LTX 2.3 produces trash....how are people creating amazing videos using simple prompts and when i do the same using text2image or image2video, i get clearly awful 1970's CGI crap??

A viral Reddit post is exposing a painful reality in the AI video generation space: the gap between dazzling demo reels and practical, consistent results. The user, after five months of dedicated late-night study, expresses sheer frustration with models like LTX 2.3, WAN 2.2, and Qwen2-VL, describing their outputs as 'actual trash' and '1970's CGI crap.' This stands in stark contrast to the flood of YouTube tutorials showing seemingly effortless, high-quality video creation using the same 'basic ComfyUI LTX 2.3 workflow' with simple prompts. The post underscores a critical, often unspoken barrier to entry in generative AI.

The core issue isn't necessarily the model's raw capability, but the immense hidden complexity in achieving good results. The user's detailed, action-packed prompt for a cinematic flying kick scene—which logically should work—fails spectacularly. This highlights the 'black box' problem where success depends on obscure workflow tweaks, specific node configurations in tools like ComfyUI, and latent knowledge not covered in surface-level tutorials. The emotional plea, 'SAVE ME!!!!' resonates with many beginners hitting the same wall, where promised democratization clashes with a steep, undocumented learning curve. It signals a maturation phase for the industry, where ease of use and reliability must catch up to raw technological potential.