import os
import gradio as gr
from random import randint
from all_models import models
from datetime import datetime
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 # If private or gated models aren't used, ENV setting is unnecessary.
now2 = 0
nb_models=24
inference_timeout = 300
MAX_SEED = 2**32-1
def split_models(models,nb_models):
models_temp=[]
models_lis_temp=[]
i=0
for m in models:
models_temp.append(m)
i=i+1
if i%nb_models==0:
models_lis_temp.append(models_temp)
models_temp=[]
if len(models_temp)>1:
models_lis_temp.append(models_temp)
return models_lis_temp
def split_models_axb(models,a,b):
models_temp=[]
models_lis_temp=[]
i=0
nb_models=b
for m in models:
for j in range(a):
models_temp.append(m)
i=i+1
if i%nb_models==0:
models_lis_temp.append(models_temp)
models_temp=[]
if len(models_temp)>1:
models_lis_temp.append(models_temp)
return models_lis_temp , a*b
def split_models_8x3(models,nb_models):
models_temp=[]
models_lis_temp=[]
i=0
nb_models_x3=8
for m in models:
models_temp.append(m)
i=i+1
if i%nb_models_x3==0:
models_lis_temp.append(models_temp+models_temp+models_temp)
models_temp=[]
if len(models_temp)>1:
models_lis_temp.append(models_temp+models_temp+models_temp)
return models_lis_temp
"""models_test=split_models_x3(models,nb_models)"""
"""models_test=split_models(models,nb_models)"""
models_test , nb_models =split_models_axb(models,2,20)
def get_current_time():
now = datetime.now()
now2 = now
current_time = now2.strftime("%Y-%m-%d %H:%M:%S")
kii = "" # ?
ki = f'{kii} {current_time}'
return ki
def load_fn_original(models):
global models_load
global num_models
global default_models
models_load = {}
num_models = len(models)
if num_models!=0:
default_models = models[:num_models]
else:
default_models = {}
for model in models:
if model not in models_load.keys():
try:
m = gr.load(f'models/{model}')
except Exception as error:
m = gr.Interface(lambda txt: None, ['text'], ['image'])
print(error)
models_load.update({model: m})
def load_fn(models):
global models_load
global num_models
global default_models
models_load = {}
num_models = len(models)
if num_models!=0:
default_models = models[:num_models]
else:
default_models = {}
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:
m = gr.Interface(lambda txt: None, ['text'], ['image'])
print(error)
models_load.update({model: m})
"""models = models_test[1]"""
#load_fn_original
load_fn(models)
"""models = {}
load_fn(models)"""
def extend_choices(choices):
return choices + (nb_models - len(choices)) * ['NA']
"""return choices + (num_models - len(choices)) * ['NA']"""
def extend_choices_b(choices):
choices_plus = extend_choices(choices)
return [gr.Textbox(m, visible=False) for m in choices_plus]
def update_imgbox(choices):
choices_plus = extend_choices(choices)
return [gr.Image(None, label=m, visible=(m != 'NA')) for m in choices_plus]
def choice_group_a(group_model_choice):
for m in models_test:
if group_model_choice==m[1]:
choice=m
print(choice)
return choice
def choice_group_b(group_model_choice):
choice=choice_group_a(group_model_choice)
choice = extend_choices(choice)
"""return [gr.Image(label=m, min_width=170, height=170) for m in choice]"""
return [gr.Image(None, label=m, visible=(m != 'NA')) for m in choice]
def choice_group_c(group_model_choice):
choice=choice_group_a(group_model_choice)
choice = extend_choices(choice)
return [gr.Textbox(m, visible=False) for m in choice]
def choice_group_d(var_Test):
(gen_button,stop_button,output,current_models, txt_input)=var_Test
for m, o in zip(current_models, output):
gen_event = gen_button.click(gen_fn, [m, txt_input], o)
stop_button.click(lambda: gr.update(interactive=False), None, stop_button, cancels=[gen_event])
return gen_event
def test_pass(test):
if test==os.getenv('p'):
#if True:
print("ok")
return gr.Dropdown(label="test Model", show_label=False, choices=list(models_test) , allow_custom_value=True)
