10 Stable Diffusion Models Tested With Optimal Settings!
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.
Takeaways
- π The video compares 10 different stable diffusion models with optimal settings to address issues from a previous flawed methodology.
- π― The author spent the weekend optimizing settings for each model and shared them on Pixel Dojo.
- π The AI Image Creator offers a free trial and is priced at $5 a month for unlimited image creations.
- βοΈ The three main settings discussed are inference steps, the noise removal algorithm (scheduler), and the guidance scale.
- π Inference steps determine how many times the neural network processes the image; not always better with higher numbers.
- π οΈ Schedulers like Uler or Caris DD IM influence how noise is removed and the style of the final image.
- π The guidance scale (CFG scale) controls how closely the final image adheres to the prompt, with higher values increasing precision but reducing creativity.
- π©βπ¦° Examples are given using Juggernaut XL models to illustrate the impact of guidance scale on image quality and artifacting.
- π The video demonstrates how to upscale images for more detail and higher resolution.
- π Each model has specific optimal settings, which can be found on their model card or determined through trial and error.
- π¨ Models like Animag and Kandinsky offer unique aesthetics and are suited for different styles of image creation.
- β‘ 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.
Outlines
π 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.
πΌοΈ 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.
π¨ 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.
Mindmap
Keywords
π‘Stable Diffusion Models
π‘Inference Steps
π‘Scheduler
π‘Guidance Scale (CFG Scale)
π‘Artifacting
π‘Pixel Dojo
π‘AI Image Creator
π‘Upscale
π‘Prompt
π‘Turbo Model
π‘Ancestral
Highlights
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.