metadata
library_name: diffusers
license: apache-2.0
datasets:
- Drozdik/tattoo_v0
language:
- en
tags:
- art
Model Details
Abstract:
*Trained a Unconditional Diffusion Model on tattoo dataset with DDIM noise scheduler *
Inference
DDPM models can use discrete noise schedulers such as:
for inference. Note that while the ddpm scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the ddim or pndm schedulers instead.
See the following code:
# !pip install diffusers
from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
model_id = "google/DDIM-tattoo-32"
# load model and scheduler
ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference
# run pipeline in inference (sample random noise and denoise)
image = ddpm().images[0]
# save image
image.save("ddpm_generated_image.png")