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Add mask to the FaceDetailer #1053

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1d723c0
Add mask to the FaceDetailer
benstaniford Jul 16, 2025
b55f916
Merge branch 'ltdrdata:Main' into Main
benstaniford Oct 13, 2025
f2dfd6c
Merge branch 'ltdrdata:Main' into Main
benstaniford Dec 12, 2025
864ad70
Merge branch 'ltdrdata:Main' into Main
benstaniford Dec 30, 2025
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24 changes: 15 additions & 9 deletions modules/impact/impact_pack.py
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Original file line number Diff line number Diff line change
Expand Up @@ -782,6 +782,7 @@ def INPUT_TYPES(s):
"scheduler_func_opt": ("SCHEDULER_FUNC",),
"tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
"tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
"mask": ("MASK", {"tooltip": "Optional mask to limit face detection to specific areas. Only areas not masked (white areas) will be processed."}),
}}

RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "MASK", "DETAILER_PIPE", "IMAGE")
Expand All @@ -801,13 +802,17 @@ def enhance_face(image, model, clip, vae, guide_size, guide_size_for_bbox, max_s
sam_mask_hint_use_negative, drop_size,
bbox_detector, segm_detector=None, sam_model_opt=None, wildcard_opt=None, detailer_hook=None,
refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, cycle=1,
inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None, tiled_encode=False, tiled_decode=False):
inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None, tiled_encode=False, tiled_decode=False, mask=None):

# make default prompt as 'face' if empty prompt for CLIPSeg
bbox_detector.setAux('face')
segs = bbox_detector.detect(image, bbox_threshold, bbox_dilation, bbox_crop_factor, drop_size, detailer_hook=detailer_hook)
bbox_detector.setAux(None)

# Apply mask filter if provided
if mask is not None:
segs = core.segs_bitwise_and_mask(segs, mask)

# bbox + sam combination
if sam_model_opt is not None:
sam_mask = core.make_sam_mask(sam_model_opt, segs, image, sam_detection_hint, sam_dilation,
Expand Down Expand Up @@ -861,7 +866,7 @@ def doit(self, image, model, clip, vae, guide_size, guide_size_for, max_size, se
sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold,
sam_mask_hint_use_negative, drop_size, bbox_detector, wildcard, cycle=1,
sam_model_opt=None, segm_detector_opt=None, detailer_hook=None, inpaint_model=False, noise_mask_feather=0,
scheduler_func_opt=None, tiled_encode=False, tiled_decode=False):
scheduler_func_opt=None, tiled_encode=False, tiled_decode=False, mask=None):

result_img = None
result_mask = None
Expand All @@ -873,17 +878,17 @@ def doit(self, image, model, clip, vae, guide_size, guide_size_for, max_size, se
logging.warning("[Impact Pack] WARN: FaceDetailer is not a node designed for video detailing. If you intend to perform video detailing, please use Detailer For AnimateDiff.")

for i, single_image in enumerate(image):
enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask, cnet_pil_list = FaceDetailer.enhance_face(
enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask_result, cnet_pil_list = FaceDetailer.enhance_face(
single_image.unsqueeze(0), model, clip, vae, guide_size, guide_size_for, max_size, seed + i, steps, cfg, sampler_name, scheduler,
positive, negative, denoise, feather, noise_mask, force_inpaint,
bbox_threshold, bbox_dilation, bbox_crop_factor,
sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold,
sam_mask_hint_use_negative, drop_size, bbox_detector, segm_detector_opt, sam_model_opt, wildcard, detailer_hook,
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt,
tiled_encode=tiled_encode, tiled_decode=tiled_decode)
tiled_encode=tiled_encode, tiled_decode=tiled_decode, mask=mask)

result_img = torch.cat((result_img, enhanced_img), dim=0) if result_img is not None else enhanced_img
result_mask = torch.cat((result_mask, mask), dim=0) if result_mask is not None else mask
result_mask = torch.cat((result_mask, mask_result), dim=0) if result_mask is not None else mask_result
result_cropped_enhanced.extend(cropped_enhanced)
result_cropped_enhanced_alpha.extend(cropped_enhanced_alpha)
result_cnet_images.extend(cnet_pil_list)
Expand Down Expand Up @@ -1674,6 +1679,7 @@ def INPUT_TYPES(s):
"scheduler_func_opt": ("SCHEDULER_FUNC",),
"tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
"tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
"mask": ("MASK", {"tooltip": "Optional mask to limit face detection to specific areas. Only areas not masked (white areas) will be processed."}),
}
}

Expand All @@ -1691,7 +1697,7 @@ def doit(self, image, detailer_pipe, guide_size, guide_size_for, max_size, seed,
sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion,
sam_mask_hint_threshold, sam_mask_hint_use_negative, drop_size, refiner_ratio=None,
cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None,
tiled_encode=False, tiled_decode=False):
tiled_encode=False, tiled_decode=False, mask=None):

result_img = None
result_mask = None
Expand All @@ -1706,7 +1712,7 @@ def doit(self, image, detailer_pipe, guide_size, guide_size_for, max_size, seed,
refiner_model, refiner_clip, refiner_positive, refiner_negative = detailer_pipe

for i, single_image in enumerate(image):
enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask, cnet_pil_list = FaceDetailer.enhance_face(
enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask_result, cnet_pil_list = FaceDetailer.enhance_face(
single_image.unsqueeze(0), model, clip, vae, guide_size, guide_size_for, max_size, seed + i, steps, cfg, sampler_name, scheduler,
positive, negative, denoise, feather, noise_mask, force_inpaint,
bbox_threshold, bbox_dilation, bbox_crop_factor,
Expand All @@ -1715,10 +1721,10 @@ def doit(self, image, detailer_pipe, guide_size, guide_size_for, max_size, seed,
refiner_ratio=refiner_ratio, refiner_model=refiner_model,
refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative,
cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt,
tiled_encode=tiled_encode, tiled_decode=tiled_decode)
tiled_encode=tiled_encode, tiled_decode=tiled_decode, mask=mask)

result_img = torch.cat((result_img, enhanced_img), dim=0) if result_img is not None else enhanced_img
result_mask = torch.cat((result_mask, mask), dim=0) if result_mask is not None else mask
result_mask = torch.cat((result_mask, mask_result), dim=0) if result_mask is not None else mask_result
result_cropped_enhanced.extend(cropped_enhanced)
result_cropped_enhanced_alpha.extend(cropped_enhanced_alpha)
result_cnet_images.extend(cnet_pil_list)
Expand Down