Instructions to use H-oliday/SwiftVR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use H-oliday/SwiftVR with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("H-oliday/SwiftVR", 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
update readme: Add LightX2V to Community Works
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by ZhangBilang - opened
README.md
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- [2026/06] Release the inference code and pretrained weights 🎉
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- [2026/06] Release the inference code and pretrained weights 🎉
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## Community Works
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* [LightX2V](https://github.com/ModelTC/LightX2V) brings **faster inference and lower GPU memory usage** to SwiftVR (**1.91× speedup and 65.71% lower peak GPU memory per request** on a single H100). It also supports **multi-GPU acceleration**. Ready-to-use scripts cover image and video super-resolution, offline inference, and API serving. **[Get started with LightX2V →](https://github.com/ModelTC/LightX2V/tree/main/scripts/swiftvr)**
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