From ee7d5b42411eb37f9e5e5c518b25258bb20b1408 Mon Sep 17 00:00:00 2001 From: kijai <40791699+kijai@users.noreply.github.com> Date: Tue, 9 Jul 2024 00:08:10 +0300 Subject: [PATCH] revert this for compatibility --- liveportrait/live_portrait_pipeline.py | 2 +- liveportrait/live_portrait_wrapper.py | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/liveportrait/live_portrait_pipeline.py b/liveportrait/live_portrait_pipeline.py index 303dff7..ae10dbd 100644 --- a/liveportrait/live_portrait_pipeline.py +++ b/liveportrait/live_portrait_pipeline.py @@ -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 diff --git a/liveportrait/live_portrait_wrapper.py b/liveportrait/live_portrait_wrapper.py index 448cf27..1cbd474 100644 --- a/liveportrait/live_portrait_wrapper.py +++ b/liveportrait/live_portrait_wrapper.py @@ -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