v22.6.1

.github/workflows/typos.yaml

@@ -18,4 +18,4 @@ jobs:
       - uses: actions/checkout@v4
 
       - name: typos-action
-        uses: crate-ci/typos@v1.16.26
+        uses: crate-ci/typos@v1.17.2

.release

@@ -1 +1 @@
-v22.6.0
+v22.6.1

README.md

@@ -503,13 +503,19 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
 
 
 ## Change History
+* 2024/02/15 (v22.6.1)
+- Add support for multi-gpu parameters in the GUI under the "Parameters > Advanced" tab.
+- Significant rewrite of how parameters are created in the code. I hope I did not break anything in the process... Will make the code easier to update.
+- Update TW locallisation
+- Update gradio module version to latest 3.x
+
 * 2024/01/27 (v22.6.0)
 - Merge sd-scripts v0.8.3 code update
   - Fixed a bug that the training crashes when `--fp8_base` is specified with `--save_state`. PR [#1079](https://github.com/kohya-ss/sd-scripts/pull/1079) Thanks to feffy380!
     - `safetensors` is updated. Please see [Upgrade](#upgrade) and update the library.
   - Fixed a bug that the training crashes when `network_multiplier` is specified with multi-GPU training. PR [#1084](https://github.com/kohya-ss/sd-scripts/pull/1084) Thanks to fireicewolf!
   - Fixed a bug that the training crashes when training ControlNet-LLLite.
-  
+
 - Merge sd-scripts v0.8.2 code update
   - [Experimental] The `--fp8_base` option is added to the training scripts for LoRA etc. The base model (U-Net, and Text Encoder when training modules for Text Encoder) can be trained with fp8. PR [#1057](https://github.com/kohya-ss/sd-scripts/pull/1057) Thanks to KohakuBlueleaf!
     - Please specify `--fp8_base` in `train_network.py` or `sdxl_train_network.py`.
@@ -522,15 +528,15 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
     - For example, if you train with state A as `1.0` and state B as `-1.0`, you may be able to generate by switching between state A and B depending on the LoRA application rate.
     - Also, if you prepare five states and train them as `0.2`, `0.4`, `0.6`, `0.8`, and `1.0`, you may be able to generate by switching the states smoothly depending on the application rate.
     - Please specify `network_multiplier` in `[[datasets]]` in `.toml` file.
-  
+
   - Some options are added to `networks/extract_lora_from_models.py` to reduce the memory usage.
     - `--load_precision` option can be used to specify the precision when loading the model. If the model is saved in fp16, you can reduce the memory usage by specifying `--load_precision fp16` without losing precision.
     - `--load_original_model_to` option can be used to specify the device to load the original model. `--load_tuned_model_to` option can be used to specify the device to load the derived model. The default is `cpu` for both options, but you can specify `cuda` etc. You can reduce the memory usage by loading one of them to GPU. This option is available only for SDXL.
 
   - The gradient synchronization in LoRA training with multi-GPU is improved. PR [#1064](https://github.com/kohya-ss/sd-scripts/pull/1064) Thanks to KohakuBlueleaf!
-  
+
   - The code for Intel IPEX support is improved. PR [#1060](https://github.com/kohya-ss/sd-scripts/pull/1060) Thanks to akx!
-  
+
   - Fixed a bug in multi-GPU Textual Inversion training.
 
   - `.toml` example for network multiplier
@@ -556,7 +562,7 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
 
   - Fixed a bug that the VRAM usage without Text Encoder training is larger than before in training scripts for LoRA etc (`train_network.py`, `sdxl_train_network.py`).
     - Text Encoders were not moved to CPU.
-  
+
   - Fixed typos. Thanks to akx! [PR #1053](https://github.com/kohya-ss/sd-scripts/pull/1053)
 
