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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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LTX-Video/.gitattributes

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+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text

LTX-Video/.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
+
+# From inference.py
+outputs/
+video_output_*.mp4

LTX-Video/.pre-commit-config.yaml

@@ -0,0 +1,16 @@
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    # Ruff version.
+    rev: v0.2.2
+    hooks:
+      # Run the linter.
+      - id: ruff
+        args: [--fix]  # Automatically fix issues if possible.
+        types: [python]  # Ensure it only runs on .py files.
+
+  - repo: https://github.com/psf/black
+    rev: 24.2.0  # Specify the version of Black you want
+    hooks:
+      - id: black
+        name: Black code formatter
+        language_version: python3  # Use the Python version you're targeting (e.g., 3.10)

LTX-Video/LICENSE

@@ -0,0 +1,201 @@
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+
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LTX-Video/MODEL_DIR/.gitattributes

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LTX-Video/MODEL_DIR/README.md

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+---
+license: apache-2.0
+---

LTX-Video/MODEL_DIR/ltx-video-2b-v0.9.5.safetensors

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LTX-Video/MODEL_DIR/model_index.json

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+  "_diffusers_version": "0.32.0.dev0",
+  "scheduler": [
+    "diffusers",
+    "FlowMatchEulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "T5EncoderModel"
+  ],
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+    "transformers",
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+  ],
+  "transformer": [
+    "diffusers",
+    "LTXVideoTransformer3DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKLLTXVideo"
+  ]
+}

LTX-Video/MODEL_DIR/scheduler/scheduler_config.json

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LTX-Video/MODEL_DIR/tokenizer/added_tokens.json

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LTX-Video/MODEL_DIR/tokenizer/special_tokens_map.json

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LTX-Video/MODEL_DIR/tokenizer/spiece.model

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LTX-Video/MODEL_DIR/tokenizer/tokenizer_config.json

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LTX-Video/MODEL_DIR/vae/config.json

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+{
+  "_class_name": "AutoencoderKLLTXVideo",
+  "_diffusers_version": "0.32.0.dev0",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "decoder_causal": false,
+  "encoder_causal": true,
+  "in_channels": 3,
+  "latent_channels": 128,
+  "layers_per_block": [
+    4,
+    3,
+    3,
+    3,
+    4
+  ],
+  "out_channels": 3,
+  "patch_size": 4,
+  "patch_size_t": 1,
+  "resnet_norm_eps": 1e-06,
+  "scaling_factor": 1.0,
+  "spatio_temporal_scaling": [
+    true,
+    true,
+    true,
+    false
+  ]
+}

