| 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 | |
| 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) | |
| 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 = 'Prompt Input Text. 77 Token (Keyword or Symbol) Maximum'), | |
| gr.Slider(2, 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 = .5, 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 1.0 see https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0 <br><br>Upload an Image (<b>MUST Be .PNG and 512x512 or 768x768</b>) enter a Prompt, or let it just do its Thing, then click submit. 10 Iterations takes about ~900-1200 seconds currently. 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: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").queue(max_size=5).launch() |