10 Stable Diffusion Models Tested With Optimal Settings!
TLDRIn this video, the creator reviews and compares 10 different stable diffusion models using optimal settings to improve their performance. Initially, the testing methodology was flawed as it didn't adjust settings between different models, leading to an unfair disadvantage for some. Over the weekend, the creator fine-tuned each model to find its best settings, which are now available on Pixel Dojo. The video discusses three key settings: inference steps, scheduler, and guidance scale (CFG scale). These settings influence the image generation process, with the number of steps determining the image's refinement, the scheduler affecting the noise removal process, and the guidance scale controlling how closely the final image adheres to the prompt. The creator provides examples using models like Juggernaut XL, Proteus V2, and SSD 1B, showing how adjusting these settings can enhance image quality and avoid artifacts. The video also demonstrates the use of an upscaler to add more detail and realism to the generated images. Each model's optimal settings are discussed, highlighting their unique strengths and the kind of images they produce. The creator encourages viewers to try out the models on Pixel Dojo and provides links for further exploration.
Takeaways
- 🔧 The initial testing methodology was flawed as it didn't adjust settings between different models, leading to an unfair disadvantage for some.
- 🔄 The creator spent the weekend optimizing settings for each of the 10 models to find the best performance, which are now available on Pixel Dojo.
- 💲 The pricing for the AI Image Creator was lowered to $5 a month for unlimited image creations.
- ⚙️ The importance of adjusting three key settings for each model was emphasized: inference steps, scheduler, and guidance scale.
- 🔢 Inference steps determine how many times the model processes the image to refine it, but more is not always better beyond a certain threshold.
- 🛠️ The scheduler, such as Uler or Caris DDPM, is the algorithm that removes noise from the image and can affect the final image's style.
- 📏 The guidance scale or CFG scale dictates how closely the final image adheres to the prompt, with higher values increasing precision but reducing creativity.
- 🧩 Artifacting can occur when the guidance scale is too high, as demonstrated with Juggernaut XL Version 9.
- 🔄 By adjusting the guidance scale and other settings, different models can be fine-tuned to produce better results, as shown with Juggernaut V8 and V9.
- 📈 The upscaler can be used to enhance images generated by faster models, adding detail and doubling the resolution.
- 🌟 Each model has unique optimal settings, and the video provided specific recommendations for models like Proteus V2, SSD 1B, Playground V2, and others.
Q & A
What was the issue with the initial testing methodology for the stable diffusion models?
-The initial testing methodology was flawed because it didn't change any of the settings between generations with different models. Every model used the same number of inference steps, the same guidance scale, and everything else, which gave an unfair disadvantage to some models.
What did the creator do to address the issue with the initial testing?
-The creator spent the weekend going through each of the 10 models, trying to find the best settings for each, which were then uploaded to Pixel Dojo.
What is the significance of the number of inference steps in the image generation process?
-The number of inference steps is related to how many times the model iterates through the neural network to remove noise from the image. It does not always mean that higher is better; there is a threshold where adding more steps increases the time taken without improving the result.
What is the role of the scheduler in the image generation process?
-The scheduler is the algorithm used to remove noise from the image. Changing the scheduler can influence the way the image is created and the style of the image at the end, making it very much model-specific.
How does the guidance scale or CFG scale impact the final image?
-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 guidance scale increases precision but may reduce creativity and introduce artifacts.
What is the recommended guidance scale for Juggernaut XL Version 9?
-For Juggernaut XL Version 9, the default guidance scale is set to one, which is very low and still adheres to the prompt but avoids the overbaked artifact look.
What is the advantage of using SSD 1B model?
-SSD 1B has 50% fewer parameters, meaning it generates images more quickly—about 60% faster than SDXL. It's a good model for quick image generation and testing.
How does the upscaler tool 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, significantly improving the image quality.
What settings does Playground V2 prefer for optimal image generation?
-Playground V2 prefers lower guidance scales around two and around 30 inference steps for soft, well-lit images.
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 inference steps as V8, which results in a significant improvement in realism and lighting compared to V8.
What is the recommended guidance scale for the Animag model to achieve high-quality anime images?
