import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("alkiskoudounas/sd-pokemon-256px", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Model Card for Stable Diffusion - Pokemon, 256px
Model developed for the Unit 1 of the Diffusion Models Class 🧨.
This model is a diffusion model for unconditional image generation of Pokemon 
It is trained on a small collection of pokemon pictures and trained for 50 epochs, with 🤗 Accelerate.
Usage
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('alkiskoudounas/sd-pokemon-256px')
Example
Here you can find an example of the output of the model, in a batch of 8 images:
- Downloads last month
- 8
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support