FULLY Automated MidJourney & LumaLabs Faceless Videos

The paragraph introduces a video tutorial focused on automating the creation of faceless videos for platforms like TikTok and YouTube using Mid Journey and Luma Labs. The process involves building a database in Airtable to define scenes for the video, which are then used to generate images and animations. The video will guide viewers through setting up the system from scratch, including creating an Airtable database and automating the scene generation process.

This section delves into the detailed setup of an Airtable database designed to manage scenes for video creation. It includes creating fields for movie dimensions, aspect ratio, and linking to scenes. The process of defining scenes with status, image prompts, and using Mid Journey CF directives for consistency is explained. The paragraph also covers the automation backend setup that triggers image generation and the subsequent steps of image selection and video creation.

The paragraph explains how to link individual scenes to a movie project in Airtable and manage 'orphan scenes' that are not yet assigned to a movie. It details the process of creating a new table for scenes, setting up fields for scene management, and using filters to separate scenes that are part of a movie from those that are not. The setup includes options for image selection and upscale tasks, which are crucial for the video generation process.

This part of the script describes the process of building automation for image generation using Make.com. It includes setting up a webhook trigger in Airtable that calls a custom webhook in Make.com, where the automation script is written to interact with the Mid Journey API. The script handles the generation of images based on prompts and conditions set in Airtable, showcasing the integration between the database and the automation platform.

The paragraph discusses the iterative process of image processing and upscaling within the automation workflow. It explains how to handle the generation of upscale images after the initial images are created, including the use of Airtable's record ID and task ID to track the status of image generation. The process involves checking for successful image generation, handling errors, and preparing images for the next stage of video animation.

This section describes the final steps in image selection and the triggering of video animation using the upscaled images. It covers the process of choosing the best image from the generated set, updating the Airtable record, and initiating the video creation process. The paragraph also touches on the importance of prompt crafting for video animation and the integration of the video generation process with the overall automation workflow.

The paragraph explains how to link the generated videos back to their respective scenes and movies in the Airtable database. It details the process of updating the Airtable records with video URLs and task IDs, and how these updates are used to track the progress of video generation. The integration of video data with the existing scene and movie records is emphasized, showcasing the comprehensive management of the video creation process.

This section describes the use of a repeater in the automation workflow to periodically check the status of video generation. It explains how the system loops, making API calls to check if the video is ready, and updates the Airtable records accordingly. The paragraph also discusses the handling of API responses and the conditions under which the repeater continues looping or stops once the video generation is complete.

The paragraph outlines the process of combining individual video scenes into a final movie. It describes the setup of a trigger in Airtable that initiates the movie generation process, and the use of an API call to a service like Json to video to stitch the scenes together. The process includes creating a JSON structure that defines the order and properties of the scenes, and making a final API call to generate the complete movie.

This section wraps up the movie generation process by detailing the final steps of receiving the completed movie from the API and updating the Airtable records. It explains how to handle the movie file, update the Airtable movie record with the movie URL, and ensure that all scenes are correctly linked to the final movie. The paragraph concludes with a summary of the entire process and the ability to create new movies by repeating the steps outlined in the tutorial.