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update cli

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This commit is contained in:
Vladimir Mandic
2023-05-15 16:55:10 -04:00
parent e737419ba4
commit 7fe0587557
7 changed files with 6614 additions and 104 deletions
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cli/train/setup.log
+6477
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File diff suppressed because one or more lines are too long
cli/train/train.py
+126 -73
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@@ -6,22 +6,22 @@ import re
import gc
import sys
import json
import http
import time
import shutil
import pathlib
import asyncio
import logging
import tempfile
import argparse
import warnings
warnings.filterwarnings(action="ignore", category=DeprecationWarning)
warnings.filterwarnings(action="ignore", category=UserWarning)
warnings.filterwarnings(action="ignore", category=FutureWarning)
sys.path.append('.')
# 3rd party imports
import filetype
import torch
import requests
import urllib3
from tqdm.rich import tqdm
# local imports
@@ -31,27 +31,39 @@ import process
import latents
import options
# console handler
from rich.pretty import install as pretty_install
from rich.traceback import install as traceback_install
from rich.console import Console
console = Console(log_time=True, log_time_format='%H:%M:%S-%f')
pretty_install(console=console)
traceback_install(console=console, extra_lines=1, width=console.width, word_wrap=False, indent_guides=False, suppress=[torch,asyncio,http,urllib3,requests])
# lora imports
lora_path = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, 'modules', 'lora'))
sys.path.append(lora_path)
lycoris_path = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, 'modules', 'lycoris'))
sys.path.append(lycoris_path)
import train_network
# globals
args = None
log = logging.getLogger(__name__)
valid_steps = ['original', 'face', 'body', 'blur', 'range', 'upscale', 'restore', 'interrogate', 'resize', 'square', 'segment']
# methods
def setup_logging(clean=False):
try:
if clean and os.path.isfile('setup.log'):
os.remove('setup.log')
time.sleep(0.1) # prevent race condition
except:
pass
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s | %(levelname)s | %(pathname)s | %(message)s', filename='setup.log', filemode='a', encoding='utf-8', force=True)
from rich.theme import Theme
from rich.logging import RichHandler
from rich.console import Console
from rich.pretty import install as pretty_install
from rich.traceback import install as traceback_install
console = Console(log_time=True, log_time_format='%H:%M:%S-%f', theme=Theme({
"traceback.border": "black",
"traceback.border.syntax_error": "black",
"inspect.value.border": "black",
}))
pretty_install(console=console)
traceback_install(console=console, extra_lines=1, width=console.width, word_wrap=False, indent_guides=False, suppress=[])
rh = RichHandler(show_time=True, omit_repeated_times=False, show_level=True, show_path=False, markup=False, rich_tracebacks=True, log_time_format='%H:%M:%S-%f', level=logging.DEBUG if args.debug else logging.INFO, console=console)
rh.set_name(logging.DEBUG if args.debug else logging.INFO)
log.addHandler(rh)
def mem_stats():
gc.collect()
if torch.cuda.is_available():
@@ -62,7 +74,7 @@ def mem_stats():
torch.cuda.ipc_collect()
mem = util.get_memory()
peak = { 'active': mem['gpu-active']['peak'], 'allocated': mem['gpu-allocated']['peak'], 'reserved': mem['gpu-reserved']['peak'] }
console.log(f"memory cpu: {mem.ram} gpu current: {mem.gpu} gpu peak: {peak}")
log.debug(f"memory cpu: {mem.ram} gpu current: {mem.gpu} gpu peak: {peak}")
def parse_args():
@@ -71,8 +83,8 @@ def parse_args():
group_main = parser.add_argument_group('Main')
group_main.add_argument('--type', type=str, choices=['embedding', 'lora', 'lycoris', 'dreambooth'], default=None, required=True, help='training type')
