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Expanded README.md with installation, configuration, and usage instructions.

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Added detailed instructions for installing dependencies, configuring the project, and using different generation modes.

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  1. README.md +81 -12
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- ---
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- title: LTX Video Fast
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- emoji: 🎥
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- colorFrom: yellow
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- colorTo: pink
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- sdk: gradio
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- sdk_version: 5.29.1
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- app_file: app.py
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- pinned: false
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- short_description: ultra-fast video model, LTX 0.9.7 13B distilled
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- ---
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-
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
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+ ---
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+ title: LTX Video Fast
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+ emoji: 🎥
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+ colorFrom: yellow
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+ colorTo: pink
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+ sdk: gradio
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+ sdk_version: 5.29.1
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+ app_file: app.py
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+ pinned: false
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+ short_description: ultra-fast video model, LTX 0.9.7 13B distilled
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+ ---
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+
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+ # LTX Video Fast
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+
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+ This project provides an ultra-fast video generation model, LTX 0.9.7 13B distilled.
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+
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+ ## Installation
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+
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+ 1. **Clone the repository:**
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+
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+ ```bash
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+ git clone <repository_url>
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+ cd <repository_directory>
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+ ```
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+
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+ 2. **Install the dependencies:**
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+
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+ ```bash
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+ pip install -r requirements.txt
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+ ```
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+
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+ The `requirements.txt` file includes the following dependencies:
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+
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+ ```
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+ accelerate
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+ transformers
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+ sentencepiece
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+ pillow
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+ numpy
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+ torchvision
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+ huggingface_hub
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+ spaces
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+ opencv-python
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+ imageio
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+ imageio-ffmpeg
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+ einops
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+ timm
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+ av
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+ git+https://github.com/huggingface/diffusers.git@main
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+ ```
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+
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+ ## Configuration
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+
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+ The project uses YAML configuration files to define model parameters and pipeline settings. Example configuration files are located in the `configs` directory.
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+
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+ * `configs/ltxv-13b-0.9.7-dev.yaml`
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+ * `configs/ltxv-13b-0.9.7-distilled.yaml`
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+ * `configs/ltxv-2b-0.9.1.yaml`
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+ * `configs/ltxv-2b-0.9.5.yaml`
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+ * `configs/ltxv-2b-0.9.6-dev.yaml`
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+ * `configs/ltxv-2b-0.9.6-distilled.yaml`
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+ * `configs/ltxv-2b-0.9.yaml`
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+
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+ To use a specific configuration, you can specify its path when running the generation scripts.
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+
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+ ## Usage
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+
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+ The project supports different generation modes: text-to-video, image-to-video, and video-to-video.
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+ ### Text-to-Video
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+
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+ To generate a video from a text prompt, use the `inference.py` script with the `--mode text2video` argument. You must also specify the path to the desired config file using `--config` and a text prompt using `--prompt`. For example:
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+
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+ ### Image-to-Video
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+
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+ To generate a video from an image, use the `inference.py` script with the `--mode image2video` argument. You must also specify the path to the desired config file using `--config` and the path to the input image using `--image_path`. For example:
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+
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+ ### Video-to-Video
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+ To generate a video from another video, use the `inference.py` script with the `--mode video2video` argument. You must also specify the path to the desired config file using `--config` and the path to the input video using `--video_path`. For example:
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+
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  Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference