Instructions to use aellaboudy/generativeModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use aellaboudy/generativeModel with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("aellaboudy/generativeModel") prompt = "gold jewelry" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
metadata
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: gold jewelry
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
LoRA DreamBooth - aellaboudy/generativeModel
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on gold jewelry using DreamBooth. You can find some example images in the following.
LoRA for the text encoder was enabled: False.