group_main.add_argument('--model', type=str, default='', required=False, help='base model to use for training, default: current loaded model')
group_main.add_argument('--name', type=str, default=None, required=True, help='output filename')
group_main.add_argument('--overwrite', default = False, action='store_true', help = "overwrite existing training, default: %(default)s")
group_main.add_argument('--tag', type=str, default='person', required=False, help='primary tags, default: %(default)s')
group_data = parser.add_argument_group('Dataset')
@@ -91,6 +103,10 @@ def parse_args():
group_train.add_argument('--repeats', type=int, default=10, required=False, help='number of repeats per image, default: %(default)s')
group_train.add_argument('--alpha', type=float, default=0, required=False, help='alpha for weights scaling, default: dim/2')
group_other = parser.add_argument_group('Other')
group_other.add_argument('--overwrite', default = False, action='store_true', help = "overwrite existing training, default: %(default)s")
group_other.add_argument('--debug', default = False, action='store_true', help = "enable debug level logging, default: %(default)s")
args = parser.parse_args()
@@ -99,10 +115,10 @@ def prepare_server():
server_status = util.Map(sdapi.progress())
server_state = server_status['state']
except:
console.log('server error:', server_status)
log.error(f'server error: {server_status}')
exit(1)
if server_state['job_count'] > 0:
console.log('server not idle:', server_state)
log.error(f'server not idle: {server_state}')
exit(1)
server_options = util.Map(sdapi.options())
@@ -114,36 +130,57 @@ def prepare_server():
server_options.options.training_image_repeats_per_epoch = args.repeats
server_options.options.training_write_csv_every = 0
sdapi.postsync('/sdapi/v1/options', server_options.options)
console.log('updated server options')
log.info('updated server options')
def verify_args():
server_options = util.Map(sdapi.options())
args.model = server_options.options.sd_model_checkpoint.split(' [')[0]
if args.model != '':
if not os.path.isfile(args.model):
log.error(f'cannot find loaded model: {args.model}')
exit(1)
server_options.options.sd_model_checkpoint = args.model
sdapi.postsync('/sdapi/v1/options', server_options.options)
else:
args.model = server_options.options.sd_model_checkpoint.split(' [')[0]
args.lora_dir = server_options.options.lora_dir
if not os.path.isabs(args.model) and not os.path.exists(args.model):
args.model = os.path.abspath(os.path.join(args.lora_dir, os.pardir, 'Stable-diffusion', args.model))
if not os.path.exists(args.model) or not os.path.isfile(args.model):
console.log('cannot find model:', args.model)
args.lyco_dir = server_options.options.lyco_dir
args.ckpt_dir = server_options.options.ckpt_dir
args.embeddings_dir = server_options.options.embeddings_dir
if not os.path.isfile(args.model):
attempt = os.path.abspath(os.path.join(args.ckpt_dir, args.model))
args.model = attempt if os.path.isfile(attempt) else args.model
if not os.path.isfile(args.model):
attempt = os.path.abspath(os.path.join(args.ckpt_dir, '..', args.model))
args.model = attempt if os.path.isfile(attempt) else args.model
if not os.path.isfile(args.model):
log.error(f'cannot find loaded model: {args.model}')
exit(1)
if not os.path.exists(args.ckpt_dir) or not os.path.isdir(args.ckpt_dir):
log.error(f'cannot find models folder: {args.ckpt_dir}')
exit(1)
if not os.path.exists(args.input) or not os.path.isdir(args.input):
console.log('cannot find training folder:', args.input)
log.error(f'cannot find training folder: {args.input}')
exit(1)
if not os.path.exists(args.lora_dir) or not os.path.isdir(args.lora_dir):
console.log('cannot find lora folder:', args.lora_dir)
log.error(f'cannot find lora folder: {args.lora_dir}')
exit(1)
if not os.path.exists(args.lyco_dir) or not os.path.isdir(args.lyco_dir):
log.error(f'cannot find lyco folder: {args.lyco_dir}')
exit(1)
if args.output != '':
args.process_dir = args.output
else:
args.process_dir = os.path.join(tempfile.gettempdir(), 'train', args.name)
