A lively discussion is transforming the landscape of AI image generation, as users share their experiences with models running on the powerful 4070 Ti. Current frontrunners such as Qwen, WAN 2.2, and Flux Kontext are sparking both support and skepticism among people seeking the best performance.
With everyone striving for optimal results, some users feel overwhelmed by the choices available. One user remarked, "I am reading and using AI every day but I just can't keep up anymore." This sentiment echoes in many posts as community members seek guidance on which model maximizes their hardware's potential.
Qwen continues to stand out, with numerous users lauding its speed and prompt accuracy. One expressed, "Qwen is consistently faster and amazing prompt adherence for literally anything you can imagine." Others support Qwen, suggesting it pairs well with GGUF files for enhanced capabilities.
"Qwen GGUF should work - there are multiple edit effects and prompt in the video if it fits your case," shared one enthusiast, hinting at available resources for optimal setup.
While WAN 2.2 gets praise for its competitive nature, some voices in the forums indicate it struggles to keep pace with the enthusiasm surrounding Qwen. A recent comment ranked models, stating, "1: wan 2: qwen 3: flux krea," briefly echoing mixed feelings about their effectiveness.
Conversely, Flux Kontext seems to be falling short, with one user bluntly stating, "It doesn't compete for me."
People are fine-tuning their setups to boost performance. Suggestions about pairing lightning loras with GGUF files have gained traction, with one user promising output in under a minute on a different GPU model.
Interestingly, users are turning to ComfyUI as a recommended setup, but consistency remains a hotly debated topic. "Okay, I will try out Qwen then thanks! You got some links for the best setup right now? Is ComfyUI still the go-to?" asked another.
โก Qwen remains the favored choice for many, especially for speed.
โ ๏ธ Flux Kontext is less favored, with some deeming it ineffective.
๐ก Combining lightning loras and GGUF files creates a more responsive setup.
As 2025 unfolds, the conversation surrounding image generation shows no signs of slowing down. Will new models emerge to contest the current reign of Qwen, or will user feedback lead to breakthroughs in technology tailored to meet the community's evolving demands?
Looking ahead, there's an inclination for real-time feedback and collaborative environments where people can share performance metrics. The next few months will likely determine how AI image generation evolves to better suit user needs while advancing technological capabilities.