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Fooocus
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feat: progress bar improvements (#2962) 

* feat: align progress bar vertically

* feat: use fixed width for status text, remove ordinals

* refactor: align progress to actions
This commit is contained in:
Manuel Schmid 2024-05-19 20:43:11 +02:00 committed by GitHub
parent e94b97604f
commit c995511705
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4 changed files with 26 additions and 22 deletions
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6
css/style.css
View File

@ -27,6 +27,7 @@ progress {
border-radius: 5px; /* Round the corners of the progress bar */ border-radius: 5px; /* Round the corners of the progress bar */
background-color: #f3f3f3; /* Light grey background */ background-color: #f3f3f3; /* Light grey background */
width: 100%; width: 100%;
vertical-align: middle !important;
} }
/* Style the progress bar container */ /* Style the progress bar container */
@ -69,6 +70,11 @@ progress::after {
height: 30px !important; height: 30px !important;
} }
.progress-bar span {
text-align: right;
width: 200px;
}
.type_row{ .type_row{
height: 80px !important; height: 80px !important;
} }

37
modules/async_worker.py
View File

@ -49,7 +49,7 @@ def worker():
from modules.private_logger import log from modules.private_logger import log
from extras.expansion import safe_str from extras.expansion import safe_str
from modules.util import (remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil, 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) parse_lora_references_from_prompt, apply_wildcards)
from modules.upscaler import perform_upscale from modules.upscaler import perform_upscale
from modules.flags import Performance from modules.flags import Performance
@ -72,7 +72,7 @@ def worker():
async_task.yields.append(['preview', (number, text, None)]) 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, 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): if not isinstance(imgs, list):
imgs = [imgs] imgs = [imgs]
@ -456,7 +456,7 @@ def worker():
extra_positive_prompts = prompts[1:] if len(prompts) > 1 else [] extra_positive_prompts = prompts[1:] if len(prompts) > 1 else []
extra_negative_prompts = negative_prompts[1:] if len(negative_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) loras = parse_lora_references_from_prompt(prompt, loras, modules.config.default_max_lora_number)
@ -523,25 +523,25 @@ def worker():
if use_expansion: if use_expansion:
for i, t in enumerate(tasks): 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']) expansion = pipeline.final_expansion(t['task_prompt'], t['task_seed'])
print(f'[Prompt Expansion] {expansion}') print(f'[Prompt Expansion] {expansion}')
t['expansion'] = expansion t['expansion'] = expansion
t['positive'] = copy.deepcopy(t['positive']) + [expansion] # Deep copy. t['positive'] = copy.deepcopy(t['positive']) + [expansion] # Deep copy.
for i, t in enumerate(tasks): 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']) t['c'] = pipeline.clip_encode(texts=t['positive'], pool_top_k=t['positive_top_k'])
for i, t in enumerate(tasks): for i, t in enumerate(tasks):
if abs(float(cfg_scale) - 1.0) < 1e-4: if abs(float(cfg_scale) - 1.0) < 1e-4:
t['uc'] = pipeline.clone_cond(t['c']) t['uc'] = pipeline.clone_cond(t['c'])
else: 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']) t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=t['negative_top_k'])
if len(goals) > 0: if len(goals) > 0:
progressbar(async_task, 13, 'Image processing ...') progressbar(async_task, 7, 'Image processing ...')
if 'vary' in goals: if 'vary' in goals:
if 'subtle' in uov_method: if 'subtle' in uov_method:
@ -562,7 +562,7 @@ def worker():
uov_input_image = set_image_shape_ceil(uov_input_image, shape_ceil) uov_input_image = set_image_shape_ceil(uov_input_image, shape_ceil)
initial_pixels = core.numpy_to_pytorch(uov_input_image) 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( candidate_vae, _ = pipeline.get_candidate_vae(
steps=steps, steps=steps,
@ -579,7 +579,7 @@ def worker():
if 'upscale' in goals: if 'upscale' in goals:
H, W, C = uov_input_image.shape 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) uov_input_image = perform_upscale(uov_input_image)
print(f'Image upscaled.') print(f'Image upscaled.')
@ -628,7 +628,7 @@ def worker():
denoising_strength = overwrite_upscale_strength denoising_strength = overwrite_upscale_strength
initial_pixels = core.numpy_to_pytorch(uov_input_image) 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( candidate_vae, _ = pipeline.get_candidate_vae(
steps=steps, steps=steps,
@ -686,7 +686,7 @@ def worker():
do_not_show_finished_images=True) do_not_show_finished_images=True)
return 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_fill = core.numpy_to_pytorch(inpaint_worker.current_task.interested_fill)
inpaint_pixel_image = core.numpy_to_pytorch(inpaint_worker.current_task.interested_image) inpaint_pixel_image = core.numpy_to_pytorch(inpaint_worker.current_task.interested_image)
@ -706,7 +706,7 @@ def worker():
latent_swap = None latent_swap = None
if candidate_vae_swap is not 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( latent_swap = core.encode_vae(
vae=candidate_vae_swap, vae=candidate_vae_swap,
pixels=inpaint_pixel_fill)['samples'] pixels=inpaint_pixel_fill)['samples']
@ -832,16 +832,17 @@ def worker():
zsnr=False)[0] zsnr=False)[0]
print(f'Using {scheduler_name} scheduler.') 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): def callback(step, x0, x, total_steps, y):
done_steps = current_task_id * steps + step done_steps = current_task_id * steps + step
async_task.yields.append(['preview', ( async_task.yields.append(['preview', (
int(15.0 + 85.0 * float(done_steps) / float(all_steps)), int(flags.preparation_step_count + (100 - flags.preparation_step_count) * float(done_steps) / float(all_steps)),
f'Step {step}/{total_steps} in the {current_task_id + 1}{ordinal_suffix(current_task_id + 1)} Sampling', f'Sampling step {step + 1}/{total_steps}, image {current_task_id + 1}/{image_number} ...', y)])
y)])
for current_task_id, task in enumerate(tasks): 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() execution_start_time = time.perf_counter()
try: try:
@ -884,12 +885,12 @@ def worker():
imgs = [inpaint_worker.current_task.post_process(x) for x in imgs] imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]
img_paths = [] 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: if modules.config.default_black_out_nsfw or black_out_nsfw:
progressbar(async_task, current_progress, 'Checking for NSFW content ...') progressbar(async_task, current_progress, 'Checking for NSFW content ...')
imgs = default_censor(imgs) 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: for x in imgs:
d = [('Prompt', 'prompt', task['log_positive_prompt']), d = [('Prompt', 'prompt', task['log_positive_prompt']),
('Negative Prompt', 'negative_prompt', task['log_negative_prompt']), ('Negative Prompt', 'negative_prompt', task['log_negative_prompt']),

1
modules/flags.py
View File

@ -93,6 +93,7 @@ metadata_scheme = [
] ]
controlnet_image_count = 4 controlnet_image_count = 4
preparation_step_count = 13
class OutputFormat(Enum): class OutputFormat(Enum):

4
modules/util.py
View File

@ -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))) 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): def makedirs_with_log(path):
try: try:
os.makedirs(path, exist_ok=True) os.makedirs(path, exist_ok=True)