Text-to-Video
Diffusers
Safetensors
WanPipeline
video-generation
wan2.1
dmd
quantization-aware-training
nvfp4
sparse-attention
Instructions to use memset0/vsqa-preview-14b-200steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use memset0/vsqa-preview-14b-200steps 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-200steps", 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: 811 Bytes
ce069e0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"status": "passed_strict_reload_and_manifest",
"step": 200,
"checkpoint": "/mnt/lustre/vlm-miz055/vsqa-14b/runs/fastvideo-v0362-14b-c256-dmd1000-260922-123321/checkpoints/checkpoint-200",
"output": "/mnt/lustre/vlm-miz055/vsqa-14b/runs/fastvideo-v0362-14b-c256-dmd1000-260922-123321/export-step200",
"student_dcp_keys": 1175,
"elapsed_sec": 234.01916241645813,
"exporter_sha256": "d688074bb5aa84c944ca7b30be8b9b5a9263d389570135951c96528ae654dc31",
"raw_metadata_sha256": "52f6aa570252b124318ee8547e86e78212d91d840372b9fe440c38ea5a4e9055",
"export_metadata_sha256": "ea927901d98fcc71e1c73dfdcbf1902b60806441551970e5377733d5fc5f615e",
"representation": "native normalized export; original raw metadata preserved",
"native_strict_reload": true,
"native_parent_payload_validation": true
}
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