LTX-Video/MODEL_DIR/vae/diffusion_pytorch_model.safetensors

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+size 1676798532

LTX-Video/README.md

@@ -0,0 +1,280 @@
+<div align="center">
+
+# LTX-Video
+
+This is the official repository for LTX-Video.
+
+[Website](https://www.lightricks.com/ltxv) |
+[Model](https://huggingface.co/Lightricks/LTX-Video) |
+[Demo](https://app.ltx.studio/ltx-video) |
+[Paper](https://arxiv.org/abs/2501.00103)
+
+</div>
+
+## Table of Contents
+
+- [Introduction](#introduction)
+- [What's new](#news)
+- [Quick Start Guide](#quick-start-guide)
+  - [Online demo](#online-demo)
+  - [Run locally](#run-locally)
+    - [Installation](#installation)
+    - [Inference](#inference)
+  - [ComfyUI Integration](#comfyui-integration)
+  - [Diffusers Integration](#diffusers-integration)
+- [Model User Guide](#model-user-guide)
+- [Community Contribution](#community-contribution)
+- [Training](#trining)
+- [Join Us!](#join-us)
+- [Acknowledgement](#acknowledgement)
+
+# Introduction
+
+LTX-Video is the first DiT-based video generation model that can generate high-quality videos in *real-time*.
+It can generate 24 FPS videos at 768x512 resolution, faster than it takes to watch them.
+The model is trained on a large-scale dataset of diverse videos and can generate high-resolution videos
+with realistic and diverse content.
+
+The model supports text-to-image, image-to-video, keyframe-based animation, video extension (both forward and backward), video-to-video transformations, and any combination of these features.
+
+| | | | |
+|:---:|:---:|:---:|:---:|
+| ![example1](./docs/_static/ltx-video_example_00001.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A woman with long brown hair and light skin smiles at another woman...</summary>A woman with long brown hair and light skin smiles at another woman with long blonde hair. The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek. The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and natural, likely from the setting sun, casting a soft glow on the scene. The scene appears to be real-life footage.</details> | ![example2](./docs/_static/ltx-video_example_00002.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A woman walks away from a white Jeep parked on a city street at night...</summary>A woman walks away from a white Jeep parked on a city street at night, then ascends a staircase and knocks on a door. The woman, wearing a dark jacket and jeans, walks away from the Jeep parked on the left side of the street, her back to the camera; she walks at a steady pace, her arms swinging slightly by her sides; the street is dimly lit, with streetlights casting pools of light on the wet pavement; a man in a dark jacket and jeans walks past the Jeep in the opposite direction; the camera follows the woman from behind as she walks up a set of stairs towards a building with a green door; she reaches the top of the stairs and turns left, continuing to walk towards the building; she reaches the door and knocks on it with her right hand; the camera remains stationary, focused on the doorway; the scene is captured in real-life footage.</details> | ![example3](./docs/_static/ltx-video_example_00003.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A woman with blonde hair styled up, wearing a black dress...</summary>A woman with blonde hair styled up, wearing a black dress with sequins and pearl earrings, looks down with a sad expression on her face. The camera remains stationary, focused on the woman's face. The lighting is dim, casting soft shadows on her face. The scene appears to be from a movie or TV show.</details> | ![example4](./docs/_static/ltx-video_example_00004.gif)<br><details style="max-width: 300px; margin: auto;"><summary>The camera pans over a snow-covered mountain range...</summary>The camera pans over a snow-covered mountain range, revealing a vast expanse of snow-capped peaks and valleys.The mountains are covered in a thick layer of snow, with some areas appearing almost white while others have a slightly darker, almost grayish hue. The peaks are jagged and irregular, with some rising sharply into the sky while others are more rounded. The valleys are deep and narrow, with steep slopes that are also covered in snow. The trees in the foreground are mostly bare, with only a few leaves remaining on their branches. The sky is overcast, with thick clouds obscuring the sun. The overall impression is one of peace and tranquility, with the snow-covered mountains standing as a testament to the power and beauty of nature.</details> |
+| ![example5](./docs/_static/ltx-video_example_00005.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A woman with light skin, wearing a blue jacket and a black hat...</summary>A woman with light skin, wearing a blue jacket and a black hat with a veil, looks down and to her right, then back up as she speaks; she has brown hair styled in an updo, light brown eyebrows, and is wearing a white collared shirt under her jacket; the camera remains stationary on her face as she speaks; the background is out of focus, but shows trees and people in period clothing; the scene is captured in real-life footage.</details> | ![example6](./docs/_static/ltx-video_example_00006.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A man in a dimly lit room talks on a vintage telephone...</summary>A man in a dimly lit room talks on a vintage telephone, hangs up, and looks down with a sad expression. He holds the black rotary phone to his right ear with his right hand, his left hand holding a rocks glass with amber liquid. He wears a brown suit jacket over a white shirt, and a gold ring on his left ring finger. His short hair is neatly combed, and he has light skin with visible wrinkles around his eyes. The camera remains stationary, focused on his face and upper body. The room is dark, lit only by a warm light source off-screen to the left, casting shadows on the wall behind him. The scene appears to be from a movie.</details> | ![example7](./docs/_static/ltx-video_example_00007.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A prison guard unlocks and opens a cell door...</summary>A prison guard unlocks and opens a cell door to reveal a young man sitting at a table with a woman. The guard, wearing a dark blue uniform with a badge on his left chest, unlocks the cell door with a key held in his right hand and pulls it open; he has short brown hair, light skin, and a neutral expression. The young man, wearing a black and white striped shirt, sits at a table covered with a white tablecloth, facing the woman; he has short brown hair, light skin, and a neutral expression. The woman, wearing a dark blue shirt, sits opposite the young man, her face turned towards him; she has short blonde hair and light skin. The camera remains stationary, capturing the scene from a medium distance, positioned slightly to the right of the guard. The room is dimly lit, with a single light fixture illuminating the table and the two figures. The walls are made of large, grey concrete blocks, and a metal door is visible in the background. The scene is captured in real-life footage.</details> | ![example8](./docs/_static/ltx-video_example_00008.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A woman with blood on her face and a white tank top...</summary>A woman with blood on her face and a white tank top looks down and to her right, then back up as she speaks. She has dark hair pulled back, light skin, and her face and chest are covered in blood. The camera angle is a close-up, focused on the woman's face and upper torso. The lighting is dim and blue-toned, creating a somber and intense atmosphere. The scene appears to be from a movie or TV show.</details> |
+| ![example9](./docs/_static/ltx-video_example_00009.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A man with graying hair, a beard, and a gray shirt...</summary>A man with graying hair, a beard, and a gray shirt looks down and to his right, then turns his head to the left. The camera angle is a close-up, focused on the man's face. The lighting is dim, with a greenish tint. The scene appears to be real-life footage. Step</details> | ![example10](./docs/_static/ltx-video_example_00010.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A clear, turquoise river flows through a rocky canyon...</summary>A clear, turquoise river flows through a rocky canyon, cascading over a small waterfall and forming a pool of water at the bottom.The river is the main focus of the scene, with its clear water reflecting the surrounding trees and rocks. The canyon walls are steep and rocky, with some vegetation growing on them. The trees are mostly pine trees, with their green needles contrasting with the brown and gray rocks. The overall tone of the scene is one of peace and tranquility.</details> | ![example11](./docs/_static/ltx-video_example_00011.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A man in a suit enters a room and speaks to two women...</summary>A man in a suit enters a room and speaks to two women sitting on a couch. The man, wearing a dark suit with a gold tie, enters the room from the left and walks towards the center of the frame. He has short gray hair, light skin, and a serious expression. He places his right hand on the back of a chair as he approaches the couch. Two women are seated on a light-colored couch in the background. The woman on the left wears a light blue sweater and has short blonde hair. The woman on the right wears a white sweater and has short blonde hair. The camera remains stationary, focusing on the man as he enters the room. The room is brightly lit, with warm tones reflecting off the walls and furniture. The scene appears to be from a film or television show.</details> | ![example12](./docs/_static/ltx-video_example_00012.gif)<br><details style="max-width: 300px; margin: auto;"><summary>The waves crash against the jagged rocks of the shoreline...</summary>The waves crash against the jagged rocks of the shoreline, sending spray high into the air.The rocks are a dark gray color, with sharp edges and deep crevices. The water is a clear blue-green, with white foam where the waves break against the rocks. The sky is a light gray, with a few white clouds dotting the horizon.</details> |
+| ![example13](./docs/_static/ltx-video_example_00013.gif)<br><details style="max-width: 300px; margin: auto;"><summary>The camera pans across a cityscape of tall buildings...</summary>The camera pans across a cityscape of tall buildings with a circular building in the center. The camera moves from left to right, showing the tops of the buildings and the circular building in the center. The buildings are various shades of gray and white, and the circular building has a green roof. The camera angle is high, looking down at the city. The lighting is bright, with the sun shining from the upper left, casting shadows from the buildings. The scene is computer-generated imagery.</details> | ![example14](./docs/_static/ltx-video_example_00014.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A man walks towards a window, looks out, and then turns around...</summary>A man walks towards a window, looks out, and then turns around. He has short, dark hair, dark skin, and is wearing a brown coat over a red and gray scarf. He walks from left to right towards a window, his gaze fixed on something outside. The camera follows him from behind at a medium distance. The room is brightly lit, with white walls and a large window covered by a white curtain. As he approaches the window, he turns his head slightly to the left, then back to the right. He then turns his entire body to the right, facing the window. The camera remains stationary as he stands in front of the window. The scene is captured in real-life footage.</details> | ![example15](./docs/_static/ltx-video_example_00015.gif)<br><details style="max-width: 300px; margin: auto;"><summary>Two police officers in dark blue uniforms and matching hats...</summary>Two police officers in dark blue uniforms and matching hats enter a dimly lit room through a doorway on the left side of the frame. The first officer, with short brown hair and a mustache, steps inside first, followed by his partner, who has a shaved head and a goatee. Both officers have serious expressions and maintain a steady pace as they move deeper into the room. The camera remains stationary, capturing them from a slightly low angle as they enter. The room has exposed brick walls and a corrugated metal ceiling, with a barred window visible in the background. The lighting is low-key, casting shadows on the officers' faces and emphasizing the grim atmosphere. The scene appears to be from a film or television show.</details> | ![example16](./docs/_static/ltx-video_example_00016.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A woman with short brown hair, wearing a maroon sleeveless top...</summary>A woman with short brown hair, wearing a maroon sleeveless top and a silver necklace, walks through a room while talking, then a woman with pink hair and a white shirt appears in the doorway and yells. The first woman walks from left to right, her expression serious; she has light skin and her eyebrows are slightly furrowed. The second woman stands in the doorway, her mouth open in a yell; she has light skin and her eyes are wide. The room is dimly lit, with a bookshelf visible in the background. The camera follows the first woman as she walks, then cuts to a close-up of the second woman's face. The scene is captured in real-life footage.</details> |
