diff --git a/app/sana_pipeline.py b/app/sana_pipeline.py
index cfafe2b..e89b063 100644
--- a/app/sana_pipeline.py
+++ b/app/sana_pipeline.py
@@ -231,86 +231,87 @@ def forward(
                 ),
                 torch.tensor([[1.0]], device=self.device).repeat(num_images_per_prompt, 1),
             )
+
             for _ in range(num_images_per_prompt):
-                with torch.no_grad():
-                    prompts.append(
-                        prepare_prompt_ar(prompt, self.base_ratios, device=self.device, show=False)[0].strip()
-                    )
+                prompts.append(
+                    prepare_prompt_ar(prompt, self.base_ratios, device=self.device, show=False)[0].strip()
+                )
 
-                    # prepare text feature
-                    if not self.config.text_encoder.chi_prompt:
-                        max_length_all = self.config.text_encoder.model_max_length
-                        prompts_all = prompts
-                    else:
-                        chi_prompt = "\n".join(self.config.text_encoder.chi_prompt)
-                        prompts_all = [chi_prompt + prompt for prompt in prompts]
-                        num_chi_prompt_tokens = len(self.tokenizer.encode(chi_prompt))
-                        max_length_all = (
-                            num_chi_prompt_tokens + self.config.text_encoder.model_max_length - 2
-                        )  # magic number 2: [bos], [_]
-
-                    caption_token = self.tokenizer(
-                        prompts_all,
-                        max_length=max_length_all,
-                        padding="max_length",
-                        truncation=True,
-                        return_tensors="pt",
-                    ).to(device=self.device)
-                    select_index = [0] + list(range(-self.config.text_encoder.model_max_length + 1, 0))
-                    caption_embs = self.text_encoder(caption_token.input_ids, caption_token.attention_mask)[0][:, None][
-                        :, :, select_index
-                    ].to(self.weight_dtype)
-                    emb_masks = caption_token.attention_mask[:, select_index]
-                    null_y = self.null_caption_embs.repeat(len(prompts), 1, 1)[:, None].to(self.weight_dtype)
-
-                    n = len(prompts)
-                    if latents is None:
-                        z = torch.randn(
-                            n,
-                            self.config.vae.vae_latent_dim,
-                            self.latent_size_h,
-                            self.latent_size_w,
-                            generator=generator,
-                            device=self.device,
-                            dtype=self.weight_dtype,
-                        )
-                    else:
-                        z = latents.to(self.weight_dtype).to(self.device)
-                    model_kwargs = dict(data_info={"img_hw": hw, "aspect_ratio": ar}, mask=emb_masks)
-                    if self.vis_sampler == "flow_euler":
-                        flow_solver = FlowEuler(
-                            self.model,
-                            condition=caption_embs,
-                            uncondition=null_y,
-                            cfg_scale=guidance_scale,
-                            model_kwargs=model_kwargs,
-                        )
-                        sample = flow_solver.sample(
-                            z,
-                            steps=num_inference_steps,
-                        )
-                    elif self.vis_sampler == "flow_dpm-solver":
-                        scheduler = DPMS(
-                            self.model,
-                            condition=caption_embs,
-                            uncondition=null_y,
-                            guidance_type=self.guidance_type,
-                            cfg_scale=guidance_scale,
-                            pag_scale=pag_guidance_scale,
-                            pag_applied_layers=self.config.model.pag_applied_layers,
-                            model_type="flow",
-                            model_kwargs=model_kwargs,
-                            schedule="FLOW",
-                        )
-                        scheduler.register_progress_bar(self.progress_fn)
-                        sample = scheduler.sample(
-                            z,
-                            steps=num_inference_steps,
-                            order=2,
-                            skip_type="time_uniform_flow",
-                            method="multistep",
-                            flow_shift=self.flow_shift,
-                        )
+            with torch.no_grad():
+                # prepare text feature
+                if not self.config.text_encoder.chi_prompt:
+                    max_length_all = self.config.text_encoder.model_max_length
+                    prompts_all = prompts
+                else:
+                    chi_prompt = "\n".join(self.config.text_encoder.chi_prompt)
+                    prompts_all = [chi_prompt + prompt for prompt in prompts]
+                    num_chi_prompt_tokens = len(self.tokenizer.encode(chi_prompt))
+                    max_length_all = (
+                        num_chi_prompt_tokens + self.config.text_encoder.model_max_length - 2
+                    )  # magic number 2: [bos], [_]
+
+                caption_token = self.tokenizer(
+                    prompts_all,
+                    max_length=max_length_all,
+                    padding="max_length",
+                    truncation=True,
+                    return_tensors="pt",
+                ).to(device=self.device)
+                select_index = [0] + list(range(-self.config.text_encoder.model_max_length + 1, 0))
+                caption_embs = self.text_encoder(caption_token.input_ids, caption_token.attention_mask)[0][:, None][
+                    :, :, select_index
+                ].to(self.weight_dtype)
+                emb_masks = caption_token.attention_mask[:, select_index]
+                null_y = self.null_caption_embs.repeat(len(prompts), 1, 1)[:, None].to(self.weight_dtype)
+
+                n = len(prompts)
+                if latents is None:
+                    z = torch.randn(
+                        n,
+                        self.config.vae.vae_latent_dim,
+                        self.latent_size_h,
+                        self.latent_size_w,
+                        generator=generator,
+                        device=self.device,
+                        dtype=self.weight_dtype,
+                    )
+                else:
+                    z = latents.to(self.weight_dtype).to(self.device)
+                model_kwargs = dict(data_info={"img_hw": hw, "aspect_ratio": ar}, mask=emb_masks)
+                if self.vis_sampler == "flow_euler":
+                    flow_solver = FlowEuler(
+                        self.model,
+                        condition=caption_embs,
+                        uncondition=null_y,
+                        cfg_scale=guidance_scale,
+                        model_kwargs=model_kwargs,
+                    )
+                    sample = flow_solver.sample(
+                        z,
+                        steps=num_inference_steps,
+                    )
+                elif self.vis_sampler == "flow_dpm-solver":
+                    scheduler = DPMS(
+                        self.model,
+                        condition=caption_embs,
+                        uncondition=null_y,
+                        guidance_type=self.guidance_type,
+                        cfg_scale=guidance_scale,
+                        pag_scale=pag_guidance_scale,
+                        pag_applied_layers=self.config.model.pag_applied_layers,
+                        model_type="flow",
+                        model_kwargs=model_kwargs,
+                        schedule="FLOW",
+                    )
+                    scheduler.register_progress_bar(self.progress_fn)
+                    sample = scheduler.sample(
+                        z,
+                        steps=num_inference_steps,
+                        order=2,
+                        skip_type="time_uniform_flow",
+                        method="multistep",
+                        flow_shift=self.flow_shift,
+                    )
 
             sample = sample.to(self.weight_dtype)
             with torch.no_grad():