metadata

base_model: stabilityai/stable-diffusion-3.5-medium
library_name: diffusers
license: other
instance_prompt: '=A ID card ICAO photo'
widget:
  - text: A ID card ICAO photo
    output:
      url: image_0.png
  - text: A ID card ICAO photo
    output:
      url: image_1.png
  - text: A ID card ICAO photo
    output:
      url: image_2.png
  - text: A ID card ICAO photo
    output:
      url: image_3.png
tags:
  - text-to-image
  - diffusers-training
  - diffusers
  - lora
  - template:sd-lora
  - sd3
  - sd3-diffusers
  - text-to-image
  - diffusers-training
  - diffusers
  - lora
  - template:sd-lora
  - sd3
  - sd3-diffusers

SD3 DreamBooth LoRA - izangalefacephi/std3

Model description

These are izangalefacephi/std3 DreamBooth LoRA weights for stabilityai/stable-diffusion-3.5-medium.

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 ID card ICAO photo 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-3.5-medium, torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('izangalefacephi/std3', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('A ID card ICAO photo').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]