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feat: progress bar improvements #2962

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merged 3 commits into from
May 19, 2024
Merged

feat: progress bar improvements #2962

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d2255a9
feat: align progress bar vertically
mashb1t May 19, 2024
888de56
feat: use fixed width for status text, remove ordinals
mashb1t May 19, 2024
5d8eb52
refactor: align progress to actions
mashb1t May 19, 2024
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6 changes: 6 additions & 0 deletions css/style.css
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,7 @@ progress {
border-radius: 5px; /* Round the corners of the progress bar */
background-color: #f3f3f3; /* Light grey background */
width: 100%;
vertical-align: middle !important;
}

/* Style the progress bar container */
Expand Down Expand Up @@ -69,6 +70,11 @@ progress::after {
height: 30px !important;
}

.progress-bar span {
text-align: right;
width: 200px;
}

.type_row{
height: 80px !important;
}
Expand Down
37 changes: 19 additions & 18 deletions modules/async_worker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,7 @@ def worker():
from modules.private_logger import log
from extras.expansion import safe_str
from modules.util import (remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil,
get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix, get_enabled_loras,
get_shape_ceil, resample_image, erode_or_dilate, get_enabled_loras,
parse_lora_references_from_prompt, apply_wildcards)
from modules.upscaler import perform_upscale
from modules.flags import Performance
Expand All @@ -72,7 +72,7 @@ def progressbar(async_task, number, text):
async_task.yields.append(['preview', (number, text, None)])

def yield_result(async_task, imgs, black_out_nsfw, censor=True, do_not_show_finished_images=False,
progressbar_index=13):
progressbar_index=flags.preparation_step_count):
if not isinstance(imgs, list):
imgs = [imgs]

Expand Down Expand Up @@ -456,7 +456,7 @@ def handler(async_task):
extra_positive_prompts = prompts[1:] if len(prompts) > 1 else []
extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else []

progressbar(async_task, 3, 'Loading models ...')
progressbar(async_task, 2, 'Loading models ...')

loras = parse_lora_references_from_prompt(prompt, loras, modules.config.default_max_lora_number)

Expand Down Expand Up @@ -523,25 +523,25 @@ def handler(async_task):

if use_expansion:
for i, t in enumerate(tasks):
progressbar(async_task, 5, f'Preparing Fooocus text #{i + 1} ...')
progressbar(async_task, 4, f'Preparing Fooocus text #{i + 1} ...')
expansion = pipeline.final_expansion(t['task_prompt'], t['task_seed'])
print(f'[Prompt Expansion] {expansion}')
t['expansion'] = expansion
t['positive'] = copy.deepcopy(t['positive']) + [expansion] # Deep copy.

for i, t in enumerate(tasks):
progressbar(async_task, 7, f'Encoding positive #{i + 1} ...')
progressbar(async_task, 5, f'Encoding positive #{i + 1} ...')
t['c'] = pipeline.clip_encode(texts=t['positive'], pool_top_k=t['positive_top_k'])

for i, t in enumerate(tasks):
if abs(float(cfg_scale) - 1.0) < 1e-4:
t['uc'] = pipeline.clone_cond(t['c'])
else:
progressbar(async_task, 10, f'Encoding negative #{i + 1} ...')
progressbar(async_task, 6, f'Encoding negative #{i + 1} ...')
t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=t['negative_top_k'])

if len(goals) > 0:
progressbar(async_task, 13, 'Image processing ...')
progressbar(async_task, 7, 'Image processing ...')

if 'vary' in goals:
if 'subtle' in uov_method:
Expand All @@ -562,7 +562,7 @@ def handler(async_task):
uov_input_image = set_image_shape_ceil(uov_input_image, shape_ceil)

initial_pixels = core.numpy_to_pytorch(uov_input_image)
progressbar(async_task, 13, 'VAE encoding ...')
progressbar(async_task, 8, 'VAE encoding ...')

candidate_vae, _ = pipeline.get_candidate_vae(
steps=steps,
Expand All @@ -579,7 +579,7 @@ def handler(async_task):

if 'upscale' in goals:
H, W, C = uov_input_image.shape
progressbar(async_task, 13, f'Upscaling image from {str((H, W))} ...')
progressbar(async_task, 9, f'Upscaling image from {str((H, W))} ...')
uov_input_image = perform_upscale(uov_input_image)
print(f'Image upscaled.')

