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()
HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None
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}', hf_token=HF_TOKEN)
except Exception as error:
print(error)
m = gr.Interface(lambda: None, ['text'], ['image'])
models_load.update({model: m})
load_fn(models)
num_models = 6
max_images = 6
inference_timeout = 300
default_models = models[:num_models]
def extend_choices(choices):
return choices[:num_models] + (num_models - len(choices[:num_models])) * ['NA']
def update_imgbox(choices):
choices_plus = extend_choices(choices[:num_models])
return [gr.Image(None, label = m, visible = (m != 'NA')) for m in choices_plus]
def random_choices():
import random
random.seed()
return random.choices(models, k = num_models)
# https://huggingface.co/docs/api-inference/detailed_parameters
# https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
async def infer(model_str, prompt, nprompt="", height=None, width=None, steps=None, cfg=None, timeout=inference_timeout):
from pathlib import Path
kwargs = {}
if height is not None and height >= 256: kwargs["height"] = height
if width is not None and width >= 256: kwargs["width"] = width
if steps is not None and steps >= 1: kwargs["num_inference_steps"] = steps
if cfg is not None and cfg > 0: cfg = kwargs["guidance_scale"] = cfg
noise = ""
rand = randint(1, 500)
for i in range(rand):
noise += " "
task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn,
prompt=f'{prompt} {noise}', negative_prompt=nprompt, **kwargs, token=HF_TOKEN))
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_fn(model_str, prompt, nprompt="", height=None, width=None, steps=None, cfg=None):
if model_str == 'NA':
return None
try:
loop = asyncio.new_event_loop()
result = loop.run_until_complete(infer(model_str, prompt, nprompt,
height, width, steps, cfg, inference_timeout))
except (Exception, asyncio.CancelledError) as e:
print(e)
print(f"Task aborted: {model_str}")
result = None
finally:
loop.close()
return result
def add_gallery(image, model_str, gallery):
if gallery is None: gallery = []
with lock:
if image is not None: gallery.insert(0, (image, model_str))
return gallery
CSS="""
#container { max-width: 1200px; margin: 0 auto; !important; }
.output { width=112px; height=112px; !important; }
.gallery { width=100%; min_height=768px; !important; }
.guide { text-align: center; !important; }
"""
with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=CSS) as demo:
with gr.Tab('The Dream'):
with gr.Column(scale=2):
with gr.Group():
txt_input = gr.Textbox(label='Your prompt:', lines=4)
neg_input = gr.Textbox(label='Negative prompt:', lines=1)
with gr.Accordion("Advanced", open=False, visible=True):
width = gr.Number(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
height = gr.Number(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
steps = gr.Number(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
cfg = gr.Number(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
with gr.Row():
gen_button = gr.Button(f'Generate up to {int(num_models)} images in up to 3 minutes total', scale=3)
random_button = gr.Button(f'Random {int(num_models)} 🎲', variant='secondary', scale=1)
stop_button = gr.Button('Stop', variant='secondary', interactive=False, scale=1)
gen_button.click(lambda: gr.update(interactive=True), None, stop_button)
gr.Markdown("Scroll down to see more images and select models.", elem_classes="guide")
with gr.Column(scale=1):
with gr.Group():
with gr.Row():
output = [gr.Image(label=m, show_download_button=True, elem_classes="output",
interactive=False, min_width=80, show_share_button=False, format=".png",
visible=True) for m in default_models]
current_models = [gr.Textbox(m, visible=False) for m in default_models]
with gr.Column(scale=2):
gallery = gr.Gallery(label="Output", show_download_button=True, elem_classes="gallery",
interactive=False, show_share_button=True, container=True, format="png",
preview=True, object_fit="cover", columns=2, rows=2)
for m, o in zip(current_models, output):
gen_event = gr.on(triggers=[gen_button.click, txt_input.submit], fn=gen_fn,
inputs=[m, txt_input, neg_input, height, width, steps, cfg], outputs=[o])
o.change(add_gallery, [o, m, gallery], [gallery])
stop_button.click(lambda: gr.update(interactive=False), None, stop_button, cancels=[gen_event])
with gr.Column(scale=4):
with gr.Accordion('Model selection'):
model_choice = gr.CheckboxGroup(models, label = f'Choose up to {int(num_models)} different models from the {len(models)} available!', value=default_models, interactive=True)
model_choice.change(update_imgbox, model_choice, output)
model_choice.change(extend_choices, model_choice, current_models)
random_button.click(random_choices, None, model_choice)
with gr.Tab('Single model'):
with gr.Column(scale=2):
model_choice2 = gr.Dropdown(models, label='Choose model', value=models[0])
with gr.Group():
txt_input2 = gr.Textbox(label='Your prompt:', lines=4)
neg_input2 = gr.Textbox(label='Negative prompt:', lines=1)
with gr.Accordion("Advanced", open=False, visible=True):
width2 = gr.Number(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
height2 = gr.Number(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
steps2 = gr.Number(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
cfg2 = gr.Number(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
num_images = gr.Slider(1, max_images, value=max_images, step=1, label='Number of images')
with gr.Row():
gen_button2 = gr.Button('Generate', scale=2)
stop_button2 = gr.Button('Stop', variant='secondary', interactive=False, scale=1)
gen_button2.click(lambda: gr.update(interactive=True), None, stop_button2)
with gr.Column(scale=1):
with gr.Group():
with gr.Row():
output2 = [gr.Image(label='', show_download_button=True, elem_classes="output",
interactive=False, min_width=80, visible=True, format=".png",
show_share_button=False, show_label=False) for _ in range(max_images)]
with gr.Column(scale=2):
gallery2 = gr.Gallery(label="Output", show_download_button=True, elem_classes="gallery",
interactive=False, show_share_button=True, container=True, format="png",
preview=True, object_fit="cover", columns=2, rows=2)
for i, o in enumerate(output2):
img_i = gr.Number(i, visible = False)
num_images.change(lambda i, n: gr.update(visible = (i < n)), [img_i, num_images], o)
gen_event2 = gr.on(triggers=[gen_button2.click, txt_input2.submit],
fn=lambda i, n, m, t1, t2, n1, n2, n3, n4: gen_fn(m, t1, t2, n1, n2, n3, n4) if (i < n) else None,
inputs=[img_i, num_images, model_choice2, txt_input2, neg_input2,
height2, width2, steps2, cfg2], outputs=[o])
o.change(add_gallery, [o, model_choice2, gallery2], [gallery2])
stop_button2.click(lambda: gr.update(interactive=False), None, stop_button2, cancels=[gen_event2])
gr.Markdown("Based on the [TestGen](https://huggingface.co/spaces/derwahnsinn/TestGen) Space by derwahnsinn, the [SpacIO](https://huggingface.co/spaces/RdnUser77/SpacIO_v1) Space by RdnUser77 and Omnibus's Maximum Multiplier!")
demo.queue()
demo.launch()