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("dminhk/deepracer-example-sdxl-lora")
prompt = "a photo of sks deepracer"
image = pipe(prompt).images[0]LoRA DreamBooth - dminhk/deepracer-example-sdxl-lora
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks deepracer using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
Data set: AWS DeepRacer stock images from amazon.com (10 images)
Train steps: 1,000
Example images:
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Model tree for dminhk/deepracer-example-sdxl-lora
Base model
stabilityai/stable-diffusion-xl-base-1.0