Do you agree? SD3 model release. But hands are bad?

The video discusses the SD3 model release and its limitations in rendering hands, acknowledging the issue but expressing optimism for future improvements. By drawing parallels to the evolution of SDXL and highlighting the role of community training and new model introductions, the speaker conveys hope for SD3 to become a leading model in AI rendering despite its initial shortcomings.

The video discusses the SD3 model release and its purported shortcomings, particularly in the area of rendering hands. The speaker acknowledges that SD3 is indeed lacking in hand rendering capabilities, which may come as a surprise to viewers. However, they quickly move on to argue that this issue is not a significant cause for concern. Drawing a parallel to the lounge of SDXL, which was also initially subpar but improved over time with community training and the introduction of new models like LCM, lightning, and turbo, the speaker suggests that SD3 may follow a similar trajectory.

The speaker expresses optimism about the potential for SD3 to become the new standard in AI rendering despite its current limitations. They highlight the evolution of SDXL from a basic model to a more advanced and widely used tool in AI rendering, suggesting that SD3 could undergo a similar transformation. By emphasizing the role of community training and the introduction of new models in improving rendering quality, the speaker builds a case for why SD3 may eventually overcome its initial shortcomings and be embraced as a staple in the field.

The mention of LCM, lightning, and turbo models as examples of how the rendering capabilities of SDXL have advanced over time serves to underscore the idea that progress and innovation are ongoing in the realm of AI rendering. The speaker’s positive outlook on the potential for SD3 to establish itself as a leading model in this space is based on the premise that advancements in technology and community input can drive significant improvements in rendering quality over time.

While acknowledging the current limitations of SD3 in rendering hands, the speaker shifts the focus towards the broader context of the model’s development trajectory. They suggest that the history of SDXL’s evolution serves as a promising indicator of what may lie ahead for SD3, with improvements in rendering quality likely to be driven by community engagement and the introduction of new models. This forward-looking perspective encourages viewers to consider the long-term potential of SD3 as a key player in the field of AI rendering.

In conclusion, the video presents a nuanced view of the SD3 model release, acknowledging its current shortcomings while also highlighting the possibilities for improvement and advancement in rendering capabilities. By drawing parallels to the evolution of SDXL and emphasizing the role of community training and new model introductions, the speaker conveys a sense of optimism about the future prospects of SD3 as a potential standard in AI rendering.