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transformer.py
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transformer.py
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import math
import comfy.latent_formats
import comfy.model_base
import comfy.model_management
import comfy.model_patcher
import comfy.sd
import comfy.supported_models_base
import comfy.utils
import torch
from ltx_video.models.autoencoders.vae_encode import get_vae_size_scale_factor
from .img2vid import encode_media_conditioning
from .model import LTXVSampling
from .nodes_registry import comfy_node
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}),
"img_compression": (
"INT",
{
"default": 29,
"min": 0,
"max": 100,
"tooltip": "Amount of compression to apply on conditioning image.",
},
),
},
"optional": {
"conditioning": (
"IMAGE",
{"tooltip": "Optional conditioning image or video."},
),
"initial_latent": (
"LATENT",
{
"tooltip": "initial latent that is combined with conditioning if given"
},
),
},
}
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 configure_sizes(
self,
model,
vae,
preset,
width,
height,
frames_number,
frame_rate,
batch,
mixed_precision,
img_compression,
conditioning=None,
initial_latent=None,
):
load_device = comfy.model_management.get_torch_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)
initial_latent = (
None
if initial_latent is None
else initial_latent["samples"].to(load_device)
)
guiding_latent = None
if conditioning is not None:
latent = encode_media_conditioning(
conditioning,
vae,
width,
height,
frames_number,
image_compression=img_compression,
initial_latent=initial_latent,
)
conditioning_mask[:, :, 0] = 1.0
guiding_latent = latent[:, :, :1, ...]
else:
latent = torch.zeros(latent_shape, dtype=torch.float32, device=load_device)
if initial_latent is not None:
latent[:, :, : initial_latent.shape[2], ...] = initial_latent
_, vae_scale_factor, _ = get_vae_size_scale_factor(vae.first_stage_model)
patcher = model.clone()
patcher.add_object_patch("diffusion_model.conditioning_mask", conditioning_mask)
patcher.add_object_patch("diffusion_model.latent_frame_rate", latent_frame_rate)
patcher.add_object_patch("diffusion_model.vae_scale_factor", vae_scale_factor)
patcher.add_object_patch(
"model_sampling", LTXVSampling(conditioning_mask, guiding_latent)
)
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,)