Open qianmoxiaogege opened 9 months ago
with torch.no_grad(): I = align_face(frame, landmarkpredictor) I = transform(I).unsqueeze(dim=0).to(device) s_w = pspencoder(I) s_w = vtoonify.zplus2wplus(s_w) s_w[:,:7] = exstyle[:,:7]
# followed by downsampling the parsing maps x_p = F.interpolate(parsingpredictor(2*(F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)))[0], scale_factor=0.5, recompute_scale_factor=False).detach() # we give parsing maps lower weight (1/16) inputs = torch.cat((x, x_p/16.), dim=1) # d_s has no effect when backbone is toonify y_tilde = vtoonify(inputs, s_w.repeat(inputs.size(0), 1, 1), d_s = 0.5) y_tilde = torch.clamp(y_tilde, -1, 1)
Every time I run torch, colab often crashes
with torch.no_grad(): I = align_face(frame, landmarkpredictor) I = transform(I).unsqueeze(dim=0).to(device) s_w = pspencoder(I) s_w = vtoonify.zplus2wplus(s_w) s_w[:,:7] = exstyle[:,:7]
parsing network works best on 512x512 images, so we predict parsing maps on upsmapled frames