Spaces:
Runtime error
Runtime error
import gradio as gr | |
import os | |
import sys | |
from pathlib import Path | |
import time | |
text_gen=gr.Interface.load("spaces/Omnibus/MagicPrompt-Stable-Diffusion",live=True, preprocess=True) | |
proc1=gr.Interface.load("models/nitrosocke/Arcane-Diffusion", live=True, every=5,preprocess=True, postprocess=False) | |
proc2=gr.Interface.load("models/naclbit/trinart_stable_diffusion_v2", live=False, preprocess=True, postprocess=False) | |
proc3=gr.Interface.load("models/nitrosocke/redshift-diffusion", live=False, preprocess=True, postprocess=False) | |
proc4=gr.Interface.load("models/runwayml/stable-diffusion-v1-5", live=False, preprocess=True, postprocess=False) | |
proc5=gr.Interface.load("models/claudfuen/photorealistic-fuen-v1", live=False, preprocess=True, postprocess=False) | |
proc6=gr.Interface.load("models/CompVis/stable-diffusion-v1-4", live=False, preprocess=True, postprocess=False) | |
proc7=gr.Interface.load("models/Linaqruf/anything-v3.0", live=False, preprocess=True, postprocess=False) | |
proc8=gr.Interface.load("models/andite/anything-v4.0", live=False, preprocess=True, postprocess=False) | |
proc9=gr.Interface.load("models/dreamlike-art/dreamlike-photoreal-1.0", live=False, preprocess=True, postprocess=False) | |
proc10=gr.Interface.load("models/prompthero/openjourney", live=False, preprocess=True, postprocess=False) | |
proc11=gr.Interface.load("models/prompthero/midjourney-v4-diffusion", live=False, preprocess=True, postprocess=False) | |
proc12=gr.Interface.load("models/wavymulder/Analog-Diffusion", live=False, preprocess=True, postprocess=False) | |
#proc13=gr.Interface.load("models/dreamlike-art/dreamlike-photoreal-2.0") | |
#proc14=gr.Interface.load("models/wavymulder/Analog-Diffusion") | |
#proc15=gr.Interface.load("models/nitrosocke/redshift-diffusion") | |
#proc16=gr.Interface.load("models/prompthero/midjourney-v4-diffusion") | |
# Import the library signal | |
#import signal | |
# Define an alarm clock for delivery of signal | |
#signal.signal(signal.SIGALRM, lambda signum,frame: print('\nYour time got over')) | |
def get_prompts(prompt_text): | |
return text_gen(prompt_text) | |
# Import the libraries | |
from threading import Timer | |
# Take seconds as integer input | |
# for the time limit per question | |
input_time = 5 | |
# Set the timer for the specified time and call the | |
# function to print the message when time is over | |
t = Timer(input_time, lambda: print("\nYour writing time is over!!\nEnter / to quit the program")) | |
def send_it1(inputs,proc1=proc1): | |
############# | |
output1=None | |
# Start the timer | |
t.start() | |
while t < input_time: | |
output1=proc1(inputs) | |
else: | |
output1=None | |
pass | |
t.cancel() | |
return output1 | |
def send_it2(inputs,proc2=proc2): | |
timeout = time.time() + 60/5 # 5 minutes from now | |
while True: | |
output2=proc2(inputs) | |
if time.time() > timeout: | |
break | |
return(output2) | |
def send_it3(inputs,proc3=proc3): | |
timeout = time.time() + 60/5 # 5 minutes from now | |
while True: | |
output3=proc3(inputs) | |
if time.time() > timeout: | |
break | |
return(output3) | |
def send_it4(inputs,proc4=proc4): | |
timeout = time.time() + 60/5 # 5 minutes from now | |
while True: | |
output4=proc4(inputs) | |
if time.time() > timeout: | |
break | |
return(output4) | |
def send_it5(inputs,proc5=proc5): | |
output5=proc5(inputs) | |
return(output5) | |
def send_it6(inputs,proc6=proc6): | |
output6=proc6(inputs) | |
