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

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Michael Kupchick 2024-12-06 12:46:08 +02:00 committed by GitHub
parent 1e21f4c14e
commit 005d2d3a13
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2 changed files with 18 additions and 1 deletions
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13
comfy/ldm/lightricks/model.py
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@ -379,6 +379,7 @@ class LTXVModel(torch.nn.Module):
positional_embedding_max_pos=[20, 2048, 2048], positional_embedding_max_pos=[20, 2048, 2048],
dtype=None, device=None, operations=None, **kwargs): dtype=None, device=None, operations=None, **kwargs):
super().__init__() super().__init__()
self.generator = None
self.dtype = dtype self.dtype = dtype
self.out_channels = in_channels self.out_channels = in_channels
self.inner_dim = num_attention_heads * attention_head_dim self.inner_dim = num_attention_heads * attention_head_dim
@ -417,6 +418,7 @@ class LTXVModel(torch.nn.Module):
def forward(self, x, timestep, context, attention_mask, frame_rate=25, guiding_latent=None, transformer_options={}, **kwargs): def forward(self, x, timestep, context, attention_mask, frame_rate=25, guiding_latent=None, transformer_options={}, **kwargs):
patches_replace = transformer_options.get("patches_replace", {}) patches_replace = transformer_options.get("patches_replace", {})
image_noise_scale = transformer_options.get("image_noise_scale", 0.15)
indices_grid = self.patchifier.get_grid( indices_grid = self.patchifier.get_grid(
orig_num_frames=x.shape[2], orig_num_frames=x.shape[2],
@ -435,6 +437,17 @@ class LTXVModel(torch.nn.Module):
timestep = self.patchifier.patchify(ts) timestep = self.patchifier.patchify(ts)
input_x = x.clone() input_x = x.clone()
x[:, :, 0] = guiding_latent[:, :, 0] 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) orig_shape = list(x.shape)

6
comfy_extras/nodes_lt.py
View File

@ -1,3 +1,4 @@
import io
import nodes import nodes
import node_helpers import node_helpers
import torch import torch
@ -77,6 +78,7 @@ class ModelSamplingLTXV:
return {"required": { "model": ("MODEL",), return {"required": { "model": ("MODEL",),
"max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step":0.01}), "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}), "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",), } "optional": {"latent": ("LATENT",), }
} }
@ -86,7 +88,7 @@ class ModelSamplingLTXV:
CATEGORY = "advanced/model" 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() m = model.clone()
if latent is None: if latent is None:
@ -109,6 +111,8 @@ class ModelSamplingLTXV:
model_sampling = ModelSamplingAdvanced(model.model.model_config) model_sampling = ModelSamplingAdvanced(model.model.model_config)
model_sampling.set_parameters(shift=shift) model_sampling.set_parameters(shift=shift)
m.add_object_patch("model_sampling", model_sampling) m.add_object_patch("model_sampling", model_sampling)
m.model_options.setdefault("transformer_options", {})["image_noise_scale"] = image_noise_scale
return (m, ) return (m, )