Files
DiffSynth-Studio/diffsynth/utils/data/media_io_ltx2.py
2026-02-02 19:53:07 +08:00

150 lines
4.8 KiB
Python

from fractions import Fraction
import torch
import av
from tqdm import tqdm
from PIL import Image
import numpy as np
from io import BytesIO
from collections.abc import Generator, Iterator
def _resample_audio(
container: av.container.Container, audio_stream: av.audio.AudioStream, frame_in: av.AudioFrame
) -> None:
cc = audio_stream.codec_context
# Use the encoder's format/layout/rate as the *target*
target_format = cc.format or "fltp" # AAC → usually fltp
target_layout = cc.layout or "stereo"
target_rate = cc.sample_rate or frame_in.sample_rate
audio_resampler = av.audio.resampler.AudioResampler(
format=target_format,
layout=target_layout,
rate=target_rate,
)
audio_next_pts = 0
for rframe in audio_resampler.resample(frame_in):
if rframe.pts is None:
rframe.pts = audio_next_pts
audio_next_pts += rframe.samples
rframe.sample_rate = frame_in.sample_rate
container.mux(audio_stream.encode(rframe))
# flush audio encoder
for packet in audio_stream.encode():
container.mux(packet)
def _write_audio(
container: av.container.Container, audio_stream: av.audio.AudioStream, samples: torch.Tensor, audio_sample_rate: int
) -> None:
if samples.ndim == 1:
samples = samples[:, None]
if samples.shape[1] != 2 and samples.shape[0] == 2:
samples = samples.T
if samples.shape[1] != 2:
raise ValueError(f"Expected samples with 2 channels; got shape {samples.shape}.")
# Convert to int16 packed for ingestion; resampler converts to encoder fmt.
if samples.dtype != torch.int16:
samples = torch.clip(samples, -1.0, 1.0)
samples = (samples * 32767.0).to(torch.int16)
frame_in = av.AudioFrame.from_ndarray(
samples.contiguous().reshape(1, -1).cpu().numpy(),
format="s16",
layout="stereo",
)
frame_in.sample_rate = audio_sample_rate
_resample_audio(container, audio_stream, frame_in)
def _prepare_audio_stream(container: av.container.Container, audio_sample_rate: int) -> av.audio.AudioStream:
"""
Prepare the audio stream for writing.
"""
audio_stream = container.add_stream("aac", rate=audio_sample_rate)
audio_stream.codec_context.sample_rate = audio_sample_rate
audio_stream.codec_context.layout = "stereo"
audio_stream.codec_context.time_base = Fraction(1, audio_sample_rate)
return audio_stream
def write_video_audio_ltx2(
video: list[Image.Image],
audio: torch.Tensor | None,
output_path: str,
fps: int = 24,
audio_sample_rate: int | None = 24000,
) -> None:
width, height = video[0].size
container = av.open(output_path, mode="w")
stream = container.add_stream("libx264", rate=int(fps))
stream.width = width
stream.height = height
stream.pix_fmt = "yuv420p"
if audio is not None:
if audio_sample_rate is None:
raise ValueError("audio_sample_rate is required when audio is provided")
audio_stream = _prepare_audio_stream(container, audio_sample_rate)
for frame in tqdm(video, total=len(video)):
frame = av.VideoFrame.from_image(frame)
for packet in stream.encode(frame):
container.mux(packet)
# Flush encoder
for packet in stream.encode():
container.mux(packet)
if audio is not None:
_write_audio(container, audio_stream, audio, audio_sample_rate)
container.close()
def encode_single_frame(output_file: str, image_array: np.ndarray, crf: float) -> None:
container = av.open(output_file, "w", format="mp4")
try:
stream = container.add_stream("libx264", rate=1, options={"crf": str(crf), "preset": "veryfast"})
# Round to nearest multiple of 2 for compatibility with video codecs
height = image_array.shape[0] // 2 * 2
width = image_array.shape[1] // 2 * 2
image_array = image_array[:height, :width]
stream.height = height
stream.width = width
av_frame = av.VideoFrame.from_ndarray(image_array, format="rgb24").reformat(format="yuv420p")
container.mux(stream.encode(av_frame))
container.mux(stream.encode())
finally:
container.close()
def decode_single_frame(video_file: str) -> np.array:
container = av.open(video_file)
try:
stream = next(s for s in container.streams if s.type == "video")
frame = next(container.decode(stream))
finally:
container.close()
return frame.to_ndarray(format="rgb24")
def ltx2_preprocess(image: np.array, crf: float = 33) -> np.array:
if crf == 0:
return image
with BytesIO() as output_file:
encode_single_frame(output_file, image, crf)
video_bytes = output_file.getvalue()
with BytesIO(video_bytes) as video_file:
image_array = decode_single_frame(video_file)
return image_array