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# notes https://huggingface.co/spaces/Joeythemonster/Text-To-image-AllModels/blob/main/app.py
import tensorflow as tf
from diffusers import DiffusionPipeline
import spaces
# import torch
import PIL.Image
import gradio as gr
import gradio.components as grc
import numpy as np
from huggingface_hub import from_pretrained_keras
import keras
import time
import os
# os.environ['KMP_DUPLICATE_LIB_OK']='TRUE'
# options = ['Placeholder A', 'Placeholder B', 'Placeholder C']
# 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):
# print("HI")
# 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()
from diffusers import StableDiffusionPipeline
import torch
modellist=['nathanReitinger/MNIST-diffusion-oneImage',
'nathanReitinger/MNIST-diffusion',
# 'nathanReitinger/MNIST-GAN',
# 'nathanReitinger/MNIST-GAN-noDropout'
]
# pipeline = DiffusionPipeline.from_pretrained("nathanReitinger/MNIST-diffusion-oneImage")
# device = "cuda" if torch.cuda.is_available() else "cpu"
# pipeline = pipeline.to(device=device)
def getModel(model):
model_id = model
(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()
RANDO = str(time.time())
file_path = 'tester/' + model_id.replace("/", "-") + "/" + RANDO + '/'
os.makedirs(file_path)
train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')
train_images = (train_images - 127.5) / 127.5 # Normalize the images to [-1, 1]
print(model_id)
if 'diffusion' in model_id:
pipe = DiffusionPipeline.from_pretrained(model_id)
pipe = pipe.to("cpu")
image = pipe(generator= torch.manual_seed(42), num_inference_steps=40).images[0]
else:
pipe = DiffusionPipeline.from_pretrained('nathanReitinger/MNIST-diffusion')
pipe = pipe.to("cpu")
test = from_pretrained_keras('nathanReitinger/MNIST-GAN')
image = pipe(generator= torch.manual_seed(42), num_inference_steps=40).images[0]
return image
import gradio as gr
interface = gr.Interface(fn=getModel, inputs=[gr.Dropdown(modellist)], css="#output_image{width: 256px !important; height: 256px !important;}", outputs="image", title='Model Problems (infringement)')
interface.launch()
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