metadata
base_model: stabilityai/stable-diffusion-3-medium-diffusers
library_name: diffusers
license: openrail++
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- sd3
- sd3-diffusers
- template:sd-lora
instance_prompt: Toyota Corolla Cross XLE 2022 car
widget:
- text: Toyota Corolla Cross XLE 2022 car near beach.
output:
url: image_0.png
- text: Toyota Corolla Cross XLE 2022 car near beach.
output:
url: image_1.png
- text: Toyota Corolla Cross XLE 2022 car near beach.
output:
url: image_2.png
- text: Toyota Corolla Cross XLE 2022 car near beach.
output:
url: image_3.png
SD3 DreamBooth LoRA - gaurav-raul/corolla-sd3-lora_v3
Model description
These are gaurav-raul/corolla-sd3-lora_v3 DreamBooth LoRA weights for stabilityai/stable-diffusion-3-medium-diffusers.
The weights were trained using DreamBooth with the SD3 diffusers trainer.
Was LoRA for the text encoder enabled? False.
Trigger words
You should use Toyota Corolla Cross XLE 2022 car
to trigger the image generation.
Download model
Download the *.safetensors LoRA in the Files & versions tab.
Use it with the 🧨 diffusers library
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-3-medium-diffusers', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('gaurav-raul/corolla-sd3-lora_v3', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('Toyota Corolla Cross XLE 2022 car near beach.').images[0]
Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- LoRA: download
diffusers_lora_weights.safetensors
here 💾.- Rename it and place it on your
models/Lora
folder. - On AUTOMATIC1111, load the LoRA by adding
<lora:your_new_name:1>
to your prompt. On ComfyUI just load it as a regular LoRA.
- Rename it and place it on your
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
License
Please adhere to the licensing terms as described here.
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]