refactor: move methods to handler scope
This commit is contained in:
Manuel Schmid
parent
c1f62361ba
commit
c79f6bd461
|
|
@ -227,256 +227,6 @@ def worker():
|
|||
async_task.results = async_task.results + [wall]
|
||||
return
|
||||
|
||||
@torch.no_grad()
|
||||
@torch.inference_mode()
|
||||
def handler(async_task: AsyncTask):
|
||||
preparation_start_time = time.perf_counter()
|
||||
async_task.processing = True
|
||||
|
||||
async_task.outpaint_selections = [o.lower() for o in async_task.outpaint_selections]
|
||||
base_model_additional_loras = []
|
||||
async_task.uov_method = async_task.uov_method.lower()
|
||||
|
||||
if fooocus_expansion in async_task.style_selections:
|
||||
use_expansion = True
|
||||
async_task.style_selections.remove(fooocus_expansion)
|
||||
else:
|
||||
use_expansion = False
|
||||
|
||||
use_style = len(async_task.style_selections) > 0
|
||||
|
||||
if async_task.base_model_name == async_task.refiner_model_name:
|
||||
print(f'Refiner disabled because base model and refiner are same.')
|
||||
async_task.refiner_model_name = 'None'
|
||||
|
||||
if async_task.performance_selection == Performance.EXTREME_SPEED:
|
||||
set_lcm_defaults(async_task)
|
||||
elif async_task.performance_selection == Performance.LIGHTNING:
|
||||
set_lightning_defaults(async_task)
|
||||
elif async_task.performance_selection == Performance.HYPER_SD:
|
||||
set_hyper_sd_defaults(async_task)
|
||||
|
||||
if async_task.translate_prompts:
|
||||
translate_prompts(async_task)
|
||||
|
||||
print(f'[Parameters] Adaptive CFG = {async_task.adaptive_cfg}')
|
||||
print(f'[Parameters] CLIP Skip = {async_task.clip_skip}')
|
||||
print(f'[Parameters] Sharpness = {async_task.sharpness}')
|
||||
print(f'[Parameters] ControlNet Softness = {async_task.controlnet_softness}')
|
||||
print(f'[Parameters] ADM Scale = '
|
||||
f'{async_task.adm_scaler_positive} : '
|
||||
f'{async_task.adm_scaler_negative} : '
|
||||
f'{async_task.adm_scaler_end}')
|
||||
print(f'[Parameters] Seed = {async_task.seed}')
|
||||
|
||||
apply_patch_settings(async_task)
|
||||
|
||||
print(f'[Parameters] CFG = {async_task.cfg_scale}')
|
||||
|
||||
initial_latent = None
|
||||
denoising_strength = 1.0
|
||||
tiled = False
|
||||
|
||||
width, height = async_task.aspect_ratios_selection.replace('×', ' ').split(' ')[:2]
|
||||
width, height = int(width), int(height)
|
||||
|
||||
skip_prompt_processing = False
|
||||
|
||||
inpaint_parameterized = async_task.inpaint_engine != 'None'
|
||||
inpaint_image = None
|
||||
inpaint_mask = None
|
||||
inpaint_head_model_path = None
|
||||
|
||||
use_synthetic_refiner = False
|
||||
|
||||
controlnet_canny_path = None
|
||||
controlnet_cpds_path = None
|
||||
clip_vision_path, ip_negative_path, ip_adapter_path, ip_adapter_face_path = None, None, None, None
|
||||
|
||||
goals = []
|
||||
tasks = []
|
||||
|
||||
if async_task.input_image_checkbox:
|
||||
if (async_task.current_tab == 'uov' or (
|
||||
async_task.current_tab == 'ip' and async_task.mixing_image_prompt_and_vary_upscale)) \
|
||||
and async_task.uov_method != flags.disabled and async_task.uov_input_image is not None:
|
||||
async_task.uov_input_image = HWC3(async_task.uov_input_image)
|
||||
if 'vary' in async_task.uov_method:
|
||||
goals.append('vary')
|
||||
elif 'upscale' in async_task.uov_method:
|
||||
goals.append('upscale')
|
||||
if 'fast' in async_task.uov_method:
|
||||
skip_prompt_processing = True
|
||||
else:
|
||||
async_task.steps = async_task.performance_selection.steps_uov()
|
||||
|
||||
progressbar(async_task, 1, 'Downloading upscale models ...')
