Instructions to use WaveCut/Cosmos3-Super-Text2Image-Quanto-FP8-Transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WaveCut/Cosmos3-Super-Text2Image-Quanto-FP8-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-Quanto-FP8-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

- Xet hash:
- 59456d76cabf444705809d1dd999f982f554d1bfa01fc15f8c68a6a704b9f9a8
- Size of remote file:
- 1.45 MB
- SHA256:
- 8f65440660e972cd94590dee97e5fb696556cafca99f5d03189e2682f990ac3f
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