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from PIL import Image
import torch
import re
import gradio as gr
import random
import time
from diffusers import AutoPipelineForText2Image
from diffusers import AutoPipelineForImage2Image
from diffusers.utils import load_image
pipeline_text2image = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo").to("cuda")
pipeline_image2image = AutoPipelineForImage2Image.from_pipe(pipeline_text2image).to("cuda")
def text2img(prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe.",guidance_scale=0.0, num_inference_steps=1):
image = pipeline_text2image(prompt=prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
return image
def img2img(image,prompt="A cinematic shot of a baby racoon wearing an intricate italian priest robe.", guidance_scale=0.0, num_inference_steps=1,strength=0.5):
init_image = load_image(image)
init_image = init_image.resize((512, 512))
image = pipeline_image2image(prompt, image=init_image, strength=strength, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
return image
gradio_app_text2img = gr.Interface(
fn=text2img,
inputs=[
gr.Text(),
gr.Slider(0.0, 2.0, value=1,step=0.1),
gr.Slider(2.0, 20.0, value=1,step=1)
],
outputs="image",
)
gradio_app_img2img = gr.Interface(
fn=img2img,
inputs=[
gr.Image(type='filepath'),
gr.Text(),
gr.Slider(0.0, 2.0, value=1,step=0.1),
gr.Slider(2, 20.0, value=1,step=1),
gr.Slider(0.0, 1.0, value=0.5,step=0.05),
],
outputs="image",
)
demo = gr.TabbedInterface([gradio_app_text2img,gradio_app_img2img], ["text2img","img2img"])
if __name__ == "__main__":
demo.launch() |