Merge pull request #78 from bmaltais/dev

Dev
2023-01-27 19:46:55 -05:00 · 2023-01-27 19:46:55 -05:00 · fdd1b02a26
commit fdd1b02a26
parent 08d189400d a4957cfea7
6 changed files with 50 additions and 12 deletions
--- a/README.md
+++ b/README.md
@ -2,6 +2,16 @@

 This repository repository is providing a Gradio GUI for kohya's Stable Diffusion trainers found here: https://github.com/kohya-ss/sd-scripts. The GUI allow you to set the training parameters and generate and run the required CLI command to train the model.

+## Tutorials
+
+How to create a LoRA part 1, dataset preparation:
+
+[![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/N4_-fB62Hwk/0.jpg)](https://www.youtube.com/watch?v=N4_-fB62Hwk)
+
+How to create a LoRA part 2, training the model:
+
+[![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/k5imq01uvUY/0.jpg)](https://www.youtube.com/watch?v=k5imq01uvUY)
+
 ## Required Dependencies

 Python 3.10.6+ and Git:
@ -131,6 +141,9 @@ Then redo the installation instruction within the kohya_ss venv.

 ## Change history

+* 2023/01/27 (v20.5.1):
+    - Fix issue: https://github.com/bmaltais/kohya_ss/issues/70
+    - Fix issue https://github.com/bmaltais/kohya_ss/issues/71
 * 2023/01/26 (v20.5.0):
    - Add new `Dreambooth TI` tab for training of Textual Inversion embeddings
    - Add Textual Inversion training. Documentation is [here](./train_ti_README-ja.md) (in Japanese.)
--- a/dreambooth_gui.py
+++ b/dreambooth_gui.py
@ -82,7 +82,7 @@ def save_configuration(
    max_data_loader_n_workers,
    mem_eff_attn,
    gradient_accumulation_steps,
-    model_list,
+    model_list, keep_tokens,
 ):
    # Get list of function parameters and values
    parameters = list(locals().items())
@ -166,7 +166,7 @@ def open_configuration(
    max_data_loader_n_workers,
    mem_eff_attn,
    gradient_accumulation_steps,
-    model_list,
+    model_list, keep_tokens,
 ):
    # Get list of function parameters and values
    parameters = list(locals().items())
@ -235,6 +235,7 @@ def train_model(
    mem_eff_attn,
    gradient_accumulation_steps,
    model_list,  # Keep this. Yes, it is unused here but required given the common list used
+    keep_tokens,
 ):
    if pretrained_model_name_or_path == '':
        msgbox('Source model information is missing')
@ -396,6 +397,7 @@ def train_model(
        full_fp16=full_fp16,
        xformers=xformers,
        use_8bit_adam=use_8bit_adam,
+        keep_tokens=keep_tokens,
    )

    print(run_cmd)
@ -602,6 +604,7 @@ def dreambooth_tab(
                max_token_length,
                max_train_epochs,
                max_data_loader_n_workers,
+                keep_tokens,
            ) = gradio_advanced_training()
            color_aug.change(
                color_aug_changed,
@ -665,6 +668,7 @@ def dreambooth_tab(
        mem_eff_attn,
        gradient_accumulation_steps,
        model_list,
+        keep_tokens,
    ]

    button_open_config.click(
--- a/finetune_gui.py
+++ b/finetune_gui.py
@ -78,7 +78,7 @@ def save_configuration(
    color_aug,
    model_list,
    cache_latents,
-    use_latent_files,
+    use_latent_files, keep_tokens,
 ):
    # Get list of function parameters and values
    parameters = list(locals().items())
@ -168,7 +168,7 @@ def open_config_file(
    color_aug,
    model_list,
    cache_latents,
-    use_latent_files,
+    use_latent_files, keep_tokens,
 ):
    # Get list of function parameters and values
    parameters = list(locals().items())
@ -243,7 +243,7 @@ def train_model(
    color_aug,
    model_list,  # Keep this. Yes, it is unused here but required given the common list used
    cache_latents,
-    use_latent_files,
+    use_latent_files, keep_tokens,
 ):
    # create caption json file
    if generate_caption_database:
@ -381,6 +381,7 @@ def train_model(
        full_fp16=full_fp16,
        xformers=xformers,
        use_8bit_adam=use_8bit_adam,
+        keep_tokens=keep_tokens,
    )

    print(run_cmd)
@ -585,6 +586,7 @@ def finetune_tab():
                max_token_length,
                max_train_epochs,
                max_data_loader_n_workers,
+                keep_tokens,
            ) = gradio_advanced_training()
            color_aug.change(
                color_aug_changed,
@ -644,6 +646,7 @@ def finetune_tab():
        model_list,
        cache_latents,
        use_latent_files,
+        keep_tokens,
    ]

    button_run.click(train_model, inputs=settings_list)
--- a/library/common_gui.py
+++ b/library/common_gui.py
@ -519,6 +519,9 @@ def gradio_advanced_training():
        shuffle_caption = gr.Checkbox(
            label='Shuffle caption', value=False
        )
+        keep_tokens = gr.Slider(
+            label='Keen n tokens', value='0', minimum=0, maximum=32, step=1
+        )
        use_8bit_adam = gr.Checkbox(label='Use 8bit adam', value=True)
        xformers = gr.Checkbox(label='Use xformers', value=True)
    with gr.Row():
@ -572,6 +575,7 @@ def gradio_advanced_training():
        max_token_length,
        max_train_epochs,
        max_data_loader_n_workers,
+        keep_tokens,
    )

 def run_cmd_advanced_training(**kwargs):
@ -596,6 +600,10 @@ def run_cmd_advanced_training(**kwargs):
        if kwargs.get('resume')
        else '',
        
