Merge branch 'master' into embed-embeddings-in-images

1 files changed, 11 insertions, 1 deletions

diff --git a/modules/ui.py b/modules/ui.py
index 202c4866..0f6427a6 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1029,6 +1029,8 @@ def create_ui(wrap_gradio_gpu_call):
 

                     process_src = gr.Textbox(label='Source directory')

                     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)

 

                     with gr.Row():

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

@@ -1043,13 +1045,16 @@ def create_ui(wrap_gradio_gpu_call):
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')

 

                 with gr.Group():

-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")

+                    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>")

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

                     learn_rate = gr.Number(label='Learning rate', value=5.0e-03)

                     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")

                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))

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

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

                     steps = gr.Number(label='Max steps', value=100000, precision=0)

+                    num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)

                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)

                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)

                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)

@@ -1093,6 +1098,8 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[

                 process_src,

                 process_dst,

+                process_width,

+                process_height,

                 process_flip,

                 process_split,

                 process_caption,

@@ -1111,7 +1118,10 @@ def create_ui(wrap_gradio_gpu_call):
                 learn_rate,

                 dataset_directory,

                 log_directory,

+                training_width,

+                training_height,

                 steps,

+                num_repeats,

                 create_image_every,

                 save_embedding_every,

                 template_file,