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Sponsored by
YouArt
merve / lego-sdxl-dora-3
Model's Last Updated: February 26 2024
text-to-image
Introduction of lego-sdxl-dora-3
Model Details of lego-sdxl-dora-3
SDXL LoRA DreamBooth - merve/lego-sdxl-dora-3
- Prompt
- a lego set in the style of <s0><s1>, an astronaut riding a horse
- Prompt
- a lego set in the style of <s0><s1>, an astronaut riding a horse
- Prompt
- a lego set in the style of <s0><s1>, an astronaut riding a horse
- Prompt
- a lego set in the style of <s0><s1>, an astronaut riding a horse
Model description
These are merve/lego-sdxl-dora-3 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
Download model
Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
-
LoRA
: download
lego-sdxl-dora-3.safetensorshere 💾 .-
Place it on your
models/Lorafolder. -
On AUTOMATIC1111, load the LoRA by adding
<lora:lego-sdxl-dora-3:1>to your prompt. On ComfyUI just load it as a regular LoRA .
-
Place it on your
-
Embeddings
: download
lego-sdxl-dora-3_emb.safetensorshere 💾 .-
Place it on it on your
embeddingsfolder -
Use it by adding
lego-sdxl-dora-3_embto your prompt. For example,lego set in the style of lego-sdxl-dora-3_emb(you need both the LoRA and the embeddings as they were trained together for this LoRA)
-
Place it on it on your
Use it with the 🧨 diffusers library
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('merve/lego-sdxl-dora-3', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='merve/lego-sdxl-dora-3', filename='lego-sdxl-dora-3_emb.safetensors', repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('a lego set in the style of <s0><s1>, an astronaut riding a horse').images[0]
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept
TOK
→ use
<s0><s1>
in your prompt
Details
All Files & versions .
The weights were trained using 🧨 diffusers Advanced Dreambooth Training Script .
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
Runs of merve lego-sdxl-dora-3 on huggingface.co
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30-day runs
More Information About lego-sdxl-dora-3 huggingface.co Model
Url of lego-sdxl-dora-3
lego-sdxl-dora-3 huggingface.co Url
Provider of lego-sdxl-dora-3 huggingface.co
Other API from merve
Updated:May 26 2026
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