import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Norod78/ddpm-EmojiAlignedFaces-64", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]ddpm-EmojiAlignedFaces-64
Model description
This diffusion model is trained with the 🤗 Diffusers library on the Norod78/EmojiFFHQAlignedFaces dataset.
How to use
from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
def main():
model_id = "Norod78/ddpm-EmojiAlignedFaces-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()["sample"]
# save image
image[0].save("ddpm_generated_image.jpg")
image[0].show()
if __name__ == '__main__':
main()
Training data
Training results
📈 TensorBoard logs
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