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Initial Noise 是怎么统计得来 #12
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Hi @13718413797 , An illustrative demo is as follows (for simplicity, the specific function details are omitted) : cnt=0
with torch.no_grad(), torch.cuda.amp.autocast():
# ----------- Traverse the video dataset to obtain its latent space tensor. ---------------
for root, dirs, files in os.walk(gt_path):
for file in files[:args.total-1]:
file_path = os.path.join(root, file)
gt_video=get_gt(file_path,video_size=(args.height, args.width)).to("cuda")
gt_video=get_latent_z(model,gt_video)
# ---------- For the mean, simply sum each tensor element-wise and divide by the total number. -----
# For variance, we use the formula Var(x) = E(x²) - E²(x). Therefore, we need to calculate E(x²), which is computed as gt_video**2 / sum.
if cnt==0:
sum=gt_video
sum_for_var=gt_video**2
else:
sum+=gt_video
sum_for_var+=gt_video**2
cnt+=1
# The mean has been calculated.
expectation_X_0=sum/cnt
# Here, the sum of the element-wise variances is calculated.
tr_covar_X_0=sum_for_var/cnt-expectation_X_0**2
tr_Cov=tr_covar_X_0.sum()
# Divide the sum of variances by the total number of elements in the tensor.
shape_ = expectation_X_0.shape
d = 1
for dim in shape_:
d *= dim
var=tr_Cov/d
#------------------After calculating the mean and variance, apply the final weighting.-------------
sqrt_alpha_t=model.get_sqrt_alpha_t_bar(gt_video,torch.tensor([args.sdedit_t-1]).to('cuda'))
# Final mean.
mu_p=sqrt_alpha_t*expectation_X_0
# Final std
alpha_t=sqrt_alpha_t**2
sigma_p=torch.sqrt(1-alpha_t + alpha_t*var) |
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Initial Noise 是怎么统计得来的 是否有相关的代码demo
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