Comparison of Stable Diffusion Best Sampling Method

November 27


The stable diffusion machine learning model gradually removes noises from resulting samples and diffuses these images to create new, artistic, innovative content. Prompts, characteristics, and instructions given to the model should be separated by commas and concise to give the desired output. Since the output of your prompt and idea is based on a key sampler that has to be established beforehand, understanding the stable diffusion best sampling method can help you improve the output and help you produce better-quality content.

Are you wondering how to make unique and eye-catching art with stable diffusion? 

It's a tool that's more popular than you might think!  Over 12 billion images have been created using this technology, and with the stable diffusion best sampling method, you can be part of this revolution. ? 

That's huge! Plus, the stable diffusion models learned from over 2 billion pictures, so they're pretty smart.

 Every day, people like you create over 2 million images with this cool tool. And guess what? Models on Civitai, a popular platform for this, have been downloaded more than 7 million times! 

This article is here to guide you through the different ways you can use stable diffusion to make your own amazing images. We'll look at the best methods and explain how everything works, so you can get started on your own artistic journey.

What Is Sampling? How Do Samplers Work?

Sampling in stable diffusion is an essential step in creating digital artwork. When we talk about the stable diffusion best sampling method,' we're referring to how the AI transforms a random assortment of pixels into a coherent image based on the user's prompt.

 But what exactly is sampling? It's a process where the AI systematically reduces noise from an initially random pixel set. This noise reduction is done repeatedly, refining the image with each step.

The process of reducing noise from the random image is called sampling. The method of denoising or reduction is called samplers. Based on the sampler preference you have used or is used by default, the final output can have varying sharpness, quality, and innovative ideas. 

Although the basic idea remains the same, there are multiple ways to carry out the denoising process. Depending upon the sample you have used, the speed, accuracy, and noise schedule vary and influence the finished image. Every sampler has a common goal to solve the instructions and create a finished image, either by using ordinary differential equation solvers or other means.

Understanding the nuances of each stable diffusion best sampling method allows artists to control the outcome of their digital creations better. In essence, samplers are the artist's tools in the digital realm, shaping the raw material of random pixels into a piece of art. 

Best Sampling Methods For Stable Diffusion

There are over 19 different types of samplers available in Stable diffusion web UI. Each has its techniques to solve the ODE and create images with varying noise schedules. The noise schedule is usually higher at the initial step and gradually reduces to a clear one. 

Among the options you have available in the stable diffusion web UI, they can be broadly categorized as follows:

Traditional ODE Solvers

Euler, Heun, and LMS are examples of the traditional ODE solvers that have been used for different purposes. 

Traditional ODE Solvers are mathematical tools used in various fields, including the area of stable diffusion, specifically in the stable diffusion best sampling method. ODE stands for Ordinary Differential Equations, which are equations involving functions and their derivatives. In the context of stable diffusion, these solvers are used to tackle the problem of noise reduction in the image generation process.

How do they work? In simple terms, traditional ODE solvers methodically compute the steps needed to transition from a noisy, random set of pixels to a clear, coherent image as dictated by the user's prompt. They do this by solving a series of complex equations that describe the behavior of pixel values over time. This involves calculating the changes needed at each step to reduce noise and improve image clarity gradually.

The role of ODE solvers in stable diffusion is crucial. They ensure that the transition from randomness to the final image is smooth and accurate, adhering closely to the user's original vision. By efficiently managing the denoising process, traditional ODE solvers contribute significantly to the effectiveness of the 'stable diffusion best sampling method', helping create high-quality, detailed, and visually appealing images.

Ancestral Samplers

Euler a, DPM2 a, DPM++ 2S a, and DPM++ 2S a Karras are examples of ancestral samplers that add noise to the image at each step before denoising. 

Ancestral Samplers" represent a different approach within the realm of image generation, particularly in relation to the 'stable diffusion best sampling method'. Unlike the more mathematical ODE solvers, Ancestral Samplers follow a more probabilistic and sequential process in image creation.

