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mlx-video/mlx_video/generate.py

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import argparse
import time
from pathlib import Path
from typing import Optional
import mlx.core as mx
import mlx.nn as nn
import numpy as np
from PIL import Image
from tqdm import tqdm
class Colors:
"""ANSI color codes for terminal output."""
CYAN = "\033[96m"
BLUE = "\033[94m"
GREEN = "\033[92m"
YELLOW = "\033[93m"
RED = "\033[91m"
MAGENTA = "\033[95m"
BOLD = "\033[1m"
DIM = "\033[2m"
RESET = "\033[0m"
from mlx_video.models.ltx.config import LTXModelConfig, LTXModelType, LTXRopeType
from mlx_video.models.ltx.ltx import LTXModel
from mlx_video.models.ltx.transformer import Modality
from mlx_video.convert import sanitize_transformer_weights, sanitize_vae_encoder_weights
from mlx_video.utils import to_denoised, load_image, prepare_image_for_encoding
from mlx_video.models.ltx.video_vae.decoder import load_vae_decoder
from mlx_video.models.ltx.video_vae.encoder import load_vae_encoder
from mlx_video.models.ltx.video_vae.tiling import TilingConfig
from mlx_video.models.ltx.upsampler import load_upsampler, upsample_latents
from mlx_video.conditioning import VideoConditionByLatentIndex, apply_conditioning
from mlx_video.conditioning.latent import LatentState, create_initial_state, apply_denoise_mask, add_noise_with_state
from mlx_video.utils import get_model_path
# Distilled sigma schedules
STAGE_1_SIGMAS = [1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0]
STAGE_2_SIGMAS = [0.909375, 0.725, 0.421875, 0.0]
def create_position_grid(
batch_size: int,
num_frames: int,
height: int,
width: int,
temporal_scale: int = 8,
spatial_scale: int = 32,
fps: float = 24.0,
causal_fix: bool = True,
) -> mx.array:
"""Create position grid for RoPE in pixel space.
Args:
batch_size: Batch size
num_frames: Number of frames (latent)
height: Height (latent)
width: Width (latent)
temporal_scale: VAE temporal scale factor (default 8)
spatial_scale: VAE spatial scale factor (default 32)
fps: Frames per second (default 24.0)
causal_fix: Apply causal fix for first frame (default True)
Returns:
Position grid of shape (B, 3, num_patches, 2) in pixel space
where dim 2 is [start, end) bounds for each patch
"""
# Patch size is (1, 1, 1) for LTX-2 - no spatial patching
patch_size_t, patch_size_h, patch_size_w = 1, 1, 1
# Generate grid coordinates for each dimension (frame, height, width)
t_coords = np.arange(0, num_frames, patch_size_t)
h_coords = np.arange(0, height, patch_size_h)
w_coords = np.arange(0, width, patch_size_w)
# Create meshgrid with indexing='ij' for (frame, height, width) order
t_grid, h_grid, w_grid = np.meshgrid(t_coords, h_coords, w_coords, indexing='ij')
# Stack to get shape (3, grid_t, grid_h, grid_w)
patch_starts = np.stack([t_grid, h_grid, w_grid], axis=0)
# Calculate end coordinates (start + patch_size)
patch_size_delta = np.array([patch_size_t, patch_size_h, patch_size_w]).reshape(3, 1, 1, 1)
patch_ends = patch_starts + patch_size_delta
# Stack start and end: shape (3, grid_t, grid_h, grid_w, 2)
latent_coords = np.stack([patch_starts, patch_ends], axis=-1)
# Flatten spatial/temporal dims: (3, num_patches, 2)
num_patches = num_frames * height * width
latent_coords = latent_coords.reshape(3, num_patches, 2)
# Broadcast to batch: (batch, 3, num_patches, 2)
latent_coords = np.tile(latent_coords[np.newaxis, ...], (batch_size, 1, 1, 1))
# Convert latent coords to pixel coords by scaling with VAE factors
scale_factors = np.array([temporal_scale, spatial_scale, spatial_scale]).reshape(1, 3, 1, 1)
pixel_coords = (latent_coords * scale_factors).astype(np.float32)
# Apply causal fix for first frame temporal axis
if causal_fix:
# VAE temporal stride for first frame is 1 instead of temporal_scale
pixel_coords[:, 0, :, :] = np.clip(
pixel_coords[:, 0, :, :] + 1 - temporal_scale,
a_min=0,
a_max=None
)
# Convert temporal to time in seconds by dividing by fps
pixel_coords[:, 0, :, :] = pixel_coords[:, 0, :, :] / fps
# Always return float32 for RoPE precision - bfloat16 causes quality degradation
return mx.array(pixel_coords, dtype=mx.float32)
def denoise(
latents: mx.array,
positions: mx.array,
text_embeddings: mx.array,
transformer: LTXModel,
sigmas: list,
verbose: bool = True,
state: Optional[LatentState] = None,
) -> mx.array:
"""Run denoising loop with optional conditioning.
