cookbook-recipes.json

{
  "version": 7,
  "recipes": [
    {
      "id": "fastwan21-t2v",
      "family": "wan",
      "stage": "inference",
      "task": "Text to video",
      "label": "FastWan2.1 1.3B (distilled + VSA)",
      "summary": "Generate a video in three denoising steps with the distilled FastWan2.1 1.3B checkpoint and video sparse attention.",
      "model": "FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
      "source": "scripts/inference/inference_wan_VSA_DMD_1_3B.yaml",
      "command": "FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN fastvideo generate --config scripts/inference/inference_wan_VSA_DMD_1_3B.yaml",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "MP4 videos under outputs_video_dmd_1.3B/"
    },
    {
      "id": "wan22-t2v",
      "family": "wan",
      "stage": "inference",
      "task": "Text to video",
      "label": "Wan2.2 A14B",
      "summary": "The maintained high-capacity Wan2.2 text-to-video example with CPU offload settings encoded in its checked-in Python source.",
      "model": "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
      "source": "examples/inference/basic/basic_wan2_2.py",
      "command": "python examples/inference/basic/basic_wan2_2.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 2, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "MP4 videos under video_samples_wan2_2_14B_t2v/"
    },
    {
      "id": "wan21-i2v",
      "family": "wan",
      "stage": "inference",
      "task": "Image to video",
      "label": "Wan2.1 14B 480P",
      "summary": "Animate an input image at 480P using the maintained Wan2.1 YAML configuration and its recorded offload settings.",
      "model": "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers",
      "source": "scripts/inference/inference_wan_i2v.yaml",
      "command": "fastvideo generate --config scripts/inference/inference_wan_i2v.yaml",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 2, "evidence": "source-configured"},
      "evidence": "Source-backed"
    },
    {
      "id": "wan22-ti2v",
      "family": "wan",
      "stage": "inference",
      "task": "Text or image to video",
      "label": "Wan2.2 TI2V 5B",
      "summary": "Use one maintained 5B checkpoint for text-to-video or add an image input to switch the same recipe to image-to-video.",
      "model": "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
      "source": "examples/inference/basic/basic_wan2_2_ti2v.py",
      "command": "python examples/inference/basic/basic_wan2_2_ti2v.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "MP4 videos under video_samples_wan2_2_5B_ti2v/"
    },
    {
      "id": "fastmetal-1-3b-mlx",
      "family": "wan",
      "stage": "inference",
      "task": "Text to video",
      "label": "FastMetal 1.3B",
      "summary": "Run the released FastMetal 1.3B QAD checkpoint through FastVideo's native Apple Silicon MLX path.",
      "model": "FastVideo/FastMetal-1.3B-QAD",
      "source": "examples/inference/basic/mlx_wan_prompt_to_video.py",
      "command": "hf download FastVideo/FastMetal-1.3B-QAD --local-dir ./FastMetal-1.3B-QAD\npython examples/inference/basic/mlx_wan_prompt_to_video.py --model-root ./FastMetal-1.3B-QAD --mlx-checkpoint ./FastMetal-1.3B-QAD --height 480 --width 832 --num-frames 81 --prompt \"A bird's-eye view of a misty forest valley at dawn.\" --output-path ./outputs/fastmetal_1_3b.mp4",
      "gpu_types": ["Apple Silicon"],
      "hardware": {
        "platform": "mlx",
        "accelerator": "Apple M4 Max",
        "system_memory": "36 GB unified memory",
        "minimum_memory": "16 GB+ unified memory",
        "peak_memory": "3.87 GiB peak MLX memory",
        "evidence": "validated",
        "evidence_url": "https://github.com/hao-ai-lab/FastVideo/pull/1638"
      },
      "evidence": "Verified",
      "expected_artifact": "MP4 video at outputs/fastmetal_1_3b.mp4",
      "modes": ["T2V", "temporal --fast", "spatial --fast-spatial", "two-pass --refine"],
      "limitations": [
        "Native MLX FastMetal T2V. Add --fast for temporal RIFE, --fast-spatial to denoise at half resolution, or --refine for a two-pass upsample. basic_mps.py is the older PyTorch MPS demo."
