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import random | |
import gradio as gr | |
from datasets import load_dataset | |
from PIL import Image | |
# from model import get_sd_small, get_sd_tiny, get_sd_every | |
from trans_google import google_translator | |
import replicate | |
from i18n import i18nTranslator | |
word_list_dataset = load_dataset("Gustavosta/Stable-Diffusion-Prompts") | |
word_list = word_list_dataset["train"]['Prompt'] | |
# | |
# from diffusers import EulerDiscreteScheduler, DDIMScheduler, KDPM2AncestralDiscreteScheduler, \ | |
# UniPCMultistepScheduler, DPMSolverSinglestepScheduler, DEISMultistepScheduler, PNDMScheduler, \ | |
# DPMSolverMultistepScheduler, HeunDiscreteScheduler, EulerAncestralDiscreteScheduler, DDPMScheduler, \ | |
# LMSDiscreteScheduler, KDPM2DiscreteScheduler | |
# import torch | |
# import base64 | |
# from io import BytesIO | |
is_gpu_busy = False | |
# translator = i18nTranslator() | |
# translator.init(path='locales') | |
samplers = [ | |
"EulerDiscrete", | |
"EulerAncestralDiscrete", | |
"UniPCMultistep", | |
"DPMSolverSinglestep", | |
"DPMSolverMultistep", | |
"KDPM2Discrete", | |
"KDPM2AncestralDiscrete", | |
"DEISMultistep", | |
"HeunDiscrete", | |
"PNDM", | |
"DDPM", | |
"DDIM", | |
"LMSDiscrete", | |
] | |
re_sampler = [ | |
"DDIM", | |
"K_EULER", | |
"DPMSolverMultistep", | |
"K_EULER_ANCESTRAL", | |
"PNDM", | |
"KLMS" | |
] | |
rand = random.Random() | |
translator = google_translator() | |
# tiny_pipe = get_sd_tiny() | |
# small_pipe = get_sd_small() | |
# every_pipe = get_sd_every() | |
# def get_pipe(width: int, height: int): | |
# if width == 512 and height == 512: | |
# return tiny_pipe | |
# elif width == 256 and height == 256: | |
# return small_pipe | |
# else: | |
# return every_pipe | |
def infer(prompt: str, negative: str, width: int, height: int, sampler: str, steps: int, seed: int, scale): | |
global is_gpu_busy | |
if seed == 0: | |
seed = rand.randint(0, 10000) | |
else: | |
seed = int(seed) | |
# | |
# pipeline = get_pipe(width, height) | |
# | |
images = [] | |
# if torch.cuda.is_available(): | |
# generator = torch.Generator(device="cuda").manual_seed(seed) | |
# else: | |
# generator = None | |
# if sampler == "EulerDiscrete": | |
# pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config) | |
# elif sampler == "EulerAncestralDiscrete": | |
# pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config) | |
# elif sampler == "KDPM2Discrete": | |
# pipeline.scheduler = KDPM2DiscreteScheduler.from_config(pipeline.scheduler.config) | |
# elif sampler == "KDPM2AncestralDiscrete": | |
# pipeline.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipeline.scheduler.config) | |
# elif sampler == "UniPCMultistep": | |
# pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config) | |
# elif sampler == "DPMSolverSinglestep": | |
# pipeline.scheduler = DPMSolverSinglestepScheduler.from_config(pipeline.scheduler.config) | |
# elif sampler == "DPMSolverMultistep": | |
# pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config) | |
# elif sampler == "HeunDiscrete": | |
# pipeline.scheduler = HeunDiscreteScheduler.from_config(pipeline.scheduler.config) | |
# elif sampler == "DEISMultistep": | |
# pipeline.scheduler = DEISMultistepScheduler.from_config(pipeline.scheduler.config) | |
# elif sampler == "PNDM": | |
# pipeline.scheduler = PNDMScheduler.from_config(pipeline.scheduler.config) | |
# elif sampler == "DDPM": | |
# pipeline.scheduler = DDPMScheduler.from_config(pipeline.scheduler.config) | |
# elif sampler == "DDIM": | |
# pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config) | |
# elif sampler == "LMSDiscrete": | |
# pipeline.scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config) | |
try: | |
translate_prompt = translator.translate(prompt, lang_tgt='en') | |
translate_negative = translator.translate(negative, lang_tgt='en') | |
except Exception as ex: | |
print(ex) | |
translate_prompt = prompt | |
translate_negative = negative | |
output = replicate.run( | |
"stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf", | |
input={ | |
"prompt": translate_prompt, | |
"negative_prompt": translate_negative, | |
"guidance_scale": scale, | |
"num_inference_steps": steps, | |
"seed": seed, | |
"scheduler": sampler, | |
} | |
) | |
