Optional[bool] = False,
     clip_skip=None,
+    layer=None,
     **kwargs,
 ):
     r"""
@@ -1837,11 +1891,11 @@ def get_weighted_text_embeddings(
         prompt = [prompt]
 
     if not skip_parsing:
-        prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2)
+        prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2, layer=layer)
         if uncond_prompt is not None:
             if isinstance(uncond_prompt, str):
                 uncond_prompt = [uncond_prompt]
-            uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2)
+            uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2, layer=layer)
     else:
         prompt_tokens = [token[1:-1] for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True).input_ids]
         prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
@@ -2221,7 +2275,7 @@ def main(args):
                     if metadata is not None:
                         print(f"metadata for: {network_weight}: {metadata}")
 
-                network = imported_module.create_network_from_weights(
+                network, weights_sd = imported_module.create_network_from_weights(
                     network_mul, network_weight, vae, text_encoder, unet, **net_kwargs
                 )
             else:
@@ -2229,13 +2283,19 @@ def main(args):
             if network is None:
                 return
 
-            network.apply_to(text_encoder, unet)
+            if not args.network_merge:
+                network.apply_to(text_encoder, unet)
+                info = network.load_state_dict(weights_sd, False)       # network.load_weightsを使うようにするとよい
+                print(f"weights are loaded: {info}")
 
-            if args.opt_channels_last:
-                network.to(memory_format=torch.channels_last)
-            network.to(dtype).to(device)
+                if args.opt_channels_last:
+                    network.to(memory_format=torch.channels_last)
+                network.to(dtype).to(device)
+
+                networks.append(network)
+            else:
+                network.merge_to(text_encoder, unet, weights_sd, dtype, device)
 
-            networks.append(network)
     else:
         networks = []
 
@@ -2289,6 +2349,11 @@ def main(args):
     if args.diffusers_xformers:
         pipe.enable_xformers_memory_efficient_attention()
 
+    if args.XTI_embeddings:
+        diffusers.models.UNet2DConditionModel.forward = unet_forward_XTI
+        diffusers.models.unet_2d_blocks.CrossAttnDownBlock2D.forward = downblock_forward_XTI
+        diffusers.models.unet_2d_blocks.CrossAttnUpBlock2D.forward = upblock_forward_XTI
+
     # Textual Inversionを処理する
     if args.textual_inversion_embeddings:
         token_ids_embeds = []
@@ -2335,6 +2400,71 @@ def main(args):
             for token_id, embed in zip(token_ids, embeds):
                 token_embeds[token_id] = embed
 
+    if args.XTI_embeddings:
+        XTI_layers = [
+            "IN01",
+            "IN02",
+            "IN04",
+            "IN05",
+            "IN07",
+            "IN08",
+            "MID",
+            "OUT03",
+            "OUT04",
+            "OUT05",
+            "OUT06",
+            "OUT07",
+            "OUT08",
+            "OUT09",
+            "OUT10",
+            "OUT11",
+        ]
+        token_ids_embeds_XTI = []
+        for embeds_file in args.XTI_embeddings:
+            if model_util.is_safetensors(embeds_file):
+                from safetensors.torch import load_file
+
+                data = load_file(embeds_file)
+            else:
+                data = torch.load(embeds_file, map_location="cpu")
+            if set(data.keys()) != set(XTI_layers):
+                raise ValueError("NOT XTI")
+            embeds = torch.concat(list(data.values()))
+            num_vectors_per_token = data["MID"].size()[0]
+
+            token_string = os.path.splitext(os.path.basename(embeds_file))[0]
+            token_strings = [token_string] + [f"{token_string}{i+1}" for i in range(num_vectors_per_token - 1)]
+
+            # add new word to tokenizer, count is num_vectors_per_token
+            num_added_tokens = tokenizer.add_tokens(token_strings)
+            assert (
+                num_added_tokens == num_vectors_per_token
+            ), f"tokenizer has same word to token string (filename). please rename the file / 指定した名前（ファイル名）のトークンが既に存在します。ファイルをリネームしてください: {embeds_file}"
+
+            token_ids = tokenizer.convert_tokens_to_ids(token_strings)
+            print(f"XTI embeddings `{token_string}` loaded. Tokens are added: {token_ids}")
+
+            # if num_vectors_per_token > 1:
+            pipe.add_token_replacement(token_ids[0], token_ids)
+
+            token_strings_XTI = []
+            for layer_name in XTI_layers:
+                token_strings_XTI += [f"{t}_{layer_name}" for t in token_strings]
+            tokenizer.add_tokens(token_strings_XTI)
+            token_ids_XTI = tokenizer.convert_tokens_to_ids(token_strings_XTI)
+            token_ids_embeds_XTI.append((token_ids_XTI, embeds))
+            for t in token_ids:
+                t_XTI_dic = {}
+                for i, layer_name in enumerate(XTI_layers):
+                    t_XTI_dic[layer_name] = t + (i + 1) * num_added_tokens
+                pipe.add_token_replacement_XTI(t, t_XTI_dic)
+
+            text_encoder.resize_token_embeddings(len(tokenizer))
+            token_embeds = text_encoder.get_input_embeddings().weight.data
+            for token_ids, embeds in token_ids_embeds_XTI:
+                for token_id, embed in zip(token_ids, embeds):
+                    token_embeds[token_id] = embed
+
     # promptを取得する
     if args.from_file is not None:
         print(f"reading prompts from {args.from_file}")
@@ -2983,6 +3113,7 @@ def setup_parser() -> argparse.ArgumentParser:
         "--network_args", type=str, default=None, nargs="*", help="additional argmuments for network (key=value) / ネットワークへの追加の引数"
     )
     parser.add_argument("--network_show_meta", action="store_true", help="show metadata of network model / ネットワークモデルのメタデータを表示する")
+    parser.add_argument("--network_merge", action="store_true", help="merge network weights to original model / ネットワークの重みをマージする")
     parser.add_argument(
         "--textual_inversion_embeddings",
         type=str,
@@ -2990,6 +3121,13 @@ def setup_parser() -> argparse.ArgumentParser:
         nargs="*",
         help="Embeddings files of Textual Inversion / Textual Inversionのembeddings",
     )
+    parser.add_argument(
+        "--XTI_embeddings",
+        type=str,
+        default=None,
+        nargs="*",
+        help="Embeddings files of Extended Textual Inversion / Extended Textual Inversionのembeddings",
+    )
     parser.add_argument("--clip_skip", type=int, default=None, help="layer number from bottom to use in CLIP / CLIPの後ろからn層目の出力を使う")
     parser.add_argument(
         "--max_embeddings_multiples",

library/config_util.py

@@ -445,7 +445,7 @@ def generate_dreambooth_subsets_config_by_subdirs(train_data_dir: Optional[str]
     try:
       n_repeats = int(tokens[0])
     except ValueError as e:
-      print(f"ignore directory without repeats / 繰り返し回数のないディレクトリを無視します: {dir}")
+      print(f"ignore directory without repeats / 繰り返し回数のないディレクトリを無視します: {name}")
       return 0, ""
     caption_by_folder = '_'.join(tokens[1:])
     return n_repeats, caption_by_folder
@@ -486,7 +486,8 @@ def load_user_config(file: str) -> dict:
 
   if file.name.lower().endswith('.json'):
     try:
-      config = json.load(file)
+      with open(file, 'r') as f:
+        config = json.load(f)
     except Exception:
       print(f"Error on parsing JSON config file. Please check the format. / JSON 形式の設定ファイルの読み込みに失敗しました。文法が正しいか確認してください。: {file}")
       raise

library/train_util.py

@@ -404,6 +404,8 @@ class BaseDataset(torch.utils.data.Dataset):
 
         self.token_padding_disabled = False
         self.tag_frequency = {}
+        self.XTI_layers = None
+        self.token_strings = None
 
         self.enable_bucket = False
         self.bucket_manager: BucketManager = None  # not initialized
@@ -464,6 +466,10 @@ class BaseDataset(torch.utils.data.Dataset):
     def disable_token_padding(self):
         self.token_padding_disabled = True
 
