Instructions to use armhebb/sample_lora_train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use armhebb/sample_lora_train with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("armhebb/sample_lora_train") 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
metadata
license: creativeml-openrail-m
dataset: None
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
LoRA text2image fine-tuning - armhebb/lora_license-id_style-name
These are LoRA adaption weights for /sdxl_j. The weights were fine-tuned on the None dataset. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.