Ancestral Samplers work by building an image pixel by pixel or region by region, in a manner akin to how an artist might draw on a canvas, starting from one point and gradually expanding outwards. This method relies heavily on the concept of probability; each new pixel or region is generated based on the likelihood of its occurrence, given the existing pixels and the user's prompt.

This approach allows for a high level of detail and control in the image generation process, as each new addition to the image is carefully calculated based on the surrounding context. It's a method that can yield highly creative and unique results, particularly in scenarios where the desired output is complex or highly specific.

The application of Ancestral Samplers in stable diffusion is particularly noteworthy for those seeking a blend of creativity, precision, and a touch of unpredictability in their digital art creations. It stands as a testament to the diversity of techniques available under the umbrella of the 'stable diffusion best sampling method', catering to a wide range of artistic visions and styles.

Karras Noise Schedule

Samplers with Karras attached to the name tag have been found to have a greater quality image at the final stage. However, the noise variations are quite huge initially, and it might not be easy to achieve reproducibility. 

The "Karras Noise Schedule" is a sophisticated concept within the field of image generation, particularly relevant in discussions about the 'stable diffusion best sampling method'. Named after Tero Karras, a notable figure in AI and computer graphics, this method introduces a unique approach to managing noise in the process of image creation.

At its core, the Karras Noise Schedule is a technique that carefully controls the rate and pattern of noise reduction during the image generation process. Unlike traditional methods that might uniformly reduce noise across the entire image, the Karras method applies a more nuanced schedule. It strategically varies the amount and intensity of noise reduction at different stages, based on the specific requirements of the image being generated.

This tailored approach allows for greater control over the final appearance of the image. By adjusting the noise reduction schedule, the Karras method can influence factors such as texture, sharpness, and even stylistic elements of the generated image. This makes it an invaluable tool in the arsenal of techniques under the 'stable diffusion best sampling method', especially for creators who are aiming for a high degree of realism or specific aesthetic qualities in their digital artworks.

 Karras Noise Schedule represents a significant advancement in the field of AI-driven image generation, offering a level of precision and customization that helps artists and creators bring their visions to life with stunning accuracy and detail.


Denoising diffusion implicit model and pseudo linear multi-step method samplers have existed ever since the stable diffusion v1 was released. These are considered outdated and not really used anymore. 

DDIM and PLMS are two advanced techniques in the field of image generation, especially within the scope of the 'stable diffusion best sampling method'. These methods are instrumental in refining how images are generated from noise, each offering unique advantages and characteristics.

DDIM, short for Denoising Diffusion Implicit Models, is a novel approach that differs from traditional diffusion models. It allows for a more controlled and predictable image generation process. By manipulating the noise reduction steps more explicitly, DDIM provides a clearer path from the initial noisy image to the final output. This method is particularly useful for applications where consistency and control over the generation process are paramount, making it a popular choice in various artistic and design contexts.

PLMS, or Pseudo Likelihood Monte Carlo Sampling, on the other hand, is a technique that enhances the efficiency of the sampling process in stable diffusion models. It strategically samples portions of the image space to reconstruct the final image, balancing the need for computational efficiency with the desire for high-quality results. PLMS is known for its ability to generate detailed and coherent images while requiring fewer computational resources compared to some other methods.

Together, DDIM and PLMS represent two important pillars in the 'stable diffusion best sampling method' framework. DDIM offers a more deterministic approach, giving creators a higher degree of control over the end result, while PLMS emphasizes efficiency and quality, ensuring that even complex images can be generated effectively. Both methods showcase the versatility and innovation present in the field of AI-driven image generation, catering to a wide range of artistic needs and preferences.

DPM and DPM++

Diffusion probabilistic model solvers are one of the new samplers recently added in 2022. DPM++ is an improved sampler with better capabilities than DPM. DPM and DPM2 are quite similar in technique but the latter being more accurate but slower. 