+        f' --keep_tokens="{kwargs.get("keep_tokens", "")}"'
+        if int(kwargs.get('keep_tokens', 0)) > 0
+        else '',
+        
        ' --save_state' if kwargs.get('save_state') else '',
        
        ' --mem_eff_attn' if kwargs.get('mem_eff_attn') else '',
--- a/lora_gui.py
+++ b/lora_gui.py
@ -90,7 +90,7 @@ def save_configuration(
    max_train_epochs,
    max_data_loader_n_workers,
    network_alpha,
-    training_comment,
+    training_comment, keep_tokens,
 ):
    # Get list of function parameters and values
    parameters = list(locals().items())
@ -159,7 +159,7 @@ def open_configuration(
    stop_text_encoder_training,
    use_8bit_adam,
    xformers,
-    save_model_as_dropdown,
+    save_model_as,
    shuffle_caption,
    save_state,
    resume,
@ -179,7 +179,7 @@ def open_configuration(
    max_train_epochs,
    max_data_loader_n_workers,
    network_alpha,
-    training_comment,
+    training_comment, keep_tokens,
 ):
    # Get list of function parameters and values
    parameters = list(locals().items())
@ -252,7 +252,7 @@ def train_model(
    max_train_epochs,
    max_data_loader_n_workers,
    network_alpha,
-    training_comment,
+    training_comment, keep_tokens,
 ):
    if pretrained_model_name_or_path == '':
        msgbox('Source model information is missing')
@ -425,6 +425,7 @@ def train_model(
        full_fp16=full_fp16,
        xformers=xformers,
        use_8bit_adam=use_8bit_adam,
+        keep_tokens=keep_tokens,
    )

    print(run_cmd)
@ -660,6 +661,7 @@ def lora_tab(
                max_token_length,
                max_train_epochs,
                max_data_loader_n_workers,
+                keep_tokens,
            ) = gradio_advanced_training()
            color_aug.change(
                color_aug_changed,
@ -733,6 +735,7 @@ def lora_tab(
        max_data_loader_n_workers,
        network_alpha,
        training_comment,
+        keep_tokens,
    ]

    button_open_config.click(
--- a/textual_inversion_gui.py
+++ b/textual_inversion_gui.py
@ -82,7 +82,7 @@ def save_configuration(
    max_data_loader_n_workers,
    mem_eff_attn,
    gradient_accumulation_steps,
-    model_list, token_string, init_word, num_vectors_per_token, max_train_steps, weights, template,
+    model_list, token_string, init_word, num_vectors_per_token, max_train_steps, weights, template, keep_tokens,
 ):
    # Get list of function parameters and values
    parameters = list(locals().items())
@ -104,6 +104,10 @@ def save_configuration(
    if file_path == None or file_path == '':
        return original_file_path  # In case a file_path was provided and the user decide to cancel the open action

+    directory = os.path.dirname(file_path)
+    if not os.path.exists(directory):
+        os.makedirs(directory)
+
    # Return the values of the variables as a dictionary
    variables = {
        name: value
@ -166,7 +170,7 @@ def open_configuration(
    max_data_loader_n_workers,
    mem_eff_attn,
    gradient_accumulation_steps,
-    model_list, token_string, init_word, num_vectors_per_token, max_train_steps, weights, template,
+    model_list, token_string, init_word, num_vectors_per_token, max_train_steps, weights, template, keep_tokens,
 ):
    # Get list of function parameters and values
    parameters = list(locals().items())
@ -235,7 +239,7 @@ def train_model(
    mem_eff_attn,
    gradient_accumulation_steps,
    model_list,  # Keep this. Yes, it is unused here but required given the common list used
-    token_string, init_word, num_vectors_per_token, max_train_steps, weights, template,
+    token_string, init_word, num_vectors_per_token, max_train_steps, weights, template, keep_tokens,
 ):
    if pretrained_model_name_or_path == '':
        msgbox('Source model information is missing')
@ -412,6 +416,7 @@ def train_model(
        full_fp16=full_fp16,
        xformers=xformers,
        use_8bit_adam=use_8bit_adam,
+        keep_tokens=keep_tokens,
    )
    run_cmd += f' --token_string={token_string}'
    run_cmd += f' --init_word={init_word}'
@ -665,6 +670,7 @@ def ti_tab(
                max_token_length,
                max_train_epochs,
                max_data_loader_n_workers,
+                keep_tokens,
            ) = gradio_advanced_training()
            color_aug.change(
                color_aug_changed,
@ -729,6 +735,7 @@ def ti_tab(
        gradient_accumulation_steps,
        model_list,
        token_string, init_word, num_vectors_per_token, max_train_steps, weights, template,
+        keep_tokens,
    ]

    button_open_config.click(