Instructions to use socialtrait/flux-3-image-klein-infendpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use socialtrait/flux-3-image-klein-infendpoint with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("socialtrait/flux-3-image-klein-infendpoint", 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 handler.py from socialtrait/flux-3-image-klein-infendpoint: direct link, hf CLI and curl.
- Browser
- Download file 405 Bytes
-
https://huggingface.co/socialtrait/flux-3-image-klein-infendpoint/resolve/main/handler.py
- Command line
-
hf download hf://socialtrait/flux-3-image-klein-infendpoint/handler.py
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curl -L -o handler.py https://huggingface.co/socialtrait/flux-3-image-klein-infendpoint/resolve/main/handler.py
405 Bytes
| """HF Inference Endpoints entry point, same base64 response as the SDXL repo.""" | |
| from flux3_engine import Engine | |
| class EndpointHandler: | |
| def __init__(self, path=''): | |
| self.engine = Engine(model_path=path or None) | |
| self.engine.load() | |
| def __call__(self, data): | |
| # HF and the existing persona caller send the legacy SDXL-shaped payload. | |
| return self.engine.legacy(data) | |