Args:
latents: Noisy latent tensor (B, C, F, H, W)
positions: Position embeddings
text_embeddings: Text conditioning embeddings
transformer: LTX model
sigmas: List of sigma values for denoising schedule
verbose: Whether to show progress bar
state: Optional LatentState for I2V conditioning
Returns:
Denoised latent tensor
"""
# If state is provided, use its latent (which may have conditioning applied)
dtype = latents.dtype
if state is not None:
latents = state.latent
for i in tqdm(range(len(sigmas) - 1), desc="Denoising", disable=not verbose):
sigma, sigma_next = sigmas[i], sigmas[i + 1]
b, c, f, h, w = latents.shape
num_tokens = f * h * w
latents_flat = mx.transpose(mx.reshape(latents, (b, c, -1)), (0, 2, 1))
# Compute per-token timesteps
# For I2V: conditioned tokens get timestep=0 (mask=0), unconditioned get timestep=sigma (mask=1)
if state is not None:
# Reshape denoise_mask from (B, 1, F, 1, 1) to (B, num_tokens)
denoise_mask_flat = mx.reshape(state.denoise_mask, (b, 1, f, 1, 1))
denoise_mask_flat = mx.broadcast_to(denoise_mask_flat, (b, 1, f, h, w))
denoise_mask_flat = mx.reshape(denoise_mask_flat, (b, num_tokens))
# Per-token timesteps: sigma * mask (preserve dtype)
timesteps = mx.array(sigma, dtype=dtype) * denoise_mask_flat
else:
# All tokens get the same timestep (use latent dtype)
timesteps = mx.full((b, num_tokens), sigma, dtype=dtype)
video_modality = Modality(
latent=latents_flat,
timesteps=timesteps,
positions=positions,
context=text_embeddings,
context_mask=None,
enabled=True,
)
velocity, _ = transformer(video=video_modality, audio=None)
mx.eval(velocity)
velocity = mx.reshape(mx.transpose(velocity, (0, 2, 1)), (b, c, f, h, w))
denoised = to_denoised(latents, velocity, sigma)
# Apply conditioning mask if state is provided
if state is not None:
denoised = apply_denoise_mask(denoised, state.clean_latent, state.denoise_mask)
mx.eval(denoised)
# Euler step (preserve dtype by converting Python floats to arrays)
if sigma_next > 0:
sigma_next_arr = mx.array(sigma_next, dtype=dtype)
sigma_arr = mx.array(sigma, dtype=dtype)
latents = denoised + sigma_next_arr * (latents - denoised) / sigma_arr
else:
latents = denoised
mx.eval(latents)
return latents
def generate_video(
model_repo: str,
text_encoder_repo: str,
prompt: str,
height: int = 512,
width: int = 512,
num_frames: int = 33,
seed: int = 42,
fps: int = 24,
output_path: str = "output.mp4",
save_frames: bool = False,
verbose: bool = True,
enhance_prompt: bool = False,
max_tokens: int = 512,
temperature: float = 0.7,
image: Optional[str] = None,
image_strength: float = 1.0,
image_frame_idx: int = 0,
tiling: str = "auto",
stream: bool = False,
):
"""Generate video from text prompt, optionally conditioned on an image.
