-
Notifications
You must be signed in to change notification settings - Fork 47
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
stablemultidiffusion_pipeline.py code is incomplete about the code that does not apply to the lcm method. Can you provide a copy? #11
Comments
Ah, yes. I do have a plan to support other types of acceleration LoRAs (I'm trying Hyper-SD right now). If you have a list of request for other types of schedulers, please let me know. Thanks! |
DDIMScheduler
not using any acceleration, I want to use the normal pipeline with DDIMScheduler, 20-50 step. But the code currently does not support. Does this algorithm require an acceleration solution? |
Ah the algorithm is originally about acceleration stuff, but I will include other standardized samplers as well. |
This algorithm is great. When the mask area is small during inpainting, I wanted a more detailed and controlled generation, while the result is a bit rough. I'm wandering whether the acceleration affects. |
Curious if you manage to add support to LCM-LORA. I see this is already supported in the original StreamDiffusion, so shouldn't it work here as well? |
LCM-LoRA's already in support, though I have found that LCM-SDXL has lower quality and relatively severe style leakage issues compared to newer acceleration modules (Lightning/Hyper-SD/etc...). It'll be nice if you suggest more acceleration methods, so I can put them into this :) |
Sure, here are a few accelerators:
Also check OneDiff, FaserLCM, DeepCache, if these are useful. |
Copy that! |
stablemultidiffusion_pipeline.py code is incomplete about the code that does not apply to the lcm method. Can you provide a copy? there is something wrong with the scheduler
The text was updated successfully, but these errors were encountered: