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import gradio as gr | |
import torch | |
from torch import autocast | |
from diffusers import StableDiffusionPipeline | |
from datasets import load_dataset | |
from PIL import Image | |
import re | |
import streamlit as st | |
model_id = "CompVis/stable-diffusion-v1-4" | |
device = "cpu" | |
#If you are running this code locally, you need to either do a 'huggingface-cli login` or paste your User Access Token from here https://huggingface.co./settings/tokens into the use_auth_token field below. | |
pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=st.secrets["AUTH_KEY"], torch_dtype=torch.float32) | |
def dummy(images, **kwargs): return images, False | |
pipe.safety_checker = dummy | |
def infer(prompt, width, height, steps, scale, seed): | |
if seed == -1: | |
images_list = pipe( | |
[prompt], | |
height=height, | |
width=width, | |
num_inference_steps=steps, | |
guidance_scale=scale, | |
generator=torch.Generator(device=device).manual_seed(seed)) | |
else: | |
images_list = pipe( | |
[prompt], | |
height=height, | |
width=width, | |
num_inference_steps=steps, | |
guidance_scale=scale) | |
return images_list["sample"] | |
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; | |
} | |
#gallery { | |
min-height: 22rem; | |
margin-bottom: 15px; | |
margin-left: auto; | |
margin-right: auto; | |
border-bottom-right-radius: .5rem !important; | |
border-bottom-left-radius: .5rem !important; | |
} | |
#gallery>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%; | |
} | |
""" | |
block = gr.Blocks(css=css) | |
with block: | |
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; | |
" | |
> | |
<h1 style="font-weight: 900; margin-bottom: 7px;"> | |
Stable Diffusion CPU | |
</h1> | |
</div> | |
</div> | |
""" | |
) | |
with gr.Group(): | |
with gr.Box(): | |
with gr.Row().style(mobile_collapse=False, equal_height=True): | |
text = gr.Textbox( | |
label="Enter your prompt", | |
show_label=False, | |
max_lines=1, | |
placeholder="Enter your prompt", | |
).style( | |
border=(True, False, True, True), | |
rounded=(True, False, False, True), | |
container=False, | |
) | |
btn = gr.Button("Generate image").style( | |
margin=False, | |
rounded=(False, True, True, False), | |
) | |
gallery = gr.Gallery( | |
label="Generated images", show_label=False, elem_id="gallery" | |
).style(grid=[2], height="auto") | |
with gr.Row().style(mobile_collapse=False, equal_height=True): | |
width = gr.Slider(label="Width", minimum=32, maximum=1024, value=512, step=8) | |
height = gr.Slider(label="Height", minimum=32, maximum=1024, value=512, step=8) | |
with gr.Row(): | |
steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=30, step=1) | |
scale = gr.Slider( | |
label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1 | |
) | |
seed = gr.Slider( | |
label="Seed", | |
minimum=-1, | |
maximum=2147483647, | |
step=1, | |
value=-1, | |
) | |
text.submit(infer, inputs=[text, width, height, steps, scale, seed], outputs=gallery) | |
btn.click(infer, inputs=[text, width, height, steps, scale, seed], outputs=gallery) | |
block.queue(max_size=10).launch() |