fix stability mode for the old model, add vae dist mode selection

hyvideo/diffusion/pipelines/pipeline_hunyuan_video.py

@@ -320,6 +320,8 @@ class HunyuanVideoPipeline(DiffusionPipeline):
         elif image_cond_latents is not None and i2v_stability:
             if image_cond_latents.shape[2] == 1:
                 img_latents = image_cond_latents.repeat(1, 1, video_length, 1, 1)
+            else:
+                img_latents = image_cond_latents
             t = torch.tensor([0.999]).to(device=device)
             latents = noise * t + img_latents * (1 - t)
             latents = latents.to(dtype=self.base_dtype)
@@ -728,8 +730,8 @@ 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":
@@ -737,7 +739,7 @@ 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":
+                    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:

hyvideo/modules/models.py

@@ -201,7 +201,6 @@ class MMDoubleStreamBlock(nn.Module):
         first_frame_token_num: int = None,
         condition_type: str = None,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
-        
         if condition_type == "token_replace":
             img_mod1, token_replace_img_mod1 = self.img_mod(vec, condition_type=condition_type, \
                                                             token_replace_vec=token_replace_vec)

nodes.py

@@ -1489,6 +1489,7 @@ class HyVideoEncode:
                     "optional": {
                         "noise_aug_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Strength of noise augmentation, helpful for leapfusion I2V where some noise can add motion and give sharper results"}),
                         "latent_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Additional latent multiplier, helpful for leapfusion I2V where lower values allow for more motion"}),
+                        "latent_dist": (["sample", "mode"], {"default": "sample", "tooltip": "Sampling mode for the VAE, sample uses the latent distribution, mode uses the mode of the latent distribution"}),
                     }
                 }
 
@@ -1497,7 +1498,8 @@ class HyVideoEncode:
     FUNCTION = "encode"
     CATEGORY = "HunyuanVideoWrapper"
 
-    def encode(self, vae, image, enable_vae_tiling, temporal_tiling_sample_size, auto_tile_size, spatial_tile_sample_min_size, noise_aug_strength=0.0, latent_strength=1.0):
+    def encode(self, vae, image, enable_vae_tiling, temporal_tiling_sample_size, auto_tile_size, 
+               spatial_tile_sample_min_size, noise_aug_strength=0.0, latent_strength=1.0, latent_dist="sample"):
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
 
@@ -1523,7 +1525,10 @@ class HyVideoEncode:
         
         if enable_vae_tiling:
             vae.enable_tiling()
-        latents = vae.encode(image).latent_dist.sample(generator)
+        if latent_dist == "sample":
+            latents = vae.encode(image).latent_dist.sample(generator)
+        elif latent_dist == "mode":
+            latents = vae.encode(image).latent_dist.mode()
         if latent_strength != 1.0:
             latents *= latent_strength
         #latents = latents * vae.config.scaling_factor