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mirror of https://github.com/vladmandic/automatic synced 2026-09-19 17:24:32 +02:00
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facehires support batch size&count, add override strength

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Vladimir Mandic
2024-03-14 11:40:58 -04:00
parent 5b4aca87d1
commit 940b6d02ba
8 changed files with 39 additions and 33 deletions
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scripts/face-details.py
+10 -10
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@@ -92,7 +92,7 @@ class FaceRestorerYolo(FaceRestoration):
from modules import devices, processing_class
if not hasattr(p, 'facehires'):
p.facehires = 0
if np_image is None or p.facehires >= p.batch_size:
if np_image is None or p.facehires >= p.batch_size * p.n_iter:
return np_image
self.load()
if self.model is None:
@@ -119,7 +119,7 @@ class FaceRestorerYolo(FaceRestoration):
'sampler_name': orig_p.get('hr_sampler_name', 'default'),
'steps': orig_p.get('hr_second_pass_steps', 0),
'negative_prompt': orig_p.get('refiner_negative', ''),
'denoising_strength': orig_p.get('denoising_strength', 0.3),
'denoising_strength': shared.opts.facehires_strength if shared.opts.facehires_strength > 0 else orig_p.get('denoising_strength', 0.3),
'styles': [],
'prompt': orig_p.get('refiner_prompt', ''),
# TODO facehires expose as tunable
@@ -137,6 +137,7 @@ class FaceRestorerYolo(FaceRestoration):
if len(p.negative_prompt) == 0:
p.negative_prompt = orig_p.get('all_negative_prompts', [''])[0]
shared.log.debug(f'Face HiRes: faces={[f.__dict__ for f in faces]} strength={p.denoising_strength} blur={p.mask_blur} padding={p.inpaint_full_res_padding} steps={p.steps}')
for face in faces:
if face.mask is None:
continue
@@ -145,21 +146,20 @@ class FaceRestorerYolo(FaceRestoration):
continue
p.init_images = [image]
p.image_mask = [face.mask]
shared.log.debug(f'Face HiRes: face={p.facehires} {face.__dict__} strength={p.denoising_strength} blur={p.mask_blur} padding={p.inpaint_full_res_padding} steps={p.steps}')
p.recursion = True
pp = processing.process_images_inner(p)
del p.recursion
p.overlay_images = None # skip applying overlay twice
if pp is not None and pp.images is not None and len(pp.images) > 0:
image = pp.images[0]
if np_image is None or getattr(p, 'facehires', 0) >= p.batch_size:
p.facehires = 0
image = pp.images[0] # update image to be reused for next face
# restore pipeline
p = processing_class.switch_class(p, orig_cls, orig_p)
p.init_images = getattr(orig_p, 'init_images', None)
p.image_mask = getattr(orig_p, 'image_mask', None)
shared.opts.data['mask_apply_overlay'] = orig_apply_overlay
if pp is not None and pp.images is not None and len(pp.images) > 0:
image = pp.images[0]
np_image = np.array(image)
np_image = np.array(image)
# shared.log.debug(f'Face HiRes complete: faces={len(faces)} time={t1-t0:.3f}')
return np_image