else:
print("nop")
return gr.Dropdown(label="test Model", show_label=False, choices=list([]) , allow_custom_value=True)
# 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, seed=-1, 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 = ""
if seed >= 0: kwargs["seed"] = seed
else:
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, seed=-1):
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, seed, inference_timeout))
except (Exception, asyncio.CancelledError) as e:
print(e)
print(f"Task aborted: {model_str}")
result = None
finally:
loop.close()
return result
def gen_fn_original(model_str, prompt):
if model_str == 'NA':
return None
noise = str(randint(0, 9999))
try :
m=models_load[model_str](f'{prompt} {noise}')
except Exception as error :
print("error : " + model_str)
print(error)
m=False
return m
def make_me():
# with gr.Tab('The Dream'):
with gr.Row():
#txt_input = gr.Textbox(lines=3, width=300, max_height=100)
#txt_input = gr.Textbox(label='Your prompt:', lines=3, width=300, max_height=100)
with gr.Column(scale=4):
with gr.Group():
txt_input = gr.Textbox(label='Your prompt:', lines=3)
with gr.Accordion("Advanced", open=False, visible=True):
neg_input = gr.Textbox(label='Negative prompt:', lines=1)
with gr.Row():
width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
with gr.Row():
steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
#gen_button = gr.Button('Generate images', width=150, height=30)
#stop_button = gr.Button('Stop', variant='secondary', interactive=False, width=150, height=30)
gen_button = gr.Button('Generate images', scale=3)
stop_button = gr.Button('Stop', variant='secondary', interactive=False, scale=1)
gen_button.click(lambda: gr.update(interactive=True), None, stop_button)
#gr.HTML("""
#
#
#
#
#""")
with gr.Row():
"""output = [gr.Image(label=m, min_width=170, height=170) for m in default_models]
current_models = [gr.Textbox(m, visible=False) for m in default_models]"""
"""choices=[models_test[0][0]]"""
choices=models_test[0]
"""output = [gr.Image(label=m, min_width=170, height=170) for m in choices]
current_models = [gr.Textbox(m, visible=False) for m in choices]"""
output = update_imgbox([choices[0]])
current_models = extend_choices_b([choices[0]])
for m, o in zip(current_models, output):
#gen_event = gen_button.click(gen_fn_original, [m, txt_input], o)
gen_event = gr.on(triggers=[gen_button.click, txt_input.submit], fn=gen_fn,
inputs=[m, txt_input, neg_input, height, width, steps, cfg, seed], outputs=[o])
stop_button.click(lambda: gr.update(interactive=False), None, stop_button, cancels=[gen_event])
"""with gr.Accordion('Model selection'):
model_choice = gr.CheckboxGroup(models, label=f' {num_models} different models selected', value=default_models, multiselect=True, max_choices=num_models, interactive=True, filterable=False)
model_choice.change(update_imgbox, (gen_button,stop_button,group_model_choice), output)
model_choice.change(extend_choices, model_choice, current_models)
"""
with gr.Accordion("test", open=True):
"""group_model_choice = gr.Dropdown(label="test Model", show_label=False, choices=list(models_test) , allow_custom_value=True)"""
group_model_choice = gr.Dropdown(label="test Model", show_label=False, choices=list([]) , allow_custom_value=True)
group_model_choice.change(choice_group_b,group_model_choice,output)
group_model_choice.change(choice_group_c,group_model_choice,current_models)
"""group_model_choice.change(choice_group_d,(gen_button,stop_button,output,current_models,txt_input),gen_event)"""
with gr.Row():
#txt_input_p = gr.Textbox(label='test', lines=1, width=300, max_height=100)
txt_input_p = gr.Textbox(label='test', lines=1)
#test_button = gr.Button('test', width=30, height=10)
test_button = gr.Button('test')
test_button.click(test_pass,txt_input_p,group_model_choice)
with gr.Row():
gr.HTML("""