Instructions to use merve/lego-dreambooth-sdxl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use merve/lego-dreambooth-sdxl 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", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("merve/lego-dreambooth-sdxl") prompt = "a picture of <s1><s2> minifigure" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
LoRA DreamBooth - merve/lego-lora-trained-xl
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a picture of minifigure using DreamBooth. You can find some example images in the following.
You can use this code 👇
from huggingface_hub.repocard import RepoCard
from diffusers import DiffusionPipeline
import torch
lora_model_id = "merve/lego-lora-trained-xl"
card = RepoCard.load(lora_model_id)
base_model_id = card.data.to_dict()["base_model"]
pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
pipe.load_lora_weights(lora_model_id)
pipe("a picture of <s1><s2> minifigure as lana del rey, high quality", num_inference_steps=35).images[0]
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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Model tree for merve/lego-dreambooth-sdxl
Base model
stabilityai/stable-diffusion-xl-base-1.0


