The ComfyUI-native NVFP4 QAD diffusion model for FastH3 Omni. It generates video with synchronized 32-kHz stereo audio from a prompt and ordered image, video and audio references in eight sampling steps. Download a compatible text encoder, video VAE and audio VAE from FastH3 Omni Comfy and place them in `ComfyUI/models/text_encoders/` and `ComfyUI/models/vae/`. The audio VAE and the text encoder's reference-vision support are required. Select a text-encoder format supported by your GPU. Accelerated NVFP4 inference requires NVIDIA Blackwell hardware. Use BF16 or FP8 on other GPU families. Quantized output can differ from BF16. This is the ComfyUI format; use the separate FastVideo-format model for FastVideo inference.
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The compact LoRA releases for FastH3 Preview v1 from FastVideo. Each adapter reconstructs one four-forward FastH3 transformer from the MiniMax H3 base model. > The three VSA adapters require FastVideo's VSA-H3 backend and kernel. Use > FastVideo's launchers rather than a generic PEFT loader; these adapters also > contain exact delta and VSA gate tensors. Install uv, then use the CUDA 13 / Blackwell path below. It selects FastVideo's published CUDA kernel wheel instead of compiling the kernel locally. See the installation guide for other platforms.
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The recommended FastH3 Preview v1 checkpoint from FastVideo. It generates synchronized video and audio from text with four transformer forwards. This step-1300 model was trained with data-free DMD2 and VSA-H3 at 90% sparsity. > This checkpoint requires FastVideo's VSA-H3 attention backend. Use the > matching LoRA above if you prefer to download only the distilled adapter. Install uv, then use the CUDA 13 / Blackwell path below. It selects FastVideo's published CUDA kernel wheel instead of compiling the kernel locally. See the installation guide for other platforms.
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Original model repository: https://huggingface.co/FastVideo/FastVideo-FastH3-8-Step-V2 FastH3 V2 with its transformer quantized to NVFP4, for NVIDIA Blackwell GPUs: RTX 50 series, RTX PRO 6000 and DGX Spark. It is built from `fastvideo_fasth3_8step_v2_pruned_bf16.safetensors` in FastVideo/FastVideo-FastH3-Comfy, which also has the BF16 and INT8 builds for other GPUs. Use the **FastVideo FastH3: Text to Video** template (ComfyUI 0.36.0 or later) and select `fastvideo_fasth3_8step_v2_pruned_nvfp4.safetensors` (13.6 GB) in its `UNETLoader`. Keep the template's sampler settings: 8 steps, `res_multistep`, `simple`, and the `MiniMaxH3SigmaShift` node at 10 (video) and 3 (audio).
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Original model repository: https://huggingface.co/FastVideo/FastVideo-FastH3-8-Step-V2
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Original model repository: https://huggingface.co/FastVideo/FastVideo-FastH3-Trim-8-Step FastH3 Trim is an experimental pruned FastH3 model: 42 transformer blocks instead of 50 and a rank-16 timestep conditioning, sampled in 8 steps. It is 4.2× smaller than base H3 and faster than FastH3 V2. Use the **FastVideo FastH3: Text to Video** template (ComfyUI 0.36.0 or later) and pick one of the files below in its `UNETLoader`. Keep the template's sampler settings: 8 steps, `res_multistep`, `simple`, and the `MiniMaxH3SigmaShift` node at 10 (video) and 3 (audio).
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We're excited to introduce the **FastWan2.2 series**—a new line of models finetuned with our novel **Sparse-distill** strategy. This approach jointly integrates DMD and VSA in a single training process, combining the benefits of both **distillation** to shorten diffusion steps and **sparse attention** to reduce attention computations, enabling even faster video generation. FastWan2.2-TI2V-5B-Full-Diffusers is built upon Wan-AI/Wan2.2-TI2V-5B-Diffusers. It supports efficient **3-step inference** and produces high-quality videos at 121×704×1280 resolution. For training, we used simulated forward for the generator model, making the process data-free. **The current FastWan2.2-TI2V-5B-Full-Diffusers model is trained using only DMD**. - 3-step inference is supported. - Our model is trained on **121×704×1280** resolution, but it supports generating videos with **any resolution**.(quality may degrade) - Finetuning and inference scripts are available in the FastVideo repository: - 1 Node/GPU debugging finetuning script - Slurm training example script - Inference script
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