Text-to-Image
Diffusers
FlaxStableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
jax-diffusers-event
Instructions to use bguisard/stable-diffusion-nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bguisard/stable-diffusion-nano with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bguisard/stable-diffusion-nano", 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
Training details
#1
by bielca98 - opened
Hello,
I was wondering which parts of the model were exactly fine-tuned. Did you only fine-tune the VAE and keep the backbone model unchanged, or did you also retrain the UNET?
Thanks.
It's actually the other way around. It uses the same VAE used in stable diffusion 1.5 and the unet was tuned for 100,000 steps.
There are some basic details in the model card, but I recommend using Stable Diffusion Nano 2.1, which we spent a bit more time working on.