+
+# News
+
+## March, 5th, 2025: New checkpoint v0.9.5
+- New license for commercial use ([OpenRail-M](https://huggingface.co/Lightricks/LTX-Video/ltx-video-2b-v0.9.5.license.txt))
+- Release a new checkpoint v0.9.5 with improved quality
+- Support keyframes and video extension
+- Support higher resolutions
+- Improved prompt understanding
+- Improved VAE
+- New online web app in [LTX-Studio](https://app.ltx.studio/ltx-video)
+- Automatic prompt enhancement
+
+## February, 20th, 2025: More inference options
+- Improve STG (Spatiotemporal Guidance) for LTX-Video
+- Support MPS on macOS with PyTorch 2.3.0
+- Add support for 8-bit model, LTX-VideoQ8
+- Add TeaCache for LTX-Video
+- Add [ComfyUI-LTXTricks](#comfyui-integration)
+- Add Diffusion-Pipe
+
+## December 31st, 2024: Research paper
+- Release the [research paper](https://arxiv.org/abs/2501.00103)
+
+## December 20th, 2024: New checkpoint v0.9.1
+- Release a new checkpoint v0.9.1 with improved quality
+- Support for STG / PAG
+- Support loading checkpoints of LTX-Video in Diffusers format (conversion is done on-the-fly)
+- Support offloading unused parts to CPU
+- Support the new timestep-conditioned VAE decoder
+- Reference contributions from the community in the readme file
+- Relax transformers dependency
+
+## November 21th, 2024: Initial release v0.9.0
+- Initial release of LTX-Video
+- Support text-to-video and image-to-video generation
+
+# Quick Start Guide
+
+## Online inference
+The model is accessible right away via the following links:
+- [LTX-Studio image-to-video](https://app.ltx.studio/ltx-video)
+- [Fal.ai text-to-video](https://fal.ai/models/fal-ai/ltx-video)
+- [Fal.ai image-to-video](https://fal.ai/models/fal-ai/ltx-video/image-to-video)
+- [Replicate text-to-video and image-to-video](https://replicate.com/lightricks/ltx-video)
+
+## Run locally
+
+### Installation
+The codebase was tested with Python 3.10.5, CUDA version 12.2, and supports PyTorch >= 2.1.2.
+On macos, MPS was tested with PyTorch 2.3.0, and should support PyTorch == 2.3 or >= 2.6.
+
+```bash
+git clone https://github.com/Lightricks/LTX-Video.git
+cd LTX-Video
+
+# create env
+python -m venv env
+source env/bin/activate
+python -m pip install -e .\[inference-script\]
+```
+
+Then, download the model from [Hugging Face](https://huggingface.co/Lightricks/LTX-Video)
+
+```python
+from huggingface_hub import hf_hub_download
+
+model_dir = 'MODEL_DIR'   # The local directory to save downloaded checkpoint
+hf_hub_download(repo_id="Lightricks/LTX-Video", filename="ltx-video-2b-v0.9.5.safetensors", local_dir=model_dir, local_dir_use_symlinks=False, repo_type='model')
+```
+
+### Inference
+
+To use our model, please follow the inference code in [inference.py](./inference.py):
+
+#### For text-to-video generation:
+
+```bash
+python inference.py --ckpt_path 'PATH' --prompt "PROMPT" --height HEIGHT --width WIDTH --num_frames NUM_FRAMES --seed SEED
+```
+
+#### For image-to-video generation:
+
+```bash
+python inference.py --ckpt_path 'PATH' --prompt "PROMPT" --conditioning_media_paths IMAGE_PATH --conditioning_start_frames 0 --height HEIGHT --width WIDTH --num_frames NUM_FRAMES --seed SEED
+```
+
+#### Extending a video:
+
+📝 **Note:** Input video segments must contain a multiple of 8 frames plus 1 (e.g., 9, 17, 25, etc.), and the target frame number should be a multiple of 8.
+
+
+```bash
+python inference.py --ckpt_path 'PATH' --prompt "PROMPT" --conditioning_media_paths VIDEO_PATH --conditioning_start_frames START_FRAME --height HEIGHT --width WIDTH --num_frames NUM_FRAMES --seed SEED
+```
+
+#### For video generation with multiple conditions:
+
+You can now generate a video conditioned on a set of images and/or short video segments.
+Simply provide a list of paths to the images or video segments you want to condition on, along with their target frame numbers in the generated video. You can also specify the conditioning strength for each item (default: 1.0).
+
+```bash
+python inference.py --ckpt_path 'PATH' --prompt "PROMPT" --conditioning_media_paths IMAGE_OR_VIDEO_PATH_1 IMAGE_OR_VIDEO_PATH_2 --conditioning_start_frames TARGET_FRAME_1 TARGET_FRAME_2 --height HEIGHT --width WIDTH --num_frames NUM_FRAMES --seed SEED
+```
+
+## ComfyUI Integration
+To use our model with ComfyUI, please follow the instructions at [https://github.com/Lightricks/ComfyUI-LTXVideo/](https://github.com/Lightricks/ComfyUI-LTXVideo/).
+
+## Diffusers Integration
+To use our model with the Diffusers Python library, check out the [official documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video).
+
+Diffusers also support an 8-bit version of LTX-Video, [see details below](#ltx-videoq8)
+
+# Model User Guide
+
+## 📝 Prompt Engineering
+
+When writing prompts, focus on detailed, chronological descriptions of actions and scenes. Include specific movements, appearances, camera angles, and environmental details - all in a single flowing paragraph. Start directly with the action, and keep descriptions literal and precise. Think like a cinematographer describing a shot list. Keep within 200 words. For best results, build your prompts using this structure:
+
+* Start with main action in a single sentence
+* Add specific details about movements and gestures
+* Describe character/object appearances precisely
+* Include background and environment details
+* Specify camera angles and movements
+* Describe lighting and colors
+* Note any changes or sudden events
+* See [examples](#introduction) for more inspiration.
+
+### Automatic Prompt Enhancement
+When using `inference.py`, shorts prompts (below `prompt_enhancement_words_threshold` words) are automatically enhanced by a language model. This is supported with text-to-video and image-to-video (first-frame conditioning).
+
+When using `LTXVideoPipeline` directly, you can enable prompt enhancement by setting `enhance_prompt=True`.
+
+## 🎮 Parameter Guide
+
+* Resolution Preset: Higher resolutions for detailed scenes, lower for faster generation and simpler scenes. The model works on resolutions that are divisible by 32 and number of frames that are divisible by 8 + 1 (e.g. 257). In case the resolution or number of frames are not divisible by 32 or 8 + 1, the input will be padded with -1 and then cropped to the desired resolution and number of frames. The model works best on resolutions under 720 x 1280 and number of frames below 257
+* Seed: Save seed values to recreate specific styles or compositions you like
+* Guidance Scale: 3-3.5 are the recommended values
+* Inference Steps: More steps (40+) for quality, fewer steps (20-30) for speed
+
+📝 For advanced parameters usage, please see `python inference.py --help`
+
+## Community Contribution
+
+### ComfyUI-LTXTricks 🛠️
+
+A community project providing additional nodes for enhanced control over the LTX Video model. It includes implementations of advanced techniques like RF-Inversion, RF-Edit, FlowEdit, and more. These nodes enable workflows such as Image and Video to Video (I+V2V), enhanced sampling via Spatiotemporal Skip Guidance (STG), and interpolation with precise frame settings.
+
+- **Repository:** [ComfyUI-LTXTricks](https://github.com/logtd/ComfyUI-LTXTricks)
+- **Features:**
+  - 🔄 **RF-Inversion:** Implements [RF-Inversion](https://rf-inversion.github.io/) with an [example workflow here](https://github.com/logtd/ComfyUI-LTXTricks/blob/main/example_workflows/example_ltx_inversion.json).
+  - ✂️ **RF-Edit:** Implements [RF-Solver-Edit](https://github.com/wangjiangshan0725/RF-Solver-Edit) with an [example workflow here](https://github.com/logtd/ComfyUI-LTXTricks/blob/main/example_workflows/example_ltx_rf_edit.json).
+  - 🌊 **FlowEdit:** Implements [FlowEdit](https://github.com/fallenshock/FlowEdit) with an [example workflow here](https://github.com/logtd/ComfyUI-LTXTricks/blob/main/example_workflows/example_ltx_flow_edit.json).
+  - 🎥 **I+V2V:** Enables Video to Video with a reference image. [Example workflow](https://github.com/logtd/ComfyUI-LTXTricks/blob/main/example_workflows/example_ltx_iv2v.json).
+  - ✨ **Enhance:** Partial implementation of [STGuidance](https://junhahyung.github.io/STGuidance/). [Example workflow](https://github.com/logtd/ComfyUI-LTXTricks/blob/main/example_workflows/example_ltxv_stg.json).
+  - 🖼️ **Interpolation and Frame Setting:** Nodes for precise control of latents per frame. [Example workflow](https://github.com/logtd/ComfyUI-LTXTricks/blob/main/example_workflows/example_ltx_interpolation.json).
+
+
+### LTX-VideoQ8 🎱 <a id="ltx-videoq8"></a>
+
+**LTX-VideoQ8** is an 8-bit optimized version of [LTX-Video](https://github.com/Lightricks/LTX-Video), designed for faster performance on NVIDIA ADA GPUs.
+
+- **Repository:** [LTX-VideoQ8](https://github.com/KONAKONA666/LTX-Video)
+- **Features:**
+  - 🚀 Up to 3X speed-up with no accuracy loss
+  - 🎥 Generate 720x480x121 videos in under a minute on RTX 4060 (8GB VRAM)
+  - 🛠️ Fine-tune 2B transformer models with precalculated latents
+- **Community Discussion:** [Reddit Thread](https://www.reddit.com/r/StableDiffusion/comments/1h79ks2/fast_ltx_video_on_rtx_4060_and_other_ada_gpus/)
+- **Diffusers integration:** A diffusers integration for the 8-bit model is already out! [Details here](https://github.com/sayakpaul/q8-ltx-video)
+
+
+### TeaCache for LTX-Video 🍵 <a id="TeaCache"></a>
+
+**TeaCache** is a training-free caching approach that leverages timestep differences across model outputs to accelerate LTX-Video inference by up to 2x without significant visual quality degradation.
+
+- **Repository:** [TeaCache4LTX-Video](https://github.com/ali-vilab/TeaCache/tree/main/TeaCache4LTX-Video)
+- **Features:**
+  - 🚀 Speeds up LTX-Video inference.
+  - 📊 Adjustable trade-offs between speed (up to 2x) and visual quality using configurable parameters.
+  - 🛠️ No retraining required: Works directly with existing models.
+
+### Your Contribution
+
+...is welcome! If you have a project or tool that integrates with LTX-Video,
+please let us know by opening an issue or pull request.
+
+# Training
+
+## Diffusers
+
+Diffusers implemented [LoRA support](https://github.com/huggingface/diffusers/pull/10228),
+with a training script for fine-tuning.
+More information and training script in
+[finetrainers](https://github.com/a-r-r-o-w/finetrainers?tab=readme-ov-file#training).
+
+## Diffusion-Pipe
+
+An experimental training framework with pipeline parallelism, enabling fine-tuning of large models like **LTX-Video** across multiple GPUs.
+
+- **Repository:** [Diffusion-Pipe](https://github.com/tdrussell/diffusion-pipe)
+- **Features:**
+  - 🛠️ Full fine-tune support for LTX-Video using LoRA
+  - 📊 Useful metrics logged to Tensorboard
+  - 🔄 Training state checkpointing and resumption
+  - ⚡ Efficient pre-caching of latents and text embeddings for multi-GPU setups
+
+
+# Join Us 🚀
+
+Want to work on cutting-edge AI research and make a real impact on millions of users worldwide?
+
+At **Lightricks**, an AI-first company, we're revolutionizing how visual content is created.
+
+If you are passionate about AI, computer vision, and video generation, we would love to hear from you!
+
+Please visit our [careers page](https://careers.lightricks.com/careers?query=&office=all&department=R%26D) for more information.
+
+# Acknowledgement
+
+We are grateful for the following awesome projects when implementing LTX-Video:
+* [DiT](https://github.com/facebookresearch/DiT) and [PixArt-alpha](https://github.com/PixArt-alpha/PixArt-alpha): vision transformers for image generation.
+
+
+## Citation
+
+📄 Our tech report is out! If you find our work helpful, please ⭐️ star the repository and cite our paper.
+
+```
+@article{HaCohen2024LTXVideo,
+  title={LTX-Video: Realtime Video Latent Diffusion},
+  author={HaCohen, Yoav and Chiprut, Nisan and Brazowski, Benny and Shalem, Daniel and Moshe, Dudu and Richardson, Eitan and Levin, Eran and Shiran, Guy and Zabari, Nir and Gordon, Ori and Panet, Poriya and Weissbuch, Sapir and Kulikov, Victor and Bitterman, Yaki and Melumian, Zeev and Bibi, Ofir},
+  journal={arXiv preprint arXiv:2501.00103},
+  year={2024}
+}
+```

