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import random
import gradio as gr
import requests
from concurrent.futures import ThreadPoolExecutor
from MonsterAPIClient import MClient
from typing import Tuple
client = MClient()
def generate_model_output(model: str, input_text: str, neg_prompt: str, samples: int, steps: int,
aspect_ratio: str, guidance_scale: float, random_seed: str) -> str:
"""
Generate output from a specific model.
Parameters:
model (str): The name of the model.
input_text (str): Your input text prompt.
neg_prompt (str): Negative text prompt.
samples (int): No. of images to be generated.
steps (int): Sampling steps per image.
aspect_ratio (str): Aspect ratio of the generated image.
guidance_scale (float): Prompt guidance scale.
random_seed (str): Random number used to initialize the image generation.
Returns:
str: The generated output text or image URL.
"""
try:
response = client.get_response(model, {
"prompt": input_text,
"negprompt": neg_prompt,
"samples": samples,
"steps": steps,
"aspect_ratio": aspect_ratio,
"guidance_scale": guidance_scale,
"seed": random_seed,
})
output = client.wait_and_get_result(response['process_id'])
if 'output' in output:
return output['output']
else:
return "No output available."
except Exception as e:
return f"Error occurred: {str(e)}"
def generate_output(input_text: str, neg_prompt: str, samples: int, steps: int,
aspect_ratio: str, guidance_scale: float, random_seed: str):
with ThreadPoolExecutor() as executor:
# Schedule the function calls asynchronously
future_sdxl_base = executor.submit(generate_model_output, 'sdxl-base', input_text, neg_prompt, samples, steps,
aspect_ratio, guidance_scale, random_seed)
future_txt2img = executor.submit(generate_model_output, 'txt2img', input_text, neg_prompt, samples, steps,
aspect_ratio, guidance_scale, random_seed)
# Get the results from the completed futures
sdxl_base_output = future_sdxl_base.result()
txt2img_output = future_txt2img.result()
return [sdxl_base_output, txt2img_output]
# Function to stitch
input_components = [
gr.inputs.Textbox(label="Input Prompt"),
gr.inputs.Textbox(label="Negative Prompt"),
gr.inputs.Slider(label="No. of Images to Generate", minimum=1, maximum=3, default=1),
gr.inputs.Slider(label="Sampling Steps per Image", minimum=30, maximum=40, default=30),
gr.inputs.Dropdown(label="Aspect Ratio", choices=["square", "landscape", "portrait"], default="square"),
gr.inputs.Slider(label="Prompt Guidance Scale", minimum=0.1, maximum=20.0, default=7.5),
gr.inputs.Textbox(label="Random Seed", default=random.randint(0, 1000000)),
]
output_component_sdxl_base = gr.Gallery(label="Stable Diffusion V2.0 Output", type="pil", container = True)
output_component_txt2img = gr.Gallery(label="Stable Diffusion V1.5 Output", type="pil", container = True)
interface = gr.Interface(
fn=generate_output,
inputs=input_components,
outputs=[output_component_sdxl_base, output_component_txt2img],
live=False,
capture_session=True,
title="Stable Diffusion Evaluation powered by MonsterAPI",
description="""This HuggingFace Space has been designed to help you compare the outputs between Stable-Diffusion V1.5 vs V2.0. These models are hosted on [MonsterAPI](https://monsterapi.ai/?utm_source=llm-evaluation&utm_medium=referral) - An AI infrastructure platform built for easily accessing AI models via scalable APIs and [finetuning LLMs](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm) at very low cost with our no-code implementation. MonsterAPI is powered by our low cost and highly scalable GPU computing platform - [Q Blocks](https://www.qblocks.cloud?utm_source=llm-evaluation&utm_medium=referral). These LLMs are accessible via scalable REST APIs. Checkout our [API documentation](https://documenter.getpostman.com/view/13759598/2s8ZDVZ3Yi) to integrate them in your AI powered applications.""",
css="body {background-color: black}"
)
# Launch the Gradio app
interface.launch() |