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README.md
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# ComfyUI-LTXVideo
ComfyUI-LTXVideo is a collection of custom nodes for ComfyUI designed to integrate the LTXVideo diffusion model. These nodes enable workflows for text-to-video, image-to-video, and video-to-video generation. The main LTXVideo repository can be found [here](https://github.com/Lightricks/LTX-Video).
## Installation
1. Install ComfyUI
2. Clone this repository to `custom-nodes` folder in your ComfyUI installation directory.
3. Install the required packages:
```bash
cd custom_nodes/ComfyUI-LTXVideo && pip install -r requirements.txt
```
4. Download [ltx-video-2b-v0.9.safetensors](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.safetensors) from Hugging Face and place it under `models/checkpoints`.
5. Clone the text encoder model to `models/text_encoders`:
```bash
cd models/text_encoders && git clone https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS
```
## Example workflows
Note that to run the example workflows, you need to have [ComfyUI-VideoHelperSuite](https://github.com/kosinkadink/ComfyUI-VideoHelperSuite) installed.
### Text-to-video
[Download workflow](assets/ltxvideo-t2v.json)
![workflow](assets/ltxvideo-t2v.png)
### Image-to-video
[Download workflow](assets/ltxvideo-i2v.json)
![workflow](assets/ltxvideo-i2v.png)
### Video-to-video
[Download workflow](assets/ltxvideo-v2v.json)
![workflow](assets/ltxvideo-v2v.png)
__init__.py
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from .nodes_registry import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
from .transformer import LTXVModelConfigurator, LTXVShiftSigmas
from .t5_encoder import LTXVCLIPModelLoader
from .loader_node import LTXVLoader
# Export so that ComfyUI can pick them up.
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
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{
"id": 110,
"type": "VHS_LoadVideo",
"pos": [
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-10
],
"size": [
283.6130065917969,
473.7097473144531
],
"flags": {},
"order": 3,
"mode": 0,
"inputs": [
{
"name": "meta_batch",
"type": "VHS_BatchManager",
"link": null,
"shape": 7
},
{
"name": "vae",
"type": "VAE",
"link": null,
"shape": 7
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
271
],
"slot_index": 0
},
{
"name": "frame_count",
"type": "INT",
"links": null
},
{
"name": "audio",
"type": "AUDIO",
"links": null
},
{
"name": "video_info",
"type": "VHS_VIDEOINFO",
"links": null
}
],
"properties": {
"Node name for S&R": "VHS_LoadVideo"
},
"widgets_values": {
"video": "video.mp4",
"force_rate": 0,
"force_size": "Disabled",
"custom_width": 512,
"custom_height": 512,
"frame_load_cap": 0,
"skip_first_frames": 0,
"select_every_nth": 1,
"choose video to upload": "image",
"videopreview": {
"hidden": false,
"paused": false,
"params": {
"force_rate": 0,
"frame_load_cap": 0,
"skip_first_frames": 0,
"select_every_nth": 1,
"filename": "video.mp4",
"type": "input",
"format": "video/mp4"
},
"muted": false
}
}
},
{
"id": 102,
"type": "LTXVLoader",
"pos": [
-461,
-249
],
"size": [
315,
78
],
"flags": {},
"order": 4,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "model",
"type": "MODEL",
"links": [
254
],
"slot_index": 0
},
{
"name": "vae",
"type": "VAE",
"links": [
255,
256
],
"slot_index": 1
}
],
"properties": {
"Node name for S&R": "LTXVLoader"
},
"widgets_values": [
"ltx-video-2b-v0.9.safetensors"
]
}
],
"links": [
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[
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[
