import gradio as gr import modin.pandas as pd import torch import numpy as np from PIL import Image from diffusers import AutoPipelineForImage2Image from diffusers.utils import load_image import math device = "cuda" if torch.cuda.is_available() else "cpu" pipe = AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16) if torch.cuda.is_available() else AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo") pipe = pipe.to(device) def resize(value,img): img = Image.open(img) img = img.resize((value,value)) return img def infer(source_img, prompt, steps, seed, Strength): generator = torch.Generator(device).manual_seed(seed) if int(steps * Strength) < 1: steps = math.ceil(1 / max(0.10, Strength)) source_image = resize(512, source_img) source_image.save('source.png') image = pipe(prompt, image=source_image, strength=Strength, guidance_scale=0.0, num_inference_steps=steps).images[0] return image gr.Interface(fn=infer, inputs=[ gr.Image(sources=["upload", "webcam", "clipboard"], type="filepath", label="Raw Image."), gr.Textbox(label = 'Creative Touch(prompt)'), gr.Slider(1, 5, value = 2, step = 1, label = 'Number of Iterations'), gr.Slider(label = "Seed", minimum = 0, maximum = 987654321987654321, step = 1, randomize = True), gr.Slider(label='Strength', minimum = 0.1, maximum = 1, step = .05, value = .5)], outputs='image', title = "Creative Touch").queue(max_size=10).launch()