81daf3f67d
- Introduced `enhance_prompt`, `max_tokens`, and `temperature` parameters in `generate_video` function for improved prompt handling. - Implemented prompt enhancement logic using the new `enhance_t2v` method in the text encoder. - Added command-line arguments for prompt enhancement options. - Created new system prompt files for T2V and I2V generation to guide the enhancement process.
438 lines
14 KiB
Python
438 lines
14 KiB
Python
import argparse
|
|
import time
|
|
from pathlib import Path
|
|
|
|
import mlx.core as mx
|
|
import numpy as np
|
|
from PIL import Image
|
|
from tqdm import tqdm
|
|
|
|
# ANSI color codes
|
|
class Colors:
|
|
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
|
|
from mlx_video.utils import to_denoised
|
|
from mlx_video.models.ltx.video_vae.decoder import load_vae_decoder
|
|
from mlx_video.models.ltx.upsampler import load_upsampler, upsample_latents
|
|
|
|
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
|
|
|
|
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,
|
|
) -> mx.array:
|
|
"""Run denoising loop."""
|
|
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
|
|
latents_flat = mx.transpose(mx.reshape(latents, (b, c, -1)), (0, 2, 1))
|
|
|
|
video_modality = Modality(
|
|
latent=latents_flat,
|
|
timesteps=mx.full((1,), sigma),
|
|
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)
|
|
mx.eval(denoised)
|
|
|
|
if sigma_next > 0:
|
|
latents = denoised + sigma_next * (latents - denoised) / sigma
|
|
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,
|
|
):
|
|
"""Generate video from text prompt.
|
|
|
|
Args:
|
|
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
|
|
"""
|
|
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
|
|
|
|
|
|
print(f"{Colors.BOLD}{Colors.CYAN}🎬 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}")
|
|
|
|
# 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)
|
|
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)
|
|
|
|
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())
|
|
|
|
# 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)
|
|
latents = mx.random.normal((1, 128, latent_frames, stage1_h, stage1_w))
|
|
mx.eval(latents)
|
|
|
|
positions = create_position_grid(1, latent_frames, stage1_h, stage1_w)
|
|
mx.eval(positions)
|
|
|
|
latents = denoise(latents, positions, text_embeddings, transformer, STAGE_1_SIGMAS, verbose=verbose)
|
|
|
|
# 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=True
|
|
)
|
|
|
|
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}")
|
|
positions = create_position_grid(1, latent_frames, stage2_h, stage2_w)
|
|
mx.eval(positions)
|
|
|
|
# Add noise for refinement
|
|
noise_scale = STAGE_2_SIGMAS[0]
|
|
noise = mx.random.normal(latents.shape)
|
|
latents = noise * noise_scale + latents * (1 - noise_scale)
|
|
mx.eval(latents)
|
|
|
|
latents = denoise(latents, positions, text_embeddings, transformer, STAGE_2_SIGMAS, verbose=verbose)
|
|
|
|
del transformer
|
|
mx.clear_cache()
|
|
|
|
# Decode to video
|
|
print(f"{Colors.BLUE}🎞️ Decoding video...{Colors.RESET}")
|
|
video = vae_decoder(latents)
|
|
mx.eval(video)
|
|
mx.clear_cache()
|
|
|
|
# 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 outputs
|
|
output_path = Path(output_path)
|
|
output_path.parent.mkdir(parents=True, exist_ok=True)
|
|
|
|
try:
|
|
import cv2
|
|
height, width = video_np.shape[1], video_np.shape[2]
|
|
fourcc = cv2.VideoWriter_fourcc(*'avc1')
|
|
out = cv2.VideoWriter(str(output_path), fourcc, fps, (width, height))
|
|
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",
|
|
formatter_class=argparse.RawDescriptionHelpFormatter,
|
|
epilog="""
|
|
Examples:
|
|
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
|
|
"""
|
|
)
|
|
|
|
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)"
|
|
)
|
|
args = parser.parse_args()
|
|
|
|
generate_video(
|
|
**vars(args)
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|