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import gradio as gr
from torch import autocast
import requests
import torch
from PIL import Image
from io import BytesIO
from diffusers import StableDiffusionImg2ImgPipeline
# load the pipeline
device = "cuda"
model_id_or_path = "CompVis/stable-diffusion-v1-4"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
model_id_or_path,
revision="fp16",
torch_dtype=torch.float16,
use_auth_token='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'
)
# or download via git clone https://huggingface.co/CompVis/stable-diffusion-v1-4
# and pass `model_id_or_path="./stable-diffusion-v1-4"` without having to use `use_auth_token=True`.
pipe = pipe.to(device)
def diffuse(x, param):
print('in callback')
x = Image.fromarray(np.uint8(x))
init_image = x.resize((768, 512))
prompt = 'st petersburg logo'
if param == 'Эрмитаж':
prompt = "st petersburg logo winter palace image on background hermitage vector style"
elif param == 'Казанский собор':
prompt = "st petersburg logo kazansky sobor image on background"
elif param == 'Мосты':
prompt = 'st petersburg logo bridges over neva image on background beutiful high quality'
with autocast("cuda"):
images = pipe(prompt=prompt, init_image=init_image, strength=0.7, guidance_scale=7.5).images
return [images[0], param]
import numpy as np
import gradio as gr
def flip_image(x, param):
return [np.fliplr(x), 'функция приняла на вход ' + param]
with gr.Blocks() as demo:
gr.Markdown("Слово 'Санкт-Петербург'")
with gr.Tab("Санкт-Петербург"):
with gr.Row():
image_input = gr.Image()
param_input = gr.Radio(["Эрмитаж", "Мосты", "Казанский собор"], label='Что для тебя Санкт-Петербург?')
image_output = gr.Image()
param_out = gr.Markdown()
image_button = gr.Button("GET IMAGE")
image_button.click(diffuse, [image_input, param_input], [image_output, param_out])
demo.launch()
#def greet(name):
# return "Hello " + name + "!!"
#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
#iface.launch()
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