{
  "_comment": "Wan2.2 i2v MoE 4-step distill, GGUF quantized. Uses QuantStack/Wan2.2-I2V-A14B-GGUF checkpoints instead of fp8 safetensors. GGUF does not support block-level offload so offload_granularity is set to 'model' — the entire DIT is moved to GPU when active. With Q4_K_M (~9.65 GB per expert) this fits comfortably in 24+ GB VRAM. high_noise_quantized_ckpt / low_noise_quantized_ckpt are filled in at runtime by server/video_models/wan22.py. IMPORTANT: GGUF dequantizes to fp16, so you must set DTYPE=FP16 in the container environment.",

  "infer_steps": 4,
  "target_video_length": 81,
  "text_len": 512,

  "resize_mode": "adaptive",
  "resolution": "480p",
  "target_height": 480,
  "target_width": 480,
  "fps": 16,

  "self_attn_1_type": "flash_attn3",
  "cross_attn_1_type": "flash_attn3",
  "cross_attn_2_type": "flash_attn3",

  "sample_guide_scale": [3.5, 3.5],
  "sample_shift": 5.0,
  "enable_cfg": false,

  "cpu_offload": true,
  "offload_granularity": "model",
  "t5_cpu_offload": true,
  "vae_cpu_offload": false,

  "use_image_encoder": false,

  "boundary_step_index": 2,
  "denoising_step_list": [1000, 750, 500, 250],

  "dit_quantized": true,
  "dit_quant_scheme": "gguf-Q4_K_M",
  "t5_quantized": false
}