|
||||
modules.config.downloading_upscale_model()
|
||||
if (async_task.current_tab == 'inpaint' or (
|
||||
async_task.current_tab == 'ip' and async_task.mixing_image_prompt_and_inpaint)) \
|
||||
and isinstance(async_task.inpaint_input_image, dict):
|
||||
inpaint_image = async_task.inpaint_input_image['image']
|
||||
inpaint_mask = async_task.inpaint_input_image['mask'][:, :, 0]
|
||||
|
||||
if async_task.inpaint_mask_upload_checkbox:
|
||||
if isinstance(async_task.inpaint_mask_image_upload, dict):
|
||||
if (isinstance(async_task.inpaint_mask_image_upload['image'], np.ndarray)
|
||||
and isinstance(async_task.inpaint_mask_image_upload['mask'], np.ndarray)
|
||||
and async_task.inpaint_mask_image_upload['image'].ndim == 3):
|
||||
async_task.inpaint_mask_image_upload = np.maximum(async_task.inpaint_mask_image_upload['image'], async_task.inpaint_mask_image_upload['mask'])
|
||||
if isinstance(async_task.inpaint_mask_image_upload, np.ndarray) and async_task.inpaint_mask_image_upload.ndim == 3:
|
||||
H, W, C = inpaint_image.shape
|
||||
async_task.inpaint_mask_image_upload = resample_image(async_task.inpaint_mask_image_upload, width=W, height=H)
|
||||
async_task.inpaint_mask_image_upload = np.mean(async_task.inpaint_mask_image_upload, axis=2)
|
||||
async_task.inpaint_mask_image_upload = (async_task.inpaint_mask_image_upload > 127).astype(np.uint8) * 255
|
||||
async_task.inpaint_mask = np.maximum(inpaint_mask, async_task.inpaint_mask_image_upload)
|
||||
|
||||
if int(async_task.inpaint_erode_or_dilate) != 0:
|
||||
async_task.inpaint_mask = erode_or_dilate(async_task.inpaint_mask, async_task.inpaint_erode_or_dilate)
|
||||
|
||||
if async_task.invert_mask_checkbox:
|
||||
async_task.inpaint_mask = 255 - async_task.inpaint_mask
|
||||
|
||||
inpaint_image = HWC3(inpaint_image)
|
||||
if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \
|
||||
and (np.any(inpaint_mask > 127) or len(async_task.outpaint_selections) > 0):
|
||||
progressbar(async_task, 1, 'Downloading upscale models ...')
|
||||
modules.config.downloading_upscale_model()
|
||||
if inpaint_parameterized:
|
||||
progressbar(async_task, 1, 'Downloading inpainter ...')
|
||||
inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(
|
||||
async_task.inpaint_engine)
|
||||
base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]
|
||||
print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
|
||||
if async_task.refiner_model_name == 'None':
|
||||
use_synthetic_refiner = True
|
||||
async_task.refiner_switch = 0.8
|
||||
else:
|
||||
inpaint_head_model_path, inpaint_patch_model_path = None, None
|
||||
print(f'[Inpaint] Parameterized inpaint is disabled.')
|
||||
if async_task.inpaint_additional_prompt != '':
|
||||
if async_task.prompt == '':
|
||||
async_task.prompt = async_task.inpaint_additional_prompt
|
||||
else:
|
||||
async_task.prompt = async_task.inpaint_additional_prompt + '\n' + async_task.prompt
|
||||
goals.append('inpaint')
|
||||
if async_task.current_tab == 'ip' or \
|
||||
async_task.mixing_image_prompt_and_vary_upscale or \
|
||||
async_task.mixing_image_prompt_and_inpaint:
|
||||
goals.append('cn')
|
||||
progressbar(async_task, 1, 'Downloading control models ...')