+    def enable_XTI(self, layers=None, token_strings=None):
+        self.XTI_layers = layers
+        self.token_strings = token_strings
+
     def add_replacement(self, str_from, str_to):
         self.replacements[str_from] = str_to
 
@@ -909,9 +915,22 @@ class BaseDataset(torch.utils.data.Dataset):
             latents_list.append(latents)
 
             caption = self.process_caption(subset, image_info.caption)
-            captions.append(caption)
+            if self.XTI_layers:
+                caption_layer = []
+                for layer in self.XTI_layers:
+                    token_strings_from = " ".join(self.token_strings)
+                    token_strings_to = " ".join([f"{x}_{layer}" for x in self.token_strings])
+                    caption_ = caption.replace(token_strings_from, token_strings_to)
+                    caption_layer.append(caption_)
+                captions.append(caption_layer)
+            else:
+                captions.append(caption)
             if not self.token_padding_disabled:  # this option might be omitted in future
-                input_ids_list.append(self.get_input_ids(caption))
+                if self.XTI_layers:
+                    token_caption = self.get_input_ids(caption_layer)
+                else:
+                    token_caption = self.get_input_ids(caption)
+                input_ids_list.append(token_caption)
 
         example = {}
         example["loss_weights"] = torch.FloatTensor(loss_weights)
@@ -1314,6 +1333,10 @@ class DatasetGroup(torch.utils.data.ConcatDataset):
     #   for dataset in self.datasets:
     #     dataset.make_buckets()
 
+    def enable_XTI(self, *args, **kwargs):
+        for dataset in self.datasets:
+            dataset.enable_XTI(*args, **kwargs)
+
     def cache_latents(self, vae, vae_batch_size=1):
         for i, dataset in enumerate(self.datasets):
             print(f"[Dataset {i}]")
@@ -2437,7 +2460,7 @@ def get_scheduler_fix(args, optimizer: Optimizer, num_processes: int):
     Unified API to get any scheduler from its name.
     """
     name = args.lr_scheduler
-    num_warmup_steps = args.lr_warmup_steps
+    num_warmup_steps: Optional[int] = args.lr_warmup_steps
     num_training_steps = args.max_train_steps * num_processes * args.gradient_accumulation_steps
     num_cycles = args.lr_scheduler_num_cycles
     power = args.lr_scheduler_power
@@ -2461,6 +2484,11 @@ def get_scheduler_fix(args, optimizer: Optimizer, num_processes: int):
 
             lr_scheduler_kwargs[key] = value
 
+    def wrap_check_needless_num_warmup_steps(return_vals):
+        if num_warmup_steps is not None and num_warmup_steps != 0:
+            raise ValueError(f"{name} does not require `num_warmup_steps`. Set None or 0.")
+        return return_vals
+
     # using any lr_scheduler from other library
     if args.lr_scheduler_type:
         lr_scheduler_type = args.lr_scheduler_type
@@ -2473,7 +2501,7 @@ def get_scheduler_fix(args, optimizer: Optimizer, num_processes: int):
             lr_scheduler_type = values[-1]
         lr_scheduler_class = getattr(lr_scheduler_module, lr_scheduler_type)
         lr_scheduler = lr_scheduler_class(optimizer, **lr_scheduler_kwargs)
-        return lr_scheduler
+        return wrap_check_needless_num_warmup_steps(lr_scheduler)
 
     if name.startswith("adafactor"):
         assert (
@@ -2481,12 +2509,12 @@ def get_scheduler_fix(args, optimizer: Optimizer, num_processes: int):
         ), f"adafactor scheduler must be used with Adafactor optimizer / adafactor schedulerはAdafactorオプティマイザと同時に使ってください"
         initial_lr = float(name.split(":")[1])
         # print("adafactor scheduler init lr", initial_lr)
-        return transformers.optimization.AdafactorSchedule(optimizer, initial_lr)
+        return wrap_check_needless_num_warmup_steps(transformers.optimization.AdafactorSchedule(optimizer, initial_lr))
 
     name = SchedulerType(name)
     schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]
     if name == SchedulerType.CONSTANT:
-        return schedule_func(optimizer)
+        return wrap_check_needless_num_warmup_steps(schedule_func(optimizer))
 
     # All other schedulers require `num_warmup_steps`
     if num_warmup_steps is None:
@@ -2617,14 +2645,15 @@ def prepare_dtype(args: argparse.Namespace):
     return weight_dtype, save_dtype
 
 
-def load_target_model(args: argparse.Namespace, weight_dtype):
+def load_target_model(args: argparse.Namespace, weight_dtype, device='cpu'):
     name_or_path = args.pretrained_model_name_or_path
     name_or_path = os.readlink(name_or_path) if os.path.islink(name_or_path) else name_or_path
     load_stable_diffusion_format = os.path.isfile(name_or_path)  # determine SD or Diffusers
     if load_stable_diffusion_format:
         print("load StableDiffusion checkpoint")
-        text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, name_or_path)
+        text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, name_or_path, device)
     else:
+        # Diffusers model is loaded to CPU
         print("load Diffusers pretrained models")
         try:
             pipe = StableDiffusionPipeline.from_pretrained(name_or_path, tokenizer=None, safety_checker=None)

networks/extract_lora_from_models.py

@@ -145,8 +145,8 @@ def svd(args):
     lora_sd[lora_name + '.alpha'] = torch.tensor(down_weight.size()[0])
 
   # load state dict to LoRA and save it
-  lora_network_save = lora.create_network_from_weights(1.0, None, None, text_encoder_o, unet_o, weights_sd=lora_sd)
-  lora_network_save.apply_to(text_encoder_o, unet_o)        # create internal module references for state_dict
+  lora_network_save, lora_sd = lora.create_network_from_weights(1.0, None, None, text_encoder_o, unet_o, weights_sd=lora_sd)
+  lora_network_save.apply_to(text_encoder_o, unet_o)  # create internal module references for state_dict  
 
   info = lora_network_save.load_state_dict(lora_sd)
   print(f"Loading extracted LoRA weights: {info}")

networks/merge_lora.py

@@ -1,4 +1,3 @@
-
 import math
 import argparse
 import os
@@ -9,216 +8,236 @@ import lora
 
 
 def load_state_dict(file_name, dtype):
-  if os.path.splitext(file_name)[1] == '.safetensors':
-    sd = load_file(file_name)
-  else:
-    sd = torch.load(file_name, map_location='cpu')
-  for key in list(sd.keys()):
-    if type(sd[key]) == torch.Tensor:
-      sd[key] = sd[key].to(dtype)
-  return sd
+    if os.path.splitext(file_name)[1] == ".safetensors":
+        sd = load_file(file_name)
+    else:
+        sd = torch.load(file_name, map_location="cpu")
+    for key in list(sd.keys()):
+        if type(sd[key]) == torch.Tensor:
+            sd[key] = sd[key].to(dtype)
+    return sd
 
 
 def save_to_file(file_name, model, state_dict, dtype):
-  if dtype is not None:
-    for key in list(state_dict.keys()):
-      if type(state_dict[key]) == torch.Tensor:
-        state_dict[key] = state_dict[key].to(dtype)
+    if dtype is not None:
+        for key in list(state_dict.keys()):
+            if type(state_dict[key]) == torch.Tensor:
+                state_dict[key] = state_dict[key].to(dtype)
 
