Instructions to use dreamcomputing/Flux-Dev-8-step with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dreamcomputing/Flux-Dev-8-step with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dreamcomputing/Flux-Dev-8-step", 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:
- f768552023b894cef92d2ed6a3ccc46a8096508f43c6acc576bc556433dbcba0
- Size of remote file:
- 1.58 GB
- SHA256:
- 1c6534420dda5ca01cad617deb236990771c5e9536fb4d771fea066c77838c86
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