LTX-Video/file_list.txt

@@ -0,0 +1,46 @@
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+ out=MODEL_DIR/ltx-video-2b-v0.9.5.safetensors
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+https://huggingface.co/Isi99999/LTX-Video/resolve/main/transformer/diffusion_pytorch_model-00001-of-00002.safetensors
+ out=MODEL_DIR/transformer/diffusion_pytorch_model-00001-of-00002.safetensors
+https://huggingface.co/Isi99999/LTX-Video/resolve/main/transformer/diffusion_pytorch_model-00002-of-00002.safetensors
+ out=MODEL_DIR/transformer/diffusion_pytorch_model-00002-of-00002.safetensors
+https://huggingface.co/Isi99999/LTX-Video/resolve/main/transformer/diffusion_pytorch_model.safetensors.index.json
+ out=MODEL_DIR/transformer/diffusion_pytorch_model.safetensors.index.json
+https://huggingface.co/Isi99999/LTX-Video/resolve/main/vae/config.json
+ out=MODEL_DIR/vae/config.json
+https://huggingface.co/Isi99999/LTX-Video/resolve/main/vae/diffusion_pytorch_model.safetensors
+ out=MODEL_DIR/vae/diffusion_pytorch_model.safetensors

LTX-Video/inference.py

@@ -0,0 +1,758 @@
+import argparse
+import os
+import random
+from datetime import datetime
+from pathlib import Path
+from diffusers.utils import logging
+from typing import Optional, List, Union
+
+import imageio
+import numpy as np
+import torch
+from PIL import Image
+from transformers import (
+    T5EncoderModel,
+    T5Tokenizer,
+    AutoModelForCausalLM,
+    AutoProcessor,
+    AutoTokenizer,
+)
+
+from ltx_video.models.autoencoders.causal_video_autoencoder import (
+    CausalVideoAutoencoder,
+)
+from ltx_video.models.transformers.symmetric_patchifier import SymmetricPatchifier
+from ltx_video.models.transformers.transformer3d import Transformer3DModel
+from ltx_video.pipelines.pipeline_ltx_video import ConditioningItem, LTXVideoPipeline
+from ltx_video.schedulers.rf import RectifiedFlowScheduler
+from ltx_video.utils.skip_layer_strategy import SkipLayerStrategy
+
+MAX_HEIGHT = 720
+MAX_WIDTH = 1280
+MAX_NUM_FRAMES = 257
+
+logger = logging.get_logger("LTX-Video")
+
+
+def get_total_gpu_memory():
+    if torch.cuda.is_available():
+        total_memory = torch.cuda.get_device_properties(0).total_memory / (1024**3)
+        return total_memory
+    return 0
+
+
+def get_device():
+    if torch.cuda.is_available():
+        return "cuda"
+    elif torch.backends.mps.is_available():
+        return "mps"
+    return "cpu"
+
+
+def load_image_to_tensor_with_resize_and_crop(
+    image_input: Union[str, Image.Image],
+    target_height: int = 512,
+    target_width: int = 768,
+) -> torch.Tensor:
+    """Load and process an image into a tensor.
+
+    Args:
+        image_input: Either a file path (str) or a PIL Image object
+        target_height: Desired height of output tensor
+        target_width: Desired width of output tensor
+    """
+    if isinstance(image_input, str):
+        image = Image.open(image_input).convert("RGB")
+    elif isinstance(image_input, Image.Image):
+        image = image_input
+    else:
+        raise ValueError("image_input must be either a file path or a PIL Image object")
+
+    input_width, input_height = image.size
+    aspect_ratio_target = target_width / target_height
+    aspect_ratio_frame = input_width / input_height
+    if aspect_ratio_frame > aspect_ratio_target:
+        new_width = int(input_height * aspect_ratio_target)
+        new_height = input_height
+        x_start = (input_width - new_width) // 2
+        y_start = 0
+    else:
+        new_width = input_width
+        new_height = int(input_width / aspect_ratio_target)
+        x_start = 0
+        y_start = (input_height - new_height) // 2
+
+    image = image.crop((x_start, y_start, x_start + new_width, y_start + new_height))
+    image = image.resize((target_width, target_height))
+    frame_tensor = torch.tensor(np.array(image)).permute(2, 0, 1).float()
+    frame_tensor = (frame_tensor / 127.5) - 1.0
+    # Create 5D tensor: (batch_size=1, channels=3, num_frames=1, height, width)
+    return frame_tensor.unsqueeze(0).unsqueeze(2)
+
+
+def calculate_padding(
+    source_height: int, source_width: int, target_height: int, target_width: int
+) -> tuple[int, int, int, int]:
+
+    # Calculate total padding needed
+    pad_height = target_height - source_height
+    pad_width = target_width - source_width
+
+    # Calculate padding for each side
+    pad_top = pad_height // 2
+    pad_bottom = pad_height - pad_top  # Handles odd padding
+    pad_left = pad_width // 2
+    pad_right = pad_width - pad_left  # Handles odd padding
+
+    # Return padded tensor
+    # Padding format is (left, right, top, bottom)
+    padding = (pad_left, pad_right, pad_top, pad_bottom)
+    return padding
+
+
+def convert_prompt_to_filename(text: str, max_len: int = 20) -> str:
+    # Remove non-letters and convert to lowercase
+    clean_text = "".join(
+        char.lower() for char in text if char.isalpha() or char.isspace()
+    )
+
+    # Split into words
+    words = clean_text.split()
+
+    # Build result string keeping track of length
+    result = []
+    current_length = 0
+
+    for word in words:
+        # Add word length plus 1 for underscore (except for first word)
+        new_length = current_length + len(word)
+
+        if new_length <= max_len:
+            result.append(word)
+            current_length += len(word)
+        else:
+            break
+
+    return "-".join(result)
+
+
+# Generate output video name
+def get_unique_filename(
+    base: str,
+    ext: str,
+    prompt: str,
+    seed: int,
+    resolution: tuple[int, int, int],
+    dir: Path,
+    endswith=None,
+    index_range=1000,
+) -> Path:
+    base_filename = f"{base}_{convert_prompt_to_filename(prompt, max_len=30)}_{seed}_{resolution[0]}x{resolution[1]}x{resolution[2]}"
+    for i in range(index_range):
+        filename = dir / f"{base_filename}_{i}{endswith if endswith else ''}{ext}"
+        if not os.path.exists(filename):
+            return filename
+    raise FileExistsError(
+        f"Could not find a unique filename after {index_range} attempts."
+    )
+
+
+def seed_everething(seed: int):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+    if torch.backends.mps.is_available():
+        torch.mps.manual_seed(seed)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Load models from separate directories and run the pipeline."
+    )
+
+    # Directories
+    parser.add_argument(
+        "--ckpt_path",
+        type=str,
+        required=True,
+        help="Path to a safetensors file that contains all model parts.",
+    )
+    parser.add_argument(
+        "--output_path",
+        type=str,
+        default=None,
+        help="Path to the folder to save output video, if None will save in outputs/ directory.",
+    )
+    parser.add_argument("--seed", type=int, default="171198")
+
+    # Pipeline parameters
+    parser.add_argument(
+        "--num_inference_steps", type=int, default=40, help="Number of inference steps"
+    )
+    parser.add_argument(
+        "--num_images_per_prompt",
+        type=int,
+        default=1,
+        help="Number of images per prompt",
+    )
+    parser.add_argument(
+        "--guidance_scale",
+        type=float,
+        default=3,
+        help="Guidance scale.",
+    )
+    parser.add_argument(
+        "--stg_scale",
+        type=float,
+        default=1,
+        help="Spatiotemporal guidance scale. 0 to disable STG.",
+    )
+    parser.add_argument(
+        "--stg_rescale",
+        type=float,
+        default=0.7,
+        help="Spatiotemporal guidance rescaling scale. 1 to disable rescale.",
+    )
+    parser.add_argument(
+        "--stg_mode",
+        type=str,
+        default="attention_values",
+        help="Spatiotemporal guidance mode. "
+        "It can be one of 'attention_values' (default), 'attension_skip', 'residual', or 'transformer_block'.",
+    )
+    parser.add_argument(
+        "--stg_skip_layers",
+        type=str,
+        default="19",
+        help="Layers to block for spatiotemporal guidance. Comma separated list of integers.",
+    )
+    parser.add_argument(
+        "--image_cond_noise_scale",
+        type=float,
+        default=0.15,
+        help="Amount of noise to add to the conditioned image",
+    )
+    parser.add_argument(
+        "--height",
+        type=int,
+        default=480,
+        help="Height of the output video frames. Optional if an input image provided.",
+    )
+    parser.add_argument(
+        "--width",
+        type=int,
+        default=704,
+        help="Width of the output video frames. If None will infer from input image.",
+    )
+    parser.add_argument(
+        "--num_frames",
+        type=int,
+        default=121,
+        help="Number of frames to generate in the output video",
+    )
+    parser.add_argument(
+        "--frame_rate", type=int, default=25, help="Frame rate for the output video"
+    )
+    parser.add_argument(
+        "--device",
+        default=None,
+        help="Device to run inference on. If not specified, will automatically detect and use CUDA or MPS if available, else CPU.",
+    )
+    parser.add_argument(
+        "--precision",
+        choices=["bfloat16", "mixed_precision"],
+        default="bfloat16",
+        help="Sets the precision for the transformer and tokenizer. Default is bfloat16. If 'mixed_precision' is enabled, it moves to mixed-precision.",
