Instructions to use SedatAl/Crypto-Punk-LoRa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SedatAl/Crypto-Punk-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("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("SedatAl/Crypto-Punk-LoRa") prompt = "pixel art cryptopunks, human, female, medium skin tone, Big Shades, Crazy Hair" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
metadata
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: creativeml-openrail-m
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
inference: true
widget:
- text: >-
pixel art cryptopunks, human, female, medium skin tone, Big Shades, Crazy
Hair
output:
url: images/example_ccse1jdid.png
- text: >-
pixel art cryptopunks, human, female, dark skin tone, Tassle Hat, Blue Eye
Shadow, Purple Lipstick, Earring
output:
url: images/example_e9bha5sos.png
LoRA text2image fine-tuning - SedatAl/Crypto-Punk-LoRa
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the SedatAl/modified-cryptopunks dataset. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]
