Merge pull request #3197 from AUTOMATIC1111/training-help-text

Training UI Changes

1 files changed, 13 insertions, 4 deletions

diff --git a/modules/ui.py b/modules/ui.py
index 716f14b8..f6a92ddc 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1211,6 +1211,7 @@ def create_ui(wrap_gradio_gpu_call):
                     new_embedding_name = gr.Textbox(label="Name")

                     initialization_text = gr.Textbox(label="Initialization text", value="*")

                     nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)

+                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding")

 

                     with gr.Row():

                         with gr.Column(scale=3):

@@ -1224,6 +1225,7 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])

                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")

                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")

+                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")

                     new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu"])

 

                     with gr.Row():

@@ -1238,6 +1240,7 @@ def create_ui(wrap_gradio_gpu_call):
                     process_dst = gr.Textbox(label='Destination directory')

                     process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)

                     process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)

+                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])

 

                     with gr.Row():

                         process_flip = gr.Checkbox(label='Create flipped copies')

@@ -1253,14 +1256,17 @@ def create_ui(wrap_gradio_gpu_call):
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')

 

                 with gr.Tab(label="Train"):

-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")

+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")

                     with gr.Row():

                         train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))

                         create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")

                     with gr.Row():

                         train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])

                         create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")

-                    learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")

+                    with gr.Row():

+                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")

+                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")

+                    

                     batch_size = gr.Number(label='Batch size', value=1, precision=0)

                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")

                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")

@@ -1294,6 +1300,7 @@ def create_ui(wrap_gradio_gpu_call):
                 new_embedding_name,

                 initialization_text,

                 nvpt,

+                overwrite_old_embedding,

             ],

             outputs=[

                 train_embedding_name,

@@ -1307,6 +1314,7 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[

                 new_hypernetwork_name,

                 new_hypernetwork_sizes,

+                overwrite_old_hypernetwork,

                 new_hypernetwork_layer_structure,

                 new_hypernetwork_add_layer_norm,

                 new_hypernetwork_activation_func,

@@ -1326,6 +1334,7 @@ def create_ui(wrap_gradio_gpu_call):
                 process_dst,

                 process_width,

                 process_height,

+                preprocess_txt_action,

                 process_flip,

                 process_split,

                 process_caption,

@@ -1342,7 +1351,7 @@ def create_ui(wrap_gradio_gpu_call):
             _js="start_training_textual_inversion",

             inputs=[

                 train_embedding_name,

-                learn_rate,

+                embedding_learn_rate,

                 batch_size,

                 dataset_directory,

                 log_directory,

@@ -1367,7 +1376,7 @@ def create_ui(wrap_gradio_gpu_call):
             _js="start_training_textual_inversion",

             inputs=[

                 train_hypernetwork_name,

-                learn_rate,

+                hypernetwork_learn_rate,

                 batch_size,

                 dataset_directory,

                 log_directory,