Instructions to use shaharh/lowpoly with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use shaharh/lowpoly with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("undefined", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("shaharh/lowpoly") prompt = "lowpoly style" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from shaharh/lowpoly: direct link, hf CLI and curl.
- Browser
- Download file 555 Bytes
-
https://huggingface.co/shaharh/lowpoly/resolve/main/README.md
- Command line
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hf download hf://shaharh/lowpoly/README.md
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curl -L -o README.md https://huggingface.co/shaharh/lowpoly/resolve/main/README.md
555 Bytes
metadata
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: undefined
instance_prompt: lowpoly style
license: other
lowpoly
Model description
Trigger words
You should use lowpoly style to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Training at fal.ai
Training was done using fal.ai/models/fal-ai/flux-krea-trainer.