README.md

metadata

library_name: diffusers
license: apache-2.0
datasets:
  - valhalla/emoji-dataset
language:
  - en
tags:
  - art

Model Details

Abstract:

*Trained an Unconditional Diffusion Model on emoji dataset with DDPM noise scheduler *

Inference

DDPM models can use discrete noise schedulers such as:

• scheduling_ddpm
• scheduling_ddim
• scheduling_pndm

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/DDPM-emoji-64"
# 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")

Samples Generated

1. 
2. 
3. 
4.