return(output6) | |
def send_it7(inputs,proc7=proc7): | |
output7=proc7(inputs) | |
return(output7) | |
def send_it8(inputs,proc8=proc8): | |
output8=proc8(inputs) | |
return(output8) | |
def send_it9(inputs,proc9=proc9): | |
output9=proc9(inputs) | |
return(output9) | |
def send_it10(inputs,proc10=proc10): | |
output10=proc10(inputs) | |
return(output10) | |
def send_it11(inputs,proc11=proc11): | |
output11=proc11(inputs) | |
return(output11) | |
def send_it12(inputs,proc12=proc12): | |
output12=proc12(inputs) | |
return(output12) | |
''' | |
def up_it1(inputs,proc1=proc1): | |
output1=proc1(inputs) | |
return(output1) | |
def up_it2(inputs,proc2=proc2): | |
output2=proc2(inputs) | |
return(output2) | |
def up_it3(inputs,proc3=proc3): | |
output3=proc3(inputs) | |
return(output3) | |
def up_it4(inputs,proc4=proc4): | |
output4=proc4(inputs) | |
return(output4) | |
def up_it5(inputs,proc5=proc5): | |
output5=proc5(inputs) | |
return(output5) | |
def up_it6(inputs,proc6=proc6): | |
output6=proc6(inputs) | |
return(output6) | |
def up_it7(inputs,proc7=proc7): | |
output7=proc7(inputs) | |
return(output7) | |
def up_it8(inputs,proc8=proc8): | |
output8=proc8(inputs) | |
return(output8) | |
def up_it9(inputs,proc9=proc9): | |
output9=proc9(inputs) | |
return(output9) | |
def up_it10(inputs,proc10=proc10): | |
output10=proc10(inputs) | |
return(output10) | |
def up_it11(inputs,proc11=proc11): | |
output11=proc11(inputs) | |
return(output11) | |
def up_it12(inputs,proc12=proc12): | |
output12=proc12(inputs) | |
return(output12) | |
#def send_it13(inputs,proc13=proc13): | |
# output13=proc13(inputs) | |
# return(output13) | |
#def send_it14(inputs,proc14=proc14): | |
# output14=proc14(inputs) | |
# return(output14) | |
#def send_it15(inputs,proc15=proc15): | |
# output15=proc15(inputs) | |
# return(output15) | |
#def send_it16(inputs,proc16=proc16): | |
# output16=proc16(inputs) | |
# return(output16) | |
''' | |
def main(): | |
#test = 0 | |
#batch=True, max_batch_size=30 | |
#max_batch_size=20 | |
with gr.Blocks(batch=False) as myface: | |
with gr.Row(): | |
with gr.Tab("Title"): | |
gr.HTML(""" <title>Maximum Diffusion</title><div style="text-align: center; max-width: 1500px; margin: 0 auto;"> | |
<div | |
style=" | |
display: inline-flex; | |
align-items: center; | |
gap: 0.8rem; | |
font-size: 1.75rem; | |
margin-bottom: 10px; | |
line-height: 1em; | |
" | |
> | |
<h1 style="font-weight: 1200; margin-top: 9px;"> | |
Maximum Diffusion | |
</h1> | |
</div> | |
<br> | |
<br> | |
<p style="margin-bottom: 10px;font-size: 94%;font-weight: 100;line-height: 1.5em;"> | |
Text to Image Model Comparison Space - CPU | |
</p><br><br> | |
<p style="margin-bottom: 10px;font-size: 94%;font-weight: 100;line-height: 1.5em;"> | |
Freaky Fast, when it's fresh<br> | |
Try a Mirror, or DIY for Maximum Diffusion in minimum time. | |
</p><br> | |
</div> | |
""") | |
with gr.Tab("Description"): | |
gr.HTML("""<div style="text-align:center;"> | |
<h4>Enter your Prompt into the "Short Prompt" box and click "Magic Prompt" to load a prettified version of your prompt<br> | |
When you are satisfied with the prompt that is in the "Text to Image" box, click "Launch" to load the Models.<br><br> | |
Images load faster with a simpler prompt.<br> | |
Most images should load within 2 minutes.<br> | |
Some models become stuck on certain prompts, and refreshing the page seems to fix it.<br><br> | |
Not responsible for content, use at your own risk. | |
</h4> | |
</div>""") | |
with gr.Tab("DIY"): | |