console.log(f'args: {vars(args)}')
log.debug(f'args: {vars(args)}')
log.debug(f'server flags: {server_options.flags}')
log.debug(f'server options: {server_options.options}')
async def training_loop():
async def async_train():
res = await sdapi.post('/sdapi/v1/train/embedding', options.embedding)
console.log(f'train embedding result: {res}')
log.info(f'train embedding result: {res}')
async def async_monitor():
await asyncio.sleep(3)
@@ -164,49 +201,74 @@ async def training_loop():
def train_embedding():
console.log(f'{args.type} options: {options.embedding}')
log.info(f'{args.type} options: {options.embedding}')
create_options = util.Map({
"name": args.name,
"num_vectors_per_token": args.dim,
"overwrite_old": False,
"init_text": args.tag,
})
server_options = util.Map(sdapi.options())
fn = os.path.join(server_options.options.embeddings_dir, args.name) + '.pt'
fn = os.path.join(args.embeddings_dir, args.name) + '.pt'
if os.path.exists(fn) and args.overwrite:
console.log(f'delete existing embedding {fn}')
log.warning(f'delete existing embedding {fn}')
os.remove(fn)
else:
console.log(f'embedding exists {fn}')
log.error(f'embedding exists {fn}')
return
console.log(f'create embedding {create_options}')
log.info(f'create embedding {create_options}')
res = sdapi.postsync('/sdapi/v1/create/embedding', create_options)
if 'info' in res and 'error' in res['info']: # formatted error
console.log(res.info)
log.error(res.info)
elif 'info' in res: # no error
asyncio.run(training_loop())
else: # unknown error
console.log(f'create embedding error {res}')
log.error(f'create embedding error {res}')
def train_lora():
fn = os.path.join(args.lora_dir, args.name)
fn = os.path.join(options.lora.output_dir, args.name)
for ext in ['.ckpt', '.pt', '.safetensors']:
if os.path.exists(fn + ext):
if args.overwrite:
console.log(f'delete existing lora: {fn + ext}')
log.warning(f'delete existing lora: {fn + ext}')
os.remove(fn + ext)
else:
console.log(f'lora exists: {fn + ext}')
log.error(f'lora exists: {fn + ext}')
return
console.log(f'{args.type} options: {options.lora}')
log.info(f'{args.type} options: {options.lora}')
# lora imports
lora_path = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, 'modules', 'lora'))
sys.path.append(lora_path)
if args.type == 'lycoris':
lycoris_path = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir, 'modules', 'lycoris'))
sys.path.append(lycoris_path)
log.debug('importing lora lib')
import train_network
train_network.train(options.lora)
if args.type == 'lycoris':
log.debug('importing lycoris lib')
import importlib
_network_module = importlib.import_module(options.lora.network_module)
def prepare_options():
if args.type == 'embedding':
log.info('train embedding')
options.lora.in_json = None
if args.type == 'dreambooth':
log.info('train using dreambooth style training')
options.lora.in_json = None
if args.type == 'lora':
log.info('train using lora style training')
options.lora.output_dir = args.lora_dir
options.lora.in_json = os.path.join(args.process_dir, args.name + '.json')
if args.type == 'lycoris':
log.info('train using lycoris network')
options.lora.output_dir = args.lyco_dir
options.lora.network_module = 'lycoris.kohya'
options.lora.in_json = os.path.join(args.process_dir, args.name + '.json')
# lora specific
options.lora.pretrained_model_name_or_path = args.model
options.lora.output_dir = args.lora_dir
options.lora.output_name = args.name
options.lora.max_train_steps = args.steps
options.lora.network_dim = args.dim
@@ -216,19 +278,6 @@ def prepare_options():
options.lora.train_batch_size = args.batch
options.lora.network_alpha = args.dim // 2 if args.alpha == 0 else args.alpha
options.lora.train_data_dir = args.process_dir
if args.type == 'lycoris':
console.log('train using lycoris network')
options.lora.network_module = 'lycoris.kohya'
options.lora.in_json = os.path.join(args.process_dir, args.name + '.json')
if args.type == 'dreambooth':