Expand Down Expand Up @@ -628,7 +628,7 @@ def handler(async_task):
denoising_strength = overwrite_upscale_strength

initial_pixels = core.numpy_to_pytorch(uov_input_image)
progressbar(async_task, 13, 'VAE encoding ...')
progressbar(async_task, 10, 'VAE encoding ...')

candidate_vae, _ = pipeline.get_candidate_vae(
steps=steps,
Expand Down Expand Up @@ -686,7 +686,7 @@ def handler(async_task):
do_not_show_finished_images=True)
return

progressbar(async_task, 13, 'VAE Inpaint encoding ...')
progressbar(async_task, 11, 'VAE Inpaint encoding ...')

inpaint_pixel_fill = core.numpy_to_pytorch(inpaint_worker.current_task.interested_fill)
inpaint_pixel_image = core.numpy_to_pytorch(inpaint_worker.current_task.interested_image)
Expand All @@ -706,7 +706,7 @@ def handler(async_task):

latent_swap = None
if candidate_vae_swap is not None:
progressbar(async_task, 13, 'VAE SD15 encoding ...')
progressbar(async_task, 12, 'VAE SD15 encoding ...')
latent_swap = core.encode_vae(
vae=candidate_vae_swap,
pixels=inpaint_pixel_fill)['samples']
Expand Down Expand Up @@ -832,16 +832,17 @@ def handler(async_task):
zsnr=False)[0]
print(f'Using {scheduler_name} scheduler.')

async_task.yields.append(['preview', (13, 'Moving model to GPU ...', None)])
async_task.yields.append(['preview', (flags.preparation_step_count, 'Moving model to GPU ...', None)])

def callback(step, x0, x, total_steps, y):
done_steps = current_task_id * steps + step
async_task.yields.append(['preview', (
int(15.0 + 85.0 * float(done_steps) / float(all_steps)),
f'Step {step}/{total_steps} in the {current_task_id + 1}{ordinal_suffix(current_task_id + 1)} Sampling',
y)])
int(flags.preparation_step_count + (100 - flags.preparation_step_count) * float(done_steps) / float(all_steps)),
f'Sampling step {step + 1}/{total_steps}, image {current_task_id + 1}/{image_number} ...', y)])

for current_task_id, task in enumerate(tasks):
current_progress = int(flags.preparation_step_count + (100 - flags.preparation_step_count) * float(current_task_id * steps) / float(all_steps))
progressbar(async_task, current_progress, f'Preparing task {current_task_id + 1}/{image_number} ...')
execution_start_time = time.perf_counter()

try:
Expand Down Expand Up @@ -884,12 +885,12 @@ def callback(step, x0, x, total_steps, y):
imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]

img_paths = []
current_progress = int(15.0 + 85.0 * float((current_task_id + 1) * steps) / float(all_steps))
current_progress = int(flags.preparation_step_count + (100 - flags.preparation_step_count) * float((current_task_id + 1) * steps) / float(all_steps))
if modules.config.default_black_out_nsfw or black_out_nsfw:
progressbar(async_task, current_progress, 'Checking for NSFW content ...')
imgs = default_censor(imgs)

progressbar(async_task, current_progress, 'Saving image to system ...')
progressbar(async_task, current_progress, f'Saving image {current_task_id + 1}/{image_number} to system ...')
for x in imgs:
d = [('Prompt', 'prompt', task['log_positive_prompt']),
('Negative Prompt', 'negative_prompt', task['log_negative_prompt']),
Expand Down
1 change: 1 addition & 0 deletions modules/flags.py
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Original file line number Diff line number Diff line change
Expand Up @@ -93,6 +93,7 @@ class MetadataScheme(Enum):
]

controlnet_image_count = 4
preparation_step_count = 13


class OutputFormat(Enum):
Expand Down
4 changes: 0 additions & 4 deletions modules/util.py
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Expand Up @@ -372,10 +372,6 @@ def get_file_from_folder_list(name, folders):
return os.path.abspath(os.path.realpath(os.path.join(folders[0], name)))


def ordinal_suffix(number: int) -> str:
return 'th' if 10 <= number % 100 <= 20 else {1: 'st', 2: 'nd', 3: 'rd'}.get(number % 10, 'th')


def makedirs_with_log(path):
try:
os.makedirs(path, exist_ok=True)
Expand Down