Load textencoder to RAM by default

nodes.py

@@ -634,6 +634,7 @@ class DownloadAndLoadHyVideoTextEncoder:
                 "apply_final_norm": ("BOOLEAN", {"default": False}),
                 "hidden_state_skip_layer": ("INT", {"default": 2}),
                 "quantization": (['disabled', 'bnb_nf4', "fp8_e4m3fn"], {"default": 'disabled'}),
+                "load_device": (["main_device", "offload_device"], {"default": "offload_device"}),
             }
         }
 
@@ -643,7 +644,7 @@ class DownloadAndLoadHyVideoTextEncoder:
     CATEGORY = "HunyuanVideoWrapper"
     DESCRIPTION = "Loads Hunyuan text_encoder model from 'ComfyUI/models/LLM'"
 
-    def loadmodel(self, llm_model, clip_model, precision,  apply_final_norm=False, hidden_state_skip_layer=2, quantization="disabled"):
+    def loadmodel(self, llm_model, clip_model, precision,  apply_final_norm=False, hidden_state_skip_layer=2, quantization="disabled", load_device="offload_device"):
         lm_type_mapping = {
             "Kijai/llava-llama-3-8b-text-encoder-tokenizer": "llm",
             "xtuner/llava-llama-3-8b-v1_1-transformers": "vlm",
@@ -651,6 +652,9 @@ class DownloadAndLoadHyVideoTextEncoder:
         lm_type = lm_type_mapping[llm_model]
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
+
+        text_encoder_load_device = device if load_device == "main_device" else offload_device
+
         dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
         quantization_config = None
         if quantization == "bnb_nf4":
@@ -682,7 +686,7 @@ class DownloadAndLoadHyVideoTextEncoder:
             text_encoder_precision=precision,
             tokenizer_type="clipL",
             logger=log,
-            device=device,
+            device=text_encoder_load_device,
         )
         else:
             text_encoder_2 = None
@@ -706,7 +710,7 @@ class DownloadAndLoadHyVideoTextEncoder:
             hidden_state_skip_layer=hidden_state_skip_layer,
             apply_final_norm=apply_final_norm,
             logger=log,
-            device=device,
+            device=text_encoder_load_device,
             dtype=dtype,
             quantization_config=quantization_config
         )
@@ -1401,7 +1405,8 @@ class HyVideoEncode:
                     "auto_tile_size": ("BOOLEAN", {"default": True, "tooltip": "Automatically set tile size based on defaults, above settings are ignored"}),
                     },
                     "optional": {
-                        "noise_aug_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Strength of noise augmentation"}),
+                        "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"}),
                     }
                 }
 
@@ -1410,7 +1415,7 @@ 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):
+    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):
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
 
@@ -1437,6 +1442,8 @@ class HyVideoEncode:
         if enable_vae_tiling:
             vae.enable_tiling()
         latents = vae.encode(image).latent_dist.sample(generator)
+        if latent_strength != 1.0:
+            latents *= latent_strength
         #latents = latents * vae.config.scaling_factor
         vae.to(offload_device)
         print("encoded latents shape",latents.shape)