End-to-End Text-to-Image Generation Pipeline using Diffusion Models

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Overview:

This project showcases an end-to-end pipeline for generating high-resolution images (2048 x 2048) from textual prompts describing individuals and their backgrounds. Leveraging state-of-the-art diffusion models, the pipeline ensures robustness, stability, and superior image quality compared to traditional GANs and VAEs. Below is a detailed breakdown of the experimentation process, findings, and evaluation of the pipeline.

Familiarity with Diffusion Models:

Diffusion models have been chosen for their superior performance in image generation tasks, surpassing traditional GANs and VAEs. They offer enhanced stability during training and mitigate issues like model collapse.

Experimentation Process:

1. Platform and Setup:
   • Platform: Jupyter notebook
   • Language: Python
   • Hardware: GPU (RTX 4090 24GB), CPU (i9-13700KF)
2. Models Explored:
   • Explored various AI/ML models on Hugging Face.
   • Selected "stabilityai/stable-diffusion-xl-base-1.0” for text-to-image generation and “databuzzword/esrgan” (RRDB_ESRGAN_x4.pth) for upscaling.
3. Automatic Workflow:
   • Developed an automatic workflow:
     1. Load model from Hugging Face.
     2. Input textual prompt.
     3. Generate and save image.
     4. Download and save upscaling model.
     5. Upscale generated image to 2048 x 2048.
4. Image Generation from Text Prompt:
   • Input: Text prompt describing a person and their background.
   • Utilized diffusion models to generate a 1024 x 1024 image.
5. Upscaling:
   • Employed super resolution to scale images from 1024 x 1024 to 2048 x 2048.

Reporting and Findings:

1. Approach:
   • Utilized Hugging Face models and pre-existing codes for upscaling.
2. Experiments:
   • Experimented with Google Colab, faced CUDA OUT OF MEMORY error, shifted to local Jupyter runtime.
   • Successful execution on workstation with specified specifications.
3. Inferences:
   • Inferences drawn directly from the notebook, allowing for prompt-based generation.

Evaluation:

1. Effectiveness:

   • Evaluated model effectiveness in achieving photorealism and prompt steerability.
   • Effectiveness adjustable via epochs and noise ratio.

2. Creativity and Efficiency:

   • Assessed pipeline creativity and efficiency in terms of resource utilization and computational complexity.
   • Requires substantial resources but delivers desired results.

3. Results:

   • Prompt: "Middle-Aged Gentleman Standing on a Luxurious Yacht"

   

Conclusion:

The pipeline demonstrates the effectiveness of utilizing diffusion models for text-to-image generation. While pre-trained models and codes were employed, customizations can be accommodated based on specific requirements. With the potential for further enhancements, this pipeline proves beneficial for various applications and would be a valuable addition to your team.

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Note:

• This README provides a comprehensive overview of the project, including its objectives, methodology, findings, and evaluation.
• It serves as a guide for stakeholders interested in understanding the project's scope, outcomes, and potential implications.