feat: gradio acceleration (#123)

* fix: typo

* feat: gradio acceleration

* doc: update readme.md

---------

Co-authored-by: Jianzhu Guo <guojianzhu@kuaishou.com>

app.py

@@ -5,6 +5,7 @@ The entrance of the gradio
 """
 
 import tyro
+import subprocess
 import gradio as gr
 import os.path as osp
 from src.utils.helper import load_description
@@ -18,10 +19,22 @@ def partial_fields(target_class, kwargs):
     return target_class(**{k: v for k, v in kwargs.items() if hasattr(target_class, k)})
 
 
+def fast_check_ffmpeg():
+    try:
+        subprocess.run(["ffmpeg", "-version"], capture_output=True, check=True)
+        return True
+    except:
+        return False
+
 # set tyro theme
 tyro.extras.set_accent_color("bright_cyan")
 args = tyro.cli(ArgumentConfig)
 
+if not fast_check_ffmpeg():
+    raise ImportError(
+        "FFmpeg is not installed. Please install FFmpeg before running this script. https://ffmpeg.org/download.html"
+    )
+
 # specify configs for inference
 inference_cfg = partial_fields(InferenceConfig, args.__dict__)  # use attribute of args to initial InferenceConfig
 crop_cfg = partial_fields(CropConfig, args.__dict__)  # use attribute of args to initial CropConfig

readme.md

@@ -148,6 +148,13 @@ python app.py
 
 You can specify the `--server_port`, `--share`, `--server_name` arguments to satisfy your needs!
 
+🚀 We also provide an acceleration option `--flag_do_torch_compile`. The first-time inference triggers an optimization process (about one minute), making subsequent inferences 20-30% faster. Performance gains may vary with different CUDA versions.
+```bash
+# enable torch.compile for faster inference
+python app.py --flag_do_torch_compile
+```
+**Note**: This method has not been fully tested. e.g., on Windows.
+
 **Or, try it out effortlessly on [HuggingFace](https://huggingface.co/spaces/KwaiVGI/LivePortrait) 🤗**
 
 ### 5. Inference speed evaluation 🚀🚀🚀

src/config/argument_config.py

@@ -23,7 +23,7 @@ class ArgumentConfig(PrintableConfig):
     flag_crop_driving_video: bool = False  # whether to crop the driving video, if the given driving info is a video
     device_id: int = 0 # gpu device id
     flag_force_cpu: bool = False # force cpu inference, WIP!
-    flag_lip_zero : bool = True # whether let the lip to close state before animation, only take effect when flag_eye_retargeting and flag_lip_retargeting is False
+    flag_lip_zero: bool = True # whether let the lip to close state before animation, only take effect when flag_eye_retargeting and flag_lip_retargeting is False
     flag_eye_retargeting: bool = False # not recommend to be True, WIP
     flag_lip_retargeting: bool = False # not recommend to be True, WIP
     flag_stitching: bool = True  # recommend to True if head movement is small, False if head movement is large
@@ -45,3 +45,4 @@ class ArgumentConfig(PrintableConfig):
     server_port: Annotated[int, tyro.conf.arg(aliases=["-p"])]  = 8890 # port for gradio server
     share: bool = False # whether to share the server to public
     server_name: Optional[str] = "127.0.0.1"  # set the local server name, "0.0.0.0" to broadcast all
+    flag_do_torch_compile: bool = False  # whether to use torch.compile to accelerate generation

src/config/inference_config.py

@@ -14,7 +14,7 @@ from .base_config import PrintableConfig, make_abs_path
 
 @dataclass(repr=False)  # use repr from PrintableConfig
 class InferenceConfig(PrintableConfig):
-    # MODEL CONFIG, NOT EXPOERTED PARAMS
+    # MODEL CONFIG, NOT EXPORTED PARAMS
     models_config: str = make_abs_path('./models.yaml')  # portrait animation config
     checkpoint_F: str = make_abs_path('../../pretrained_weights/liveportrait/base_models/appearance_feature_extractor.pth')  # path to checkpoint of F
     checkpoint_M: str = make_abs_path('../../pretrained_weights/liveportrait/base_models/motion_extractor.pth')  # path to checkpoint pf M
@@ -22,7 +22,7 @@ class InferenceConfig(PrintableConfig):
     checkpoint_W: str = make_abs_path('../../pretrained_weights/liveportrait/base_models/warping_module.pth')  # path to checkpoint of W
     checkpoint_S: str = make_abs_path('../../pretrained_weights/liveportrait/retargeting_models/stitching_retargeting_module.pth')  # path to checkpoint to S and R_eyes, R_lip
 
-    # EXPOERTED PARAMS
+    # EXPORTED PARAMS
     flag_use_half_precision: bool = True
     flag_crop_driving_video: bool = False
     device_id: int = 0
@@ -35,8 +35,9 @@ class InferenceConfig(PrintableConfig):
     flag_do_crop: bool = True
     flag_do_rot: bool = True
     flag_force_cpu: bool = False 
+    flag_do_torch_compile: bool = False  
 
-    # NOT EXPOERTED PARAMS
+    # NOT EXPORTED PARAMS
     lip_zero_threshold: float = 0.03 # threshold for flag_lip_zero
     anchor_frame: int = 0 # TO IMPLEMENT
 

src/live_portrait_wrapper.py

@@ -24,6 +24,7 @@ class LivePortraitWrapper(object):
 
         self.inference_cfg = inference_cfg
         self.device_id = inference_cfg.device_id
+        self.compile = inference_cfg.flag_do_torch_compile
         if inference_cfg.flag_force_cpu:
             self.device = 'cpu'
         else:
@@ -48,8 +49,10 @@ class LivePortraitWrapper(object):
             log(f'Load stitching_retargeting_module done.')
         else:
             self.stitching_retargeting_module = None
-
-        
+        # Optimize for inference
+        if self.compile:
+            self.warping_module = torch.compile(self.warping_module, mode='max-autotune')  
+            self.spade_generator = torch.compile(self.spade_generator, mode='max-autotune')  
         
         self.timer = Timer()
 
@@ -261,6 +264,9 @@ class LivePortraitWrapper(object):
         # The line 18 in Algorithm 1: D(W(f_s; x_s, x′_d,i)）
         with torch.no_grad():
             with torch.autocast(device_type=self.device[:4], dtype=torch.float16, enabled=self.inference_cfg.flag_use_half_precision):
+                if self.compile:
+                    # Mark the beginning of a new CUDA Graph step
+                    torch.compiler.cudagraph_mark_step_begin()
                 # get decoder input
                 ret_dct = self.warping_module(feature_3d, kp_source=kp_source, kp_driving=kp_driving)
                 # decode