nodes/kijai-ComfyUI-LivePortraitKJ
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revert this for compatibility

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kijai
2024-07-09 00:08:10 +03:00
parent 03df9f35cd
commit ee7d5b4241
2 changed files with 5 additions and 5 deletions
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liveportrait/live_portrait_pipeline.py
+1 -1
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@@ -106,7 +106,7 @@ class LivePortraitPipeline(object):
c_d_lip_before_animation = [0.0]
combined_lip_ratio_tensor_before_animation = (
self.live_portrait_wrapper.calc_combined_lip_ratio(
c_d_lip_before_animation, source_lmk, inference_cfg
c_d_lip_before_animation, source_lmk
)
)
# TODO: expose lip_zero_threshold
liveportrait/live_portrait_wrapper.py
+4 -4
View File
@@ -301,20 +301,20 @@ class LivePortraitWrapper(object):
input_lip_ratio_lst.append(calc_lip_close_ratio(lmk[None]))
return input_eye_ratio_lst, input_lip_ratio_lst
def calc_combined_eye_ratio(self, input_eye_ratio, source_lmk, inference_cfg):
def calc_combined_eye_ratio(self, input_eye_ratio, source_lmk):
eye_close_ratio = calc_eye_close_ratio(source_lmk[None])
eye_close_ratio_tensor = torch.from_numpy(eye_close_ratio).float().to(self.device_id)
input_eye_ratio_tensor = torch.Tensor([input_eye_ratio[0][0]]).reshape(1, 1).to(self.device_id) * inference_cfg.eyes_retargeting_multiplier
input_eye_ratio_tensor = torch.Tensor([input_eye_ratio[0][0]]).reshape(1, 1).to(self.device_id)
# [c_s,eyes, c_d,eyes,i]
combined_eye_ratio_tensor = torch.cat([eye_close_ratio_tensor, input_eye_ratio_tensor], dim=1)
return combined_eye_ratio_tensor
def calc_combined_lip_ratio(self, input_lip_ratio, source_lmk, inference_cfg):
def calc_combined_lip_ratio(self, input_lip_ratio, source_lmk):
lip_close_ratio = calc_lip_close_ratio(source_lmk[None])
lip_close_ratio_tensor = torch.from_numpy(lip_close_ratio).float().to(self.device_id)
# [c_s,lip, c_d,lip,i]
input_lip_ratio_tensor = torch.Tensor([input_lip_ratio[0]]).to(self.device_id)
if input_lip_ratio_tensor.shape != [1, 1]:
input_lip_ratio_tensor = input_lip_ratio_tensor.reshape(1, 1)
combined_lip_ratio_tensor = torch.cat([lip_close_ratio_tensor, input_lip_ratio_tensor], dim=1) * inference_cfg.lip_retargeting_multiplier
combined_lip_ratio_tensor = torch.cat([lip_close_ratio_tensor, input_lip_ratio_tensor], dim=1)
return combined_lip_ratio_tensor