app.py

import torch

import numpy as np
import random
import os
import yaml
import argparse
from pathlib import Path
import imageio
import tempfile
if latent_upsampler_instance: 
    latent_upsampler_instance.to(target_inference_device)


# --- Helper function for dimension calculation ---
MIN_DIM_SLIDER = 256
TARGET_FIXED_SIDE = 768

def calculate_new_dimensions(orig_w, orig_h):
    """
    both are multiples of 32, and within [MIN_DIM_SLIDER, MAX_IMAGE_SIZE].
    """
    if orig_w == 0 or orig_h == 0:

        return int(TARGET_FIXED_SIDE), int(TARGET_FIXED_SIDE)

    if orig_w >= orig_h:
        new_h = TARGET_FIXED_SIDE
        aspect_ratio = orig_w / orig_h
        new_w_ideal = new_h * aspect_ratio


        new_w = round(new_w_ideal / 32) * 32


        new_w = max(MIN_DIM_SLIDER, min(new_w, MAX_IMAGE_SIZE))

        new_h = max(MIN_DIM_SLIDER, min(new_h, MAX_IMAGE_SIZE)) 
    else:
        new_w = TARGET_FIXED_SIDE
        aspect_ratio = orig_h / orig_w
        new_h_ideal = new_w * aspect_ratio


        new_h = round(new_h_ideal / 32) * 32


        new_h = max(MIN_DIM_SLIDER, min(new_h, MAX_IMAGE_SIZE))

        new_w = max(MIN_DIM_SLIDER, min(new_w, MAX_IMAGE_SIZE))

    return int(new_h), int(new_w)

def generate(prompt, negative_prompt="worst quality, inconsistent motion, blurry, jittery, distorted", 
             input_image_filepath=None, input_video_filepath=None,
             height_ui=512, width_ui=704, mode="text-to-video",
             duration_ui=2.0, ui_frames_to_use=9,
             seed_ui=42, randomize_seed=True, ui_guidance_scale=None, improve_texture_flag=True):














    if randomize_seed:
        seed_ui = random.randint(0, 2**32 - 1)
    seed_everething(int(seed_ui))
    
    if ui_guidance_scale is None:
        ui_guidance_scale = PIPELINE_CONFIG_YAML.get("first_pass", {}).get("guidance_scale", 1.0)
    
    target_frames_ideal = duration_ui * FPS
    target_frames_rounded = round(target_frames_ideal)
    if target_frames_rounded < 1: 

    height_padded = ((actual_height - 1) // 32 + 1) * 32
    width_padded = ((actual_width - 1) // 32 + 1) * 32
    num_frames_padded = ((actual_num_frames - 2) // 8 + 1) * 8 + 1


    
    padding_values = calculate_padding(actual_height, actual_width, height_padded, width_padded)

            call_kwargs["conditioning_items"] = [ConditioningItem(media_tensor.to(target_inference_device), 0, 1.0)]
        except Exception as e:
            print(f"Error loading image {input_image_filepath}: {e}")
            raise RuntimeError(f"Could not load image: {e}")
    elif mode == "video-to-video" and input_video_filepath:
        try:
            call_kwargs["media_items"] = load_media_file(
            ).to(target_inference_device)
        except Exception as e:
            print(f"Error loading video {input_video_filepath}: {e}")
            raise RuntimeError(f"Could not load video: {e}")

    print(f"Moving models to {target_inference_device} for inference (if not already there)...")
    
    result_images_tensor = None
    if improve_texture_flag:
        if not active_latent_upsampler:
            raise RuntimeError("Spatial upscaler model not loaded or improve_texture not selected, cannot use multi-scale.")
        
        multi_scale_pipeline_obj = LTXMultiScalePipeline(pipeline_instance, active_latent_upsampler)
        
        first_pass_args = PIPELINE_CONFIG_YAML.get("first_pass", {}).copy()
        first_pass_args["guidance_scale"] = float(ui_guidance_scale)

        first_pass_args.pop("num_inference_steps", None)


        second_pass_args = PIPELINE_CONFIG_YAML.get("second_pass", {}).copy()
        second_pass_args["guidance_scale"] = float(ui_guidance_scale)

        second_pass_args.pop("num_inference_steps", None)
        
        multi_scale_call_kwargs = call_kwargs.copy()
        first_pass_config_from_yaml = PIPELINE_CONFIG_YAML.get("first_pass", {})

        single_pass_call_kwargs["timesteps"] = first_pass_config_from_yaml.get("timesteps")
        single_pass_call_kwargs["guidance_scale"] = float(ui_guidance_scale)
        single_pass_call_kwargs["stg_scale"] = first_pass_config_from_yaml.get("stg_scale")
        single_pass_call_kwargs["rescaling_scale"] = first_pass_config_from_yaml.get("rescaling_scale")
        single_pass_call_kwargs["skip_block_list"] = first_pass_config_from_yaml.get("skip_block_list")
        

        single_pass_call_kwargs.pop("num_inference_steps", None) 
        single_pass_call_kwargs.pop("first_pass", None) 
        single_pass_call_kwargs.pop("second_pass", None)
        result_images_tensor = pipeline_instance(**single_pass_call_kwargs).images

    if result_images_tensor is None:
        raise RuntimeError("Generation failed.")