-  if os.path.splitext(file_name)[1] == '.safetensors':
-    save_file(model, file_name)
-  else:
-    torch.save(model, file_name)
+    if os.path.splitext(file_name)[1] == ".safetensors":
+        save_file(model, file_name)
+    else:
+        torch.save(model, file_name)
 
 
 def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
-  text_encoder.to(merge_dtype)
-  unet.to(merge_dtype)
+    text_encoder.to(merge_dtype)
+    unet.to(merge_dtype)
 
-  # create module map
-  name_to_module = {}
-  for i, root_module in enumerate([text_encoder, unet]):
-    if i == 0:
-      prefix = lora.LoRANetwork.LORA_PREFIX_TEXT_ENCODER
-      target_replace_modules = lora.LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE
-    else:
-      prefix = lora.LoRANetwork.LORA_PREFIX_UNET
-      target_replace_modules = lora.LoRANetwork.UNET_TARGET_REPLACE_MODULE
-
-    for name, module in root_module.named_modules():
-      if module.__class__.__name__ in target_replace_modules:
-        for child_name, child_module in module.named_modules():
-          if child_module.__class__.__name__ == "Linear" or child_module.__class__.__name__ == "Conv2d":
-            lora_name = prefix + '.' + name + '.' + child_name
-            lora_name = lora_name.replace('.', '_')
-            name_to_module[lora_name] = child_module
-
-  for model, ratio in zip(models, ratios):
-    print(f"loading: {model}")
-    lora_sd = load_state_dict(model, merge_dtype)
-
-    print(f"merging...")
-    for key in lora_sd.keys():
-      if "lora_down" in key:
-        up_key = key.replace("lora_down", "lora_up")
-        alpha_key = key[:key.index("lora_down")] + 'alpha'
-
-        # find original module for this lora
-        module_name = '.'.join(key.split('.')[:-2])               # remove trailing ".lora_down.weight"
-        if module_name not in name_to_module:
-          print(f"no module found for LoRA weight: {key}")
-          continue
-        module = name_to_module[module_name]
-        # print(f"apply {key} to {module}")
-
-        down_weight = lora_sd[key]
-        up_weight = lora_sd[up_key]
-
-        dim = down_weight.size()[0]
-        alpha = lora_sd.get(alpha_key, dim)
-        scale = alpha / dim
-
-        # W <- W + U * D
-        weight = module.weight
-        # print(module_name, down_weight.size(), up_weight.size())
-        if len(weight.size()) == 2:
-          # linear
-          weight = weight + ratio * (up_weight @ down_weight) * scale
-        elif down_weight.size()[2:4] == (1, 1):
-          # conv2d 1x1
-          weight = weight + ratio * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)
-                                     ).unsqueeze(2).unsqueeze(3) * scale
+    # create module map
+    name_to_module = {}
+    for i, root_module in enumerate([text_encoder, unet]):
+        if i == 0:
+            prefix = lora.LoRANetwork.LORA_PREFIX_TEXT_ENCODER
+            target_replace_modules = lora.LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE
         else:
-          # conv2d 3x3
-          conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
-          # print(conved.size(), weight.size(), module.stride, module.padding)
-          weight = weight + ratio * conved * scale
+            prefix = lora.LoRANetwork.LORA_PREFIX_UNET
+            target_replace_modules = (
+                lora.LoRANetwork.UNET_TARGET_REPLACE_MODULE + lora.LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
+            )
 
-        module.weight = torch.nn.Parameter(weight)
+        for name, module in root_module.named_modules():
+            if module.__class__.__name__ in target_replace_modules:
+                for child_name, child_module in module.named_modules():
+                    if child_module.__class__.__name__ == "Linear" or child_module.__class__.__name__ == "Conv2d":
+                        lora_name = prefix + "." + name + "." + child_name
+                        lora_name = lora_name.replace(".", "_")
+                        name_to_module[lora_name] = child_module
+
+    for model, ratio in zip(models, ratios):
+        print(f"loading: {model}")
+        lora_sd = load_state_dict(model, merge_dtype)
+
+        print(f"merging...")
+        for key in lora_sd.keys():
+            if "lora_down" in key:
+                up_key = key.replace("lora_down", "lora_up")
+                alpha_key = key[: key.index("lora_down")] + "alpha"
+
+                # find original module for this lora
+                module_name = ".".join(key.split(".")[:-2])  # remove trailing ".lora_down.weight"
+                if module_name not in name_to_module:
+                    print(f"no module found for LoRA weight: {key}")
+                    continue
+                module = name_to_module[module_name]
+                # print(f"apply {key} to {module}")
+
+                down_weight = lora_sd[key]
+                up_weight = lora_sd[up_key]
+
+                dim = down_weight.size()[0]
+                alpha = lora_sd.get(alpha_key, dim)
+                scale = alpha / dim
+
+                # W <- W + U * D
+                weight = module.weight
+                # print(module_name, down_weight.size(), up_weight.size())
+                if len(weight.size()) == 2:
+                    # linear
+                    weight = weight + ratio * (up_weight @ down_weight) * scale
+                elif down_weight.size()[2:4] == (1, 1):
+                    # conv2d 1x1
+                    weight = (
+                        weight
+                        + ratio
+                        * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
+                        * scale
+                    )
+                else:
+                    # conv2d 3x3
+                    conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
+                    # print(conved.size(), weight.size(), module.stride, module.padding)
+                    weight = weight + ratio * conved * scale
+
+                module.weight = torch.nn.Parameter(weight)
 
 
 def merge_lora_models(models, ratios, merge_dtype):
-  base_alphas = {}                          # alpha for merged model
-  base_dims = {}
+    base_alphas = {}  # alpha for merged model
+    base_dims = {}
 
-  merged_sd = {}
-  for model, ratio in zip(models, ratios):
-    print(f"loading: {model}")
-    lora_sd = load_state_dict(model, merge_dtype)
+    merged_sd = {}
+    for model, ratio in zip(models, ratios):
+        print(f"loading: {model}")
+        lora_sd = load_state_dict(model, merge_dtype)
 
-    # get alpha and dim
-    alphas = {}                             # alpha for current model
-    dims = {}                               # dims for current model
-    for key in lora_sd.keys():
-      if 'alpha' in key:
-        lora_module_name = key[:key.rfind(".alpha")]
-        alpha = float(lora_sd[key].detach().numpy())
-        alphas[lora_module_name] = alpha
-        if lora_module_name not in base_alphas:
-          base_alphas[lora_module_name] = alpha
-      elif "lora_down" in key:
-        lora_module_name = key[:key.rfind(".lora_down")]
-        dim = lora_sd[key].size()[0]
-        dims[lora_module_name] = dim
-        if lora_module_name not in base_dims:
-          base_dims[lora_module_name] = dim
+        # get alpha and dim
+        alphas = {}  # alpha for current model
+        dims = {}  # dims for current model
+        for key in lora_sd.keys():
+            if "alpha" in key:
+                lora_module_name = key[: key.rfind(".alpha")]
+                alpha = float(lora_sd[key].detach().numpy())
+                alphas[lora_module_name] = alpha
+                if lora_module_name not in base_alphas:
+                    base_alphas[lora_module_name] = alpha
+            elif "lora_down" in key:
+                lora_module_name = key[: key.rfind(".lora_down")]
+                dim = lora_sd[key].size()[0]
+                dims[lora_module_name] = dim
+                if lora_module_name not in base_dims:
+                    base_dims[lora_module_name] = dim
 
