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from diffusers import DiffusionPipeline
import spaces
import torch
import PIL.Image
import gradio as gr
import gradio.components as grc
import numpy as np
# import time
# models=[
# "runwayml/stable-diffusion-v1-5",
# "claudfuen/photorealistic-fuen-v1",
# "nitrosocke/redshift-diffusion",
# ]
# model_box=[
# gr.Interface.load(f"models/{models[0]}",live=True,preprocess=True),
# gr.Interface.load(f"models/{models[1]}",live=True,preprocess=True),
# gr.Interface.load(f"models/{models[2]}",live=True,preprocess=True),
# ]
# current_model=model_box[0]
pipeline = DiffusionPipeline.from_pretrained("nathanReitinger/MNIST-diffusion-oneImage")
device = "cuda" if torch.cuda.is_available() else "cpu"
pipeline = pipeline.to(device=device)
@spaces.GPU
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]
gr.Interface(
predict,
inputs=[
grc.Slider(0, 1000, label='Inference Steps', value=42, step=1),
grc.Slider(0, 2147483647, label='Seed', value=42, step=1),
],
outputs=gr.Image(height=28, width=28, type="pil", elem_id="output_image"),
css="#output_image{width: 256px !important; height: 256px !important;}",
title="Model Problems: Infringing on MNIST!",
description="Opening the black box.",
).queue().launch() |