Args:
model_repo: Model repository ID
text_encoder_repo: Text encoder repository ID
prompt: Text description of the video to generate
height: Output video height (must be divisible by 64)
width: Output video width (must be divisible by 64)
num_frames: Number of frames (must be 1 + 8*k, e.g., 33, 65, 97)
seed: Random seed for reproducibility
fps: Frames per second for output video
output_path: Path to save the output video
save_frames: Whether to save individual frames as images
verbose: Whether to print progress
enhance_prompt: Whether to enhance prompt using Gemma
max_tokens: Max tokens for prompt enhancement
temperature: Temperature for prompt enhancement
image: Path to conditioning image for I2V (Image-to-Video)
image_strength: Conditioning strength (1.0 = full denoise, 0.0 = keep original)
image_frame_idx: Frame index to condition (0 = first frame)
tiling: Tiling mode for VAE decoding. Options:
- "auto": Automatically determine based on video size (default)
- "none": Disable tiling
- "default": 512px spatial, 64 frame temporal
- "aggressive": 256px spatial, 32 frame temporal (lowest memory)
- "conservative": 768px spatial, 96 frame temporal (faster)
- "spatial": Spatial tiling only
- "temporal": Temporal tiling only
"""
start_time = time.time()
# Validate dimensions
assert height % 64 == 0, f"Height must be divisible by 64, got {height}"
assert width % 64 == 0, f"Width must be divisible by 64, got {width}"
if num_frames % 8 != 1:
adjusted_num_frames = round((num_frames - 1) / 8) * 8 + 1
print(f"{Colors.YELLOW}⚠️ Number of frames must be 1 + 8*k. Using nearest valid value: {adjusted_num_frames}{Colors.RESET}")
num_frames = adjusted_num_frames
is_i2v = image is not None
mode_str = "I2V" if is_i2v else "T2V"
print(f"{Colors.BOLD}{Colors.CYAN}🎬 [{mode_str}] Generating {width}x{height} video with {num_frames} frames{Colors.RESET}")
print(f"{Colors.DIM}Prompt: {prompt[:80]}{'...' if len(prompt) > 80 else ''}{Colors.RESET}")
if is_i2v:
print(f"{Colors.DIM}Image: {image} (strength={image_strength}, frame={image_frame_idx}){Colors.RESET}")
# Get model path
model_path = get_model_path(model_repo)
text_encoder_path = model_path if text_encoder_repo is None else get_model_path(text_encoder_repo)
# Calculate latent dimensions
stage1_h, stage1_w = height // 2 // 32, width // 2 // 32
stage2_h, stage2_w = height // 32, width // 32
latent_frames = 1 + (num_frames - 1) // 8
mx.random.seed(seed)
# Load text encoder
print(f"{Colors.BLUE}📝 Loading text encoder...{Colors.RESET}")
from mlx_video.models.ltx.text_encoder import LTX2TextEncoder
text_encoder = LTX2TextEncoder()
text_encoder.load(model_path=model_path, text_encoder_path=text_encoder_path)
mx.eval(text_encoder.parameters())
# Optionally enhance the prompt
if enhance_prompt:
print(f"{Colors.MAGENTA}✨ Enhancing prompt...{Colors.RESET}")
prompt = text_encoder.enhance_t2v(prompt, max_tokens=max_tokens, temperature=temperature, seed=seed, verbose=verbose)
print(f"{Colors.DIM}Enhanced: {prompt[:150]}{'...' if len(prompt) > 150 else ''}{Colors.RESET}")
text_embeddings, _ = text_encoder(prompt, return_audio_embeddings=False)
model_dtype = text_embeddings.dtype # bfloat16 from text encoder
mx.eval(text_embeddings)
del text_encoder
mx.clear_cache()
# Load transformer
print(f"{Colors.BLUE}🤖 Loading transformer...{Colors.RESET}")
raw_weights = mx.load(str(model_path / 'ltx-2-19b-distilled.safetensors'))
sanitized = sanitize_transformer_weights(raw_weights)
# Convert transformer weights to bfloat16 for memory efficiency
sanitized = {k: v.astype(mx.bfloat16) if v.dtype == mx.float32 else v for k, v in sanitized.items()}