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[
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"groups": [],
"config": {},
"extra": {
"ds": {
"scale": 0.6115909044841471,
"offset": [
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]
}
},
"version": 0.4
}
assets/ltxvideo-v2v.png
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assets/video.mp4
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version https://git-lfs.github.com/spec/v1
oid sha256:610240226d1f9f7e89e6d585176d952787d79523403d1c9a6c2feaba85e8ed35
size 1538809
img2vid.py
+43
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import comfy.model_base
import comfy.model_management
import comfy.supported_models_base
import comfy.utils
import comfy.model_patcher
import comfy.sd
import comfy.latent_formats
import nodes
import torch
from .nodes_registry import comfy_node
from ltx_video.models.autoencoders.vae_encode import get_vae_size_scale_factor
def pad_tensor(tensor, target_len):
dim = 2
repeat_factor = target_len - tensor.shape[dim] # Ceiling division
last_element = tensor.select(dim, -1).unsqueeze(dim)
padding = last_element.repeat(1, 1, repeat_factor, 1, 1)
return torch.cat([tensor, padding], dim=dim)
def encode_media_conditioning(init_media, vae, width, height, frames_number):
pixels = comfy.utils.common_upscale(
init_media.movedim(-1, 1), width, height, "bilinear", "center"
).movedim(1, -1)
encode_pixels = pixels[:, :, :, :3]
init_latents = vae.encode(encode_pixels).float()
video_scale_factor, _, _ = get_vae_size_scale_factor(vae.first_stage_model)
video_scale_factor = video_scale_factor if frames_number > 1 else 1
target_len = (frames_number // video_scale_factor) + 1
init_latents = init_latents[:, :, :target_len]
init_image_frame_number = init_media.shape[0]
if init_image_frame_number == 1:
result = pad_tensor(init_latents, target_len)
elif init_image_frame_number % 8 != 1:
result = pad_tensor(init_latents, target_len)
else:
result = init_latents
return result
loader_node.py
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from pathlib import Path
import safetensors.torch
import torch
import comfy
import comfy.model_management
import comfy.model_patcher
import folder_paths
from ltx_video.models.transformers.transformer3d import Transformer3DModel
from ltx_video.models.autoencoders.causal_video_autoencoder import CausalVideoAutoencoder
from ltx_video.models.autoencoders.vae_encode import get_vae_size_scale_factor
from ltx_video.models.transformers.symmetric_patchifier import SymmetricPatchifier
from .model import LTXVTransformer3D, LTXVModel, LTXVModelConfig
from .vae import LTXVVAE
from .nodes_registry import comfy_node
@comfy_node(name="LTXVLoader")
class LTXVLoader:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"ckpt_name": (
folder_paths.get_filename_list("checkpoints"),
{"tooltip": "The name of the checkpoint (model) to load."},
),
}
}
RETURN_TYPES = ("MODEL", "VAE")
RETURN_NAMES = ("model", "vae")
FUNCTION = "load"
CATEGORY = "lightricks/LTXV"
TITLE = "LTXV Loader"
OUTPUT_NODE = False
def load(self, ckpt_name):
load_device = comfy.model_management.get_torch_device()
offload_device = comfy.model_management.unet_offload_device()
ckpt_path = Path(folder_paths.get_full_path("checkpoints", ckpt_name))
weights = safetensors.torch.load_file(ckpt_path, device=str(load_device))
vae = self._load_vae(weights)
num_latent_channels = vae.first_stage_model.config.latent_channels
model = self._load_unet(
load_device, offload_device, weights, num_latent_channels
)
return (model, vae)
def _load_vae(self, weights):
vae_prefix = "vae."