 * 2024/01/15 (v22.5.0)
@@ -574,10 +580,10 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
   - IPEX library is updated. PR [#1030](https://github.com/kohya-ss/sd-scripts/pull/1030) Thanks to Disty0!
   - Fixed a bug that Diffusers format model cannot be saved.
 - Fix LoRA config display after load that would sometime hide some of the feilds
-  
+
 * 2024/01/02 (v22.4.1)
 - Minor bug fixed and enhancements.
-  
+
 * 2023/12/28 (v22.4.0)
 - Fixed to work `tools/convert_diffusers20_original_sd.py`. Thanks to Disty0! PR [#1016](https://github.com/kohya-ss/sd-scripts/pull/1016)
 - The issues in multi-GPU training are fixed. Thanks to Isotr0py! PR [#989](https://github.com/kohya-ss/sd-scripts/pull/989) and [#1000](https://github.com/kohya-ss/sd-scripts/pull/1000)
@@ -592,13 +598,13 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
 - The optimizer `PagedAdamW` is added. Thanks to xzuyn! PR [#955](https://github.com/kohya-ss/sd-scripts/pull/955)
 - NaN replacement in SDXL VAE is sped up. Thanks to liubo0902! PR [#1009](https://github.com/kohya-ss/sd-scripts/pull/1009)
 - Fixed the path error in `finetune/make_captions.py`. Thanks to CjangCjengh! PR [#986](https://github.com/kohya-ss/sd-scripts/pull/986)
-  
+
 * 2023/12/20 (v22.3.1)
 - Add goto button to manual caption utility
-- Add missing options for various LyCORIS training algorythms
+- Add missing options for various LyCORIS training algorithms
 - Refactor how feilds are shown or hidden
 - Made max value for network and convolution rank 512 except for LyCORIS/LoKr.
-  
+
 * 2023/12/06 (v22.3.0)
 - Merge sd-scripts updates:
   - `finetune\tag_images_by_wd14_tagger.py` now supports the separator other than `,` with `--caption_separator` option. Thanks to KohakuBlueleaf! PR [#913](https://github.com/kohya-ss/sd-scripts/pull/913)
@@ -612,4 +618,4 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b
     - `--ds_ratio` option denotes the ratio of the Deep Shrink. `0.5` means the half of the original latent size for the Deep Shrink.
     - `--dst1`, `--dst2`, `--dsd1`, `--dsd2` and `--dsr` prompt options are also available.
   - Add GLoRA support
-- 
+-

dreambooth_gui.py

@@ -17,7 +17,6 @@
     color_aug_changed,
     save_inference_file,
     run_cmd_advanced_training,
-    run_cmd_training,
     update_my_data,
     check_if_model_exist,
     output_message,
@@ -101,6 +100,10 @@ def save_configuration(
     flip_aug,
     clip_skip,
     vae,
+    num_processes,
+    num_machines,
+    multi_gpu,
+    gpu_ids,
     output_name,
     max_token_length,
     max_train_epochs,
@@ -225,6 +228,10 @@ def open_configuration(
     flip_aug,
     clip_skip,
     vae,
+    num_processes,
+    num_machines,
+    multi_gpu,
+    gpu_ids,
     output_name,
     max_token_length,
     max_train_epochs,
@@ -344,6 +351,10 @@ def train_model(
     flip_aug,
     clip_skip,
     vae,
+    num_processes,
+    num_machines,
+    multi_gpu,
+    gpu_ids,
     output_name,
     max_token_length,
     max_train_epochs,
@@ -539,123 +550,100 @@ def train_model(
     log.info(f"lr_warmup_steps = {lr_warmup_steps}")
 
     # run_cmd = f'accelerate launch --num_cpu_threads_per_process={num_cpu_threads_per_process} "train_db.py"'
-    run_cmd = (
-        f"accelerate launch --num_cpu_threads_per_process={num_cpu_threads_per_process}"
+    run_cmd = "accelerate launch"
+
+    run_cmd += run_cmd_advanced_training(
+        num_processes=num_processes,
+        num_machines=num_machines,
+        multi_gpu=multi_gpu,
+        gpu_ids=gpu_ids,
+        num_cpu_threads_per_process=num_cpu_threads_per_process,
     )
+
     if sdxl:
         run_cmd += f' "./sdxl_train.py"'
     else:
         run_cmd += f' "./train_db.py"'
 
