Spaces:
Runtime error
Runtime error
File size: 6,170 Bytes
bea4577 2abe079 bea4577 2abe079 bea4577 2abe079 bea4577 2abe079 bea4577 2abe079 bea4577 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 |
import torch
from datasets import load_dataset
import transformers
from diffusers import StableDiffusionPipeline
import gradio as gr
from random import randrange
import os
MY_SECRET_TOKEN = os.environ.get('stable-diffusion')
data = load_dataset("mfumanelli/movies-small")
data = data['train'].to_pandas()
model_id = 'CompVis/stable-diffusion-v1-4'
device = torch.device('cpu' if not torch.cuda.is_available() else 'cuda')
pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=MY_SECRET_TOKEN, revision='fp16')
pipe = pipe.to(device)
def infer(prompt, samples, steps, scale):
generator = torch.Generator(device=device)
if device.type == 'cuda':
with torch.autocast(device.type):
images_list = pipe(
[prompt] * samples,
num_inference_steps=steps,
guidance_scale=scale,
generator=generator,
)
else:
images_list = pipe(
[prompt] * samples,
num_inference_steps=steps,
guidance_scale=scale,
generator=generator,
)
return images_list
def generate_movie():
seed = randrange(data.shape[0])
plot = data.iloc[seed]["plot_synopsis_sum"]
image = infer(plot, 1, 50, 7.5)
return image["sample"][0], seed
def movie_title(seed):
return data.iloc[int(seed)]["title"]
css = """
.gradio-container {
font-family: 'IBM Plex Sans', sans-serif;
}
.gr-button {
color: white;
border-color: black;
background: black;
}
input[type='range'] {
accent-color: black;
}
.dark input[type='range'] {
accent-color: #dfdfdf;
}
.container {
max-width: 730px;
margin: auto;
padding-top: 1.5rem;
}
#iamge {
min-height: 22rem;
margin-bottom: 15px;
margin-left: auto;
margin-right: auto;
border-bottom-right-radius: .5rem !important;
border-bottom-left-radius: .5rem !important;
}
#iamge>div>.h-full {
min-height: 20rem;
}
.details:hover {
text-decoration: underline;
}
.gr-button {
white-space: nowrap;
}
.gr-button:focus {
border-color: rgb(147 197 253 / var(--tw-border-opacity));
outline: none;
box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
--tw-border-opacity: 1;
--tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
--tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
--tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
--tw-ring-opacity: .5;
}
.footer {
margin-bottom: 45px;
margin-top: 35px;
text-align: center;
border-bottom: 1px solid #e5e5e5;
}
.footer>p {
font-size: .8rem;
display: inline-block;
padding: 0 10px;
transform: translateY(10px);
background: white;
}
.dark .footer {
border-color: #303030;
}
.dark .footer>p {
background: #0b0f19;
}
.acknowledgments h4{
margin: 1.25em 0 .25em 0;
font-weight: bold;
font-size: 115%;
}
"""
with gr.Blocks(css=css) as demo:
gr.HTML(
"""
<div style="text-align: center; max-width: 650px; margin: 0 auto;">
<div
style="
display: inline-flex;
align-items: center;
gap: 0.8rem;
font-size: 1.75rem;
"
>
<svg style="color: red" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 256 256", height="0.85em" width="0.85em">
<rect width="18em" height="18em" fill="none"></rect>
<path d="M128,216S28,160,28,92A52,52,0,0,1,128,72h0A52,52,0,0,1,228,92C228,160,128,216,128,216Z" fill="#d63e25" stroke="#d63e25" stroke-linecap="round"
stroke-linejoin="round" stroke-width="12"></path></svg>
<h1 style="font-weight: 900; margin-bottom: 7px;">
Stable Diffusion Loves Cinema
</h1>
</div>
<p style="margin-bottom: 20px; font-size: 94%">
Stable Diffusion is a state-of-the-art text-to-image model that generates images from text,
in this demo it is used to generate movie scenes from their storyline. <br></p>
<hr style="height:2px;border-width:0;color:gray;background-color:gray">
<br>
<p align="left" style="margin-bottom: 10px; font-size: 94%">
<b>Instructions</b>: press the "Generate a movie scene!" button to generate an image and try to see if you can guess the movie.
You can see if you guessed right by pressing the "Tell me the title" button.
</p>
</div>
"""
)
with gr.Group():
with gr.Box():
with gr.Row().style(mobile_collapse=False, equal_height=True):
b1 = gr.Button("Generate a movie scene!").style(
margin=False,
rounded=(False, True, True, False),
)
b2 = gr.Button("Tell me the title").style(
margin=False,
rounded=(False, True, True, False),
)
text = gr.Textbox(label="Title:")
image = gr.Image(
label="Generated images", show_label=False, elem_id="image"
).style(height="auto")
seed = gr.Number(visible=False)
b1.click(generate_movie, inputs=None, outputs=[image, seed])
b2.click(movie_title, inputs=seed, outputs=text)
demo.launch()
|