-    for lora_module_name in dims.keys():
-      if lora_module_name not in alphas:
-        alpha = dims[lora_module_name]
-        alphas[lora_module_name] = alpha
-        if lora_module_name not in base_alphas:
-          base_alphas[lora_module_name] = alpha
+        for lora_module_name in dims.keys():
+            if lora_module_name not in alphas:
+                alpha = dims[lora_module_name]
+                alphas[lora_module_name] = alpha
+                if lora_module_name not in base_alphas:
+                    base_alphas[lora_module_name] = alpha
 
-    print(f"dim: {list(set(dims.values()))}, alpha: {list(set(alphas.values()))}")
+        print(f"dim: {list(set(dims.values()))}, alpha: {list(set(alphas.values()))}")
 
-    # merge
-    print(f"merging...")
-    for key in lora_sd.keys():
-      if 'alpha' in key:
-        continue
+        # merge
+        print(f"merging...")
+        for key in lora_sd.keys():
+            if "alpha" in key:
+                continue
 
-      lora_module_name = key[:key.rfind(".lora_")]
+            lora_module_name = key[: key.rfind(".lora_")]
 
-      base_alpha = base_alphas[lora_module_name]
-      alpha = alphas[lora_module_name]
+            base_alpha = base_alphas[lora_module_name]
+            alpha = alphas[lora_module_name]
 
-      scale = math.sqrt(alpha / base_alpha) * ratio
+            scale = math.sqrt(alpha / base_alpha) * ratio
 
-      if key in merged_sd:
-        assert merged_sd[key].size() == lora_sd[key].size(
-        ), f"weights shape mismatch merging v1 and v2, different dims? / 重みのサイズが合いません。v1とv2、または次元数の異なるモデルはマージできません"
-        merged_sd[key] = merged_sd[key] + lora_sd[key] * scale
-      else:
-        merged_sd[key] = lora_sd[key] * scale
+            if key in merged_sd:
+                assert (
+                    merged_sd[key].size() == lora_sd[key].size()
+                ), f"weights shape mismatch merging v1 and v2, different dims? / 重みのサイズが合いません。v1とv2、または次元数の異なるモデルはマージできません"
+                merged_sd[key] = merged_sd[key] + lora_sd[key] * scale
+            else:
+                merged_sd[key] = lora_sd[key] * scale
 
-  # set alpha to sd
-  for lora_module_name, alpha in base_alphas.items():
-    key = lora_module_name + ".alpha"
-    merged_sd[key] = torch.tensor(alpha)
+    # set alpha to sd
+    for lora_module_name, alpha in base_alphas.items():
+        key = lora_module_name + ".alpha"
+        merged_sd[key] = torch.tensor(alpha)
 
-  print("merged model")
-  print(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}")
+    print("merged model")
+    print(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}")
 
-  return merged_sd
+    return merged_sd
 
 
 def merge(args):
-  assert len(args.models) == len(args.ratios), f"number of models must be equal to number of ratios / モデルの数と重みの数は合わせてください"
+    assert len(args.models) == len(args.ratios), f"number of models must be equal to number of ratios / モデルの数と重みの数は合わせてください"
 
-  def str_to_dtype(p):
-    if p == 'float':
-      return torch.float
-    if p == 'fp16':
-      return torch.float16
-    if p == 'bf16':
-      return torch.bfloat16
-    return None
+    def str_to_dtype(p):
+        if p == "float":
+            return torch.float
+        if p == "fp16":
+            return torch.float16
+        if p == "bf16":
+            return torch.bfloat16
+        return None
 
-  merge_dtype = str_to_dtype(args.precision)
-  save_dtype = str_to_dtype(args.save_precision)
-  if save_dtype is None:
-    save_dtype = merge_dtype
+    merge_dtype = str_to_dtype(args.precision)
+    save_dtype = str_to_dtype(args.save_precision)
+    if save_dtype is None:
+        save_dtype = merge_dtype
 
-  if args.sd_model is not None:
-    print(f"loading SD model: {args.sd_model}")
+    if args.sd_model is not None:
+        print(f"loading SD model: {args.sd_model}")
 
-    text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.sd_model)
+        text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.sd_model)
 
-    merge_to_sd_model(text_encoder, unet, args.models, args.ratios, merge_dtype)
+        merge_to_sd_model(text_encoder, unet, args.models, args.ratios, merge_dtype)
 
-    print(f"saving SD model to: {args.save_to}")
-    model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet,
-                                                args.sd_model, 0, 0, save_dtype, vae)
-  else:
-    state_dict = merge_lora_models(args.models, args.ratios, merge_dtype)
+        print(f"saving SD model to: {args.save_to}")
+        model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet, args.sd_model, 0, 0, save_dtype, vae)
+    else:
+        state_dict = merge_lora_models(args.models, args.ratios, merge_dtype)
 
-    print(f"saving model to: {args.save_to}")
-    save_to_file(args.save_to, state_dict, state_dict, save_dtype)
+        print(f"saving model to: {args.save_to}")
+        save_to_file(args.save_to, state_dict, state_dict, save_dtype)
 
 
 def setup_parser() -> argparse.ArgumentParser:
-  parser = argparse.ArgumentParser()
-  parser.add_argument("--v2", action='store_true',
-                      help='load Stable Diffusion v2.x model / Stable Diffusion 2.xのモデルを読み込む')
-  parser.add_argument("--save_precision", type=str, default=None,
-                      choices=[None, "float", "fp16", "bf16"], help="precision in saving, same to merging if omitted / 保存時に精度を変更して保存する、省略時はマージ時の精度と同じ")
-  parser.add_argument("--precision", type=str, default="float",
-                      choices=["float", "fp16", "bf16"], help="precision in merging (float is recommended) / マージの計算時の精度（floatを推奨）")
-  parser.add_argument("--sd_model", type=str, default=None,
-                      help="Stable Diffusion model to load: ckpt or safetensors file, merge LoRA models if omitted / 読み込むモデル、ckptまたはsafetensors。省略時はLoRAモデル同士をマージする")
-  parser.add_argument("--save_to", type=str, default=None,
-                      help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors")
-  parser.add_argument("--models", type=str, nargs='*',
-                      help="LoRA models to merge: ckpt or safetensors file / マージするLoRAモデル、ckptまたはsafetensors")
-  parser.add_argument("--ratios", type=float, nargs='*',
-                      help="ratios for each model / それぞれのLoRAモデルの比率")
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--v2", action="store_true", help="load Stable Diffusion v2.x model / Stable Diffusion 2.xのモデルを読み込む")
+    parser.add_argument(
+        "--save_precision",
+        type=str,
+        default=None,
+        choices=[None, "float", "fp16", "bf16"],
+        help="precision in saving, same to merging if omitted / 保存時に精度を変更して保存する、省略時はマージ時の精度と同じ",
+    )
+    parser.add_argument(
+        "--precision",
+        type=str,
+        default="float",
+        choices=["float", "fp16", "bf16"],
+        help="precision in merging (float is recommended) / マージの計算時の精度（floatを推奨）",
+    )
+    parser.add_argument(
+        "--sd_model",
+        type=str,
+        default=None,
+        help="Stable Diffusion model to load: ckpt or safetensors file, merge LoRA models if omitted / 読み込むモデル、ckptまたはsafetensors。省略時はLoRAモデル同士をマージする",
+    )
+    parser.add_argument(
+        "--save_to", type=str, default=None, help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors"
+    )
+    parser.add_argument(
+        "--models", type=str, nargs="*", help="LoRA models to merge: ckpt or safetensors file / マージするLoRAモデル、ckptまたはsafetensors"
+    )
+    parser.add_argument("--ratios", type=float, nargs="*", help="ratios for each model / それぞれのLoRAモデルの比率")
 
