10 Stable Diffusion Models Tested With Optimal Settings!

All Your Tech AI
4 Mar 202412:24

TLDRThe video discusses the optimization of 10 different stable diffusion models for image generation. Initially, a flaw in the testing methodology was identified, where all models used the same settings, leading to an unfair disadvantage for some. To rectify this, the presenter spent the weekend fine-tuning the settings for each model and uploaded the results to Pixel Dojo. The video then delves into the three key settings: inference steps, scheduler, and guidance scale, explaining their impact on image creation. Various examples are provided, such as Juggernaut XL Version 9, which benefits from a lower guidance scale to avoid overbaked artifacts. The presenter also demonstrates the effectiveness of these settings on other models like Proteus V2, SSD 1B, and others, highlighting the importance of model-specific settings. The video concludes with a call to action, inviting viewers to try the models themselves on Pixel Dojo and share their thoughts.


  • πŸ” The video compares 10 different stable diffusion models with optimal settings to address issues from a previous flawed methodology.
  • 🎯 The author spent the weekend optimizing settings for each model and shared them on Pixel Dojo.
  • πŸ“‰ The AI Image Creator offers a free trial and is priced at $5 a month for unlimited image creations.
  • βš™οΈ The three main settings discussed are inference steps, the noise removal algorithm (scheduler), and the guidance scale.
  • πŸ” Inference steps determine how many times the neural network processes the image; not always better with higher numbers.
  • πŸ› οΈ Schedulers like Uler or Caris DD IM influence how noise is removed and the style of the final image.
  • πŸ“œ The guidance scale (CFG scale) controls how closely the final image adheres to the prompt, with higher values increasing precision but reducing creativity.
  • πŸ‘©β€πŸ¦° Examples are given using Juggernaut XL models to illustrate the impact of guidance scale on image quality and artifacting.
  • πŸš€ The video demonstrates how to upscale images for more detail and higher resolution.
  • 🌟 Each model has specific optimal settings, which can be found on their model card or determined through trial and error.
  • 🎨 Models like Animag and Kandinsky offer unique aesthetics and are suited for different styles of image creation.
  • ⚑ Turbo models like Dream Shaper XL Turbo can generate images quickly with fewer inference steps.

Q & A

  • What was the flaw in the original testing methodology of the 10 stable diffusion models?

    -The flaw was that the same settings were used for all models, which did not allow for the optimal settings for each model to be utilized, giving an unfair disadvantage to some models.

  • What is the significance of the inference steps in the image generation process?

    -Inference steps refer to the number of times the model iterates through the neural network to remove noise from the image. It affects the quality and detail of the final image, but adding more steps beyond a certain threshold does not improve the result and only increases the generation time.

  • How does the choice of the scheduler affect the image generation?

    -The scheduler is the algorithm used to remove noise from the image. Different schedulers can influence the style and quality of the final image, making it model-specific.

  • What is the role of the guidance scale (CFG scale) in the image generation?

    -The guidance scale determines how closely the final image adheres to the prompt. A lower guidance scale results in more creativity and less adherence to the prompt, while a higher scale increases precision but may reduce creativity and introduce artifacts.

  • Why did the video creator lower the pricing for the AI Image Creator tool?

    -The pricing was lowered to $5 a month to allow more users to access the tool and perform unlimited image creations at a low cost.

  • What is the difference between Juggernaut XL Version 9 and Version 8 in terms of image quality?

    -Juggernaut XL Version 9 has a more realistic and higher quality image output compared to Version 8. It has improved lighting and reduced artifacts when using a lower guidance scale.

  • How does the SSD 1B model differ from other models in terms of speed and parameter count?

    -SSD 1B has 50% fewer parameters than other models like SDSL, which means it generates images more quickly, approximately 60% faster.

  • What is the advantage of using a fast model like SSD 1B for image generation?

    -A fast model like SSD 1B can be used to quickly generate a baseline image, which can then be upscaled and enhanced for additional detail and realism.

  • What settings were found to be optimal for Playground V2?

    -For Playground V2, lower guidance scales around two and around 30 inference steps were found to produce soft, well-lit images that are visually appealing.

  • How does the Animag model differ from the others in terms of guidance scale and steps?

    -Animag prefers a higher guidance scale of 12 and a higher number of inference steps, up to 50, to produce crisp images with less noise, which is ideal for high-quality anime-style images.

  • What is the recommended approach for using the Dream Shaper XL Turbo model?

    -For the Dream Shaper XL Turbo model, it is recommended to use a guidance scale of two and not to reduce the inference steps below 10 to avoid grainy and noisy images, despite its ability to generate images quickly.



πŸ” Refining Stable Diffusion Models for Optimal Settings

The speaker acknowledges a flaw in their previous video's testing methodology, where they compared 10 different stable diffusion models using identical settings. They spent the weekend fine-tuning the best settings for each model and uploaded them to Pixel Dojo. The speaker explains the importance of adjusting inference steps, schedulers, and guidance scale (CFG scale) for each model to achieve the best results. They provide examples of how varying these settings can drastically change the outcome, such as reducing artifacts in Juggernaut XL Version 9 by lowering the guidance scale.


