Stable Diffusion Deep Dive - Samplers - which are worth using and which are redundant?
TLDRThis video script offers an in-depth analysis of the 19 Samplers available in the stable diffusion automatic 1111 GUI. The creator runs various prompts to compare the output, convergence, and processing speed of different Samplers, categorizing them into three groups with an outlier. The study reveals that Group 1 and 1B Samplers generally converge and produce lower quality images, while Group 2 Samplers are non-convergent and take longer to generate images. The new sde Samplers in Group 3 are unique but occasionally match other groups' outputs. The video concludes with recommendations for a core and expanded set of Samplers for optimal variety and efficiency in AI image generation.
Takeaways
- ๐จ The video discusses the art of generating good images using stable diffusion and the challenges in selecting the right samplers.
- ๐ The presenter conducted experiments with 19 samplers in the stable diffusion automatic 1111 GUI to determine their impact on image generation.
- ๐ The analysis involved running various prompts with constant seed and CFG, focusing on different subjects like portraits, animals, and landscapes.
- ๐ฅ The results showed that samplers could be grouped into clusters based on their output similarities, with some outliers.
- ๐ Convergence was evaluated qualitatively as the sampler's ability to reach a specific output within 150 steps without substantial change.
- ๐ Processing speed was measured by the time taken per step, with samplers categorized as fast or slow.
- ๐ The new SDE samplers in group 3 were found to be typically unique in their outputs.
- ๐ก The video recommends a selection of samplers for use based on their performance in convergence, processing speed, and image quality.
- ๐ ๏ธ Viewers are advised to compare outputs from different prompts to find preferred styles and to select samplers accordingly.
- โ๏ธ The video provides guidance on how to deactivate unwanted samplers in the automatic 1111 web interface.
- โณ The content of the video is subject to change as stable diffusion and web UI evolve rapidly, potentially becoming obsolete within weeks or days.
Q & A
What is the main focus of the video?
-The main focus of the video is to analyze and evaluate the 19 Samplers available in the stable diffusion automatic 1111 GUI and provide recommendations on which ones to use for generating good image results.
How does the speaker approach the evaluation of Samplers?
-The speaker runs a variety of prompts with each Sampler at various steps, keeping the seed and CFG constant, and uses different subject matter to ensure the results are not impacted by the content type.
What did the speaker find about the correlation of Sampler outputs?
-The speaker found that many Samplers were strongly or completely correlated in their outputs, with three distinct clusters identified, and one outlier Sampler.
What is the definition of convergence used in the video?
-Convergence is defined as the sampler reaching a specific output within 150 steps that doesn't change substantially with additional steps.
Which Samplers were found to be convergent?
-All of the Group 1 and Group 1B Samplers, DPM adaptive, and the new SDE Samplers from Group 3 were found to be convergent.
How was processing speed measured for the Samplers?
-Processing speed was measured by how much time each step takes, with Samplers being categorized as fast or slow based on this metric.
What are the recommended core Samplers for maximum variety with few samples?
-The recommended core Samplers are DPM plus plus 2m from Group 1, DPM plus plus 2m Keras from Group 1B, DPM plus plus 2s a from Group 2A, DPM plus plus 2A Karis from Group 2B, and from Group 3, DPM plus plus sde and DPM plus plus sde Keras.
Which Samplers are considered for expanded use?
-The expanded Samplers recommended are DPM adaptive, DPM fast, hewn, and ddim.
How can unused Samplers be deactivated in the automatic 1111 web interface?
-To deactivate unused Samplers, go to the settings tab, scroll to the bottom, select the Samplers to remove, click apply, close the command line window, and then restart for the changes to take effect.
What is the disclaimer given by the speaker regarding the content of the video?
-The speaker notes that stable diffusion and the web UI are rapidly evolving, and the contents of the video may become obsolete within weeks or even days.
