Merge pull request #2222 from bmaltais/dev

v23.1.1

.release

@@ -1 +1 @@
-v23.1.0
+v23.1.1

README.md

@@ -42,6 +42,7 @@ The GUI allows you to set the training parameters and generate and run the requi
   - [SDXL training](#sdxl-training)
   - [Masked loss](#masked-loss)
   - [Change History](#change-history)
+    - [2024/04/07 (v23.1.1)](#20240407-v2311)
     - [2024/04/07 (v23.1.0)](#20240407-v2310)
     - [2024/03/21 (v23.0.15)](#20240321-v23015)
     - [2024/03/19 (v23.0.14)](#20240319-v23014)
@@ -403,6 +404,10 @@ ControlNet dataset is used to specify the mask. The mask images should be the RG
 
 ## Change History
 
+### 2024/04/07 (v23.1.1)
+
+- Added support for Huber loss under the Parameters / Advanced tab.
+
 ### 2024/04/07 (v23.1.0)
 
 - Update sd-scripts to 0.8.7
@@ -444,7 +449,7 @@ ControlNet dataset is used to specify the mask. The mask images should be the RG
       - See [Dataset config](./docs/config_README-en.md) for details.
     - The dataset with DreamBooth method supports caching image information (size, caption). PR [#1178](https://github.com/kohya-ss/sd-scripts/pull/1178) and [#1206](https://github.com/kohya-ss/sd-scripts/pull/1206) Thanks to KohakuBlueleaf! See [DreamBooth method specific options](./docs/config_README-en.md#dreambooth-specific-options) for details.
 
-  - Image tagging
+  - Image tagging (not implemented yet in the GUI)
     - The support for v3 repositories is added to `tag_image_by_wd14_tagger.py` (`--onnx` option only). PR [#1192](https://github.com/kohya-ss/sd-scripts/pull/1192) Thanks to sdbds!
       - Onnx may need to be updated. Onnx is not installed by default, so please install or update it with `pip install onnx==1.15.0 onnxruntime-gpu==1.17.1` etc. Please also check the comments in `requirements.txt`.
     - The model is now saved in the subdirectory as `--repo_id` in `tag_image_by_wd14_tagger.py` . This caches multiple repo_id models. Please delete unnecessary files under `--model_dir`.

config example.toml

@@ -74,11 +74,14 @@ full_bf16 = false                         # Full bf16 training (experimental)
 full_fp16 = false                         # Full fp16 training (experimental)
 gradient_accumulation_steps = 1           # Gradient accumulation steps
 gradient_checkpointing = false            # Gradient checkpointing
+huber_c = 0.1                             # The huber loss parameter. Only used if one of the huber loss modes (huber or smooth l1) is selected with loss_type
+huber_schedule = "snr"                    # The type of loss to use and whether it's scheduled based on the timestep
 ip_noise_gamma = 0                        # IP noise gamma
 ip_noise_gamma_random_strength = false    # IP noise gamma random strength (true, false)
 keep_tokens = 0                           # Keep tokens
 log_tracker_config_dir = "./logs"         # Log tracker configs directory
 log_tracker_name = ""                     # Log tracker name
+loss_type = "l2"                          # Loss type (l2, huber, smooth_l1)
 masked_loss = false                       # Masked loss
 max_data_loader_n_workers = "0"           # Max data loader n workers (string)
 max_timestep = 1000                       # Max timestep

kohya_gui/class_advanced_training.py

@@ -128,6 +128,33 @@ def list_vae_files(path):
                 placeholder='(Optional) Use to provide additional parameters not handled by the GUI. Eg: --some_parameters "value"',
                 value=self.config.get("advanced.additional_parameters", ""),
             )
+        with gr.Accordion("Scheduled Huber Loss", open=False):
+            with gr.Row():
+                self.loss_type = gr.Dropdown(
+                    label="Loss type",
+                    choices=["huber", "smooth_l1", "l2"],
+                    value=self.config.get("advanced.loss_type", "l2"),
+                    info="The type of loss to use and whether it's scheduled based on the timestep",
+                )
+                self.huber_schedule = gr.Dropdown(
+                    label="Huber schedule",
+                    choices=[
+                        "constant",
+                        "exponential",
+                        "snr",
+                    ],
+                    value=self.config.get("advanced.huber_schedule", "snr"),
+                    info="The type of loss to use and whether it's scheduled based on the timestep",
+                )
+                self.huber_c = gr.Number(
+                    label="Huber C",
+                    value=self.config.get("advanced.huber_c", 0.1),
+                    minimum=0.0,
+                    maximum=1.0,
+                    step=0.01,
+                    info="The huber loss parameter. Only used if one of the huber loss modes (huber or smooth l1) is selected with loss_type",
+                )
+
         with gr.Row():
             self.save_every_n_steps = gr.Number(
                 label="Save every N steps",

