Text-to-Video
Diffusers
Safetensors
WanPipeline
video-generation
wan2.1
dmd
quantization-aware-training
nvfp4
sparse-attention
Instructions to use memset0/vsqa-preview-14b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use memset0/vsqa-preview-14b-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("memset0/vsqa-preview-14b-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
File size: 837 Bytes
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"output": "/mnt/lustre/vlm-miz055/vsqa-14b/runs/fastvideo-v0403-14b-c256-dmd200-critic2e6-gs1-260924-094401/export-step200",
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