DPM and DPM++ are integral components in the evolving landscape of AI-based image generation, particularly within the framework of the 'stable diffusion best sampling method'. These methods represent advanced steps in the diffusion process model, each bringing unique attributes to the table.

DPM, short for Diffusion Probabilistic Models, is a technique that focuses on gradually transforming a distribution of random noise into a structured image. This transformation is guided by a carefully designed process that incrementally reduces randomness (or noise) in each step, moving closer to a coherent image. The key aspect of DPM is its ability to control this transformation with precision, allowing for the generation of high-quality images that are both diverse and faithful to the input prompt.

DPM++ builds upon the foundation laid by DPM, introducing enhancements that improve the efficiency and quality of the image generation process. These improvements might include optimized noise reduction schedules, more effective handling of the diffusion steps, or advancements in the underlying algorithms that govern the transformation from noise to image. The "++" in DPM++ signifies these incremental but significant improvements, pushing the boundaries of what's possible in stable diffusion image generation.

Both DPM and DPM++ are crucial for understanding the depth and capabilities of the 'stable diffusion best sampling method'. They exemplify the continuous evolution in AI image generation technology, offering creators and developers more powerful tools to bring their artistic visions to life. Whether it's through the foundational strengths of DPM or the enhanced capabilities of DPM++, these methods are at the forefront of creating detailed, accurate, and visually stunning images through AI.


Unified Predictor Corrector is one of the latest samplers released in 2023. These are the results of the latest predictor-corrector method to solve ordinary differential equations. These are quite fast, as great results can be achieved within 5-10 steps. 

UniPC, short for Unified Prediction and Correction, is a cutting-edge technique in the realm of AI-driven image generation, particularly relevant to discussions around the 'stable diffusion best sampling method'. This method is part of the broader category of probabilistic models used in the process of generating images from textual prompts.

The UniPC approach is designed to streamline and enhance the efficiency of the image generation process. It does this by integrating two key phases: prediction and correction. In the prediction phase, the model makes an initial guess about the image based on the input prompt. This guess is then refined in the correction phase, where the model adjusts its initial prediction to reduce errors and improve the overall quality of the generated image.

One of the standout features of UniPC is its ability to balance speed and accuracy. By unifying the prediction and correction steps into a single, cohesive process, UniPC can generate high-quality images more quickly than methods that treat these steps separately. This efficiency makes UniPC an attractive option for applications where both time and image quality are crucial factors.

In the context of the 'stable diffusion best sampling method', UniPC represents a significant advancement. It offers a harmonious blend of predictive power and corrective precision, enabling creators to efficiently produce images that are both aesthetically pleasing and closely aligned with their creative vision. As such, UniPC is a key player in the ongoing evolution of AI-based image generation, pushing the boundaries of what's possible in this exciting and rapidly developing field.


Comparison Across the Best Sampling Methods

The following picture can help you get an idea regarding the samplers and how denoising works in each step. The picture below depicts the comparison between samplers for the same prompt and the same settings. 

Credit: Twitter

The ancestral samplers offer a traditional artistic image that is not often reproducible. Although all the other models have different noise samples initially the final image is very much similar due to the same stable diffusion prompt being used.  The denoising process clearly illustrates that DDLM and Karras noise schedule models are able to achieve a presentable result faster than other samplers. 

These final images were created using all the samplers mentioned above for comparison.

DPM++ quickly failed to load an image that can adhere to the industry standard. The ancestral samplers failed to converge properly and thus the image of a kitten is being given out, while all other models were able to output the image of a cat. The better among these is a question of perspective and as long as you are happy with the result, the ability of the sampler cannot be questioned.


Sampling method for anime

When exploring the optimal sampling method for creating anime-style images using AI, it's crucial to understand the role of the sampling process. While the 'stable diffusion best sampling method' offers various options, the choice of sampler often influences the efficiency of the image generation rather than the specific style or appearance of the final image.

