tarot

LoRA DreamBooth - jtlowell/tarot

These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.

The weights were trained on the concept prompt:

tarot_style

Use this keyword to trigger your custom model in your prompts.

LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

Usage

Make sure to upgrade diffusers to >= 0.19.0:

pip install diffusers --upgrade

In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark:

pip install invisible_watermark transformers accelerate safetensors

To just use the base model, you can run:

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

vae = AutoencoderKL.from_pretrained('madebyollin/sdxl-vae-fp16-fix', torch_dtype=torch.float16)

pipe = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    vae=vae, torch_dtype=torch.float16, variant="fp16",
    use_safetensors=True
)

# This is where you load your trained weights
pipe.load_lora_weights('jtlowell/tarot')

pipe.to("cuda")

prompt = "A majestic tarot_style jumping from a big stone at night"

image = pipe(prompt=prompt, num_inference_steps=50).images[0]