app.py

import gradio as gr
import torch
import numpy as np
import tempfile
import os
import spaces
from diffusers import LTXLatentUpsamplePipeline
from pipeline_ltx_condition_control import LTXConditionPipeline
from diffusers.utils import export_to_video, load_video
from torchvision import transforms
import random
from controlnet_aux import CannyDetector
# from image_gen_aux import DepthPreprocessor
# import mediapipe as mp
from PIL import Image
import cv2


dtype = torch.bfloat16 
device = "cuda" if torch.cuda.is_available() else "cpu"

#pipeline = LTXConditionPipeline.from_pretrained("Lightricks/LTX-Video-0.9.7-dev", torch_dtype=dtype)
pipeline = LTXConditionPipeline.from_pretrained("Lightricks/LTX-Video-0.9.7-distilled", torch_dtype=torch.bfloat16)

pipe_upsample = LTXLatentUpsamplePipeline.from_pretrained("Lightricks/ltxv-spatial-upscaler-0.9.7", vae=pipeline.vae, torch_dtype=dtype)
pipeline.to(device)
pipe_upsample.to(device)
pipeline.vae.enable_tiling()

CONTROL_LORAS = {
    "canny": {
        "repo": "Lightricks/LTX-Video-ICLoRA-canny-13b-0.9.7",
        "weight_name": "ltxv-097-ic-lora-canny-control-diffusers.safetensors",
        "adapter_name": "canny_lora"
    },
    "depth": {
        "repo": "Lightricks/LTX-Video-ICLoRA-depth-13b-0.9.7", 
        "weight_name": "ltxv-097-ic-lora-depth-control-diffusers.safetensors",
        "adapter_name": "depth_lora"
    },
    "pose": {
        "repo": "Lightricks/LTX-Video-ICLoRA-pose-13b-0.9.7",
        "weight_name": "ltxv-097-ic-lora-pose-control-diffusers.safetensors", 
        "adapter_name": "pose_lora"
    }
}

# load canny lora
pipeline.load_lora_weights(
            CONTROL_LORAS["canny"]["repo"],
            weight_name=CONTROL_LORAS["canny"]["weight_name"],
            adapter_name=CONTROL_LORAS["canny"]["adapter_name"]
        )
pipeline.set_adapters([CONTROL_LORAS["canny"]["adapter_name"]], adapter_weights=[1.0])

# Initialize MediaPipe pose estimation
# mp_drawing = mp.solutions.drawing_utils
# mp_drawing_styles = mp.solutions.drawing_styles
# mp_pose = mp.solutions.pose

canny_processor = CannyDetector()

@spaces.GPU()
def read_video(video) -> torch.Tensor:
    """
    Reads a video file and converts it into a torch.Tensor with the shape [F, C, H, W].
    """
    
    to_tensor_transform = transforms.ToTensor()
    video_tensor = torch.stack([to_tensor_transform(img) for img in video])
    return video_tensor

def round_to_nearest_resolution_acceptable_by_vae(height, width, vae_temporal_compression_ratio):
    height = height - (height % vae_temporal_compression_ratio)
    width = width - (width % vae_temporal_compression_ratio)
    return height, width

@spaces.GPU()
def load_control_lora(control_type, current_lora_state):
    """Load the specified control LoRA, unloading any previous one"""
    
    if control_type not in CONTROL_LORAS:
        raise ValueError(f"Unknown control type: {control_type}")
    
    # If same LoRA is already loaded, do nothing
    if current_lora_state == control_type:
        print(f"{control_type} LoRA already loaded")
        return current_lora_state
    
    # Unload current LoRA if any
    if current_lora_state is not None:
        try:
            pipeline.unload_lora_weights()
            print(f"Unloaded previous LoRA: {current_lora_state}")
        except Exception as e:
            print(f"Warning: Could not unload previous LoRA: {e}")
    