|
||||
if len(async_task.cn_tasks[flags.cn_canny]) > 0:
|
||||
controlnet_canny_path = modules.config.downloading_controlnet_canny()
|
||||
if len(async_task.cn_tasks[flags.cn_cpds]) > 0:
|
||||
controlnet_cpds_path = modules.config.downloading_controlnet_cpds()
|
||||
if len(async_task.cn_tasks[flags.cn_ip]) > 0:
|
||||
clip_vision_path, ip_negative_path, ip_adapter_path = modules.config.downloading_ip_adapters('ip')
|
||||
if len(async_task.cn_tasks[flags.cn_ip_face]) > 0:
|
||||
clip_vision_path, ip_negative_path, ip_adapter_face_path = modules.config.downloading_ip_adapters(
|
||||
'face')
|
||||
|
||||
|
||||
# Load or unload CNs
|
||||
progressbar(async_task, 1, 'Loading control models ...')
|
||||
pipeline.refresh_controlnets([controlnet_canny_path, controlnet_cpds_path])
|
||||
ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path)
|
||||
ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_face_path)
|
||||
|
||||
height, switch, width = apply_overrides(async_task, height, width)
|
||||
|
||||
print(f'[Parameters] Sampler = {async_task.sampler_name} - {async_task.scheduler_name}')
|
||||
print(f'[Parameters] Steps = {async_task.steps} - {switch}')
|
||||
|
||||
progressbar(async_task, 1, 'Initializing ...')
|
||||
|
||||
tasks = []
|
||||
if not skip_prompt_processing:
|
||||
tasks, use_expansion = process_prompt(async_task, base_model_additional_loras, use_expansion, use_style,
|
||||
use_synthetic_refiner)
|
||||
|
||||
if len(goals) > 0:
|
||||
progressbar(async_task, 7, 'Image processing ...')
|
||||
|
||||
if 'vary' in goals:
|
||||
height, initial_latent, width = apply_vary(async_task, denoising_strength, switch)
|
||||
|
||||
if 'upscale' in goals:
|
||||
try:
|
||||
denoising_strength, height, initial_latent, tiled, width = apply_upscale(async_task, switch)
|
||||
except EarlyReturnException:
|
||||
return
|
||||
if 'inpaint' in goals:
|
||||
try:
|
||||
denoising_strength, initial_latent, height, width = apply_inpaint(async_task, initial_latent,
|
||||
inpaint_head_model_path, inpaint_image,
|
||||
inpaint_mask, inpaint_parameterized,
|
||||
switch)
|
||||
except EarlyReturnException:
|
||||
return
|
||||
|
||||
if 'cn' in goals:
|
||||
apply_control_nets(async_task, height, ip_adapter_face_path, ip_adapter_path, width)
|
||||
if async_task.debugging_cn_preprocessor:
|
||||
return
|
||||
|
||||
if async_task.freeu_enabled:
|
||||
apply_freeu(async_task)
|
||||
|
||||
all_steps = async_task.steps * async_task.image_number
|
||||
|
||||
print(f'[Parameters] Denoising Strength = {denoising_strength}')
|
||||
|
||||
if isinstance(initial_latent, dict) and 'samples' in initial_latent:
|
||||
log_shape = initial_latent['samples'].shape
|
||||
else:
|
||||
log_shape = f'Image Space {(height, width)}'
|
||||
|
||||
print(f'[Parameters] Initial Latent shape: {log_shape}')
|
||||
|
||||
preparation_time = time.perf_counter() - preparation_start_time
|
||||
print(f'Preparation time: {preparation_time:.2f} seconds')
|
||||
|
||||
final_scheduler_name = patch_samplers(async_task)
|
||||
print(f'Using {final_scheduler_name} scheduler.')
