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: a photo of Nongshim Squid Snack 83g
widget:
  - text: A photo of Nongshim Squid Snack 83g in the refrigerator
    output:
      url: image_0.png

SD3 DreamBooth LoRA - BangDoon/lora-Nongshim_Squid_Snack_83g-SD3

Prompt
A photo of Nongshim Squid Snack 83g in the refrigerator

Model description

These are BangDoon/lora-Nongshim_Squid_Snack_83g-SD3 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 a photo of Nongshim Squid Snack 83g 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('BangDoon/lora-Nongshim_Squid_Snack_83g-SD3', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('A photo of Nongshim Squid Snack 83g in the refrigerator').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]