File size: 3,904 Bytes
9d9638e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 |
import os
import sys
import gradio as gr
import subprocess
import json
# Set environment variables for HF Spaces
os.environ["GRADIO_SERVER_NAME"] = "0.0.0.0"
os.environ["GRADIO_SERVER_PORT"] = "7860"
# Pre-download models cache
os.environ["HF_HUB_CACHE"] = "/tmp/hf_cache"
os.environ["HUGGINGFACE_HUB_CACHE"] = "/tmp/hf_cache"
# Fix potential Hunyuan Video Avatar issues
os.environ["TOKENIZERS_PARALLELISM"] = "false"
def setup_environment():
"""Setup environment for HF Spaces with WanGP v6.3"""
# Install additional dependencies if needed
try:
import sageattention
except ImportError:
print("Installing SageAttention...")
subprocess.run([sys.executable, "-m", "pip", "install", "sageattention==1.0.6"], check=True)
# Ensure face detection libs are available
try:
import insightface
import facexlib
except ImportError:
print("Installing face processing libraries...")
subprocess.run([sys.executable, "-m", "pip", "install", "insightface", "facexlib"], check=True)
def download_essential_models():
"""Pre-download essential models for faster startup"""
from huggingface_hub import snapshot_download
print("Downloading Hunyuan Video Avatar models...")
try:
# Download Hunyuan Video Avatar base models
snapshot_download(
repo_id="tencent/HunyuanVideo-Avatar",
cache_dir="/tmp/hf_cache",
allow_patterns=["*.safetensors", "*.json", "*.txt", "*.bin"],
ignore_patterns=["*.mp4", "*.avi", "*.mov"] # Skip demo videos
)
# Download base Hunyuan Video model
snapshot_download(
repo_id="tencent/HunyuanVideo",
cache_dir="/tmp/hf_cache",
allow_patterns=["*.safetensors", "*.json", "*.txt"],
ignore_patterns=["*.mp4", "*.avi"]
)
except Exception as e:
print(f"Model download warning: {e}")
print("Models will be downloaded on-demand during first use.")
def create_hf_config():
"""Create optimized config for HF Spaces deployment"""
config = {
"model_settings": {
"profile": 3, # Optimized for A10G Large
"quantize_transformer": True,
"attention_mode": "sage",
"compile": False, # Disable for stability on HF
"teacache": "2.0"
},
"avatar_settings": {
"max_frames": 120, # ~5 seconds at 24fps
"resolution": "512x512", # Balanced quality/performance
"emotion_control": True,
"multi_character": True
},
"memory_optimization": {
"enable_vae_tiling": True,
"enable_cpu_offload": True,
"max_batch_size": 1,
"gradient_checkpointing": True
},
"audio_processing": {
"sample_rate": 16000,
"max_duration": 15, # seconds
"supported_formats": ["wav", "mp3", "m4a"]
}
}
with open("/tmp/hf_config.json", "w") as f:
json.dump(config, f, indent=2)
return config
if __name__ == "__main__":
print("🚀 Starting WanGP v6.3 with Hunyuan Video Avatar...")
setup_environment()
config = create_hf_config()
download_essential_models()
print("✅ Setup complete! Launching application...")
# Import and run the main application
from wgp import main
main(
profile=3, # Higher profile for A10G Large
attention="sage", # Use Sage attention for better performance
server_name="0.0.0.0",
server_port=7860,
quantize_transformer=True,
teacache="2.0", # Enable TeaCache for Avatar acceleration
compile=False, # Disabled for HF Spaces stability
share=False, # HF Spaces handles sharing
config_file="/tmp/hf_config.json"
)
|