Instructions to use naclbit/trinart_characters_19.2m_stable_diffusion_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use naclbit/trinart_characters_19.2m_stable_diffusion_v1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("naclbit/trinart_characters_19.2m_stable_diffusion_v1", 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
Same as model inside the waifu-diffusion-v1-4 repo
#1
by joujiboi - opened
Produces literally the exact same results as waifu-diffusion-v1-4
What is that about? π
You are probably confusing between autoencoder/VAE (which is separately provided) and UNet model. Autoencoder model is the part where latent diffusion model produces noise and has a slight effect on smaller parts, details and overall style. You need to load both autoencoder and UNet model.
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