+    )
+
+    # VAE noise augmentation
+    parser.add_argument(
+        "--decode_timestep",
+        type=float,
+        default=0.025,
+        help="Timestep for decoding noise",
+    )
+    parser.add_argument(
+        "--decode_noise_scale",
+        type=float,
+        default=0.0125,
+        help="Noise level for decoding noise",
+    )
+
+    # Prompts
+    parser.add_argument(
+        "--prompt",
+        type=str,
+        help="Text prompt to guide generation",
+    )
+    parser.add_argument(
+        "--negative_prompt",
+        type=str,
+        default="worst quality, inconsistent motion, blurry, jittery, distorted",
+        help="Negative prompt for undesired features",
+    )
+
+    parser.add_argument(
+        "--low_vram",
+        action="store_true",
+    )
+
+    parser.add_argument(
+        "--offload_to_cpu",
+        action="store_true",
+        help="Offloading unnecessary computations to CPU.",
+    )
+
+    parser.add_argument(
+        "--text_encoder_model_name_or_path",
+        type=str,
+        default="PixArt-alpha/PixArt-XL-2-1024-MS",
+        help="Local path or model identifier for both the tokenizer and text encoder. Defaults to pretrained model on Hugging Face.",
+    )
+
+    # Conditioning arguments
+    parser.add_argument(
+        "--conditioning_media_paths",
+        type=str,
+        nargs="*",
+        help="List of paths to conditioning media (images or videos). Each path will be used as a conditioning item.",
+    )
+    parser.add_argument(
+        "--conditioning_strengths",
+        type=float,
+        nargs="*",
+        help="List of conditioning strengths (between 0 and 1) for each conditioning item. Must match the number of conditioning items.",
+    )
+    parser.add_argument(
+        "--conditioning_start_frames",
+        type=int,
+        nargs="*",
+        help="List of frame indices where each conditioning item should be applied. Must match the number of conditioning items.",
+    )
+    parser.add_argument(
+        "--sampler",
+        type=str,
+        choices=["uniform", "linear-quadratic"],
+        default=None,
+        help="Sampler to use for noise scheduling. Can be either 'uniform' or 'linear-quadratic'. If not specified, uses the sampler from the checkpoint.",
+    )
+
+    # Prompt enhancement
+    parser.add_argument(
+        "--prompt_enhancement_words_threshold",
+        type=int,
+        default=50,
+        help="Enable prompt enhancement only if input prompt has fewer words than this threshold. Set to 0 to disable enhancement completely.",
+    )
+    parser.add_argument(
+        "--prompt_enhancer_image_caption_model_name_or_path",
+        type=str,
+        default="MiaoshouAI/Florence-2-large-PromptGen-v2.0",
+        help="Path to the image caption model",
+    )
+    parser.add_argument(
+        "--prompt_enhancer_llm_model_name_or_path",
+        type=str,
+        default="unsloth/Llama-3.2-3B-Instruct",
+        help="Path to the LLM model, default is Llama-3.2-3B-Instruct, but you can use other models like Llama-3.1-8B-Instruct, or other models supported by Hugging Face",
+    )
+
+    args = parser.parse_args()
+    logger.warning(f"Running generation with arguments: {args}")
+    infer(**vars(args))
+
+
+def create_ltx_video_pipeline(
+    ckpt_path: str,
+    precision: str,
+    text_encoder_model_name_or_path: str,
+    sampler: Optional[str] = None,
+    device: Optional[str] = None,
+    lowVram: bool = False,
+    enhance_prompt: bool = False,
+    prompt_enhancer_image_caption_model_name_or_path: Optional[str] = None,
+    prompt_enhancer_llm_model_name_or_path: Optional[str] = None,
+) -> LTXVideoPipeline:
+    ckpt_path = Path(ckpt_path)
+    assert os.path.exists(
+        ckpt_path
+    ), f"Ckpt path provided (--ckpt_path) {ckpt_path} does not exist"
+    vae = CausalVideoAutoencoder.from_pretrained(ckpt_path)
+    transformer = Transformer3DModel.from_pretrained(ckpt_path)
+
+    # Use constructor if sampler is specified, otherwise use from_pretrained
+    if sampler:
+        scheduler = RectifiedFlowScheduler(
+            sampler=("Uniform" if sampler.lower() == "uniform" else "LinearQuadratic")
+        )
+    else:
+        scheduler = RectifiedFlowScheduler.from_pretrained(ckpt_path)
+
+    text_encoder = T5EncoderModel.from_pretrained(text_encoder_model_name_or_path, subfolder="text_encoder")
+    patchifier = SymmetricPatchifier(patch_size=1)
+    tokenizer = T5Tokenizer.from_pretrained(
+        text_encoder_model_name_or_path, subfolder="tokenizer"
+    )
+
+    if torch.cuda.is_available() and not lowVram:
+        text_encoder = text_encoder.to(device)
+    else:
+        text_encoder = text_encoder.to("cpu")
+        text_encoder = text_encoder.to(dtype=torch.bfloat16, device="cpu")
+
+    transformer = transformer.to(device)
+    vae = vae.to(device)
+    # text_encoder = text_encoder.to(device)
+
+    if enhance_prompt:
+        prompt_enhancer_image_caption_model = AutoModelForCausalLM.from_pretrained(
+            prompt_enhancer_image_caption_model_name_or_path, trust_remote_code=True
+        )
+        prompt_enhancer_image_caption_processor = AutoProcessor.from_pretrained(
+            prompt_enhancer_image_caption_model_name_or_path, trust_remote_code=True
+        )
+        prompt_enhancer_llm_model = AutoModelForCausalLM.from_pretrained(
+            prompt_enhancer_llm_model_name_or_path,
+            torch_dtype="bfloat16",
+        )
+        prompt_enhancer_llm_tokenizer = AutoTokenizer.from_pretrained(
+            prompt_enhancer_llm_model_name_or_path,
+        )
+    else:
+        prompt_enhancer_image_caption_model = None
+        prompt_enhancer_image_caption_processor = None
+        prompt_enhancer_llm_model = None
+        prompt_enhancer_llm_tokenizer = None
+
+    vae = vae.to(torch.bfloat16)
+    if precision == "bfloat16" and transformer.dtype != torch.bfloat16:
+        transformer = transformer.to(torch.bfloat16)
+    # text_encoder = text_encoder.to(torch.bfloat16)
+
+    # Use submodels for the pipeline
+    submodel_dict = {
+        "transformer": transformer,
+        "patchifier": patchifier,
+        "text_encoder": text_encoder,
+        "tokenizer": tokenizer,
+        "scheduler": scheduler,
+        "vae": vae,
+        "prompt_enhancer_image_caption_model": prompt_enhancer_image_caption_model,
+        "prompt_enhancer_image_caption_processor": prompt_enhancer_image_caption_processor,
+        "prompt_enhancer_llm_model": prompt_enhancer_llm_model,
+        "prompt_enhancer_llm_tokenizer": prompt_enhancer_llm_tokenizer,
+    }
+
+    pipeline = LTXVideoPipeline(**submodel_dict)
+    if torch.cuda.is_available() and not lowVram:
+        pipeline = pipeline.to("cuda")
+    return pipeline
+
+
+def infer(
+    ckpt_path: str,
+    output_path: Optional[str],
+    seed: int,
+    num_inference_steps: int,
+    num_images_per_prompt: int,
+    guidance_scale: float,
+    stg_scale: float,
+    stg_rescale: float,
+    stg_mode: str,
+    stg_skip_layers: str,
+    image_cond_noise_scale: float,
+    height: Optional[int],
+    width: Optional[int],
+    num_frames: int,
+    frame_rate: int,
+    precision: str,
+    decode_timestep: float,
+    decode_noise_scale: float,
+    prompt: str,
+    negative_prompt: str,
+    low_vram: bool,
+    offload_to_cpu: bool,
+    text_encoder_model_name_or_path: str,
+    conditioning_media_paths: Optional[List[str]] = None,
+    conditioning_strengths: Optional[List[float]] = None,
+    conditioning_start_frames: Optional[List[int]] = None,
+    sampler: Optional[str] = None,
+    device: Optional[str] = None,
+    prompt_enhancement_words_threshold: int = 50,
+    prompt_enhancer_image_caption_model_name_or_path: str = "MiaoshouAI/Florence-2-large-PromptGen-v2.0",
+    prompt_enhancer_llm_model_name_or_path: str = "unsloth/Llama-3.2-3B-Instruct",
+    **kwargs,
+):
+    if kwargs.get("input_image_path", None):
+        logger.warning(
+            "Please use conditioning_media_paths instead of input_image_path."
+        )
+        assert not conditioning_media_paths and not conditioning_start_frames
+        conditioning_media_paths = [kwargs["input_image_path"]]
+        conditioning_start_frames = [0]
+
+    # Validate conditioning arguments
+    if conditioning_media_paths:
+        # Use default strengths of 1.0
+        if not conditioning_strengths:
+            conditioning_strengths = [1.0] * len(conditioning_media_paths)
+        if not conditioning_start_frames:
+            raise ValueError(
+                "If `conditioning_media_paths` is provided, "
+                "`conditioning_start_frames` must also be provided"
+            )
+        if len(conditioning_media_paths) != len(conditioning_strengths) or len(
+            conditioning_media_paths
+        ) != len(conditioning_start_frames):
+            raise ValueError(
+                "`conditioning_media_paths`, `conditioning_strengths`, "
+                "and `conditioning_start_frames` must have the same length"
+            )
+        if any(s < 0 or s > 1 for s in conditioning_strengths):
+            raise ValueError("All conditioning strengths must be between 0 and 1")
+        if any(f < 0 or f >= num_frames for f in conditioning_start_frames):
+            raise ValueError(
+                f"All conditioning start frames must be between 0 and {num_frames-1}"