      ]
    },
    {
      "id": "fastmetal-5b-mlx",
      "family": "wan",
      "stage": "inference",
      "task": "Text to video",
      "label": "FastMetal 5B",
      "summary": "Run the released Wan2.2 5B FastMetal checkpoint as MLX T2V with MLX DiT denoising and MLX TAEHV decode. The CUDA Wan2.2 TI2V 5B recipe is the image-capable path.",
      "model": "FastVideo/FastMetal-5B-QAD",
      "source": "examples/inference/basic/mlx_wan22_generate.py",
      "command": "hf download FastVideo/FastMetal-5B-QAD --local-dir ./FastMetal-5B-QAD\npython examples/inference/basic/mlx_wan22_generate.py --mlx-checkpoint ./FastMetal-5B-QAD --text-encoder-root ./FastMetal-5B-QAD --vae-root ./FastMetal-5B-QAD/vae --height 704 --width 1280 --num-frames 81 --prompt \"A cinematic portrait with soft neon lighting and smooth camera motion.\" --output-path ./outputs/fastmetal_5b.mp4",
      "gpu_types": ["Apple Silicon"],
      "hardware": {
        "platform": "mlx",
        "accelerator": "Apple M4 Max",
        "system_memory": "36 GB unified memory",
        "minimum_memory": "16 GB+ unified memory",
        "peak_memory": "9.34 GiB peak MLX memory",
        "evidence": "validated",
        "evidence_url": "https://github.com/hao-ai-lab/FastVideo/pull/1638"
      },
      "evidence": "Verified",
      "expected_artifact": "MP4 video at outputs/fastmetal_5b.mp4",
      "modes": ["T2V", "temporal --fast", "spatial --fast-spatial", "two-pass --refine"],
      "limitations": [
        "The checked-in MLX example is T2V. Image-to-video is not in mlx_wan22_generate.py. Add --fast, --fast-spatial, or --refine on the same script."
      ]
    },
    {
      "id": "fastmetal-14b-mlx",
      "family": "wan",
      "stage": "inference",
      "task": "Text to video",
      "label": "FastMetal 14B",
      "summary": "Run the released 14B FastMetal QAD checkpoint through the same Apple Silicon MLX entrypoint as the 1.3B release.",
      "model": "FastVideo/FastMetal-14B-QAD",
      "source": "examples/inference/basic/mlx_wan_prompt_to_video.py",
      "command": "hf download FastVideo/FastMetal-14B-QAD --local-dir ./FastMetal-14B-QAD\npython examples/inference/basic/mlx_wan_prompt_to_video.py --model-root ./FastMetal-14B-QAD --mlx-checkpoint ./FastMetal-14B-QAD --height 480 --width 832 --num-frames 81 --prompt \"A wide cinematic landscape at sunrise.\" --output-path ./outputs/fastmetal_14b.mp4",
      "gpu_types": ["Apple Silicon"],
      "hardware": {
        "platform": "mlx",
        "accelerator": "Apple M4 Max",
        "system_memory": "36 GB unified memory",
        "minimum_memory": "36 GB+ unified memory",
        "peak_memory": "21.68 GiB peak MLX memory",
        "evidence": "validated",
        "evidence_url": "https://github.com/hao-ai-lab/FastVideo/pull/1638"
      },
      "evidence": "Verified",
      "expected_artifact": "MP4 video at outputs/fastmetal_14b.mp4",
      "modes": ["T2V", "temporal --fast", "spatial --fast-spatial", "two-pass --refine"],
      "limitations": [
        "Native MLX FastMetal T2V on 36 GB+ unified memory. Same --fast, --fast-spatial, and --refine flags as the 1.3B script."
      ]
    },
    {
      "id": "turbodiffusion-wan21-1-3b-t2v",
      "family": "turbodiffusion",
      "stage": "inference",
      "task": "Text to video",
      "label": "TurboWan2.1 1.3B",
      "summary": "A TurboDiffusion-accelerated Wan2.1 1.3B text-to-video run from its maintained single-GPU example.",
      "model": "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers",
      "source": "examples/inference/basic/basic_turbodiffusion.py",
      "command": "python examples/inference/basic/basic_turbodiffusion.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "MP4 videos under video_samples_turbodiffusion/",
      "related": ["turbodiffusion-wan21-14b-t2v", "turbowan22-i2v"]
    },
    {
      "id": "turbodiffusion-wan21-14b-t2v",
      "family": "turbodiffusion",
      "stage": "inference",
      "task": "Text to video",
      "label": "TurboWan2.1 14B",
      "summary": "TurboDiffusion acceleration applied to the 14B Wan2.1 text-to-video checkpoint; the checked-in source is configured for two GPUs.",
      "model": "loayrashid/TurboWan2.1-T2V-14B-Diffusers",
