Fresh deploy: gradio 3.50.2, Docker, CPU mode

Dockerfile

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+FROM python:3.10-slim
+
+RUN apt-get update && apt-get install -y \
+    git ffmpeg libgl1 libglib2.0-0 curl \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# Install PyTorch CPU
+RUN pip install --no-cache-dir \
+    torch==2.3.0+cpu torchvision==0.18.0+cpu torchaudio==2.3.0+cpu \
+    --index-url https://download.pytorch.org/whl/cpu
+
+# Install gradio 3.x (stable, no schema bug)
+RUN pip install --no-cache-dir \
+    "gradio==3.50.2" \
+    "gradio_client==0.6.1" \
+    huggingface_hub \
+    omegaconf einops tqdm rich tyro \
+    "imageio[ffmpeg]" opencv-python-headless \
+    scikit-image lmdb safetensors \
+    diffusers transformers
+
+# Clone and install LivePortrait
+RUN git clone --depth=1 https://github.com/KlingAIResearch/LivePortrait.git /app/LivePortrait && \
+    pip install --no-cache-dir -r /app/LivePortrait/requirements.txt 2>/dev/null || true
+
+COPY app.py /app/app.py
+
+EXPOSE 7860
+
+CMD ["python", "/app/app.py"]

README.md

@@ -1,10 +1,12 @@
 ---
 title: LivePortrait
-emoji: 🐨
-colorFrom: gray
-colorTo: purple
+emoji: 🎭
+colorFrom: purple
+colorTo: blue
 sdk: docker
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# LivePortrait
+Bring portraits to life!

app.py

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+import sys
+import os
+import tempfile
+
+os.environ["CUDA_VISIBLE_DEVICES"] = ""
+
+from huggingface_hub import snapshot_download
+
+weights_dir = "/app/LivePortrait/pretrained_weights"
+if not os.path.exists(os.path.join(weights_dir, "appearance_feature_extractor.safetensors")):
+    print("Downloading model weights (~2GB)...")
+    snapshot_download(
+        repo_id="KlingTeam/LivePortrait",
+        local_dir=weights_dir,
+        ignore_patterns=["*.git*", "README.md", "docs/*"],
+    )
+    print("Weights ready ✓")
+
+sys.path.insert(0, "/app/LivePortrait")
+os.chdir("/app/LivePortrait")
+
+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
+
+infer_cfg = InferenceConfig()
+infer_cfg.flag_force_cpu = True
+
+pipeline = LivePortraitPipeline(
+    inference_cfg=infer_cfg,
+    crop_cfg=CropConfig(),
+)
+
+def animate(source_image, driving_video):
+    if source_image is None or driving_video is None:
+        return None
+    out_dir = tempfile.mkdtemp()
+    args = ArgumentConfig(
+        source=source_image,
+        driving=driving_video,
+        output_dir=out_dir,
+    )
+    wfp, _ = pipeline.execute(args)
+    return wfp
+
+import gradio as gr
+
+with gr.Blocks(title="LivePortrait 🎭") as demo:
+    gr.Markdown("# 🎭 LivePortrait\nAnimate any portrait with a driving video.\n> ⚠️ Running on CPU — takes a few minutes.")
+    with gr.Row():
+        with gr.Column():
+            src_image = gr.Image(label="Source Portrait", type="filepath")
+            drv_video = gr.Video(label="Driving Video")
+            btn = gr.Button("✨ Animate!", variant="primary")
+        with gr.Column():
+            out_video = gr.Video(label="Result")
+    btn.click(fn=animate, inputs=[src_image, drv_video], outputs=[out_video])
+
+demo.launch(server_name="0.0.0.0", server_port=7860)