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
Download examples/04_rain_market_cross_section_fp8.png from WaveCut/Cosmos3-Super-Text2Image-Quanto-FP8-Transformer: direct link, hf CLI and curl.
- Browser
- Download file 1.69 MB
-
https://huggingface.co/WaveCut/Cosmos3-Super-Text2Image-Quanto-FP8-Transformer/resolve/main/examples/04_rain_market_cross_section_fp8.png
- Command line
-
hf download hf://WaveCut/Cosmos3-Super-Text2Image-Quanto-FP8-Transformer/examples/04_rain_market_cross_section_fp8.png
-
curl -L -o 04_rain_market_cross_section_fp8.png https://huggingface.co/WaveCut/Cosmos3-Super-Text2Image-Quanto-FP8-Transformer/resolve/main/examples/04_rain_market_cross_section_fp8.png
1.69 MB

- Xet hash:
- 909b40dee62da4ceaf95711c3c5702c0f1901c89cab558a35cc38c5194b2ea6e
- Size of remote file:
- 1.69 MB
- SHA256:
- d5d4ed1c526a6dbc551484a83fcad6252c942dadcccf5c187dc893d1fda21978
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