10 Stable Diffusion Models Tested With Optimal Settings!

In a recent video, the creator discusses the optimization of 10 different stable diffusion models to achieve the best results. Initially, a flaw in the testing methodology was identified where all models used the same settings, leading to an unfair disadvantage for some. Over the weekend, the creator fine-tuned the settings for each model and shared the optimal parameters on Pixel Dojo. The summary covers the importance of adjusting inference steps, the choice of scheduler, and the guidance scale (CFG scale) for each model to enhance image creation quality and reduce artifacts. The video provides examples of the differences in image quality with varying settings, especially noting the improvements in Juggernaut V9 compared to earlier versions. The creator also demonstrates the use of an upscaler to add detail and realism to images generated by faster models. The discussion includes specific settings for models like Proteus V2, SSD 1B, Playground V2, Juggernaut V8 and V9, Animag, Kandinsky, Realviz XL, and Dream Shaper XL Turbo, highlighting the unique characteristics and optimal settings for each to achieve the best outcomes in image generation.

Takeaways

• 🔍 The video compares 10 different stable diffusion models using optimal settings to improve the fairness of comparison.
• 🔧 The initial testing methodology was flawed as it used the same settings for all models, disadvantaging some.
• 📈 The creator spent the weekend optimizing settings for each of the 10 models and shared the best settings on Pixel Dojo.
• 💡 The number of inference steps (or 'steps') is crucial for image generation, but more is not always better beyond a certain threshold.
• 🎛️ The 'scheduler' used in the model influences how noise is removed from the image, affecting the final style and quality.
• 📉 A lower guidance scale (CFG scale) results in more creativity but less adherence to the prompt, while a higher scale increases precision but can introduce artifacts.
• 👩‍🦰 An example of the impact of settings is seen with Juggernaut XL Version 9, where a higher guidance scale led to overbaked and artifacted images.
• 🚀 For quick image generation, models like SSD 1B can be used with fewer parameters and faster generation times, albeit with slightly lower quality.
• 📚 Upscaling can be used to enhance baseline images from fast models, adding detail and doubling the resolution.
• 🌟 Different models have different optimal settings; for instance, Juggernaut V9 prefers a lower guidance scale and Oiler scheduler for better results.
• ⚙️ Each model's settings can be adjusted in the AI Image Creator on Pixel Dojo, allowing for fine-tuning to achieve the best image quality.

Q & A

What was the issue with the initial testing methodology of the 10 stable diffusion models?

The initial testing methodology was flawed because it didn't change any of the settings between generations with different models, which gave an unfair disadvantage to some models.

What is the purpose of adjusting the inference steps in stable diffusion models?

Adjusting the inference steps is related to the number of times the model iterates through the neural network to remove noise from the image and steer it towards the prompt. However, higher steps do not always result in better images and can increase generation time without improving results.

How does changing the scheduler influence the image creation process?

The scheduler is the algorithm used to remove noise from the image. By changing the scheduler, one can influence the way the image is created and the style of the final image, as different schedulers work better for different models.

What is the role of the guidance scale or CFG scale in 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 can lead to artifacting and a loss of creativity.

Why did the video creator lower the pricing of the AI image creator on Pixel Dojo?

The pricing was lowered to $5 a month to allow users to jump in and do unlimited image creations at a very low price.

What is the significance of the model card in relation to the guidance scale?

The model card typically provides information on the recommended guidance scale for a specific model, which is useful for achieving the best results with that model.

How did the creator test the different models to find the optimal settings?

The creator spent the weekend going through each of the 10 models, adjusting the steps, scheduler, and guidance scale to find the best settings for each one.

What is the advantage of using a model with fewer parameters like SSD 1B?

SSD 1B has 50% fewer parameters, which means it generates images more quickly, making it a good choice if you need results faster and are willing to compromise on some image quality.

How does the upscaler feature enhance the image quality?

The upscaler not only sharpens and adds more realism and detail to the image but also doubles the resolution to 2048 by 2048, resulting in a significant improvement in image quality.

What is the key difference between Juggernaut V8 and Juggernaut V9 in terms of settings?

Juggernaut V9 prefers a lower guidance scale of one and the same number of steps as V8, but it produces images with more realism and better lighting, indicating a significant improvement in image quality.

Why is it recommended to experiment with different settings for each model?

Different models have different optimal settings for steps, scheduler, and guidance scale. Experimenting with these settings allows users to achieve the best results tailored to each specific model's capabilities.

