Text-to-Image
Diffusers
TensorBoard
diffusers-training
lora
template:sd-lora
stable-diffusion-xl
stable-diffusion-xl-diffusers
Instructions to use biashacker/amygdala_style_LoRA_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use biashacker/amygdala_style_LoRA_v2 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-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("biashacker/amygdala_style_LoRA_v2") prompt = "digital drawing in AMYGDALA style" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
SDXL LoRA DreamBooth - biashacker/amygdala_style_LoRA_v2
Model description
These are biashacker/amygdala_style_LoRA_v2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
Trigger words
You should use digital drawing in AMYGDALA style to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
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]
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Model tree for biashacker/amygdala_style_LoRA_v2
Base model
stabilityai/stable-diffusion-xl-base-1.0