diff --git a/hyvideo/diffusion/pipelines/pipeline_hunyuan_video.py b/hyvideo/diffusion/pipelines/pipeline_hunyuan_video.py
index fe93443..6a623ef 100644
--- a/hyvideo/diffusion/pipelines/pipeline_hunyuan_video.py
+++ b/hyvideo/diffusion/pipelines/pipeline_hunyuan_video.py
@@ -684,6 +684,9 @@ class HunyuanVideoPipeline(DiffusionPipeline):
                 if self.interrupt:
                     continue
 
+                if image_cond_latents is not None and i2v_condition_type == "token_replace":
+                    latents = torch.concat([original_image_latents, latents[:, :, 1:, :, :]], dim=2)
+
                 latent_model_input = latents
                 input_prompt_embeds = prompt_embeds
                 input_prompt_mask = prompt_mask 
@@ -730,8 +733,8 @@ class HunyuanVideoPipeline(DiffusionPipeline):
 
                 t_expand = t.repeat(latent_model_input.shape[0])
 
-                #if leapfusion_img2vid:
-                #    latent_model_input[:, :, [0,], :, :] = original_latents[:, :, [0,], :, :].to(latent_model_input)
+                if leapfusion_img2vid:
+                    latent_model_input[:, :, [0,], :, :] = original_latents[:, :, [0,], :, :].to(latent_model_input)
 
                 if image_cond_latents is not None and not use_context_schedule:
                     if i2v_condition_type == "latent_concat":
@@ -739,11 +742,6 @@ class HunyuanVideoPipeline(DiffusionPipeline):
                         i2v_mask = torch.cat([i2v_mask] * 2) if cfg_enabled else i2v_mask
                         latent_image_input = torch.cat([latent_image_input, i2v_mask], dim=1)
                         latent_model_input = torch.cat([latent_model_input, latent_image_input], dim=1)
-                    elif i2v_condition_type == "token_replace" or leapfusion_img2vid:
-                        latent_image_input = (torch.cat([original_image_latents] * 2) if cfg_enabled else original_image_latents)
-                        latent_model_input = torch.cat([latent_image_input, latent_model_input[:, :, 1:, :, :]], dim=2)
-                    else:
-                        latent_model_input = torch.cat([latent_model_input, latent_image_input], dim=1)
 
                 if self.transformer.guidance_embed:
                     if cfg_enabled: