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model.py
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model.py
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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]))
def extra_conds(self, **kwargs):
out = super().extra_conds(**kwargs)
attention_mask = kwargs.get("attention_mask", None)
if attention_mask is not None:
out["attention_mask"] = comfy.conds.CONDRegular(attention_mask)
return out
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,
attention_mask,
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)
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=attention_mask,
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,
attention_mask,
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,
attention_mask,
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