File size: 2,138 Bytes
9fd4f11 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | """VBVR-Pro-LTX2.3 image-to-audio-video inference example.
Usage:
python example.py --model_path Video-Reason/VBVR-Pro-LTX2.3 \
--image input.png --prompt "Your video instruction"
"""
import argparse
import torch
from diffusers import LTX2ImageToVideoPipeline
from diffusers.utils import encode_video, load_image
parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str, default="Video-Reason/VBVR-Pro-LTX2.3")
parser.add_argument("--image", type=str, required=True, help="Path or URL to input image")
parser.add_argument("--prompt", type=str, required=True, help="Video instruction")
parser.add_argument(
"--negative_prompt",
type=str,
default="blurry, low quality, flickering, motion blur, distorted",
)
parser.add_argument("--output", type=str, default="output.mp4")
parser.add_argument("--width", type=int, default=768)
parser.add_argument("--height", type=int, default=512)
parser.add_argument("--num_frames", type=int, default=49)
parser.add_argument("--steps", type=int, default=40)
parser.add_argument("--guidance_scale", type=float, default=5.0)
parser.add_argument("--fps", type=int, default=24)
parser.add_argument("--seed", type=int, default=42)
args = parser.parse_args()
print(f"Loading model from: {args.model_path}")
pipe = LTX2ImageToVideoPipeline.from_pretrained(
args.model_path, torch_dtype=torch.bfloat16
)
pipe.enable_model_cpu_offload()
image = load_image(args.image).convert("RGB")
print(f"Input image: {args.image} ({image.size[0]}x{image.size[1]})")
video, audio = pipe(
image=image,
prompt=args.prompt,
negative_prompt=args.negative_prompt,
height=args.height,
width=args.width,
num_frames=args.num_frames,
frame_rate=args.fps,
num_inference_steps=args.steps,
guidance_scale=args.guidance_scale,
generator=torch.manual_seed(args.seed),
output_type="np",
return_dict=False,
)
encode_video(
video[0][: args.num_frames],
fps=args.fps,
output_path=args.output,
audio=audio[0].float().cpu(),
audio_sample_rate=pipe.vocoder.config.output_sampling_rate,
)
print(f"Saved to: {args.output}")
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