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inference.py
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inference.py
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import argparse
import os
import random
from datetime import datetime
from pathlib import Path
from diffusers.utils import logging
import imageio
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from transformers import T5EncoderModel, T5Tokenizer
from ltx_video.models.autoencoders.causal_video_autoencoder import (
CausalVideoAutoencoder,
)
from ltx_video.models.transformers.symmetric_patchifier import SymmetricPatchifier
from ltx_video.models.transformers.transformer3d import Transformer3DModel
from ltx_video.pipelines.pipeline_ltx_video import LTXVideoPipeline
from ltx_video.schedulers.rf import RectifiedFlowScheduler
from ltx_video.utils.conditioning_method import ConditioningMethod
from ltx_video.utils.skip_layer_strategy import SkipLayerStrategy
MAX_HEIGHT = 720
MAX_WIDTH = 1280
MAX_NUM_FRAMES = 257
def get_total_gpu_memory():
if torch.cuda.is_available():
total_memory = torch.cuda.get_device_properties(0).total_memory / (1024**3)
return total_memory
return None
def load_image_to_tensor_with_resize_and_crop(
image_path, target_height=512, target_width=768
):
image = Image.open(image_path).convert("RGB")
input_width, input_height = image.size
aspect_ratio_target = target_width / target_height
aspect_ratio_frame = input_width / input_height
if aspect_ratio_frame > aspect_ratio_target:
new_width = int(input_height * aspect_ratio_target)
new_height = input_height
x_start = (input_width - new_width) // 2
y_start = 0
else:
new_width = input_width
new_height = int(input_width / aspect_ratio_target)
x_start = 0
y_start = (input_height - new_height) // 2
image = image.crop((x_start, y_start, x_start + new_width, y_start + new_height))
image = image.resize((target_width, target_height))
frame_tensor = torch.tensor(np.array(image)).permute(2, 0, 1).float()
frame_tensor = (frame_tensor / 127.5) - 1.0
# Create 5D tensor: (batch_size=1, channels=3, num_frames=1, height, width)
return frame_tensor.unsqueeze(0).unsqueeze(2)
def calculate_padding(
source_height: int, source_width: int, target_height: int, target_width: int
) -> tuple[int, int, int, int]:
# Calculate total padding needed
pad_height = target_height - source_height
pad_width = target_width - source_width
# Calculate padding for each side
pad_top = pad_height // 2
pad_bottom = pad_height - pad_top # Handles odd padding
pad_left = pad_width // 2
pad_right = pad_width - pad_left # Handles odd padding
# Return padded tensor
# Padding format is (left, right, top, bottom)
padding = (pad_left, pad_right, pad_top, pad_bottom)
return padding
def convert_prompt_to_filename(text: str, max_len: int = 20) -> str:
# Remove non-letters and convert to lowercase
clean_text = "".join(
char.lower() for char in text if char.isalpha() or char.isspace()
)
# Split into words
words = clean_text.split()
# Build result string keeping track of length
result = []
current_length = 0
for word in words:
# Add word length plus 1 for underscore (except for first word)
new_length = current_length + len(word)
if new_length <= max_len:
result.append(word)
current_length += len(word)
else:
break
return "-".join(result)
# Generate output video name
def get_unique_filename(
base: str,
ext: str,
prompt: str,
seed: int,
resolution: tuple[int, int, int],
dir: Path,
endswith=None,
index_range=1000,
) -> Path:
base_filename = f"{base}_{convert_prompt_to_filename(prompt, max_len=30)}_{seed}_{resolution[0]}x{resolution[1]}x{resolution[2]}"
for i in range(index_range):
filename = dir / f"{base_filename}_{i}{endswith if endswith else ''}{ext}"
if not os.path.exists(filename):
return filename
raise FileExistsError(
f"Could not find a unique filename after {index_range} attempts."
)
def seed_everething(seed: int):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
def main():
parser = argparse.ArgumentParser(
description="Load models from separate directories and run the pipeline."
