显卡是NVIDIA RTX 3080 10G，生成模型统一采用Chilloutmix-Ni，采样步数(Sampling steps)一律设成30步，图片size设为800*800，并且不额外使用其它的ControlNet与Lora等影响生图速度的套件。仅单纯地用内建功能使用同一组prompt随机生图，一次生4张。并且生图过程中不会开启其它软件或对电脑做其余操作，以免影响生图速度。
@torch.no_grad() def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """Ancestral sampling with Euler method steps.""" extra_args = {} if extra_args is None else extra_args noise_sampler = default...
除了LMS、DMP2 a 和PLMS在20步以下出现明显的瑕疵,其他的都很不错。 UniPC 的创意性更足,基本上5个step给了5个惊喜。 最常用的DPM++ SDE Karras,在20 step之后很稳定,构图基本没变化。 采样步数(Sampling steps) 一般采样步数选择20-50之间,也是视大模型而定。 但AI技术始终在进化,SD最新嵌入的UniPC采样器...
('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}), ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}), ('DPM++ 2M Karras', 'sample_dpmpp_2m...
With any of my models, generated images are screwed up in the last step(s). I can see the generation doing great when I run a script, outputting every step until the last steps. Then it is as if there was a sort of sharpening taking place in certain places, most noticeably faces. ...
In the last few steps of sampling, you will find that this method deviates from the true region. You can verify this by plotting the image. So we only execute this method for half of the steps. You may question that if the trajectory of x is a concave function, this theory does not...
The accuracies of higher-order sliding modes under the one step Euler integration method with variable sampling steps were calculated in Levant (2011). A new discrete time super-twisting-like SOSM algorithm was proposed in Salgado, Kamal, Chairez, Bandyopadhyay, and Fridman (2011). However, ...
for the one-dimensional case, may be another way to resolve this issue. it involves a rejection-sampling algorithm and, when applicable, returns exact draws from any finite-dimensional distribution of the solution to the sde. the method has been further extended to multivariate diffusions in ...
Moreover, we propose a fast EM method via the exponential-sum-approximation technique to reduce the EM method's computational cost. More specifically, if one disregards the Monte Carlo sampling error, then the fast EM method reduces the computational cost fromO(N2)\\documentclass[12pt]{minimal...
Homework Statement Consider the differential equation \begin{equation} y'''-y''=u \end{equation} Discretize (1) using a forward-Euler scheme with sampling...