vae = LTXVVAE.from_config_and_state_dict(
vae_class=CausalVideoAutoencoder,
config={
"_class_name": "CausalVideoAutoencoder",
"dims": 3,
"in_channels": 3,
"out_channels": 3,
"latent_channels": 128,
"blocks": [
["res_x", 4],
["compress_all", 1],
["res_x_y", 1],
["res_x", 3],
["compress_all", 1],
["res_x_y", 1],
["res_x", 3],
["compress_all", 1],
["res_x", 3],
["res_x", 4],
],
"scaling_factor": 1.0,
"norm_layer": "pixel_norm",
"patch_size": 4,
"latent_log_var": "uniform",
"use_quant_conv": False,
"causal_decoder": False,
},
state_dict={
key.removeprefix(vae_prefix): value
for key, value in weights.items()
if key.startswith(vae_prefix)
},
)
return vae
def _load_unet(self, load_device, offload_device, weights, num_latent_channels):
config = {
"_class_name": "Transformer3DModel",
"_diffusers_version": "0.25.1",
"_name_or_path": "PixArt-alpha/PixArt-XL-2-256x256",
"activation_fn": "gelu-approximate",
"attention_bias": True,
"attention_head_dim": 64,
"attention_type": "default",
"caption_channels": 4096,
"cross_attention_dim": 2048,
"double_self_attention": False,
"dropout": 0.0,
"in_channels": 128,
"norm_elementwise_affine": False,
"norm_eps": 1e-06,
"norm_num_groups": 32,
"num_attention_heads": 32,
"num_embeds_ada_norm": 1000,
"num_layers": 28,
"num_vector_embeds": None,
"only_cross_attention": False,
"out_channels": 128,
"project_to_2d_pos": True,
"upcast_attention": False,
"use_linear_projection": False,
"qk_norm": "rms_norm",
"standardization_norm": "rms_norm",
"positional_embedding_type": "rope",
"positional_embedding_theta": 10000.0,
"positional_embedding_max_pos": [20, 2048, 2048],
"timestep_scale_multiplier": 1000,
}
transformer = Transformer3DModel.from_config(config)
unet_prefix = "model.diffusion_model."
transformer.load_state_dict(
{
key.removeprefix(unet_prefix): value
for key, value in weights.items()
if key.startswith(unet_prefix)
}
)
transformer.to(torch.float32).to(load_device).eval()
diffusion_model = LTXVTransformer3D(transformer, SymmetricPatchifier(1))
model = LTXVModel(
LTXVModelConfig(num_latent_channels),
model_type=comfy.model_base.ModelType.FLOW,
device=comfy.model_management.get_torch_device(),
)
model.diffusion_model = diffusion_model
patcher = comfy.model_patcher.ModelPatcher(model, load_device, offload_device)
return patcher
model.py
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from contextlib import nullcontext
import torch
import torch.nn as nn
import comfy.model_base
import comfy.model_management
import comfy.supported_models_base
import comfy.utils
import comfy.model_patcher
import comfy.sd
import comfy.latent_formats
import comfy.model_sampling
from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
import math
from ltx_video.models.transformers.transformer3d import Transformer3DModel
from ltx_video.models.transformers.symmetric_patchifier import SymmetricPatchifier
from ltx_video.models.autoencoders.vae_encode import get_vae_size_scale_factor
class LTXVModelConfig:
def __init__(self, latent_channels):
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 = torch.float32
self.sampling_settings = {"multiplier": 1.0}
self.memory_usage_factor = 1
# 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):
super().__init__()
self.condition_mask = condition_mask
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(1, timesteps + 1, 1) / timesteps) * multiplier)
self.register_buffer("sigmas", ts)
@property
def sigma_min(self):
return 0.0
@property
def sigma_max(self):
return 1.0
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 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.