# image = pipeline(prompt=translate_prompt, | |
# negative_prompt=translate_negative, | |
# guidance_scale=scale, | |
# num_inference_steps=steps, | |
# generator=generator, | |
# height=height, | |
# width=width).images[0] | |
# buffered = BytesIO() | |
# image.save(buffered, format="JPEG") | |
# img_str = base64.b64encode(buffered.getvalue()) | |
# img_base64 = bytes("data:image/jpeg;base64,", encoding='utf-8') + img_str | |
images.append(output[0]) | |
return images | |
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: 1130px; | |
margin: auto; | |
padding-top: 1.5rem; | |
} | |
#prompt-column { | |
min-height: 500px | |
} | |
#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; | |
} | |
#advanced-btn { | |
font-size: .7rem !important; | |
line-height: 19px; | |
margin-top: 12px; | |
margin-bottom: 12px; | |
padding: 2px 8px; | |
border-radius: 14px !important; | |
} | |
#advanced-options { | |
display: none; | |
margin-bottom: 20px; | |
} | |
.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%; | |
} | |
.animate-spin { | |
animation: spin 1s linear infinite; | |
} | |
@keyframes spin { | |
from { | |
transform: rotate(0deg); | |
} | |
to { | |
transform: rotate(360deg); | |
} | |
} | |
#share-btn-container { | |
display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem; | |
margin-top: 10px; | |
margin-left: auto; | |
} | |
#share-btn { | |
all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0; | |
} | |
#share-btn * { | |
all: unset; | |
} | |
#share-btn-container div:nth-child(-n+2){ | |
width: auto !important; | |
min-height: 0px !important; | |
} | |
#share-btn-container .wrap { | |
display: none !important; | |
} | |
.gr-form{ | |
flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0; | |
} | |
#prompt-container{ | |
gap: 0; | |
} | |
#prompt-text-input, #negative-prompt-text-input{padding: .45rem 0.625rem} | |
#component-16{border-top-width: 1px!important;margin-top: 1em} | |
.image_duplication{position: absolute; width: 100px; left: 50px} | |
.generate-container {display: flex; justify-content: flex-end;} | |
#generate-btn {background: linear-gradient(to bottom right, #ffedd5, #fdba74)} | |
""" | |
block = gr.Blocks(css=css) | |
# text, negative, width, height, sampler, steps, seed, guidance_scale | |
# examples = [ | |
# [ | |
# 'A high tech solarpunk utopia in the Amazon rainforest', | |
# 'low quality', | |
# 512, | |
# 512, | |
# 'ddim', | |
# 30, | |
# 0, | |
# 9 | |
# ], | |
# [ | |
# 'A pikachu fine dining with a view to the Eiffel Tower', | |
# 'low quality', | |
# 512, | |
# 512, | |
# 'ddim', | |
# 30, | |
# 0, | |
# 9 | |
# ], | |
# [ | |
# 'A mecha robot in a favela in expressionist style', | |
# 'low quality, 3d, photorealistic', | |
# 512, | |
# 512, | |
# 'ddim', | |
# 30, | |
# 0, | |
# 9 | |
# ], | |
# [ | |
# 'an insect robot preparing a delicious meal', | |
# 'low quality, illustration', | |
# 512, | |
# 512, | |
# 'ddim', | |
# 30, | |
# 0, | |
# 9 | |
# ], | |
# [ | |
# "A small cabin on top of a snowy mountain in the style of Disney, artstation", | |
# 'low quality, ugly', | |
# 512, | |
# 512, | |
# 'ddim', | |
# 30, | |
# 0, | |
# 9 | |
# ], | |
# ] | |
examples = list(map(lambda x: [ | |
x, | |
'low quality', | |
512, | |
512, | |
'ddim', | |
30, | |
0, | |
9 | |
], word_list))[:500] | |
with block: | |
gr.HTML( | |
""" | |
<div style="text-align: center; margin: 0 auto;"> | |
<div | |
style=" | |
display: inline-flex; | |
align-items: center; | |
gap: 0.8rem; | |
font-size: 1.75rem; | |
" | |
> | |
<svg | |
width="0.65em" | |
height="0.65em" | |
viewBox="0 0 115 115" | |
fill="none" | |
xmlns="http://www.w3.org/2000/svg" | |
> | |
<rect width="23" height="23" fill="white"></rect> | |
<rect y="69" width="23" height="23" fill="white"></rect> | |
<rect x="23" width="23" height="23" fill="#AEAEAE"></rect> | |
<rect x="23" y="69" width="23" height="23" fill="#AEAEAE"></rect> | |
<rect x="46" width="23" height="23" fill="white"></rect> | |
<rect x="46" y="69" width="23" height="23" fill="white"></rect> | |
<rect x="69" width="23" height="23" fill="black"></rect> | |
<rect x="69" y="69" width="23" height="23" fill="black"></rect> | |
<rect x="92" width="23" height="23" fill="#D9D9D9"></rect> | |
<rect x="92" y="69" width="23" height="23" fill="#AEAEAE"></rect> | |
<rect x="115" y="46" width="23" height="23" fill="white"></rect> | |
<rect x="115" y="115" width="23" height="23" fill="white"></rect> | |
<rect x="115" y="69" width="23" height="23" fill="#D9D9D9"></rect> | |
<rect x="92" y="46" width="23" height="23" fill="#AEAEAE"></rect> | |
<rect x="92" y="115" width="23" height="23" fill="#AEAEAE"></rect> | |
