Diffusers
Safetensors
CogVideoXPipeline
diffusion
video-generation
multi-scene
autoregressive
transformer
computer-vision
cvpr2025
Instructions to use qth/Mask2DiT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use qth/Mask2DiT with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("qth/Mask2DiT", 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
- Xet hash:
- 68443050a0cdf08a6d0eaf7a73c9d34866ccc99cc885453bcc2e5b31cba73245
- Size of remote file:
- 406 kB
- SHA256:
- 2f65e63bd92ae9376368e2c115670b2cd823f430ee4b8f4499c117f82d092ae7
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