@ -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 = ( 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 = ' ' )
reuse_seed = gr . Button ( reuse_symbol , elem_id = ' ' )
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 = ' ' ) :
seed_checkbox = gr . Checkbox ( label = ' Extra ' , elem_id = ' ' , 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 = 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 = ' ' )
reuse_subseed = gr . Button ( reuse_symbol , elem_id = ' ' )
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 "
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
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 , 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 ,
AUTOMATIC1111
3 years ago
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GitHub