config = LTXModelConfig(
model_type=LTXModelType.VideoOnly,
num_attention_heads=32,
attention_head_dim=128,
in_channels=128,
out_channels=128,
num_layers=48,
cross_attention_dim=4096,
caption_channels=3840,
rope_type=LTXRopeType.SPLIT,
double_precision_rope=True,
positional_embedding_theta=10000.0,
positional_embedding_max_pos=[20, 2048, 2048],
use_middle_indices_grid=True,
timestep_scale_multiplier=1000,
)
transformer = LTXModel(config)
transformer.load_weights(list(sanitized.items()), strict=False)
mx.eval(transformer.parameters())
# Load VAE encoder and encode image for I2V conditioning
stage1_image_latent = None
stage2_image_latent = None
if is_i2v:
print(f"{Colors.BLUE}🖼️ Loading VAE encoder and encoding image...{Colors.RESET}")
vae_encoder = load_vae_encoder(str(model_path / 'ltx-2-19b-distilled.safetensors'))
mx.eval(vae_encoder.parameters())
# Load and prepare image for stage 1 (half resolution)
input_image = load_image(image, height=height // 2, width=width // 2, dtype=model_dtype)
stage1_image_tensor = prepare_image_for_encoding(input_image, height // 2, width // 2, dtype=model_dtype)
stage1_image_latent = vae_encoder(stage1_image_tensor)
mx.eval(stage1_image_latent)
print(f" Stage 1 image latent: {stage1_image_latent.shape}")
# Load and prepare image for stage 2 (full resolution)
input_image = load_image(image, height=height, width=width, dtype=model_dtype)
stage2_image_tensor = prepare_image_for_encoding(input_image, height, width, dtype=model_dtype)
stage2_image_latent = vae_encoder(stage2_image_tensor)
mx.eval(stage2_image_latent)
print(f" Stage 2 image latent: {stage2_image_latent.shape}")
del vae_encoder
mx.clear_cache()
# Stage 1: Generate at half resolution
print(f"{Colors.YELLOW}⚡ Stage 1: Generating at {width//2}x{height//2} (8 steps)...{Colors.RESET}")
mx.random.seed(seed)
# Position grids stay float32 for RoPE precision
positions = create_position_grid(1, latent_frames, stage1_h, stage1_w)
mx.eval(positions)
# Apply I2V conditioning if provided
state1 = None
if is_i2v and stage1_image_latent is not None:
# PyTorch flow: create zeros -> apply conditioning -> apply noiser
# Create initial state with zeros
latent_shape = (1, 128, latent_frames, stage1_h, stage1_w)
state1 = LatentState(
latent=mx.zeros(latent_shape, dtype=model_dtype),
clean_latent=mx.zeros(latent_shape, dtype=model_dtype),
denoise_mask=mx.ones((1, 1, latent_frames, 1, 1), dtype=model_dtype),
)
conditioning = VideoConditionByLatentIndex(
latent=stage1_image_latent,
frame_idx=image_frame_idx,
strength=image_strength,
)
state1 = apply_conditioning(state1, [conditioning])
# Apply noiser: latent = noise * (mask * noise_scale) + latent * (1 - mask * noise_scale)
# For Stage 1, noise_scale = 1.0 (first sigma)
noise = mx.random.normal(latent_shape, dtype=model_dtype)
noise_scale = mx.array(STAGE_1_SIGMAS[0], dtype=model_dtype) # 1.0
scaled_mask = state1.denoise_mask * noise_scale
state1 = LatentState(
latent=noise * scaled_mask + state1.latent * (mx.array(1.0, dtype=model_dtype) - scaled_mask),
clean_latent=state1.clean_latent,
denoise_mask=state1.denoise_mask,
)
latents = state1.latent
mx.eval(latents)
else:
# T2V: just use random noise
latents = mx.random.normal((1, 128, latent_frames, stage1_h, stage1_w), dtype=model_dtype)
mx.eval(latents)
latents = denoise(latents, positions, text_embeddings, transformer, STAGE_1_SIGMAS, verbose=verbose, state=state1)
# Upsample latents
print(f"{Colors.MAGENTA}🔍 Upsampling latents 2x...{Colors.RESET}")