-  return parser
+    return parser
 
 
-if __name__ == '__main__':
-  parser = setup_parser()
+if __name__ == "__main__":
+    parser = setup_parser()
 
-  args = parser.parse_args()
-  merge(args)
+    args = parser.parse_args()
+    merge(args)

train_README-ja.md

@@ -801,7 +801,7 @@ model_dirオプションでモデルの保存先フォルダを指定できま
 キャプションをメタデータに入れるには、作業フォルダ内で以下を実行してください（キャプションを学習に使わない場合は実行不要です）（実際は1行で記述します、以下同様）。`--full_path` オプションを指定してメタデータに画像ファイルの場所をフルパスで格納します。このオプションを省略すると相対パスで記録されますが、フォルダ指定が `.toml` ファイル内で別途必要になります。
 
 ```
-python merge_captions_to_metadata.py --full_apth <教師データフォルダ>
+python merge_captions_to_metadata.py --full_path <教師データフォルダ>
 　  --in_json <読み込むメタデータファイル名> <メタデータファイル名>
 ```
 

train_network.py

@@ -25,23 +25,38 @@ from library.config_util import (
     BlueprintGenerator,
 )
 import library.custom_train_functions as custom_train_functions
-from library.custom_train_functions import apply_snr_weight 
+from library.custom_train_functions import apply_snr_weight
 
 
 # TODO 他のスクリプトと共通化する
 def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler):
     logs = {"loss/current": current_loss, "loss/average": avr_loss}
 
-    if args.network_train_unet_only:
-        logs["lr/unet"] = float(lr_scheduler.get_last_lr()[0])
-    elif args.network_train_text_encoder_only:
-        logs["lr/textencoder"] = float(lr_scheduler.get_last_lr()[0])
-    else:
-        logs["lr/textencoder"] = float(lr_scheduler.get_last_lr()[0])
-        logs["lr/unet"] = float(lr_scheduler.get_last_lr()[-1])  # may be same to textencoder
+    lrs = lr_scheduler.get_last_lr()
 
-    if args.optimizer_type.lower() == "DAdaptation".lower():  # tracking d*lr value of unet.
-        logs["lr/d*lr"] = lr_scheduler.optimizers[-1].param_groups[0]["d"] * lr_scheduler.optimizers[-1].param_groups[0]["lr"]
+    if args.network_train_text_encoder_only or len(lrs) <= 2:  # not block lr (or single block)
+        if args.network_train_unet_only:
+            logs["lr/unet"] = float(lrs[0])
+        elif args.network_train_text_encoder_only:
+            logs["lr/textencoder"] = float(lrs[0])
+        else:
+            logs["lr/textencoder"] = float(lrs[0])
+            logs["lr/unet"] = float(lrs[-1])  # may be same to textencoder
+
+        if args.optimizer_type.lower() == "DAdaptation".lower():  # tracking d*lr value of unet.
+            logs["lr/d*lr"] = lr_scheduler.optimizers[-1].param_groups[0]["d"] * lr_scheduler.optimizers[-1].param_groups[0]["lr"]
+    else:
+        idx = 0
+        if not args.network_train_unet_only:
+            logs["lr/textencoder"] = float(lrs[0])
+            idx = 1
+
+        for i in range(idx, len(lrs)):
+            logs[f"lr/group{i}"] = float(lrs[i])
+            if args.optimizer_type.lower() == "DAdaptation".lower():
+                logs[f"lr/d*lr/group{i}"] = (
+                    lr_scheduler.optimizers[-1].param_groups[i]["d"] * lr_scheduler.optimizers[-1].param_groups[i]["lr"]
+                )
 
     return logs
 
@@ -99,10 +114,10 @@ def train(args):
     blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
     train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
 
-    current_epoch = Value('i',0)
-    current_step = Value('i',0)
+    current_epoch = Value("i", 0)
+    current_step = Value("i", 0)
     ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
-    collater = train_util.collater_class(current_epoch,current_step, ds_for_collater)
+    collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
 
     if args.debug_dataset:
         train_util.debug_dataset(train_dataset_group)
@@ -127,12 +142,24 @@ def train(args):
     weight_dtype, save_dtype = train_util.prepare_dtype(args)
 
     # モデルを読み込む
-    text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype)
+    for pi in range(accelerator.state.num_processes):
+        # TODO: modify other training scripts as well
+        if pi == accelerator.state.local_process_index:
+            print(f"loading model for process {accelerator.state.local_process_index}/{accelerator.state.num_processes}")
 
-    # work on low-ram device
-    if args.lowram:
-        text_encoder.to("cuda")
-        unet.to("cuda")
+            text_encoder, vae, unet, _ = train_util.load_target_model(
+                args, weight_dtype, accelerator.device if args.lowram else "cpu"
+            )
+
+            # work on low-ram device
+            if args.lowram:
+                text_encoder.to(accelerator.device)
+                unet.to(accelerator.device)
+                vae.to(accelerator.device)
+
+            gc.collect()
+            torch.cuda.empty_cache()
+        accelerator.wait_for_everyone()
 
     # モデルに xformers とか memory efficient attention を組み込む
     train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
@@ -166,15 +193,18 @@ def train(args):
     network = network_module.create_network(1.0, args.network_dim, args.network_alpha, vae, text_encoder, unet, **net_kwargs)
     if network is None:
         return
-
-    if args.network_weights is not None:
-        print("load network weights from:", args.network_weights)
-        network.load_weights(args.network_weights)
+    
+    if hasattr(network, "prepare_network"):
+        network.prepare_network(args)
 
     train_unet = not args.network_train_text_encoder_only
     train_text_encoder = not args.network_train_unet_only
     network.apply_to(text_encoder, unet, train_text_encoder, train_unet)
 
+    if args.network_weights is not None:
+        info = network.load_weights(args.network_weights)
+        print(f"load network weights from {args.network_weights}: {info}")
+
     if args.gradient_checkpointing:
         unet.enable_gradient_checkpointing()
         text_encoder.gradient_checkpointing_enable()
@@ -183,13 +213,19 @@ def train(args):
     # 学習に必要なクラスを準備する
     print("prepare optimizer, data loader etc.")
 
-    trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr)
+    # 後方互換性を確保するよ
+    try:
+        trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr, args.learning_rate)
+    except TypeError:
+        print("Deprecated: use prepare_optimizer_params(text_encoder_lr, unet_lr, learning_rate) instead of prepare_optimizer_params(text_encoder_lr, unet_lr)")
+        trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr)
+
     optimizer_name, optimizer_args, optimizer = train_util.get_optimizer(args, trainable_params)
 
     # dataloaderを準備する
     # DataLoaderのプロセス数：0はメインプロセスになる
     n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
-    
+
     train_dataloader = torch.utils.data.DataLoader(
         train_dataset_group,
         batch_size=1,
@@ -201,7 +237,9 @@ def train(args):
 
     # 学習ステップ数を計算する
     if args.max_train_epochs is not None:
-        args.max_train_steps = args.max_train_epochs * math.ceil(len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps)
+        args.max_train_steps = args.max_train_epochs * math.ceil(
+            len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
+        )
         if is_main_process:
             print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
 
