36 lines
1.2 KiB
JSON
36 lines
1.2 KiB
JSON
{
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"_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.",
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"infer_steps": 4,
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"target_video_length": 81,
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"text_len": 512,
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"resize_mode": "adaptive",
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"resolution": "480p",
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"target_height": 480,
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"target_width": 480,
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"fps": 16,
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"self_attn_1_type": "flash_attn3",
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"cross_attn_1_type": "flash_attn3",
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"cross_attn_2_type": "flash_attn3",
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"sample_guide_scale": [3.5, 3.5],
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"sample_shift": 5.0,
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"enable_cfg": false,
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"cpu_offload": true,
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"offload_granularity": "model",
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"t5_cpu_offload": true,
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"vae_cpu_offload": false,
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"use_image_encoder": false,
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"boundary_step_index": 2,
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"denoising_step_list": [1000, 750, 500, 250],
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"dit_quantized": true,
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"dit_quant_scheme": "gguf-Q4_K_M",
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"t5_quantized": false
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}
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