upsampler = load_upsampler(str(model_path / 'ltx-2-spatial-upscaler-x2-1.0.safetensors'))
mx.eval(upsampler.parameters())
vae_decoder = load_vae_decoder(
str(model_path / 'ltx-2-19b-distilled.safetensors'),
timestep_conditioning=None # Auto-detect from model metadata
)
latents = upsample_latents(latents, upsampler, vae_decoder.latents_mean, vae_decoder.latents_std)
mx.eval(latents)
del upsampler
mx.clear_cache()
# Stage 2: Refine at full resolution
print(f"{Colors.YELLOW}⚡ Stage 2: Refining at {width}x{height} (3 steps)...{Colors.RESET}")
# Position grids stay float32 for RoPE precision
positions = create_position_grid(1, latent_frames, stage2_h, stage2_w)
mx.eval(positions)
# Apply I2V conditioning for stage 2 if provided
state2 = None
if is_i2v and stage2_image_latent is not None:
# PyTorch flow: start with upscaled latent -> apply conditioning -> apply noiser
state2 = LatentState(
latent=latents, # Start with upscaled latent
clean_latent=mx.zeros_like(latents),
denoise_mask=mx.ones((1, 1, latent_frames, 1, 1), dtype=model_dtype),
)
conditioning = VideoConditionByLatentIndex(
latent=stage2_image_latent,
frame_idx=image_frame_idx,
strength=image_strength,
)
state2 = apply_conditioning(state2, [conditioning])
# Apply noiser: latent = noise * (mask * noise_scale) + latent * (1 - mask * noise_scale)
# For Stage 2, noise_scale = stage_2_sigmas[0]
# Conditioned frames (mask=0) keep image latent, unconditioned get partial noise
noise = mx.random.normal(latents.shape).astype(model_dtype)
noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype)
scaled_mask = state2.denoise_mask * noise_scale
state2 = LatentState(
latent=noise * scaled_mask + state2.latent * (mx.array(1.0, dtype=model_dtype) - scaled_mask),
clean_latent=state2.clean_latent,
denoise_mask=state2.denoise_mask,
)
latents = state2.latent
mx.eval(latents)
else:
# T2V: add noise to all frames for refinement
noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype)
one_minus_scale = mx.array(1.0 - STAGE_2_SIGMAS[0], dtype=model_dtype)
noise = mx.random.normal(latents.shape).astype(model_dtype)
latents = noise * noise_scale + latents * one_minus_scale
mx.eval(latents)
latents = denoise(latents, positions, text_embeddings, transformer, STAGE_2_SIGMAS, verbose=verbose, state=state2)
del transformer
mx.clear_cache()
# Decode to video with tiling
print(f"{Colors.BLUE}🎞️ Decoding video...{Colors.RESET}")
# Select tiling configuration
if tiling == "none":
tiling_config = None
elif tiling == "auto":
tiling_config = TilingConfig.auto(height, width, num_frames)
elif tiling == "default":
tiling_config = TilingConfig.default()
elif tiling == "aggressive":
tiling_config = TilingConfig.aggressive()
elif tiling == "conservative":
tiling_config = TilingConfig.conservative()
elif tiling == "spatial":
tiling_config = TilingConfig.spatial_only()
elif tiling == "temporal":
tiling_config = TilingConfig.temporal_only()
else:
print(f"{Colors.YELLOW} Unknown tiling mode '{tiling}', using auto{Colors.RESET}")
tiling_config = TilingConfig.auto(height, width, num_frames)
# Save outputs
output_path = Path(output_path)
output_path.parent.mkdir(parents=True, exist_ok=True)
# Stream mode: write frames as they're decoded
video_writer = None
stream_pbar = None
if stream and tiling_config is not None:
import cv2
fourcc = cv2.VideoWriter_fourcc(*'avc1')
video_writer = cv2.VideoWriter(str(output_path), fourcc, fps, (width, height))
stream_pbar = tqdm(total=num_frames, desc="Streaming", unit="frame")
def on_frames_ready(frames: mx.array, start_idx: int):
"""Callback to write frames as they're finalized."""
