File size: 7,730 Bytes
82ea528 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 |
import math
from contextlib import nullcontext
import comfy.latent_formats
import comfy.model_base
import comfy.model_management
import comfy.model_patcher
import comfy.model_sampling
import comfy.sd
import comfy.supported_models_base
import comfy.utils
import torch
import torch.nn as nn
from ltx_video.models.transformers.symmetric_patchifier import SymmetricPatchifier
from ltx_video.models.transformers.transformer3d import Transformer3DModel
class LTXVModelConfig:
def __init__(self, latent_channels, dtype):
self.unet_config = {}
self.unet_extra_config = {}
self.latent_format = comfy.latent_formats.LatentFormat()
self.latent_format.latent_channels = latent_channels
self.manual_cast_dtype = dtype
self.sampling_settings = {"multiplier": 1.0}
self.memory_usage_factor = 2.7
# denoiser is handled by extension
self.unet_config["disable_unet_model_creation"] = True
class LTXVSampling(torch.nn.Module, comfy.model_sampling.CONST):
def __init__(self, condition_mask, guiding_latent=None):
super().__init__()
self.condition_mask = condition_mask
self.guiding_latent = guiding_latent
self.set_parameters(shift=1.0, multiplier=1)
def set_parameters(self, shift=1.0, timesteps=1000, multiplier=1000):
self.shift = shift
self.multiplier = multiplier
ts = self.sigma((torch.arange(0, timesteps + 1, 1) / timesteps) * multiplier)
self.register_buffer("sigmas", ts)
@property
def sigma_min(self):
return self.sigmas[0]
@property
def sigma_max(self):
return self.sigmas[-1]
def timestep(self, sigma):
return sigma * self.multiplier
def sigma(self, timestep):
return timestep
def percent_to_sigma(self, percent):
if percent <= 0.0:
return 1.0
if percent >= 1.0:
return 0.0
return 1.0 - percent
def calculate_input(self, sigma, noise):
if self.guiding_latent is not None:
noise = (
noise * (1 - self.condition_mask)
+ self.guiding_latent * self.condition_mask
)
return noise
def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
self.condition_mask = self.condition_mask.to(latent_image.device)
scaled = latent_image * (1 - sigma) + noise * sigma
result = latent_image * self.condition_mask + scaled * (1 - self.condition_mask)
return result
def calculate_denoised(self, sigma, model_output, model_input):
sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
result = model_input - model_output * sigma
# In order to d * dT to be zero in euler step, we need to set result equal to input in first latent frame.
if self.guiding_latent is not None:
result = (
result * (1 - self.condition_mask)
+ self.guiding_latent * self.condition_mask
)
else:
result = (
result * (1 - self.condition_mask) + model_input * self.condition_mask
)
return result
class LTXVModel(comfy.model_base.BaseModel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.model_sampling = LTXVSampling(torch.zeros([1]))
class LTXVTransformer3D(nn.Module):
def __init__(
self,
transformer: Transformer3DModel,
patchifier: SymmetricPatchifier,
conditioning_mask,
latent_frame_rate,
vae_scale_factor,
):
super().__init__()
self.dtype = transformer.dtype
self.transformer = transformer
self.patchifier = patchifier
self.conditioning_mask = conditioning_mask
self.latent_frame_rate = latent_frame_rate
self.vae_scale_factor = vae_scale_factor
def indices_grid(
self,
latent_shape,
device,
):
use_rope = self.transformer.use_rope
scale_grid = (
(1 / self.latent_frame_rate, self.vae_scale_factor, self.vae_scale_factor)
if use_rope
else None
)
indices_grid = self.patchifier.get_grid(
orig_num_frames=latent_shape[2],
orig_height=latent_shape[3],
orig_width=latent_shape[4],
batch_size=latent_shape[0],
scale_grid=scale_grid,
device=device,
)
return indices_grid
def wrapped_transformer(
self,
latent,
timesteps,
context,
indices_grid,
skip_layer_mask=None,
skip_layer_strategy=None,
img_hw=None,
aspect_ratio=None,
mixed_precision=True,
**kwargs,
):
# infer mask from context padding, assumes padding vectors are all zero.
latent = latent.to(self.transformer.dtype)
latent_patchified = self.patchifier.patchify(latent)
context_mask = (context != 0).any(dim=2).to(self.transformer.dtype)
if mixed_precision:
context_manager = torch.autocast("cuda", dtype=torch.bfloat16)
else:
context_manager = nullcontext()
with context_manager:
noise_pred = self.transformer(
latent_patchified.to(self.transformer.dtype).to(
self.transformer.device
),
indices_grid.to(self.transformer.device),
encoder_hidden_states=context.to(self.transformer.device),
encoder_attention_mask=context_mask.to(self.transformer.device).to(
torch.int64
),
timestep=timesteps,
skip_layer_mask=skip_layer_mask,
skip_layer_strategy=skip_layer_strategy,
return_dict=False,
)[0]
result = self.patchifier.unpatchify(
latents=noise_pred,
output_height=latent.shape[3],
output_width=latent.shape[4],
output_num_frames=latent.shape[2],
out_channels=latent.shape[1] // math.prod(self.patchifier.patch_size),
)
return result
def forward(self, x, timesteps, context, img_hw=None, aspect_ratio=None, **kwargs):
transformer_options = kwargs.get("transformer_options", {})
ptb_index = transformer_options.get("ptb_index", None)
mixed_precision = transformer_options.get("mixed_precision", False)
cond_or_uncond = transformer_options.get("cond_or_uncond", [])
skip_block_list = transformer_options.get("skip_block_list", [])
skip_layer_strategy = transformer_options.get("skip_layer_strategy", None)
mask = self.patchifier.patchify(self.conditioning_mask).squeeze(-1).to(x.device)
ndim_mask = mask.ndimension()
expanded_timesteps = timesteps.view(timesteps.size(0), *([1] * (ndim_mask - 1)))
timesteps_masked = expanded_timesteps * (1 - mask)
skip_layer_mask = None
if ptb_index is not None and ptb_index in cond_or_uncond:
skip_layer_mask = self.transformer.create_skip_layer_mask(
skip_block_list,
1,
len(cond_or_uncond),
len(cond_or_uncond) - 1 - cond_or_uncond.index(ptb_index),
)
result = self.wrapped_transformer(
x,
timesteps_masked,
context,
indices_grid=self.indices_grid(x.shape, x.device),
mixed_precision=mixed_precision,
skip_layer_mask=skip_layer_mask,
skip_layer_strategy=skip_layer_strategy,
)
return result
|