console.log('train using dreambooth style training')
options.lora.in_json = None
if args.type == 'lora':
console.log('train using lora style training')
options.lora.in_json = os.path.join(args.process_dir, args.name + '.json')
if args.type == 'embedding':
console.log('train embedding')
options.lora.in_json = None
# embedding specific
options.embedding.embedding_name = args.name
options.embedding.learn_rate = str(args.lr)
@@ -243,17 +292,20 @@ def process_inputs():
pathlib.Path(args.process_dir).mkdir(parents=True, exist_ok=True)
processing_options = args.process.split(',') if isinstance(args.process, str) else args.process
processing_options = [opt.strip() for opt in re.split(',| ', args.process)]
console.log(f'processing steps: {processing_options}')
log.info(f'processing steps: {processing_options}')
for step in processing_options:
if step not in valid_steps:
console.log(f'invalid processing step: {[step]}')
log.error(f'invalid processing step: {[step]}')
exit(1)
for root, _sub_dirs, folder in os.walk(args.input):
files = [os.path.join(root, f) for f in folder if filetype.is_image(os.path.join(root, f))]
console.log(f'processing input images: {len(files)}')
log.info(f'processing input images: {len(files)}')
if os.path.exists(args.process_dir):
console.log('removing existing processed folder:', args.process_dir)
shutil.rmtree(args.process_dir, ignore_errors=True)
if args.overwrite:
log.warning(f'removing existing processed folder: {args.process_dir}')
shutil.rmtree(args.process_dir, ignore_errors=True)
else:
log.info(f'processed folder exists: {args.process_dir}')
steps = [step for step in processing_options if step in ['face', 'body', 'original']]
process.reset()
metadata = {}
@@ -264,7 +316,7 @@ def process_inputs():
opts = [step for step in processing_options if step not in ['face', 'original', 'upscale', 'restore']] # body does not perform upscale or restore
if step == 'original':
opts = [step for step in processing_options if step not in ['face', 'body', 'upscale', 'restore', 'blur', 'range', 'segment']] # original does not perform most steps
console.log(f'processing current step: {opts}')
log.info(f'processing current step: {opts}')
tag = step
if tag == 'original' and args.tag is not None:
concept = args.tag.split(',')[0].strip()
@@ -274,8 +326,8 @@ def process_inputs():
folder = os.path.join(args.process_dir, str(args.repeats) + '_' + concept) # separate concepts per folder
if args.type in ['embedding']:
folder = os.path.join(args.process_dir) # everything into same folder
console.log('processing concept:', concept)
console.log('processing output folder:', folder)
log.info(f'processing concept: {concept}')
log.info(f'processing output folder: {folder}')
pathlib.Path(folder).mkdir(parents=True, exist_ok=True)
results = {}
for f in files:
@@ -290,9 +342,9 @@ def process_inputs():
if options.lora.in_json is None:
with open(res.output.replace(options.process.format, '.txt'), "w", encoding='utf-8') as outfile:
outfile.write(res.caption)
console.log(f"processing {'saved' if res.image is not None else 'skipped'}: {f} => {res.output} {res.ops} {res.message}")
log.info(f"processing {'saved' if res.image is not None else 'skipped'}: {f} => {res.output} {res.ops} {res.message}")
folders = [os.path.join(args.process_dir, folder) for folder in os.listdir(args.process_dir) if os.path.isdir(os.path.join(args.process_dir, folder))]
console.log(f'input datasets {folders}')
log.info(f'input datasets {folders}')
if options.lora.in_json is not None:
with open(options.lora.in_json, "w", encoding='utf-8') as outfile: # write json at the end only
outfile.write(json.dumps(metadata, indent=2))
@@ -300,17 +352,18 @@ def process_inputs():
latents.create_vae_latents(util.Map({ 'input': folder, 'json': options.lora.in_json }))
latents.unload_vae()
r = { 'inputs': len(files), 'outputs': results, 'metadata': options.lora.in_json }
console.log(f'processing steps result: {r}')
log.info(f'processing steps result: {r}')