Outlines
๐จ Introduction to Samplers in Stable Diffusion
This paragraph introduces the topic of the video, which is an in-depth exploration of the 19 Samplers available in the Stable Diffusion Automatic 1111 GUI. The speaker, Silicon Pharma, discusses the art of generating good images and the challenges faced due to conflicting information in the community. The aim is to test the impact of different settings and provide recommendations on which Samplers to use. The video begins with an experiment where various prompts were run through each sampler at different steps, keeping seed and CFG constant to ensure the results were not influenced by the subject matter.
๐ Analysis and Findings on Samplers
In this paragraph, the analysis of the Samplers is presented. The speaker found that many samples were strongly correlated in their output, forming three distinct clusters with one outlier. The convergence of the Samplers was evaluated, with most from Group 1 and 1B, DPM adaptive, and those from Group 3 converging. Group 2 Samplers mostly did not converge, except for DPM adaptive. Processing speed was also measured, with all Samplers except one falling into either fast or slow categories. The number of steps needed for a decent output was tested, with DPM fast being unique but not as efficient as others. The paragraph concludes with a qualitative judgment on the Samplers' performance.
๐ Recommendations and Conclusion
The speaker provides recommendations on which Samplers to use based on the analysis. Group 1 and 1B Samplers were found to generate lower quality images, with some exceptions like hewn for its sharpness and Euler for its softness. The speaker recommends keeping at least one from each group. The unique output of DPM fast and the versatility of DPM adaptive were discussed. A list of recommended core and expanded Samplers is provided for maximum variety and efficiency. The speaker also explains how to deactivate unwanted Samplers in the Automatic 1111 web interface. The video ends with a reminder that the information might become obsolete due to the rapid evolution of Stable Diffusion and the web UI, and encourages viewers to like, subscribe, and comment for future content.
Mindmap
Keywords
๐กstable diffusion
๐กsampler
๐กconvergence
๐กprocessing speed
๐กCFG
๐กoutput
๐กsubject matter
๐กexperiments
๐กDPM
๐กEuler a
๐กSDE
Highlights
The video discusses the art of generating good images and stable effusion in the context of 19 Samplers available in the stable diffusion automatic 1111 GUI.
The presenter conducted experiments to isolate and test the impact of certain settings on the output of the Samplers.
Different subject matters such as portraits, full-body people, animals, and landscapes were used to ensure the results were not biased by the subject matter.
Three distinct clusters of Samplers were identified based on their output correlation, with one outlier.
Group 1 and 1B Samplers usually share the same output, but Group 1BSamplers, which end in Keras, can have different outputs.
DDIM could result in either a Group 1 or Group 1B output, making it slightly unpredictable.
Group 2 is more varied with 2A and 2B being mostly distinct but capable of producing the same output.
Euler A and DPM Adaptive are wildcards, producing outputs in roughly equal proportions across different groups.
The new SDE Samplers in Group 3 were typically unique but could occasionally produce the same output.
Convergence was evaluated qualitatively, with Group 1 and 1B Samplers, DPM Adaptive, and DPM Plus Plus SDE Keras from Group 3 converging.
Group 2 Samplers mostly did not converge, with the exception of DPM Adaptive.
Processing speed was measured in terms of the power consumption per step, with all Samplers except one falling into either fast or slow categories.
DPM Adaptive removes steps as a variable and uses CFG, resulting in unique images but is less efficient for exploration and screening.
The recommended core Samplers for maximum variety with fewest samples include DPM Plus Plus 2M from Group 1, DPM Plus Plus 2M Keras from Group 1B, DPM Plus Plus 2S A from Group 2A, and DPM Plus Plus 2A Karis from Group 2B.
Optional or expanded Samplers recommended are DPM Adaptive, DPM Fast, Hewn, and DDIM.
Samplers can be deactivated in the automatic 1111 web GUI by selecting them in the settings tab and restarting the command line window.
The content of the video may become obsolete quickly due to the rapid evolution of stable diffusion and the web UI.