Text-to-Image
Diffusers
Trained with AutoTrain
stable-diffusion-xl
stable-diffusion-xl-diffusers
lora
template:sd-lora
Instructions to use Kvisten/nocco-apple-v7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Kvisten/nocco-apple-v7 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("Kvisten/nocco-apple-v7") prompt = "photo of a Nocco bcaa energy drink with the taste of apple" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
AutoTrain SDXL LoRA DreamBooth - Kvisten/nocco-apple-v7
Model description
These are Kvisten/nocco-apple-v7 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: True.
Special VAE used for training: None.
Trigger words
You should use photo of a Nocco bcaa energy drink with the taste of apple to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
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Model tree for Kvisten/nocco-apple-v7
Base model
stabilityai/stable-diffusion-xl-base-1.0