Instructions to use EcoCy/jultest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use EcoCy/jultest 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-2-1-base", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("EcoCy/jultest") prompt = "jultest01" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("EcoCy/jultest")
prompt = "jultest01"
image = pipe(prompt).images[0]LoRA DreamBooth - jultest
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on the instance prompt "jultest01" using DreamBooth. You can find some example images in the following.
- Downloads last month
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Model tree for EcoCy/jultest
Base model
stabilityai/stable-diffusion-2-1-base