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
):
super().__init__()
self.transformer = transformer
self.dtype = transformer.dtype
self.patchifier = patchifier
def forward(
self,
latent,
timesteps,
context,
indices_grid,
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).float()
l = latent_patchified
if mixed_precision:
context_manager = torch.autocast("cuda", dtype=torch.bfloat16)
else:
context_manager = nullcontext()
with context_manager:
noise_pred = self.transformer(
l.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,
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
class LTXVTransformer3DWrapper(nn.Module):
def __init__(
self,
transformer: LTXVTransformer3D,
patchifier: SymmetricPatchifier,
conditioning_mask,
indices_grid,
):
super().__init__()
self.indices_grid = indices_grid
self.dtype = transformer.dtype
self.wrapped_transformer = transformer
self.patchifier = patchifier
self.conditioning_mask = conditioning_mask
self.conditioning_mask_patchified = patchifier.patchify(
self.conditioning_mask
).squeeze(-1)
def forward(self, x, timesteps, context, img_hw=None, aspect_ratio=None, **kwargs):
transformer_options = kwargs.get("transformer_options", {})
mixed_precision = transformer_options.get("mixed_precision", False)
mask = self.conditioning_mask_patchified.to(x.device)
ndim_mask = mask.ndimension()
expanded_timesteps = timesteps.view(timesteps.size(0), *([1] * (ndim_mask - 1)))
timesteps_masked = expanded_timesteps * (1 - mask)
result = self.wrapped_transformer.forward(
x,
timesteps_masked,
context,
self.indices_grid,
mixed_precision=mixed_precision,
)
return result
nodes_registry.py
+132
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import re
from typing import Callable, Optional, Type
from easy_nodes import initialize_easy_nodes
NODE_CLASS_MAPPINGS = {}
NODE_DISPLAY_NAME_MAPPINGS = {}
NODES_DISPLAY_NAME_PREFIX = "🅛🅣🅧"
EXPERIMENTAL_DISPLAY_NAME_PREFIX = "(Experimental 🧪)"
DEPRECATED_DISPLAY_NAME_PREFIX = "(Deprecated 🚫)"
DEFAULT_CATEGORY_NAME = "Lightricks"
initialize_easy_nodes(default_category=DEFAULT_CATEGORY_NAME, auto_register=True)
def register_node(node_class: Type, name: str, description: str) -> None:
"""
Register a ComfyUI node class to ComfyUI's global nodes' registry.
Args:
node_class (Type): The class of the node to be registered.
name (str): The name of the node.
description (str): The short user-friendly description of the node.
Raises:
ValueError: If `node_class` is not a class, or `class_name` or `display_name` is not a string.
"""
if not isinstance(node_class, type):
raise ValueError("`node_class` must be a class")
if not isinstance(name, str):
raise ValueError("`name` must be a string")
if not isinstance(description, str):
raise ValueError("`description` must be a string")
NODE_CLASS_MAPPINGS[name] = node_class
NODE_DISPLAY_NAME_MAPPINGS[name] = description
def comfy_node(
node_class: Optional[Type] = None,
name: Optional[str] = None,
description: Optional[str] = None,
experimental: bool = False,
deprecated: bool = False,
skip: bool = False,
) -> Callable:
"""
Decorator for registering a node class with optional name, description, and status flags.
Args:
node_class (Type): The class of the node to be registered.
name (str, optional): The name of the class. If not provided, the class name will be used.
description (str, optional): The description of the class.
If not provided, an auto-formatted description will be used based on the class name.
experimental (bool): Flag indicating if the class is experimental. Defaults to False.
deprecated (bool): Flag indicating if the class is deprecated. Defaults to False.
skip (bool): Flag indicating if the node registration should be skipped. Defaults to False.
This is useful for conditionally registering nodes based on certain conditions
(e.g. unavailability of certain dependencies).
Returns:
Callable: The decorator function.
Raises:
ValueError: If `node_class` is not a class.
