Instructions to use vantagewithai/FastVideo-FastH3-8-Step-V2-ComfyUI-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vantagewithai/FastVideo-FastH3-8-Step-V2-ComfyUI-GGUF with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("vantagewithai/FastVideo-FastH3-8-Step-V2-ComfyUI-GGUF", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("vantagewithai/FastVideo-FastH3-8-Step-V2-ComfyUI-GGUF", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]GGUF quants of FastVideo-FastH3-8-Step-V2 for ComfyUI.
Original model repository: https://huggingface.co/FastVideo/FastVideo-FastH3-8-Step-V2
Watch us on Youtube: @VantageWithAI
FastVideo-FastH3-8-Step-V2
The FastH3 8-Step V2 checkpoint from FastVideo. It generates synchronized video and audio from text with eight transformer forwards. This step-1300 model was trained with data-free DMD2 and VSA-H3 at 80% sparsity.
This checkpoint requires FastVideo's VSA-H3 attention backend. Its video scheduler shift is 10, not the base model's 12.
Scope
This checkpoint supports text-to-audio-video generation. FL2VA and Ref2VA were not distilled. Difficult motion, fine detail, and some audio may remain below the base MiniMax H3 model. This checkpoint inherits the MiniMax H3 Community License.
Acknowledgements
We thank Nuva Lab for bringing production grounding to FastH3 through its experience with real-world creative video-agent workloads. Its production-aligned post-training insights help bridge open-source research to practical data-assisted distillation for commercial video workflows, with Omni Ref as the next focus.
We thank the NVIDIA FastGen team for the DMD2 framework and H3 reference experiment that helped us align the score clock, modality shifts, and backward simulation.
We also thank MiniMax for releasing H3-Base, and the vLLM project, NVIDIA, and MBZUAI for their continued sponsorship and support of FastVideo.
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Base model
MiniMaxAI/MiniMax-H3