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import requests |
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from PIL import Image |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from io import BytesIO |
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import base64 |
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import gradio as gr |
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import os |
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backend_url = os.environ["BACKEND_SERVER"] |
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def compose_predictions(images, caption=None): |
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increased_h = 0 if caption is None else 48 |
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w, h = images[0].size[0], images[0].size[1] |
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img = Image.new("RGB", (len(images)*w, h + increased_h)) |
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for i, img_ in enumerate(images): |
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img.paste(img_, (i*w, increased_h)) |
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if caption is not None: |
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draw = ImageDraw.Draw(img) |
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font = ImageFont.truetype("/usr/share/fonts/truetype/liberation2/LiberationMono-Bold.ttf", 40) |
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draw.text((20, 3), caption, (255,255,255), font=font) |
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return img |
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class ServiceError(Exception): |
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def __init__(self, status_code): |
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self.status_code = status_code |
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def get_images_from_ngrok(prompt): |
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r = requests.post( |
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backend_url, |
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json={"prompt": prompt} |
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) |
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if r.status_code == 200: |
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images = r.json()["images"] |
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images = [Image.open(BytesIO(base64.b64decode(img))) for img in images] |
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return images |
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else: |
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raise ServiceError(r.status_code) |
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def run_inference(prompt): |
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try: |
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images = get_images_from_ngrok(prompt) |
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predictions = compose_predictions(images) |
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output_title = f""" |
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<p style="font-size:22px; font-style:bold">Best predictions</p> |
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<p>We asked our model to generate 128 candidates for your prompt:</p> |
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<pre> |
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<b>{prompt}</b> |
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</pre> |
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<p>We then used a pre-trained <a href="https://huggingface.co/openai/clip-vit-base-patch32">CLIP model</a> to score them according to the |
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similarity of the text and the image representations.</p> |
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<p>This is the result:</p> |
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""" |
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output_description = """ |
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<p>Read our <a style="color:blue;" href="https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA">full report</a> for more details on how this works.<p> |
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<p style='text-align: center'>Created with <a style="color:blue;" href="https://github.com/borisdayma/dalle-mini">DALL路E mini</a></p> |
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""" |
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except ServiceError: |
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output_title = f""" |
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Sorry, there was an error retrieving the images. Please, try again later or <a href="mailto:pcuenca-dalle@guenever.net">contact us here</a>. |
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""" |
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predictions = None |
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output_description = "" |
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return (output_title, predictions, output_description) |
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outputs = [ |
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gr.outputs.HTML(label=""), |
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gr.outputs.Image(label=''), |
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gr.outputs.HTML(label=""), |
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] |
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description = """ |
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Welcome to DALL路E-mini, a text-to-image generation model. |
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""" |
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gr.Interface(run_inference, |
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inputs=[gr.inputs.Textbox(label='Prompt')], |
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outputs=outputs, |
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title='DALL路E mini', |
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description=description, |
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article="<p style='text-align: center'> DALLE路mini by Boris Dayma et al. | <a href='https://github.com/borisdayma/dalle-mini'>GitHub</a></p>", |
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layout='vertical', |
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theme='huggingface', |
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examples=[['an armchair in the shape of an avocado'], ['snowy mountains by the sea']], |
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allow_flagging=False, |
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live=False, |
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).launch() |
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