<rect x="92" y="69" width="23" height="23" fill="white"></rect> | |
<rect x="69" y="46" width="23" height="23" fill="white"></rect> | |
<rect x="69" y="115" width="23" height="23" fill="white"></rect> | |
<rect x="69" y="69" width="23" height="23" fill="#D9D9D9"></rect> | |
<rect x="46" y="46" width="23" height="23" fill="black"></rect> | |
<rect x="46" y="115" width="23" height="23" fill="black"></rect> | |
<rect x="46" y="69" width="23" height="23" fill="black"></rect> | |
<rect x="23" y="46" width="23" height="23" fill="#D9D9D9"></rect> | |
<rect x="23" y="115" width="23" height="23" fill="#AEAEAE"></rect> | |
<rect x="23" y="69" width="23" height="23" fill="black"></rect> | |
</svg> | |
<h1 style="font-weight: 900; margin-bottom: 7px;margin-top:5px"> | |
Stable Diffusion 2.1 Demo | |
</h1> | |
</div> | |
<p style="margin-bottom: 10px; font-size: 94%; line-height: 23px;"> | |
small stable diffusion Demo App. <br /> | |
Click <strong>Generate image</strong> Button to generate image. <br /> | |
Also Change params to have a try <br /> | |
more size may cost more time. <br /> | |
It's just a simplified demo, you can use more advanced features optimize image quality <br /> | |
</p> | |
</div> | |
""" | |
) | |
with gr.Group(): | |
with gr.Box(): | |
with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True): | |
with gr.Column(elem_id="prompt-column"): | |
text = gr.Textbox( | |
label="Enter your prompt", | |
show_label=False, | |
max_lines=1, | |
placeholder="Enter your prompt", | |
elem_id="prompt-text-input", | |
).style( | |
border=(True, False, True, True), | |
rounded=(True, False, False, True), | |
container=False, | |
) | |
negative = gr.Textbox( | |
label="Enter your negative prompt", | |
show_label=False, | |
max_lines=1, | |
placeholder="Enter a negative prompt", | |
elem_id="negative-prompt-text-input", | |
).style( | |
border=(True, False, True, True), | |
rounded=(True, False, False, True), | |
container=False, | |
) | |
with gr.Row(elem_id="txt2img_size", scale=4): | |
width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, | |
elem_id="txt2img_width") | |
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, | |
elem_id="txt2img_height") | |
with gr.Row(elem_id="txt2img_sampler", scale=4): | |
seed = gr.Number(value=0, label="Seed", elem_id="txt2img_seed") | |
sampler = gr.Dropdown( | |
re_sampler, value="DPMSolverMultistep", | |
multiselect=False, | |
label="Sampler", | |
info="sampler select" | |
) | |
steps = gr.Slider(minimum=1, maximum=80, step=1, elem_id=f"steps", label="Sampling steps", | |
value=20) | |
with gr.Accordion("Advanced settings", open=False): | |
# gr.Markdown("Advanced settings are temporarily unavailable") | |
# samples = gr.Slider(label="Images", minimum=1, maximum=4, value=4, step=1) | |
# steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=45, step=1) | |
guidance_scale = gr.Slider( | |
label="Guidance Scale", minimum=0, maximum=50, value=9, step=0.1 | |
) | |
with gr.Row(elem_id="generate-container", elem_classes="generate-container").style(height="100"): | |
btn = gr.Button("Generate image", elem_id="generate-btn", elem_classes="generate-btn").style( | |
margin=False, | |
rounded=(False, True, True, False), | |
full_width=False, | |
) | |
gallery = gr.Gallery( | |
label="Generated images", show_label=False, elem_id="gallery" | |
).style() | |
# with gr.Group(elem_id="container-advanced-btns"): | |
# # advanced_button = gr.Button("Advanced options", elem_id="advanced-btn") | |
# with gr.Group(elem_id="share-btn-container"): | |
# community_icon = gr.HTML(community_icon_html) | |
# loading_icon = gr.HTML(loading_icon_html) | |
# share_button = gr.Button("Share to community", elem_id="share-btn") | |
ex = gr.Examples(examples=examples, fn=infer, | |
inputs=[text, negative, width, height, sampler, steps, seed, guidance_scale], | |
outputs=[gallery], | |
examples_per_page=5, | |
cache_examples=False) | |
ex.dataset.headers = [""] | |
negative.submit(infer, inputs=[text, negative, width, height, sampler, steps, seed, guidance_scale], | |
outputs=[gallery], postprocess=False) | |
text.submit(infer, inputs=[text, negative, width, height, sampler, steps, seed, guidance_scale], | |
outputs=[gallery], postprocess=False) | |
btn.click(infer, inputs=[text, negative, width, height, sampler, steps, seed, guidance_scale], | |
outputs=[gallery], postprocess=False) | |
block.queue(concurrency_count=80, | |
max_size=100).launch( | |
max_threads=150, | |
server_port=6006, | |
share=True, | |
) | |