Outlines

00:00

🔍 Refining Stable Diffusion Models for Better Image Generation

The video script discusses the process of optimizing 10 different stable diffusion models for image generation. Initially, the testing methodology was flawed as it didn't adjust settings between generations for different models. The narrator spent the weekend fine-tuning the best settings for each model and uploaded them to Pixel Dojo. The video provides an overview of the AI Image Creator tool, its pricing, and the importance of adjusting settings such as inference steps, schedulers, and guidance scale to achieve better results. It also illustrates the impact of these settings on image quality using examples from different models, highlighting the need for model-specific adjustments.

05:01

🎨 Customizing Model Settings for Enhanced Image Quality

The narrator continues to delve into the customization of model settings, emphasizing the impact of the guidance scale and inference steps on the final image's adherence to the prompt and its overall quality. The video showcases the results from various models, such as Proteus V2, SSD 1B, and Playground V2, with different settings like schedulers and steps, to demonstrate how they affect the image generation process. It also explores the use of an upscaler to enhance images and add more detail. The narrator shares the findings for each model, including the optimal settings and the visual outcomes, to guide viewers in achieving the best results from their image creation process.

10:03

🚀 Turbo Models and Their Fast, High-Quality Image Generation

The final paragraph focuses on the performance of turbo models, which are capable of generating high-quality images quickly with fewer inference steps. The narrator discusses the settings for models like Dream Shaper XL Turbo, emphasizing the need for a balance between speed and detail. It also provides a comparison with other models and highlights the trade-offs between speed and image quality. The video concludes with a call to action for viewers to try out the models on Pixel Dojo and share their opinions on which model produces the best results.

Keywords

💡Stable Diffusion Models

Stable diffusion models are AI-driven algorithms used to generate images from textual descriptions, often referred to as prompts. In the video, the creator discusses testing 10 different models to find the optimal settings for each, which is crucial for producing high-quality images that closely match the desired prompt. These models are the central focus of the video.

💡Inference Steps

Inference steps refer to the number of iterations the AI goes through to refine the generated image by removing noise and adhering to the prompt. The video explains that increasing these steps does not always lead to better results and can instead increase the time taken to generate an image without significant improvement.

💡Scheduler

The scheduler, also known as the noise removal algorithm, dictates how the noise is taken out of the image during the generation process. Different schedulers can influence the style and quality of the final image, making it a model-specific setting that the video explores for optimization.

💡Guidance Scale (CFG Scale)

The guidance scale, or CFG scale, is a parameter that determines how closely the final image adheres to the input prompt. A lower guidance scale results in more creativity but less adherence to the prompt, while a higher scale leads to more precise images but potentially at the cost of creativity and with the risk of artifacting.

💡Artifacting

Artifacting refers to the visual anomalies or imperfections that can appear in the generated images when the guidance scale is set too high or the model is not properly optimized. The video uses examples to show how adjusting the guidance scale can reduce artifacting and produce more realistic images.

💡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 serves as a resource for viewers to access and utilize the optimized settings for their own image generation, indicating a community or shared space for AI image creation.

💡AI Image Creator

AI Image Creator is a tool within the Pixel Dojo platform that allows users to generate images using various models. The video discusses the different settings available in this tool, such as steps, scheduler, and guidance scale, which are essential for customizing the image generation process.

💡Upscale

Upscaling in the context of the video refers to the process of enhancing a generated image to add more detail and realism, as well as doubling the resolution. The creator demonstrates how using a fast model to create a baseline image can be followed by upscaling to achieve higher quality results.

💡Model Card

A model card is a document or section within the software that provides information about a specific stable diffusion model, including recommended settings like the guidance scale. The video suggests that most models will have their ideal settings listed on their respective model cards.

💡Turbo Model

Turbo models are a type of stable diffusion model that generates images more quickly than others, often at the cost of some image quality. The video notes that for turbo models like Dream Shaper XL Turbo, fewer inference steps can still produce high-detail images, albeit with some noise.

💡Ancestral

In the context of the video, 'ancestral' likely refers to a specific type of scheduler or algorithm used within the AI image generation process. It is mentioned as a preferred setting for certain models, suggesting it is a term specific to the software or models being discussed.

Highlights

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

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

The best settings for each model have been found and uploaded to Pixel Dojo.

Pixel Dojo's AI Image Creator offers a free trial and is $5 a month for unlimited image creations.

Different models have different optimal settings for steps, scheduler, and guidance scale.

Higher inference steps do not always result in better image quality.

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

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

High guidance scale can lead to overbaked images with artifacts.

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

SSD 1B is a fast model with 50% fewer parameters, generating images 60% faster.

Upscaling can add more realism, detail, and doubles the resolution of images.

Playground V2 works well with lower guidance scales for soft, well-lit images.

Juggernaut V8 and V9 show significant improvements in image quality 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.

Real Viz XL is great for portrait photography with its natural look and soft lighting.

Dream Shaper XL Turbo generates high detail quality images quickly with few inference steps.