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
- Draw Things
- DiffusionBee
- Xet hash:
- 76cd8e5078061c198b36405b138867ddf5d1293dfd31d5c9f9a0db7e72c7914f
- Size of remote file:
- 246 MB
- SHA256:
- f2d7e83d85c915df4f9f5a04c43dc173c279850fe453e3da8891b5941cd67034
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