Instructions to use cerspense/zeroscope_v2_576w with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cerspense/zeroscope_v2_576w with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Seed behaviour
#34
by raphosaurus - opened
I wonder if it is possible to give this model a seed an be able to replicate former results. I'm experimenting right now for a scientific paper an it would be crucial to be able to experiment with different properties/settings but the same seed to compare different results. I checked the Colab Notebooks and couldn't find any sufficient solutions,