ltxv: add noise to guidance image to ensure generated motion. (#5937)

comfy/ldm/lightricks/model.py

@@ -379,6 +379,7 @@ def __init__(self,
                  positional_embedding_max_pos=[20, 2048, 2048],
                  dtype=None, device=None, operations=None, **kwargs):
         super().__init__()
+        self.generator = None
         self.dtype = dtype
         self.out_channels = in_channels
         self.inner_dim = num_attention_heads * attention_head_dim
@@ -417,6 +418,7 @@ def __init__(self,
 
     def forward(self, x, timestep, context, attention_mask, frame_rate=25, guiding_latent=None, transformer_options={}, **kwargs):
         patches_replace = transformer_options.get("patches_replace", {})
+        image_noise_scale = transformer_options.get("image_noise_scale", 0.15)
 
         indices_grid = self.patchifier.get_grid(
             orig_num_frames=x.shape[2],
@@ -435,6 +437,17 @@ def forward(self, x, timestep, context, attention_mask, frame_rate=25, guiding_l
             timestep = self.patchifier.patchify(ts)
             input_x = x.clone()
             x[:, :, 0] = guiding_latent[:, :, 0]
+            if image_noise_scale > 0:
+                if self.generator is None:
+                    self.generator = torch.Generator(device=x.device).manual_seed(42)
+                elif self.generator.device != x.device:
+                    self.generator = torch.Generator(device=x.device).set_state(self.generator.get_state())
+
+                noise_shape = [guiding_latent.shape[0], guiding_latent.shape[1], 1, guiding_latent.shape[3], guiding_latent.shape[4]]
+                guiding_noise = image_noise_scale * (input_ts ** 2) * torch.randn(size=noise_shape, device=x.device, generator=self.generator)
+
+                x[:, :, 0] += guiding_noise[:, :, 0]
+
 
         orig_shape = list(x.shape)
 

comfy_extras/nodes_lt.py

@@ -1,3 +1,4 @@
+import io
 import nodes
 import node_helpers
 import torch
@@ -77,6 +78,7 @@ def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
                               "max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step":0.01}),
                               "base_shift": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 100.0, "step":0.01}),
+                              "image_noise_scale": ("FLOAT", {"default": 0.15, "min": 0, "max": 100, "step": 0.01, "tooltip": "Amount of noise to apply on conditioning image latent."})
                               },
                 "optional": {"latent": ("LATENT",), }
                 }
@@ -86,7 +88,7 @@ def INPUT_TYPES(s):
 
     CATEGORY = "advanced/model"
 
-    def patch(self, model, max_shift, base_shift, latent=None):
+    def patch(self, model, max_shift, base_shift, image_noise_scale, latent=None):
         m = model.clone()
 
         if latent is None:
@@ -109,6 +111,8 @@ class ModelSamplingAdvanced(sampling_base, sampling_type):
         model_sampling = ModelSamplingAdvanced(model.model.model_config)
         model_sampling.set_parameters(shift=shift)
         m.add_object_patch("model_sampling", model_sampling)
+        m.model_options.setdefault("transformer_options", {})["image_noise_scale"] = image_noise_scale
+
         return (m, )