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:
- ec6366c0da2122cc83b8edb3e8a3bd7cedeb06aefcb87efb9a763f81ecfc3099
- Size of remote file:
- 4.99 GB
- SHA256:
- a7390844be24cb8405e5c4888a0d81003ebf634af7b9ed97dc8d9122accd9d0a
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