Ani14/Video-agent

1"""2Configuration file for WAN-VACE video generation application3"""4import os5 6# Hugging Face token (must be set as environment variable)7HF_TOKEN = os.getenv("HF_TOKEN")8 9# Model paths and configurations10MODEL_CONFIG = {11    "transformer_path": "https://huggingface.co/calcuis/wan-gguf/blob/main/wan2.1-v5-vace-1.3b-q4_0.gguf",12    "text_encoder_path": "chatpig/umt5xxl-encoder-gguf",13    "text_encoder_file": "umt5xxl-encoder-q4_0.gguf",14    "vae_path": "callgg/wan-decoder",15    "pipeline_path": "callgg/wan-decoder"16}17 18# Default generation parameters19DEFAULT_PARAMS = {20    "width": 720,21    "height": 480,22    "num_frames": 57,23    "num_inference_steps": 24,24    "guidance_scale": 2.5,25    "conditioning_scale": 0.0,26    "fps": 16,27    "flow_shift": 3.028}29 30# UI configuration31#32# The title and description here emphasise the agentic nature of the app:33# you provide a concept and the system plans the prompts for you.  Feel free34# to adjust these strings to suit your needs or branding.35UI_CONFIG = {36    "title": "🎬 Agentic WAN-VACE Video Generation",37    "description": (38        "Generate high-quality videos from simple concepts. "39        "Provide a short description of what you want to see, and the agent "40        "will craft a refined prompt and negative prompt before generating a cinematic "41        "vertical video using the WAN‑VACE model."42    ),43    "theme": "default"44}45 46# Server configuration47SERVER_CONFIG = {48    "host": "0.0.0.0",49    "port": 7860,50    "share": False51}52