gr.HTML("""<div style="text-align:Left;"> | |
<h4>Copy/Paste this code in your app.py file<br><br> | |
import gradio as gr<br> | |
max_d=gr.Interface.load("spaces/Omnibus/maximum_diffusion")<br> | |
max_d.launch()<br> | |
</h4> | |
</div>""") | |
with gr.Tab("Mirrors"): | |
gr.HTML("""<div style="text-align:center;vertical-align:center;"> | |
<h4>Queue loading slow? Try a Mirror:<br><br> | |
<a href="https://huggingface.co/spaces/Omnibus/maximum-diffusion-light">Maximum Diffusion Light 1</a><br> | |
<p><a href="https://huggingface.co/spaces/Omnibus/maximum-diffusion-light-1">Maximum Diffusion Light 2</a><br> | |
<p><a href="https://huggingface.co/spaces/Omnibus/maximum-diffusion-light-2">Maximum Diffusion Light 3</a> | |
</h4> | |
</div>""") | |
with gr.Tab("Credits"): | |
with gr.Row(): | |
gr.Column() | |
with gr.Column(style="text-align:left;"): | |
gr.HTML(""" | |
<div style="vertical-align:center"> | |
<br> | |
<p>I learned everything I know from: | |
<p><a href="https://huggingface.co/spaces/anzorq/finetuned_diffusion">Finetuned Diffusion</a></p> | |
<p><a href="https://huggingface.co/spaces/Gustavosta/MagicPrompt-Stable-Diffusion">Magic Prompt Stable Diffusion</a></p> | |
<p><a href="https://huggingface.co/spaces/huggingface-projects/magic-diffusion">Magic Diffusion</a></p> | |
<p>Models by <a href="https://huggingface.co/Gustavosta">@Gustavosta</a>, <a href="https://twitter.com/haruu1367">@haruu1367</a>, <a href="https://twitter.com/DGSpitzer">@Helixngc7293</a>, <a href="https://twitter.com/dal_mack">@dal_mack</a>, <a href="https://twitter.com/prompthero">@prompthero</a> and others.</p> | |
</div> | |
""") | |
gr.Column() | |
with gr.Tab("Tools"): | |
with gr.Tab("View"): | |
with gr.Column(style="width=50%, height=70%"): | |
gr.Pil(label="Crop") | |
with gr.Column(style="width=50%, height=70%"): | |
gr.Pil(label="Crop") | |
with gr.Tab("Draw"): | |
with gr.Column(style="width=50%, height=70%"): | |
gr.Pil(label="Crop") | |
with gr.Column(style="width=50%, height=70%"): | |
gr.Pil(label="Draw") | |
gr.ImagePaint(label="Draw") | |
with gr.Tab("Text"): | |
with gr.Row(): | |
with gr.Column(scale=50): | |
gr.Textbox(label="", lines=8, interactive=True) | |
with gr.Column(scale=50): | |
gr.Textbox(label="", lines=8, interactive=True) | |
with gr.Tab("Color Picker"): | |
with gr.Row(): | |
with gr.Column(scale=50): | |
gr.ColorPicker(label="Color", interactive=True) | |
with gr.Column(scale=50): | |
gr.ImagePaint(label="Draw", interactive=True) | |
with gr.Row(): | |
input_text=gr.Textbox(label="Short Prompt") | |
see_prompts=gr.Button("Magic Prompt") | |
with gr.Row(): | |
prompt=gr.Textbox(label="Text to Image") | |
run=gr.Button("Launch") | |
# with gr.Row(): | |
# gr.Column() | |
# with gr.Column(): | |
# clear_btn=gr.Button("Test") | |
with gr.Row(): | |
output1=gr.Image(label="nitrosocke/Arcane-Diffusion") | |
output2=gr.Image(label="naclbit/trinart_stable_diffusion_v2") | |
output3=gr.Image(label="nitrosocke/redshift-diffusion") | |
output4=gr.Image(label="runwayml/stable-diffusion-v1-5") | |
with gr.Row(): | |
output5=gr.Image(label="claudfuen/photorealistic-fuen-v1") | |
output6=gr.Image(label="CompVis/stable-diffusion-v1-4") | |
output7=gr.Image(label="Linaqruf/anything-v3.0") | |
output8=gr.Image(label="andite/anything-v4.0") | |
with gr.Row(): | |
output9=gr.Image(label="dreamlike-art/dreamlike-photoreal-1.0") | |
output10=gr.Image(label="prompthero/openjourney") | |
output11=gr.Image(label="prompthero/midjourney-v4-diffusion") | |
