| 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 | |
| demo = gr.Interface( | |
| fn=infer, | |
| inputs=[ | |
| gr.Image(sources=["upload", "webcam", "clipboard"], type="filepath", label="Raw Image."), | |
| gr.Textbox(label='Prompt Input Text.'), | |
| gr.Slider(1, 5, value=2, step=1, label='Number of Iterations'), | |
| gr.Slider(label="Seed", minimum=0, maximum=67, step=1, randomize=True), | |
| gr.Slider(label='Strength', minimum=0.1, maximum=1, step=.05, value=.5) | |
| ], | |
| outputs='image', | |
| title="Generative Images", | |
| description="Upload an Image, Use your Cam, or Paste an Image. Then enter a Prompt, then click submit.", | |
| article="<a href=\"https://Agent5.com\">Agent 5</a>", | |
| css="footer {visibility: hidden}" | |
| ) | |
| demo.queue(max_size=10).launch() |