)
# Directories
parser.add_argument(
"--ckpt_path",
type=str,
required=True,
help="Path to a safetensors file that contains all model parts.",
)
parser.add_argument(
"--input_video_path",
type=str,
help="Path to the input video file (first frame used)",
)
parser.add_argument(
"--input_image_path", type=str, help="Path to the input image file"
)
parser.add_argument(
"--output_path",
type=str,
default=None,
help="Path to the folder to save output video, if None will save in outputs/ directory.",
)
parser.add_argument("--seed", type=int, default="171198")
# Pipeline parameters
parser.add_argument(
"--num_inference_steps", type=int, default=40, help="Number of inference steps"
)
parser.add_argument(
"--num_images_per_prompt",
type=int,
default=1,
help="Number of images per prompt",
)
parser.add_argument(
"--guidance_scale",
type=float,
default=3,
help="Guidance scale for the pipeline",
)
parser.add_argument(
"--stg_scale",
type=float,
default=1,
help="Spatiotemporal guidance scale for the pipeline. 0 to disable STG.",
)
parser.add_argument(
"--stg_rescale",
type=float,
default=0.7,
help="Spatiotemporal guidance rescaling scale for the pipeline. 1 to disable rescale.",
)
parser.add_argument(
"--stg_mode",
type=str,
default="stg_a",
help="Spatiotemporal guidance mode for the pipeline. Can be either stg_a or stg_r.",
)
parser.add_argument(
"--stg_skip_layers",
type=str,
default="19",
help="Attention layers to skip for spatiotemporal guidance. Comma separated list of integers.",
)
parser.add_argument(
"--image_cond_noise_scale",
type=float,
default=0.15,
help="Amount of noise to add to the conditioned image",
)
parser.add_argument(
"--height",
type=int,
default=480,
help="Height of the output video frames. Optional if an input image provided.",
)
parser.add_argument(
"--width",
type=int,
default=704,
help="Width of the output video frames. If None will infer from input image.",
)
parser.add_argument(
"--num_frames",
type=int,
default=121,
help="Number of frames to generate in the output video",
)
parser.add_argument(
"--frame_rate", type=int, default=25, help="Frame rate for the output video"
)
parser.add_argument(
"--precision",
choices=["bfloat16", "mixed_precision"],
default="bfloat16",
help="Sets the precision for the transformer and tokenizer. Default is bfloat16. If 'mixed_precision' is enabled, it moves to mixed-precision.",
)
# VAE noise augmentation
parser.add_argument(
"--decode_timestep",
type=float,
default=0.05,
help="Timestep for decoding noise",
)
parser.add_argument(
"--decode_noise_scale",
type=float,
default=0.025,
help="Noise level for decoding noise",
)
# Prompts
parser.add_argument(
"--prompt",
type=str,
help="Text prompt to guide generation",
)
parser.add_argument(
"--negative_prompt",
type=str,
default="worst quality, inconsistent motion, blurry, jittery, distorted",
help="Negative prompt for undesired features",
)
parser.add_argument(
"--offload_to_cpu",
action="store_true",
help="Offloading unnecessary computations to CPU.",
)
logger = logging.get_logger(__name__)
args = parser.parse_args()
logger.warning(f"Running generation with arguments: {args}")
seed_everething(args.seed)
offload_to_cpu = False if not args.offload_to_cpu else get_total_gpu_memory() < 30
output_dir = (
Path(args.output_path)
if args.output_path
else Path(f"outputs/{datetime.today().strftime('%Y-%m-%d')}")
)
output_dir.mkdir(parents=True, exist_ok=True)
# Load image
if args.input_image_path:
media_items_prepad = load_image_to_tensor_with_resize_and_crop(
args.input_image_path, args.height, args.width
)
else:
media_items_prepad = None
height = args.height if args.height else media_items_prepad.shape[-2]
width = args.width if args.width else media_items_prepad.shape[-1]
num_frames = args.num_frames
if height > MAX_HEIGHT or width > MAX_WIDTH or num_frames > MAX_NUM_FRAMES:
logger.warning(
f"Input resolution or number of frames {height}x{width}x{num_frames} is too big, it is suggested to use the resolution below {MAX_HEIGHT}x{MAX_WIDTH}x{MAX_NUM_FRAMES}."