@@ -333,6 +371,7 @@ def train(args):
         "ss_caption_tag_dropout_rate": args.caption_tag_dropout_rate,
         "ss_face_crop_aug_range": args.face_crop_aug_range,
         "ss_prior_loss_weight": args.prior_loss_weight,
+        "ss_min_snr_gamma": args.min_snr_gamma,
     }
 
     if use_user_config:
@@ -461,8 +500,6 @@ def train(args):
     # add extra args
     if args.network_args:
         metadata["ss_network_args"] = json.dumps(net_kwargs)
-        # for key, value in net_kwargs.items():
-        #   metadata["ss_arg_" + key] = value
 
     # model name and hash
     if args.pretrained_model_name_or_path is not None:
@@ -505,7 +542,7 @@ def train(args):
     for epoch in range(num_train_epochs):
         if is_main_process:
             print(f"epoch {epoch+1}/{num_train_epochs}")
-        current_epoch.value = epoch+1
+        current_epoch.value = epoch + 1
 
         metadata["ss_epoch"] = str(epoch + 1)
 
@@ -556,9 +593,9 @@ def train(args):
 
                 loss_weights = batch["loss_weights"]  # 各sampleごとのweight
                 loss = loss * loss_weights
-                 
+
                 if args.min_snr_gamma:
-                  loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
+                    loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
 
