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
Commit History
-
Clarify Quanto FP8 quantization
Verified signature3a5ee2a -
Add FP8 stress example gallery
Verified signature0d516b5 -
Update README.md
Verified signature44ed06b -
Add benchmark example images
Verified signaturef147518 -
Add model card with FP8 benchmarks
Verified signature455f5a7 -
Add files using upload-large-folder tool
Verified signature0979f41 -
initial commit
Verified signature9e3f837