alvarobartt's picture
alvarobartt HF staff
End of training
786faaa verified
|
raw
history blame
3.07 kB
metadata
base_model: stabilityai/stable-diffusion-3.5-large
library_name: diffusers
license: other
tags:
  - text-to-image
  - diffusers-training
  - diffusers
  - lora
  - sd3
  - sd3-diffusers
  - template:sd-lora
instance_prompt: >-
  Ghibli style human with pig face, staring while being mouth full of food, in a
  restaurant, warm light on night, sweating a lot
widget: []

SD3 DreamBooth LoRA - alvarobartt/ghibli-characters-sd3.5-lora

Prompt
Ghibli style futuristic stormtrooper with glossy white armor and a sleek helmet, standing heroically on a lush alien planet, vibrant flowers blooming around, soft sunlight illuminating the scene, a gentle breeze rustling the leaves

Model description

These are alvarobartt/ghibli-characters-sd3.5-lora 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 Ghibli style human with pig face, staring while being mouth full of food, in a restaurant, warm light on night, sweating a lot 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('alvarobartt/ghibli-characters-sd3.5-lora', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('Ghibli style human with pig face, staring while being mouth full of food, in a restaurant, warm light on night, sweating a lot').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]