-    if v2:
-        run_cmd += " --v2"
-    if v_parameterization:
-        run_cmd += " --v_parameterization"
-    if enable_bucket:
-        run_cmd += f" --enable_bucket --min_bucket_reso={min_bucket_reso} --max_bucket_reso={max_bucket_reso}"
-    if no_token_padding:
-        run_cmd += " --no_token_padding"
-    if weighted_captions:
-        run_cmd += " --weighted_captions"
-    run_cmd += f' --pretrained_model_name_or_path="{pretrained_model_name_or_path}"'
-    run_cmd += f' --train_data_dir="{train_data_dir}"'
-    if len(reg_data_dir):
-        run_cmd += f' --reg_data_dir="{reg_data_dir}"'
-    run_cmd += f' --resolution="{max_resolution}"'
-    run_cmd += f' --output_dir="{output_dir}"'
-    if not logging_dir == "":
-        run_cmd += f' --logging_dir="{logging_dir}"'
-    if not stop_text_encoder_training == 0:
-        run_cmd += f" --stop_text_encoder_training={stop_text_encoder_training}"
-    if not save_model_as == "same as source model":
-        run_cmd += f" --save_model_as={save_model_as}"
-    # if not resume == '':
-    #     run_cmd += f' --resume={resume}'
-    if not float(prior_loss_weight) == 1.0:
-        run_cmd += f" --prior_loss_weight={prior_loss_weight}"
-    if full_bf16:
-        run_cmd += " --full_bf16"
-    if not vae == "":
-        run_cmd += f' --vae="{vae}"'
-    if not output_name == "":
-        run_cmd += f' --output_name="{output_name}"'
-    if not lr_scheduler_num_cycles == "":
-        run_cmd += f' --lr_scheduler_num_cycles="{lr_scheduler_num_cycles}"'
-    else:
-        run_cmd += f' --lr_scheduler_num_cycles="{epoch}"'
-    if not lr_scheduler_power == "":
-        run_cmd += f' --lr_scheduler_power="{lr_scheduler_power}"'
-    if int(max_token_length) > 75:
-        run_cmd += f" --max_token_length={max_token_length}"
-    if not max_train_epochs == "":
-        run_cmd += f' --max_train_epochs="{max_train_epochs}"'
-    if not max_data_loader_n_workers == "":
-        run_cmd += f' --max_data_loader_n_workers="{max_data_loader_n_workers}"'
-    if int(gradient_accumulation_steps) > 1:
-        run_cmd += f" --gradient_accumulation_steps={int(gradient_accumulation_steps)}"
-
-    if sdxl:
-        run_cmd += f' --learning_rate_te1="{learning_rate_te1}"'
-        run_cmd += f' --learning_rate_te2="{learning_rate_te2}"'
-    else:
-        run_cmd += f' --learning_rate_te="{learning_rate_te}"'
-
-    run_cmd += run_cmd_training(
+    run_cmd += run_cmd_advanced_training(
+        adaptive_noise_scale=adaptive_noise_scale,
+        additional_parameters=additional_parameters,
+        bucket_no_upscale=bucket_no_upscale,
+        bucket_reso_steps=bucket_reso_steps,
+        cache_latents=cache_latents,
+        cache_latents_to_disk=cache_latents_to_disk,
+        caption_dropout_every_n_epochs=caption_dropout_every_n_epochs,
+        caption_dropout_rate=caption_dropout_rate,
+        caption_extension=caption_extension,
+        clip_skip=clip_skip,
+        color_aug=color_aug,
+        enable_bucket=enable_bucket,
+        epoch=epoch,
+        flip_aug=flip_aug,
+        full_bf16=full_bf16,
+        full_fp16=full_fp16,
+        gradient_accumulation_steps=gradient_accumulation_steps,
+        gradient_checkpointing=gradient_checkpointing,
+        keep_tokens=keep_tokens,
         learning_rate=learning_rate,
+        learning_rate_te1=learning_rate_te1 if sdxl else None,
+        learning_rate_te2=learning_rate_te2 if sdxl else None,
+        learning_rate_te=learning_rate_te if not sdxl else None,
+        logging_dir=logging_dir,
         lr_scheduler=lr_scheduler,
+        lr_scheduler_args=lr_scheduler_args,
+        lr_scheduler_num_cycles=lr_scheduler_num_cycles,
+        lr_scheduler_power=lr_scheduler_power,
         lr_warmup_steps=lr_warmup_steps,
-        train_batch_size=train_batch_size,
+        max_bucket_reso=max_bucket_reso,
+        max_data_loader_n_workers=max_data_loader_n_workers,
+        max_resolution=max_resolution,
+        max_timestep=max_timestep,
+        max_token_length=max_token_length,
+        max_train_epochs=max_train_epochs,
         max_train_steps=max_train_steps,
-        save_every_n_epochs=save_every_n_epochs,
+        mem_eff_attn=mem_eff_attn,
+        min_bucket_reso=min_bucket_reso,
+        min_snr_gamma=min_snr_gamma,
+        min_timestep=min_timestep,
         mixed_precision=mixed_precision,
-        save_precision=save_precision,
-        seed=seed,
-        caption_extension=caption_extension,
-        cache_latents=cache_latents,
-        cache_latents_to_disk=cache_latents_to_disk,
+        multires_noise_discount=multires_noise_discount,
+        multires_noise_iterations=multires_noise_iterations,
+        no_token_padding=no_token_padding,
+        noise_offset=noise_offset,
+        noise_offset_type=noise_offset_type,
         optimizer=optimizer,
         optimizer_args=optimizer_args,
-        lr_scheduler_args=lr_scheduler_args,
-    )
-
-    run_cmd += run_cmd_advanced_training(
-        max_train_epochs=max_train_epochs,
-        max_data_loader_n_workers=max_data_loader_n_workers,
-        max_token_length=max_token_length,
-        resume=resume,
-        save_state=save_state,
-        mem_eff_attn=mem_eff_attn,
-        clip_skip=clip_skip,
-        flip_aug=flip_aug,
-        color_aug=color_aug,
-        shuffle_caption=shuffle_caption,
-        gradient_checkpointing=gradient_checkpointing,
-        full_fp16=full_fp16,
-        xformers=xformers,
-        keep_tokens=keep_tokens,
+        output_dir=output_dir,
+        output_name=output_name,
         persistent_data_loader_workers=persistent_data_loader_workers,
-        bucket_no_upscale=bucket_no_upscale,
+        pretrained_model_name_or_path=pretrained_model_name_or_path,
+        prior_loss_weight=prior_loss_weight,
         random_crop=random_crop,
-        bucket_reso_steps=bucket_reso_steps,
-        v_pred_like_loss=v_pred_like_loss,
-        caption_dropout_every_n_epochs=caption_dropout_every_n_epochs,
-        caption_dropout_rate=caption_dropout_rate,
-        noise_offset_type=noise_offset_type,
-        noise_offset=noise_offset,
-        adaptive_noise_scale=adaptive_noise_scale,
-        multires_noise_iterations=multires_noise_iterations,
-        multires_noise_discount=multires_noise_discount,
-        additional_parameters=additional_parameters,
-        vae_batch_size=vae_batch_size,
-        min_snr_gamma=min_snr_gamma,
+        reg_data_dir=reg_data_dir,
+        resume=resume,
+        save_every_n_epochs=save_every_n_epochs,
         save_every_n_steps=save_every_n_steps,
         save_last_n_steps=save_last_n_steps,
         save_last_n_steps_state=save_last_n_steps_state,
+        save_model_as=save_model_as,
+        save_precision=save_precision,
+        save_state=save_state,
+        scale_v_pred_loss_like_noise_pred=scale_v_pred_loss_like_noise_pred,
+        seed=seed,
+        shuffle_caption=shuffle_caption,
+        stop_text_encoder_training=stop_text_encoder_training,
+        train_batch_size=train_batch_size,
+        train_data_dir=train_data_dir,
         use_wandb=use_wandb,
+        v2=v2,
+        v_parameterization=v_parameterization,
+        v_pred_like_loss=v_pred_like_loss,
+        vae=vae,
+        vae_batch_size=vae_batch_size,
         wandb_api_key=wandb_api_key,
-        scale_v_pred_loss_like_noise_pred=scale_v_pred_loss_like_noise_pred,
-        min_timestep=min_timestep,
-        max_timestep=max_timestep,
+        weighted_captions=weighted_captions,
+        xformers=xformers,
     )
 
