SD3.5-Large DreamBooth LoRA - Santus108/newnat

Model description

These are Santus108/newnat DreamBooth LoRA weights for stable-diffusion-3.5-large.

The weights were trained using DreamBooth with the SD3 diffusers trainer.

Was LoRA for the text encoder enabled? False.

Trigger words

You should use SANKTOS sitting at her desk, focused on her computer screen, captured from a close-up angle that highlights her expression and concentration; soft ambient lighting illuminates her face and workspace, with vibrant colors on the computer screen adding a creative energy. 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(stable-diffusion-3.5-large, torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Santus108/newnat', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('SANKTOS sitting at her desk, focused on her computer screen, captured from a close-up angle that highlights her expression and concentration; soft ambient lighting illuminates her face and workspace, with vibrant colors on the computer screen adding a creative energy.').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.

Training details

Trained on Replicate using: lucataco/stable-diffusion-3.5-large-lora-trainer

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]