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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-08-05 03:32:37 +00:00
images history improvement
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yfszzx
@@ -39,6 +39,7 @@ import modules.generation_parameters_copypaste
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from modules import prompt_parser
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from modules.images import save_image
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import modules.textual_inversion.ui
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import modules.hypernetwork.ui
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import modules.images_history as img_his
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# 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
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@@ -312,7 +313,7 @@ def interrogate(image):
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def interrogate_deepbooru(image):
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prompt = get_deepbooru_tags(image)
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prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold)
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return gr_show(True) if prompt is None else prompt
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@@ -525,7 +526,7 @@ def create_ui(wrap_gradio_gpu_call):
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denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
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with gr.Row():
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batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
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batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
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batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
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cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
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@@ -712,7 +713,7 @@ def create_ui(wrap_gradio_gpu_call):
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tiling = gr.Checkbox(label='Tiling', value=False)
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with gr.Row():
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batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
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batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
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batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
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with gr.Group():
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@@ -964,7 +965,7 @@ def create_ui(wrap_gradio_gpu_call):
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extras_send_to_inpaint.click(
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fn=lambda x: image_from_url_text(x),
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_js="extract_image_from_gallery_img2img",
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_js="extract_image_from_gallery_inpaint",
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inputs=[result_images],
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outputs=[init_img_with_mask],
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)
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@@ -1025,13 +1026,27 @@ def create_ui(wrap_gradio_gpu_call):
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gr.HTML(value="")
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with gr.Column():
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create_embedding = gr.Button(value="Create", variant='primary')
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create_embedding = gr.Button(value="Create embedding", variant='primary')
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with gr.Group():
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gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new hypernetwork</p>")
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new_hypernetwork_name = gr.Textbox(label="Name")
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with gr.Row():
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with gr.Column(scale=3):
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gr.HTML(value="")
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with gr.Column():
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create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
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with gr.Group():
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gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")
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process_src = gr.Textbox(label='Source directory')
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process_dst = gr.Textbox(label='Destination directory')
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process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
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process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
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with gr.Row():
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process_flip = gr.Checkbox(label='Create flipped copies')
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@@ -1046,24 +1061,25 @@ def create_ui(wrap_gradio_gpu_call):
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run_preprocess = gr.Button(value="Preprocess", variant='primary')
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with gr.Group():
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gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
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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>")
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train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
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train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
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learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
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dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
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log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
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template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
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training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
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training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
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steps = gr.Number(label='Max steps', value=100000, precision=0)
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num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
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create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
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save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
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preview_image_prompt = gr.Textbox(label='Preview prompt', value="")
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with gr.Row():
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with gr.Column(scale=2):
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gr.HTML(value="")
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with gr.Column():
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with gr.Row():
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interrupt_training = gr.Button(value="Interrupt")
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train_embedding = gr.Button(value="Train", variant='primary')
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interrupt_training = gr.Button(value="Interrupt")
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train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
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train_embedding = gr.Button(value="Train Embedding", variant='primary')
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with gr.Column():
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progressbar = gr.HTML(elem_id="ti_progressbar")
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@@ -1089,12 +1105,26 @@ def create_ui(wrap_gradio_gpu_call):
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]
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)
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create_hypernetwork.click(
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fn=modules.hypernetwork.ui.create_hypernetwork,
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inputs=[
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new_hypernetwork_name,
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],
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outputs=[
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train_hypernetwork_name,
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ti_output,
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ti_outcome,
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]
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)
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run_preprocess.click(
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fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
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_js="start_training_textual_inversion",
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inputs=[
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process_src,
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process_dst,
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process_width,
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process_height,
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process_flip,
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process_split,
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process_caption,
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@@ -1113,10 +1143,34 @@ def create_ui(wrap_gradio_gpu_call):
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learn_rate,
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dataset_directory,
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log_directory,
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training_width,
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training_height,
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steps,
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num_repeats,
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create_image_every,
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save_embedding_every,
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template_file,
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preview_image_prompt,
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],
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outputs=[
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ti_output,
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ti_outcome,
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]
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)
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train_hypernetwork.click(
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fn=wrap_gradio_gpu_call(modules.hypernetwork.ui.train_hypernetwork, extra_outputs=[gr.update()]),
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_js="start_training_textual_inversion",
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inputs=[
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train_hypernetwork_name,
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learn_rate,
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dataset_directory,
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log_directory,
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steps,
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create_image_every,
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save_embedding_every,
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template_file,
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preview_image_prompt,
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],
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outputs=[
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ti_output,
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@@ -1130,6 +1184,7 @@ def create_ui(wrap_gradio_gpu_call):
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outputs=[],
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)
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def create_setting_component(key):
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def fun():
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return opts.data[key] if key in opts.data else opts.data_labels[key].default
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@@ -1335,7 +1390,7 @@ Requested path was: {f}
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with gr.Tabs() as tabs:
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for interface, label, ifid in interfaces:
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with gr.TabItem(label, id=ifid):
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with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
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interface.render()
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if os.path.exists(os.path.join(script_path, "notification.mp3")):
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