metadata

base_model: stabilityai/stable-diffusion-3-medium-diffusers
library_name: diffusers
license: other
instance_prompt: >-
  A photo of buffered bicycle lanes displaying a flush-surface buffer between
  the bicycle lane and vehicular traffic. The buffer is a minimum of 18 inches
  wide, with a preferred width of 3 feet. A 4-inch solid white line separates
  vehicular traffic from the buffer, and another 4-inch solid white line marks
  the boundary between the bicycle lane and the buffer. The buffer is detailed
  with hatched 4-inch solid white chevrons spaced every 15 feet.
widget: []
tags:
  - text-to-image
  - diffusers-training
  - diffusers
  - lora
  - template:sd-lora
  - sd3
  - sd3-diffusers

SD3 DreamBooth LoRA - SteveWCG/trained-sd3_buffered

Model description

These are SteveWCG/trained-sd3_buffered 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? True.

Trigger words

You should use A photo of buffered bicycle lanes displaying a flush-surface buffer between the bicycle lane and vehicular traffic. The buffer is a minimum of 18 inches wide, with a preferred width of 3 feet. A 4-inch solid white line separates vehicular traffic from the buffer, and another 4-inch solid white line marks the boundary between the bicycle lane and the buffer. The buffer is detailed with hatched 4-inch solid white chevrons spaced every 15 feet. 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('SteveWCG/trained-sd3_buffered', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('A photo of buffered bicycle lanes displaying a flush-surface buffer between the bicycle lane and vehicular traffic. The buffer is a minimum of 18 inches wide, with a preferred width of 3 feet. A 4-inch solid white line separates vehicular traffic from the buffer, and another 4-inch solid white line marks the boundary between the bicycle lane and the buffer. The buffer is detailed with hatched 4-inch solid white chevrons spaced every 15 feet.').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.

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]