kohya_gui/common_gui.py

@@ -1098,6 +1098,12 @@ def run_cmd_advanced_training(**kwargs):
 
     if kwargs.get("gradient_checkpointing"):
         run_cmd += " --gradient_checkpointing"
+
+    if kwargs.get("huber_c"):
+        run_cmd += fr' --huber_c="{kwargs.get("huber_c")}"'
+
+    if kwargs.get("huber_schedule"):
+        run_cmd += fr' --huber_schedule="{kwargs.get("huber_schedule")}"'
 
     if kwargs.get("ip_noise_gamma"):
         if float(kwargs["ip_noise_gamma"]) > 0:
@@ -1152,6 +1158,9 @@ def run_cmd_advanced_training(**kwargs):
     lora_network_weights = kwargs.get("lora_network_weights")
     if lora_network_weights:
         run_cmd += f' --network_weights="{lora_network_weights}"'  # Yes, the parameter is now called network_weights instead of lora_network_weights
+
+    if "loss_type" in kwargs:
+        run_cmd += fr' --loss_type="{kwargs.get("loss_type")}"'
 
     lr_scheduler = kwargs.get("lr_scheduler")
     if lr_scheduler:

kohya_gui/dreambooth_gui.py

@@ -135,6 +135,9 @@ def save_configuration(
     sample_sampler,
     sample_prompts,
     additional_parameters,
+    loss_type,
+    huber_schedule,
+    huber_c,
     vae_batch_size,
     min_snr_gamma,
     weighted_captions,
@@ -275,6 +278,9 @@ def open_configuration(
     sample_sampler,
     sample_prompts,
     additional_parameters,
+    loss_type,
+    huber_schedule,
+    huber_c,
     vae_batch_size,
     min_snr_gamma,
     weighted_captions,
@@ -410,6 +416,9 @@ def train_model(
     sample_sampler,
     sample_prompts,
     additional_parameters,
+    loss_type,
+    huber_schedule,
+    huber_c,
     vae_batch_size,
     min_snr_gamma,
     weighted_captions,
@@ -665,6 +674,9 @@ def train_model(
         "weighted_captions": weighted_captions,
         "xformers": xformers,
         "additional_parameters": additional_parameters,
+        "loss_type": loss_type,
+        "huber_schedule": huber_schedule,
+        "huber_c": huber_c,
     }
 
     # Conditionally include specific keyword arguments based on sdxl
@@ -897,6 +909,9 @@ def dreambooth_tab(
             sample.sample_sampler,
             sample.sample_prompts,
             advanced_training.additional_parameters,
+            advanced_training.loss_type,
+            advanced_training.huber_schedule,
+            advanced_training.huber_c,
             advanced_training.vae_batch_size,
             advanced_training.min_snr_gamma,
             advanced_training.weighted_captions,

kohya_gui/finetune_gui.py

@@ -144,6 +144,9 @@ def save_configuration(
     sample_sampler,
     sample_prompts,
     additional_parameters,
+    loss_type,
+    huber_schedule,
+    huber_c,
     vae_batch_size,
     min_snr_gamma,
     weighted_captions,
@@ -291,6 +294,9 @@ def open_configuration(
     sample_sampler,
     sample_prompts,
     additional_parameters,
+    loss_type,
+    huber_schedule,
+    huber_c,
     vae_batch_size,
     min_snr_gamma,
     weighted_captions,
@@ -445,6 +451,9 @@ def train_model(
     sample_sampler,
     sample_prompts,
     additional_parameters,
+    loss_type,
+    huber_schedule,
+    huber_c,
     vae_batch_size,
     min_snr_gamma,
     weighted_captions,
@@ -695,6 +704,9 @@ def train_model(
         "weighted_captions": weighted_captions,
         "xformers": xformers,
         "additional_parameters": additional_parameters,
+        "loss_type": loss_type,
+        "huber_schedule": huber_schedule,
+        "huber_c": huber_c,
     }
 
