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 Settings
- Draw Things
- DiffusionBee
Cosmos3-Super-Text2Image-SDNQ-Int8-Transformer / examples /07_kitchen_storm_chess_table_sdnq_int8.png

- Xet hash:
- 75280d3ac921be5cbc3ca52b0f53a4fc476726163922b8799741a474cbe97d78
- Size of remote file:
- 1.51 MB
- SHA256:
- e57a49220f4786875cd360f191a8cb98d8d0c866c9348b20097296ebbd19c076
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