      "source": "examples/inference/basic/basic_turbodiffusion_14b.py",
      "command": "python examples/inference/basic/basic_turbodiffusion_14b.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 2, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "MP4 videos under video_samples_turbodiffusion_14B/",
      "related": ["turbodiffusion-wan21-1-3b-t2v", "turbowan22-i2v"]
    },
    {
      "id": "turbowan22-i2v",
      "family": "turbodiffusion",
      "stage": "inference",
      "task": "Image to video",
      "label": "TurboWan2.2 A14B",
      "summary": "A one-to-four-step image-to-video path using TurboDiffusion and the SLA attention backend from its maintained example.",
      "model": "loayrashid/TurboWan2.2-I2V-A14B-Diffusers",
      "source": "examples/inference/basic/basic_turbodiffusion_i2v.py",
      "command": "python examples/inference/basic/basic_turbodiffusion_i2v.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 2, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "related": ["turbodiffusion-wan21-14b-t2v"]
    },
    {
      "id": "ltx2-distilled-t2v",
      "family": "ltx2",
      "stage": "inference",
      "task": "Text to video",
      "label": "LTX-2 distilled",
      "summary": "The distilled LTX-2 text-to-video checkpoint with audio, from its maintained example. The source is configured for four GPUs.",
      "model": "FastVideo/LTX2-Distilled-Diffusers",
      "source": "examples/inference/basic/basic_ltx2_distilled.py",
      "command": "python examples/inference/basic/basic_ltx2_distilled.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 4, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "MP4 (video with audio) at outputs_video/ltx2_basic/output_ltx2_distilled_t2v.mp4",
      "related": ["ltx23-base-t2v"]
    },
    {
      "id": "ltx23-base-t2v",
      "family": "ltx2",
      "stage": "inference",
      "task": "Text to video",
      "label": "LTX-2 base (1088p)",
      "summary": "Base LTX-2 text-to-video at 1088x1920 using FastVideo default sampling for LTX2 base. The example loads a community Diffusers mirror of the base checkpoint; registered aliases include Lightricks/LTX-2 and FastVideo/LTX2-Diffusers.",
      "model": "Davids048/LTX2-Base-Diffusers",
      "source": "examples/inference/basic/basic_ltx2.py",
      "command": "python examples/inference/basic/basic_ltx2.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "MP4 (video with audio) at outputs_video/ltx2_basic/output_ltx2_base_t2v_1088_1920_1.1.mp4",
      "limitations": ["The maintained example loads the Davids048/LTX2-Base-Diffusers community mirror rather than a Lightricks upstream ID."],
      "related": ["ltx2-distilled-t2v"]
    },
    {
      "id": "hy15-t2v-480p",
      "family": "hunyuan",
      "stage": "inference",
      "task": "Text to video",
      "label": "HunyuanVideo 1.5 480P",
      "summary": "HunyuanVideo 1.5 text-to-video at 480P with CPU offload enabled in the checked-in source for smaller GPUs.",
      "model": "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v",
      "source": "examples/inference/basic/basic_hy15.py",
      "command": "python examples/inference/basic/basic_hy15.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "MP4 videos under video_samples_hy15/",
      "related": ["hy15-1080p-upscale"]
    },
    {
      "id": "hy15-1080p-upscale",
      "family": "hunyuan",
      "stage": "inference",
      "task": "Text to video (upscaled)",
      "label": "HunyuanVideo 1.5 1080P upscale",
      "summary": "Run HunyuanVideo 1.5 through the 480p to 720p to 1080p upscale chain in one maintained script.",
      "model": "weizhou03/HunyuanVideo-1.5-Diffusers-1080p-2SR",
      "source": "examples/inference/basic/basic_hy15_1080p.py",
      "command": "python examples/inference/basic/basic_hy15_1080p.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "MP4 videos under video_samples_hy15_1080p/",
      "related": ["hy15-t2v-480p"]
    },
    {
      "id": "cosmos25-t2w",
      "family": "cosmos",
      "stage": "inference",
      "task": "Text to world",
      "label": "Cosmos Predict 2.5 2B",
      "summary": "Generate a navigable world video from a text prompt with Cosmos Predict 2.5 2B on a single GPU.",
