Pietra
New York, NY
New York, NY
Jupyter Notebook, Sagemaker, AWS Bedrock
In an era where creative and practical applications of AI are continuously expanding, Pietra, a trailblazer in e-commerce solutions, sought to harness the power of generative AI models to enhance their online store offerings. This initiative aimed to conduct a meticulous comparative analysis of the Stability Diffusion and DALL-E models using advanced techniques. The project’s cornerstone was the development on AWS Sagemaker to evaluate these models side-by-side on a uniform set of user-provided prompts. The primary goal was to explore and document each model’s capabilities, efficiency, and practical application potential in various creative scenarios.
The project entailed establishing a Sagemaker environment, fine-tuning the Stability Diffusion model on relevant datasets from e-commerce domain, generating and analyzing results from identical prompts, and comparing these with the results from the DALL-E model. The goal was to extract actionable insights to guide strategic model deployment in business operations.
With the ever advancing Generative AI technology, there is a growing demand to leverage the best models; however, this brand-new creations come with challenges:
Being at the forefront is what allows a company to be the highlight of their domain; incurring into a barely explored field is nothing short of innovative. By leveraging and understanding which models and techniques are the most impactful to the use case, the project provided significant value to the client:
To present a feasible model, state of the art models (DALL-E and Stability Diffusion various models, mainly SDXL) were compared via Jupyter Runbooks, to allow developers to test different approaches:
Architecture Diagram for training and inference. Having a defined a diagram helps the comparison task.
Applied advanced fine-tuning techniques to the Stability Diffusion model, including:
LoRA model:
Output :
Output :
Textual Inversion
Output :
Control Net
Output :
Generation with LoRA vs DALL-E:
Image to image:
SDXL-turbo Fine Tuned with LoRA
ControlNet
DALL-E. It does not allow to add variations to the image with text, it is very restrictive.
Virtual try-on for men.
Virtual try-on for women.
This project, executed on AWS SageMaker, successfully demonstrated the power of fine-tuning the Stability AI model, which notably outperformed the DALL-E model. By deploying advanced SageMaker notebooks, we conducted rigorous evaluations that not only highlighted the enhanced performance of the Stability AI model but also its superior creative capabilities. This comparative study provided PieStraStudio with deep insights into the potential of customized AI solutions, enabling strategic, informed decisions for their ongoing and future AI initiatives. Overall, the project epitomized the transformative power of tailored AI technologies, leveraging the advanced capabilities of AWS SageMaker to fulfill and exceed the client’s strategic objectives.
Pietra
New York, NY
New York, NY
Jupyter Notebook, Sagemaker, AWS Bedrock
In an era where creative and practical applications of AI are continuously expanding, Pietra, a trailblazer in e-commerce solutions, sought to harness the power of generative AI models to enhance their online store offerings. This initiative aimed to conduct a meticulous comparative analysis of the Stability Diffusion and DALL-E models using advanced techniques. The project’s cornerstone was the development on AWS Sagemaker to evaluate these models side-by-side on a uniform set of user-provided prompts. The primary goal was to explore and document each model’s capabilities, efficiency, and practical application potential in various creative scenarios.
The project entailed establishing a Sagemaker environment, fine-tuning the Stability Diffusion model on relevant datasets from e-commerce domain, generating and analyzing results from identical prompts, and comparing these with the results from the DALL-E model. The goal was to extract actionable insights to guide strategic model deployment in business operations.
With the ever advancing Generative AI technology, there is a growing demand to leverage the best models; however, this brand-new creations come with challenges:
Being at the forefront is what allows a company to be the highlight of their domain; incurring into a barely explored field is nothing short of innovative. By leveraging and understanding which models and techniques are the most impactful to the use case, the project provided significant value to the client:
To present a feasible model, state of the art models (DALL-E and Stability Diffusion various models, mainly SDXL) were compared via Jupyter Runbooks, to allow developers to test different approaches:
Architecture Diagram for training and inference. Having a defined a diagram helps the comparison task.
Applied advanced fine-tuning techniques to the Stability Diffusion model, including:
LoRA model:
Output :
Output :
Textual Inversion
Output :
Control Net
Output :
Generation with LoRA vs DALL-E:
Image to image:
SDXL-turbo Fine Tuned with LoRA
ControlNet
DALL-E. It does not allow to add variations to the image with text, it is very restrictive.
Virtual try-on for men.
Virtual try-on for women.
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