|
||||
|
||||
async_task.yields.append(['preview', (flags.preparation_step_count, 'Moving model to GPU ...', None)])
|
||||
|
||||
processing_start_time = time.perf_counter()
|
||||
|
||||
def callback(step, x0, x, total_steps, y):
|
||||
done_steps = current_task_id * async_task.steps + step
|
||||
async_task.yields.append(['preview', (
|
||||
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}/{async_task.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 * 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()
|
||||
|
||||
try:
|
||||
process_task(all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path,
|
||||
current_task_id, denoising_strength, final_scheduler_name, goals, initial_latent,
|
||||
switch, task, tasks, tiled, use_expansion, width, height)
|
||||
except ldm_patched.modules.model_management.InterruptProcessingException:
|
||||
if async_task.last_stop == 'skip':
|
||||
print('User skipped')
|
||||
async_task.last_stop = False
|
||||
continue
|
||||
else:
|
||||
print('User stopped')
|
||||
break
|
||||
|
||||
execution_time = time.perf_counter() - execution_start_time
|
||||
print(f'Generating and saving time: {execution_time:.2f} seconds')
|
||||
|
||||
async_task.processing = False
|
||||
|
||||
processing_time = time.perf_counter() - processing_start_time
|
||||
print(f'Processing time (total): {processing_time:.2f} seconds')
|
||||
|
||||
def process_task(all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path, current_task_id,
|
||||
denoising_strength, final_scheduler_name, goals, initial_latent, switch, task, tasks,
|
||||
tiled, use_expansion, width, height):
|
||||
|
|
@ -649,22 +399,22 @@ def worker():
|
|||
if len(all_ip_tasks) > 0:
|
||||
pipeline.final_unet = ip_adapter.patch_model(pipeline.final_unet, all_ip_tasks)
|
||||
|
||||
def apply_vary(async_task, uov_input_image, denoising_strength, switch):
|
||||
def apply_vary(async_task, denoising_strength, switch):
|
||||
if 'subtle' in async_task.uov_method:
|
||||
async_task.denoising_strength = 0.5
|
||||
if 'strong' in async_task.uov_method:
|
||||
async_task.denoising_strength = 0.85
|
||||
if async_task.overwrite_vary_strength > 0:
|
||||
async_task.denoising_strength = async_task.overwrite_vary_strength
|
||||
shape_ceil = get_image_shape_ceil(uov_input_image)
|
||||
shape_ceil = get_image_shape_ceil(async_task.uov_input_image)
|
||||
if shape_ceil < 1024:
|
||||
print(f'[Vary] Image is resized because it is too small.')
|
||||
shape_ceil = 1024
|
||||
elif shape_ceil > 2048:
|
||||
print(f'[Vary] Image is resized because it is too big.')
|
||||
shape_ceil = 2048
|
||||
uov_input_image = set_image_shape_ceil(uov_input_image, shape_ceil)
|
||||
initial_pixels = core.numpy_to_pytorch(uov_input_image)
|
||||
async_task.uov_input_image = set_image_shape_ceil(async_task.uov_input_image, shape_ceil)
|
||||
initial_pixels = core.numpy_to_pytorch(async_task.uov_input_image)
|
||||
progressbar(async_task, 8, 'VAE encoding ...')
|
||||
candidate_vae, _ = pipeline.get_candidate_vae(
|
||||
steps=async_task.steps,
|
||||
|
|
@ -680,7 +430,7 @@ def worker():
|
|||
return initial_latent, width, height
|
||||
|
||||
def apply_inpaint(async_task, initial_latent, inpaint_head_model_path, inpaint_image,
|
||||
inpaint_mask, inpaint_parameterized, switch):
|
||||
inpaint_mask, inpaint_parameterized, switch):
|
||||
if len(async_task.outpaint_selections) > 0:
|
||||
H, W, C = inpaint_image.shape
|
||||
if 'top' in async_task.outpaint_selections:
|
||||
|
|
@ -833,7 +583,7 @@ def worker():
|
|||
return height, switch, width
|
||||
|
||||
def process_prompt(async_task, base_model_additional_loras, use_expansion, use_style,
|
||||
use_synthetic_refiner):
|
||||
use_synthetic_refiner):
|
||||
prompts = remove_empty_str([safe_str(p) for p in async_task.prompt.splitlines()], default='')
|
||||
negative_prompts = remove_empty_str([safe_str(p) for p in async_task.negative_prompt.splitlines()], default='')
|
||||
prompt = prompts[0]
|
||||
|
|
@ -1021,6 +771,256 @@ def worker():
|
|||
async_task.adm_scaler_negative = 1.0
|
||||
async_task.adm_scaler_end = 0.0
|
||||
|
||||
@torch.no_grad()
|
||||
@torch.inference_mode()
|
||||
def handler(async_task: AsyncTask):
|
||||
preparation_start_time = time.perf_counter()
|
||||
async_task.processing = True
|
||||
|
||||
async_task.outpaint_selections = [o.lower() for o in async_task.outpaint_selections]
|
||||
base_model_additional_loras = []
|
||||
async_task.uov_method = async_task.uov_method.lower()
|
||||
|
||||
if fooocus_expansion in async_task.style_selections:
|
||||
use_expansion = True
|
||||
async_task.style_selections.remove(fooocus_expansion)
|
||||
else:
|
||||
use_expansion = False
|
||||
|
||||
use_style = len(async_task.style_selections) > 0
|
||||
|
||||
if async_task.base_model_name == async_task.refiner_model_name:
|
||||
print(f'Refiner disabled because base model and refiner are same.')