      "model": "KyleShao/Cosmos-Predict2.5-2B-Diffusers",
      "source": "examples/inference/basic/basic_cosmos2_5_t2w.py",
      "command": "python examples/inference/basic/basic_cosmos2_5_t2w.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed"
    },
    {
      "id": "kandinsky5-t2v-lite-sft",
      "family": "kandinsky5",
      "stage": "inference",
      "task": "Text to video",
      "label": "Kandinsky 5.0 T2V Lite SFT",
      "summary": "Kandinsky 5.0 text-to-video (Lite SFT variant) from the maintained example; alternative Lite/Pro checkpoints are listed in the source.",
      "model": "kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers",
      "source": "examples/inference/basic/basic_kandinsky5_t2v.py",
      "command": "python examples/inference/basic/basic_kandinsky5_t2v.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {
        "platform": "cuda",
        "gpu_count": 1,
        "accelerator": "NVIDIA B200",
        "evidence": "validated",
        "evidence_url": "https://github.com/hao-ai-lab/FastVideo/pull/1471"
      },
      "evidence": "Verified",
      "expected_artifact": "MP4 videos under video_samples_kandinsky5_t2v/",
      "related": ["kandinsky5-i2v-pro-distilled"]
    },
    {
      "id": "kandinsky5-i2v-pro-distilled",
      "family": "kandinsky5",
      "stage": "inference",
      "task": "Image to video",
      "label": "Kandinsky 5.0 I2V Pro distilled",
      "summary": "Animate an input image with Kandinsky 5.0 I2V Pro (distilled) on a single GPU.",
      "model": "kandinskylab/Kandinsky-5.0-I2V-Pro-distilled-5s-Diffusers",
      "source": "examples/inference/basic/basic_kandinsky5_i2v.py",
      "command": "python examples/inference/basic/basic_kandinsky5_i2v.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {
        "platform": "cuda",
        "gpu_count": 1,
        "accelerator": "NVIDIA B200",
        "peak_memory": "10,365.89 MB peak GPU memory",
        "evidence": "validated",
        "evidence_url": "https://github.com/hao-ai-lab/FastVideo/pull/1471"
      },
      "evidence": "Verified",
      "expected_artifact": "MP4 videos under video_samples_kandinsky5_i2v/",
      "related": ["kandinsky5-t2v-lite-sft"]
    },
    {
      "id": "flux2-klein-t2i",
      "family": "flux",
      "stage": "inference",
      "task": "Text to image",
      "label": "FLUX.2 Klein 4B",
      "summary": "Generate an image in four denoising steps with the distilled FLUX.2 Klein checkpoint.",
      "model": "black-forest-labs/FLUX.2-klein-4B",
      "source": "examples/inference/basic/basic_flux2_klein.py",
      "command": "python examples/inference/basic/basic_flux2_klein.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "PNG image at outputs/flux2/flux2_klein.png",
      "related": ["flux2-dev-t2i"]
    },
    {
      "id": "flux2-dev-t2i",
      "family": "flux",
      "stage": "inference",
      "task": "Text to image",
      "label": "FLUX.2 dev",
      "summary": "Full FLUX.2 dev text-to-image with embedded guidance and the Mistral3 text encoder, from its maintained example.",
      "model": "black-forest-labs/FLUX.2-dev",
      "source": "examples/inference/basic/basic_flux2.py",
      "command": "python examples/inference/basic/basic_flux2.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "PNG image at outputs/flux2/flux2.png",
      "related": ["flux2-klein-t2i"]
    },
    {
      "id": "flux1-dev-t2i",
      "family": "flux",
      "stage": "inference",
      "task": "Text to image",
      "label": "FLUX.1 dev",
      "summary": "FLUX.1 dev text-to-image through the Diffusers-backed pipeline. The example defaults to a local weights directory, so this recipe passes the Hugging Face ID explicitly.",
      "model": "black-forest-labs/FLUX.1-dev",
      "source": "examples/inference/basic/basic_flux_dev.py",
      "command": "python examples/inference/basic/basic_flux_dev.py --model-path black-forest-labs/FLUX.1-dev",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "PNG images under outputs/flux_dev/samples/",
      "limitations": ["FLUX.1 is loadable by ID but registers no model_family in fastvideo/registry.py; it is grouped under FLUX for documentation only."]