# frames: (B, 3, num_frames, H, W)
frames = mx.squeeze(frames, axis=0) # (3, num_frames, H, W)
frames = mx.transpose(frames, (1, 2, 3, 0)) # (num_frames, H, W, 3)
frames = mx.clip((frames + 1.0) / 2.0, 0.0, 1.0)
frames = (frames * 255).astype(mx.uint8)
frames_np = np.array(frames)
for frame in frames_np:
video_writer.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
stream_pbar.update(1)
else:
on_frames_ready = None
if tiling_config is not None:
spatial_info = f"{tiling_config.spatial_config.tile_size_in_pixels}px" if tiling_config.spatial_config else "none"
temporal_info = f"{tiling_config.temporal_config.tile_size_in_frames}f" if tiling_config.temporal_config else "none"
print(f"{Colors.DIM} Tiling ({tiling}): spatial={spatial_info}, temporal={temporal_info}{Colors.RESET}")
video = vae_decoder.decode_tiled(latents, tiling_config=tiling_config, tiling_mode=tiling, debug=verbose, on_frames_ready=on_frames_ready)
else:
print(f"{Colors.DIM} Tiling: disabled{Colors.RESET}")
video = vae_decoder(latents)
mx.eval(video)
mx.clear_cache()
# Close progressive video writer if used
if video_writer is not None:
video_writer.release()
if stream_pbar is not None:
stream_pbar.close()
print(f"{Colors.GREEN}✅ Streamed video to{Colors.RESET} {output_path}")
# Still need video_np for save_frames option
video = mx.squeeze(video, axis=0)
video = mx.transpose(video, (1, 2, 3, 0))
video = mx.clip((video + 1.0) / 2.0, 0.0, 1.0)
video = (video * 255).astype(mx.uint8)
video_np = np.array(video)
else:
# Convert to uint8 frames
video = mx.squeeze(video, axis=0) # (C, F, H, W)
video = mx.transpose(video, (1, 2, 3, 0)) # (F, H, W, C)
video = mx.clip((video + 1.0) / 2.0, 0.0, 1.0)
video = (video * 255).astype(mx.uint8)
video_np = np.array(video)
# Save video normally
try:
import cv2
h, w = video_np.shape[1], video_np.shape[2]
fourcc = cv2.VideoWriter_fourcc(*'avc1')
out = cv2.VideoWriter(str(output_path), fourcc, fps, (w, h))
for frame in video_np:
out.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
out.release()
print(f"{Colors.GREEN}✅ Saved video to{Colors.RESET} {output_path}")
except Exception as e:
print(f"{Colors.RED}❌ Could not save video: {e}{Colors.RESET}")
if save_frames:
frames_dir = output_path.parent / f"{output_path.stem}_frames"
frames_dir.mkdir(exist_ok=True)
for i, frame in enumerate(video_np):
Image.fromarray(frame).save(frames_dir / f"frame_{i:04d}.png")
print(f"{Colors.GREEN}✅ Saved {len(video_np)} frames to {frames_dir}{Colors.RESET}")
elapsed = time.time() - start_time
print(f"{Colors.BOLD}{Colors.GREEN}🎉 Done! Generated in {elapsed:.1f}s ({elapsed/num_frames:.2f}s/frame){Colors.RESET}")
print(f"{Colors.BOLD}{Colors.GREEN}✨ Peak memory: {mx.get_peak_memory() / (1024 ** 3):.2f}GB{Colors.RESET}")
return video_np
def main():
parser = argparse.ArgumentParser(
description="Generate videos with MLX LTX-2 (T2V and I2V)",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
# Text-to-Video (T2V)
python -m mlx_video.generate --prompt "A cat walking on grass"
python -m mlx_video.generate --prompt "Ocean waves at sunset" --height 768 --width 768
