import gradio as gr import modin.pandas as pd import torch import time 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("Lykon/dreamshaper-xl-v2-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): start_time = time.time() 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] end_time = time.time() elapsed_time = end_time - start_time print("η”Ÿζˆζ—Άι—΄",elapsed_time) return image gr.Interface(fn=infer, inputs=[ gr.Image(sources=["upload", "webcam", "clipboard"], type="filepath", label="Raw Image."), gr.Textbox(label = 'Prompt Input Text. 77 Token (Keyword or Symbol) Maximum'), 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 = "Stable Diffusion XL Turbo Image to Image Pipeline CPU", description = "For more information on Stable Diffusion XL Turbo see https://huggingface.co./stabilityai/sdxl-turbo

Upload an Image, Use your Cam, or Paste an Image. Then enter a Prompt, or let it just do its Thing, then click submit. For more informationon about Stable Diffusion or Suggestions for prompts, keywords, artists or styles see https://github.com/Maks-s/sd-akashic", article = "Code Monkey: Manjushri").queue(max_size=10).launch()