    pad_left, pad_right, pad_top, pad_bottom = padding_values
    slice_h_end = -pad_bottom if pad_bottom > 0 else None
    ]

    video_np = result_images_tensor[0].permute(1, 2, 3, 0).cpu().float().numpy()

    video_np = np.clip(video_np, 0, 1) 
    video_np = (video_np * 255).astype(np.uint8)

    timestamp = random.randint(10000, 99999)
    output_video_path = f"output_{timestamp}.mp4"

    
    try:
        with imageio.get_writer(output_video_path, fps=call_kwargs["frame_rate"], macro_block_size=1) as video_writer:
            for frame_idx in range(video_np.shape[0]):

                video_writer.append_data(video_np[frame_idx])
                if frame_idx % 10 == 0:
                    print(f"Saving frame {frame_idx + 1}/{video_np.shape[0]}")
    except Exception as e:
        print(f"Error saving video with macro_block_size=1: {e}")
        try:
            with imageio.get_writer(output_video_path, fps=call_kwargs["frame_rate"], format='FFMPEG', codec='libx264', quality=8) as video_writer:
                for frame_idx in range(video_np.shape[0]):

                    video_writer.append_data(video_np[frame_idx])
                    if frame_idx % 10 == 0:
                        print(f"Saving frame {frame_idx + 1}/{video_np.shape[0]} (fallback)")
        except Exception as e2:
            print(f"Fallback video saving error: {e2}")
            raise RuntimeError(f"Failed to save video: {e2}")
            
    return output_video_path, seed_ui

def main():
    parser = argparse.ArgumentParser(description="LTX Video Generation from Command Line")
    parser.add_argument("--prompt", required=True, help="Text prompt for video generation")
    parser.add_argument("--negative-prompt", default="worst quality, inconsistent motion, blurry, jittery, distorted", 
                        help="Negative prompt")
    parser.add_argument("--mode", choices=["text-to-video", "image-to-video", "video-to-video"], 
                        default="text-to-video", help="Generation mode")
    parser.add_argument("--input-image", help="Input image path for image-to-video mode")
    parser.add_argument("--input-video", help="Input video path for video-to-video mode")
    parser.add_argument("--duration", type=float, default=2.0, help="Video duration in seconds (0.3-8.5)")
    parser.add_argument("--height", type=int, default=512, help="Video height (must be divisible by 32)")
    parser.add_argument("--width", type=int, default=704, help="Video width (must be divisible by 32)")
    parser.add_argument("--seed", type=int, default=42, help="Random seed")
    parser.add_argument("--randomize-seed", action="store_true", help="Use random seed")
    parser.add_argument("--guidance-scale", type=float, help="Guidance scale for generation")
    parser.add_argument("--no-improve-texture", action="store_true", help="Disable texture improvement (faster)")
    parser.add_argument("--frames-to-use", type=int, default=9, help="Frames to use from input video (for video-to-video)")



    
    args = parser.parse_args()
































































































    
    # Validate parameters
    if args.mode == "image-to-video" and not args.input_image:
        print("Error: --input-image is required for image-to-video mode")
        return















    
    if args.mode == "video-to-video" and not args.input_video:
        print("Error: --input-video is required for video-to-video mode")
        return

    
    # Ensure dimensions are divisible by 32
    args.height = ((args.height - 1) // 32 + 1) * 32
    args.width = ((args.width - 1) // 32 + 1) * 32
    
    print(f"Starting video generation...")
    print(f"Prompt: {args.prompt}")
    print(f"Mode: {args.mode}")
    print(f"Duration: {args.duration}s")
    print(f"Resolution: {args.width}x{args.height}")
    
    try:
        output_path, used_seed = generate(
            prompt=args.prompt,
            negative_prompt=args.negative_prompt,
            input_image_filepath=args.input_image,
            input_video_filepath=args.input_video,
            height_ui=args.height,
            width_ui=args.width,
            mode=args.mode,
            duration_ui=args.duration,
            ui_frames_to_use=args.frames_to_use,
            seed_ui=args.seed,
            randomize_seed=args.randomize_seed,
            ui_guidance_scale=args.guidance_scale,
            improve_texture_flag=not args.no_improve_texture
        )
        
        print(f"\nVideo generation completed!")
        print(f"Output saved to: {output_path}")
        print(f"Used seed: {used_seed}")
        
    except Exception as e:
        print(f"Error during generation: {e}")
        raise

if __name__ == "__main__":
    if os.path.exists(models_dir) and os.path.isdir(models_dir):
        print(f"Model directory: {Path(models_dir).resolve()}")
    
    main()