| 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() |
