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modular pipeline prototype

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-09-24 11:40:20 -04:00
parent 35c319578d
commit 91dab5703a
5 changed files with 127 additions and 8 deletions
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modules/processing_helpers.py
+19 -3
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@@ -9,6 +9,7 @@ import cv2
from PIL import Image
from blendmodes.blend import blendLayers, BlendType
from modules import shared, devices, images, sd_models, sd_samplers, sd_vae, sd_hijack_hypertile, processing_vae, timer
from modules.api import helpers
debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
@@ -16,6 +17,10 @@ debug_steps = shared.log.trace if os.environ.get('SD_STEPS_DEBUG', None) is not
debug_steps('Trace: STEPS')
def is_modular():
return sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.MODULAR
def is_txt2img():
return sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.TEXT_2_IMAGE
@@ -278,7 +283,10 @@ def validate_sample(tensor):
def resize_init_images(p):
if getattr(p, 'image', None) is not None and getattr(p, 'init_images', None) is None:
p.init_images = [p.image]
if getattr(p, 'init_images', None) is not None and len(p.init_images) > 0:
if isinstance(p.init_images[0], str):
p.init_images = [helpers.decode_base64_to_image(i, quiet=True) for i in p.init_images]
vae_scale_factor = sd_vae.get_vae_scale_factor()
tgt_width, tgt_height = vae_scale_factor * math.ceil(p.init_images[0].width / vae_scale_factor), vae_scale_factor * math.ceil(p.init_images[0].height / vae_scale_factor)
if p.init_images[0].size != (tgt_width, tgt_height):
@@ -287,11 +295,17 @@ def resize_init_images(p):
p.height = tgt_height
p.width = tgt_width
sd_hijack_hypertile.hypertile_set(p)
if getattr(p, 'mask', None) is not None and p.mask.size != (tgt_width, tgt_height):
if getattr(p, 'mask', None) is not None and p.mask is not None and p.mask.size != (tgt_width, tgt_height):
if isinstance(p.mask[0], str):
p.mask = [helpers.decode_base64_to_image(i, quiet=True) for i in p.mask]
p.mask = images.resize_image(1, p.mask, tgt_width, tgt_height, upscaler_name=None)
if getattr(p, 'init_mask', None) is not None and p.init_mask.size != (tgt_width, tgt_height):
if getattr(p, 'init_mask', None) is not None and p.init_mask is not None and p.init_mask.size != (tgt_width, tgt_height):
if isinstance(p.init_mask[0], str):
p.init_mask = [helpers.decode_base64_to_image(i, quiet=True) for i in p.init_mask]
p.init_mask = images.resize_image(1, p.init_mask, tgt_width, tgt_height, upscaler_name=None)
if getattr(p, 'mask_for_overlay', None) is not None and p.mask_for_overlay.size != (tgt_width, tgt_height):
if getattr(p, 'mask_for_overlay', None) is not None and p.mask_for_overlay is not None and p.mask_for_overlay.size != (tgt_width, tgt_height):
if isinstance(p.mask_for_overlay, str):
p.mask_for_overlay = helpers.decode_base64_to_image(p.mask_for_overlay, quiet=True)
p.mask_for_overlay = images.resize_image(1, p.mask_for_overlay, tgt_width, tgt_height, upscaler_name=None)
return tgt_width, tgt_height
return p.width, p.height
@@ -374,6 +388,8 @@ def calculate_base_steps(p, use_denoise_start, use_refiner_start):
cls = shared.sd_model.__class__.__name__
if 'Flex' in cls or 'Kontext' in cls or 'Edit' in cls or 'Wan' in cls:
steps = p.steps
elif is_modular():
steps = p.steps
elif not is_txt2img():
if cls in sd_models.i2i_pipes:
steps = p.steps