In anime image generation, the sampling method primarily affects the number of steps required to produce a decent image. It's less about crafting the distinct visual style of anime and more about the process efficiency. Different prompts will interact with different samplers in unique ways, and there's no definitive way to predict the outcome. This unpredictability suggests that while experimenting with different samplers can yield varied results, these variations aren't necessarily aligned with consistent stylistic changes.

For those aiming to create anime art, it's often recommended to start with the default sampler and focus on refining the prompts. Additionally, seeking out models that are fine-tuned to the anime style can be more impactful than changing the sampler. Experimenting with the sampler settings can be an interesting exploration, but it's important to understand that such adjustments are more likely to affect the image generation process rather than consistently alter the stylistic elements of the anime genre. Therefore, the key to achieving high-quality anime images lies more in the prompt and model selection than in the choice of the sampling method.

Stable Diffusion best sampling method for realistic images

When striving for realistic human forms in AI-generated images, the choice of sampler in the 'stable diffusion best sampling method' is crucial. Euler_a stands out for its ability to effectively render human figures, minimizing issues like unintended extra limbs or mutations that are more common with samplers like Euler or Heun. While Euler is known for capturing fine details, such as skin textures and imperfections, Euler_a excels in producing images with smooth skin, akin to the polished look often seen in high-fashion photo retouching.

Heun, on the other hand, can be thought of as an intensified version of Euler - it generates similar images but with heightened detail. However, for that sleek, retouched appearance, particularly in skin textures, Euler_a is typically the better choice.

Regardless of the sampler used, achieving perfect facial details might still require post-processing tools like GFPGAN or Codeformer for that final touch of realism.

It's important to note that all three samplers - Euler, Euler_a, and Heun - are capable of producing photorealistic images. The realism in the output heavily relies on the prompts used. Incorporating specific details like camera model, lens type, photo style, and film type into your prompts can significantly enhance the photo-realistic quality of the results. In summary, while the sampler plays a role in the texture and detail of the image, the key to achieving high-quality, realistic images in stable diffusion lies in a combination of the right sampler and carefully crafted prompts.

Dalle & Stable diffusion

DALL·E and Stable Diffusion are both cutting-edge AI models, but they serve different purposes and operate on distinct principles, though they share the common goal of generating images from textual descriptions.

DALL·E, developed by OpenAI, is primarily known for its ability to generate highly creative and often whimsical images from complex and abstract prompts. It uses a version of the GPT (Generative Pre-trained Transformer) architecture, which is also used for language models, and adapts it for image generation. DALL·E is particularly adept at understanding and interpreting intricate and imaginative prompts, creating images that are not only visually striking but also often display a level of creativity and abstraction that mimics human artistic thought processes.

Stable Diffusion, on the other hand, is focused on a slightly different aspect of image generation. As the name suggests, it employs diffusion models, a class of generative models that start with noise and gradually refine this noise into a coherent image based on the provided prompt. The "stable" in its name refers to the stability of the generation process, which aims to produce high-quality images that are true to the input prompts. Stable Diffusion is often lauded for its ability to generate images that are realistic and detailed, making it particularly suitable for tasks where fidelity to the prompt is crucial.

The connection between DALL·E and Stable Diffusion lies in their shared foundation of using AI to bridge the gap between textual descriptions and visual representations. While DALL·E leans more towards creative and abstract interpretations, Stable Diffusion excels in the clarity and realism of its outputs. Both are powerful tools in the realm of AI-generated art, demonstrating the vast potential of AI in understanding and visualizing human language and concepts in visual forms.


The best sampling method that suits your particular use case depends on what you are after. If you want something that can quickly perform the task with convergence and decent quality, DPM++ 2M Karras and UniPC are the obvious choices.

If you are solely focused on image quality and not worried about convergence or reproducibility, DPM++ SDE Karras and DDIM are the best options. Ancestral samplers might not work out for everyone as it does not give reproducible images. 

If you are looking for something simple without much hassle, traditional ODE solvers like Euler and Heun might work out just fine. 


Leave a Reply