Instructions to use multimodalart/cat-toy-z with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use multimodalart/cat-toy-z 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("multimodalart/cat-toy-z") prompt = "cttoyz" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
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("multimodalart/cat-toy-z")
prompt = "cttoyz"
image = pipe(prompt).images[0]cat-toy-z Dreambooth LoRA model trained by multimodalart with Hugging Face Dreambooth Training Space with the v1-5 base model
You run your new concept via diffusers Colab Notebook for Inference. Don't forget to use the concept prompts!
Sample pictures of:
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Model tree for multimodalart/cat-toy-z
Base model
runwayml/stable-diffusion-v1-5