)
# Adjust dimensions to be divisible by 32 and num_frames to be (N * 8 + 1)
height_padded = ((height - 1) // 32 + 1) * 32
width_padded = ((width - 1) // 32 + 1) * 32
num_frames_padded = ((num_frames - 2) // 8 + 1) * 8 + 1
padding = calculate_padding(height, width, height_padded, width_padded)
logger.warning(
f"Padded dimensions: {height_padded}x{width_padded}x{num_frames_padded}"
)
if media_items_prepad is not None:
media_items = F.pad(
media_items_prepad, padding, mode="constant", value=-1
) # -1 is the value for padding since the image is normalized to -1, 1
else:
media_items = None
ckpt_path = Path(args.ckpt_path)
vae = CausalVideoAutoencoder.from_pretrained(ckpt_path)
transformer = Transformer3DModel.from_pretrained(ckpt_path)
scheduler = RectifiedFlowScheduler.from_pretrained(ckpt_path)
text_encoder = T5EncoderModel.from_pretrained(
"PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="text_encoder"
)
patchifier = SymmetricPatchifier(patch_size=1)
tokenizer = T5Tokenizer.from_pretrained(
"PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="tokenizer"
)
if torch.cuda.is_available():
transformer = transformer.cuda()
vae = vae.cuda()
text_encoder = text_encoder.cuda()
vae = vae.to(torch.bfloat16)
if args.precision == "bfloat16" and transformer.dtype != torch.bfloat16:
transformer = transformer.to(torch.bfloat16)
text_encoder = text_encoder.to(torch.bfloat16)
# Set spatiotemporal guidance
skip_block_list = [int(x.strip()) for x in args.stg_skip_layers.split(",")]
skip_layer_strategy = (
SkipLayerStrategy.Attention
if args.stg_mode.lower() == "stg_a"
else SkipLayerStrategy.Residual
)
# Use submodels for the pipeline
submodel_dict = {
"transformer": transformer,
"patchifier": patchifier,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"scheduler": scheduler,
"vae": vae,
}
pipeline = LTXVideoPipeline(**submodel_dict)
if torch.cuda.is_available():
pipeline = pipeline.to("cuda")
# Prepare input for the pipeline
sample = {
"prompt": args.prompt,
"prompt_attention_mask": None,
"negative_prompt": args.negative_prompt,
"negative_prompt_attention_mask": None,
"media_items": media_items,
}
generator = torch.Generator(
device="cuda" if torch.cuda.is_available() else "cpu"
).manual_seed(args.seed)
images = pipeline(
num_inference_steps=args.num_inference_steps,
num_images_per_prompt=args.num_images_per_prompt,
guidance_scale=args.guidance_scale,
skip_layer_strategy=skip_layer_strategy,
skip_block_list=skip_block_list,
stg_scale=args.stg_scale,
do_rescaling=args.stg_rescale != 1,
rescaling_scale=args.stg_rescale,
generator=generator,
output_type="pt",
callback_on_step_end=None,
height=height_padded,
width=width_padded,
num_frames=num_frames_padded,
frame_rate=args.frame_rate,
**sample,
is_video=True,
vae_per_channel_normalize=True,
conditioning_method=(
ConditioningMethod.FIRST_FRAME
if media_items is not None
else ConditioningMethod.UNCONDITIONAL
),
image_cond_noise_scale=args.image_cond_noise_scale,
decode_timestep=args.decode_timestep,
decode_noise_scale=args.decode_noise_scale,
mixed_precision=(args.precision == "mixed_precision"),
offload_to_cpu=offload_to_cpu,
).images
# Crop the padded images to the desired resolution and number of frames
(pad_left, pad_right, pad_top, pad_bottom) = padding
pad_bottom = -pad_bottom
pad_right = -pad_right
if pad_bottom == 0:
pad_bottom = images.shape[3]
if pad_right == 0:
pad_right = images.shape[4]
images = images[:, :, :num_frames, pad_top:pad_bottom, pad_left:pad_right]
for i in range(images.shape[0]):
# Gathering from B, C, F, H, W to C, F, H, W and then permuting to F, H, W, C
video_np = images[i].permute(1, 2, 3, 0).cpu().float().numpy()
# Unnormalizing images to [0, 255] range
video_np = (video_np * 255).astype(np.uint8)
fps = args.frame_rate
height, width = video_np.shape[1:3]
# In case a single image is generated
if video_np.shape[0] == 1:
output_filename = get_unique_filename(
f"image_output_{i}",
".png",
prompt=args.prompt,
seed=args.seed,
resolution=(height, width, num_frames),
dir=output_dir,
)
imageio.imwrite(output_filename, video_np[0])
else:
if args.input_image_path:
base_filename = f"img_to_vid_{i}"
else:
base_filename = f"text_to_vid_{i}"
output_filename = get_unique_filename(
base_filename,
".mp4",
prompt=args.prompt,
seed=args.seed,
resolution=(height, width, num_frames),
dir=output_dir,
)
# Write video
with imageio.get_writer(output_filename, fps=fps) as video:
for frame in video_np:
video.append_data(frame)
# Write condition image
if args.input_image_path:
reference_image = (
(
media_items_prepad[0, :, 0].permute(1, 2, 0).cpu().data.numpy()
+ 1.0
)
/ 2.0
* 255
)
imageio.imwrite(
get_unique_filename(
base_filename,
".png",
prompt=args.prompt,
seed=args.seed,
resolution=(height, width, num_frames),
dir=output_dir,
endswith="_condition",
),
reference_image.astype(np.uint8),
)
logger.warning(f"Output saved to {output_dir}")
if __name__ == "__main__":
main()