Instructions to use caikybaldo999/ZYI-Turbo-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use caikybaldo999/ZYI-Turbo-1.0 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("caikybaldo999/ZYI-Turbo-1.0", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
ZYI Turbo 0.1
Small experimental general-purpose text-to-image diffusion denoiser.
Model
- Trainable parameters: 18.77M
- Resolution: 128x128
- Architecture: Diffusers
UNet2DConditionModel - Text encoder:
openai/clip-vit-base-patch32 - VAE:
stabilityai/sd-vae-ft-mse - Recommended inference steps: 12
- Recommended CFG: 5.0
Important
This repository contains the trained ZYI Turbo denoiser.
The CLIP text encoder and VAE are not necessarily bundled with the model and may need to be loaded separately during inference.
- Downloads last month
- -