output12=gr.Image(label="wavymulder/Analog-Diffusion") | |
with gr.Row(): | |
stat1=gr.Textbox(visible=False, value="running") | |
stat2=gr.Textbox(visible=False, value="running") | |
stat3=gr.Textbox(visible=False, value="running") | |
stat4=gr.Textbox(visible=False, value="running") | |
stat5=gr.Textbox(visible=False, value="running") | |
stat6=gr.Textbox(visible=False, value="running") | |
stat7=gr.Textbox(visible=False, value="running") | |
stat8=gr.Textbox(visible=False, value="running") | |
stat9=gr.Textbox(visible=False, value="running") | |
stat10=gr.Textbox(visible=False, value="running") | |
stat11=gr.Textbox(visible=False, value="running") | |
stat12=gr.Textbox(visible=False, value="running") | |
#with gr.Row(): | |
# output13=gr.Image(label="dreamlike-photoreal-2.0") | |
# output14=gr.Image(label="Analog-Diffusion") | |
# | |
# output15=gr.Image(label="redshift-diffusion") | |
# output16=gr.Image(label="midjourney-v4-diffusion") | |
#def set_models(model_name1, model_name2, model_name3, model_name4): | |
#return(proc1,proc2,proc3,proc4) | |
#run.click(set_models, inputs=[model_name1, model_name2, model_name3, model_name4], outputs=[proc1,proc2,proc3,proc4]) | |
#run.click(send_it, inputs=[prompt], outputs=[output1, output2, output3, output4]) | |
def clear_queue(): | |
myface.queue.clear() | |
#clear_btn.click(clear_queue, inputs=None, outputs=None) | |
see_prompts.click(get_prompts, inputs=[input_text], outputs=[prompt]) | |
#timeout = time.time() + 5 #60*1 #1 minute # 5 minutes from now | |
run.click(send_it1, inputs=[prompt], outputs=[output1]) | |
run.click(send_it2, inputs=[prompt], outputs=[output2]) | |
run.click(send_it3, inputs=[prompt], outputs=[output3]) | |
run.click(send_it4, inputs=[prompt], outputs=[output4]) | |
run.click(send_it5, inputs=[prompt], outputs=[output5]) | |
run.click(send_it6, inputs=[prompt], outputs=[output6]) | |
run.click(send_it7, inputs=[prompt], outputs=[output7]) | |
run.click(send_it8, inputs=[prompt], outputs=[output8]) | |
run.click(send_it9, inputs=[prompt], outputs=[output9]) | |
run.click(send_it10, inputs=[prompt], outputs=[output10]) | |
run.click(send_it11, inputs=[prompt], outputs=[output11]) | |
run.click(send_it12, inputs=[prompt], outputs=[output12]) | |
#output1.change(up_it1, inputs=[], outputs=[stat1]) | |
#output2.change(up_it2, inputs=[], outputs=[stat2]) | |
#output3.change(up_it3, inputs=[], outputs=[stat3]) | |
#output4.change(up_it4, inputs=[], outputs=[stat4]) | |
#output5.change(up_it5, inputs=[], outputs=[stat5]) | |
#output6.change(up_it6, inputs=[], outputs=[stat6]) | |
#output7.change(up_it7, inputs=[], outputs=[stat7]) | |
#output8.change(up_it8, inputs=[], outputs=[stat8]) | |
#output9.change(up_it9, inputs=[], outputs=[stat9]) | |
#output10.change(up_it10, inputs=[], outputs=[stat10]) | |
#output11.change(up_it11, inputs=[], outputs=[stat11]) | |
#output12.change(up_it12, inputs=[], outputs=[stat12]) | |
#run.click(send_it13, inputs=[prompt], outputs=[output13]) | |
#run.click(send_it14, inputs=[prompt], outputs=[output14]) | |
#run.click(send_it15, inputs=[prompt], outputs=[output15]) | |
#run.click(send_it16, inputs=[prompt], outputs=[output16]) | |
#myface.queue(default_enabled=False) | |
#myface.queue(concurrency_count=240,status_update_rate=1) | |
myface.queue(concurrency_count=800,status_update_rate=1) | |
myface.launch(enable_queue=True,inline=True,max_threads=800) | |
#myface.launch(enable_queue=True, max_threads=20) | |
if __name__ == "__main__": | |
main() | |