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!pip install transformers gradio scipy ftfy "ipywidgets>=7,<8" datasets diffusers

import gradio as gr
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline

model_id = "hakurei/waifu-diffusion"
device = "cuda"

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# model: https://huggingface.co/osanseviero/BigGAN-deep-128
# HF blog: https://huggingface.co/blog/gradio-spaces
# pip install gradio before running code below

import gradio as gr

gr.Interface.load("huggingface/osanseviero/BigGAN-deep-128").launch()

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# launch your own Gradio Web Demo of Arcane style transfer by following the steps below
# open a jupyter notebook, code editor (vs code etc), or google colab
# pip install gradio
# copy the code below into a file or cell in a python notebook and run it
# that's it, a web demo will appear in your python notebook or web browser
# github: https://github.com/jjeamin/anime_style_transfer_pytorch
# HF blog: https://huggingface.co/blog/gradio-spaces

import gradio as gr

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# blog: https://huggingface.co/blog/gradio-spaces
# pip install gradio before running the code below

import gradio as gr

title = "New AnimeganV2"
description = "this is a test"
gr.Interface.load("spaces/akhaliq/AnimeGANv2", title=title, description=description).launch()

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# blog: https://huggingface.co/blog/gradio-spaces
# It's so easy to demonstrate a Machine Learning project thanks to Gradio.

# In this blog post, we'll walk you through:

# the recent Gradio integration that helps you demo models from the Hub seamlessly with few lines of code leveraging the Inference API.
# how to use Hugging Face Spaces to host demos of your own models.
# Hugging Face Hub Integration in Gradio
# You can demonstrate your models in the Hub easily. You only need to define the Interface that includes: