import sys from pathlib import Path # Add packages to Python path current_dir = Path(__file__).parent sys.path.insert(0, str(current_dir / "packages" / "ltx-pipelines" / "src")) sys.path.insert(0, str(current_dir / "packages" / "ltx-core" / "src")) import spaces import gradio as gr from gradio_client import Client, handle_file import numpy as np import random import torch from typing import Optional from pathlib import Path from huggingface_hub import hf_hub_download from gradio_client import Client from ltx_pipelines.distilled import DistilledPipeline from ltx_core.tiling import TilingConfig from ltx_core.loader.primitives import LoraPathStrengthAndSDOps from ltx_core.loader.sd_ops import LTXV_LORA_COMFY_RENAMING_MAP from ltx_pipelines.constants import ( DEFAULT_SEED, DEFAULT_HEIGHT, DEFAULT_WIDTH, DEFAULT_NUM_FRAMES, DEFAULT_FRAME_RATE, DEFAULT_LORA_STRENGTH, ) MAX_SEED = np.iinfo(np.int32).max # Default prompt from docstring example 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. Fine lunar dust lifts and drifts outward with each movement, floating in slow arcs before settling back onto the ground. The astronaut pushes free in a deliberate, weightless motion, small fragments of the egg tumbling and spinning through the air. In the background, the deep darkness of space subtly shifts as stars glide with the camera's movement, emphasizing vast depth and scale. The camera performs a smooth, cinematic slow push-in, with natural parallax between the foreground dust, the astronaut, and the distant starfield. Ultra-realistic detail, physically accurate low-gravity motion, cinematic lighting, and a breath-taking, movie-like shot." # HuggingFace Hub defaults DEFAULT_REPO_ID = "Lightricks/LTX-2" DEFAULT_CHECKPOINT_FILENAME = "ltx-2-19b-dev-fp8.safetensors" DEFAULT_DISTILLED_LORA_FILENAME = "ltx-2-19b-distilled-lora-384.safetensors" DEFAULT_SPATIAL_UPSAMPLER_FILENAME = "ltx-2-spatial-upscaler-x2-1.0.safetensors" # Text encoder space URL TEXT_ENCODER_SPACE = "linoyts/gemma-text-encoder" def get_hub_or_local_checkpoint(repo_id: Optional[str] = None, filename: Optional[str] = None): """Download from HuggingFace Hub or use local checkpoint.""" if repo_id is None and filename is None: raise ValueError("Please supply at least one of `repo_id` or `filename`") if repo_id is not None: if filename is None: raise ValueError("If repo_id is specified, filename must also be specified.") print(f"Downloading {filename} from {repo_id}...") ckpt_path = hf_hub_download(repo_id=repo_id, filename=filename) print(f"Downloaded to {ckpt_path}") else: ckpt_path = filename return ckpt_path # Initialize pipeline at startup print("=" * 80) print("Loading LTX-2 Distilled pipeline...") print("=" * 80) checkpoint_path = get_hub_or_local_checkpoint(DEFAULT_REPO_ID, DEFAULT_CHECKPOINT_FILENAME) distilled_lora_path = get_hub_or_local_checkpoint(DEFAULT_REPO_ID, DEFAULT_DISTILLED_LORA_FILENAME) spatial_upsampler_path = get_hub_or_local_checkpoint(DEFAULT_REPO_ID, DEFAULT_SPATIAL_UPSAMPLER_FILENAME) print(f"Initializing pipeline with:") print(f" checkpoint_path={checkpoint_path}") print(f" distilled_lora_path={distilled_lora_path}") print(f" spatial_upsampler_path={spatial_upsampler_path}") print(f" text_encoder_space={TEXT_ENCODER_SPACE}") # Load distilled LoRA as a regular LoRA loras = [ LoraPathStrengthAndSDOps( path=distilled_lora_path, strength=DEFAULT_LORA_STRENGTH, sd_ops=LTXV_LORA_COMFY_RENAMING_MAP, ) ] # Initialize pipeline WITHOUT text encoder (gemma_root=None) # Text encoding will be done by external space pipeline = DistilledPipeline( checkpoint_path=checkpoint_path, spatial_upsampler_path=spatial_upsampler_path, gemma_root=None, # No text encoder in this space loras=loras, fp8transformer=True, local_files_only=False, ) # Initialize text encoder client print(f"Connecting to text encoder space: {TEXT_ENCODER_SPACE}") try: text_encoder_client = Client(TEXT_ENCODER_SPACE) print("✓ Text encoder client connected!") except Exception as e: print(f"⚠ Warning: Could not connect to text encoder space: {e}") text_encoder_client = None print("=" * 80) print("Pipeline fully loaded and ready!") print("=" * 80) @spaces.GPU(duration=300) def generate_video( input_image, prompt: str, duration: float, enhance_prompt: bool = True, seed: int = 42, randomize_seed: bool = True, height: int = DEFAULT_HEIGHT, width: int = DEFAULT_WIDTH, progress=gr.Progress(track_tqdm=True) ): """Generate a video based on the given parameters.""" try: # Randomize seed if checkbox is enabled current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed) # Calculate num_frames from duration (using fixed 24 fps) frame_rate = 24.0 num_frames = int(duration * frame_rate) + 1 # +1 to ensure we meet the duration # Create output directory if it doesn't exist output_dir = Path("outputs") output_dir.mkdir(exist_ok=True) output_path = output_dir / f"video_{current_seed}.mp4" # Handle image input images = [] temp_image_path = None # Initialize to None if input_image is not None: # Save uploaded image temporarily temp_image_path = output_dir / f"temp_input_{current_seed}.jpg" if hasattr(input_image, 'save'): input_image.save(temp_image_path) else: # If it's a file path already temp_image_path = Path(input_image) # Format: (image_path, frame_idx, strength) images = [(str(temp_image_path), 0, 1.0)] # Get embeddings from text encoder space print(f"Encoding prompt: {prompt}") if text_encoder_client is None: raise RuntimeError( f"Text encoder client not connected. Please ensure the text encoder space " f"({TEXT_ENCODER_SPACE}) is running and accessible." ) try: # Prepare image for upload if it exists image_input = None if temp_image_path is not None: image_input = handle_file(str(temp_image_path)) result = text_encoder_client.predict( prompt=prompt, enhance_prompt=enhance_prompt, input_image=image_input, seed=current_seed, negative_prompt="", api_name="/encode_prompt" ) embedding_path = result[0] # Path to .pt file print(f"Embeddings received from: {embedding_path}") # Load embeddings embeddings = torch.load(embedding_path) video_context = embeddings['video_context'] audio_context = embeddings['audio_context'] print("✓ Embeddings loaded successfully") except Exception as e: raise RuntimeError( f"Failed to get embeddings from text encoder space: {e}\n" f"Please ensure {TEXT_ENCODER_SPACE} is running properly." ) # Run inference - progress automatically tracks tqdm from pipeline pipeline( prompt=prompt, output_path=str(output_path), seed=current_seed, height=height, width=width, num_frames=num_frames, frame_rate=frame_rate, images=images, tiling_config=TilingConfig.default(), video_context=video_context, audio_context=audio_context, ) return str(output_path), current_seed except Exception as e: import traceback error_msg = f"Error: {str(e)}\n{traceback.format_exc()}" print(error_msg) return None # Create Gradio interface with gr.Blocks(title="LTX-2 Video Distilled 🎥🔈") as demo: gr.Markdown("# LTX-2 Distilled 🎥🔈: The First Open Source Audio-Video Model") gr.Markdown("Fast, state-of-the-art video & audio generation with [Lightricks LTX-2 TI2V model](https://huggingface.co/Lightricks/LTX-2) and [distillation LoRA](https://huggingface.co/Lightricks/LTX-2/blob/main/ltx-2-19b-distilled-lora-384.safetensors) for accelerated inference. Read more: [[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", info="for best results - make it as elaborate as possible", value="Make this image come alive with cinematic motion, smooth animation", lines=3, placeholder="Describe the motion and animation you want..." ) with gr.Row(): duration = gr.Slider( label="Duration (seconds)", minimum=1.0, maximum=10.0, value=3.0, step=0.1 ) enhance_prompt = gr.Checkbox( label="Enhance Prompt", value=True ) generate_btn = gr.Button("Generate Video", variant="primary", size="lg") with