INNER CODE UNIT · Python
latent
wenhaochai/StableVideo · app.py:198
latent = torch.randn((1, 4, H // 8, W // 8)).cuda()
samples, _ = self.canny_ddim_sampler.sample(ddim_steps, num_samples,
shape, cond, verbose=False, eta=eta,
unconditional_guidance_scale=scale,
unconditional_conditioning=un_cond,
x_T=latent)
else:
last_atlas = f_atlas[i-1:i].permute(0, 3, 2, 1)
mapped_img = F.grid_sample(last_atlas, self.crops['foreground_uvs'][i].reshape(1, -1, 1, 2), mode="bilinear", align_corners=self.data.config["align_corners"]).clamp(min=0.0, max=1.0).reshape((3, current_img.size[1], current_img.size[0]))
mapped_img = transforms.ToPILImage()(mapped_img)
mapped_img = mapped_img.resize((W, H))
mapped_img = np.array(mapped_img).astype(np.float32) / 255.0
mapped_img = mapped_img[None].transpose(0, 3, 1, 2)
mapped_img = torch.from_numpy(mapped_img).cuda()
mapped_img = 2. * mapped_img - 1.
latent = self.canny_model.get_first_stage_encoding(self.canny_model.encode_first_stage(mapped_img))