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metadata
base_model: stabilityai/stable-diffusion-3.5-large
library_name: diffusers
license: other
instance_prompt: an icon of trpfrog
widget:
  - text: an icon of trpfrog eating ramen
    output:
      url: image_0.png
  - text: an icon of trpfrog eating ramen
    output:
      url: image_1.png
  - text: an icon of trpfrog eating ramen
    output:
      url: image_2.png
  - text: an icon of trpfrog eating ramen
    output:
      url: image_3.png
  - text: an icon of trpfrog eating ramen
    output:
      url: image_4.png
  - text: an icon of trpfrog eating ramen
    output:
      url: image_5.png
  - text: an icon of trpfrog eating ramen
    output:
      url: image_6.png
  - text: an icon of trpfrog eating ramen
    output:
      url: image_7.png
tags:
  - text-to-image
  - diffusers-training
  - diffusers
  - lora
  - template:sd-lora
  - sd3.5-large
  - sd3.5
  - sd3.5-diffusers

SD3.5-Large DreamBooth LoRA - Prgckwb/trpfrog-sd3.5l-lora-2

Prompt
an icon of trpfrog eating ramen
Prompt
an icon of trpfrog holding signs that says "Hello World!"
Prompt
an icon of trpfrog wearing sunglasses
Prompt
an icon of trpfrog in fire
Prompt
an icon of trpfrog saying “TrpFrog”

Model description

These are Prgckwb/trpfrog-sd3.5l-lora-2 DreamBooth LoRA weights for stabilityai/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 an icon of trpfrog 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-large, torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Prgckwb/trpfrog-sd3.5l-lora-2', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('an icon of trpfrog eating ramen').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]