import os
import gradio as gr
from random import randint
from operator import itemgetter
import bisect
from all_models import tags_plus_models,models,models_plus_tags
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
inference_timeout = 300
MAX_SEED = 2**32-1
nb_rep=2
nb_mod_dif=20
nb_models=nb_mod_dif*nb_rep
cache_image={}
cache_image_actu={}
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
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
def construct_list_models(tags_plus_models,nb_rep,nb_mod_dif):
list_temp=[]
output=[]
for tag_plus_models in tags_plus_models:
list_temp=split_models_axb(tag_plus_models[2],nb_rep,nb_mod_dif)
list_temp2=[]
i=0
for elem in list_temp:
list_temp2.append([tag_plus_models[0]+"_"+str(i)+" : "+elem[0]+" - "+elem[len(elem)-1] ,elem])
i+=1
output.append([tag_plus_models[0] + " (" + str(tag_plus_models[1]) + ")",list_temp2])
return output
models_test = []
models_test = construct_list_models(tags_plus_models,nb_rep,nb_mod_dif)
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)
i=0
if num_models!=0:
default_models = models[:num_models]
else:
default_models = {}
for model in models:
i+=1
if i%50==0:
print("\n\n\n-------"+str(i)+'/'+str(len(models))+"-------\n\n\n")
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,interactive=False, visible=(m != 'NA')) for m in choices_plus]
def choice_group_a(group_model_choice):
return group_model_choice
def choice_group_b(group_model_choice):
choiceTemp =choice_group_a(group_model_choice)
choiceTemp = extend_choices(choiceTemp)
"""return [gr.Image(label=m, min_width=170, height=170) for m in choice]"""
return [gr.Image(None, label=m,interactive=False, visible=(m != 'NA')) for m in choiceTemp]
def choice_group_c(group_model_choice):
choiceTemp=choice_group_a(group_model_choice)
choiceTemp = extend_choices(choiceTemp)
return [gr.Textbox(m, visible=False) for m in choiceTemp]
def cutStrg(longStrg,start,end):
shortStrg=''
for i in range(end-start):
shortStrg+=longStrg[start+i]
return shortStrg
def aff_models_perso(txt_list_perso,nb_models=nb_models,models=models):
list_perso=[]
t1=True
start=txt_list_perso.find('\"')
if start!=-1:
while t1:
start+=1
end=txt_list_perso.find('\"',start)
if end != -1:
txtTemp=cutStrg(txt_list_perso,start,end)
if txtTemp in models:
list_perso.append(cutStrg(txt_list_perso,start,end))
else :
t1=False
start=txt_list_perso.find('\"',end+1)
if start==-1:
t1=False
if len(list_perso)>=nb_models:
t1=False
return list_perso
def aff_models_perso_b(txt_list_perso):
return choice_group_b(aff_models_perso(txt_list_perso))
def aff_models_perso_c(txt_list_perso):
return choice_group_c(aff_models_perso(txt_list_perso))
def tag_choice(group_tag_choice):
return gr.Dropdown(label="List of Models with the chosen Tag", show_label=True, choices=list(group_tag_choice) , interactive = True , filterable = False)
def test_pass(test):
if test==os.getenv('p'):
print("ok")
return gr.Dropdown(label="Lists Tags", show_label=True, choices=list(models_test) , interactive = True)
else:
print("nop")
return gr.Dropdown(label="Lists Tags", show_label=True, choices=list([]) , interactive = True)
def test_pass_aff(test):
if test==os.getenv('p'):
return gr.Accordion( open=True, visible=True)
else:
return gr.Accordion( open=True, visible=False)
# 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 add_gallery(image, model_str, gallery):
if gallery is None: gallery = []
#with lock:
