Compare-6 / app.py
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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
# Create a lock to ensure thread safety when accessing shared resources
lock = RLock()
# Load Hugging Face token from environment variable, if available
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.
# Function to load all models specified in the 'models' list
def load_fn(models):
global models_load
models_load = {}
# Iterate through all models to load them
for model in models:
if model not in models_load.keys():
try:
# Log model loading attempt
print(f"Attempting to load model: {model}")
# Load model interface using externalmod function
m = gr_Interface_load(f'models/{model}', hf_token=HF_TOKEN)
print(f"Successfully loaded model: {model}")
except Exception as error:
# In case of an error, print it and create a placeholder interface
print(f"Error loading model {model}: {error}")
m = gr.Interface(lambda: None, ['text'], ['image'])
# Update the models_load dictionary with the loaded model
models_load.update({model: m})
# Load all models defined in the 'models' list
print("Loading models...")
load_fn(models)
print("Models loaded successfully.")
num_models = 6
# Set the default models to use for inference
default_models = models[:num_models]
inference_timeout = 600
MAX_SEED = 3999999999
# Generate a starting seed randomly between 1941 and 2024
starting_seed = randint(1941, 2024)
print(f"Starting seed: {starting_seed}")
# Extend the choices list to ensure it contains 'num_models' elements
def extend_choices(choices):
print(f"Extending choices: {choices}")
extended = choices[:num_models] + (num_models - len(choices[:num_models])) * ['NA']
print(f"Extended choices: {extended}")
return extended
# Update the image boxes based on selected models
def update_imgbox(choices):
print(f"Updating image boxes with choices: {choices}")
choices_plus = extend_choices(choices[:num_models])
imgboxes = [gr.Image(None, label=m, visible=(m != 'NA')) for m in choices_plus]
print(f"Updated image boxes: {imgboxes}")
return imgboxes
# Asynchronous function to perform inference on a given model
async def infer(model_str, prompt, seed=1, timeout=inference_timeout):
from pathlib import Path
kwargs = {}
noise = ""
kwargs["seed"] = seed
# Create an asynchronous task to run the model inference
print(f"Starting inference for model: {model_str} with prompt: '{prompt}' and 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) # Allow other tasks to run
try:
# Wait for the task to complete within the specified timeout
result = await asyncio.wait_for(task, timeout=timeout)
print(f"Inference completed for model: {model_str}")
except (Exception, asyncio.TimeoutError) as e:
# Handle any exceptions or timeout errors
print(f"Error during inference for model {model_str}: {e}")
if not task.done():
task.cancel()
print(f"Task cancelled for model: {model_str}")
result = None
# If the task completed successfully, save the result as an image
if task.done() and result is not None:
with lock:
png_path = "image.png"
result.save(png_path)
image = str(Path(png_path).resolve())
print(f"Result saved as image: {image}")
return image
print(f"No result for model: {model_str}")
return None
# Function to generate an image based on the given model, prompt, and seed
def gen_fnseed(model_str, prompt, seed=1):
if model_str == 'NA':
print(f"Model is 'NA', skipping generation.")
return None
try:
# Create a new event loop to run the asynchronous inference function
print(f"Generating image for model: {model_str} with prompt: '{prompt}' and seed: {seed}")
loop = asyncio.new_event_loop()
result = loop.run_until_complete(infer(model_str, prompt, seed, inference_timeout))
except (Exception, asyncio.CancelledError) as e:
# Handle any exceptions or cancelled tasks
print(f"Error during generation for model {model_str}: {e}")
result = None
finally:
# Close the event loop
loop.close()
print(f"Event loop closed for model: {model_str}")
return result
# Create the Gradio Blocks interface with a custom theme
print("Creating Gradio interface...")
with gr.Blocks(theme="Nymbo/Nymbo_Theme") as demo:
gr.HTML("<center><h1>Compare-6</h1></center>")
with gr.Tab('Compare-6'):
# Text input for user prompt
txt_input = gr.Textbox(label='Your prompt:', lines=4)
# Button to generate images
gen_button = gr.Button('Generate up to 6 images in up to 3 minutes total')
with gr.Row():
# Slider to select a seed for reproducibility
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)
# Button to randomize the seed
seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary", scale=1)
# Set up click event to randomize the seed
seed_rand.click(randomize_seed, None, [seed], queue=False)
print("Seed randomization button set up.")
# Button click to start generation
gen_button.click(lambda s: gr.update(interactive=True), None)
print("Generation button set up.")
with gr.Row():
# Create image output components for each model
output = [gr.Image(label=m, min_width=480) for m in default_models]
# Create hidden textboxes to store the current models
current_models = [gr.Textbox(m, visible=False) for m in default_models]
# Set up generation events for each model and output image
for m, o in zip(current_models, output):
print(f"Setting up generation event for model: {m.value}")
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)
# The commented stop button could be used to cancel the generation event
#stop_button.click(lambda s: gr.update(interactive=False), None, stop_button, cancels=[gen_event])
# Accordion to allow model selection
with gr.Accordion('Model selection'):
# Checkbox group to select up to 'num_models' different models
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)
# Update image boxes and current models based on model selection
model_choice.change(update_imgbox, model_choice, output)
model_choice.change(extend_choices, model_choice, current_models)
print("Model selection setup complete.")
with gr.Row():
# Placeholder HTML to add additional UI elements if needed
gr.HTML(
)
# Queue settings for handling multiple concurrent requests
print("Setting up queue...")
demo.queue(default_concurrency_limit=200, max_size=200)
print("Launching Gradio interface...")
demo.launch(show_api=False, max_threads=400)
print("Gradio interface launched successfully.")