gr.Accordion("Advanced Settings", open=False): seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, value=DEFAULT_SEED, 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] ) # Add example gr.Examples( examples=[ [ "kill_bill.jpeg", "A low, subsonic drone pulses as Uma Thurman's character, Beatrix Kiddo, holds her razor-sharp katana blade steady in the cinematic lighting. A faint electrical hum fills the silence. Suddenly, accompanied by a deep metallic groan, the polished steel begins to soften and distort, like heated metal starting to lose its structural integrity. Discordant strings swell as the blade's perfect edge slowly warps and droops, molten steel beginning to flow downward in silvery rivulets while maintaining its metallic sheen—each drip producing a wet, viscous stretching sound. The transformation starts subtly at first—a slight bend in the blade—then accelerates as the metal becomes increasingly fluid, the groaning intensifying. The camera holds steady on her face as her piercing eyes gradually narrow, not with lethal focus, but with confusion and growing alarm as she watches her weapon dissolve before her eyes. She whispers under her breath, voice flat with disbelief: 'Wait, what?' Her heartbeat rises in the mix—thump... thump-thump—as her breathing quickens slightly while she witnesses this impossible transformation. Sharp violin stabs punctuate each breath. The melting intensifies, the katana's perfect form becoming increasingly abstract, dripping like liquid mercury from her grip. Molten droplets fall to the ground with soft, bell-like pings. Unintelligible whispers fade in and out as her expression shifts from calm readiness to bewilderment and concern, her heartbeat now pounding like a war drum, as her legendary instrument of vengeance literally liquefies in her hands, leaving her defenseless and disoriented. All sound cuts to silence—then a single devastating bass drop as the final droplet falls, leaving only her unsteady breathing in the dark.", 5.0, ], [ "wednesday.png", "A cinematic close-up of Wednesday Addams frozen mid-dance on a dark, blue-lit ballroom floor as students move indistinctly behind her, their footsteps and muffled music reduced to a distant, underwater thrum; the audio foregrounds her steady breathing and the faint rustle of fabric as she slowly raises one arm, never breaking eye contact with the camera, then after a deliberately long silence she speaks in a flat, dry, perfectly controlled voice, “I don’t dance… I vibe code,” each word crisp and unemotional, followed by an abrupt cutoff of her voice as the background sound swells slightly, reinforcing the deadpan humor, with precise lip sync, minimal facial movement, stark gothic lighting, and cinematic realism.", 5.0, ], [ "astronaut.jpg", "An astronaut hatches from a fragile egg on the surface of the Moon, the shell cracking and peeling apart in gentle low-gravity motion. Fine lunar dust lifts and drifts outward with each movement, floating in slow arcs before settling back onto the ground. The astronaut pushes free in a deliberate, weightless motion, small fragments of the egg tumbling and spinning through the air. In the background, the deep darkness of space subtly shifts as stars glide with the camera's movement, emphasizing vast depth and scale. The camera performs a smooth, cinematic slow push-in, with natural parallax between the foreground dust, the astronaut, and the distant starfield. Ultra-realistic detail, physically accurate low-gravity motion, cinematic lighting, and a breath-taking, movie-like shot.", 3.0, ] ], fn=generate_video, inputs=[input_image, prompt, duration], outputs = [output_video, seed], label="Example", cache_examples=True, cache_mode="lazy", ) css = ''' .gradio-container .contain{max-width: 1200px !important; margin: 0 auto !important} ''' if __name__ == "__main__": demo.launch(theme=gr.themes.Citrus(), css=css)