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91 lines (78 loc) · 3.45 KB
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#!/usr/bin/env python
# coding: utf-8
from PIL import Image
import gradio as gr
def compose_predictions(images, caption=None):
increased_h = 0 if caption is None else 48
w, h = images[0].size[0], images[0].size[1]
img = Image.new("RGB", (len(images)*w, h + increased_h))
for i, img_ in enumerate(images):
img.paste(img_, (i*w, increased_h))
if caption is not None:
draw = ImageDraw.Draw(img)
font = ImageFont.truetype("/usr/share/fonts/truetype/liberation2/LiberationMono-Bold.ttf", 40)
draw.text((20, 3), caption, (255,255,255), font=font)
return img
def compose_predictions_grid(images):
cols = 4
rows = len(images) // cols
w, h = images[0].size[0], images[0].size[1]
img = Image.new("RGB", (w * cols, h * rows))
for i, img_ in enumerate(images):
row = i // cols
col = i % cols
img.paste(img_, (w * col, h * row))
return img
def top_k_predictions_real(prompt, num_candidates=32, k=8):
images = hallucinate(prompt, num_images=num_candidates)
images = clip_top_k(prompt, images, k=num_preds)
return images
def top_k_predictions(prompt, num_candidates=32, k=8):
images = []
for i in range(k):
image = Image.open(f"sample_images/image_{i}.jpg")
images.append(image)
return images
def run_inference(prompt, num_images=32, num_preds=8):
images = top_k_predictions(prompt, num_candidates=num_images, k=num_preds)
predictions = compose_predictions(images)
output_title = f"""
<p style="font-size:22px; font-style:bold">Best predictions</p>
<p>We asked our model to generate 32 candidates for your prompt:</p>
<pre>
<b>{prompt}</b>
</pre>
<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
similarity of the text and the image representations.</p>
<p>This is the result:</p>
"""
output_description = """
<p>Read more about the process <a href="https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA">in our report</a>.<p>
<p style='text-align: center'>Created with <a href="https://github.com/borisdayma/dalle-mini">DALLE·mini</a></p>
"""
return (output_title, predictions, output_description)
outputs = [
gr.outputs.HTML(label=""), # To be used as title
gr.outputs.Image(label=''),
gr.outputs.HTML(label=""), # Additional text that appears in the screenshot
]
description = """
Welcome to our demo of DALL·E-mini. This project was created on TPU v3-8s during the 🤗 Flax / JAX Community Week.
It reproduces the essential characteristics of OpenAI's DALL·E, at a fraction of the size.
Please, write what you would like the model to generate, or select one of the examples below.
"""
gr.Interface(run_inference,
inputs=[gr.inputs.Textbox(label='Prompt')], #, gr.inputs.Slider(1,64,1,8, label='Candidates to generate'), gr.inputs.Slider(1,8,1,1, label='Best predictions to show')],
outputs=outputs,
title='DALL·E mini',
description=description,
article="<p style='text-align: center'> DALLE·mini by Boris Dayma et al. | <a href='https://github.com/borisdayma/dalle-mini'>GitHub</a></p>",
layout='vertical',
theme='huggingface',
examples=[['an armchair in the shape of an avocado'], ['snowy mountains by the sea']],
allow_flagging=False,
live=False,
server_port=8999
).launch(
share=True # Creates temporary public link if true
)