|
||||
async_task.refiner_model_name = 'None'
|
||||
|
||||
if async_task.performance_selection == Performance.EXTREME_SPEED:
|
||||
set_lcm_defaults(async_task)
|
||||
elif async_task.performance_selection == Performance.LIGHTNING:
|
||||
set_lightning_defaults(async_task)
|
||||
elif async_task.performance_selection == Performance.HYPER_SD:
|
||||
set_hyper_sd_defaults(async_task)
|
||||
|
||||
if async_task.translate_prompts:
|
||||
translate_prompts(async_task)
|
||||
|
||||
print(f'[Parameters] Adaptive CFG = {async_task.adaptive_cfg}')
|
||||
print(f'[Parameters] CLIP Skip = {async_task.clip_skip}')
|
||||
print(f'[Parameters] Sharpness = {async_task.sharpness}')
|
||||
print(f'[Parameters] ControlNet Softness = {async_task.controlnet_softness}')
|
||||
print(f'[Parameters] ADM Scale = '
|
||||
f'{async_task.adm_scaler_positive} : '
|
||||
f'{async_task.adm_scaler_negative} : '
|
||||
f'{async_task.adm_scaler_end}')
|
||||
print(f'[Parameters] Seed = {async_task.seed}')
|
||||
|
||||
apply_patch_settings(async_task)
|
||||
|
||||
print(f'[Parameters] CFG = {async_task.cfg_scale}')
|
||||
|
||||
initial_latent = None
|
||||
denoising_strength = 1.0
|
||||
tiled = False
|
||||
|
||||
width, height = async_task.aspect_ratios_selection.replace('×', ' ').split(' ')[:2]
|
||||
width, height = int(width), int(height)
|
||||
|
||||
skip_prompt_processing = False
|
||||
|
||||
inpaint_parameterized = async_task.inpaint_engine != 'None'
|
||||
inpaint_image = None
|
||||
inpaint_mask = None
|
||||
inpaint_head_model_path = None
|
||||
|
||||
use_synthetic_refiner = False
|
||||
|
||||
controlnet_canny_path = None
|
||||
controlnet_cpds_path = None
|
||||
clip_vision_path, ip_negative_path, ip_adapter_path, ip_adapter_face_path = None, None, None, None
|
||||
|
||||
goals = []
|
||||
tasks = []
|
||||
|
||||
if async_task.input_image_checkbox:
|
||||
if (async_task.current_tab == 'uov' or (
|
||||
async_task.current_tab == 'ip' and async_task.mixing_image_prompt_and_vary_upscale)) \
|
||||
and async_task.uov_method != flags.disabled and async_task.uov_input_image is not None:
|
||||
async_task.uov_input_image = HWC3(async_task.uov_input_image)
|
||||
if 'vary' in async_task.uov_method:
|
||||
goals.append('vary')
|
||||
elif 'upscale' in async_task.uov_method:
|
||||
goals.append('upscale')
|
||||
if 'fast' in async_task.uov_method:
|
||||
skip_prompt_processing = True
|
||||
else:
|
||||
async_task.steps = async_task.performance_selection.steps_uov()
|
||||
|
||||
progressbar(async_task, 1, 'Downloading upscale models ...')
