Instructions to use WaveCut/Cosmos3-Super-Text2Image-SDNQ-Int8-Transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WaveCut/Cosmos3-Super-Text2Image-SDNQ-Int8-Transformer with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WaveCut/Cosmos3-Super-Text2Image-SDNQ-Int8-Transformer", 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
- Local Apps
- Draw Things
- DiffusionBee

- Xet hash:
- 9e0187a16bf0f9da6e86a12c75295d0a1afb80a3877893a8b6b063f890c4af40
- Size of remote file:
- 1.22 MB
- SHA256:
- 2c4ed931255ab651b91247d711cc81e0f08ad34cdcd99448a7e326fd89836792
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