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feat: optimize progress bar, now correctly uses uov steps and overrides

This commit is contained in:
Manuel Schmid 2024-06-20 02:43:14 +02:00
parent 5012fb7067
commit eada51dee4
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GPG Key ID: 32C4F7569B40B84B
1 changed files with 39 additions and 17 deletions
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56
modules/async_worker.py
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@ -306,7 +306,7 @@ def worker():
del positive_cond, negative_cond # Save memory
if inpaint_worker.current_task is not None:
imgs = [inpaint_worker.current_task.post_process(x) for x in imgs]
current_progress = int(base_progress + (100 - preparation_steps) * float((current_task_id + 1) * steps) / float(all_steps))
current_progress = int(base_progress + (100 - preparation_steps) / float(all_steps) * steps)
if modules.config.default_black_out_nsfw or async_task.black_out_nsfw:
progressbar(async_task, current_progress, 'Checking for NSFW content ...')
imgs = default_censor(imgs)
@ -950,7 +950,7 @@ def worker():
processing_time = time.perf_counter() - processing_start_time
print(f'Processing time (total): {processing_time:.2f} seconds')
def process_enhance(all_steps, async_task, base_progress, callback, controlnet_canny_path, controlnet_cpds_path,
def process_enhance(all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path,
current_progress, current_task_id, denoising_strength, inpaint_disable_initial_latent,
inpaint_engine, inpaint_respective_field, inpaint_strength,
negative_prompt, prompt, final_scheduler_name, goals, height, img, mask,
@ -966,11 +966,10 @@ def worker():
async_task, async_task.enhance_uov_method, img, denoising_strength, switch, current_progress)
if 'upscale' in goals:
direct_return, img, denoising_strength, initial_latent, tiled, width, height, current_progress = apply_upscale(
async_task, img, async_task.enhance_uov_method, switch, current_progress,
advance_progress=True)
async_task, img, async_task.enhance_uov_method, switch, current_progress)
if direct_return:
return current_progress, img
if 'inpaint' in goals and inpaint_parameterized:
progressbar(async_task, current_progress, 'Downloading inpainter ...')
inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(
@ -1003,7 +1002,7 @@ def worker():
final_scheduler_name, goals, initial_latent, steps, switch,
task_enhance['c'], task_enhance['uc'], task_enhance,
tasks_enhance, tiled, use_expansion, width, height,
base_progress, preparation_steps, total_count)
current_progress, preparation_steps, total_count)
del task_enhance['c'], task_enhance['uc'] # Save memory
return current_progress, imgs[0]
@ -1161,6 +1160,14 @@ def worker():
if async_task.enhance_checkbox and len(async_task.enhance_ctrls) != 0:
all_steps += async_task.image_number * len(async_task.enhance_ctrls) * async_task.steps
enhance_upscale_steps = 0
enhance_upscale_steps_total = 0
if 'upscale' not in goals and async_task.enhance_uov_method != flags.disabled:
enhance_upscale_steps = async_task.overwrite_step if async_task.overwrite_step > 0 else async_task.performance_selection.steps_uov()
enhance_upscale_steps_total = async_task.image_number * enhance_upscale_steps
all_steps += enhance_upscale_steps_total
print(f'[Parameters] Denoising Strength = {denoising_strength}')
if isinstance(initial_latent, dict) and 'samples' in initial_latent:
@ -1180,20 +1187,20 @@ def worker():
processing_start_time = time.perf_counter()
preparation_steps = base_progress = current_progress
preparation_steps = current_progress
total_count = async_task.image_number
def callback(step, x0, x, total_steps, y):
done_steps = current_task_id * async_task.steps + step + 1
if step == 0:
async_task.callback_steps = 0
async_task.callback_steps += (100 - preparation_steps) / float(all_steps)
async_task.yields.append(['preview', (
int(base_progress + (100 - preparation_steps) * float(done_steps) / float(all_steps)),
int(current_progress + async_task.callback_steps),
f'Sampling step {step + 1}/{total_steps}, image {current_task_id + 1}/{total_count} ...', y)])
generated_imgs = {}
for current_task_id, task in enumerate(tasks):
current_progress = int(base_progress + (100 - preparation_steps) * float(
current_task_id * async_task.steps) / float(all_steps))
progressbar(async_task, current_progress,
f'Preparing task {current_task_id + 1}/{async_task.image_number} ...')