+            )
+
+    seed_everething(seed)
+    if offload_to_cpu and not torch.cuda.is_available():
+        logger.warning(
+            "offload_to_cpu is set to True, but offloading will not occur since the model is already running on CPU."
+        )
+        offload_to_cpu = False
+    else:
+        offload_to_cpu = offload_to_cpu and get_total_gpu_memory() < 30
+
+    output_dir = (
+        Path(output_path)
+        if output_path
+        else Path(f"outputs/{datetime.today().strftime('%Y-%m-%d')}")
+    )
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Adjust dimensions to be divisible by 32 and num_frames to be (N * 8 + 1)
+    height_padded = ((height - 1) // 32 + 1) * 32
+    width_padded = ((width - 1) // 32 + 1) * 32
+    num_frames_padded = ((num_frames - 2) // 8 + 1) * 8 + 1
+
+    padding = calculate_padding(height, width, height_padded, width_padded)
+
+    logger.warning(
+        f"Padded dimensions: {height_padded}x{width_padded}x{num_frames_padded}"
+    )
+
+    prompt_word_count = len(prompt.split())
+    enhance_prompt = (
+        prompt_enhancement_words_threshold > 0
+        and prompt_word_count < prompt_enhancement_words_threshold
+    )
+
+    if prompt_enhancement_words_threshold > 0 and not enhance_prompt:
+        logger.info(
+            f"Prompt has {prompt_word_count} words, which exceeds the threshold of {prompt_enhancement_words_threshold}. Prompt enhancement disabled."
+        )
+
+    pipeline = create_ltx_video_pipeline(
+        ckpt_path=ckpt_path,
+        precision=precision,
+        text_encoder_model_name_or_path=text_encoder_model_name_or_path,
+        sampler=sampler,
+        device=kwargs.get("device", get_device()),
+        lowVram=low_vram,
+        enhance_prompt=enhance_prompt,
+        prompt_enhancer_image_caption_model_name_or_path=prompt_enhancer_image_caption_model_name_or_path,
+        prompt_enhancer_llm_model_name_or_path=prompt_enhancer_llm_model_name_or_path,
+    )
+
+    conditioning_items = (
+        prepare_conditioning(
+            conditioning_media_paths=conditioning_media_paths,
+            conditioning_strengths=conditioning_strengths,
+            conditioning_start_frames=conditioning_start_frames,
+            height=height,
+            width=width,
+            num_frames=num_frames,
+            padding=padding,
+            pipeline=pipeline,
+        )
+        if conditioning_media_paths
+        else None
+    )
+
+    # Set spatiotemporal guidance
+    skip_block_list = [int(x.strip()) for x in stg_skip_layers.split(",")]
+    if stg_mode.lower() == "stg_av" or stg_mode.lower() == "attention_values":
+        skip_layer_strategy = SkipLayerStrategy.AttentionValues
+    elif stg_mode.lower() == "stg_as" or stg_mode.lower() == "attention_skip":
+        skip_layer_strategy = SkipLayerStrategy.AttentionSkip
+    elif stg_mode.lower() == "stg_r" or stg_mode.lower() == "residual":
+        skip_layer_strategy = SkipLayerStrategy.Residual
+    elif stg_mode.lower() == "stg_t" or stg_mode.lower() == "transformer_block":
+        skip_layer_strategy = SkipLayerStrategy.TransformerBlock
+    else:
+        raise ValueError(f"Invalid spatiotemporal guidance mode: {stg_mode}")
+
+    # Prepare input for the pipeline
+    sample = {
+        "prompt": prompt,
+        "prompt_attention_mask": None,
+        "negative_prompt": negative_prompt,
+        "negative_prompt_attention_mask": None,
+    }
+
+    device = device or get_device()
+    generator = torch.Generator(device=device).manual_seed(seed)
+
+    images = pipeline(
+        num_inference_steps=num_inference_steps,
+        num_images_per_prompt=num_images_per_prompt,
+        guidance_scale=guidance_scale,
+        skip_layer_strategy=skip_layer_strategy,
+        skip_block_list=skip_block_list,
+        stg_scale=stg_scale,
+        do_rescaling=stg_rescale != 1,
+        rescaling_scale=stg_rescale,
+        generator=generator,
+        output_type="pt",
+        callback_on_step_end=None,
+        height=height_padded,
+        width=width_padded,
+        num_frames=num_frames_padded,
+        frame_rate=frame_rate,
+        **sample,
+        conditioning_items=conditioning_items,
+        is_video=True,
+        vae_per_channel_normalize=True,
+        image_cond_noise_scale=image_cond_noise_scale,
+        decode_timestep=decode_timestep,
+        decode_noise_scale=decode_noise_scale,
+        mixed_precision=(precision == "mixed_precision"),
+        offload_to_cpu=offload_to_cpu,
+        device=device,
+        enhance_prompt=enhance_prompt,
+    ).images
+
+    # Crop the padded images to the desired resolution and number of frames
+    (pad_left, pad_right, pad_top, pad_bottom) = padding
+    pad_bottom = -pad_bottom
+    pad_right = -pad_right
+    if pad_bottom == 0:
+        pad_bottom = images.shape[3]
+    if pad_right == 0:
+        pad_right = images.shape[4]
+    images = images[:, :, :num_frames, pad_top:pad_bottom, pad_left:pad_right]
+
+    for i in range(images.shape[0]):
+        # Gathering from B, C, F, H, W to C, F, H, W and then permuting to F, H, W, C
+        video_np = images[i].permute(1, 2, 3, 0).cpu().float().numpy()
+        # Unnormalizing images to [0, 255] range
+        video_np = (video_np * 255).astype(np.uint8)
+        fps = frame_rate
+        height, width = video_np.shape[1:3]
+        # In case a single image is generated
+        if video_np.shape[0] == 1:
+            output_filename = get_unique_filename(
+                f"image_output_{i}",
+                ".png",
+                prompt=prompt,
+                seed=seed,
+                resolution=(height, width, num_frames),
+                dir=output_dir,
+            )
+            imageio.imwrite(output_filename, video_np[0])
+        else:
+            output_filename = get_unique_filename(
+                f"video_output_{i}",
+                ".mp4",
+                prompt=prompt,
+                seed=seed,
+                resolution=(height, width, num_frames),
+                dir=output_dir,
+            )
+
+            # Write video
+            with imageio.get_writer(output_filename, fps=fps) as video:
+                for frame in video_np:
+                    video.append_data(frame)
+
+        logger.warning(f"Output saved to {output_dir}")
+
+
+def prepare_conditioning(
+    conditioning_media_paths: List[str],
+    conditioning_strengths: List[float],
+    conditioning_start_frames: List[int],
+    height: int,
+    width: int,
+    num_frames: int,
+    padding: tuple[int, int, int, int],
+    pipeline: LTXVideoPipeline,
+) -> Optional[List[ConditioningItem]]:
+    """Prepare conditioning items based on input media paths and their parameters.
+
+    Args:
+        conditioning_media_paths: List of paths to conditioning media (images or videos)
+        conditioning_strengths: List of conditioning strengths for each media item
+        conditioning_start_frames: List of frame indices where each item should be applied
+        height: Height of the output frames
+        width: Width of the output frames
+        num_frames: Number of frames in the output video
+        padding: Padding to apply to the frames
+        pipeline: LTXVideoPipeline object used for condition video trimming
+
+    Returns:
+        A list of ConditioningItem objects.
+    """
+    conditioning_items = []
+    for path, strength, start_frame in zip(
+        conditioning_media_paths, conditioning_strengths, conditioning_start_frames
+    ):
+        # Check if the path points to an image or video
+        is_video = any(
+            path.lower().endswith(ext) for ext in [".mp4", ".avi", ".mov", ".mkv"]
+        )
+
+        if is_video:
+            reader = imageio.get_reader(path)
+            orig_num_input_frames = reader.count_frames()
+            num_input_frames = pipeline.trim_conditioning_sequence(
+                start_frame, orig_num_input_frames, num_frames
+            )
+            if num_input_frames < orig_num_input_frames:
+                logger.warning(
+                    f"Trimming conditioning video {path} from {orig_num_input_frames} to {num_input_frames} frames."
+                )
+
+            # Read and preprocess the relevant frames from the video file.
+            frames = []
+            for i in range(num_input_frames):
+                frame = Image.fromarray(reader.get_data(i))
+                frame_tensor = load_image_to_tensor_with_resize_and_crop(
+                    frame, height, width
+                )
+                frame_tensor = torch.nn.functional.pad(frame_tensor, padding)
+                frames.append(frame_tensor)
+            reader.close()
+
+            # Stack frames along the temporal dimension
+            video_tensor = torch.cat(frames, dim=2)
+            conditioning_items.append(
+                ConditioningItem(video_tensor, start_frame, strength)
+            )
+        else:  # Input image
+            frame_tensor = load_image_to_tensor_with_resize_and_crop(
+                path, height, width
+            )
+            frame_tensor = torch.nn.functional.pad(frame_tensor, padding)
+            conditioning_items.append(
+                ConditioningItem(frame_tensor, start_frame, strength)
+            )
+
+    return conditioning_items
+
+
+if __name__ == "__main__":
+    main()