"""
def decorator(node_class: Type) -> Type:
if skip:
return node_class
if not isinstance(node_class, type):
raise ValueError("`node_class` must be a class")
nonlocal name, description
if name is None:
name = node_class.__name__
# Remove possible "Node" suffix from the class name, e.g. "EditImageNode -> EditImage"
if name is not None and name.endswith("Node"):
name = name[:-4]
description = _format_description(description, name, experimental, deprecated)
register_node(node_class, name, description)
return node_class
# If the decorator is used without parentheses
if node_class is None:
return decorator
else:
return decorator(node_class)
def _format_description(
description: str, class_name: str, experimental: bool, deprecated: bool
) -> str:
"""Format nodes display name to a standard format"""
# If description is not provided, auto-generate one based on the class name
if description is None:
description = _camel_case_to_spaces(class_name)
# Strip the prefix if it's already there
prefix_len = len(NODES_DISPLAY_NAME_PREFIX)
if description.startswith(NODES_DISPLAY_NAME_PREFIX):
description = description[prefix_len:].lstrip()
# Add the deprecated / experimental prefixes
if deprecated:
description = f"{DEPRECATED_DISPLAY_NAME_PREFIX} {description}"
elif experimental:
description = f"{EXPERIMENTAL_DISPLAY_NAME_PREFIX} {description}"
# Add the prefix
description = f"{NODES_DISPLAY_NAME_PREFIX} {description}"
return description
def _camel_case_to_spaces(text: str) -> str:
# Add space before each capital letter except the first one
spaced_text = re.sub(r"(?<=[a-z])(?=[A-Z])", " ", text)
# Handle sequences of uppercase letters followed by a lowercase letter
spaced_text = re.sub(r"(?<=[A-Z])(?=[A-Z][a-z])", " ", spaced_text)
# Handle sequences of uppercase letters not followed by a lowercase letter
spaced_text = re.sub(r"(?<=[A-Z])(?=[A-Z][A-Z][a-z])", " ", spaced_text)
return spaced_text
requirements.txt
+3
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comfyui-easynodes
ltx-video@git+https://github.com/Lightricks/LTX-Video.git@ltx-video-0.9
huggingface_hub==0.25.2
t5_encoder.py
+143
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import torch
import comfy.clip_model
import comfy.model_base
import comfy.model_management
import comfy.sd1_clip
import comfy.supported_models_base
import comfy.utils
import comfy.model_patcher
import comfy.sd
import comfy.latent_formats
import folder_paths
from pathlib import Path
from .nodes_registry import comfy_node
from transformers import T5EncoderModel, T5Tokenizer
class LTXVTokenizer(comfy.sd1_clip.SDTokenizer):
def __init__(self, tokenizer_path: str):
self.tokenizer = T5Tokenizer.from_pretrained(
tokenizer_path, local_files_only=True
)
def tokenize_with_weights(self, text: str, return_word_ids: bool = False):
"""
Takes a prompt and converts it to a list of (token, weight, word id) elements.
Tokens can both be integer tokens and pre computed CLIP tensors.
Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens.
Returned list has the dimensions NxM where M is the input size of CLIP
"""
text = text.lower().strip()
text_inputs = self.tokenizer(
text,
padding="max_length", # do_not_pad, longest, max_length
max_length=128,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_attention_mask = text_inputs.attention_mask
out = {
"t5xxl": [
(token, weight, i)
for i, (token, weight) in enumerate(
zip(text_input_ids[0], prompt_attention_mask[0])
)
]
}
if not return_word_ids:
out = {k: [(t, w) for t, w, _ in v] for k, v in out.items()}
return out
class LTXVTextEncoderModel(torch.nn.Module):
def __init__(
self, encoder_path, dtype_t5=None, device="cpu", dtype=None, model_options={}
):
super().__init__()
dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
self.t5xxl = (
T5EncoderModel.from_pretrained(encoder_path, local_files_only=True)
.to(dtype_t5)
.to(device)
)
self.dtypes = set([dtype, dtype_t5])