    },
    {
      "id": "glm-image-t2i",
      "family": "glm_image",
      "stage": "inference",
      "task": "Text to image",
      "label": "GLM-Image",
      "summary": "GLM-Image text-to-image generation from its maintained example.",
      "model": "zai-org/GLM-Image",
      "source": "examples/inference/basic/basic_glm_image.py",
      "command": "python examples/inference/basic/basic_glm_image.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "PNG image at image_output/landscape.png",
      "related": ["glm-image-edit"]
    },
    {
      "id": "glm-image-edit",
      "family": "glm_image",
      "stage": "inference",
      "task": "Image editing",
      "label": "GLM-Image editing",
      "summary": "Edit an input image with an instruction prompt using GLM-Image, from its maintained editing example.",
      "model": "zai-org/GLM-Image",
      "source": "examples/inference/basic/edit_glm_image.py",
      "command": "python examples/inference/basic/edit_glm_image.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "PNG image at image_output/edited.png (input: assets/images/couple.jpg)",
      "related": ["glm-image-t2i"]
    },
    {
      "id": "zimage-turbo-t2i",
      "family": "zimage",
      "stage": "inference",
      "task": "Text to image",
      "label": "Z-Image Turbo",
      "summary": "Z-Image Turbo text-to-image on a single GPU from its maintained example.",
      "model": "Tongyi-MAI/Z-Image-Turbo",
      "source": "examples/inference/basic/basic_zimage.py",
      "command": "python examples/inference/basic/basic_zimage.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "PNG image at outputs/zimage/zimage_turbo.png"
    },
    {
      "id": "sd35-medium-t2i",
      "family": "sd35",
      "stage": "inference",
      "task": "Text to image",
      "label": "Stable Diffusion 3.5 Medium",
      "summary": "Stable Diffusion 3.5 Medium text-to-image over a small built-in prompt set, from its maintained example.",
      "model": "stabilityai/stable-diffusion-3.5-medium",
      "source": "examples/inference/basic/basic_sd35_t2i.py",
      "command": "python examples/inference/basic/basic_sd35_t2i.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "PNG images under outputs/sd35/samples/"
    },
    {
      "id": "minimax-h3-t2v",
      "family": "minimax_h3",
      "stage": "inference",
      "task": "Text to video (with audio)",
      "label": "MiniMax H3 T2VA",
      "summary": "Generate synchronized video and stereo audio from a structured text prompt with the full MiniMax H3 checkpoint.",
      "model": "MiniMaxAI/MiniMax-H3",
      "source": "examples/inference/basic/basic_minimax_h3_t2v.py",
      "command": "python examples/inference/basic/basic_minimax_h3_t2v.py --prompt \"(S1) A presenter says <d>[English] FastVideo runs MiniMax H3.</d>\"",
      "gpu_types": ["NVIDIA"],
      "hardware": {"platform": "cuda", "gpu_count": 4, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "MP4 with synchronized audio at outputs/minimax_h3_t2v/minimax_h3_t2v.mp4",
      "modes": ["T2VA"],
      "knobs": [
        {"key": "num_gpus", "label": "GPUs", "hint": "Sequence-parallel degree", "flag": "--num-gpus", "options": [1, 2, 4, 8], "default": 4}
      ],
      "limitations": ["The checked-in example defaults to four-way sequence parallelism. It does not record a GPU model or memory requirement."]
    },
    {
      "id": "fasth3-preview-cuda",
      "group": "fasth3-preview",
      "group_label": "FastH3 Preview",
      "group_task": "4-step text to video + audio",
      "family": "minimax_h3",
      "stage": "inference",
      "task": "Few-step text to video (with audio)",
      "label": "FastH3 Preview on CUDA",
      "summary": "Run the DMD2-distilled FastH3 Preview with four DiT forwards, trained H3 sparse attention, compiled decode, and synchronized audio.",
      "model": "FastVideo/FastVideo-Minimax-FastH3-Preview-v0.2",
      "source": "examples/inference/basic/basic_fasth3.py",
      "serving": {
        "source": "examples/serving/openai_fasth3.yaml",
        "install": "UV_TORCH_BACKEND=cu130 uv pip install -e \".[fasth3]\""
      },
      "command": "UV_TORCH_BACKEND=cu130 uv pip install -e \".[fasth3]\"\npython examples/inference/basic/basic_fasth3.py --prompt \"(S1) A presenter says <d>[English] FastVideo runs FastH3.</d>\" --profile all",
      "gpu_types": ["NVIDIA"],
      "hardware": {
        "platform": "cuda",
        "gpu_count": 4,
        "accelerator": "NVIDIA GB200",
        "evidence": "validated",
        "evidence_url": "https://github.com/hao-ai-lab/FastVideo/pull/1731"
      },
      "evidence": "Verified",
      "expected_artifact": "Warmup and measured MP4 files under outputs/fasth3/",
      "modes": ["T2VA", "4-step FastH3"],
      "knobs": [
        {"key": "num_gpus", "label": "GPUs", "hint": "Sequence-parallel degree", "flag": "--num-gpus", "options": [1, 2, 4, 8], "default": 4},
        {"key": "video_decode_backend", "label": "VAE decode", "hint": "Fidelity vs. speed", "flag": "--video-decode-backend", "options": [{"value": "h3-vae", "label": "Full H3 VAE"}, {"value": "taeh3", "label": "TAEH3 preview"}], "default": "h3-vae"}
      ],
      "limitations": ["The default all profile is the measured GB200 performance route and can change floating-point operation order. Use --profile strict --no-inference-torch-compile for the eager strict route."]