|
||||
modules.config.downloading_upscale_model()
|
||||
if (async_task.current_tab == 'inpaint' or (
|
||||
async_task.current_tab == 'ip' and async_task.mixing_image_prompt_and_inpaint)) \
|
||||
and isinstance(async_task.inpaint_input_image, dict):
|
||||
inpaint_image = async_task.inpaint_input_image['image']
|
||||
inpaint_mask = async_task.inpaint_input_image['mask'][:, :, 0]
|
||||
|
||||
if async_task.inpaint_mask_upload_checkbox:
|
||||
if isinstance(async_task.inpaint_mask_image_upload, dict):
|
||||
if (isinstance(async_task.inpaint_mask_image_upload['image'], np.ndarray)
|
||||
and isinstance(async_task.inpaint_mask_image_upload['mask'], np.ndarray)
|
||||
and async_task.inpaint_mask_image_upload['image'].ndim == 3):
|
||||
async_task.inpaint_mask_image_upload = np.maximum(async_task.inpaint_mask_image_upload['image'], async_task.inpaint_mask_image_upload['mask'])
|
||||
if isinstance(async_task.inpaint_mask_image_upload, np.ndarray) and async_task.inpaint_mask_image_upload.ndim == 3:
|
||||
H, W, C = inpaint_image.shape
|
||||
async_task.inpaint_mask_image_upload = resample_image(async_task.inpaint_mask_image_upload, width=W, height=H)
|
||||
async_task.inpaint_mask_image_upload = np.mean(async_task.inpaint_mask_image_upload, axis=2)
|
||||
async_task.inpaint_mask_image_upload = (async_task.inpaint_mask_image_upload > 127).astype(np.uint8) * 255
|
||||
async_task.inpaint_mask = np.maximum(inpaint_mask, async_task.inpaint_mask_image_upload)
|
||||
|
||||
if int(async_task.inpaint_erode_or_dilate) != 0:
|
||||
async_task.inpaint_mask = erode_or_dilate(async_task.inpaint_mask, async_task.inpaint_erode_or_dilate)
|
||||
|
||||
if async_task.invert_mask_checkbox:
|
||||
async_task.inpaint_mask = 255 - async_task.inpaint_mask
|
||||
|
||||
inpaint_image = HWC3(inpaint_image)
|
||||
if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \
|
||||
and (np.any(inpaint_mask > 127) or len(async_task.outpaint_selections) > 0):
|
||||
progressbar(async_task, 1, 'Downloading upscale models ...')
|
||||
modules.config.downloading_upscale_model()
|
||||
if inpaint_parameterized:
|
||||
progressbar(async_task, 1, 'Downloading inpainter ...')
|
||||
inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models(
|
||||
async_task.inpaint_engine)
|
||||
base_model_additional_loras += [(inpaint_patch_model_path, 1.0)]
|
||||
print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
|
||||
if async_task.refiner_model_name == 'None':
|
||||
use_synthetic_refiner = True
|
||||
async_task.refiner_switch = 0.8
|
||||
else:
|
||||
inpaint_head_model_path, inpaint_patch_model_path = None, None
|
||||
print(f'[Inpaint] Parameterized inpaint is disabled.')
|
||||
if async_task.inpaint_additional_prompt != '':
|
||||
if async_task.prompt == '':
|
||||
async_task.prompt = async_task.inpaint_additional_prompt
|
||||
else:
|
||||
async_task.prompt = async_task.inpaint_additional_prompt + '\n' + async_task.prompt
|
||||
goals.append('inpaint')
|
||||
if async_task.current_tab == 'ip' or \
|
||||
async_task.mixing_image_prompt_and_vary_upscale or \
|
||||
async_task.mixing_image_prompt_and_inpaint:
|
||||
goals.append('cn')
|
||||
progressbar(async_task, 1, 'Downloading control models ...')
|
||||
if len(async_task.cn_tasks[flags.cn_canny]) > 0:
|
||||
controlnet_canny_path = modules.config.downloading_controlnet_canny()
|
||||
if len(async_task.cn_tasks[flags.cn_cpds]) > 0:
|
||||
controlnet_cpds_path = modules.config.downloading_controlnet_cpds()
|
||||
if len(async_task.cn_tasks[flags.cn_ip]) > 0:
|
||||
clip_vision_path, ip_negative_path, ip_adapter_path = modules.config.downloading_ip_adapters('ip')
|
||||
if len(async_task.cn_tasks[flags.cn_ip_face]) > 0:
|
||||
clip_vision_path, ip_negative_path, ip_adapter_face_path = modules.config.downloading_ip_adapters(
|
||||
'face')
|
||||
|
||||
|
||||
# Load or unload CNs
|
||||
progressbar(async_task, 1, 'Loading control models ...')
