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Create app.py
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app.py
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import gradio as gr
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from diffusers import DiffusionPipeline
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from PIL import Image
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import numpy as np
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pipeline = DiffusionPipeline.from_pretrained("Qilex/neoGen2")
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def generate_pets(num_to_generate):
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images = pipeline(num_to_generate)["sample"]
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return images
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def concatenate_imgs(imgs):
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length = len(imgs)
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if length == 1:
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return imgs[0]
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top = Image.fromarray(np.concatenate([np.array(x) for x in imgs[:2]],axis=1))
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if length == 2:
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return top
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if len(imgs)==3:
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fake = np.zeros([128,128,3],dtype=np.uint8)
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fake[:] = 255
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bottom = Image.fromarray(np.concatenate([imgs[2], fake],axis=1))
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elif len(imgs)==4:
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bottom = Image.fromarray(np.concatenate([imgs[2], imgs[3]],axis=1))
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return Image.fromarray(np.concatenate([top,bottom],axis=0))
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def go(num):
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imgs = generate_pets(num)
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grid = concatenate_imgs(imgs)
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print(type(grid))
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return grid
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title = "VirtualPet Dream"
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description = """
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This AI will 'dream' you up a virtual pet.
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\nThis is a denoising diffusion model trained in 48 hours for a hackathon, so the images can be pretty wonky.
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\nImages are 128x128px.
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"""
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article = '''Here's a gallery of some of the better pets:
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<div style="display: flex; justify-content:space-evenly">
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<img src="https://alexlyman.org/external_images/sample_10.png">
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<img src="https://alexlyman.org/external_images/sample_5.png" >
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<img src="https://alexlyman.org/external_images/sample_4.png" >
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<img src="https://alexlyman.org/external_images/sample_8.png" >
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</div>'''
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gr.Interface(
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fn=go,
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inputs= gr.Slider(1, 4, value = 2, step = 1, label="Number of images to generate (more takes longer)"),
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outputs=gr.Image(),
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title=title,
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description=description,
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article = article,
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).launch()
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