if image is not None: gallery.append((image, model_str))
return gallery
def reset_gallery(gallery):
return add_gallery(None,"",[])
def load_gallery(gallery,id):
gallery = reset_gallery(gallery)
for c in cache_image[f"{id}"]:
gallery=add_gallery(c[0],c[1],gallery)
return gallery
def load_gallery_actu(gallery,id):
gallery = reset_gallery(gallery)
#for c in cache_image_actu:
for c in cache_image_actu[f"{id}"]:
gallery=add_gallery(c[0],c[1],gallery)
return gallery
def add_cache_image(image, model_str,id,cache_image=cache_image):
if image is not None:
cache_image[f"{id}"].append((image,model_str))
#cache_image=sorted(cache_image, key=itemgetter(1))
return
def add_cache_image_actu(image, model_str,id,cache_image_actu=cache_image_actu):
if image is not None:
bisect.insort(cache_image_actu[f"{id}"],(image, model_str), key=itemgetter(1))
#cache_image_actu=sorted(cache_image_actu, key=itemgetter(1))
return
def reset_cache_image(id,cache_image=cache_image):
cache_image[f"{id}"].clear()
return
def reset_cache_image_actu(id,cache_image_actu=cache_image_actu):
cache_image_actu[f"{id}"].clear()
return
def reset_cache_image_all_sessions(cache_image=cache_image):
for key, listT in cache_image.items():
listT.clear()
return
def set_session(id):
if id==0:
randTemp=randint(1,MAX_SEED)
cache_image[f"{randTemp}"]=[]
cache_image_actu[f"{randTemp}"]=[]
return gr.Number(visible=False,value=randTemp)
else :
return id
def print_info_sessions():
lenTot=0
print("numbre of sessions : "+str(len(cache_image)))
for key, listT in cache_image.items():
print(key+str(len(listT)))
lenTot+=len(listT)
print("images total = "+str(lenTot))
return
def disp_models(group_model_choice,nb_rep=nb_rep):
listTemp=[]
strTemp='\n'
i=0
for m in group_model_choice:
if m not in listTemp:
listTemp.append(m)
for m in listTemp:
i+=1
strTemp+="\"" + m + "\",\n"
if i%(8/nb_rep)==0:
strTemp+="\n"
return gr.Textbox(label="models",value=strTemp)
def search_models(str_search,tags_plus_models=tags_plus_models):
output1="\n"
output2=""
for m in tags_plus_models[0][2]:
if m.find(str_search)!=-1:
output1+="\"" + m + "\",\n"
outputPlus="\n From tags : \n\n"
for tag_plus_models in tags_plus_models:
if str_search.lower() == tag_plus_models[0].lower() and str_search!="":
for m in tag_plus_models[2]:
output2+="\"" + m + "\",\n"
if output2 != "":
output=output1+outputPlus+output2
else :
output=output1
return gr.Textbox(label="out",value=output)
def search_info(txt_search_info,models_plus_tags=models_plus_tags):
outputList=[]
if txt_search_info.find("\"")!=-1:
start=txt_search_info.find("\"")+1
end=txt_search_info.find("\"",start)
m_name=cutStrg(txt_search_info,start,end)
else :
m_name = txt_search_info
for m in models_plus_tags:
if m_name == m[0]:
outputList=m[1]
if len(outputList)==0:
outputList.append("Model Not Find")
return gr.Textbox(label="out",value=outputList)
def ratio_chosen(choice_ratio,width,height):
if choice_ratio == [None,None]:
return width , height
else :
return gr.Slider(label="Width", info="If 0, the default value is used.", maximum=2024, step=32, value=choice_ratio[0]), gr.Slider(label="Height", info="If 0, the default value is used.", maximum=2024, step=32, value=choice_ratio[1])
list_ratios=[["None",[None,None]],
["4:1 (2048 x 512)",[2048,512]],
["12:5 (1536 x 640)",[1536,640]],
["~16:9 (1344 x 768)",[1344,768]],
["~3:2 (1216 x 832)",[1216,832]],
["~4:3 (1152 x 896)",[1152,896]],
["1:1 (1024 x 1024)",[1024,1024]],
["~3:4 (896 x 1152)",[896,1152]],
