Comments (3)
Hi @WikiChao , does your code contain this line?: https://github.com/LuChengTHU/dpm-solver/blob/main/dpm_solver_pytorch.py#L105
If so, could you please print the first 5 and last 5 items of log_alphas
?
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Thanks for the prompt reply! The trick did help, it seems I am using the previous version and missing such a line of code.
The results make sense now, but they are still worse than DDIM. I have tried different settings, e.g., "multistep" or "single step", "order = 2 or 3", "step = 10 to 100", but cannot beat DDIM. Are there any tricks in choosing hyperparameters, for example, clipping log-SNR by different values?
Thanks a lot!
Chao
from dpm-solver.
Hi @WikiChao ,
"but they are still worse than DDIM": In fact, order=1 is exactly the DDIM. You can try to reproduce the results of DDIM by manually setting the timestep in
dpm-solver/dpm_solver_pytorch.py
Line 453 in 5c6ee9f
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Related Issues (20)
- Question regarding likelihood evaluation HOT 5
- discrete-time and continuous-time diffusion models HOT 5
- how to set parameters when sampling HOT 2
- sampling in a deterministic way like DDIM? HOT 2
- an error happens in "x0 = (x - sigma_t * noise) / alpha_t" HOT 2
- Control net HOT 5
- timestep_respacing HOT 2
- How to use dpm for guided diffision model(ddpm) HOT 4
- Is this error at single step 1st-order code? HOT 2
- Wonder you may integrate the dpm-solver to guided diffusion repo: HOT 1
- With all due respect which part of your model is this going to be added to, the training or the testing part? HOT 1
- Results of Imagenet256 using DPM++ not reproducible HOT 7
- Can DPM-Solver support self-conditioning? HOT 2
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- Worse in high guidance scale HOT 4
- The results are blurry images with vanilla ddpm HOT 2
- problem with order 2 CIFAR-10 (VP deep continuous-time model ) HOT 1
- Multiple Guidance in Sampling? HOT 2
- How to use an accelerator to quickly predict noise reduction in sequence data (3D tensor)?
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