--- title: FastVideo-FastH3-Omni-8-Step-V1-ComfyUI-GGUF canonical_url: "https://www.modelscope.cn/models/vantagewithai/FastVideo-FastH3-Omni-8-Step-V1-ComfyUI-GGUF" md_url: "https://www.modelscope.cn/models/vantagewithai/FastVideo-FastH3-Omni-8-Step-V1-ComfyUI-GGUF.md" repository: vantagewithai/FastVideo-FastH3-Omni-8-Step-V1-ComfyUI-GGUF last_updated: 2026-10-21 license: other base_model: - MiniMaxAI/MiniMax-H3 base_model_relation: quantized library_name: - gguf - pytorch frameworks: - pytorch downloads: 22 stars: 0 tags: - video-generation - reference-to-video - text-to-audio-video - fastvideo - minimax-h3 - fasth3-omni - gguf --- # FastVideo-FastH3-Omni-8-Step-V1-ComfyUI-GGUF > FastVideo-FastH3-Omni-8-Step-V1-ComfyUI-GGUF - vantagewithai 在 ModelScope 开源的模型。GGUF Quants of FastVideo-FastH3-Omni-8-Step-V1 for ComfyUI. vantagewithai/FastVideo-FastH3-Omni-8-Step-V1-ComfyUI-GGUF 是 ModelScope 魔搭社区上的机器学习模型，采用 other 许可，基于 MiniMaxAI/MiniMax-H3 构建。 - **Repository**: vantagewithai/FastVideo-FastH3-Omni-8-Step-V1-ComfyUI-GGUF - **License**: other - **Base model**: MiniMaxAI/MiniMax-H3 - **Tags**: video-generation, reference-to-video, text-to-audio-video, fastvideo, minimax-h3, fasth3-omni, gguf - **Downloads**: 22 - **Stars**: 0 - **Last updated**: 2026-10-21 Source: https://www.modelscope.cn/models/vantagewithai/FastVideo-FastH3-Omni-8-Step-V1-ComfyUI-GGUF --- **GGUF Quants of FastVideo-FastH3-Omni-8-Step-V1 for ComfyUI.** **Original model repository:** [https://huggingface.co/FastVideo/FastVideo-FastH3-Omni-8-Step-V1](https://huggingface.co/FastVideo/FastVideo-FastH3-Omni-8-Step-V1) **Watch us on Youtube:** [@VantageWithAI](https://www.youtube.com/@vantagewithai) # FastH3 Omni — BF16 Release **This is the final BF16 release checkpoint of FastH3 Omni.** It generates video with synchronized audio from a text prompt and ordered image, video and audio references, using eight transformer evaluations. [All release variants](https://huggingface.co/collections/FastVideo/fasth3-omniref) · [UniServe](https://github.com/hao-ai-lab/uniserve) · [FastVideo](https://github.com/hao-ai-lab/FastVideo) ## Choose a runtime | Hardware | Runtime and setup | | --- | --- | | NVIDIA GB200 / B200 | [UniServe deployment guide](https://github.com/hao-ai-lab/uniserve/blob/main/docs/minimax_h3/minimax_h3.md#deployments) | | NVIDIA RTX PRO 6000 Blackwell | [UniServe deployment guide](https://github.com/hao-ai-lab/uniserve/blob/main/docs/minimax_h3/minimax_h3.md#deployments) | | NVIDIA Hopper, including H100 / H200 | [UniServe requirements and setup](https://github.com/hao-ai-lab/uniserve/blob/main/docs/minimax_h3/minimax_h3.md#requirements) | | NVIDIA DGX Spark (GB10) / RTX 5090 | [FastVideo local FastH3 Omni guide](https://github.com/hao-ai-lab/FastVideo/blob/main/docs/inference/fasth3_omniref.md) with the [NVFP4 QAD variant](https://huggingface.co/FastVideo/FastH3-OmniRef-NVFP4-QAD) | For server deployment, use this BF16 checkpoint with UniServe's reference-conditioned (`ref2va`) configurations. The guide covers installation, model downloads, GPU placement, request capacities and the video/audio API. For the BF16 deployment, choose UniServe's `quality` precision preset. NVFP4 presets require Blackwell hardware. ## Local AI on DGX Spark and RTX 5090 Use **FastVideo** with the [NVFP4 QAD variant](https://huggingface.co/FastVideo/FastH3-OmniRef-NVFP4-QAD) for local inference. QAD means quantization-aware distillation; these weights are trained for NVFP4 execution on Blackwell. Start with the [FastVideo cookbook](https://haoailab.com/FastVideo/cookbook/) and [FastH3 Omni recipe builder](https://haoailab.com/FastVideo/cookbook/minimax-h3/fasth3-omniref/). | Device | Setup and inference recipe | | --- | --- | | DGX Spark (GB10, 128 GB unified memory) | [Install FastVideo](https://haoailab.com/FastVideo/getting_started/installation/spark/) · [`spark` recipe](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_fasth3_omniref_spark.yaml) | | RTX 5090 (32 GB) | [Install FastVideo](https://haoailab.com/FastVideo/getting_started/installation/gpu/) · [`rtx-5090` recipe](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_fasth3_omniref_rtx5090.yaml) | Both profiles default to 480p. Spark keeps the model components resident; the RTX 5090 offloads the text encoder and requires a precomputed AdaLN table. Follow the [local inference guide](https://github.com/hao-ai-lab/FastVideo/blob/main/docs/inference/fasth3_omniref.md) to compose the model directory, prepare the table and choose a decoder. Keep the device recipe's attention and precision settings. For FP8, see the [FastH3 Omni FP8 weights](https://huggingface.co/FastVideo/FastH3-OmniRef-FP8) and [FP8 device setup](https://github.com/hao-ai-lab/FastVideo/blob/main/docs/inference/fasth3_omniref.md#device-profiles), including the [RTX 4090 recipe](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_fasth3_omniref_rtx4090.yaml) for hardware without FP4 support. Its bundled NVFP4 text encoder requires Blackwell; non-FP4 hardware needs compatible BF16 encoder components. FP8 and NVFP4 QAD outputs can differ from BF16. The optional [NVFP4 VAE decoders](https://huggingface.co/FastVideo/FastH3-NVFP4-VAE-Decoder) offer 36-layer quality and 8-layer fast tiers. The guide documents decoder selection with `--decoder`. These local variants have their own memory and precision settings. For visual workflows, use the [ComfyUI variants](https://huggingface.co/FastVideo/FastH3-OmniRef-Comfy). ## Inputs and outputs - **Inputs:** a text prompt plus up to nine images, three videos and three audio clips, with at most twelve references in total. At least one image or video is required. - **Reference order:** use ``, `