Hello everyone! I’ve been exploring the newest version of Wan FlowEdit for both image-to-video and text-to-video creation, but I’m facing some challenges with the new workflow.
The updated pipeline feels different from what I used previously. Has anyone else observed changes in the way the model handles inputs? I’m especially confused about:
The steps for preparing image inputs
Current handling of text prompts
New parameters or settings I should consider
I’ve attempted to use the usual methods, but the output isn’t meeting my expectations. The video quality appears to fluctuate compared to older versions.
I would greatly appreciate it if someone could share their insights on the recent updates or direct me to any documentation regarding the workflow changes. Thank you for your assistance!
I’ve been using the new FlowEdit for a few weeks now, and the workflow changes are significant. The most noteworthy adjustment is how the temporal consistency settings have been altered; the frame interpolation between keyframes operates very differently now. To achieve the best results, I suggest starting with lower motion strength values and gradually increasing them based on the complexity of your content. Also, for images, utilize their new normalization pipeline to enhance stability. Moreover, the text-to-video functionality is now more finicky, requiring specific timing details like ‘smooth transition over 3 seconds’ instead of general terms. Additionally, be aware that it handles longer videos differently, so you may need to generate segments and combine them afterward. If you’re facing quality inconsistencies, consider reducing your batch size.
Been running into the same headaches with the new version. What worked for me was completely changing my preprocessing approach.
Biggest difference: the model now expects specific metadata tags in your input files. For images, strip existing EXIF data first and let FlowEdit handle reformatting internally.
For text prompts, I ditched abstract descriptions for technical language. Instead of “beautiful sunset”, I use “orange gradient transition, horizontal movement, 24fps consistency”. The model responds way better to directional and timing cues now.
Adjusting seed values saved me tons of time. The new version handles randomization differently. Lock your seed to a specific number when testing, then only change it once you’ve got the workflow dialed in.
The preview function’s broken in some setups. Don’t rely on it for quality assessment. Always render a short test segment first.
Memory management’s brutal now too. Had to upgrade my VRAM just to handle what used to work fine on my old setup.
I encountered similar challenges when I transitioned to the latest version of Wan FlowEdit. The updated workflow indeed varies from previous iterations, particularly regarding input preprocessing. A few adjustments helped me significantly: I found that ensuring consistent resolution settings for images is crucial, as the model seems to have stricter requirements for aspect ratios. Moreover, when it comes to text prompts, incorporating more detailed information has led to better outcomes. If you’re experiencing quality fluctuations, increasing the number of inference steps from the default value tends to improve results. Lastly, it’s essential to monitor your GPU memory usage, as this version demands more resources. Unfortunately, the documentation may not fully reflect these changes yet.