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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() |