LTX-Video/ltx_video.egg-info/PKG-INFO

@@ -0,0 +1,305 @@
+Metadata-Version: 2.4
+Name: ltx-video
+Version: 0.1.2
+Summary: A package for LTX-Video model
+Author-email: Sapir Weissbuch <sapir@lightricks.com>
+Classifier: Programming Language :: Python :: 3
+Classifier: Operating System :: OS Independent
+Requires-Python: >=3.10
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: torch>=2.1.0
+Requires-Dist: diffusers>=0.28.2
+Requires-Dist: transformers>=4.47.2
+Requires-Dist: sentencepiece>=0.1.96
+Requires-Dist: huggingface-hub~=0.25.2
+Requires-Dist: einops
+Requires-Dist: timm
+Provides-Extra: inference-script
+Requires-Dist: accelerate; extra == "inference-script"
+Requires-Dist: matplotlib; extra == "inference-script"
+Requires-Dist: imageio[ffmpeg]; extra == "inference-script"
+Provides-Extra: test
+Requires-Dist: pytest; extra == "test"
+Dynamic: license-file
+
+<div align="center">
+
+# LTX-Video
+
+This is the official repository for LTX-Video.
+
+[Website](https://www.lightricks.com/ltxv) |
+[Model](https://huggingface.co/Lightricks/LTX-Video) |
+[Demo](https://app.ltx.studio/ltx-video) |
+[Paper](https://arxiv.org/abs/2501.00103)
+
+</div>
+
+## Table of Contents
+
+- [Introduction](#introduction)
+- [What's new](#news)
+- [Quick Start Guide](#quick-start-guide)
+  - [Online demo](#online-demo)
+  - [Run locally](#run-locally)
+    - [Installation](#installation)
+    - [Inference](#inference)
+  - [ComfyUI Integration](#comfyui-integration)
+  - [Diffusers Integration](#diffusers-integration)
+- [Model User Guide](#model-user-guide)
+- [Community Contribution](#community-contribution)
+- [Training](#trining)
+- [Join Us!](#join-us)
+- [Acknowledgement](#acknowledgement)
+
+# Introduction
+
+LTX-Video is the first DiT-based video generation model that can generate high-quality videos in *real-time*.
+It can generate 24 FPS videos at 768x512 resolution, faster than it takes to watch them.
+The model is trained on a large-scale dataset of diverse videos and can generate high-resolution videos
+with realistic and diverse content.
+
+The model supports text-to-image, image-to-video, keyframe-based animation, video extension (both forward and backward), video-to-video transformations, and any combination of these features.
+
+| | | | |
+|:---:|:---:|:---:|:---:|
+| ![example1](./docs/_static/ltx-video_example_00001.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A woman with long brown hair and light skin smiles at another woman...</summary>A woman with long brown hair and light skin smiles at another woman with long blonde hair. The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek. The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and natural, likely from the setting sun, casting a soft glow on the scene. The scene appears to be real-life footage.</details> | ![example2](./docs/_static/ltx-video_example_00002.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A woman walks away from a white Jeep parked on a city street at night...</summary>A woman walks away from a white Jeep parked on a city street at night, then ascends a staircase and knocks on a door. The woman, wearing a dark jacket and jeans, walks away from the Jeep parked on the left side of the street, her back to the camera; she walks at a steady pace, her arms swinging slightly by her sides; the street is dimly lit, with streetlights casting pools of light on the wet pavement; a man in a dark jacket and jeans walks past the Jeep in the opposite direction; the camera follows the woman from behind as she walks up a set of stairs towards a building with a green door; she reaches the top of the stairs and turns left, continuing to walk towards the building; she reaches the door and knocks on it with her right hand; the camera remains stationary, focused on the doorway; the scene is captured in real-life footage.</details> | ![example3](./docs/_static/ltx-video_example_00003.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A woman with blonde hair styled up, wearing a black dress...</summary>A woman with blonde hair styled up, wearing a black dress with sequins and pearl earrings, looks down with a sad expression on her face. The camera remains stationary, focused on the woman's face. The lighting is dim, casting soft shadows on her face. The scene appears to be from a movie or TV show.</details> | ![example4](./docs/_static/ltx-video_example_00004.gif)<br><details style="max-width: 300px; margin: auto;"><summary>The camera pans over a snow-covered mountain range...</summary>The camera pans over a snow-covered mountain range, revealing a vast expanse of snow-capped peaks and valleys.The mountains are covered in a thick layer of snow, with some areas appearing almost white while others have a slightly darker, almost grayish hue. The peaks are jagged and irregular, with some rising sharply into the sky while others are more rounded. The valleys are deep and narrow, with steep slopes that are also covered in snow. The trees in the foreground are mostly bare, with only a few leaves remaining on their branches. The sky is overcast, with thick clouds obscuring the sun. The overall impression is one of peace and tranquility, with the snow-covered mountains standing as a testament to the power and beauty of nature.</details> |
+| ![example5](./docs/_static/ltx-video_example_00005.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A woman with light skin, wearing a blue jacket and a black hat...</summary>A woman with light skin, wearing a blue jacket and a black hat with a veil, looks down and to her right, then back up as she speaks; she has brown hair styled in an updo, light brown eyebrows, and is wearing a white collared shirt under her jacket; the camera remains stationary on her face as she speaks; the background is out of focus, but shows trees and people in period clothing; the scene is captured in real-life footage.</details> | ![example6](./docs/_static/ltx-video_example_00006.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A man in a dimly lit room talks on a vintage telephone...</summary>A man in a dimly lit room talks on a vintage telephone, hangs up, and looks down with a sad expression. He holds the black rotary phone to his right ear with his right hand, his left hand holding a rocks glass with amber liquid. He wears a brown suit jacket over a white shirt, and a gold ring on his left ring finger. His short hair is neatly combed, and he has light skin with visible wrinkles around his eyes. The camera remains stationary, focused on his face and upper body. The room is dark, lit only by a warm light source off-screen to the left, casting shadows on the wall behind him. The scene appears to be from a movie.</details> | ![example7](./docs/_static/ltx-video_example_00007.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A prison guard unlocks and opens a cell door...</summary>A prison guard unlocks and opens a cell door to reveal a young man sitting at a table with a woman. The guard, wearing a dark blue uniform with a badge on his left chest, unlocks the cell door with a key held in his right hand and pulls it open; he has short brown hair, light skin, and a neutral expression. The young man, wearing a black and white striped shirt, sits at a table covered with a white tablecloth, facing the woman; he has short brown hair, light skin, and a neutral expression. The woman, wearing a dark blue shirt, sits opposite the young man, her face turned towards him; she has short blonde hair and light skin. The camera remains stationary, capturing the scene from a medium distance, positioned slightly to the right of the guard. The room is dimly lit, with a single light fixture illuminating the table and the two figures. The walls are made of large, grey concrete blocks, and a metal door is visible in the background. The scene is captured in real-life footage.</details> | ![example8](./docs/_static/ltx-video_example_00008.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A woman with blood on her face and a white tank top...</summary>A woman with blood on her face and a white tank top looks down and to her right, then back up as she speaks. She has dark hair pulled back, light skin, and her face and chest are covered in blood. The camera angle is a close-up, focused on the woman's face and upper torso. The lighting is dim and blue-toned, creating a somber and intense atmosphere. The scene appears to be from a movie or TV show.</details> |
+| ![example9](./docs/_static/ltx-video_example_00009.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A man with graying hair, a beard, and a gray shirt...</summary>A man with graying hair, a beard, and a gray shirt looks down and to his right, then turns his head to the left. The camera angle is a close-up, focused on the man's face. The lighting is dim, with a greenish tint. The scene appears to be real-life footage. Step</details> | ![example10](./docs/_static/ltx-video_example_00010.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A clear, turquoise river flows through a rocky canyon...</summary>A clear, turquoise river flows through a rocky canyon, cascading over a small waterfall and forming a pool of water at the bottom.The river is the main focus of the scene, with its clear water reflecting the surrounding trees and rocks. The canyon walls are steep and rocky, with some vegetation growing on them. The trees are mostly pine trees, with their green needles contrasting with the brown and gray rocks. The overall tone of the scene is one of peace and tranquility.</details> | ![example11](./docs/_static/ltx-video_example_00011.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A man in a suit enters a room and speaks to two women...</summary>A man in a suit enters a room and speaks to two women sitting on a couch. The man, wearing a dark suit with a gold tie, enters the room from the left and walks towards the center of the frame. He has short gray hair, light skin, and a serious expression. He places his right hand on the back of a chair as he approaches the couch. Two women are seated on a light-colored couch in the background. The woman on the left wears a light blue sweater and has short blonde hair. The woman on the right wears a white sweater and has short blonde hair. The camera remains stationary, focusing on the man as he enters the room. The room is brightly lit, with warm tones reflecting off the walls and furniture. The scene appears to be from a film or television show.</details> | ![example12](./docs/_static/ltx-video_example_00012.gif)<br><details style="max-width: 300px; margin: auto;"><summary>The waves crash against the jagged rocks of the shoreline...</summary>The waves crash against the jagged rocks of the shoreline, sending spray high into the air.The rocks are a dark gray color, with sharp edges and deep crevices. The water is a clear blue-green, with white foam where the waves break against the rocks. The sky is a light gray, with a few white clouds dotting the horizon.</details> |
+| ![example13](./docs/_static/ltx-video_example_00013.gif)<br><details style="max-width: 300px; margin: auto;"><summary>The camera pans across a cityscape of tall buildings...</summary>The camera pans across a cityscape of tall buildings with a circular building in the center. The camera moves from left to right, showing the tops of the buildings and the circular building in the center. The buildings are various shades of gray and white, and the circular building has a green roof. The camera angle is high, looking down at the city. The lighting is bright, with the sun shining from the upper left, casting shadows from the buildings. The scene is computer-generated imagery.</details> | ![example14](./docs/_static/ltx-video_example_00014.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A man walks towards a window, looks out, and then turns around...</summary>A man walks towards a window, looks out, and then turns around. He has short, dark hair, dark skin, and is wearing a brown coat over a red and gray scarf. He walks from left to right towards a window, his gaze fixed on something outside. The camera follows him from behind at a medium distance. The room is brightly lit, with white walls and a large window covered by a white curtain. As he approaches the window, he turns his head slightly to the left, then back to the right. He then turns his entire body to the right, facing the window. The camera remains stationary as he stands in front of the window. The scene is captured in real-life footage.</details> | ![example15](./docs/_static/ltx-video_example_00015.gif)<br><details style="max-width: 300px; margin: auto;"><summary>Two police officers in dark blue uniforms and matching hats...</summary>Two police officers in dark blue uniforms and matching hats enter a dimly lit room through a doorway on the left side of the frame. The first officer, with short brown hair and a mustache, steps inside first, followed by his partner, who has a shaved head and a goatee. Both officers have serious expressions and maintain a steady pace as they move deeper into the room. The camera remains stationary, capturing them from a slightly low angle as they enter. The room has exposed brick walls and a corrugated metal ceiling, with a barred window visible in the background. The lighting is low-key, casting shadows on the officers' faces and emphasizing the grim atmosphere. The scene appears to be from a film or television show.</details> | ![example16](./docs/_static/ltx-video_example_00016.gif)<br><details style="max-width: 300px; margin: auto;"><summary>A woman with short brown hair, wearing a maroon sleeveless top...</summary>A woman with short brown hair, wearing a maroon sleeveless top and a silver necklace, walks through a room while talking, then a woman with pink hair and a white shirt appears in the doorway and yells. The first woman walks from left to right, her expression serious; she has light skin and her eyebrows are slightly furrowed. The second woman stands in the doorway, her mouth open in a yell; she has light skin and her eyes are wide. The room is dimly lit, with a bookshelf visible in the background. The camera follows the first woman as she walks, then cuts to a close-up of the second woman's face. The scene is captured in real-life footage.</details> |
