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| import gradio as gr | |
| from diffusers import DiffusionPipeline | |
| from PIL import Image | |
| import numpy as np | |
| pipeline = DiffusionPipeline.from_pretrained("Qilex/VirtualPetDiffusion2") | |
| def generate_pets(num_to_generate): | |
| images = pipeline(num_to_generate)["sample"] | |
| return images | |
| def concatenate_imgs(imgs): | |
| length = len(imgs) | |
| if length == 1: | |
| return imgs[0] | |
| top = Image.fromarray(np.concatenate([np.array(x) for x in imgs[:2]],axis=1)) | |
| if length == 2: | |
| return top | |
| if len(imgs)==3: | |
| fake = np.zeros([128,128,3],dtype=np.uint8) | |
| fake[:] = 255 | |
| bottom = Image.fromarray(np.concatenate([imgs[2], fake],axis=1)) | |
| elif len(imgs)==4: | |
| bottom = Image.fromarray(np.concatenate([imgs[2], imgs[3]],axis=1)) | |
| return Image.fromarray(np.concatenate([top,bottom],axis=0)) | |
| def go(num): | |
| imgs = generate_pets(num) | |
| grid = concatenate_imgs(imgs) | |
| print(type(grid)) | |
| return grid | |
| title = "VirtualPet Dream" | |
| description = """ | |
| This AI will 'dream' you up a virtual pet. | |
| \nThis is a denoising diffusion model trained in 48 hours for a hackathon, so the images can be pretty wonky. | |
| \nImages are 128x128px. | |
| \nBecause we're running on CPU, it takes 10-15 minutes to generate an image. Quick inference can be run in the <a href="https://colab.research.google.com/drive/19QtPOHv6HCpexyCMGXowX4vyZlF4ZZYN?usp=sharing">colab notebook</a>. | |
| \n <a href="https://github.com/ke7osm/VirtualPet-Dream">Github Repo</a> | |
| """ | |
| article = '''Here's a gallery of some of the better pets: | |
| <div style="display: flex; justify-content:space-evenly"> | |
| <img src="https://alexlyman.org/external_images/sample_10.png"> | |
| <img src="https://alexlyman.org/external_images/sample_5.png" > | |
| <img src="https://alexlyman.org/external_images/sample_4.png" > | |
| <img src="https://alexlyman.org/external_images/sample_8.png" > | |
| </div> | |
| \n | |
| ''' | |
| gr.Interface( | |
| fn=go, | |
| inputs= gr.Slider(1, 4, value = 2, step = 1, label="Number of images to generate (more takes longer)"), | |
| outputs=gr.Image(), | |
| title=title, | |
| description=description, | |
| article = article, | |
| ).launch() |