    # Load new LoRA
    lora_config = CONTROL_LORAS[control_type]
    try:
        pipeline.load_lora_weights(
            lora_config["repo"],
            weight_name=lora_config["weight_name"],
            adapter_name=lora_config["adapter_name"]
        )
        pipeline.set_adapters([lora_config["adapter_name"]], adapter_weights=[1.0])
        new_lora_state = control_type
        print(f"Loaded {control_type} LoRA successfully")
        return new_lora_state
    except Exception as e:
        print(f"Error loading {control_type} LoRA: {e}")
        raise

def process_video_for_canny(video, width, height):
    """
    Process video for canny control.
    """
    print("Processing video for canny control...")
    canny_video = []
    detect_resolution = max(video[0].size[0],video[0].size[1])
    image_resolution = max(width, height)
    
    for frame in video: 
        canny_video.append(canny_processor(frame, low_threshold=50, high_threshold=200, detect_resolution=detect_resolution, image_resolution=image_resolution))
     
    return canny_video

@spaces.GPU()
def process_video_for_pose(video):
    """
    Process video for pose control using MediaPipe pose estimation.
    Returns video frames with pose landmarks drawn on black background.
    """
    print("Processing video for pose control...")
    pose_video = []
    
    with mp_pose.Pose(
        static_image_mode=True,
        model_complexity=1,
        enable_segmentation=False,
        min_detection_confidence=0.5,
        min_tracking_confidence=0.5
    ) as pose:
        
        for frame in video:
            # Convert PIL image to numpy array
            frame_np = np.array(frame)
            
            # Convert RGB to BGR for MediaPipe
            frame_bgr = cv2.cvtColor(frame_np, cv2.COLOR_RGB2BGR)
            
            # Process the frame
            results = pose.process(frame_bgr)
            
            # Create black background with same dimensions
            pose_frame = np.zeros_like(frame_np)
            
            # Draw pose landmarks if detected
            if results.pose_landmarks:
                mp_drawing.draw_landmarks(
                    pose_frame,
                    results.pose_landmarks,
                    mp_pose.POSE_CONNECTIONS,
                    landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style()
                )
            
            # Convert back to PIL Image
            pose_pil = Image.fromarray(pose_frame)
            pose_video.append(pose_pil)
    
    return pose_video

def process_input_video(reference_video, width, height):
    """
    Process the input video for canny edges and return both processed video and preview.
    """
    if reference_video is None:
        return None
    
    try:
        # Load video into a list of PIL images
        video = load_video(reference_video)
        
        # Process video for canny edges
        processed_video = process_video_for_canny(video, width, height)
        
        # Create a preview video file for display
        fps = 24
        with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp_file:
            preview_path = tmp_file.name
            export_to_video(processed_video, preview_path, fps=fps)
        
        return preview_path
        
    except Exception as e:
        print(f"Error processing input video: {e}")
        return None

def process_video_for_control(reference_video, control_type, width, height):
    """Process video based on the selected control type - now only used for non-canny types"""
    video = load_video(reference_video)
    
    if control_type == "canny":
        # This should not be called for canny since it's pre-processed
        processed_video = process_video_for_canny(video, width, height)
    elif control_type == "depth":
        processed_video = process_video_for_depth(video)
    elif control_type == "pose":
        processed_video = process_video_for_pose(video)
    else:
        processed_video = video
    
    return processed_video

@spaces.GPU()
def generate_video(
    reference_video,
    control_video,  # New parameter for pre-processed video
    prompt,
    duration=3.0,
    negative_prompt="worst quality, inconsistent motion, blurry, jittery, distorted",
    height=768,
    width=1152,
    num_inference_steps=7,
    guidance_scale=1.0,
    seed=0,
    randomize_seed=False,
    control_type="canny",
    progress=gr.Progress()
):
    try:
        # Initialize models if needed
        # Models are already loaded at startup
        
        if reference_video is None:
            return None, "Please upload a reference video."
        
        if not prompt.strip():
            return None, "Please enter a prompt."
        
        # Handle seed
        if randomize_seed:
            seed = random.randint(0, 2**32 - 1)

        # Calculate number of frames from duration (24 fps)
        fps = 24
        num_frames = int(duration * fps) + 1  # +1 for proper frame count
        # Ensure num_frames is valid for the model (multiple of temporal compression + 1)
        temporal_compression = pipeline.vae_temporal_compression_ratio
        num_frames = ((num_frames - 1) // temporal_compression) * temporal_compression + 1
        
        progress(0.1, desc="Preparing processed video...")
        
        # Use pre-processed video frames if available (for canny), otherwise process on-demand
        print("######## control_video ", control_video)
        if control_video is not None:
            # Use the pre-processed canny frames
            processed_video = load_video(control_video)
        else:
            # Fallback to processing on-demand for other control types
            processed_video = process_video_for_control(reference_video, control_type, width, height)
        
        # Convert to tensor
        processed_video = read_video(processed_video)
        
        progress(0.2, desc="Preparing generation parameters...")
        
