Instructions to use nitindominicrai/healthy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nitindominicrai/healthy 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("nitindominicrai/healthy") prompt = "A dense canopy of very ytr healthy watermelon plants. The leaf is captured with a Canon DSLR handheld camera in natural lighting resembling a real watermelon growing environment" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
SDXL LoRA DreamBooth - nitindominicrai/healthy
Model description
These are nitindominicrai/healthy 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 A dense canopy of very ytr healthy watermelon plants. The leaf is captured with a Canon DSLR handheld camera in natural lighting resembling a real watermelon growing environment 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 nitindominicrai/healthy
Base model
stabilityai/stable-diffusion-xl-base-1.0