if args.gradient < 0:
console.log(f"setting gradient accumulation to number of images: {results['total']}")
log.info(f"setting gradient accumulation to number of images: {results['total']}")
options.lora.gradient_accumulation_steps = results['total']
options.embedding.gradient_step = results['total']
process.unload()
if __name__ == '__main__':
console.log('train script for stable diffusion')
log.info('train script for stable diffusion')
parse_args()
setup_logging()
prepare_server()
verify_args()
prepare_options()
@@ -323,7 +376,7 @@ if __name__ == '__main__':
if args.type == 'lora' or args.type == 'lycoris' or args.type == 'dreambooth':
train_lora()
except KeyboardInterrupt as e:
console.log('interrupt requested')
log.error('interrupt requested')
sdapi.interrupt()
mem_stats()
console.log('done')
log.info('done')
modules/api/api.py
-2
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@@ -182,7 +182,6 @@ class Api:
script_args = default_script_args.copy()
# position 0 in script_arg is the idx+1 of the selectable script that is going to be run when using scripts.scripts_*2img.run()
if selectable_scripts:
# TODO this can corrupt values for other scripts
script_args[selectable_scripts.args_from:selectable_scripts.args_to] = request.script_args
script_args[0] = selectable_script_idx + 1
# Now check for always on scripts
@@ -194,7 +193,6 @@ class Api:
if not alwayson_script.alwayson:
raise HTTPException(status_code=422, detail=f"Selectable script cannot be in always on params: {alwayson_script_name}")
if "args" in request.alwayson_scripts[alwayson_script_name]:
# TODO this can corrupt values for other scripts
# min between arg length in scriptrunner and arg length in the request
for idx in range(0, min((alwayson_script.args_to - alwayson_script.args_from), len(request.alwayson_scripts[alwayson_script_name]["args"]))):
script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx]
modules/generation_parameters_copypaste.py
-1
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@@ -123,7 +123,6 @@ def connect_paste_params_buttons():
destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
if binding.source_image_component and destination_image_component:
# print('HERE')
if isinstance(binding.source_image_component, gr.Gallery):
func = send_image_and_dimensions if destination_width_component else image_from_url_text
jsfunc = "extract_image_from_gallery"
modules/shared.py
+1 -1
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@@ -606,7 +606,7 @@ cmd_opts = cmd_args.compatibility_args(opts, cmd_opts)
os.makedirs(opts.hypernetwork_dir, exist_ok=True)
prompt_styles = modules.styles.StyleDatabase(opts.styles_dir)
cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.insecure
cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or (cmd_opts.server_name or False)) and not cmd_opts.insecure
devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
device = devices.device
batch_cond_uncond = opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
modules/ui.py
+10 -13
View File
@@ -14,6 +14,8 @@ from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepb
from modules.ui_components import FormRow, FormColumn, FormGroup, ToolButton, FormHTML # pylint: disable=unused-import
from modules.paths import script_path, data_path
from modules.shared import opts, cmd_opts
from modules.sd_samplers import samplers, samplers_for_img2img
from modules import prompt_parser
import modules.codeformer_model
import modules.generation_parameters_copypaste as parameters_copypaste
import modules.gfpgan_model
@@ -22,13 +24,9 @@ import modules.scripts
import modules.shared as shared
import modules.errors as errors
import modules.styles
import modules.textual_inversion.ui
from modules import prompt_parser
from modules.sd_hijack import model_hijack
from modules.sd_samplers import samplers, samplers_for_img2img
from modules.textual_inversion import textual_inversion
from modules.generation_parameters_copypaste import image_from_url_text
