Get the Most Out of Stable Diffusion 2.1: Strategies for Improved Results

Olivio Sarikas
15 Dec 202208:42

TLDRThe video script discusses the intricacies of using Stable Diffusion 2.1 for creating high-quality images. It emphasizes the importance of crafting precise prompts, including both positive and negative elements, to guide the AI in rendering images. The video also explores the impact of sampling steps and CFG scale on image quality, and provides practical examples of prompts and settings for achieving desired results in both portrait and landscape scenes. The key takeaway is finding a balance between these parameters and the power of literal interpretation in prompts for better image outputs.


  • 📝 In Stable Diffusion 2.1, prompts are interpreted more literally, allowing for better scene and style descriptions.
  • 🎨 The style and technique of the image, such as photography or 3D render, should be clearly indicated in the prompt for better results.
  • 🚫 Negative prompts are essential and should be used to exclude unwanted elements like blurriness, deformation, and ugliness.
  • 📸 Negative prompts can also be generic, such as 'blurry 3D deformed ugly distorted', to cover common undesired outcomes.
  • 📈 There's a significant impact on image quality from the sampling steps and CFG scale settings in Stable Diffusion 2.1.
  • 🔍 Experimenting with different sampling methods like Euler and DPM can yield different visual results, with Euler being softer and DPM providing more detail.
  • 🖼️ Balancing CFG scale and the number of steps used is crucial for achieving the desired image quality and appearance.
  • 🌟 A high CFG scale combined with a high step number can produce a pleasing image, but it's important to find the right balance for each scene.
  • 📍 Testing with a low step number and higher CFG scale can provide a quick preview of what the final image might look like with more steps.
  • 🎥 The positive prompt should be detailed, describing the mood, lighting, and style desired for the image, to achieve the best results.

Q & A

  • What is the main focus of the video?

    -The main focus of the video is to discuss the use of Stable Diffusion 2.1 for creating images, including the importance of prompts, negative prompts, render methods, and the steps to achieve better results.

  • How does Stable Diffusion 2.1 interpret prompts differently compared to previous versions?

    -Stable Diffusion 2.1 takes prompts more literally, allowing for more precise descriptions of elements in a scene, such as their relative positions and desired styles, like photography or 3D rendering.

  • Why is including a negative prompt important when using Stable Diffusion 2.1?

    -Including a negative prompt is important because it helps to specify what elements should not be present in the final image, greatly improving the output quality by avoiding undesired features.

  • What is the recommended resolution setting for Stable Diffusion 2.1?

    -The recommended resolution setting for Stable Diffusion 2.1 is at least 768 pixels.

  • How do sampling steps and CFG scale impact the quality of the rendered image?

    -Sampling steps and CFG scale have a significant impact on the image quality. A balance between these two parameters is necessary to achieve the desired level of detail and color saturation in the final image.

  • What sampling methods does the video mention and how do they differ?

    -The video mentions Euler and DPM sampling methods. Euler tends to produce softer images, while DPM provides more detail in the rendered images.

  • How does the video demonstrate the balance between CFG scale and steps?

    -The video uses a render grid to show how different combinations of CFG scale and steps can affect the image quality. It illustrates that a high CFG scale with a high step number can bring back nice image details, while lower settings may result in a more desaturated or less detailed image.

  • What was the purpose of the second example in the video?

    -The purpose of the second example, featuring a nature scene, was to demonstrate how adjusting the positive prompt, negative prompt, and render method (DPM plus plus 2m) can lead to a detailed and well-composed image that closely matches the desired scene.

  • What is the significance of the lighthouse scene in the video's examples?

    -The lighthouse scene serves as an example to show how the balance of steps and CFG scale can lead to an image with the correct number of lighthouses, improved detail, and better color contrast without overexposure or saturation.

  • What advice does the video give for finding the best settings for an image?

    -The video advises using a combination of a high CFG scale and a high step number for rendering, as well as testing with a low step number and a higher CFG scale for a quick preview of the final image. Ultimately, it encourages users to experiment and decide for themselves what settings yield the most pleasing results.



🎨 Understanding Prompts and Settings in Stable Diffusion 2.1

This paragraph discusses the intricacies of crafting effective prompts for the Stable Diffusion 2.1 model. It emphasizes the importance of being more literal and specific in the prompts, as the model takes them more seriously. The speaker explains how to include both positive and negative prompts to refine the output, such as avoiding undesirable elements like blurriness or distortion in the final image. They also delve into the impact of sampling steps and CFG scale on the image quality, sharing personal experiences with different sampling methods like Euler and DPM. The paragraph concludes with a detailed example of creating a portrait prompt, highlighting the balance between CFG scale and steps for optimal results.


