Instructions to use amd/FLUX.1-dev_io32_amdgpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use amd/FLUX.1-dev_io32_amdgpu with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("amd/FLUX.1-dev_io32_amdgpu", 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/model.onnx.data from amd/FLUX.1-dev_io32_amdgpu: direct link, hf CLI and curl.
- Browser
- Download file 246 MB
-
https://huggingface.co/amd/FLUX.1-dev_io32_amdgpu/resolve/main/text_encoder/model.onnx.data
- Command line
-
hf download hf://amd/FLUX.1-dev_io32_amdgpu/text_encoder/model.onnx.data
-
curl -L -o model.onnx.data https://huggingface.co/amd/FLUX.1-dev_io32_amdgpu/resolve/main/text_encoder/model.onnx.data
246 MB
- Xet hash:
- c9cd1ee53440e3d7b6c8029ffd97f85fdf7c11bfc3f9ee4ee9005ac2dbf74cf3
- Size of remote file:
- 246 MB
- SHA256:
- d2015263f98ae9f6e0ea233756bd5ea51ddb1f36ec6a2dd862e59d82611d1db7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.