execution_start_time = time.perf_counter()
@ -1229,15 +1236,25 @@ def worker():
return
progressbar(async_task, current_progress, 'Processing enhance ...')
total_count = sum([len(imgs) for _, imgs in generated_imgs.items()]) * len(async_task.enhance_ctrls)
active_enhance_tabs = len(async_task.enhance_ctrls)
should_process_uov = 'upscale' not in goals and async_task.enhance_uov_method != flags.disabled
if should_process_uov:
active_enhance_tabs += 1
total_count = sum([len(imgs) for _, imgs in generated_imgs.items()]) * active_enhance_tabs
base_progress = current_progress
current_task_id = -1
done_steps_upscaling = 0
done_steps_inpainting = 0
for imgs in generated_imgs.values():
for img in imgs:
enhancement_image_start_time = time.perf_counter()
# upscale if not disabled or already in goals
if 'upscale' not in goals and async_task.enhance_uov_method != flags.disabled:
if should_process_uov:
current_task_id += 1
current_progress = int(base_progress + (100 - preparation_steps) / float(all_steps) * (done_steps_upscaling + done_steps_inpainting))
goals_enhance = []
img, skip_prompt_processing, steps = prepare_upscale(async_task, goals_enhance, img,
async_task.enhance_uov_method,
@ -1247,26 +1264,29 @@ def worker():
if len(goals_enhance) > 0:
try:
current_progress, img = process_enhance(
all_steps, async_task, base_progress, callback, controlnet_canny_path,
all_steps, async_task, callback, controlnet_canny_path,
controlnet_cpds_path, current_progress, current_task_id, denoising_strength, False,
'None', 0.0, 0.0, async_task.negative_prompt, async_task.prompt, final_scheduler_name,
goals_enhance, height, img, None, preparation_steps, steps, switch, tiled, total_count,
use_expansion, use_style, use_synthetic_refiner, width)
# TODO check steps in progress bar, 100% wrong
except ldm_patched.modules.model_management.InterruptProcessingException:
if async_task.last_stop == 'skip':
print('User skipped')
async_task.last_stop = False
# also skip all enhance steps for this image, but add the steps to the progress bar
done_steps_inpainting += len(async_task.enhance_ctrls) * async_task.steps
continue
else:
print('User stopped')
break
finally:
done_steps_upscaling += steps
# inpaint for all other tabs
for enhance_mask_dino_prompt_text, enhance_prompt, enhance_negative_prompt, enhance_mask_model, enhance_mask_sam_model, enhance_mask_text_threshold, enhance_mask_box_threshold, enhance_mask_sam_max_detections, enhance_inpaint_disable_initial_latent, enhance_inpaint_engine, enhance_inpaint_strength, enhance_inpaint_respective_field, enhance_inpaint_erode_or_dilate, enhance_mask_invert in async_task.enhance_ctrls:
current_task_id += 1
current_progress = int(base_progress + (100 - preparation_steps) * float(current_task_id * async_task.steps) / float(all_steps))
current_progress = int(base_progress + (100 - preparation_steps) / float(all_steps) * (done_steps_upscaling + done_steps_inpainting))
progressbar(async_task, current_progress, f'Preparing enhancement {current_task_id + 1}/{total_count} ...')
enhancement_task_start_time = time.perf_counter()
@ -1310,7 +1330,7 @@ def worker():
try:
current_progress, img = process_enhance(
all_steps, async_task, base_progress, callback, controlnet_canny_path, controlnet_cpds_path,
all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path,
current_progress, current_task_id, denoising_strength,
enhance_inpaint_disable_initial_latent, enhance_inpaint_engine,
enhance_inpaint_respective_field, enhance_inpaint_strength, enhance_negative_prompt,
@ -1325,6 +1345,8 @@ def worker():
else:
print('User stopped')
break
finally:
done_steps_inpainting += async_task.steps
enhancement_task_time = time.perf_counter() - enhancement_task_start_time
print(f'Enhancement time: {enhancement_task_time:.2f} seconds')