    },
    {
      "id": "fasth3-preview-mlx",
      "serving": {
        "source": "examples/serving/mlx_fasth3.yaml",
        "install": "uv pip install -e \".[mlx]\"",
        "prepare": "hf download FastVideo/FastVideo-Minimax-FastH3-Preview-v0.2 --local-dir ./FastH3-Preview-v0.2\npython scripts/checkpoint_conversion/convert_minimax_h3_mlx.py --model-root ./FastH3-Preview-v0.2/transformer --out ./FastH3-MLX --formats \"int6\""
      },
      "group": "fasth3-preview",
      "group_label": "FastH3 Preview",
      "group_task": "4-step text to video + audio",
      "family": "minimax_h3",
      "stage": "inference",
      "task": "Few-step text to video (with audio)",
      "label": "FastH3 Preview on MLX",
      "summary": "Run FastH3 Preview on Apple Silicon with a locally converted INT6 DiT, streamed Qwen3-VL conditioning, and native MLX video and audio VAEs.",
      "model": "FastVideo/FastVideo-Minimax-FastH3-Preview-v0.2",
      "source": "examples/inference/basic/mlx_fasth3.py",
      "command": "hf download FastVideo/FastVideo-Minimax-FastH3-Preview-v0.2 --local-dir ./FastH3-Preview-v0.2\npython scripts/checkpoint_conversion/convert_minimax_h3_mlx.py --model-root ./FastH3-Preview-v0.2/transformer --out ./FastH3-MLX --formats \"int6\"\npython examples/inference/basic/mlx_fasth3.py --model-root ./FastH3-Preview-v0.2 --mlx-checkpoint ./FastH3-MLX/int6 --prompt \"(S1) A presenter says <d>[English] FastVideo runs FastH3.</d>\" --height 480 --width 832 --num-frames 124 --seed 2026 --output-path ./outputs/fasth3_int6.mp4",
      "gpu_types": ["Apple Silicon"],
      "hardware": {
        "platform": "mlx",
        "accelerator": "Apple M4 Max",
        "system_memory": "36 GB unified memory",
        "peak_memory": "19.63 GiB peak MLX memory during denoising",
        "evidence": "validated",
        "evidence_url": "https://github.com/hao-ai-lab/FastVideo/pull/1770"
      },
      "evidence": "Verified",
      "expected_artifact": "MP4 with H.264 video and stereo AAC audio at outputs/fasth3_int6.mp4",
      "modes": ["T2VA", "temporal --fast", "spatial --fast-spatial", "opt-in VSA"],
      "knobs": [
        {"key": "video_decode_backend", "label": "VAE decode", "hint": "Fidelity vs. speed", "flag": "--video-decode-backend", "options": [{"value": "h3-vae", "label": "Full H3 VAE"}, {"value": "taeh3", "label": "TAEH3 preview"}], "default": "h3-vae"}
      ],
      "limitations": [
        "The MLX path supports T2VA, optional temporal --fast, optional spatial --fast-spatial, and opt-in VSA on --include-vsa checkpoints. FL2VA, Ref2VA, and two-pass refinement are not wired."
      ]
    },
    {
      "id": "minimax-h3-fl2va",
      "family": "minimax_h3",
      "stage": "inference",
      "task": "First/last frame to video (with audio)",
      "label": "MiniMax H3 FL2VA",
      "summary": "Animate a first frame, optionally guide the final frame, and generate synchronized audio with the full MiniMax H3 checkpoint.",
      "model": "MiniMaxAI/MiniMax-H3",
      "source": "examples/inference/basic/basic_minimax_h3_fl2va.py",
      "command": "python examples/inference/basic/basic_minimax_h3_fl2va.py --image path/to/first-frame.png --prompt \"(S1) The subject turns toward the camera and says <d>[English] Hello.</d>\"",
      "gpu_types": ["NVIDIA"],
      "hardware": {"platform": "cuda", "gpu_count": 4, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "MP4 with synchronized audio at outputs/minimax_h3_fl2va/minimax_h3_fl2va.mp4",
      "modes": ["FL2VA"],
      "knobs": [
        {"key": "num_gpus", "label": "GPUs", "hint": "Sequence-parallel degree", "flag": "--num-gpus", "options": [1, 2, 4, 8], "default": 4}
      ],
      "limitations": ["Pass --last-image to constrain the final frame. The checked-in source defaults to four GPUs."]
    },
    {
      "id": "minimax-h3-ref2va",
      "family": "minimax_h3",
      "stage": "inference",
      "task": "Reference media to video (with audio)",
      "label": "MiniMax H3 Ref2VA",
      "summary": "Condition H3 on an ordered reference video and optional audio reference, then generate a new synchronized video and audio result.",
      "model": "MiniMaxAI/MiniMax-H3",
      "source": "examples/inference/basic/basic_minimax_h3_ref2va.py",
      "command": "python examples/inference/basic/basic_minimax_h3_ref2va.py --reference-video path/to/reference.mp4 --prompt \"Create a new scene that preserves the reference identity and motion language.\"",
      "gpu_types": ["NVIDIA"],
      "hardware": {"platform": "cuda", "gpu_count": 4, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "MP4 with synchronized audio at outputs/minimax_h3_ref2va/minimax_h3_ref2va.mp4",
      "modes": ["Ref2VA"],
      "knobs": [
        {"key": "num_gpus", "label": "GPUs", "hint": "Sequence-parallel degree", "flag": "--num-gpus", "options": [1, 2, 4, 8], "default": 4}
      ],
      "limitations": ["Pass --reference-audio for an additional audio reference. The checked-in source defaults to four GPUs."]