        # Calculate downscaled dimensions
        downscale_factor = 2 / 3
        downscaled_height = int(height * downscale_factor)
        downscaled_width = int(width * downscale_factor)
        downscaled_height, downscaled_width = round_to_nearest_resolution_acceptable_by_vae(
            downscaled_height, downscaled_width, pipeline.vae_temporal_compression_ratio
        )
        
        progress(0.3, desc="Generating video at lower resolution...")
        
        # 1. Generate video at smaller resolution
        latents = pipeline(
            reference_video=processed_video,  # Use processed video
            prompt=prompt,
            negative_prompt=negative_prompt,
            width=downscaled_width,
            height=downscaled_height,
            num_frames=num_frames,
            num_inference_steps=num_inference_steps,
            decode_timestep=0.05,
            decode_noise_scale=0.025,
            guidance_scale=guidance_scale,
            generator=torch.Generator().manual_seed(seed),
            output_type="latent",
        ).frames

        progress(0.6, desc="Upscaling video...")
        
        # 2. Upscale generated video
        upscaled_height, upscaled_width = downscaled_height * 2, downscaled_width * 2
        upscaled_latents = pipe_upsample(
            latents=latents,
            output_type="latent"
        ).frames

        progress(0.8, desc="Final denoising and processing...")
        
        # 3. Denoise the upscaled video
        video_output = pipeline(
            prompt=prompt,
            negative_prompt=negative_prompt,
            width=upscaled_width,
            height=upscaled_height,
            num_frames=num_frames,
            denoise_strength=0.4,
            num_inference_steps=10,
            latents=upscaled_latents,
            decode_timestep = 0.05,
            guidance_scale=guidance_scale,
            decode_noise_scale = 0.025,
            image_cond_noise_scale=0.025,
            generator=torch.Generator(device="cuda").manual_seed(seed),
            output_type="pil",
        ).frames[0]

        progress(0.9, desc="Finalizing output...")
        
        # 4. Downscale to expected resolution
        video_output = [frame.resize((width, height)) for frame in video_output]

        # Export to temporary file
        with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp_file:
            output_path = tmp_file.name
            export_to_video(video_output, output_path, fps=fps)
        
        progress(1.0, desc="Complete!")
        
        return output_path, seed
        
    except Exception as e:
        print(e)
        return None, seed

# Create Gradio interface
with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple")) as demo:
    gr.Markdown(
        """
        # Canny Control LTX Video Distilled
        
        LTX Video 0.9.7 Distilled with [control canny ICLoRA](https://huggingface.co/Lightricks/LTX-Video-ICLoRA-canny-13b-0.9.7)
        control AI video generation - by concatenation of control signals and LoRAs trained on just a few samples  ✨
        """
    )
    
    # State variables
    #current_lora_state = gr.State(value=None)
    
    with gr.Row():
        with gr.Column(scale=1):
    
            reference_video = gr.Video(
                label="Reference Video",
                height=300
            )
            
            prompt = gr.Textbox(
                label="Prompt",
                placeholder="Describe the video you want to generate...",
                lines=3,
                value="The Joker in his iconic purple suit and green hair, dancing alone in a dimly lit, run-down room. His movements are erratic and unpredictable, shifting between graceful and chaotic as he loses himself in the moment. The camera captures his theatrical gestures, his dance reflecting his unhinged personality. Moody lighting with shadows dancing across the walls, creating an atmosphere of beautiful madness."
            )
            
            # Control Type Selection
            control_type = gr.Radio(
                label="Control Type",
                choices=["canny", "depth", "pose"],
                value="canny",
                visible=False,
                info="Choose the type of control guidance for video generation"
            )
            
            duration = gr.Slider(
                label="Duration (seconds)",
                minimum=1.0,
                maximum=10.0,
                step=0.5,
                value=2.5
            )
            
            negative_prompt = gr.Textbox(
                label="Negative Prompt",
                placeholder="What you don't want in the video...",
                lines=2,
                value="worst quality, inconsistent motion, blurry, jittery, distorted"
            )
            
