Text-to-Image
Diffusers
sd3
sd3-diffusers
simpletuner
Not-For-All-Audiences
lora
template:sd-lora
lycoris
Instructions to use badul13/simpletuner-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use badul13/simpletuner-lora 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-3.5-large", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("badul13/simpletuner-lora") prompt = "A powerful earth-element bear depicted in pixel art style, featuring a strong build with fur in rich brown and earthy green tones, accented by beige highlights. Stone-like patterns on its paws and shoulders reinforce its connection to the earth, while its glowing golden eyes convey calm strength. Small pixelated rocks and soil particles surround the bear, enhancing its grounded theme, with a plain white background keeping the focus on its earthy design." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Trained for 18 epochs and 10000 steps.
Browse filesTrained with datasets ['text-embed-cache', 'my-dataset-1024', 'my-dataset-crop-1024']
Learning rate 0.0001, batch size 1, and 1 gradient accumulation steps.
Used DDPM noise scheduler for training with epsilon prediction type and rescaled_betas_zero_snr=False
Using 'trailing' timestep spacing.
Base model: stabilityai/stable-diffusion-3.5-large
VAE: None
pytorch_lora_weights.safetensors
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