if args.fixed_noise:
        b, c, h, w = latents.shape
        noises = []
        for i in range(b):
            latent_bytes = latents[i].detach().to(torch.float32).cpu().numpy().tobytes()
            hash_int = int(hashlib.sha256(latent_bytes).hexdigest(), 16) % (2**32)
            g = torch.Generator(device=latents.device).manual_seed(hash_int)
            noise_i = torch.randn((c, h, w), generator=g, device=latents.device)
            noises.append(noise_i)
        noise = torch.stack(noises, dim=0)
    else:
        noise = torch.randn_like(latents)