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

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Dec 6, 2024
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ltxv: add noise to guidance image to ensure generated motion. #5937

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13 changes: 13 additions & 0 deletions comfy/ldm/lightricks/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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],
Expand All @@ -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)

Expand Down
6 changes: 5 additions & 1 deletion comfy_extras/nodes_lt.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
import io
import nodes
import node_helpers
import torch
Expand Down Expand Up @@ -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",), }
}
Expand All @@ -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:
Expand All @@ -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, )


Expand Down