feat: Enable HunyuanVideo-Avatar on RunPod Serverless#36
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arpitdayma123 wants to merge 5 commits intoTencent-Hunyuan:mainfrom
Open
feat: Enable HunyuanVideo-Avatar on RunPod Serverless#36arpitdayma123 wants to merge 5 commits intoTencent-Hunyuan:mainfrom
arpitdayma123 wants to merge 5 commits intoTencent-Hunyuan:mainfrom
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This commit introduces the necessary changes to run the HunyuanVideo-Avatar
model on the RunPod Serverless platform.
Key changes include:
1. **RunPod Handler (`handler.py`):**
* Created a new `handler.py` to serve as the entry point for RunPod.
* The handler accepts `image_url` and `audio_url` as input.
* It downloads the provided image and audio files to temporary local storage.
2. **Inference Logic Refactoring (`hymm_sp/inference_runner.py`):**
* Refactored the core inference script `hymm_sp/sample_gpu_poor.py` into a callable function `process_video_avatar_job` within the new `hymm_sp/inference_runner.py`.
* This function now accepts direct file paths for image and audio, along with output parameters, instead of relying on command-line arguments for these.
* Hardcoded model configuration parameters (e.g., frame count, seed, FP8 usage, CPU offload) as specified for low VRAM usage.
* Adapted data loading to work with single inputs rather than a CSV file.
3. **Integration:**
* The `handler.py` now calls `process_video_avatar_job` from `inference_runner.py` to perform video generation.
* Temporary input files are cleaned up after processing.
* The path to the generated video (within the container) is returned in the handler's response.
4. **Dependencies (`requirements.txt`):**
* Added `requests` for URL downloading and `runpod` for the serverless environment.
* Removed the non-pip `ffmpeg` entry (ffmpeg is now handled as a system dependency in Docker).
5. **Dockerfile:**
* Created a `Dockerfile` to package the application.
* Uses a RunPod PyTorch base image with CUDA 11.8.
* Installs system dependencies like `ffmpeg`.
* Installs Python dependencies from `requirements.txt`, including `flash-attention`.
* Sets necessary environment variables (`MODEL_BASE`, `CPU_OFFLOAD`, `PYTHONPATH`).
* Copies the application code into the image.
* Sets the `CMD` to run `handler.py`.
These changes allow the model to be deployed as a serverless endpoint on RunPod, taking image and audio URLs as input and configured for low VRAM operation. You need to ensure model weights are correctly placed in the `/app/weights` directory within the Docker image during the build process.
This commit introduces the necessary changes to run the HunyuanVideo-Avatar
model on the RunPod Serverless platform. This is a re-attempt after a
previous Docker build error you reported. The Dockerfile in the
repository has been verified and appears correct.
Key changes include:
1. **RunPod Handler (`handler.py`):**
* I created a new `handler.py` to serve as the entry point for RunPod.
* The handler accepts `image_url` and `audio_url` as input.
* It downloads the provided image and audio files to temporary local storage.
2. **Inference Logic Refactoring (`hymm_sp/inference_runner.py`):**
* I refactored the core inference script `hymm_sp/sample_gpu_poor.py` into a callable function `process_video_avatar_job` within the new `hymm_sp/inference_runner.py`.
* This function now accepts direct file paths for image and audio, along with output parameters, instead of relying on command-line arguments for these.
* I hardcoded model configuration parameters (e.g., frame count, seed, FP8 usage, CPU offload) as specified for low VRAM usage.
* I adapted data loading to work with single inputs rather than a CSV file.
3. **Integration:**
* The `handler.py` now calls `process_video_avatar_job` from `inference_runner.py` to perform video generation.
* Temporary input files are cleaned up after processing.
* The path to the generated video (within the container) is returned in the handler's response.
4. **Dependencies (`requirements.txt`):**
* I added `requests` for URL downloading and `runpod` for the serverless environment.
* I removed the non-pip `ffmpeg` entry (ffmpeg is now handled as a system dependency in Docker).
5. **Dockerfile:**
* I created a `Dockerfile` to package the application.
* It uses a RunPod PyTorch base image with CUDA 11.8.
* It installs system dependencies like `ffmpeg`.
* It installs Python dependencies from `requirements.txt`, including `flash-attention`.
* It sets necessary environment variables (`MODEL_BASE`, `CPU_OFFLOAD`, `PYTHONPATH`).
* It copies the application code into the image.
* It sets the `CMD` to run `handler.py`.
These changes allow the model to be deployed as a serverless endpoint on RunPod, taking image and audio URLs as input and configured for low VRAM operation. You need to ensure model weights are correctly placed in the `/app/weights` directory within the Docker image during the build process.
Moved the comment for `ENV MPLCONFIGDIR` to its own line. This is to prevent potential parsing issues in certain Docker build environments that might be sensitive to inline comments on ENV lines, as suggested by persistent build errors.
Removes a commented-out `if __name__ == "__main__":` block from the end of `hymm_sp/inference_runner.py`. This block was causing a SyntaxError during runtime in the RunPod environment, likely due to formatting issues or characters within the comments that were misinterpreted. This change ensures the script only contains the necessary importable logic for `process_video_avatar_job`.
Removes a commented-out `if __name__ == "__main__":` block from the end of `hymm_sp/inference_runner.py`. This block was causing a SyntaxError during runtime in the RunPod environment, likely due to formatting issues or characters within the comments that were misinterpreted. This change ensures the script only contains the necessary importable logic for `process_video_avatar_job`.
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This commit introduces the necessary changes to run the HunyuanVideo-Avatar model on the RunPod Serverless platform.
Key changes include:
RunPod Handler (
handler.py):handler.pyto serve as the entry point for RunPod.image_urlandaudio_urlas input.Inference Logic Refactoring (
hymm_sp/inference_runner.py):hymm_sp/sample_gpu_poor.pyinto a callable functionprocess_video_avatar_jobwithin the newhymm_sp/inference_runner.py.Integration:
handler.pynow callsprocess_video_avatar_jobfrominference_runner.pyto perform video generation.Dependencies (
requirements.txt):requestsfor URL downloading andrunpodfor the serverless environment.ffmpegentry (ffmpeg is now handled as a system dependency in Docker).Dockerfile:
Dockerfileto package the application.ffmpeg.requirements.txt, includingflash-attention.MODEL_BASE,CPU_OFFLOAD,PYTHONPATH).CMDto runhandler.py.These changes allow the model to be deployed as a serverless endpoint on RunPod, taking image and audio URLs as input and configured for low VRAM operation. You need to ensure model weights are correctly placed in the
/app/weightsdirectory within the Docker image during the build process.