πŸ–ΌοΈ Testing and Optimizing Each Model's Performance

The speaker proceeds to test each model using a specific prompt and discusses the optimal settings for each. For Proteus V2, they found that using the Uler scheduler, a guidance scale of seven, and 30 inference steps produced the best images. The SSD 1B model, with fewer parameters, was found to be faster and suitable for quick image generation with a guidance scale of 13 and 20 inference steps. The upscaler tool was introduced as a way to enhance baseline images by adding detail and doubling the resolution. Playground V2 was tested with a lower guidance scale and 30 inference steps, producing soft, well-lit images. The speaker also compared different versions of the Juggernaut model, noting that each required different settings for optimal results, and highlighted the importance of matching the guidance scale to the model for the best image quality.


🎨 Exploring Aesthetics and Customizing Image Generation

The speaker continues to explore various models, discussing their unique aesthetics and how different settings affect the final image. They mention that Animag, which is trained on anime images, requires a high guidance scale and more inference steps for crisp results. Kandinsky, with its distinct aesthetic, benefits from a Caris DPM scheduler and a lower guidance scale. Real viz XL version 4 is recommended for portrait photography due to its natural look and soft lighting. Lastly, Dream Shaper XL Turbo is noted for its quick render times and high detail quality, even with fewer inference steps. The speaker concludes by encouraging viewers to try out the models on Pixel Dojo and share their opinions.



πŸ’‘Stable Diffusion Models

Stable Diffusion Models refer to a class of machine learning models that are capable of generating images from textual descriptions. These models use a process called diffusion to gradually refine an image towards the desired output. In the video, the creator tests and compares 10 different models to find the optimal settings for each, highlighting their unique capabilities and the importance of adjusting parameters for best results.

πŸ’‘Inference Steps

Inference Steps, also referred to as 'steps' in the context of diffusion models, denote the number of iterations the model goes through to refine the generated image. The video explains that increasing the number of steps does not always lead to better image quality and can instead just prolong the generation time without significant improvements.


A Scheduler in the context of diffusion models is an algorithm that determines the rate at which noise is removed from the image during the diffusion process. Different schedulers can influence the style and quality of the final image. The video discusses how certain schedulers work better with specific models to achieve desired outcomes.

πŸ’‘Guidance Scale (CFG Scale)

The Guidance Scale, or CFG Scale, is a parameter that controls how closely the generated image adheres to the input prompt. A higher Guidance Scale results in a more precise image that closely follows the prompt, while a lower scale allows for more creativity but less adherence to the prompt. The video provides examples of how varying this scale can affect the final image, such as reducing artifacts in certain models.


Artifacting refers to the presence of visual anomalies or 'noise' in the generated image that doesn't resemble the intended output. The video demonstrates how certain models with high Guidance Scales can produce images with artifacting, which appear odd or unrealistic, such as overly glossy skin or distorted facial features.

πŸ’‘Pixel Dojo

Pixel Dojo is mentioned as a platform where the creator has uploaded the best settings for each of the 10 models tested. It suggests a community or resource where users can access and utilize these optimal settings for their own image generation tasks, indicating a collaborative aspect to the AI image creation process.

πŸ’‘AI Image Creator

The AI Image Creator is a tool within the platform that allows users to generate images using various models. The video script describes it as having different models loaded and adjustable settings for users to experiment with, emphasizing its role as a user-friendly interface for creating images with AI.


Upscaling in the context of the video refers to a process where a generated image is enhanced to improve its quality, sharpness, and detail, often doubling its resolution. The video demonstrates how a fast model can produce a baseline image that, when upscaled, can result in a more refined and detailed final product.


A Prompt is a textual description or command given to the AI model to guide the generation of an image. The video discusses how the adherence to the prompt by the AI model can be controlled through the Guidance Scale, and how the final image quality is influenced by how well the model follows the prompt.

πŸ’‘Turbo Model

A Turbo Model, as mentioned in the context of Dream Shaper XL Turbo, is a type of diffusion model that is designed to generate images more quickly than standard models, often at the cost of some image quality. The video notes that even with fewer inference steps, Turbo models can still produce high-detail images, albeit with some noise or graininess.


In the context of the video, 'Ancestral' likely refers to a specific type of scheduler or algorithm used within the diffusion process. The term is used to describe a setting within the AI Image Creator tool, suggesting it as a choice that can affect the style and outcome of the generated images.


The video compares 10 different stable diffusion models with optimal settings.

The initial testing methodology was flawed as it didn't change settings between different models.

The video provides the best settings for each model, now available on Pixel Dojo.

Pixel Dojo's AI Image Creator offers a free trial and a low-cost monthly subscription.

Different models require different settings for optimal performance.

The number of inference steps is crucial and can affect the quality of the generated image.

The choice of scheduler can influence the style and quality of the final image.

Guidance scale determines how closely the final image adheres to the prompt.

High guidance scale can lead to precision but may result in loss of creativity and artifacting.

Juggernaut XL Version 9 requires a lower guidance scale to avoid overbaked artifacts.

Proteus V2 benefits from a uler scheduler, a guidance scale of seven, and 30 inference steps.

SSD 1B is a faster model with 50% fewer parameters, suitable for quick image generation.

Upscaling can improve image quality by adding detail and doubling the resolution.

Playground V2 produces soft, well-lit images with lower guidance scales and around 30 inference steps.

Juggernaut V8 and V9 models show significant improvements in image detail and realism.

Animag is ideal for high-quality anime images due to its training on thousands of anime images.

Kandinsky offers a unique aesthetic with stylized lighting and skin texture.

Realviz XL and Dreamshaper XL Turbo are good models for portrait photography and quick, high-detail image generation.