Autodetect dtype, use tqdm progress bars

examples/liveportrait_example_01.json

@@ -263,7 +263,7 @@
         "Node name for S&R": "DownloadAndLoadLivePortraitModels"
       },
       "widgets_values": [
-        "fp16"
+        "auto"
       ]
     },
     {

liveportrait/live_portrait_pipeline.py

@@ -7,7 +7,7 @@ Pipeline of LivePortrait
 import cv2
 import numpy as np
 import os.path as osp
-from rich.progress import track
+from tqdm import tqdm
 
 from .config.inference_config import InferenceConfig
 
@@ -103,7 +103,7 @@ class LivePortraitPipeline(object):
         I_p_lst = []
         R_d_0, x_d_0_info = None, None
         pbar = comfy.utils.ProgressBar(n_frames)
-        for i in track(range(n_frames), description='Animating...', total=n_frames):
+        for i in tqdm(range(n_frames), desc='Animating...', total=n_frames):
             #if is_video(args.driving_info):
             # extract kp info by M
             I_d_i = I_d_lst[i]

liveportrait/live_portrait_wrapper.py

@@ -13,6 +13,7 @@ from .utils.retargeting_utils import compute_eye_delta, compute_lip_delta
 from .utils.camera import headpose_pred_to_degree, get_rotation_matrix
 from .utils.retargeting_utils import calc_eye_close_ratio, calc_lip_close_ratio
 from .config.inference_config import InferenceConfig
+from contextlib import nullcontext
 
 from comfy.model_management import get_autocast_device
 
@@ -79,9 +80,8 @@ class LivePortraitWrapper(object):
         """ get the appearance feature of the image by F
         x: Bx3xHxW, normalized to 0~1
         """
-        with torch.no_grad():
-            with torch.autocast(device_type=get_autocast_device(self.device_id), dtype=torch.float16, enabled=self.cfg.flag_use_half_precision):
-                feature_3d = self.appearance_feature_extractor(x)
+        with torch.autocast(get_autocast_device(self.device_id), dtype=torch.float16) if self.cfg.flag_use_half_precision else nullcontext():
+            feature_3d = self.appearance_feature_extractor(x)
 
         return feature_3d.float()
 
@@ -91,15 +91,14 @@ class LivePortraitWrapper(object):
         flag_refine_info: whether to trandform the pose to degrees and the dimention of the reshape
         return: A dict contains keys: 'pitch', 'yaw', 'roll', 't', 'exp', 'scale', 'kp'
         """
-        with torch.no_grad():
-            with torch.autocast(device_type=get_autocast_device(self.device_id), dtype=torch.float16, enabled=self.cfg.flag_use_half_precision):
-                kp_info = self.motion_extractor(x)
+        with torch.autocast(get_autocast_device(self.device_id), dtype=torch.float16) if self.cfg.flag_use_half_precision else nullcontext():
+            kp_info = self.motion_extractor(x)
 
-            if self.cfg.flag_use_half_precision:
-                # float the dict
-                for k, v in kp_info.items():
-                    if isinstance(v, torch.Tensor):
-                        kp_info[k] = v.float()
+        if self.cfg.flag_use_half_precision:
+            # float the dict
+            for k, v in kp_info.items():
+                if isinstance(v, torch.Tensor):
+                    kp_info[k] = v.float()
 
         flag_refine_info: bool = kwargs.get('flag_refine_info', True)
         if flag_refine_info:
@@ -266,18 +265,17 @@ class LivePortraitWrapper(object):
         kp_driving: BxNx3
         """
         # The line 18 in Algorithm 1: D(W(f_s; x_s, x′_d,i)）
-        with torch.no_grad():
-            with torch.autocast(device_type=get_autocast_device(self.device_id), dtype=torch.float16, enabled=self.cfg.flag_use_half_precision):
-                # get decoder input
-                ret_dct = self.warping_module(feature_3d, kp_source=kp_source, kp_driving=kp_driving)
-                # decode
-                ret_dct['out'] = self.spade_generator(feature=ret_dct['out'])
+        with torch.autocast(get_autocast_device(self.device_id), dtype=torch.float16) if self.cfg.flag_use_half_precision else nullcontext():
+            # get decoder input
+            ret_dct = self.warping_module(feature_3d, kp_source=kp_source, kp_driving=kp_driving)
+            # decode
+            ret_dct['out'] = self.spade_generator(feature=ret_dct['out'])
 
