chore: setup cog configuration for replicate deployment.

.dockerignore

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+# The .dockerignore file excludes files from the container build process.
+#
+# https://docs.docker.com/engine/reference/builder/#dockerignore-file
+
+# Exclude Git files
+.git
+.github
+.gitignore
+
+# Exclude Python cache files
+__pycache__
+.mypy_cache
+.pytest_cache
+.ruff_cache
+
+# Exclude Python virtual environment
+/venv

cog.yaml

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+# Configuration for Cog ⚙️
+# Reference: https://cog.run/yaml
+
+build:
+  gpu: true
+  python_version: "3.10"
+  system_packages:
+     - "ffmpeg"
+     - "libsm6"
+     - "libxext6"
+     - "libgl1"
+  python_packages:
+    - "torch==2.3.0"
+    - "torchvision==0.18.0"
+    - "torchaudio==2.3.0"
+    - "numpy==1.26.4"
+    - "pyyaml==6.0.1"
+    - "opencv-python==4.10.0.84"
+    - "scipy==1.13.1"
+    - "imageio==2.34.2"
+    - "lmdb==1.4.1"
+    - "tqdm==4.66.4"
+    - "rich==13.7.1"
+    - "ffmpeg==1.4"
+    - "onnxruntime-gpu==1.18.0"
+    - "onnx==1.16.1"
+    - "scikit-image==0.24.0"
+    - "albumentations==1.4.10"
+    - "matplotlib==3.9.0"
+    - "imageio-ffmpeg==0.5.1"
+    - "tyro==0.8.5"
+    - "gradio==3.48.0"
+predict: "predict.py:Predictor"

predict.py

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+from cog import BasePredictor, Input, Path, File
+
+from src.config.argument_config import ArgumentConfig
+from src.config.inference_config import InferenceConfig
+from src.config.crop_config import CropConfig
+from src.live_portrait_pipeline import LivePortraitPipeline
+import requests
+
+class Predictor(BasePredictor):
+    def setup(self) -> None:
+        """Load the model into memory to make running multiple predictions efficient"""
+        self.live_portrait_pipeline = LivePortraitPipeline(
+            inference_cfg=InferenceConfig(),
+            crop_cfg=CropConfig()
+        )
+
+    def predict(
+        self,
+        input_image_path: Path = Input(description="Portrait image"),
+        input_video_path: Path = Input(description="Driving video"),
+        flag_relative_input: bool = Input(description="relative motion", default=True),
+        flag_do_crop_input: bool = Input(description="We recommend checking the do crop option when facial areas occupy a relatively small portion of your image.", default=True),
+        flag_pasteback: bool = Input(description="paste-back", default=True),
+    ) -> Path:
+        """Run a single prediction on the model"""
+
+        user_args = ArgumentConfig(
+            flag_relative=flag_relative_input,
+            flag_do_crop=flag_do_crop_input,
+            flag_pasteback=flag_pasteback,
+            source_image=input_image_path,
+            driving_info=str(input_video_path),
+            output_dir="/tmp/"
+        )
+        self.live_portrait_pipeline.cropper.update_config(user_args.__dict__)
+        self.live_portrait_pipeline.live_portrait_wrapper.update_config(user_args.__dict__)
+
+        video_path, _ = self.live_portrait_pipeline.execute(
+            user_args
+        )
+
+        return Path(video_path)