                 loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
 

train_textual_inversion_XTI.py

@@ -0,0 +1,644 @@
+import importlib
+import argparse
+import gc
+import math
+import os
+import toml
+from multiprocessing import Value
+
+from tqdm import tqdm
+import torch
+from accelerate.utils import set_seed
+import diffusers
+from diffusers import DDPMScheduler
+
+import library.train_util as train_util
+import library.config_util as config_util
+from library.config_util import (
+    ConfigSanitizer,
+    BlueprintGenerator,
+)
+import library.custom_train_functions as custom_train_functions
+from library.custom_train_functions import apply_snr_weight
+from XTI_hijack import unet_forward_XTI, downblock_forward_XTI, upblock_forward_XTI
+
+imagenet_templates_small = [
+    "a photo of a {}",
+    "a rendering of a {}",
+    "a cropped photo of the {}",
+    "the photo of a {}",
+    "a photo of a clean {}",
+    "a photo of a dirty {}",
+    "a dark photo of the {}",
+    "a photo of my {}",
+    "a photo of the cool {}",
+    "a close-up photo of a {}",
+    "a bright photo of the {}",
+    "a cropped photo of a {}",
+    "a photo of the {}",
+    "a good photo of the {}",
+    "a photo of one {}",
+    "a close-up photo of the {}",
+    "a rendition of the {}",
+    "a photo of the clean {}",
+    "a rendition of a {}",
+    "a photo of a nice {}",
+    "a good photo of a {}",
+    "a photo of the nice {}",
+    "a photo of the small {}",
+    "a photo of the weird {}",
+    "a photo of the large {}",
+    "a photo of a cool {}",
+    "a photo of a small {}",
+]
+
+imagenet_style_templates_small = [
+    "a painting in the style of {}",
+    "a rendering in the style of {}",
+    "a cropped painting in the style of {}",
+    "the painting in the style of {}",
+    "a clean painting in the style of {}",
+    "a dirty painting in the style of {}",
+    "a dark painting in the style of {}",
+    "a picture in the style of {}",
+    "a cool painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a bright painting in the style of {}",
+    "a cropped painting in the style of {}",
+    "a good painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a rendition in the style of {}",
+    "a nice painting in the style of {}",
+    "a small painting in the style of {}",
+    "a weird painting in the style of {}",
+    "a large painting in the style of {}",
+]
+
+
+def train(args):
+    if args.output_name is None:
+        args.output_name = args.token_string
+    use_template = args.use_object_template or args.use_style_template
+
+    train_util.verify_training_args(args)
+    train_util.prepare_dataset_args(args, True)
+
+    if args.sample_every_n_steps is not None or args.sample_every_n_epochs is not None:
+        print(
+            "sample_every_n_steps and sample_every_n_epochs are not supported in this script currently / sample_every_n_stepsとsample_every_n_epochsは現在このスクリプトではサポートされていません"
+        )
+
+    cache_latents = args.cache_latents
+
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    tokenizer = train_util.load_tokenizer(args)
+
+    # acceleratorを準備する
+    print("prepare accelerator")
+    accelerator, unwrap_model = train_util.prepare_accelerator(args)
+
+    # mixed precisionに対応した型を用意しておき適宜castする
+    weight_dtype, save_dtype = train_util.prepare_dtype(args)
+
+    # モデルを読み込む
+    text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype)
+
+    # Convert the init_word to token_id
+    if args.init_word is not None:
+        init_token_ids = tokenizer.encode(args.init_word, add_special_tokens=False)
+        if len(init_token_ids) > 1 and len(init_token_ids) != args.num_vectors_per_token:
+            print(
+                f"token length for init words is not same to num_vectors_per_token, init words is repeated or truncated / 初期化単語のトークン長がnum_vectors_per_tokenと合わないため、繰り返しまたは切り捨てが発生します: length {len(init_token_ids)}"
+            )
+    else:
+        init_token_ids = None
+
+    # add new word to tokenizer, count is num_vectors_per_token
+    token_strings = [args.token_string] + [f"{args.token_string}{i+1}" for i in range(args.num_vectors_per_token - 1)]
+    num_added_tokens = tokenizer.add_tokens(token_strings)
+    assert (
+        num_added_tokens == args.num_vectors_per_token
+    ), f"tokenizer has same word to token string. please use another one / 指定したargs.token_stringは既に存在します。別の単語を使ってください: {args.token_string}"
+
+    token_ids = tokenizer.convert_tokens_to_ids(token_strings)
+    print(f"tokens are added: {token_ids}")
+    assert min(token_ids) == token_ids[0] and token_ids[-1] == token_ids[0] + len(token_ids) - 1, f"token ids is not ordered"
+    assert len(tokenizer) - 1 == token_ids[-1], f"token ids is not end of tokenize: {len(tokenizer)}"
+
+    token_strings_XTI = []
+    XTI_layers = [
+        "IN01",
+        "IN02",
+        "IN04",
+        "IN05",
+        "IN07",
+        "IN08",
+        "MID",
+        "OUT03",
+        "OUT04",
+        "OUT05",
+        "OUT06",
+        "OUT07",
+        "OUT08",
+        "OUT09",
+        "OUT10",
+        "OUT11",
+    ]
+    for layer_name in XTI_layers:
+        token_strings_XTI += [f"{t}_{layer_name}" for t in token_strings]
+
+    tokenizer.add_tokens(token_strings_XTI)
+    token_ids_XTI = tokenizer.convert_tokens_to_ids(token_strings_XTI)
+    print(f"tokens are added (XTI): {token_ids_XTI}")
+    # Resize the token embeddings as we are adding new special tokens to the tokenizer
+    text_encoder.resize_token_embeddings(len(tokenizer))
+
+    # Initialise the newly added placeholder token with the embeddings of the initializer token
+    token_embeds = text_encoder.get_input_embeddings().weight.data
+    if init_token_ids is not None:
+        for i, token_id in enumerate(token_ids_XTI):
+            token_embeds[token_id] = token_embeds[init_token_ids[(i // 16) % len(init_token_ids)]]
+            # print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
+
+    # load weights
+    if args.weights is not None:
+        embeddings = load_weights(args.weights)
+        assert len(token_ids) == len(
+            embeddings
+        ), f"num_vectors_per_token is mismatch for weights / 指定した重みとnum_vectors_per_tokenの値が異なります: {len(embeddings)}"
+        # print(token_ids, embeddings.size())
+        for token_id, embedding in zip(token_ids_XTI, embeddings):
+            token_embeds[token_id] = embedding
+            # print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
+        print(f"weighs loaded")
+
+    print(f"create embeddings for {args.num_vectors_per_token} tokens, for {args.token_string}")
+
+    # データセットを準備する
+    blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, False))
+    if args.dataset_config is not None:
+        print(f"Load dataset config from {args.dataset_config}")
+        user_config = config_util.load_user_config(args.dataset_config)
+        ignored = ["train_data_dir", "reg_data_dir", "in_json"]
+        if any(getattr(args, attr) is not None for attr in ignored):
+            print(
+                "ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
+                    ", ".join(ignored)
+                )
+            )
+    else:
+        use_dreambooth_method = args.in_json is None
+        if use_dreambooth_method:
+            print("Use DreamBooth method.")
+            user_config = {
+                "datasets": [
+                    {"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)}
+                ]
+            }
+        else:
+            print("Train with captions.")
+            user_config = {
+                "datasets": [
+                    {
+                        "subsets": [
+                            {
+                                "image_dir": args.train_data_dir,
+                                "metadata_file": args.in_json,
+                            }
+                        ]
+                    }
+                ]
+            }
+
+    blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
+    train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
+    train_dataset_group.enable_XTI(XTI_layers, token_strings=token_strings)
+    current_epoch = Value("i", 0)
+    current_step = Value("i", 0)
+    ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
+    collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
+
+    # make captions: tokenstring tokenstring1 tokenstring2 ...tokenstringn という文字列に書き換える超乱暴な実装
+    if use_template:
+        print("use template for training captions. is object: {args.use_object_template}")
+        templates = imagenet_templates_small if args.use_object_template else imagenet_style_templates_small
+        replace_to = " ".join(token_strings)
+        captions = []
+        for tmpl in templates:
+            captions.append(tmpl.format(replace_to))
+        train_dataset_group.add_replacement("", captions)
+
+        if args.num_vectors_per_token > 1:
+            prompt_replacement = (args.token_string, replace_to)
+        else:
+            prompt_replacement = None
+    else:
+        if args.num_vectors_per_token > 1:
+            replace_to = " ".join(token_strings)
+            train_dataset_group.add_replacement(args.token_string, replace_to)
+            prompt_replacement = (args.token_string, replace_to)
+        else:
+            prompt_replacement = None
+
+    if args.debug_dataset:
+        train_util.debug_dataset(train_dataset_group, show_input_ids=True)
+        return
+    if len(train_dataset_group) == 0:
+        print("No data found. Please verify arguments / 画像がありません。引数指定を確認してください")
+        return
+
+    if cache_latents:
+        assert (
+            train_dataset_group.is_latent_cacheable()
+        ), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
+
+    # モデルに xformers とか memory efficient attention を組み込む
+    train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
+    diffusers.models.UNet2DConditionModel.forward = unet_forward_XTI
+    diffusers.models.unet_2d_blocks.CrossAttnDownBlock2D.forward = downblock_forward_XTI
+    diffusers.models.unet_2d_blocks.CrossAttnUpBlock2D.forward = upblock_forward_XTI
+
+    # 学習を準備する
+    if cache_latents:
+        vae.to(accelerator.device, dtype=weight_dtype)
+        vae.requires_grad_(False)
+        vae.eval()
+        with torch.no_grad():
+            train_dataset_group.cache_latents(vae, args.vae_batch_size)
+        vae.to("cpu")
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        gc.collect()
+
+    if args.gradient_checkpointing:
+        unet.enable_gradient_checkpointing()
+        text_encoder.gradient_checkpointing_enable()
+
+    # 学習に必要なクラスを準備する
+    print("prepare optimizer, data loader etc.")
+    trainable_params = text_encoder.get_input_embeddings().parameters()
+    _, _, optimizer = train_util.get_optimizer(args, trainable_params)
+
+    # dataloaderを準備する
+    # DataLoaderのプロセス数：0はメインプロセスになる
+    n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset_group,
+        batch_size=1,
+        shuffle=True,
+        collate_fn=collater,
+        num_workers=n_workers,
+        persistent_workers=args.persistent_data_loader_workers,
+    )
+
+    # 学習ステップ数を計算する
+    if args.max_train_epochs is not None:
+        args.max_train_steps = args.max_train_epochs * math.ceil(
+            len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
+        )
+        print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
+
+    # データセット側にも学習ステップを送信
+    train_dataset_group.set_max_train_steps(args.max_train_steps)
+
+    # lr schedulerを用意する
+    lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
+
+    # acceleratorがなんかよろしくやってくれるらしい
+    text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        text_encoder, optimizer, train_dataloader, lr_scheduler
+    )
+
+    index_no_updates = torch.arange(len(tokenizer)) < token_ids_XTI[0]
+    # print(len(index_no_updates), torch.sum(index_no_updates))
+    orig_embeds_params = unwrap_model(text_encoder).get_input_embeddings().weight.data.detach().clone()
+
+    # 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)
