import os import subprocess import sys # Disable torch.compile / dynamo before any torch import os.environ["TORCH_COMPILE_DISABLE"] = "1" os.environ["TORCHDYNAMO_DISABLE"] = "1" # Clone LTX-2 repo and install packages LTX_REPO_URL = "https://github.com/Lightricks/LTX-2.git" LTX_REPO_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "LTX-2") if not os.path.exists(LTX_REPO_DIR): print(f"Cloning {LTX_REPO_URL}...") subprocess.run(["git", "clone", "--depth", "1", LTX_REPO_URL, LTX_REPO_DIR], check=True) # Install ltx-core and ltx-pipelines if not already installed try: import ltx_pipelines # noqa: F401 except ImportError: print("Installing ltx-core and ltx-pipelines...") subprocess.run( [sys.executable, "-m", "pip", "install", "-e", os.path.join(LTX_REPO_DIR, "packages", "ltx-core"), "-e", os.path.join(LTX_REPO_DIR, "packages", "ltx-pipelines")], check=True, ) sys.path.insert(0, os.path.join(LTX_REPO_DIR, "packages", "ltx-pipelines", "src")) sys.path.insert(0, os.path.join(LTX_REPO_DIR, "packages", "ltx-core", "src")) import logging import random import tempfile import torch torch._dynamo.config.suppress_errors = True torch._dynamo.config.disable = True import spaces import gradio as gr import numpy as np from huggingface_hub import hf_hub_download, snapshot_download from ltx_core.model.video_vae import TilingConfig, get_video_chunks_number from ltx_core.quantization import QuantizationPolicy from ltx_pipelines.distilled import DistilledPipeline from ltx_pipelines.utils.args import ImageConditioningInput from ltx_pipelines.utils.media_io import encode_video logging.getLogger().setLevel(logging.INFO) MAX_SEED = np.iinfo(np.int32).max DEFAULT_PROMPT = ( "An astronaut hatches from a fragile egg on the surface of the Moon, " "the shell cracking and peeling apart in gentle low-gravity motion." ) DEFAULT_HEIGHT = 1024 DEFAULT_WIDTH = 1536 DEFAULT_FRAME_RATE = 24.0 # Download models from Hugging Face LTX_MODEL_REPO = "diffusers-internal-dev/ltx-23" GEMMA_MODEL_REPO = "google/gemma-3-12b-it-qat-q4_0-unquantized" print("=" * 80) print("Downloading models from Hugging Face...") print("=" * 80) DISTILLED_CHECKPOINT = hf_hub_download(repo_id=LTX_MODEL_REPO, filename="ltx-2.3-22b-distilled.safetensors") SPATIAL_UPSAMPLER = hf_hub_download(repo_id=LTX_MODEL_REPO, filename="ltx-2.3-spatial-upscaler-x2-1.0.safetensors") GEMMA_ROOT = snapshot_download(repo_id=GEMMA_MODEL_REPO) print(f"Distilled checkpoint: {DISTILLED_CHECKPOINT}") print(f"Spatial upsampler: {SPATIAL_UPSAMPLER}") print(f"Gemma root: {GEMMA_ROOT}") # Initialize pipeline print("=" * 80) print("Loading LTX-2.3 Distilled pipeline...") print("=" * 80) pipeline = DistilledPipeline( distilled_checkpoint_path=DISTILLED_CHECKPOINT, spatial_upsampler_path=SPATIAL_UPSAMPLER, gemma_root=GEMMA_ROOT, loras=[], quantization=QuantizationPolicy.fp8_cast(), ) # Preload all models so first request is fast. # On ZeroGPU, .to('cuda') is intercepted and actual GPU allocation # happens inside the @spaces.GPU decorated function. print("Preloading models...") ledger = pipeline.model_ledger _text_encoder = ledger.text_encoder() _transformer = ledger.transformer() _video_encoder = ledger.video_encoder() _video_decoder = ledger.video_decoder() _audio_decoder = ledger.audio_decoder() _vocoder = ledger.vocoder() _spatial_upsampler = ledger.spatial_upsampler() ledger.text_encoder = lambda: _text_encoder ledger.transformer = lambda: _transformer ledger.video_encoder = lambda: _video_encoder ledger.video_decoder = lambda: _video_decoder ledger.audio_decoder = lambda: _audio_decoder ledger.vocoder = lambda: _vocoder ledger.spatial_upsampler = lambda: _spatial_upsampler print("All models preloaded!") @spaces.GPU(duration=300) @torch.inference_mode() def generate_video( input_image, prompt: str, duration: float, enhance_prompt: bool, seed: int, randomize_seed: bool, height: int, width: int, progress=gr.Progress(track_tqdm=True), ): current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed) num_frames = int(duration * DEFAULT_FRAME_RATE) + 1 num_frames = ((num_frames - 1 + 7) // 8) * 8 + 1 images = [] if input_image is not None: with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as f: temp_path = f.name if hasattr(input_image, "save"): input_image.save(temp_path) else: from shutil import copy2 copy2(str(input_image), temp_path) images = [ImageConditioningInput(path=temp_path, frame_idx=0, strength=1.0)] tiling_config = TilingConfig.default() video_chunks_number = get_video_chunks_number(num_frames, tiling_config) video, audio = pipeline( prompt=prompt, seed=current_seed, height=int(height), width=int(width), num_frames=num_frames, frame_rate=DEFAULT_FRAME_RATE, images=images, tiling_config=tiling_config, enhance_prompt=enhance_prompt, ) output_path = tempfile.mktemp(suffix=".mp4") encode_video( video=video, fps=DEFAULT_FRAME_RATE, audio=audio, output_path=output_path, video_chunks_number=video_chunks_number, ) return output_path, current_seed with gr.Blocks(title="LTX-2.3 Distilled") as demo: gr.Markdown("# LTX-2.3 Distilled (22B): Fast Audio-Video Generation") gr.Markdown( "Fast video + audio generation using the distilled model (8 steps stage 1, 4 steps stage 2). " "[[model]](https://huggingface.co/Lightricks/LTX-2) " "[[code]](https://github.com/Lightricks/LTX-2)" ) with gr.Row(): with gr.Column(): input_image = gr.Image(label="Input Image (Optional)", type="pil") prompt = gr.Textbox( label="Prompt", value=DEFAULT_PROMPT, lines=3, placeholder="Describe the video you want to generate...", ) with gr.Row(): duration = gr.Slider(label="Duration (seconds)", minimum=1.0, maximum=10.0, value=5.0, step=0.5) enhance_prompt = gr.Checkbox(label="Enhance Prompt", value=True) generate_btn = gr.Button("Generate Video", variant="primary") with gr.Accordion("Advanced Settings", open=False): seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, value=10, step=1) randomize_seed = gr.Checkbox(label="Randomize Seed", value=True) with gr.Row(): width = gr.Number(label="Width", value=DEFAULT_WIDTH, precision=0) height = gr.Number(label="Height", value=DEFAULT_HEIGHT, precision=0) with gr.Column(): output_video = gr.Video(label="Generated Video", autoplay=True) generate_btn.click( fn=generate_video, inputs=[ input_image, prompt, duration, enhance_prompt, seed, randomize_seed, height, width, ], outputs=[output_video, seed], ) if __name__ == "__main__": demo.launch(share=True)