def set_clip_options(self, options):
pass
def reset_clip_options(self):
pass
def encode_token_weights(self, token_weight_pairs):
token_weight_pairs_t5 = token_weight_pairs["t5xxl"]
text_input_ids = torch.tensor(
[[t[0] for t in token_weight_pairs_t5]],
device=self.t5xxl.device,
)
prompt_attention_mask = torch.tensor(
[[w[1] for w in token_weight_pairs_t5]],
device=self.t5xxl.device,
)
out = self.t5xxl(text_input_ids, attention_mask=prompt_attention_mask)[0]
out = out * prompt_attention_mask.unsqueeze(2)
return out, None, {"attention_mask": prompt_attention_mask}
def load_sd(self, sd):
return self.t5xxl.load_state_dict(sd, strict=False)
def ltxv_clip(encoder_path, dtype_t5=None):
class LTXVTextEncoderModel_(LTXVTextEncoderModel):
def __init__(self, device="cpu", dtype=None, model_options={}):
super().__init__(
encoder_path=encoder_path,
dtype_t5=dtype_t5,
device=device,
dtype=dtype,
model_options=model_options,
)
return LTXVTextEncoderModel_
def ltxv_tokenizer(tokenizer_path):
class LTXVTokenizer_(LTXVTokenizer):
def __init__(self, embedding_directory=None, tokenizer_data={}):
super().__init__(tokenizer_path)
return LTXVTokenizer_
@comfy_node(name="LTXVCLIPModelLoader", description="LTXV CLIP Model Loader")
class LTXVCLIPModelLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": {"clip_path": (
folder_paths.get_filename_list("text_encoders"),
{"tooltip": "The name of the text encoder model to load."},
)}}
RETURN_TYPES = ("CLIP",)
RETURN_NAMES = ("clip",)
FUNCTION = "load_model"
CATEGORY = "lightricks/LTXV"
TITLE = "LTXV Model Loader"
OUTPUT_NODE = False
def load_model(self, clip_path):
path = Path(folder_paths.get_full_path("text_encoders", clip_path))
tokenizer_path = path.parents[1] / "tokenizer"
encoder_path = path.parents[1] / "text_encoder"
clip_target = comfy.supported_models_base.ClipTarget(
tokenizer=ltxv_tokenizer(tokenizer_path),
clip=ltxv_clip(encoder_path, dtype_t5=torch.bfloat16),
)
return (comfy.sd.CLIP(clip_target),)
transformer.py
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import torch
import comfy.model_base
import comfy.model_management
import comfy.supported_models_base
import comfy.utils
import comfy.model_patcher
import comfy.sd
import comfy.latent_formats
from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
import folder_paths
import math
from pathlib import Path
from .nodes_registry import comfy_node
from ltx_video.models.transformers.transformer3d import Transformer3DModel
from ltx_video.models.transformers.symmetric_patchifier import SymmetricPatchifier
from ltx_video.models.autoencoders.vae_encode import get_vae_size_scale_factor
from .model import (
LTXVTransformer3DWrapper,
LTXVTransformer3D,
LTXVModel,
LTXVModelConfig,
LTXVSampling,
)
from .img2vid import encode_media_conditioning
def get_normal_shift(
n_tokens: int,
min_tokens: int = 1024,
max_tokens: int = 4096,
min_shift: float = 0.95,
max_shift: float = 2.05,
) -> float:
m = (max_shift - min_shift) / (max_tokens - min_tokens)
b = min_shift - m * min_tokens
return m * n_tokens + b
@comfy_node(name="LTXVModelConfigurator")
class LTXVModelConfigurator:
@classmethod
def INPUT_TYPES(s):
PRESETS = [
"Custom",
"1216x704 | 41",
"1088x704 | 49",
"1056x640 | 57",
"992x608 | 65",
"896x608 | 73",
"896x544 | 81",
"832x544 | 89",
"800x512 | 97",
"768x512 | 97",
"800x480 | 105",
"736x480 | 113",
"704x480 | 121",
"704x448 | 129",
"672x448 | 137",
"640x416 | 153",
"672x384 | 161",
"640x384 | 169",
"608x384 | 177",
"576x384 | 185",
"608x352 | 193",
"576x352 | 201",
"544x352 | 209",
"512x352 | 225",
"512x352 | 233",
"544x320 | 241",
"512x320 | 249",
"512x320 | 257",
]
return {
"required": {
"model": ("MODEL",),
"vae": ("VAE",),
"preset": (
PRESETS,
{
"default": "Custom",
"tooltip": "Preset resolution and frame count. Custom allows manual input.",
},
),
"width": ("INT", {"default": 768, "min": 1, "max": 10000}),
