Using VQVAE to compress and then using Diffusion Models to model latent variables cannot generate good pictures.

[yh-xxx]

Use vae to encode x and then train vae = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device) x = vae.encode(x).latent_dist.sample().mul_(0.18215)

Sample with torch.no_grad(): z = ema_sample_method(opt.n_sample, z_shape, guide_w=opt.w) x_gen = vae.decode(z / 0.18215).sample The generation effect is poor

Hope there is a solution