     # Conditionally include specific keyword arguments based on sdxl_checkbox
@@ -999,6 +1011,9 @@ def list_presets(path):
             sample.sample_sampler,
             sample.sample_prompts,
             advanced_training.additional_parameters,
+            advanced_training.loss_type,
+            advanced_training.huber_schedule,
+            advanced_training.huber_c,
             advanced_training.vae_batch_size,
             advanced_training.min_snr_gamma,
             weighted_captions,

kohya_gui/lora_gui.py

@@ -190,6 +190,9 @@ def save_configuration(
     sample_sampler,
     sample_prompts,
     additional_parameters,
+    loss_type,
+    huber_schedule,
+    huber_c,
     vae_batch_size,
     min_snr_gamma,
     down_lr_weight,
@@ -378,6 +381,9 @@ def open_configuration(
     sample_sampler,
     sample_prompts,
     additional_parameters,
+    loss_type,
+    huber_schedule,
+    huber_c,
     vae_batch_size,
     min_snr_gamma,
     down_lr_weight,
@@ -594,6 +600,9 @@ def train_model(
     sample_sampler,
     sample_prompts,
     additional_parameters,
+    loss_type,
+    huber_schedule,
+    huber_c,
     vae_batch_size,
     min_snr_gamma,
     down_lr_weight,
@@ -1025,6 +1034,9 @@ def train_model(
         "weighted_captions": weighted_captions,
         "xformers": xformers,
         "additional_parameters": additional_parameters,
+        "loss_type": loss_type,
+        "huber_schedule": huber_schedule,
+        "huber_c": huber_c,
     }
 
     # Use the ** syntax to unpack the dictionary when calling the function
@@ -2042,6 +2054,9 @@ def update_LoRA_settings(
             sample.sample_sampler,
             sample.sample_prompts,
             advanced_training.additional_parameters,
+            advanced_training.loss_type,
+            advanced_training.huber_schedule,
+            advanced_training.huber_c,
             advanced_training.vae_batch_size,
             advanced_training.min_snr_gamma,
             down_lr_weight,

kohya_gui/textual_inversion_gui.py

@@ -135,6 +135,9 @@ def save_configuration(
     sample_sampler,
     sample_prompts,
     additional_parameters,
+    loss_type,
+    huber_schedule,
+    huber_c,
     vae_batch_size,
     min_snr_gamma,
     save_every_n_steps,
@@ -276,6 +279,9 @@ def open_configuration(
     sample_sampler,
     sample_prompts,
     additional_parameters,
+    loss_type,
+    huber_schedule,
+    huber_c,
     vae_batch_size,
     min_snr_gamma,
     save_every_n_steps,
@@ -410,6 +416,9 @@ def train_model(
     sample_sampler,
     sample_prompts,
     additional_parameters,
+    loss_type,
+    huber_schedule,
+    huber_c,
     vae_batch_size,
     min_snr_gamma,
     save_every_n_steps,
@@ -643,6 +652,9 @@ def train_model(
         wandb_run_name=wandb_run_name,
         xformers=xformers,
         additional_parameters=additional_parameters,
+        loss_type=loss_type,
+        huber_schedule=huber_schedule,
+        huber_c=huber_c,
     )
     run_cmd += f' --token_string="{token_string}"'
     run_cmd += f' --init_word="{init_word}"'
@@ -963,6 +975,9 @@ def list_embedding_files(path):
             sample.sample_sampler,
             sample.sample_prompts,
             advanced_training.additional_parameters,
+            advanced_training.loss_type,
+            advanced_training.huber_schedule,
+            advanced_training.huber_c,
             advanced_training.vae_batch_size,
             advanced_training.min_snr_gamma,
             advanced_training.save_every_n_steps,