AI Models Performance Debate | Users Call for Faster Solutions
A surge of voices in online forums highlights growing frustrations over the slow generation times of newly launched AI models. Users have expressed their concerns about several models, including Qwen and Flux Krea, often struggling to meet expectations, particularly regarding their hardware needs.
Image Generation: Users Demand Better Performance
People are sharing their experiences with generation times across various models. Those with different hardware setups express disappointment, indicating that these new tools can't keep up with previous versions.
One user commented, "With an 8GB card, generating a 1MP image takes 7 seconds," reinforcing a shared sentiment. Another user with a 12GB GPU noted, "It takes a couple of minutes to generate a decent image."
Hardware Limitations
Concerns are mounting regarding the hardware demands of these models:
SDXL: About 10 seconds per image.
Flux: Generally averages 8 to 12 seconds, some users report waiting as long as 80 seconds.
Qwen: Typically hits around 120 seconds, leading to significant user disappointment.
Wan2.2: Often takes up to 400 seconds, depending on settings.
Interestingly, a user suggested trying the Nunchaku models, stating, "It’s at least 6X faster than the original model especially with its Turbo Loras." This highlights alternatives that might perform better under similar conditions. Furthermore, users noted, all new models come with some degree of reduced quality due to their four-step LORA processing, yet many believe these still outperform older models.
Comparing Performance: New Insights
Several users have engaged in comparing various AI models:
Flux causes frustration:
"Raw Flux is just a waiting game."
Some with RTX 3090 cards reported improvements but still face delays with latest technologies.
Hardware recommendations are prevalent; one user mentioned, "I had an 8GB card myself and swapped it for an RTX 3090, which made a huge difference!"
Rising Frustration and Sentiment
While many users report dissatisfaction with current generation speeds, there is a mix of optimism that future updates could enhance performance. The prevalent frustrations are summarized as:
"Damm slow compared to SDXL" – a common complaint.
Some users remain hopeful about upcoming software tweaks.
Key Takeaways
🚀 New models underperform: Slow speeds compared to previous solutions raise concerns.
🔄 Exploration for faster alternatives: Users advocate for models like Nunchaku for better performance.
😩 Ongoing frustrations: Many feel unable to keep pace without frequent hardware upgrades.
As discussions evolve, the link between technological advancements and user satisfaction is clearer. Will developers respond by creating faster and more efficient models? Time will tell.