import gradio as gr
from random import randint
from all_models import models
from externalmod import gr_Interface_load, randomize_seed
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.
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
default_models = models[:num_models]
inference_timeout = 600
MAX_SEED=3999999999
starting_seed = randint(1941, 2024)
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]
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, 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_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
finally:
loop.close()
return result
with gr.Blocks() as demo:
gr.HTML(
"""
For more options like negative prompts, single model x6 or seeds up to 4294967295 check out Diffusion80XX4sg by John6666!
"""
)
with gr.Tab('🤗 Huggingface Diffusion 🤗'):
txt_input = gr.Textbox(label='Your prompt:', lines=4)
gen_button = gr.Button('Generate up to 6 images in up to 3 minutes total')
with gr.Row():
seed = gr.Slider(label="Use a seed to replicate the same image later (maximum 3999999999)", minimum=0, maximum=MAX_SEED, step=1, value=starting_seed, scale=3)
seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary", scale=1)
seed_rand.click(randomize_seed, None, [seed], queue=False)
#stop_button = gr.Button('Stop', variant = 'secondary', interactive = False)
gen_button.click(lambda s: gr.update(interactive = True), None)
gr.HTML(
"""
Scroll down to see more images and select models.
"""
)
with gr.Row():
output = [gr.Image(label = m, min_width=480) for m in default_models]
current_models = [gr.Textbox(m, visible = False) for m in default_models]
for m, o in zip(current_models, output):
gen_event = gr.on(triggers=[gen_button.click, txt_input.submit], fn=gen_fnseed,
inputs=[m, txt_input, seed], outputs=[o], concurrency_limit=None, queue=False)
#stop_button.click(lambda s: gr.update(interactive = False), None, stop_button, cancels = [gen_event])
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 = gr.CheckboxGroup(models, label = f'Choose up to {num_models} different models from the 2 available! Untick them to only use one!', value = default_models, multiselect = True, max_choices = num_models, interactive = True, filterable = False)
model_choice.change(update_imgbox, model_choice, output)
model_choice.change(extend_choices, model_choice, current_models)
with gr.Row():
gr.HTML(
"""