Instructions to use flymin/MagicDrive-t-60f-224x400-80k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use flymin/MagicDrive-t-60f-224x400-80k with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("flymin/MagicDrive-t-60f-224x400-80k", 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
MagicDrive-t
ArXiv | Web | License | GitHub (video-branch)
This repository contains 60-frame driving-view video generation model checkpoint.
- We train this model by loading from the 16-frame 224x400 checkpoint (we interpolate the temporal positional embeddings from 16 to 60), and train for 80k steps.
- Please check
hydra/overrides.yamlfor command overrides in the training config.
MagicDrive: Street View Generation with Diverse 3D Geometry Control
Ruiyuan Gao1*, Kai Chen2*, Enze Xie3^, Lanqing Hong3, Zhenguo Li3, Dit-Yan Yeung2, Qiang Xu1^
1CUHK 2HKUST 3Huawei Noah's Ark Lab
*Equal Contribution ^Corresponding Authors
Generated video A.
More cars in A on the right and front.
Generated video B
Change B to night.
Remove the car on the left in B.
Generated video C.
Remove the car on the right in C.
Change C to a rainy day.
Change C to a rainy day and remove the car on the right.
For more information, please refer to our GitHub: https://github.com/cure-lab/MagicDrive/tree/video (i.e., the video branch of MagicDrive repo)
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