Norod

# crapify_images.py
# Go over a folder of images and reduce the quality and/or add noise to some of them (Useful for making a Pix2Pix model more resilient) 
# @Norod78

import skimage
import skimage.io
import skimage.io._plugins.pil_plugin as pp
import numpy as np

from PIL import Image

Norod

# !pip install sentencepiece transformers tokenizers

from transformers import MarianTokenizer, MarianMTModel
from typing import List

src = "en"  # source language

trg = "he"  # target language

model_name = f"Helsinki-NLP/opus-mt-{src}-{trg}"

Norod

import gradio as gr
from transformers import pipeline

title = "Hebrew GPT-Neo Demo"
description = ""
article = "<p></p>"
examples = [
    ['צחוקים ושיגועים'],
    ["למנשה פומפרניקל יש"],
    ["פעם אחת לפני שנים רבות"]

Norod

import jax
from transformers import FlaxGPTNeoForCausalLM, AutoTokenizer

#model_name = 'Norod78/hebrew-gpt_neo-tiny'
#model_name = 'Norod78/hebrew_poetry-gpt_neo-small'
model_name = 'Norod78/hebrew-gpt_neo-small'

model = FlaxGPTNeoForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

Norod

#!/usr/bin/python
# -*- coding: utf-8 -*-

import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel, AutoConfig
from transformers.onnx import FeaturesManager, convert, export
from pathlib import Path
import os

model_id = 'gpt2-large'

Norod

Norod

Norod

#### pip install diffusers==0.2.4 transformers scipy ftfy
####

from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
import torch

def main():
    seed = 1000 #1000, 42, 420
    torch.manual_seed(seed)
    generator = torch.Generator()

Norod

import importlib


checker_location = importlib.util.find_spec('diffusers.pipelines.stable_diffusion.safety_checker').origin
checker_location_flax = importlib.util.find_spec('diffusers.pipelines.stable_diffusion.safety_checker_flax').origin
blocker_location_flax = importlib.util.find_spec('diffusers.pipelines.stable_diffusion.pipeline_flax_stable_diffusion').origin

#FlaxStableDiffusionPipeline

import fileinput

Norod

#!pip install diffusers==0.2.4

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np
from torchvision import transforms as tfms

torch_device = None
vae = None