python -m mlx_video.generate --prompt "..." --num-frames 65 --seed 123 --output my_video.mp4
# Image-to-Video (I2V)
python -m mlx_video.generate --prompt "A person dancing" --image photo.jpg
python -m mlx_video.generate --prompt "Waves crashing" --image beach.png --image-strength 0.8
"""
)
parser.add_argument(
"--prompt", "-p",
type=str,
required=True,
help="Text description of the video to generate"
)
parser.add_argument(
"--height", "-H",
type=int,
default=512,
help="Output video height (default: 512, must be divisible by 32)"
)
parser.add_argument(
"--width", "-W",
type=int,
default=512,
help="Output video width (default: 512, must be divisible by 32)"
)
parser.add_argument(
"--num-frames", "-n",
type=int,
default=100,
help="Number of frames (default: 100)"
)
parser.add_argument(
"--seed", "-s",
type=int,
default=42,
help="Random seed for reproducibility (default: 42)"
)
parser.add_argument(
"--fps",
type=int,
default=24,
help="Frames per second for output video (default: 24)"
)
parser.add_argument(
"--output-path",
type=str,
default="output.mp4",
help="Output video path (default: output.mp4)"
)
parser.add_argument(
"--save-frames",
action="store_true",
help="Save individual frames as images"
)
parser.add_argument(
"--model-repo",
type=str,
default="Lightricks/LTX-2",
help="Model repository to use (default: Lightricks/LTX-2)"
)
parser.add_argument(
"--text-encoder-repo",
type=str,
default=None,
help="Text encoder repository to use (default: None)"
)
parser.add_argument(
"--verbose",
action="store_true",
help="Verbose output"
)
parser.add_argument(
"--enhance-prompt",
action="store_true",
help="Enhance the prompt using Gemma before generation"
)
parser.add_argument(
"--max-tokens",
type=int,
default=512,
help="Maximum number of tokens to generate (default: 512)"
)
parser.add_argument(
"--temperature",
type=float,
default=0.7,
help="Temperature for prompt enhancement (default: 0.7)"
)
parser.add_argument(
"--image", "-i",
type=str,
default=None,
help="Path to conditioning image for I2V (Image-to-Video) generation"
)
parser.add_argument(
"--image-strength",
type=float,
default=1.0,
help="Conditioning strength for I2V (1.0 = full denoise, 0.0 = keep original, default: 1.0)"
)
parser.add_argument(
"--image-frame-idx",
type=int,
default=0,
help="Frame index to condition for I2V (0 = first frame, default: 0)"
)
parser.add_argument(
"--tiling",
type=str,
default="auto",
choices=["auto", "none", "default", "aggressive", "conservative", "spatial", "temporal"],
help="Tiling mode for VAE decoding (default: auto). "
"auto=based on video size, none=disabled, default=512px/64f, "
"aggressive=256px/32f (lowest memory), conservative=768px/96f, spatial=spatial only, temporal=temporal only"
)
parser.add_argument(
"--stream",
action="store_true",
help="Stream frames to output file as they're decoded (requires tiling). Allows viewing partial results sooner."
)
args = parser.parse_args()
generate_video(
**vars(args)
)
if __name__ == "__main__":
main()
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