DiffHDR: Re-Exposing LDR Videos with Video Diffusion Models

1. 1
   Prepare LDR Video Input

   Identify and prepare the 8-bit LDR video file you wish to re-expose. Ensure it's in a common format (e.g., MP4, AVI) and note its path. Recognize that this video likely suffers from clipped highlights and crushed shadows.
2. 2
   Set Up DiffHDR Environment and Model

   Establish your Python environment with necessary libraries (e.g., PyTorch, TensorFlow, OpenCV) and ensure GPU acceleration is configured. Obtain or access a pre-trained video diffusion model capable of LDR-to-HDR re-exposure. This model should be optimized for temporal consistency.
3. 3
   Pre-process LDR Video Frames

   Extract individual frames or short clips from your input LDR video. Normalize the pixel values of these frames (e.g., to a range of `[-1, 1]` or `[0, 1]`) to match the input requirements of your trained diffusion model.
4. 4
   Apply DiffHDR Model for Re-exposure

   Feed the pre-processed LDR frames/clips sequentially into the loaded video diffusion model. The model will generate corresponding HDR frames by predicting and reconstructing the lost radiance information. Monitor for temporal consistency to avoid flickering.
5. 5
   Post-process and Encode HDR Video

   De-normalize the model's output HDR pixel values to a standard HDR range (e.g., floating-point scene luminance values). Re-assemble the re-exposed HDR frames into a new video file, encoding it in an appropriate HDR format (e.g., H.265/HEVC with HDR metadata like PQ or HLG).

import os
import subprocess

def reexpose_ldr_to_hdr(input_ldr_path, output_hdr_path, model_name="diffhdr_v1"):
    """
    Simulates re-exposing an LDR video to HDR using a hypothetical DiffHDR model.
    Requires a pre-trained model and appropriate environment setup.
    """
    if not os.path.exists(input_ldr_path):
        print(f"Error: Input LDR video not found at {input_ldr_path}")
        return

    print(f"Starting HDR re-exposure for {input_ldr_path} using {model_name}...")
    
    # Placeholder for actual model invocation logic
    # In a real scenario, this would involve loading a model, processing frames,
    # and re-encoding. For demonstration, we'll simulate success.
    try:
        # Example: Using a hypothetical CLI tool or Python library function
        # subprocess.run([
        #     "diffhdr-cli",
        #     "--input", input_ldr_path,
        #     "--output", output_hdr_path,
        #     "--model", model_name,
        #     "--gpu", "0" # Assuming GPU usage
        # ], check=True)
        
        # Simulate processing time
        import time
        time.sleep(5) 
        
        # Create a dummy output file for demonstration
        with open(output_hdr_path, 'w') as f:
            f.write(f"# Simulated HDR video generated from {input_ldr_path}\n")
            f.write(f"# Processed with {model_name}\n")
        
        print(f"Successfully re-exposed LDR video to HDR at {output_hdr_path}")
    except Exception as e:
        print(f"An error occurred during HDR re-exposure: {e}")

# Example Usage (replace 'input.mp4' with your LDR video path)
# Ensure 'output_hdr.mp4' is a desired output path for the HDR video
# reexpose_ldr_to_hdr("path/to/your/input.mp4", "path/to/your/output_hdr.mp4")