+    # text_encoder.text_model.embeddings.token_embedding.requires_grad_(True)
+
+    unet.requires_grad_(False)
+    unet.to(accelerator.device, dtype=weight_dtype)
+    if args.gradient_checkpointing:  # according to TI example in Diffusers, train is required
+        unet.train()
+    else:
+        unet.eval()
+
+    if not cache_latents:
+        vae.requires_grad_(False)
+        vae.eval()
+        vae.to(accelerator.device, dtype=weight_dtype)
+
+    # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
+    if args.full_fp16:
+        train_util.patch_accelerator_for_fp16_training(accelerator)
+        text_encoder.to(weight_dtype)
+
+    # resumeする
+    if args.resume is not None:
+        print(f"resume training from state: {args.resume}")
+        accelerator.load_state(args.resume)
+
+    # epoch数を計算する
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+    if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
+        args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
+
+    # 学習する
+    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+    print("running training / 学習開始")
+    print(f"  num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
+    print(f"  num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
+    print(f"  num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
+    print(f"  num epochs / epoch数: {num_train_epochs}")
+    print(f"  batch size per device / バッチサイズ: {args.train_batch_size}")
+    print(f"  total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
+    print(f"  gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
+    print(f"  total optimization steps / 学習ステップ数: {args.max_train_steps}")
+
+    progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
+    global_step = 0
+
+    noise_scheduler = DDPMScheduler(
+        beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False
+    )
+
+    if accelerator.is_main_process:
+        accelerator.init_trackers("textual_inversion")
+
+    for epoch in range(num_train_epochs):
+        print(f"epoch {epoch+1}/{num_train_epochs}")
+        current_epoch.value = epoch + 1
+
+        text_encoder.train()
+
+        loss_total = 0
+
+        for step, batch in enumerate(train_dataloader):
+            current_step.value = global_step
+            with accelerator.accumulate(text_encoder):
+                with torch.no_grad():
+                    if "latents" in batch and batch["latents"] is not None:
+                        latents = batch["latents"].to(accelerator.device)
+                    else:
+                        # latentに変換
+                        latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
+                    latents = latents * 0.18215
+                b_size = latents.shape[0]
+
+                # Get the text embedding for conditioning
+                input_ids = batch["input_ids"].to(accelerator.device)
+                # weight_dtype) use float instead of fp16/bf16 because text encoder is float
+                encoder_hidden_states = torch.stack(
+                    [
+                        train_util.get_hidden_states(args, s, tokenizer, text_encoder, weight_dtype)
+                        for s in torch.split(input_ids, 1, dim=1)
+                    ]
+                )
+
+                # Sample noise that we'll add to the latents
+                noise = torch.randn_like(latents, device=latents.device)
+                if args.noise_offset:
+                    # https://www.crosslabs.org//blog/diffusion-with-offset-noise
+                    noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
+
+                # Sample a random timestep for each image
+                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
+                timesteps = timesteps.long()
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+
+                # Predict the noise residual
+                noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states=encoder_hidden_states).sample
+
+                if args.v_parameterization:
+                    # v-parameterization training
+                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
+                else:
+                    target = noise
+
+                loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
+                loss = loss.mean([1, 2, 3])
+
+                if args.min_snr_gamma:
+                    loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
+
+                loss_weights = batch["loss_weights"]  # 各sampleごとのweight
+                loss = loss * loss_weights
+
+                loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
+
+                accelerator.backward(loss)
+                if accelerator.sync_gradients and args.max_grad_norm != 0.0:
+                    params_to_clip = text_encoder.get_input_embeddings().parameters()
+                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
+
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad(set_to_none=True)
+
+                # Let's make sure we don't update any embedding weights besides the newly added token
+                with torch.no_grad():
+                    unwrap_model(text_encoder).get_input_embeddings().weight[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:
+                progress_bar.update(1)
+                global_step += 1
+                # TODO: fix sample_images
+                # train_util.sample_images(
+                #     accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, prompt_replacement
+                # )
+
+            current_loss = loss.detach().item()
+            if args.logging_dir is not None:
+                logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
+                if args.optimizer_type.lower() == "DAdaptation".lower():  # tracking d*lr value
+                    logs["lr/d*lr"] = (
+                        lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
+                    )
+                accelerator.log(logs, step=global_step)
+
+            loss_total += current_loss
+            avr_loss = loss_total / (step + 1)
+            logs = {"loss": avr_loss}  # , "lr": lr_scheduler.get_last_lr()[0]}
+            progress_bar.set_postfix(**logs)
+
+            if global_step >= args.max_train_steps:
+                break
+
+        if args.logging_dir is not None:
+            logs = {"loss/epoch": loss_total / len(train_dataloader)}
+            accelerator.log(logs, step=epoch + 1)
+
+        accelerator.wait_for_everyone()
+
+        updated_embs = unwrap_model(text_encoder).get_input_embeddings().weight[token_ids_XTI].data.detach().clone()
+
+        if args.save_every_n_epochs is not None:
+            model_name = train_util.DEFAULT_EPOCH_NAME if args.output_name is None else args.output_name
+
+            def save_func():
+                ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, epoch + 1) + "." + args.save_model_as
+                ckpt_file = os.path.join(args.output_dir, ckpt_name)
+                print(f"saving checkpoint: {ckpt_file}")
+                save_weights(ckpt_file, updated_embs, save_dtype)
+
+            def remove_old_func(old_epoch_no):
+                old_ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, old_epoch_no) + "." + args.save_model_as
+                old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name)
+                if os.path.exists(old_ckpt_file):
+                    print(f"removing old checkpoint: {old_ckpt_file}")
+                    os.remove(old_ckpt_file)
+
+            saving = train_util.save_on_epoch_end(args, save_func, remove_old_func, epoch + 1, num_train_epochs)
+            if saving and args.save_state:
+                train_util.save_state_on_epoch_end(args, accelerator, model_name, epoch + 1)
+
+        # TODO: fix sample_images
+        # train_util.sample_images(
+        #     accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, prompt_replacement
+        # )
+
+        # end of epoch
+
+    is_main_process = accelerator.is_main_process
+    if is_main_process:
+        text_encoder = unwrap_model(text_encoder)
+
+    accelerator.end_training()
+
+    if args.save_state:
+        train_util.save_state_on_train_end(args, accelerator)
+
+    updated_embs = text_encoder.get_input_embeddings().weight[token_ids_XTI].data.detach().clone()
+
+    del accelerator  # この後メモリを使うのでこれは消す
+
+    if is_main_process:
+        os.makedirs(args.output_dir, exist_ok=True)
+
+        model_name = train_util.DEFAULT_LAST_OUTPUT_NAME if args.output_name is None else args.output_name
+        ckpt_name = model_name + "." + args.save_model_as
+        ckpt_file = os.path.join(args.output_dir, ckpt_name)
+
+        print(f"save trained model to {ckpt_file}")
+        save_weights(ckpt_file, updated_embs, save_dtype)
+        print("model saved.")
+
+
+def save_weights(file, updated_embs, save_dtype):
+    updated_embs = updated_embs.reshape(16, -1, updated_embs.shape[-1])
+    updated_embs = updated_embs.chunk(16)
+    XTI_layers = [
+        "IN01",
+        "IN02",
+        "IN04",
+        "IN05",
+        "IN07",
+        "IN08",
+        "MID",
+        "OUT03",
+        "OUT04",
+        "OUT05",
+        "OUT06",
+        "OUT07",
+        "OUT08",
+        "OUT09",
+        "OUT10",
+        "OUT11",
+    ]
+    state_dict = {}
+    for i, layer_name in enumerate(XTI_layers):
+        state_dict[layer_name] = updated_embs[i].squeeze(0).detach().clone().to("cpu").to(save_dtype)
+
+    # if save_dtype is not None:
+    #     for key in list(state_dict.keys()):
+    #         v = state_dict[key]
+    #         v = v.detach().clone().to("cpu").to(save_dtype)
+    #         state_dict[key] = v
+
+    if os.path.splitext(file)[1] == ".safetensors":
+        from safetensors.torch import save_file
+
+        save_file(state_dict, file)
+    else:
+        torch.save(state_dict, file)  # can be loaded in Web UI
+
+
+def load_weights(file):
+    if os.path.splitext(file)[1] == ".safetensors":
+        from safetensors.torch import load_file
+
+        data = load_file(file)
+    else:
+        raise ValueError(f"NOT XTI: {file}")
+
+    if len(data.values()) != 16:
+        raise ValueError(f"NOT XTI: {file}")
+
+    emb = torch.concat([x for x in data.values()])
+
+    return emb
+
+
+def setup_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser()
+
+    train_util.add_sd_models_arguments(parser)
+    train_util.add_dataset_arguments(parser, True, True, False)
+    train_util.add_training_arguments(parser, True)
+    train_util.add_optimizer_arguments(parser)
+    config_util.add_config_arguments(parser)
+    custom_train_functions.add_custom_train_arguments(parser)
+
+    parser.add_argument(
+        "--save_model_as",
+        type=str,
+        default="pt",
+        choices=[None, "ckpt", "pt", "safetensors"],
+        help="format to save the model (default is .pt) / モデル保存時の形式（デフォルトはpt）",
+    )
+
+    parser.add_argument("--weights", type=str, default=None, help="embedding weights to initialize / 学習するネットワークの初期重み")
+    parser.add_argument(
+        "--num_vectors_per_token", type=int, default=1, help="number of vectors per token / トークンに割り当てるembeddingsの要素数"
+    )
+    parser.add_argument(
+        "--token_string",
+        type=str,
+        default=None,
+        help="token string used in training, must not exist in tokenizer / 学習時に使用されるトークン文字列、tokenizerに存在しない文字であること",
+    )
+    parser.add_argument("--init_word", type=str, default=None, help="words to initialize vector / ベクトルを初期化に使用する単語、複数可")
+    parser.add_argument(
+        "--use_object_template",
+        action="store_true",
+        help="ignore caption and use default templates for object / キャプションは使わずデフォルトの物体用テンプレートで学習する",
+    )
+    parser.add_argument(
+        "--use_style_template",
+        action="store_true",
+        help="ignore caption and use default templates for stype / キャプションは使わずデフォルトのスタイル用テンプレートで学習する",
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    parser = setup_parser()
+
+    args = parser.parse_args()
+    args = train_util.read_config_from_file(args, parser)
+
+    train(args)