dpm-solver
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Official code for "DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps" (Neurips 2022 Oral)
Would be interesting to see it in action, some community member expects that https://github.com/jina-ai/discoart/issues/162
hello,thanks to your wonderful work!i run your code with CIFAR-10 (VP deep continuous-time model ),dpms-solver order2 recently, i try so many times but still get wrong fid with the order2(table...
Hi, Thank you for your contribution and I've successfully adapted dpm-solver to text generation efficiently. Now I'm curious about: Can current dpm-solver support self-conditioning (https://arxiv.org/abs/2208.04202), since this technique is widely...
Hey fellows, really good work and thanks for sharing the code. However, I've got a question about the sampling process. It seems like no randomness is added during the sampling...
Dear authors, Thank you for generously sharing your great work! I used dpm-solver to accelerate vanilla ddpm for image purification.And if I set timesteps OF DDPM as 500,with my pretrained...
Dear authors, Thank you for sharing this great work and open source! When following this work, I found that DPM-solver++ performs worse than DDIM under high classifier guidance scale, eg,...
bugfix in set_scheduler(a, b) takes one argument but requires 2
noise_schedule = NoiseScheduleVP(schedule='linear', continuous_beta_0=0.1, continuous_beta_1=20.) model_fn = GaussianDiffusion() # model_fn = model_wrapper( # diffusion_model, # noise_schedule, # model_type="noise", # or "x_start" or "v" or "score" # model_kwargs={}, # ) dpm_solver...
I try to use the dpm to accelerate guided diffusion sample, but the results is bad. the model link is https://github.com/Janspiry/Palette-Image-to-Image-Diffusion-Models/tree/main/models/ 
Hi Cheng, Congrads on your impactful works. I'm pretty new to this field and want to know if evaluating the negative log-likelihood $(-\log P_\theta ( x_0 | c ) )$...