    },
    {
      "id": "fasth3-lora-preview",
      "family": "minimax_h3",
      "stage": "inference",
      "task": "LoRA-adapted few-step video (with audio)",
      "label": "FastH3 LoRA Preview",
      "summary": "Apply a FastH3 preview adapter at load time while keeping the shared four-forward performance profile and synchronized audio output.",
      "model": "MiniMaxAI/MiniMax-H3",
      "source": "examples/inference/basic/basic_fasth3_lora_preview.py",
      "command": "python examples/inference/basic/basic_fasth3_lora_preview.py --lora-path path/to/adapter.safetensors --prompt \"(S1) A presenter says <d>[English] This is an adapted Fast H3 run.</d>\"",
      "gpu_types": ["NVIDIA"],
      "hardware": {
        "platform": "cuda",
        "gpu_count": 4,
        "accelerator": "NVIDIA B200",
        "evidence": "validated",
        "evidence_url": "https://github.com/hao-ai-lab/FastVideo/pull/1771"
      },
      "evidence": "Verified",
      "expected_artifact": "Warmup and measured MP4 files under outputs/fasth3_lora_preview/",
      "modes": ["T2VA", "FastH3 LoRA"],
      "knobs": [
        {"key": "num_gpus", "label": "GPUs", "hint": "Sequence-parallel degree", "flag": "--num-gpus", "options": [1, 2, 4, 8], "default": 4},
        {"key": "video_decode_backend", "label": "VAE decode", "hint": "Fidelity vs. speed", "flag": "--video-decode-backend", "options": [{"value": "h3-vae", "label": "Full H3 VAE"}, {"value": "taeh3", "label": "TAEH3 preview"}], "default": "h3-vae"}
      ],
      "limitations": ["Supply a compatible FastH3 adapter. The script infers dense or VSA attention from the adapter payload unless you override it."]
    },
    {
      "id": "longcat-t2v",
      "family": "longcat",
      "stage": "inference",
      "task": "Text to video",
      "label": "LongCat Video T2V",
      "summary": "LongCat Video text-to-video at 480p (50 steps), with distilled and 720p refinement passes included in the same maintained script.",
      "model": "FastVideo/LongCat-Video-T2V-Diffusers",
      "source": "examples/inference/basic/basic_longcat_t2v.py",
      "command": "python examples/inference/basic/basic_longcat_t2v.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "MP4 videos under outputs_video/longcat_t2v_basic/, longcat_t2v_distill/, and longcat_t2v_refine_720p/",
      "related": ["longcat-i2v"]
    },
    {
      "id": "longcat-i2v",
      "family": "longcat",
      "stage": "inference",
      "task": "Image to video",
      "label": "LongCat Video I2V",
      "summary": "LongCat Video image-to-video with optional distilled and refinement passes, from its maintained example.",
      "model": "FastVideo/LongCat-Video-I2V-Diffusers",
      "source": "examples/inference/basic/basic_longcat_i2v.py",
      "command": "python examples/inference/basic/basic_longcat_i2v.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "MP4 videos under outputs_video/longcat_i2v_basic/ and longcat_i2v_distill/",
      "related": ["longcat-t2v"]
    },
    {
      "id": "stable-audio-open-t2a",
      "family": "stable_audio",
      "stage": "inference",
      "task": "Text to audio",
      "label": "Stable Audio Open 1.0",
      "summary": "Six-second text-to-audio generation with Stable Audio Open 1.0 from its maintained example; duration and steps are documented knobs in the source.",
      "model": "FastVideo/stable-audio-open-1.0-Diffusers",
      "source": "examples/inference/basic/basic_stable_audio.py",
      "command": "python examples/inference/basic/basic_stable_audio.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {
        "platform": "cuda",
        "gpu_count": 1,
        "accelerator": "NVIDIA B200",
        "evidence": "validated",
        "evidence_url": "https://github.com/hao-ai-lab/FastVideo/pull/1260"
      },
      "evidence": "Verified",
      "expected_artifact": "WAV audio at outputs_audio/stable_audio_basic/output_stable_audio.wav",
      "limitations": ["Must load the FastVideo converted Diffusers repo; upstream stabilityai monolithic checkpoints are not loader-compatible (see scripts/checkpoint_conversion/stable_audio_to_diffusers.py)."],
      "related": ["stable-audio-small-t2a"]
    },
    {
      "id": "stable-audio-small-t2a",
