Instructions to use wavespeed/FLUX.1-dev-e4m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wavespeed/FLUX.1-dev-e4m3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("wavespeed/FLUX.1-dev-e4m3", 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 text_encoder_2/pytorch_model-00001-of-00002.bin from wavespeed/FLUX.1-dev-e4m3: direct link, hf CLI and curl.
- Browser
- Download file 4.99 GB
-
https://huggingface.co/wavespeed/FLUX.1-dev-e4m3/resolve/main/text_encoder_2/pytorch_model-00001-of-00002.bin
- Command line
-
hf download hf://wavespeed/FLUX.1-dev-e4m3/text_encoder_2/pytorch_model-00001-of-00002.bin
-
curl -L -o pytorch_model-00001-of-00002.bin https://huggingface.co/wavespeed/FLUX.1-dev-e4m3/resolve/main/text_encoder_2/pytorch_model-00001-of-00002.bin
4.99 GB
- Xet hash:
- 45212c2b38f363e304c44dfd57c9a20f40f43aaa926fd5c111504bca05026dc9
- Size of remote file:
- 4.99 GB
- SHA256:
- ea05c3e1ed3dd4036f345a0025473924168d214d558eb7e6366b1bac7f6bd35a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.