Text-to-Video
Diffusers
Safetensors
MiniMaxH3ModularPipeline
video-generation
audio
fastvideo
nvfp4
qad
Instructions to use FastVideo/FastH3-QAD-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FastVideo/FastH3-QAD-v2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FastVideo/FastH3-QAD-v2", 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
[docs]: use the FastH3-QAD-v2 name consistently
Browse files
README.md
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base_model: FastVideo/FastVideo-FastH3-8-Step-V2
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tags: [video-generation, text-to-video, audio, fastvideo, nvfp4, qad]
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# FastH3-QAD-v2
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FastH3-QAD-v2 is an 8-step video-and-audio model with NVFP4 weights, trained with **quantization-aware distillation
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(QAD)**. It runs on one Blackwell GPU (RTX 5090, RTX PRO 6000) or a DGX Spark.
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Error against the BF16 teacher on 30 held-out prompts (relative L2 of the x0 prediction, lower is better):
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| Mean (video + audio) | 0.351 |
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| Video | 0.382 |
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base_model: FastVideo/FastVideo-FastH3-8-Step-V2
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tags: [video-generation, text-to-video, audio, fastvideo, nvfp4, qad]
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# FastH3-QAD-v2, 8-step, NVFP4 for one GPU
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FastH3-QAD-v2 is an 8-step video-and-audio model with NVFP4 weights, trained with **quantization-aware distillation
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(QAD)**. It runs on one Blackwell GPU (RTX 5090, RTX PRO 6000) or a DGX Spark.
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Error against the BF16 teacher on 30 held-out prompts (relative L2 of the x0 prediction, lower is better):
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| Metric | FastH3-QAD-v2 |
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| Mean (video + audio) | 0.351 |
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| Video | 0.382 |
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