import modules.extras
import modules.textual_inversion.ui
from modules.textual_inversion import textual_inversion
errors.install()
mimetypes.init()
@@ -67,7 +65,8 @@ def plaintext_to_html(text):
def send_gradio_gallery_to_image(x):
if len(x) == 0:
return None
return image_from_url_text(x[0])
return parameters_copypaste.image_from_url_text(x[0])
def visit(x, func, path=""):
if hasattr(x, 'children'):
@@ -208,7 +207,7 @@ def update_token_counter(text, steps):
prompt_schedules = [[[steps, text]]]
flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
prompts = [prompt_text for step, prompt_text in flat_prompts]
token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
token_count, max_length = max([sd_hijack.model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"
@@ -460,7 +459,7 @@ def create_ui():
res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height], show_progress=False)
txt_prompt_img.change(
fn=modules.images.image_data,
fn=modules.images.image_data, # TODO
inputs=[
txt_prompt_img
],
@@ -503,9 +502,7 @@ def create_ui():
*modules.scripts.scripts_txt2img.infotext_fields
]
parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None,
))
parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None))
txt2img_preview_params = [
txt2img_prompt,
@@ -736,7 +733,7 @@ def create_ui():
connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
img2img_prompt_img.change(
fn=modules.images.image_data,
fn=modules.images.image_data, # TODO
inputs=[
img2img_prompt_img
],
modules/ui_postprocessing.py
-14
View File
@@ -18,25 +18,17 @@ def create_ui():
with gr.Row().style(equal_height=False, variant='compact'):
with gr.Column(variant='compact'):
with gr.Tabs(elem_id="mode_extras"):
with gr.TabItem('Single Image', id="single_image", elem_id="extras_single_tab") as tab_single:
extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
with gr.TabItem('Process Batch', id="batch_process", elem_id="extras_batch_process_tab") as tab_batch:
image_batch = gr.Files(label="Batch Process", interactive=True, elem_id="extras_image_batch")
with gr.TabItem('Process Folder', id="batch_from_directory", elem_id="extras_batch_directory_tab") as tab_batch_dir:
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")
with gr.Row():
buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint"])
# submit = gr.Button('Generate', elem_id="extras_generate", variant='primary') # TODO: add all
script_inputs = scripts.scripts_postproc.setup_ui()
with gr.Column():
id_part = 'extras'
with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
@@ -45,15 +37,12 @@ def create_ui():
skip = gr.Button('Skip', elem_id=f"{id_part}_skip", variant='secondary')
skip.click(fn=lambda: shared.state.skip(), inputs=[], outputs=[])
interrupt.click(fn=lambda: shared.state.interrupt(), inputs=[], outputs=[])
result_images, html_info_x, html_info, _html_log = ui_common.create_output_panel("extras", shared.opts.outdir_extras_samples)
html_info = gr.HTML(elem_id="pnginfo_html_info")
generation_info = gr.Textbox(elem_id="pnginfo_generation_info", label="Parameters", visible=False)
generation_info_pretty = gr.Textbox(elem_id="pnginfo_generation_info_pretty", label="Parameters")
gr.HTML('Full metadata')
html2_info = gr.HTML(elem_id="pnginfo_html2_info")
for tabname, button in buttons.items():
parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=extras_image))
@@ -69,15 +58,12 @@ def create_ui():
tab_single.select(fn=lambda: 0, inputs=[], outputs=[tab_index])
tab_batch.select(fn=lambda: 1, inputs=[], outputs=[tab_index])
tab_batch_dir.select(fn=lambda: 2, inputs=[], outputs=[tab_index])
generation_info.change(fn=pretty_geninfo, inputs=[generation_info], outputs=[generation_info_pretty])
extras_image.change(
fn=wrap_gradio_call(run_pnginfo),
inputs=[extras_image],
outputs=[html_info, generation_info, html2_info],
)
submit.click(
fn=call_queue.wrap_gradio_gpu_call(submit_click, extra_outputs=[None, '']),
inputs=[