-            # float the dict
-            if self.cfg.flag_use_half_precision:
-                for k, v in ret_dct.items():
-                    if isinstance(v, torch.Tensor):
-                        ret_dct[k] = v.float()
+        # float the dict
+        if self.cfg.flag_use_half_precision:
+            for k, v in ret_dct.items():
+                if isinstance(v, torch.Tensor):
+                    ret_dct[k] = v.float()
 
         return ret_dct
 

liveportrait/template_maker.py

@@ -8,7 +8,7 @@ import os
 import cv2
 import numpy as np
 import pickle
-from rich.progress import track
+from tqdm import tqdm
 from .utils.cropper import Cropper
 
 from .utils.io import load_driving_info
@@ -41,7 +41,7 @@ class TemplateMaker:
         templates = []
 
 
-        for i in track(range(n_frames), description='Making templates...', total=n_frames):
+        for i in tqdm(range(n_frames), desc='Making templates...', total=n_frames):
             I_d_i = I_d_lst[i]
             x_d_i_info = self.live_portrait_wrapper.get_kp_info(I_d_i)
             R_d_i = get_rotation_matrix(x_d_i_info['pitch'], x_d_i_info['yaw'], x_d_i_info['roll'])

liveportrait/utils/video.py

@@ -10,7 +10,7 @@ import subprocess
 import imageio
 import cv2
 
-from rich.progress import track
+from tqdm import tqdm
 from .helper import prefix
 from .rprint import rprint as print
 
@@ -35,7 +35,7 @@ def images2video(images, wfp, **kwargs):
     )
 
     n = len(images)
-    for i in track(range(n), description='writing', transient=True):
+    for i in tqdm(range(n), desc='writing', transient=True):
         if image_mode.lower() == 'bgr':
             writer.append_data(images[i][..., ::-1])
         else:
@@ -83,7 +83,7 @@ def blend(img: np.ndarray, mask: np.ndarray, background_color=(255, 255, 255)):
 def concat_frames(I_p_lst, driving_rgb_lst, img_rgb):
     # TODO: add more concat style, e.g., left-down corner driving
     out_lst = []
-    for idx, _ in track(enumerate(I_p_lst), total=len(I_p_lst), description='Concatenating result...'):
+    for idx, _ in tqdm(enumerate(I_p_lst), total=len(I_p_lst), desc='Concatenating result...'):
         source_image_drived = I_p_lst[idx]
         image_drive = driving_rgb_lst[idx]
 

nodes.py

@@ -7,7 +7,6 @@ import comfy.utils
 
 script_directory = os.path.dirname(os.path.abspath(__file__))
 
-from .liveportrait.config.argument_config import ArgumentConfig
 from .liveportrait.live_portrait_pipeline import LivePortraitPipeline
 from .liveportrait.utils.cropper import Cropper
 from .liveportrait.modules.spade_generator import SPADEDecoder
@@ -98,10 +97,11 @@ class DownloadAndLoadLivePortraitModels:
             "optional": {
                  "precision": (
                     [
+                        'auto',
                         'fp16',
                         'fp32',
                     ], {
-                        "default": 'fp16'
+                        "default": 'auto'
                     }),
             }
         }
@@ -111,10 +111,27 @@ class DownloadAndLoadLivePortraitModels:
     FUNCTION = "loadmodel"
     CATEGORY = "LivePortrait"
 
-    def loadmodel(self, precision='fp16'):
+    def loadmodel(self, precision='auto'):
         device = mm.get_torch_device()
         mm.soft_empty_cache()
 
+        if precision == 'auto':
+            try:
+                if mm.is_device_mps(device):
+                    print("LivePortrait using fp32 for MPS")
+                    dtype = 'fp32'
+                elif mm.should_use_fp16():
+                    print("LivePortrait using fp16")
+                    dtype = 'fp16'
+                else:
+                    print("LivePortrait using fp32")
+                    dtype = 'fp32'
+            except:
+                raise AttributeError("ComfyUI version too old, can't autodetect properly. Set your dtypes manually.")
+        else:
+            dtype = precision
+            print(f"LivePortrait using {dtype}")
+
         pbar = comfy.utils.ProgressBar(3)
 
         download_path = os.path.join(folder_paths.models_dir, "liveportrait")
@@ -211,7 +228,7 @@ class DownloadAndLoadLivePortraitModels:
             self.stich_retargeting_module,
             InferenceConfig(
                 device_id=device, 
-                flag_use_half_precision = True if precision == 'fp16' else False
+                flag_use_half_precision = True if dtype == 'fp16' else False
                 )
         )