🌅 Fine-Tuning Nature Scene Rendering with Stable Diffusion 2.1

The second paragraph focuses on rendering a nature scene using Stable Diffusion 2.1, starting with crafting a positive prompt that describes the desired scene and mood, such as a wave crashing against rocks under a lighthouse. The speaker then contrasts this with a negative prompt to exclude unwanted features. They discuss the use of DPM++2m as a render method for its detailed texture and present a grid of different settings to illustrate how varying steps and CFG scale affect the final image. The paragraph ends with observations on achieving the most pleasing results by finding the right balance between these settings, and encourages viewers to decide for themselves what works best based on the examples provided.



💡Stable Diffusion 2.1

Stable Diffusion 2.1 is a version of an AI model that generates images from textual descriptions. It is noted for taking prompts more literally, meaning that the text provided by the user directly influences the output image. This version allows for more detailed scene descriptions, such as the positioning of elements relative to each other, and the inclusion of style and technique preferences like photography or 3D rendering.

💡Negative Prompts

Negative prompts are phrases included in the user's request to the AI that specify what should not be present in the final image. These are used to improve the output by explicitly telling the AI to avoid certain undesirable features, such as blurriness, deformation, or ugliness. Negative prompts are considered essential in achieving better results with Stable Diffusion 2.1.

💡Render Methods

Render methods refer to the techniques used by the AI to generate the final image from the prompt. Different methods can produce varying levels of detail and quality. In the context of the video, Euler and DPM (Deep Progressive Matrix) are mentioned as two such methods, with Euler providing softer images and DPM offering more detailed textures.

💡CFG Scale

CFG Scale, or Control Flow Graph Scale, is a parameter that influences the image generation process in Stable Diffusion 2.1. It appears to have a significant impact on the quality and detail of the output image, with higher values potentially leading to overexposure or oversaturation. Finding the right balance between the CFG Scale and the number of rendering steps is crucial for achieving desirable results.

💡Sampling Steps

Sampling steps are part of the AI's process of generating an image, where it iteratively refines the image based on the prompt. The number of steps can affect the level of detail and the final appearance of the image. In the context of the video, it is suggested that there is a correlation between the sampling steps and the CFG Scale that impacts the quality of the output.


Resolution refers to the pixel density of an image, which determines its clarity and detail. In the context of Stable Diffusion 2.1, setting the resolution to a certain value (like 768) is important as it affects the output's quality and detail level. Higher resolutions can produce more detailed images but may also require more computational resources.


In the context of the video, 'Vivid' is used to describe a quality of the image that is bright, clear, and full of life. It is specifically mentioned to avoid the common issue of photography in AI-generated images coming out as black and white. By including 'Vivid' in the prompt, the user instructs the AI to generate images with more vibrant and lively colors.

💡Hyper Realistic

Hyper realistic refers to images that are extremely detailed and closely resemble real-life objects or scenes. In the context of AI image generation, achieving hyper realistic results means that the output should look as if it could be an actual photograph or a high-quality rendering of a real-world scene.

💡Award-Winning Photography

Award-winning photography implies a high standard of quality and artistic merit. In the context of the video, this term is used to set the expectation for the AI to generate images that not only look realistic but also possess the qualities that would make them stand out and be recognized as exceptional in a competitive setting.

💡DPM Plus Plus 2M

DPM Plus Plus 2M is a specific render method mentioned in the script, which stands for Deep Progressive Matrix Plus Plus 2 Million. It is a more advanced version of the DPM method, likely indicating a higher level of detail or refinement in the generated images. The '2M' could imply a specific parameter or setting within this method that enhances the image quality.

💡Render Grid

A render grid is a visual representation or layout that displays multiple versions or iterations of an AI-generated image, often varying in parameters like CFG Scale and sampling steps. It allows users to compare and evaluate different outcomes to find the most pleasing or accurate result according to their preferences.


The importance of using more literal prompts in Stable Diffusion 2.1 is emphasized, allowing for better scene and style descriptions.

The inclusion of negative prompts greatly improves the output of images by specifying elements to avoid.

The significance of setting the resolution to at least 768 when working with Stable Diffusion 2.1 is mentioned.

The impact of sampling steps and CFG scale on the quality of the rendered image is discussed, with a correlation observed between the two.

Different sampling methods like Euler and DPM are compared, with Euler providing softer images and DPM offering more detail.

An example prompt is provided for creating a portrait, emphasizing the use of vivid colors and award-winning photography style.

The balance between CFG scale and steps used is crucial for achieving the desired image quality.

A render grid is used to demonstrate the effects of different step numbers and CFG scales on the final image.

The use of a low step number with a higher CFG scale can provide a good preview of the final image.

A second example is presented, focusing on a nature scene with specific mood and lighting described in the prompt.

DPM plus plus 2m is recommended for rendering nature scenes due to its ability to capture detailed textures.

The grid method is used again to illustrate how varying step numbers and CFG scales affect the final render.

Finding the right balance between steps and CFG scale is crucial for rendering images that closely match the prompt.

The importance of a negative prompt is reiterated, as it helps to refine the image to the desired specifications.

The video concludes with a call to action for viewers to like the content if they enjoyed it, and a farewell.

The end screen suggests other related content for viewers to explore.