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# from diffusers import DiffusionPipeline | |
from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline | |
import torch | |
import gradio as gr | |
import random | |
pipeline = DDPMPipeline.from_pretrained("google/ddpm-cat-256") | |
# pipeline.to("cuda") | |
def predict(steps, seed): | |
generator = torch.manual_seed(seed) | |
for i in range(1, steps): | |
yield pipeline(generator=generator, num_inference_steps=i).images[0] | |
random_seed = random.randint(0, 2147483647) | |
gr.Interface( | |
predict, | |
inputs=[ | |
gr.inputs.Slider(1, 100, label="Inference Steps", default=5, step=1), | |
gr.inputs.Slider(0, 2147483647, label="Seed", default=random_seed, step=1), | |
], | |
outputs=gr.Image(shape=[128, 128], type="pil", elem_id="output_image"), | |
css="#output_image{width: 256px}", | |
title="Unconditional butterflies", | |
description="εΎηηζε¨", | |
).queue().launch() | |