"height": ("INT", {"default": 512, "min": 1, "max": 10000}),
"frames_number": ("INT", {"default": 65, "min": 9, "max": 257, "step": 8, "tooltip": "Must be equal to N * 8 + 1"}),
"frame_rate": ("INT", {"default": 25, "min": 1, "max": 60}),
"batch": ("INT", {"default": 1, "min": 1, "max": 60}),
"mixed_precision": ("BOOLEAN", {"default": True}),
},
"optional": {"conditioning": ("IMAGE",{"tooltip": "Optional conditioning image or video."})},
}
RETURN_TYPES = ("MODEL", "LATENT", "FLOAT")
RETURN_NAMES = ("model", "latent", "sigma_shift")
FUNCTION = "configure_sizes"
CATEGORY = "lightricks/LTXV"
TITLE = "LTXV Model Configurator"
OUTPUT_NODE = False
def latent_shape_and_frame_rate(
self, vae, batch, height, width, frames_number, frame_rate
):
video_scale_factor, vae_scale_factor, _ = get_vae_size_scale_factor(
vae.first_stage_model
)
video_scale_factor = video_scale_factor if frames_number > 1 else 1
latent_height = height // vae_scale_factor
latent_width = width // vae_scale_factor
latent_channels = vae.first_stage_model.config.latent_channels
latent_num_frames = math.floor(frames_number / video_scale_factor) + 1
latent_frame_rate = frame_rate / video_scale_factor
latent_shape = [
batch,
latent_channels,
latent_num_frames,
latent_height,
latent_width,
]
return latent_shape, latent_frame_rate
def indices_grid(
self,
latent_shape,
vae_scale_factor,
latent_frame_rate,
patchifier,
use_rope,
device,
):
scale_grid = (
(1 / latent_frame_rate, vae_scale_factor, vae_scale_factor)
if use_rope # self.wrapped_transformer.transformer.use_rope
else None
)
indices_grid = 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 configure_sizes(
self,
model,
vae,
preset,
width,
height,
frames_number,
frame_rate,
batch,
mixed_precision,
conditioning=None,
):
load_device = comfy.model_management.get_torch_device()
offload_device = comfy.model_management.unet_offload_device()
if preset != "Custom":
preset = preset.split("|")
width, height = map(int, preset[0].strip().split("x"))
frames_number = int(preset[1].strip())
latent_shape, latent_frame_rate = self.latent_shape_and_frame_rate(
vae, batch, height, width, frames_number, frame_rate
)
mask_shape = [
latent_shape[0],
1,
latent_shape[2],
latent_shape[3],
latent_shape[4],
]
conditioning_mask = torch.zeros(mask_shape, device=load_device)
if conditioning is not None:
latent = encode_media_conditioning(
conditioning, vae, width, height, frames_number
)
conditioning_mask[:, :, 0] = 1.0
else:
latent = torch.zeros(latent_shape, dtype=torch.float32)
patchifier = SymmetricPatchifier(1)
_, vae_scale_factor, _ = get_vae_size_scale_factor(vae.first_stage_model)
use_rope = model.model.diffusion_model.transformer.use_rope
indices_grid = self.indices_grid(
latent_shape,
vae_scale_factor,
latent_frame_rate,
patchifier,
use_rope,
load_device,
)
wrapper = LTXVTransformer3DWrapper(
transformer=model.model.diffusion_model,
patchifier=patchifier,
conditioning_mask=conditioning_mask,
indices_grid=indices_grid,
)
model.model.diffusion_model.to(load_device)
new_model = LTXVModel(
LTXVModelConfig(vae.first_stage_model.config.latent_channels),
model_type=comfy.model_base.ModelType.FLOW,
device=comfy.model_management.get_torch_device(),
)
new_model.model_sampling = LTXVSampling(wrapper.conditioning_mask)
new_model.diffusion_model = wrapper
patcher = comfy.model_patcher.ModelPatcher(
new_model, load_device, offload_device
)
patcher.model_options.setdefault("transformer_options", {})[
"mixed_precision"
] = mixed_precision
num_latent_patches = latent_shape[2] * latent_shape[3] * latent_shape[4]
return (patcher, {"samples": latent}, get_normal_shift(num_latent_patches))
@comfy_node(name="LTXVShiftSigmas")
class LTXVShiftSigmas:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"sigmas": ("SIGMAS",),
"sigma_shift": ("FLOAT", {"default": 1.820833333}),
"stretch": ("BOOLEAN", {
"default": True,
"tooltip": "Stretch the sigmas to be in the range [terminal, 1]."
