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Description

Title: System Architecture of the Stable Diffusion Engine and Sampler Integration

Overview:
The chart illustrates the components and interactions of the Stable Diffusion¹ engine along with its integrated samplers, which consist of probabilistic models (DDPM, DDIM, PLMS) and numerical approach methods (Euler, Heun, LMS). The objective is to provide a comprehensive understanding of how these elements work together.

Central Element: Stable Diffusion Engine In the center of the chart, a box represents the diffusion model used for noise prediction (txt2img, img2img) This central element acts as the core of the system and controls the overall process.
Probabilistic Model Samplers: Surrounding the Stable Diffusion engine are three distinct boxes representing the probabilistic model samplers:
DDPM (Denoising Diffusion Probabilistic Model): This box showcases the DDPM sampler, which utilizes denoising techniques to estimate probability distributions from noisy observations. Arrows depict the flow of data from the Stable Diffusion engine to DDPM and back.
DDIM (Diffusion Implicit Model): The DDIM sampler box demonstrates the use of implicit generative models for learning complex data distributions. Its interactions with the Stable Diffusion engine are shown through connecting lines.
PLMS (Piecewise Linear Mapping Sampler): The PLMS sampler box represents the technique of piecewise linear mappings to approximate probability distributions. Its integration with the Stable Diffusion engine is visually connected.
Numerical Approach Methods: Aligned with the probabilistic model samplers are three additional boxes for the numerical approach methods:
Euler Method: The Euler Method box illustrates the numerical integration technique used to approximate solutions to differential equations. It's linked with the Stable Diffusion engine and the samplers it interacts with.
Heun Method: The Heun Method box demonstrates an improved numerical integration approach for accuracy. Connections are drawn to highlight its interaction with both the Stable Diffusion engine and the samplers.
LMS (Levy-based Monte Carlo Sampling): The LMS box showcases the Levy-based Monte Carlo sampling method used for complex distribution approximations. It is connected to both the Stable Diffusion engine and the probabilistic model samplers.
Interactions and Integration: Lines and arrows between the central Stable Diffusion engine and the surrounding components demonstrate the integration and flow of information. Annotations, callouts, and labels provide additional explanations where necessary.
Collaboration: This visualization has been developed in collaboration with subject matter experts to ensure accuracy and alignment with the key needs of conveying the Stable Diffusion engine's system architecture and sampler integration

¹a text-to-image latent diffusion model created by the researchers and engineers from CompVis , Stability AI and LAION

Description

Title: System Architecture of the Stable Diffusion Engine and Sampler Integration

Overview:
The chart illustrates the components and interactions of the Stable Diffusion¹ engine along with its integrated samplers, which consist of probabilistic models (DDPM, DDIM, PLMS) and numerical approach methods (Euler, Heun, LMS). The objective is to provide a comprehensive understanding of how these elements work together.

Central Element: Stable Diffusion Engine In the center of the chart, a box represents the diffusion model used for noise prediction (txt2img, img2img) This central element acts as the core of the system and controls the overall process.
Probabilistic Model Samplers: Surrounding the Stable Diffusion engine are three distinct boxes representing the probabilistic model samplers:
DDPM (Denoising Diffusion Probabilistic Model): This box showcases the DDPM sampler, which utilizes denoising techniques to estimate probability distributions from noisy observations. Arrows depict the flow of data from the Stable Diffusion engine to DDPM and back.
DDIM (Diffusion Implicit Model): The DDIM sampler box demonstrates the use of implicit generative models for learning complex data distributions. Its interactions with the Stable Diffusion engine are shown through connecting lines.
PLMS (Piecewise Linear Mapping Sampler): The PLMS sampler box represents the technique of piecewise linear mappings to approximate probability distributions. Its integration with the Stable Diffusion engine is visually connected.
Numerical Approach Methods: Aligned with the probabilistic model samplers are three additional boxes for the numerical approach methods:
Euler Method: The Euler Method box illustrates the numerical integration technique used to approximate solutions to differential equations. It's linked with the Stable Diffusion engine and the samplers it interacts with.
Heun Method: The Heun Method box demonstrates an improved numerical integration approach for accuracy. Connections are drawn to highlight its interaction with both the Stable Diffusion engine and the samplers.
LMS (Levy-based Monte Carlo Sampling): The LMS box showcases the Levy-based Monte Carlo sampling method used for complex distribution approximations. It is connected to both the Stable Diffusion engine and the probabilistic model samplers.
Interactions and Integration: Lines and arrows between the central Stable Diffusion engine and the surrounding components demonstrate the integration and flow of information. Annotations, callouts, and labels provide additional explanations where necessary.
Collaboration: This visualization has been developed in collaboration with subject matter experts to ensure accuracy and alignment with the key needs of conveying the Stable Diffusion engine's system architecture and sampler integration

¹a text-to-image latent diffusion model created by the researchers and engineers from CompVis , Stability AI and LAION