# How to Configure Training Preview and Timelapse in Faceswap: A Complete Guide > Master Faceswap training by configuring preview with -p and timelapse with input/output paths. Learn this essential skill for deepfakes efficiently. - Repository: [deepfakes/faceswap](https://github.com/deepfakes/faceswap) - Tags: how-to-guide - Published: 2026-03-06 --- **Enable the live preview by adding `-p` to your training command, and generate a timelapse by supplying all three required path arguments (`--timelapse-input-A`, `--timelapse-input-B`, and `--timelapse-output`).** The deepfakes/faceswap repository provides built-in visualization tools that let you monitor training progress in real time and export periodic snapshots for retrospective analysis. Configuring these features requires specific command-line arguments defined in the CLI parser and handled by the training subsystem. This guide explains the exact flags, file paths, and internal mechanisms that control the preview window and timelapse generation. ## Enabling the Live Training Preview The live preview opens a Tkinter window that refreshes after each save iteration, showing side-by-side source and swapped faces so you can judge model quality without stopping training. ### Command-Line Arguments Add the **`-p`** or **`--preview`** flag when launching the training script. This argument is registered in **[`lib/cli/args_train.py`](https://github.com/deepfakes/faceswap/blob/main/lib/cli/args_train.py)** (line 265) with the option tuple `("-p", "--preview")`. When present, [`scripts/train.py`](https://github.com/deepfakes/faceswap/blob/main/scripts/train.py) instantiates the `PreviewInterface` class at line 66: ```python self._preview = PreviewInterface(self._args.preview) ``` ### Preview Window Mechanics The `PreviewInterface` class (defined in **[`scripts/train.py`](https://github.com/deepfakes/faceswap/blob/main/scripts/train.py)**) coordinates with the preview subsystem located in **`tools/preview/`**. The actual window rendering is handled by **[`tools/preview/preview.py`](https://github.com/deepfakes/faceswap/blob/main/tools/preview/preview.py)**, while image composition occurs in **[`tools/preview/viewer.py`](https://github.com/deepfakes/faceswap/blob/main/tools/preview/viewer.py)**. During training, the system checks `self._preview.should_toggle_mask` and calls `self._preview.update()` around lines 322-344 of **[`scripts/train.py`](https://github.com/deepfakes/faceswap/blob/main/scripts/train.py)**. This triggers the viewer to rebuild display images via methods like `update_tk_image()` and `reload()` inside **[`tools/preview/viewer.py`](https://github.com/deepfakes/faceswap/blob/main/tools/preview/viewer.py)**, refreshing the canvas with the latest model outputs. > **Note:** Closing the preview window does not terminate training; the process continues in the background until you manually stop the script. ## Setting Up Training Timelapse A timelapse saves a snapshot image after every training iteration to a specified folder, creating a sequence you can compile into a video. ### Required Arguments You must provide **all three** arguments together or the parser raises a `FaceswapError` (validation logic in **[`lib/cli/args_train.py`](https://github.com/deepfakes/faceswap/blob/main/lib/cli/args_train.py)** lines 155-157). The arguments are defined between lines 230-263: - **`-x`** / **`--timelapse-input-A`**: Path to the alignment folder containing source faces (side A). - **`-y`** / **`--timelapse-input-B`**: Path to the alignment folder containing target faces (side B). - **`-z`** / **`--timelapse-output`**: Destination folder where timelapse frames are written. ### How Timelapse Generation Works 1. **Argument Validation:** [`scripts/train.py`](https://github.com/deepfakes/faceswap/blob/main/scripts/train.py) calls `_set_timelapse()` at line 58, which packages the three paths into a dictionary: ```python timelapse_kwargs = { "input_a": self._args.timelapse_input_a, "input_b": self._args.timelapse_input_b, "output": get_folder(self._args.timelapse_output) } ``` 2. **Trainer Integration:** The `Timelapse` object is instantiated inside **[`plugins/train/trainer/_display.py`](https://github.com/deepfakes/faceswap/blob/main/plugins/train/trainer/_display.py)** (line 77). When a save iteration occurs, the trainer passes the kwargs to `self._timelapse.output_timelapse(timelapse_kwargs)` (lines 340-341). 