SD3 DreamBooth LoRA - SteveWCG/trained-sd3_buff

Model description

These are SteveWCG/trained-sd3_buff 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 shows a dedicated bicycle lane located adjacent to a vehicular travel lane. The boundary separating vehicular traffic from the bicycle lane is marked by a 6-inch solid white line. When the bicycle lane is positioned next to the curb or another road edge, a 4-inch solid white line delineates the lane's boundary. Note that a curb-side lane line is not required if a gutter pan is present. 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_buff', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('A photo shows a dedicated bicycle lane located adjacent to a vehicular travel lane. The boundary separating vehicular traffic from the bicycle lane is marked by a 6-inch solid white line. When the bicycle lane is positioned next to the curb or another road edge, a 4-inch solid white line delineates the lane's boundary. Note that a curb-side lane line is not required if a gutter pan is present.').images[0]

Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke

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]

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