            # Advanced Settings
            with gr.Accordion("Advanced Settings", open=False):
                with gr.Row():
                    height = gr.Slider(
                        label="Height",
                        minimum=256,
                        maximum=1024,
                        step=32,
                        value=768
                    )
                    width = gr.Slider(
                        label="Width", 
                        minimum=256,
                        maximum=1536,
                        step=32,
                        value=1152
                    )
                
                num_inference_steps = gr.Slider(
                    label="Inference Steps",
                    minimum=10,
                    maximum=50,
                    step=1,
                    value=7
                )
                
                with gr.Row():
                    guidance_scale = gr.Slider(
                        label="Guidance Scale",
                        minimum=1.0,
                        maximum=15.0,
                        step=0.1,
                        value=1.0
                    )
                    
                with gr.Row():
                    randomize_seed = gr.Checkbox(
                        label="Randomize Seed",
                        value=False
                    )
                    seed = gr.Number(
                        label="Seed",
                        value=0,
                        precision=0
                    )
            
            generate_btn = gr.Button(
                "Generate Video",
                variant="primary",
                size="lg"
            )
        
        with gr.Column(scale=1):          
            output_video = gr.Video(
                label="Generated Video",
                height=400
            )
            control_video = gr.Video(
                label="Processed Control Video (Canny Edges)",
                height=400,
                visible=True 
            )
        
    gr.Examples(
        examples=[ 
            ["video_assets/vid_1.mp4", "video_assets/vid_1_canny.mp4", "A sleek cybernetic wolf sprinting through a neon-lit futuristic cityscape, its metallic form gleaming with electric blue circuits. The wolf's powerful stride carries it down rain-slicked streets between towering skyscrapers, while holographic advertisements cast colorful reflections on its chrome surface. Sparks of digital energy trail behind the creature as it moves with fluid mechanical precision through the urban maze, creating streaks of light in the misty night air.",  3, "worst quality, inconsistent motion, blurry, jittery, distorted", 768, 1152, 7, 1, 0, True, "canny"],
            ["video_assets/vid_2.mp4", "video_assets/vid_2_canny.mp4", "A translucent ghost floating in a moonlit cemetery, raising a glowing spectral lantern that casts eerie light through the darkness. The ethereal figure's wispy form shimmers as it lifts the phantom light above its head, illuminating weathered tombstones and gnarled trees. Pale mist swirls around the ghost as the lantern pulses with otherworldly energy, creating haunting shadows that dance across the graveyard in the dead of night.", 2.5, "worst quality, inconsistent motion, blurry, jittery, distorted", 768, 1152, 7, 1, 0, True, "canny"],
            ["video_assets/vid_3.mp4", "video_assets/vid_3_canny.mp4","A sleek android assassin poised in a combat stance atop a futuristic skyscraper, arms positioned for perfect balance. The chrome-plated figure gleams under neon city lights as holographic data streams flow around its metallic form. Rain droplets bead on its polished surface while the sprawling cyberpunk metropolis stretches endlessly below. Electric circuits pulse beneath the android's transparent panels as it maintains its precise, calculated pose against the backdrop of flying vehicles and towering digital billboards.", 3, "worst quality, inconsistent motion, blurry, jittery, distorted", 768, 1152, 7, 1, 0, True, "canny"],
            ["video_assets/vid_4.mp4", "video_assets/vid_4_canny.mp4", "Luminescent video game characters with glowing outlines and neon-bright details wandering through a digital landscape. Their bodies emit soft, colorful light that pulses gently as they move, creating trails of radiance behind them. The characters have a futuristic, stylized appearance with smooth surfaces that reflect their inner glow. They navigate naturally through their environment, their movements fluid and purposeful, while their bioluminescent features cast dynamic shadows and illuminate the surrounding area. The scene has a cyberpunk aesthetic with the characters' radiant presence serving as the primary light source in an otherwise darkened digital world.", 2.5, "worst quality, inconsistent motion, blurry, jittery, distorted", 768, 1152, 7, 1, 0, True, "canny"],
        ],
        inputs=[reference_video,
            control_video,
            prompt,
            duration,
            negative_prompt,
            height,
            width,
            num_inference_steps,
            guidance_scale,
            seed,
            randomize_seed,
            control_type], 
        outputs=[output_video, seed], 
        fn=generate_video, cache_examples="lazy"
    )       
    
    # Event handlers
    
    # Auto-process video when uploaded
    reference_video.upload(
        fn=process_input_video,
        inputs=[reference_video, width, height],
        outputs=[control_video],
        show_progress=True
    )
    
    generate_btn.click(
        fn=generate_video,
        inputs=[
            reference_video,
            control_video,  # Use pre-processed video
            prompt,
            duration,
            negative_prompt,
            height,
            width,
            num_inference_steps,
            guidance_scale,
            seed,
            randomize_seed,
            control_type,
        ],
        outputs=[output_video, seed],
        show_progress=True
    )

if __name__ == "__main__":
    demo.launch()