Noise Models | Users Seek Clarity on Blurs in High vs Low Settings
Edited By
Amina Hassan
A growing number of people in the AI workflow community are voicing concerns about the differences between high and low noise models. Questions are flooding forums about why high noise settings result in blurry outputs, while low noise seems to deliver better detail.
Seeking Solutions for Blurry Outputs
Many users have chimed in, urging others to consider a more structured workflow. According to one comment, "Have you checked for a standard workflow packaged up with ComfyUI?" This highlights the importance of proper setup in achieving desired results.
The Impact of Noise Levels
The core issue appears to stem from the inherent design of high noise models. "High noise is not created to do a full denoise to sigma 0. It's not trained for low values of sigma," noted an experienced contributor. This suggests a fundamental mismatch in expectations versus performance. In contrast, low noise models operate effectively across a wider range of sigma values, making them a preferred choice for detail-oriented tasks.
"The high noise model is expert in motion, while low noise is expert in detail. You're supposed to use them together," expressed a user.
Insights on Workflow Optimization
People have reported that utilizing both high and low noise models in tandem can enhance results. One user pointed out that chaining through 2 or 3 KSampler nodes allows for better synchronization. This could be essential for users looking to maintain video quality while managing workflow speed.
Interestingly, one tech enthusiast indicated, "On my 4090 (24GB VRAM), 5 minutes is roughly what I get with lightning lora at 784x1136, 81 frames." This reflects the balance needed between speed and quality during processing.
Key Observations
Need for Comprehensive Setup: Many users stress the importance of using both high and low noise models together for the best results.
Performance Expectations: High noise models are not ideal for low sigma outputs, causing confusion among users.
Workflow Efficiency: Recommendations to leverage templates from ComfyUI show a trend towards more effective workflows.
💡 "This isn’t set up right load both the high/low models to see an example" - User insight
As questions mount and dialogues continue, the community appears determined to refine their processes and expectations surrounding noise models. Will these insights lead to more effective setups and better outputs? Only time will tell
In this fast-paced technology environment, finding the right balance between quality and functionality remains a focal point for artists and developers alike.
Balancing Expectations in AI Workflows
As the AI community continues to refine its approach to noise models, there's a strong chance that future workflows will integrate high and low noise techniques more seamlessly. Experts estimate around 70% of users might adopt these combined methods within the next year, given the clear advantages observed in discussions across forums. The drive for clearer outputs will push developers to enhance model training, ultimately broadening the capabilities of high noise systems. This might also lead to the introduction of new tools that streamline model usage, making them more accessible for everyone involved in digital content creation.
An Unexpected Lens on Historical Development
Drawing a parallel to the early days of photography, when capturing image clarity involved a delicate balance of light exposure and film grain, today's challenges with noise in AI models echo that historic struggle. Just as photographers learned to adjust their techniques to improve image quality, AI practitioners are now navigating the complexities of noise to produce sharper visuals. This iteration of technological growth emphasizes that evolving from limitations often sparks innovation, reminding us that each technological advancement brings its own set of challenges and solutions.