Create a Serverless Logo Generator Application

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The Logo Generator’s primary steps

1. Text Input: The user provides a text description(prompt) of the desired logo, such as “a logo for a coffee shop, coffee”, along with a negative prompt and colors.
2. Model Processing: This text prompt is fed into the Stable Diffusion XL model, which processes the inputs and generates a corresponding logo representation.
3. Logo Output: Finally, the model outputs a unique logo based on the given text description, capturing the desired visual elements and style.

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Crafting Your Application

• Diffusers: A library specifically designed for diffusion models, offering utilities for loading, processing, and generating images.
• Inferless: A serverless platform that simplifies the deployment and scaling of machine learning models, allowing us to serve our logo generator application easily.

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Core Development Steps

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Text-to-Logo Generation

• Objective: Accept user text input, generate a logo image using the Stable Diffusion XL model with LoRA, and return the generated logo.
• Action: Implement a Python class (InferlessPythonModel) that handles the entire text-to-logo generation process, including input handling, model integration, and logo generation.

import json
import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
import base64
from io import BytesIO


class InferlessPythonModel:
    """
    Class for text-to-image generation using Stable Diffusion with LoRA
    """

    def initialize(self):
        """
        Initializes the model, scheduler, and loads model weights.
        """
        model_id = "stabilityai/stable-diffusion-xl-base-1.0"
        lora_id = "artificialguybr/LogoRedmond-LogoLoraForSDXL-V2"

        # Load the diffusion model with FP16 precision for efficiency
        self.pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16")

        # Use the high-performance DPMSolver++ scheduler for faster inference
        scheduler = DPMSolverMultistepScheduler(use_karras_sigmas=True, algorithm_type="sde-dpmsolver++")
        self.pipe.scheduler = scheduler.from_config(self.pipe.scheduler.config)

        # Load LoRA weights for text-based guidance
        self.pipe.load_lora_weights(lora_id)

        # Move model to GPU for faster processing
        self.pipe.to(device="cuda", dtype=torch.float16)

    def infer(self, inputs):
        """
        Generates an image based on the provided prompt.
        """
        prompt = inputs["prompt"]
        negative = inputs["negative_prompt"]
        color = inputs["color"]
        complete_prompt = f'logo, {prompt} colors ({color})'
  
        pipeline_output_image = self.pipe(
            prompt=complete_prompt,
            negative_prompt = negative,
            num_inference_steps=30,
            guidance_scale=7,
        ).images[0]

        # Encode the generated image as a base64 string for convenient transfer
        buff = BytesIO()
        pipeline_output_image.save(buff, format="PNG")
        img_str = base64.b64encode(buff.getvalue())
        return {"generated_image_base64": img_str.decode("utf-8")}

    def finalize(self, args):
        """
        Cleans up model resources to prevent memory leaks.
        """
        self.pipe = None

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Setting up the Environment

• Objective: Ensure all necessary libraries are installed.
• Action: Run the command below to install dependencies:

pip install accelerate==0.28.0 diffusers==0.27.0 transformers==4.38.2 peft==0.9.0

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Deploying Your Model with Inferless CLI

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Clone the repository of the model

git clone https://github.com/inferless/Logo-Generator.git

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Deploy the Model

inferless deploy --gpu A100 --runtime inferless-runtime-config.yaml

• --gpu A100: Specifies the GPU type for deployment. Available options include A10, A100, and T4.
• --runtime inferless-runtime-config.yaml: Defines the runtime configuration file. If not specified, the default Inferless runtime is used.

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Demo of the Logo Generator

Here are a few examples of logos generated from our application:

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Alternative Deployment Method

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Choosing Inferless for Deployment

1. Ease of Use: Forget the complexities of infrastructure management. With Inferless, you simply bring your model, and within minutes, you have a working endpoint. Deployment is hassle-free, without the need for in-depth knowledge of scaling or infrastructure maintenance.
2. Cold-start Times: Inferless’s unique load balancing ensures faster cold-starts. Expect around 5.32 seconds to process each logo, significantly faster than many traditional platforms.
3. Cost Efficiency: Inferless optimizes resource utilization, translating to lower operational costs. Here’s a simplified cost comparison:

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Scenario 1

• Total number of Logo: 100 daily.
  
• Inference Time: All models are hypothetically deployed on A100 80GB, taking 5.32 seconds of processing time and a cold start overhead of 11.72 seconds.
  
• Scale Down Timeout: Uniformly 60 seconds across all platforms, except Hugging Face, which requires a minimum of 15 minutes. This is assumed to happen 100 times a day.
  

1. Inference Duration:
   Processing 100 logos and each takes 5.32 seconds
   Total: 100 x 5.32 = 532 seconds (or approximately 0.15 hours)
2. Idle Timeout Duration:
   Post-processing idle time before scaling down: (60 seconds - 5.32 seconds) x 100 = 5468 seconds (or 1.52 hours approximately)
   
3. Cold Start Overhead:
   Total: 100 x 11.72 = 1172 seconds (or 0.33 hours approximately)
   

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Scenario 2

1. Inference Duration:
   Processing 1000 logos and each takes 5.32 seconds Total: 1000 x 5.32 = 5320 seconds (or approximately 1.48 hours)‍
2. Idle Timeout Duration:
   Post-processing idle time before scaling down: (60 seconds - 5.32 seconds) x 100 = 5468 seconds (or 1.52 hours approximately)
3. Cold Start Overhead:
   Total: 100 x 11.72 = 1172 seconds (or 0.33 hours approximately)

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Scenario 3

1. Inference Duration:
   Processing 10000 logos and each takes 5.32 seconds Total: 10,000 x 5.32 = 53,200 seconds (or approximately 14.78 hours)‍
2. Idle Timeout Duration:
   Post-processing idle time before scaling down: (60 seconds - 5.32 seconds) x 100 = 5468 seconds (or 1.52 hours approximately)
3. Cold Start Overhead:
   Total: 100 x 11.72 = 1172 seconds (or 0.33 hours approximately)
   

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Pricing Comparison for all the Scenario

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Conclusion