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import gradio as gr
from random import randint
from all_models import models
from externalmod import gr_Interface_load
import asyncio
import os
from threading import RLock
lock = RLock()
def load_fn(models):
global models_load
models_load = {}
for model in models:
if model not in models_load.keys():
try:
m = gr_Interface_load(f'models/{model}')
except Exception as error:
print(error)
m = gr.Interface(lambda: None, ['text'], ['image'])
models_load.update({model: m})
load_fn(models)
num_models = 1
default_models = models[:num_models]
inference_timeout = 600
MAX_SEED = 3999999999
def extend_choices(choices):
return choices + (num_models - len(choices)) * ['NA']
def update_imgbox(choices):
choices_plus = extend_choices(choices)
return [gr.Image(None, label=m, visible=(m != 'NA')) for m in choices_plus]
def gen_fn(model_str, prompt):
if model_str == 'NA':
return None
noise = str('')
return models_load[model_str](f'{prompt} {noise}')
async def infer(model_str, prompt, seed=1, timeout=inference_timeout):
from pathlib import Path
kwargs = {}
noise = ""
kwargs["seed"] = seed
task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn,
prompt=f'{prompt} {noise}', **kwargs))
await asyncio.sleep(0)
try:
result = await asyncio.wait_for(task, timeout=timeout)
except (Exception, asyncio.TimeoutError) as e:
print(e)
print(f"Task timed out: {model_str}")
if not task.done(): task.cancel()
result = None
if task.done() and result is not None:
with lock:
png_path = "image.png"
result.save(png_path)
image = str(Path(png_path).resolve())
return image
return None
def gen_fnseed(model_str, prompt, seed=1):
if model_str == 'NA':
return None
try:
loop = asyncio.new_event_loop()
result = loop.run_until_complete(infer(model_str, prompt, seed, inference_timeout))
except (Exception, asyncio.CancelledError) as e:
print(e)
print(f"Task aborted: {model_str}")
result = None
with lock:
image = "https://huggingface.co/spaces/Yntec/ToyWorld/resolve/main/error.png"
result = image
finally:
loop.close()
return result
def gen_fnsix(model_str, prompt):
if model_str == 'NA':
return None
noisesix = str(randint(1941, 2023))
return models_load[model_str](f'{prompt} {noisesix}')
with gr.Blocks() as demo:
gr.HTML(
"""
<div>
<p> <center><img src="https://huggingface.co/Yntec/OpenGenDiffusers/resolve/main/pp.png" style="height:128px; width:482px; margin-top: -22px; margin-bottom: -44px;" span title="Free ai art image generator Printing Press"></center>
</p>
"""
)
gr.HTML(
"""
<div>
<p> <center>For negative prompts, Width and Height, and other features visit John6666's <a href="https://huggingface.co/spaces/John6666/PrintingPress4">Printing Press 4</a>!</center>
</p></div>
"""
)
with gr.Tab('One Image'):
model_choice = gr.Dropdown(models, label=f'Choose a model from the {len(models)} available! Try clearing the box and typing on it to filter them!', value=models[0], filterable=True)
txt_input = gr.Textbox(label='Your prompt:')
max_imagesone = 1
num_imagesone = gr.Slider(1, max_imagesone, value=max_imagesone, step=1, label='Nobody gets to see this label so I can put here whatever I want!', visible=False)
gen_button = gr.Button('Generate')
with gr.Row():
output = [gr.Image(label='') for _ in range(max_imagesone)]
for i, o in enumerate(output):
img_in = gr.Number(i, visible=False)
num_imagesone.change(lambda i, n: gr.update(visible=(i < n)), [img_in, num_imagesone], o, show_progress=False)
gen_event = gen_button.click(lambda i, n, m, t: gen_fn(m, t) if (i < n) else None, [img_in, num_imagesone, model_choice, txt_input], o, concurrency_limit=None, queue=False)
with gr.Row():
gr.HTML(
"""
<div class="footer">
<p> Based on the <a href="https://huggingface.co/spaces/derwahnsinn/TestGen">TestGen</a> Space by derwahnsinn, the <a href="https://huggingface.co/spaces/RdnUser77/SpacIO_v1">SpacIO</a> Space by RdnUser77, Omnibus's Maximum Multiplier, and <a href="https://huggingface.co/spaces/Yntec/ToyWorld">Toy World</a>!
</p>
"""
)
with gr.Tab('Seed it!'):
model_choiceseed = gr.Dropdown(models, label=f'Choose a model from the {len(models)} available! Try clearing the box and typing on it to filter them!', value=models[0], filterable=True)
txt_inputseed = gr.Textbox(label='Your prompt:')
seed = gr.Slider(label="Use a seed to replicate the same image later", info="Max 3999999999", minimum=0, maximum=MAX_SEED, step=1, value=1)
max_imagesseed = 1
num_imagesseed = gr.Slider(1, max_imagesone, value=max_imagesone, step=1, label='One, because more would make it produce identical images with the seed', visible=False)
gen_buttonseed = gr.Button('Generate an image using the seed')
with gr.Row():
outputseed = [gr.Image(label='') for _ in range(max_imagesseed)]
for i, o in enumerate(outputseed):
img_is = gr.Number(i, visible=False)
num_imagesseed.change(lambda i, n: gr.update(visible=(i < n)), [img_is, num_imagesseed], o, show_progress=False)
gen_eventseed = gr.on(triggers=[gen_buttonseed.click, txt_inputseed.submit],
fn=lambda i, n, m, t, n1: gen_fnseed(m, t, n1) if (i < n) else None,
inputs=[img_is, num_imagesseed, model_choiceseed, txt_inputseed, seed], outputs=[o],
concurrency_limit=None, queue=False)
with gr.Row():
gr.HTML(
"""
<div class="footer">
<p> Based on the <a href="https://huggingface.co/spaces/derwahnsinn/TestGen">TestGen彼此
</p>
""") |