3. **Image Generation:** Inside [`_display.py`](https://github.com/deepfakes/faceswap/blob/main/_display.py), the `output_timelapse()` method calls `self._samples.images = self._feeder.generate_preview(is_timelapse=True)` (lines 577-580). The core face-swapping logic resides in **[`lib/training/generator.py`](https://github.com/deepfakes/faceswap/blob/main/lib/training/generator.py)** at line 859 (`generate_preview(is_timelapse=True)`), which pulls faces from the specified input folders, runs inference, and writes the results to the output directory. Each iteration produces a new image file, creating an ordered sequence (e.g., `frame_0000.jpg`, `frame_0001.jpg`) suitable for encoding with ffmpeg. ## Complete Configuration Example The following command enables both features simultaneously: ```bash python -m scripts.train \ -A /path/to/aligned_A \ -B /path/to/aligned_B \ -m /path/to/model \ -p \ -x /path/to/aligned_A \ -y /path/to/aligned_B \ -z /path/to/timelapse_output ``` - `-A` and `-B` specify your training datasets. - `-p` activates the live preview window. - `-x`, `-y`, and `-z` enable timelapse generation, reading from the alignment folders and writing frames to the designated output path. ## Summary - **Enable preview:** Add `-p` or `--preview` to open a live Tkinter window that updates after every save iteration via `PreviewInterface` in [`scripts/train.py`](https://github.com/deepfakes/faceswap/blob/main/scripts/train.py). - **Enable timelapse:** Supply all three required paths (`--timelapse-input-A`, `--timelapse-input-B`, `--timelapse-output`) defined in [`lib/cli/args_train.py`](https://github.com/deepfakes/faceswap/blob/main/lib/cli/args_train.py); the system validates these in `_set_timelapse()` and triggers generation logic in [`plugins/train/trainer/_display.py`](https://github.com/deepfakes/faceswap/blob/main/plugins/train/trainer/_display.py). - **Image creation:** Both features rely on `generate_preview()` inside [`lib/training/generator.py`](https://github.com/deepfakes/faceswap/blob/main/lib/training/generator.py) to render swapped faces, with timelapse mode setting `is_timelapse=True` to force disk writes instead of window display. - **Independence:** Preview and timelapse can operate separately or together; neither blocks the training loop managed by [`scripts/train.py`](https://github.com/deepfakes/faceswap/blob/main/scripts/train.py). ## Frequently Asked Questions ### Can I enable timelapse without the live preview window? Yes. According to the source code in [`scripts/train.py`](https://github.com/deepfakes/faceswap/blob/main/scripts/train.py), the preview and timelapse systems are independent. You can omit `-p` and still provide `-x`, `-y`, and `-z` to generate timelapse frames in the background without opening the Tkinter interface. The `_set_timelapse()` method (line 58) processes the timelapse arguments regardless of the preview flag status. ### Why does the training script require both input folders for the timelapse? The `generate_preview(is_timelapse=True)` method in [`lib/training/generator.py`](https://github.com/deepfakes/faceswap/blob/main/lib/training/generator.py) (line 859) needs access to actual face data from both sides to create a meaningful swapped output. The validation logic in [`lib/cli/args_train.py`](https://github.com/deepfakes/faceswap/blob/main/lib/cli/args_train.py) (lines 155-157) enforces that all three timelapse arguments must be present simultaneously; if any are missing, the parser raises a `FaceswapError` to prevent incomplete configuration. ### Where are the timelapse images actually saved on disk? The images are written to the path specified by `--timelapse-output` (or `-z`). In [`scripts/train.py`](https://github.com/deepfakes/faceswap/blob/main/scripts/train.py) (line 58), this path is processed through `get_folder()`, which ensures the directory exists before the trainer begins. Inside [`plugins/train/trainer/_display.py`](https://github.com/deepfakes/faceswap/blob/main/plugins/train/trainer/_display.py) (lines 577-580), each save iteration writes a new frame to this location using the naming convention defined in the generator, producing a sequentially ordered set of images. ### Does enabling the preview or timelapse slow down training? The preview window updates only after save iterations (controlled by the `save_interval` configuration), so it does not block every training step. However, timelapse generation forces an additional forward pass through the model to generate preview images, which adds minor overhead during save events. Both features run in the main thread alongside the trainer, as implemented in [`scripts/train.py`](https://github.com/deepfakes/faceswap/blob/main/scripts/train.py) around lines 322-344, but they do not significantly impact overall training speed on modern hardware.