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 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)) original_height, original_width, original_channel = np.array(source_img).shape # Limited to 1 million pixels if 1024 * 1024 < original_width * original_height: factor = ((1024 * 1024) / (original_width * original_height))**0.5 process_width = math.floor(original_width * factor) process_height = math.floor(original_height * factor) else: process_width = original_width process_height = original_height # Width and height must be multiple of 8 if (process_width % 8) != 0 or (process_height % 8) != 0: process_width = process_width - (process_width % 8) process_height = process_height - (process_height % 8) if ((process_width + 8) * (process_height + 8)) <= (1024 * 1024): process_width = process_width + 8 process_height = process_height + 8 source_image = source_img.resize((process_width, process_height)) image = pipe(prompt, image=source_image, strength=Strength, guidance_scale=0.0, num_inference_steps=steps, width = process_width, height = process_height).images[0] output_image = image.resize((original_width, original_height)) return output_image gr.Interface(fn=infer, inputs=[ gr.Image(sources=["upload", "webcam", "clipboard"], type = "pil", 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()