Instructions to use ConfettiIII/trained-sd3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ConfettiIII/trained-sd3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ConfettiIII/trained-sd3") prompt = "A photo of sks dog in a bucket" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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 sks dog
widget:
- text: A photo of sks dog in a bucket
output:
url: image_0.png
- text: A photo of sks dog in a bucket
output:
url: image_1.png
- text: A photo of sks dog in a bucket
output:
url: image_2.png
- text: A photo of sks dog in a bucket
output:
url: image_3.png
SD3 DreamBooth LoRA - ConfettiIII/trained-sd3

- Prompt
- A photo of sks dog in a bucket

- Prompt
- A photo of sks dog in a bucket

- Prompt
- A photo of sks dog in a bucket

- Prompt
- A photo of sks dog in a bucket
Model description
These are ConfettiIII/trained-sd3 DreamBooth weights for stabilityai/stable-diffusion-3-medium-diffusers.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: False.
Trigger words
You should use a photo of sks dog to trigger the image generation.
Download model
Download them in the Files & versions tab.
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]