-The recommended guidance scale for the Animag model is 12, with a higher number of inference steps (50) for a crisp look and less noise in the images.
How does the Dream Shaper XL Turbo model compare to other models in terms of inference steps?
-As a turbo model, Dream Shaper XL Turbo can typically generate images with very few inference steps. However, anything lower than 10 resulted in grainy, noisy images in the creator's experience.
Outlines
📈 Optimizing Stable Diffusion Models for Best Results
The speaker discusses their previous video where they compared 10 different stable diffusion models using a flawed testing methodology. They rectify this by spending the weekend fine-tuning the best settings for each model and uploading them to Pixel Dojo. The video provides a walkthrough of the AI Image Creator, highlighting the significance of inference steps, schedulers, and guidance scale in the image generation process. The speaker emphasizes the importance of adjusting these parameters to avoid artifacts and achieve the desired image quality, as demonstrated with examples from different models like Juggernaut XL Version 9 and Version 8.
🔍 Customizing Model Settings for Enhanced Imagery
The video script elaborates on testing various models with different settings to achieve optimal image quality. It covers the process of selecting models like Proteus V2, SSD 1B, and Playground V2, and adjusting parameters such as the scheduler, inference steps, and guidance scale to enhance image details and realism. The speaker demonstrates the use of an upscaler to improve image resolution and details, and shares their findings for each model, including the ideal settings for achieving the best results. The narrative also touches upon the trade-offs between image quality and render time, providing insights into the unique characteristics of models like Juggernaut V8, V9, and others.
🎨 Exploring Aesthetics and Settings of Different Models
The speaker continues to explore the aesthetic differences and specific settings for various stable diffusion models, including Imagina, Kandinsky, Real Viz XL, and Dream Shaper XL Turbo. Each model has unique preferences for schedulers, guidance scales, and inference steps, which are detailed in the script. The video showcases the distinct visual outcomes of these settings, from the stylized lighting of Kandinsky to the natural soft lighting suitable for portrait photography in Real Viz XL. The Dream Shaper XL Turbo model is highlighted for its quick render times and high detail quality, even at lower inference steps. The speaker concludes by encouraging viewers to try the models on Pixel Dojo and share their opinions on which model produces the best results.
Mindmap
Keywords
💡Stable Diffusion Models
💡Inference Steps
💡Scheduler
💡Guidance Scale (CFG Scale)
💡Artifacting
💡Juggernaut XL Version 9
💡Upscale
💡Playground V2
💡Turbo Model
💡Pixel Dojo
💡AI Image Creator
Highlights
A video was made comparing 10 different stable diffusion models.
The initial testing methodology was flawed as it didn't change settings between different models.
The video creator spent the weekend optimizing settings for each of the 10 models.
Optimal settings for each model are now available on Pixel Dojo.
Pixel Dojo's AI Image Creator offers a free trial and a $5/month subscription for unlimited image creations.
Different models have different ideal settings for steps, scheduler, and guidance scale.
The number of inference steps can affect the quality and generation time of an image.
The scheduler used can influence the style and noise removal process of the image.
Guidance scale determines how closely the final image adheres to the prompt, with higher values increasing precision but reducing creativity.
Artifacting can occur when the guidance scale is too high for a model.
Juggernaut XL Version 9 requires a lower guidance scale to avoid overbaked and artifacted images.
Each model's optimal settings, including scheduler, steps, and guidance scale, can be found on their model card.
Proteus V2 performs best with a scheduler of uler, a guidance scale of seven, and 30 steps.
SSD 1B generates images quickly with 50% fewer parameters than SDXL.
Upscaling can be used to enhance images generated by faster models.
Playground V2 works well with lower guidance scales and around 30 inference steps for soft, well-lit images.
Juggernaut V8 and V9 have different ideal guidance scales, with V9 benefiting from a lower scale for higher quality images.
Animag is ideal for high-quality anime images, requiring a high guidance scale and more inference steps.
Kandinsky has a unique aesthetic, preferring a different scheduler and lower guidance scale for stylized results.
Real Viz XL and Dream Shaper XL Turbo were also tested, with each having specific optimal settings for best results.