}),
"terminal": (
"FLOAT",
{
"default": 0.1, "min": 0.0, "max": 0.99, "step": 0.01,
"tooltip": "The terminal value of the sigmas after stretching."
},
),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "lightricks/LTXV"
FUNCTION = "shift_sigmas"
DESCRIPTION = "Transforms sigmas to values where the model can focus on denoising high noise."
def shift_sigmas(self, sigmas, sigma_shift, stretch, terminal):
power = 1
sigmas = torch.where(
sigmas != 0,
math.exp(sigma_shift) / (math.exp(sigma_shift) + (1 / sigmas - 1) ** power),
0,
)
# Stretch sigmas so that its final value matches the given terminal value.
if stretch:
non_zero_mask = sigmas != 0
non_zero_sigmas = sigmas[non_zero_mask]
one_minus_z = 1.0 - non_zero_sigmas
scale_factor = one_minus_z[-1] / (1.0 - terminal)
stretched = 1.0 - (one_minus_z / scale_factor)
sigmas[non_zero_mask] = stretched
return (sigmas,)
vae.py
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import torch
import comfy.model_base
import comfy.model_management
import comfy.supported_models_base
import comfy.utils
import comfy.model_patcher
import comfy.sd
import comfy.latent_formats
import folder_paths
from pathlib import Path
from .nodes_registry import comfy_node
from ltx_video.models.autoencoders.video_autoencoder import VideoAutoencoder
from ltx_video.models.autoencoders.causal_video_autoencoder import CausalVideoAutoencoder
from ltx_video.models.autoencoders.vae_encode import (
get_vae_size_scale_factor,
vae_decode,
vae_encode,
)
from ltx_video.models.transformers.symmetric_patchifier import SymmetricPatchifier
from diffusers.image_processor import VaeImageProcessor
class LTXVVAE(comfy.sd.VAE):
def __init__(self):
self.device = comfy.model_management.vae_device()
self.offload_device = comfy.model_management.vae_offload_device()
@classmethod
def from_pretrained(cls, vae_class, model_path, dtype=torch.bfloat16):
instance = cls()
model = vae_class.from_pretrained(
pretrained_model_name_or_path=model_path,
revision=None,
torch_dtype=dtype,
load_in_8bit=False,
)
instance._finalize_model(model)
return instance
@classmethod
def from_config_and_state_dict(
cls, vae_class, config, state_dict, dtype=torch.bfloat16
):
instance = cls()
model = vae_class.from_config(config)
model.load_state_dict(state_dict)
model.to(dtype)
instance._finalize_model(model)
return instance
def _finalize_model(self, model):
self.video_scale_factor, self.vae_scale_factor, _ = get_vae_size_scale_factor(
model
)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.first_stage_model = model.eval().to(self.device)
# Assumes that the input samples have dimensions in following order
# (batch, channels, frames, height, width)
def decode(self, samples_in):
is_video = samples_in.shape[2] > 1
result = vae_decode(
samples_in.to(self.device),
vae=self.first_stage_model,
is_video=is_video,
vae_per_channel_normalize=True,
)
result = self.image_processor.postprocess(
result, output_type="pt", do_denormalize=[True]
)
return (
result.squeeze(0).permute(1, 2, 3, 0).to(torch.float32)
) # .to(self.offload_device)
# Underlying VAE expects b, c, n, h, w dimensions order and dtype specific dtype.
# However in Comfy the convension is n, h, w, c.
def encode(self, pixel_samples):
preprocessed = self.image_processor.preprocess(
pixel_samples.permute(3, 0, 1, 2)
)
input = preprocessed.unsqueeze(0).to(torch.bfloat16).to(self.device)
latents = vae_encode(
input, self.first_stage_model, vae_per_channel_normalize=True
).to(comfy.model_management.get_torch_device())
return latents