mrq/stable-diffusion-webui
1
1
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge pull request #6183 from Kryptortio/add_more_element_ids

Browse Source 
Add additional elem_id/HTML ids
This commit is contained in:
AUTOMATIC1111 2023-01-02 06:10:26 +03:00 committed by GitHub
commit f39a79d143
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 133 additions and 133 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
modules/generation_parameters_copypaste.py
View File

@ -93,7 +93,7 @@ def integrate_settings_paste_fields(component_dict):
def create_buttons(tabs_list):
buttons = {}
for tab in tabs_list:
buttons[tab] = gr.Button(f"Send to {tab}")
buttons[tab] = gr.Button(f"Send to {tab}", elem_id=f"{tab}_tab")
return buttons

252
modules/ui.py
View File

@ -272,17 +272,17 @@ def interrogate_deepbooru(image):
return gr_show(True) if prompt is None else prompt
def create_seed_inputs():
def create_seed_inputs(target_interface):
with gr.Row():
with gr.Box():
with gr.Row(elem_id='seed_row'):
seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1)
with gr.Row(elem_id=target_interface + '_seed_row'):
seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
seed.style(container=False)
random_seed = gr.Button(random_symbol, elem_id='random_seed')
reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed')
random_seed = gr.Button(random_symbol, elem_id=target_interface + '_random_seed')
reuse_seed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_seed')
with gr.Box(elem_id='subseed_show_box'):
seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False)
with gr.Box(elem_id=target_interface + '_subseed_show_box'):
seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
# Components to show/hide based on the 'Extra' checkbox
seed_extras = []
@ -290,17 +290,17 @@ def create_seed_inputs():
with gr.Row(visible=False) as seed_extra_row_1:
seed_extras.append(seed_extra_row_1)
with gr.Box():
with gr.Row(elem_id='subseed_row'):
subseed = gr.Number(label='Variation seed', value=-1)
with gr.Row(elem_id=target_interface + '_subseed_row'):
subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed')
subseed.style(container=False)
random_subseed = gr.Button(random_symbol, elem_id='random_subseed')
reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed')
subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01)
random_subseed = gr.Button(random_symbol, elem_id=target_interface + '_random_subseed')
reuse_subseed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=target_interface + '_subseed_strength')
with gr.Row(visible=False) as seed_extra_row_2:
seed_extras.append(seed_extra_row_2)
seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0)
seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0)
seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=target_interface + '_seed_resize_from_w')
seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=target_interface + '_seed_resize_from_h')
random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
@ -678,28 +678,28 @@ def create_ui():
steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
with gr.Group():
width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
with gr.Row():
restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
tiling = gr.Checkbox(label='Tiling', value=False)
enable_hr = gr.Checkbox(label='Highres. fix', value=False)
restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
enable_hr = gr.Checkbox(label='Highres. fix', value=False, elem_id="txt2img_enable_hr")
with gr.Row(visible=False) as hr_options:
firstphase_width = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass width", value=0)
firstphase_height = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass height", value=0)
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
firstphase_width = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass width", value=0, elem_id="txt2img_firstphase_width")
firstphase_height = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass height", value=0, elem_id="txt2img_firstphase_height")
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
with gr.Row(equal_height=True):
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)
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
with gr.Group():
with gr.Group(elem_id="txt2img_script_container"):
custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
@ -821,10 +821,10 @@ def create_ui():
with gr.Column(variant='panel', elem_id="img2img_settings"):
with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
with gr.TabItem('img2img', id='img2img'):
with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab"):
init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool, image_mode="RGBA").style(height=480)
with gr.TabItem('Inpaint', id='inpaint'):
with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab"):
init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_inpaint_tool, image_mode="RGBA").style(height=480)
init_img_with_mask_orig = gr.State(None)
@ -843,24 +843,24 @@ def create_ui():
init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
with gr.Row():
mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
mask_alpha = gr.Slider(label="Mask transparency", interactive=use_color_sketch, visible=use_color_sketch)
mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
mask_alpha = gr.Slider(label="Mask transparency", interactive=use_color_sketch, visible=use_color_sketch, elem_id="img2img_mask_alpha")
with gr.Row():
mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
with gr.Row():
inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False, elem_id="img2img_inpaint_full_res")
inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
with gr.TabItem('Batch img2img', id='batch'):
with gr.TabItem('Batch img2img', id='batch', elem_id="img2img_batch_tab"):
hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
with gr.Row():
resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
@ -872,20 +872,20 @@ def create_ui():
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
with gr.Row():
restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
tiling = gr.Checkbox(label='Tiling', value=False)
restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
with gr.Row():
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)
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
with gr.Group():
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
with gr.Group():
with gr.Group(elem_id="img2img_script_container"):
custom_inputs = modules.scripts.scripts_img2img.setup_ui()
img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
@ -1032,45 +1032,45 @@ def create_ui():
with gr.Row().style(equal_height=False):
with gr.Column(variant='panel'):
with gr.Tabs(elem_id="mode_extras"):
with gr.TabItem('Single Image'):
extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
with gr.TabItem('Single Image', elem_id="extras_single_tab"):
extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
with gr.TabItem('Batch Process'):
image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab"):
image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id="extras_image_batch")
with gr.TabItem('Batch from Directory'):
extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
show_extras_results = gr.Checkbox(label='Show result images', value=True)
with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab"):
extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")
extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.", elem_id="extras_batch_output_dir")
show_extras_results = gr.Checkbox(label='Show result images', value=True, elem_id="extras_show_extras_results")
submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
with gr.Tabs(elem_id="extras_resize_mode"):
with gr.TabItem('Scale by'):
upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
with gr.TabItem('Scale to'):
with gr.TabItem('Scale by', elem_id="extras_scale_by_tab"):
upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
with gr.TabItem('Scale to', elem_id="extras_scale_to_tab"):
with gr.Group():
with gr.Row():
upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
with gr.Group():
extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
with gr.Group():
extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1, elem_id="extras_upscaler_2_visibility")
with gr.Group():
gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan)
gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan, elem_id="extras_gfpgan_visibility")
with gr.Group():
codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer, elem_id="extras_codeformer_visibility")
codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer, elem_id="extras_codeformer_weight")
with gr.Group():
upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False, elem_id="extras_upscale_before_face_fix")
result_images, html_info_x, html_info, html_log = create_output_panel("extras", opts.outdir_extras_samples)
@ -1117,7 +1117,7 @@ def create_ui():
with gr.Column(variant='panel'):
html = gr.HTML()
generation_info = gr.Textbox(visible=False)
generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info")
html2 = gr.HTML()
with gr.Row():
buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
@ -1144,13 +1144,13 @@ def create_ui():
tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
custom_name = gr.Textbox(label="Custom Name (Optional)")
interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
with gr.Row():
checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format")
save_as_half = gr.Checkbox(value=False, label="Save as float16")
checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
@ -1165,58 +1165,58 @@ def create_ui():
with gr.Tabs(elem_id="train_tabs"):
with gr.Tab(label="Create embedding"):
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")
new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")
with gr.Row():
with gr.Column(scale=3):
gr.HTML(value="")
with gr.Column():
create_embedding = gr.Button(value="Create embedding", variant='primary')
create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
with gr.Tab(label="Create hypernetwork"):
new_hypernetwork_name = gr.Textbox(label="Name")
new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "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_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
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'", elem_id="train_new_hypernetwork_layer_structure")
new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func")
new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")
with gr.Row():
with gr.Column(scale=3):
gr.HTML(value="")
with gr.Column():
create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
with gr.Tab(label="Preprocess images"):
process_src = gr.Textbox(label='Source directory')
process_dst = gr.Textbox(label='Destination directory')
process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
with gr.Row():
process_flip = gr.Checkbox(label='Create flipped copies')
process_split = gr.Checkbox(label='Split oversized images')
process_focal_crop = gr.Checkbox(label='Auto focal point crop')
process_caption = gr.Checkbox(label='Use BLIP for caption')
process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True)
process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
with gr.Row(visible=False) as process_split_extra_row:
process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
with gr.Row(visible=False) as process_focal_crop_row:
process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05)
process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
process_focal_crop_debug = gr.Checkbox(label='Create debug image')
process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
with gr.Row():
with gr.Column(scale=3):
@ -1224,8 +1224,8 @@ def create_ui():
with gr.Column():
with gr.Row():
interrupt_preprocessing = gr.Button("Interrupt")
run_preprocess = gr.Button(value="Preprocess", variant='primary')
interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
process_split.change(
fn=lambda show: gr_show(show),
@ -1248,31 +1248,31 @@ def create_ui():
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")
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")
embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
batch_size = gr.Number(label='Batch size', value=1, precision=0)
gradient_step = gr.Number(label='Gradient accumulation steps', 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")
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=8, label="Width", value=512)
training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
steps = gr.Number(label='Max steps', value=100000, 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)
preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"), elem_id="train_template_file")
training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
with gr.Row():
shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False)
tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0)
shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")
with gr.Row():
latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'])
latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")
with gr.Row():
interrupt_training = gr.Button(value="Interrupt")
train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
train_embedding = gr.Button(value="Train Embedding", variant='primary')
interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
@ -1490,7 +1490,7 @@ def create_ui():
return gr.update(value=value), opts.dumpjson()
with gr.Blocks(analytics_enabled=False) as settings_interface:
settings_submit = gr.Button(value="Apply settings", variant='primary')
settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
result = gr.HTML()
settings_cols = 3
@ -1541,8 +1541,8 @@ def create_ui():
download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
with gr.Row():
reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary', elem_id="settings_restart_gradio")
request_notifications.click(
fn=lambda: None,

12
style.css
View File

@ -73,7 +73,7 @@
margin-right: auto;
}
#random_seed, #random_subseed, #reuse_seed, #reuse_subseed, #open_folder{
[id$=_random_seed], [id$=_random_subseed], [id$=_reuse_seed], [id$=_reuse_subseed], #open_folder{
min-width: auto;
flex-grow: 0;
padding-left: 0.25em;
@ -84,27 +84,27 @@
display: none;
}
#seed_row, #subseed_row{
[id$=_seed_row], [id$=_subseed_row]{
gap: 0.5rem;
}
#subseed_show_box{
[id$=_subseed_show_box]{
min-width: auto;
flex-grow: 0;
}
#subseed_show_box > div{
[id$=_subseed_show_box] > div{
border: 0;
height: 100%;
}
#subseed_show{
[id$=_subseed_show]{
min-width: auto;
flex-grow: 0;
padding: 0;
}
#subseed_show label{
[id$=_subseed_show] label{
height: 100%;
}