Instructions to use danigirl/fluxLoraKitLing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use danigirl/fluxLoraKitLing with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("danigirl/fluxLoraKitLing") prompt = "kitling" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from danigirl/fluxLoraKitLing: direct link, hf CLI and curl.
- Browser
- Download file 750 Bytes
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https://huggingface.co/danigirl/fluxLoraKitLing/resolve/main/README.md
- Command line
-
hf download hf://danigirl/fluxLoraKitLing/README.md
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curl -L -o README.md https://huggingface.co/danigirl/fluxLoraKitLing/resolve/main/README.md
750 Bytes
metadata
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: kitling
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
fluxLoraKitLing
Model description
Kit Ling's Flux Dev Lora
Trigger words
You should use kitling 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-lora-fast-training.