["~2:3 (832 x 1216)",[832,1216]],
["~9:16 (768 x 1344)",[768,1344]],
["5:12 (640 x 1536)",[640,1536]],
["1:4 (512 x 2048)",[512,2048]]]
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():
choice_ratio = gr.Dropdown(label="Ratio Width/Height",
info="OverWrite Width and Height (W*H<1024*1024)",
show_label=True, choices=list(list_ratios) , interactive = True, value=list_ratios[0])
choice_ratio.change(ratio_chosen,[choice_ratio,width,height],[width,height])
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][1][0][1]
"""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 = 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.Row():
txt_input_p = gr.Textbox(label="Pass", lines=1)
test_button = gr.Button(' ')
with gr.Accordion( open=True, visible=False) as stuffs:
with gr.Accordion("Gallery",open=False):
with gr.Row():
#global cache_image
#global cache_image_actu
id_session=gr.Number(visible=False,value=0)
gen_button.click(set_session, id_session, id_session)
cache_image[f"{id_session.value}"]=[]
cache_image_actu[f"{id_session.value}"]=[]
with gr.Column():
b11 = gr.Button('Load Galerry Actu')
b12 = gr.Button('Load Galerry All')
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=4,rows=4)
with gr.Column():
b21 = gr.Button('Reset Gallery')
b22 = gr.Button('Reset Gallery All')
b23 = gr.Button('Reset All Sessions')
b24 = gr.Button('print info sessions')
b11.click(load_gallery_actu,[gallery,id_session],gallery)
b12.click(load_gallery,[gallery,id_session],gallery)
b21.click(reset_gallery,[gallery],gallery)
b22.click(reset_cache_image,[id_session],gallery)
b23.click(reset_cache_image_all_sessions,[],[])
b24.click(print_info_sessions,[],[])
for m, o in zip(current_models, output):
#o.change(add_gallery, [o, m, gallery], [gallery])
o.change(add_cache_image,[o,m,id_session],[])
o.change(add_cache_image_actu,[o,m,id_session],[])
gen_button.click(reset_cache_image_actu, [id_session], [])
gen_button.click(lambda id:gr.Button('Load Galerry All ('+str(len(cache_image_actu[f"{id}"]))+")"), [id_session], [b12])
with gr.Group():
with gr.Row():
group_tag_choice = gr.Dropdown(label="Lists Tags", show_label=True, choices=list([]) , interactive = True)
with gr.Row():
group_model_choice = gr.Dropdown(label="List of Models with the chosen Tag", show_label=True, choices=list([]) , interactive = 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_tag_choice.change(tag_choice,group_tag_choice,group_model_choice)
with gr.Row():
txt_list_models=gr.Textbox(label="Models Actu",value="")
group_model_choice.change(disp_models,group_model_choice,txt_list_models)
with gr.Row():
txt_list_perso = gr.Textbox(label='List Models Perso')
button_list_perso = gr.Button('Load')
button_list_perso.click(aff_models_perso_b,txt_list_perso,output)
button_list_perso.click(aff_models_perso_c,txt_list_perso,current_models)
with gr.Row():
txt_search = gr.Textbox(label='Search in')
txt_output_search = gr.Textbox(label='Search out')
button_search = gr.Button('Research')
button_search.click(search_models,txt_search,txt_output_search)
with gr.Row():
txt_search_info = gr.Textbox(label='Search info in')
txt_output_search_info = gr.Textbox(label='Search info out')
button_search_info = gr.Button('Research info')
button_search_info.click(search_info,txt_search_info,txt_output_search_info)
with gr.Row():
test_button.click(test_pass_aff,txt_input_p,stuffs)
test_button.click(test_pass,txt_input_p,group_tag_choice)
gr.HTML("""