Instructions to use beckhamc/FLUX.1-dev-qint8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use beckhamc/FLUX.1-dev-qint8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("beckhamc/FLUX.1-dev-qint8", torch_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
- Xet hash:
- ccac74b8fcb22a59b496e190431c07edfd1714064e5e4db819c4069199ee8ce2
- Size of remote file:
- 84.2 MB
- SHA256:
- ea74239a1003a43354c10746ef54380f86ccb49e5b1542e525307f9b1ba75f24
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