Instructions to use Shibaking/tyler-LoRa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Shibaking/tyler-LoRa 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("Shibaking/tyler-LoRa") prompt = "Tyler" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from Shibaking/tyler-LoRa: direct link, hf CLI and curl.
- Browser
- Download file 575 Bytes
-
https://huggingface.co/Shibaking/tyler-LoRa/resolve/main/README.md
- Command line
-
hf download hf://Shibaking/tyler-LoRa/README.md
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curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/Shibaking/tyler-LoRa/resolve/main/README.md
575 Bytes
metadata
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: undefined
instance_prompt: Tyler
license: other
tyler LoRa
Model description
Tyler Lora
Trigger words
You should use Tyler 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-portrait-trainer.