threestudio/models/guidance/deep_floyd_guidance.py [346:376]:
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            guidance_eval_out = self.guidance_eval(**guidance_eval_utils)
            texts = []
            for n, e, a, c in zip(
                guidance_eval_out["noise_levels"], elevation, azimuth, camera_distances
            ):
                texts.append(
                    f"n{n:.02f}\ne{e.item():.01f}\na{a.item():.01f}\nc{c.item():.02f}"
                )
            guidance_eval_out.update({"texts": texts})
            guidance_out.update({"eval": guidance_eval_out})

        return guidance_out

    @torch.cuda.amp.autocast(enabled=False)
    @torch.no_grad()
    def get_noise_pred(
        self,
        latents_noisy,
        t,
        text_embeddings,
        use_perp_neg=False,
        neg_guidance_weights=None,
    ):
        batch_size = latents_noisy.shape[0]
        if use_perp_neg:
            latent_model_input = torch.cat([latents_noisy] * 4, dim=0)
            noise_pred = self.forward_unet(
                latent_model_input,
                torch.cat([t.reshape(1)] * 4).to(self.device),
                encoder_hidden_states=text_embeddings,
            )  # (4B, 6, 64, 64)
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threestudio/models/guidance/stable_diffusion_guidance.py [478:510]:
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            guidance_eval_out = self.guidance_eval(**guidance_eval_utils)
            texts = []
            for n, e, a, c in zip(
                guidance_eval_out["noise_levels"], elevation, azimuth, camera_distances
            ):
                texts.append(
                    f"n{n:.02f}\ne{e.item():.01f}\na{a.item():.01f}\nc{c.item():.02f}"
                )
            guidance_eval_out.update({"texts": texts})
            guidance_out.update({"eval": guidance_eval_out})

        return guidance_out

    @torch.cuda.amp.autocast(enabled=False)
    @torch.no_grad()
    def get_noise_pred(
        self,
        latents_noisy,
        t,
        text_embeddings,
        use_perp_neg=False,
        neg_guidance_weights=None,
    ):
        batch_size = latents_noisy.shape[0]

        if use_perp_neg:
            # pred noise
            latent_model_input = torch.cat([latents_noisy] * 4, dim=0)
            noise_pred = self.forward_unet(
                latent_model_input,
                torch.cat([t.reshape(1)] * 4).to(self.device),
                encoder_hidden_states=text_embeddings,
            )  # (4B, 3, 64, 64)
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