Stop blaming the seed or calling the model “garbage.” After reviewing 100+ failed generations across Seedance 2.0, Wan 2.7, and Kling v3, I noticed the same six failures kept showing up. This is less a list of universal fixes and more the triage checklist I use before rerolling. Subject Drift — the character changes halfway through. Try a cleaner reference, shorter duration, and fewer simultaneous actions. If the model offers reference-strength controls, use them. Action Missing — you ask for walking; the subject just bobs. Reduce it to one subject and one clear action verb. Remove camera instructions. If it still fails repeatedly, it may be the model rather than the prompt. Camera Went Rogue — random zooms, pans, or orbits. Use one camera instruction only. Remove vague terms like “dynamic” and test a locked-off shot. Reference Ignored — the output barely resembles the input. Check the generation mode, reference limits, and whether the prompt contradicts the image before blaming the model. Anatomy Collapse — broken limbs, melting hands, impossible poses. Reduce motion, avoid heavy occlusion, shorten the clip, or test a model that handles human movement better. Semantic Misread — asked for rain, got a desert. Put the subject, action, and environment first. Move style terms to the end and remove conflicting details. I tested these patterns with the same source image, core prompt, duration, and aspect ratio where possible. I used Atlas Cloud as the API layer so I could switch between hosted models without rebuilding the request setup each time. It wasn’t a perfectly controlled benchmark, but the same categories appeared often enough to make the checklist useful. Before rerolling, I now ask: prompt problem, reference problem, or model limitation? submitted by /u/VeterinarianHairy371
Originally posted by u/VeterinarianHairy371 on r/ArtificialInteligence