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Merge branch 'feature/progress-bar'

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# Conflicts:
#	fooocus_version.py
#	modules/async_worker.py
#	webui.py
This commit is contained in:
Manuel Schmid
2024-05-19 20:54:53 +02:00
parent 84a71d1ae4 5d8eb5235a
commit dd5a14ac7f
20 changed files with 598 additions and 151 deletions
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modules/async_worker.py
+50 -37
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@@ -46,12 +46,13 @@ def worker():
import fooocus_version
import args_manager
from modules.censor import censor_batch, censor_single
from modules.sdxl_styles import get_random_style, random_style_name, apply_style, apply_wildcards, fooocus_expansion, apply_arrays
from extras.censor import default_censor
from modules.sdxl_styles import apply_style, get_random_style, fooocus_expansion, apply_arrays, random_style_name
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, get_enabled_loras
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, get_enabled_loras,
parse_lora_references_from_prompt, apply_wildcards)
from modules.upscaler import perform_upscale
from modules.flags import Performance
from modules.meta_parser import get_metadata_parser, MetadataScheme
@@ -72,13 +73,14 @@ def worker():
print(f'[Fooocus] {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):
def yield_result(async_task, imgs, black_out_nsfw, censor=True, do_not_show_finished_images=False,
progressbar_index=flags.preparation_step_count):
if not isinstance(imgs, list):
imgs = [imgs]
if censor and (modules.config.default_black_out_nsfw or black_out_nsfw):
progressbar(async_task, progressbar_index, 'Checking for NSFW content ...')
imgs = censor_batch(imgs)
imgs = default_censor(imgs)
async_task.results = async_task.results + imgs
@@ -156,7 +158,8 @@ def worker():
base_model_name = args.pop()
refiner_model_name = args.pop()
refiner_switch = args.pop()
loras = get_enabled_loras([[bool(args.pop()), str(args.pop()), float(args.pop())] for _ in range(modules.config.default_max_lora_number)])
loras = get_enabled_loras([(bool(args.pop()), str(args.pop()), float(args.pop())) for _ in
range(modules.config.default_max_lora_number)])
input_image_checkbox = args.pop()
current_tab = args.pop()
uov_method = args.pop()
@@ -206,7 +209,8 @@ def worker():
inpaint_erode_or_dilate = args.pop()
save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False
metadata_scheme = MetadataScheme(args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS
metadata_scheme = MetadataScheme(
args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS
cn_tasks = {x: [] for x in flags.ip_list}
for _ in range(flags.controlnet_image_count):
@@ -464,14 +468,17 @@ def worker():
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)
pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name,
loras=loras, base_model_additional_loras=base_model_additional_loras,
use_synthetic_refiner=use_synthetic_refiner, vae_name=vae_name)
progressbar(async_task, 3, 'Processing prompts ...')
tasks = []
for i in range(image_number):
if disable_seed_increment:
task_seed = seed % (constants.MAX_SEED + 1)
@@ -482,8 +489,10 @@ def worker():
task_prompt = apply_wildcards(prompt, task_rng, i, read_wildcards_in_order)
task_prompt = apply_arrays(task_prompt, i)
task_negative_prompt = apply_wildcards(negative_prompt, task_rng, i, read_wildcards_in_order)
task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_positive_prompts]
task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_negative_prompts]
task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in
extra_positive_prompts]
task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in
extra_negative_prompts]
positive_basic_workloads = []
negative_basic_workloads = []
@@ -526,25 +535,25 @@ def worker():
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:
@@ -565,7 +574,7 @@ def worker():
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,
@@ -582,7 +591,7 @@ def worker():
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.')
@@ -615,10 +624,11 @@ def worker():
direct_return = False
if direct_return:
d = [('Upscale', 'upscale', 'Fast 2x')]
d = [('Upscale (Fast)', 'upscale_fast', '2x')]
if modules.config.default_black_out_nsfw or black_out_nsfw:
progressbar(async_task, 100, 'Checking for NSFW content ...')
uov_input_image = censor_single(uov_input_image)
uov_input_image = default_censor(uov_input_image)
progressbar(async_task, 100, 'Saving image to system ...')
uov_input_image_path = log(uov_input_image, d, output_format=output_format)
yield_result(async_task, uov_input_image_path, black_out_nsfw, False, do_not_show_finished_images=True)
return
@@ -630,7 +640,7 @@ def worker():
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,
@@ -687,7 +697,7 @@ def worker():
yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(), black_out_nsfw, 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)
@@ -707,7 +717,7 @@ def worker():
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']
@@ -833,15 +843,17 @@ def worker():
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'Sampling Image {current_task_id + 1}/{image_number}, Step {step + 1}/{total_steps} ...', 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:
@@ -885,16 +897,19 @@ def worker():
img_paths = []
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, int(15.0 + 85.0 * float((current_task_id + 1) * steps) / float(all_steps)),
'Checking for NSFW content ...')
imgs = censor_batch(imgs)
progressbar(async_task, current_progress, 'Checking for NSFW content ...')
imgs = default_censor(imgs)
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']),
('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']),
('Styles', 'styles', str(task['styles'] if not use_expansion else [fooocus_expansion] + task['styles'])),
('Styles', 'styles',
str(task['styles'] if not use_expansion else [fooocus_expansion] + task['styles'])),
('Performance', 'performance', performance_selection.value)]
if performance_selection.steps() != steps:
@@ -917,7 +932,8 @@ def worker():
if refiner_swap_method != flags.refiner_swap_method:
d.append(('Refiner Swap Method', 'refiner_swap_method', refiner_swap_method))
if modules.patch.patch_settings[pid].adaptive_cfg != modules.config.default_cfg_tsnr:
d.append(('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))
d.append(
('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg))
d.append(('Sampler', 'sampler', sampler_name))
d.append(('Scheduler', 'scheduler', scheduler_name))
@@ -937,16 +953,13 @@ def worker():
metadata_parser.set_data(task['log_positive_prompt'], task['positive'],
task['log_negative_prompt'], task['negative'],
steps, base_model_name, refiner_model_name, loras, vae_name)
d.append(('Metadata Scheme', 'metadata_scheme', metadata_scheme.value if save_metadata_to_images else save_metadata_to_images))
d.append(('Metadata Scheme', 'metadata_scheme',
metadata_scheme.value if save_metadata_to_images else save_metadata_to_images))
d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version))
img_paths.append(log(x, d, metadata_parser, output_format, task))
yield_result(async_task, img_paths, black_out_nsfw, False,
do_not_show_finished_images=len(tasks) == 1
or disable_intermediate_results
or performance_selection == Performance.EXTREME_SPEED
or performance_selection == Performance.LIGHTNING)
do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results)
except ldm_patched.modules.model_management.InterruptProcessingException as e:
if async_task.last_stop == 'skip':
print('User skipped')