nodes/ltdrdata-ComfyUI-Impact-Pack
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improve: FaceDetailer allows image batch input

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Dr.Lt.Data
2023-12-23 13:16:12 +09:00
parent d1aaa4a097
commit 75799f5834
2 changed files with 54 additions and 24 deletions
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modules/impact/config.py
+1 -1
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@@ -2,7 +2,7 @@ import configparser
import os
version = "V4.49"
version = "V4.50"
dependency_version = 19
modules/impact/impact_pack.py
+53 -23
View File
@@ -461,15 +461,29 @@ class FaceDetailer:
sam_mask_hint_use_negative, drop_size, bbox_detector, wildcard, cycle=1,
sam_model_opt=None, segm_detector_opt=None, detailer_hook=None):
enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask, cnet_pil_list = FaceDetailer.enhance_face(
image, model, clip, vae, guide_size, guide_size_for, max_size, seed, 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)
result_img = None
result_mask = None
result_cropped_enhanced = []
result_cnet_images = []
if len(image) > 1:
print(f"[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(
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)
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_cropped_enhanced.extend(cropped_enhanced)
result_cnet_images.extend(cnet_pil_list)
pipe = (model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, None, None, None, None)
return enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask, pipe, cnet_pil_list
return result_img, result_cropped_enhanced, result_cropped_enhanced, result_mask, pipe, result_cnet_images
class LatentPixelScale:
@@ -1150,29 +1164,45 @@ class FaceDetailerPipe:
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):
result_img = None
result_mask = None
result_cropped_enhanced = []
result_cropped_enhanced_alpha = []
result_cnet_images = []
if len(image) > 1:
print(f"[Impact Pack] WARN: FaceDetailer is not a node designed for video detailing. If you intend to perform video detailing, please use Detailer For AnimateDiff.")
model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector, sam_model_opt, detailer_hook, \
refiner_model, refiner_clip, refiner_positive, refiner_negative = detailer_pipe
enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask, cnet_pil_list = FaceDetailer.enhance_face(
image, model, clip, vae, guide_size, guide_size_for, max_size, seed, 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, sam_model_opt, wildcard, detailer_hook,
refiner_ratio=refiner_ratio, refiner_model=refiner_model,
refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative,
cycle=cycle)
for i, single_image in enumerate(image):
enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask, 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, sam_model_opt, wildcard, detailer_hook,
refiner_ratio=refiner_ratio, refiner_model=refiner_model,
refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative,
cycle=cycle)
if len(cropped_enhanced) == 0:
cropped_enhanced = [empty_pil_tensor()]
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_cropped_enhanced.extend(cropped_enhanced)
result_cropped_enhanced_alpha.extend(cropped_enhanced_alpha)
result_cnet_images.extend(cnet_pil_list)
if len(cropped_enhanced_alpha) == 0:
cropped_enhanced_alpha = [empty_pil_tensor()]
if len(result_cropped_enhanced) == 0:
result_cropped_enhanced = [empty_pil_tensor()]
if len(cnet_pil_list) == 0:
cnet_pil_list = [empty_pil_tensor()]
if len(result_cropped_enhanced_alpha) == 0:
result_cropped_enhanced_alpha = [empty_pil_tensor()]
return enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask, detailer_pipe, cnet_pil_list
if len(result_cnet_images) == 0:
result_cnet_images = [empty_pil_tensor()]
return result_img, result_cropped_enhanced, result_cropped_enhanced_alpha, result_mask, detailer_pipe, result_cnet_images
class MaskDetailerPipe: