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"format": "split",
"components": [
"void_pass1",
"void_pass2"
],
"quantized": true,
"quantization_bits": 8,
"quantization_group_size": 64,
"recipe": "void-model",
"source": "netflix/void-model",
"license": "apache-2.0",
"quantization_scope": "transformer Linear weights only",
"links": [
"void-model-mlx (inference): https://github.com/dgrauet/void-model-mlx",
"VideoX-Fun-mlx (engine): https://github.com/dgrauet/VideoX-Fun-mlx"
],
"usage_url": "https://github.com/dgrauet/void-model-mlx",
"extra_links": [
"Base model weights (q8): https://huggingface.co/dgrauet/CogVideoX-Fun-V1.5-5b-InP-mlx-q8",
"bf16 variant: https://huggingface.co/dgrauet/void-model-mlx",
"q4 variant: https://huggingface.co/dgrauet/void-model-mlx-q4"
],
"cli_snippet": "python -m void_mlx.infer \\\n --sample sample/BigBen \\\n --pass1 weights/q8/void_pass1.safetensors \\\n --pass2 weights/q8/void_pass2.safetensors \\\n --base-model /path/to/CogVideoX-Fun-V1.5-5b-InP-mlx-q8 \\\n --steps 30 --max-frames 13 --height 352 --width 624 \\\n --output result.gif",
"build_note": "Good quality/memory balance (~48 GB RAM recommended for the full two-pass\npipeline). On 32 GB Macs use the q4 variant instead."
} |