diff options
Diffstat (limited to 'modules/ui.py')
| -rw-r--r-- | modules/ui.py | 83 |
1 files changed, 69 insertions, 14 deletions
diff --git a/modules/ui.py b/modules/ui.py index 1c0d35fe..948f303b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -39,6 +39,7 @@ import modules.generation_parameters_copypaste from modules import prompt_parser
from modules.images import save_image
import modules.textual_inversion.ui
+import modules.hypernetwork.ui
import modules.images_history as img_his
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
@@ -312,7 +313,7 @@ def interrogate(image): def interrogate_deepbooru(image):
- prompt = get_deepbooru_tags(image)
+ prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold)
return gr_show(True) if prompt is None else prompt
@@ -525,7 +526,7 @@ def create_ui(wrap_gradio_gpu_call): denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
with gr.Row():
- batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
+ batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
@@ -712,7 +713,7 @@ def create_ui(wrap_gradio_gpu_call): tiling = gr.Checkbox(label='Tiling', value=False)
with gr.Row():
- batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
+ batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
with gr.Group():
@@ -964,7 +965,7 @@ def create_ui(wrap_gradio_gpu_call): extras_send_to_inpaint.click(
fn=lambda x: image_from_url_text(x),
- _js="extract_image_from_gallery_img2img",
+ _js="extract_image_from_gallery_inpaint",
inputs=[result_images],
outputs=[init_img_with_mask],
)
@@ -1025,13 +1026,27 @@ def create_ui(wrap_gradio_gpu_call): gr.HTML(value="")
with gr.Column():
- create_embedding = gr.Button(value="Create", variant='primary')
+ create_embedding = gr.Button(value="Create embedding", variant='primary')
+
+ with gr.Group():
+ gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new hypernetwork</p>")
+
+ new_hypernetwork_name = gr.Textbox(label="Name")
+
+ with gr.Row():
+ with gr.Column(scale=3):
+ gr.HTML(value="")
+
+ with gr.Column():
+ create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
with gr.Group():
gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")
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')
@@ -1046,24 +1061,25 @@ 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()))
+ train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.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)
+ preview_image_prompt = gr.Textbox(label='Preview prompt', value="")
with gr.Row():
- with gr.Column(scale=2):
- gr.HTML(value="")
-
- with gr.Column():
- with gr.Row():
- interrupt_training = gr.Button(value="Interrupt")
- train_embedding = gr.Button(value="Train", variant='primary')
+ interrupt_training = gr.Button(value="Interrupt")
+ train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
+ train_embedding = gr.Button(value="Train Embedding", variant='primary')
with gr.Column():
progressbar = gr.HTML(elem_id="ti_progressbar")
@@ -1089,12 +1105,26 @@ def create_ui(wrap_gradio_gpu_call): ]
)
+ create_hypernetwork.click(
+ fn=modules.hypernetwork.ui.create_hypernetwork,
+ inputs=[
+ new_hypernetwork_name,
+ ],
+ outputs=[
+ train_hypernetwork_name,
+ ti_output,
+ ti_outcome,
+ ]
+ )
+
run_preprocess.click(
fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
_js="start_training_textual_inversion",
inputs=[
process_src,
process_dst,
+ process_width,
+ process_height,
process_flip,
process_split,
process_caption,
@@ -1113,10 +1143,34 @@ 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,
+ preview_image_prompt,
+ ],
+ outputs=[
+ ti_output,
+ ti_outcome,
+ ]
+ )
+
+ train_hypernetwork.click(
+ fn=wrap_gradio_gpu_call(modules.hypernetwork.ui.train_hypernetwork, extra_outputs=[gr.update()]),
+ _js="start_training_textual_inversion",
+ inputs=[
+ train_hypernetwork_name,
+ learn_rate,
+ dataset_directory,
+ log_directory,
+ steps,
+ create_image_every,
+ save_embedding_every,
+ template_file,
+ preview_image_prompt,
],
outputs=[
ti_output,
@@ -1130,6 +1184,7 @@ def create_ui(wrap_gradio_gpu_call): outputs=[],
)
+
def create_setting_component(key):
def fun():
return opts.data[key] if key in opts.data else opts.data_labels[key].default
@@ -1335,7 +1390,7 @@ Requested path was: {f} with gr.Tabs() as tabs:
for interface, label, ifid in interfaces:
- with gr.TabItem(label, id=ifid):
+ with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
interface.render()
if os.path.exists(os.path.join(script_path, "notification.mp3")):
|