- modified img2img to accept 2 prompts and 2 seeds (and a slerpradius and output video path)
- uses imutil.Video to create a video
- stochastic encode with noise, then decode, then add a frame to the video, for all n_iter
one thing i dont understand is why OP keeps calling `.half()` on their models. googled it and it seems to be a newish pytorch feature for "half precision" but couldnt get a clear answer on why you would want that. anyone care to share?
Comments
i find it interesting that you only took 9 commits to do it as well (+244 -62 LOC) https://github.com/CompVis/stable-diffusion/compare/main...l...
breaking it out for others curious:
- modified img2img to accept 2 prompts and 2 seeds (and a slerpradius and output video path)
- uses imutil.Video to create a video
- stochastic encode with noise, then decode, then add a frame to the video, for all n_iter
one thing i dont understand is why OP keeps calling `.half()` on their models. googled it and it seems to be a newish pytorch feature for "half precision" but couldnt get a clear answer on why you would want that. anyone care to share?
I believe using .half() lets you run Stable Diffusion on graphics cards with less vram
Half precision models reduce memory requirements without really impacting the final quality much.