      "family": "stable_audio",
      "stage": "inference",
      "task": "Text to audio",
      "label": "Stable Audio Open Small",
      "summary": "The smaller Stable Audio Open variant with its own shorter training window, from its maintained example.",
      "model": "FastVideo/stable-audio-open-small-Diffusers",
      "source": "examples/inference/basic/basic_stable_audio_small.py",
      "command": "python examples/inference/basic/basic_stable_audio_small.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "related": ["stable-audio-open-t2a"]
    },
    {
      "id": "mmaudio-v2a",
      "family": "mmaudio",
      "stage": "inference",
      "task": "Video/Text to audio",
      "label": "MMAudio large 44k v2",
      "summary": "Add synchronized audio to a video (or from a prompt) with MMAudio large 44k v2. The example reads the model path from MMAUDIO_MODEL_PATH; this recipe passes the converted Hugging Face repo explicitly.",
      "model": "FastVideo/MMAudio-large-44k-v2-Diffusers",
      "source": "examples/inference/basic/basic_mmaudio.py",
      "command": "MMAUDIO_MODEL_PATH=FastVideo/MMAudio-large-44k-v2-Diffusers python examples/inference/basic/basic_mmaudio.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "limitations": ["The upstream checkpoint must be converted to Diffusers layout via scripts/checkpoint_conversion/convert_mmaudio_to_diffusers.py unless loaded from the FastVideo converted repo as done here."]
    },
    {
      "id": "fasth3-spark-pair",
      "family": "minimax_h3",
      "stage": "inference",
      "task": "Few-step text to video (with audio)",
      "label": "FastH3 on two DGX Sparks (sequence parallel)",
      "summary": "Run one FastH3 clip across two GB10s with Ray sequence parallel over QSFP RoCE. Sequential load and lazy module load stay on because SP replicates the DiT on each node.",
      "model": "FastVideo/FastVideo-FastH3-4-step-Preview-v1-VSA-DataFree",
      "source": "examples/inference/basic/basic_fasth3_spark_pair.yaml",
      "command": "source examples/inference/optimizations/spark_pair_env.sh && FASTVIDEO_VSA_SM100A=0 FASTVIDEO_FA4=0 FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN_H3 FASTVIDEO_VAE_PARALLEL_DECODE=1 fastvideo generate --config examples/inference/basic/basic_fasth3_spark_pair.yaml",
      "gpu_types": ["NVIDIA"],
      "hardware": {
        "platform": "cuda",
        "gpu_count": 2,
        "accelerator": "NVIDIA GB10 (DGX Spark pair)",
        "evidence": "validated",
        "evidence_url": "https://github.com/hao-ai-lab/FastVideo/pull/1803"
      },
      "evidence": "Verified",
      "expected_artifact": "MP4 under outputs/fasth3_spark_pair/",
      "modes": ["T2VA", "2-Spark SP"],
      "limitations": ["Requires a two-node Ray cluster on the QSFP interconnect. Height, width, frames, and steps in the YAML are examples. Edit them or pass CLI flags. See docs/getting_started/installation/spark_pair.md."]
    },
    {
      "id": "matrix-game-2",
      "family": "matrixgame",
      "stage": "inference",
      "task": "Interactive world",
      "label": "Matrix Game 2.0",
      "summary": "Generate an interactive-world sequence from the maintained Matrix Game 2.0 example.",
      "model": "FastVideo/Matrix-Game-2.0-Base-Distilled-Diffusers",
      "source": "examples/inference/basic/basic_matrixgame2.py",
      "command": "python examples/inference/basic/basic_matrixgame2.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "related": ["matrix-game-3-i2w"]
    },
    {
      "id": "matrix-game-3-i2w",
      "family": "matrixgame",
      "stage": "inference",
      "task": "Interactive world",
      "label": "Matrix Game 3.0",
      "summary": "Drive Matrix Game 3.0 from an input image plus prompt at 720p, three steps, from its maintained example.",
      "model": "FastVideo/Matrix-Game-3.0-Base-Distilled-Diffusers",
      "source": "examples/inference/basic/basic_matrixgame3.py",
      "command": "python examples/inference/basic/basic_matrixgame3.py",
      "gpu_types": ["NVIDIA"],
      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
      "evidence": "Source-backed",
      "expected_artifact": "MP4 videos under video_samples_matrixgame3/",
      "related": ["matrix-game-2"]
    }
  ]
}