| import gradio as gr | |
| import modin.pandas as pd | |
| import torch | |
| import numpy as np | |
| from PIL import Image | |
| from diffusers import DiffusionPipeline | |
| from diffusers.utils import load_image | |
| import math | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| pipe = DiffusionPipeline.from_pretrained("radames/stable-diffusion-v1-5-img2img", 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 | |
| gr.Interface(fn=infer, inputs=[ | |
| gr.Image(sources=["upload", "webcam", "clipboard"], type="filepath", label="Raw Image."), | |
| gr.Textbox(label = 'Creative Touch(prompt)'), | |
| 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 = "Creative Touch").queue(max_size=10).launch() |