     run_cmd += run_cmd_sample(
@@ -827,6 +815,10 @@ def dreambooth_tab(
             advanced_training.flip_aug,
             advanced_training.clip_skip,
             advanced_training.vae,
+            advanced_training.num_processes,
+            advanced_training.num_machines,
+            advanced_training.multi_gpu,
+            advanced_training.gpu_ids,
             folders.output_name,
             advanced_training.max_token_length,
             basic_training.max_train_epochs,

examples/caption.ps1

@@ -1,6 +1,6 @@
 # This powershell script will create a text file for each files in the folder
 #
-# Usefull to create base caption that will be augmented on a per image basis
+# Useful to create base caption that will be augmented on a per image basis
 
 $folder = "D:\some\folder\location\"
 $file_pattern="*.*"

examples/caption_subfolders.ps1

@@ -1,19 +1,19 @@
 # This powershell script will create a text file for each files in the folder
 #
-# Usefull to create base caption that will be augmented on a per image basis
+# Useful to create base caption that will be augmented on a per image basis
 
 $folder = "D:\test\t2\"
 $file_pattern="*.*"
 $text_fir_file="bigeyes style"
 
-foreach ($file in Get-ChildItem $folder\$file_pattern -File) 
+foreach ($file in Get-ChildItem $folder\$file_pattern -File)
 {
     New-Item -ItemType file -Path $folder -Name "$($file.BaseName).txt" -Value $text_fir_file
 }
 
 foreach($directory in Get-ChildItem -path $folder -Directory)
 {
-    foreach ($file in Get-ChildItem $folder\$directory\$file_pattern) 
+    foreach ($file in Get-ChildItem $folder\$directory\$file_pattern)
     {
         New-Item -ItemType file -Path $folder\$directory -Name "$($file.BaseName).txt" -Value $text_fir_file
     }

examples/kohya_train_db_fixed_with-reg_SDv2 512 base.ps1

@@ -61,4 +61,4 @@ accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process tra
     --seed=494481440 `
     --lr_scheduler=$lr_scheduler
 
-# Add the inference yaml file along with the model for proper loading. Need to have the same name as model... Most likelly "last.yaml" in our case.
+# Add the inference yaml file along with the model for proper loading. Need to have the same name as model... Most likely "last.yaml" in our case.