|
||||
pipeline.refresh_controlnets([controlnet_canny_path, controlnet_cpds_path])
|
||||
ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path)
|
||||
ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_face_path)
|
||||
|
||||
height, switch, width = apply_overrides(async_task, height, width)
|
||||
|
||||
print(f'[Parameters] Sampler = {async_task.sampler_name} - {async_task.scheduler_name}')
|
||||
print(f'[Parameters] Steps = {async_task.steps} - {switch}')
|
||||
|
||||
progressbar(async_task, 1, 'Initializing ...')
|
||||
|
||||
if not skip_prompt_processing:
|
||||
tasks, use_expansion = process_prompt(async_task, base_model_additional_loras, use_expansion, use_style,
|
||||
use_synthetic_refiner)
|
||||
|
||||
if len(goals) > 0:
|
||||
progressbar(async_task, 7, 'Image processing ...')
|
||||
|
||||
if 'vary' in goals:
|
||||
height, initial_latent, width = apply_vary(async_task, denoising_strength, switch)
|
||||
|
||||
if 'upscale' in goals:
|
||||
try:
|
||||
denoising_strength, height, initial_latent, tiled, width = apply_upscale(async_task, switch)
|
||||
except EarlyReturnException:
|
||||
return
|
||||
if 'inpaint' in goals:
|
||||
try:
|
||||
denoising_strength, initial_latent, height, width = apply_inpaint(async_task, initial_latent,
|
||||
inpaint_head_model_path, inpaint_image,
|
||||
inpaint_mask, inpaint_parameterized,
|
||||
switch)
|
||||
except EarlyReturnException:
|
||||
return
|
||||
|
||||
if 'cn' in goals:
|
||||
apply_control_nets(async_task, height, ip_adapter_face_path, ip_adapter_path, width)
|
||||
if async_task.debugging_cn_preprocessor:
|
||||
return
|
||||
|
||||
if async_task.freeu_enabled:
|
||||
apply_freeu(async_task)
|
||||
|
||||
all_steps = async_task.steps * async_task.image_number
|
||||
|
||||
print(f'[Parameters] Denoising Strength = {denoising_strength}')
|
||||
|
||||
if isinstance(initial_latent, dict) and 'samples' in initial_latent:
|
||||
log_shape = initial_latent['samples'].shape
|
||||
else:
|
||||
log_shape = f'Image Space {(height, width)}'
|
||||
|
||||
print(f'[Parameters] Initial Latent shape: {log_shape}')
|
||||
|
||||
preparation_time = time.perf_counter() - preparation_start_time
|
||||
print(f'Preparation time: {preparation_time:.2f} seconds')
|
||||
|
||||
final_scheduler_name = patch_samplers(async_task)
|
||||
print(f'Using {final_scheduler_name} scheduler.')
|
||||
|
||||
async_task.yields.append(['preview', (flags.preparation_step_count, 'Moving model to GPU ...', None)])
|
||||
|
||||
processing_start_time = time.perf_counter()
|
||||
|
||||
def callback(step, x0, x, total_steps, y):
|
||||
done_steps = current_task_id * async_task.steps + step
|
||||
async_task.yields.append(['preview', (
|
||||
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}/{async_task.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 * 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()
|
||||
|
||||
try:
|
||||
process_task(all_steps, async_task, callback, controlnet_canny_path, controlnet_cpds_path,
|
||||
current_task_id, denoising_strength, final_scheduler_name, goals, initial_latent,
|
||||
switch, task, tasks, tiled, use_expansion, width, height)
|
||||
except ldm_patched.modules.model_management.InterruptProcessingException:
|
||||
if async_task.last_stop == 'skip':
|
||||
print('User skipped')
|
||||
async_task.last_stop = False
|
||||
continue
|
||||
else:
|
||||
print('User stopped')
|
||||
break
|
||||
|
||||
execution_time = time.perf_counter() - execution_start_time
|
||||
print(f'Generating and saving time: {execution_time:.2f} seconds')
|
||||
|
||||
async_task.processing = False
|
||||
|
||||
processing_time = time.perf_counter() - processing_start_time
|
||||
print(f'Processing time (total): {processing_time:.2f} seconds')
|
||||
|
||||
|
||||
while True:
|
||||
time.sleep(0.01)
|
||||
if len(async_tasks) > 0:
|
||||
|
|
|
|||
Loading…
Reference in New Issue