+
+# News
+
+## March, 5th, 2025: New checkpoint v0.9.5
+- New license for commercial use ([OpenRail-M](https://huggingface.co/Lightricks/LTX-Video/ltx-video-2b-v0.9.5.license.txt))
+- Release a new checkpoint v0.9.5 with improved quality
+- Support keyframes and video extension
+- Support higher resolutions
+- Improved prompt understanding
+- Improved VAE
+- New online web app in [LTX-Studio](https://app.ltx.studio/ltx-video)
+- Automatic prompt enhancement
+
+## February, 20th, 2025: More inference options
+- Improve STG (Spatiotemporal Guidance) for LTX-Video
+- Support MPS on macOS with PyTorch 2.3.0
+- Add support for 8-bit model, LTX-VideoQ8
+- Add TeaCache for LTX-Video
+- Add [ComfyUI-LTXTricks](#comfyui-integration)
+- Add Diffusion-Pipe
+
+## December 31st, 2024: Research paper
+- Release the [research paper](https://arxiv.org/abs/2501.00103)
+
+## December 20th, 2024: New checkpoint v0.9.1
+- Release a new checkpoint v0.9.1 with improved quality
+- Support for STG / PAG
+- Support loading checkpoints of LTX-Video in Diffusers format (conversion is done on-the-fly)
+- Support offloading unused parts to CPU
+- Support the new timestep-conditioned VAE decoder
+- Reference contributions from the community in the readme file
+- Relax transformers dependency
+
+## November 21th, 2024: Initial release v0.9.0
+- Initial release of LTX-Video
+- Support text-to-video and image-to-video generation
+
+# Quick Start Guide
+
+## Online inference
+The model is accessible right away via the following links:
+- [LTX-Studio image-to-video](https://app.ltx.studio/ltx-video)
+- [Fal.ai text-to-video](https://fal.ai/models/fal-ai/ltx-video)
+- [Fal.ai image-to-video](https://fal.ai/models/fal-ai/ltx-video/image-to-video)
+- [Replicate text-to-video and image-to-video](https://replicate.com/lightricks/ltx-video)
+
+## Run locally
+
+### Installation
+The codebase was tested with Python 3.10.5, CUDA version 12.2, and supports PyTorch >= 2.1.2.
+On macos, MPS was tested with PyTorch 2.3.0, and should support PyTorch == 2.3 or >= 2.6.
+
+```bash
+git clone https://github.com/Lightricks/LTX-Video.git
+cd LTX-Video
+
+# create env
+python -m venv env
+source env/bin/activate
+python -m pip install -e .\[inference-script\]
+```
+
+Then, download the model from [Hugging Face](https://huggingface.co/Lightricks/LTX-Video)
+
+```python
+from huggingface_hub import hf_hub_download
+
+model_dir = 'MODEL_DIR'   # The local directory to save downloaded checkpoint
+hf_hub_download(repo_id="Lightricks/LTX-Video", filename="ltx-video-2b-v0.9.5.safetensors", local_dir=model_dir, local_dir_use_symlinks=False, repo_type='model')
+```
+
+### Inference
+
+To use our model, please follow the inference code in [inference.py](./inference.py):
+
+#### For text-to-video generation:
+
+```bash
+python inference.py --ckpt_path 'PATH' --prompt "PROMPT" --height HEIGHT --width WIDTH --num_frames NUM_FRAMES --seed SEED
+```
+
+#### For image-to-video generation:
+
+```bash
+python inference.py --ckpt_path 'PATH' --prompt "PROMPT" --conditioning_media_paths IMAGE_PATH --conditioning_start_frames 0 --height HEIGHT --width WIDTH --num_frames NUM_FRAMES --seed SEED
+```
+
+#### Extending a video:
+
+📝 **Note:** Input video segments must contain a multiple of 8 frames plus 1 (e.g., 9, 17, 25, etc.), and the target frame number should be a multiple of 8.
+
+
+```bash
+python inference.py --ckpt_path 'PATH' --prompt "PROMPT" --conditioning_media_paths VIDEO_PATH --conditioning_start_frames START_FRAME --height HEIGHT --width WIDTH --num_frames NUM_FRAMES --seed SEED
+```
+
+#### For video generation with multiple conditions:
+
+You can now generate a video conditioned on a set of images and/or short video segments.
+Simply provide a list of paths to the images or video segments you want to condition on, along with their target frame numbers in the generated video. You can also specify the conditioning strength for each item (default: 1.0).
+
+```bash
+python inference.py --ckpt_path 'PATH' --prompt "PROMPT" --conditioning_media_paths IMAGE_OR_VIDEO_PATH_1 IMAGE_OR_VIDEO_PATH_2 --conditioning_start_frames TARGET_FRAME_1 TARGET_FRAME_2 --height HEIGHT --width WIDTH --num_frames NUM_FRAMES --seed SEED
+```
+
+## ComfyUI Integration
+To use our model with ComfyUI, please follow the instructions at [https://github.com/Lightricks/ComfyUI-LTXVideo/](https://github.com/Lightricks/ComfyUI-LTXVideo/).
+
+## Diffusers Integration
+To use our model with the Diffusers Python library, check out the [official documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video).
+
+Diffusers also support an 8-bit version of LTX-Video, [see details below](#ltx-videoq8)
+
+# Model User Guide
+
+## 📝 Prompt Engineering
+
+When writing prompts, focus on detailed, chronological descriptions of actions and scenes. Include specific movements, appearances, camera angles, and environmental details - all in a single flowing paragraph. Start directly with the action, and keep descriptions literal and precise. Think like a cinematographer describing a shot list. Keep within 200 words. For best results, build your prompts using this structure:
+
+* Start with main action in a single sentence
+* Add specific details about movements and gestures
+* Describe character/object appearances precisely
+* Include background and environment details
+* Specify camera angles and movements
+* Describe lighting and colors
+* Note any changes or sudden events
+* See [examples](#introduction) for more inspiration.
+
+### Automatic Prompt Enhancement
+When using `inference.py`, shorts prompts (below `prompt_enhancement_words_threshold` words) are automatically enhanced by a language model. This is supported with text-to-video and image-to-video (first-frame conditioning).
+
+When using `LTXVideoPipeline` directly, you can enable prompt enhancement by setting `enhance_prompt=True`.
+
+## 🎮 Parameter Guide
+
+* Resolution Preset: Higher resolutions for detailed scenes, lower for faster generation and simpler scenes. The model works on resolutions that are divisible by 32 and number of frames that are divisible by 8 + 1 (e.g. 257). In case the resolution or number of frames are not divisible by 32 or 8 + 1, the input will be padded with -1 and then cropped to the desired resolution and number of frames. The model works best on resolutions under 720 x 1280 and number of frames below 257
+* Seed: Save seed values to recreate specific styles or compositions you like
+* Guidance Scale: 3-3.5 are the recommended values
+* Inference Steps: More steps (40+) for quality, fewer steps (20-30) for speed
+
+📝 For advanced parameters usage, please see `python inference.py --help`
+
+## Community Contribution
+
+### ComfyUI-LTXTricks 🛠️
+
+A community project providing additional nodes for enhanced control over the LTX Video model. It includes implementations of advanced techniques like RF-Inversion, RF-Edit, FlowEdit, and more. These nodes enable workflows such as Image and Video to Video (I+V2V), enhanced sampling via Spatiotemporal Skip Guidance (STG), and interpolation with precise frame settings.
+
+- **Repository:** [ComfyUI-LTXTricks](https://github.com/logtd/ComfyUI-LTXTricks)
+- **Features:**
+  - 🔄 **RF-Inversion:** Implements [RF-Inversion](https://rf-inversion.github.io/) with an [example workflow here](https://github.com/logtd/ComfyUI-LTXTricks/blob/main/example_workflows/example_ltx_inversion.json).
+  - ✂️ **RF-Edit:** Implements [RF-Solver-Edit](https://github.com/wangjiangshan0725/RF-Solver-Edit) with an [example workflow here](https://github.com/logtd/ComfyUI-LTXTricks/blob/main/example_workflows/example_ltx_rf_edit.json).
+  - 🌊 **FlowEdit:** Implements [FlowEdit](https://github.com/fallenshock/FlowEdit) with an [example workflow here](https://github.com/logtd/ComfyUI-LTXTricks/blob/main/example_workflows/example_ltx_flow_edit.json).
+  - 🎥 **I+V2V:** Enables Video to Video with a reference image. [Example workflow](https://github.com/logtd/ComfyUI-LTXTricks/blob/main/example_workflows/example_ltx_iv2v.json).
+  - ✨ **Enhance:** Partial implementation of [STGuidance](https://junhahyung.github.io/STGuidance/). [Example workflow](https://github.com/logtd/ComfyUI-LTXTricks/blob/main/example_workflows/example_ltxv_stg.json).
+  - 🖼️ **Interpolation and Frame Setting:** Nodes for precise control of latents per frame. [Example workflow](https://github.com/logtd/ComfyUI-LTXTricks/blob/main/example_workflows/example_ltx_interpolation.json).
+
+
+### LTX-VideoQ8 🎱 <a id="ltx-videoq8"></a>
+
+**LTX-VideoQ8** is an 8-bit optimized version of [LTX-Video](https://github.com/Lightricks/LTX-Video), designed for faster performance on NVIDIA ADA GPUs.
+
+- **Repository:** [LTX-VideoQ8](https://github.com/KONAKONA666/LTX-Video)
+- **Features:**
+  - 🚀 Up to 3X speed-up with no accuracy loss
+  - 🎥 Generate 720x480x121 videos in under a minute on RTX 4060 (8GB VRAM)
+  - 🛠️ Fine-tune 2B transformer models with precalculated latents
+- **Community Discussion:** [Reddit Thread](https://www.reddit.com/r/StableDiffusion/comments/1h79ks2/fast_ltx_video_on_rtx_4060_and_other_ada_gpus/)
+- **Diffusers integration:** A diffusers integration for the 8-bit model is already out! [Details here](https://github.com/sayakpaul/q8-ltx-video)
+
+
+### TeaCache for LTX-Video 🍵 <a id="TeaCache"></a>
+
+**TeaCache** is a training-free caching approach that leverages timestep differences across model outputs to accelerate LTX-Video inference by up to 2x without significant visual quality degradation.
+
+- **Repository:** [TeaCache4LTX-Video](https://github.com/ali-vilab/TeaCache/tree/main/TeaCache4LTX-Video)
+- **Features:**
+  - 🚀 Speeds up LTX-Video inference.
+  - 📊 Adjustable trade-offs between speed (up to 2x) and visual quality using configurable parameters.
+  - 🛠️ No retraining required: Works directly with existing models.
+
+### Your Contribution
+
+...is welcome! If you have a project or tool that integrates with LTX-Video,
+please let us know by opening an issue or pull request.
+
+# Training
+
+## Diffusers
+
+Diffusers implemented [LoRA support](https://github.com/huggingface/diffusers/pull/10228),
+with a training script for fine-tuning.
+More information and training script in
+[finetrainers](https://github.com/a-r-r-o-w/finetrainers?tab=readme-ov-file#training).
+
+## Diffusion-Pipe
+
+An experimental training framework with pipeline parallelism, enabling fine-tuning of large models like **LTX-Video** across multiple GPUs.
+
+- **Repository:** [Diffusion-Pipe](https://github.com/tdrussell/diffusion-pipe)
+- **Features:**
+  - 🛠️ Full fine-tune support for LTX-Video using LoRA
+  - 📊 Useful metrics logged to Tensorboard
+  - 🔄 Training state checkpointing and resumption
+  - ⚡ Efficient pre-caching of latents and text embeddings for multi-GPU setups
+
+
+# Join Us 🚀
+
+Want to work on cutting-edge AI research and make a real impact on millions of users worldwide?
+
+At **Lightricks**, an AI-first company, we're revolutionizing how visual content is created.
+
+If you are passionate about AI, computer vision, and video generation, we would love to hear from you!
+
+Please visit our [careers page](https://careers.lightricks.com/careers?query=&office=all&department=R%26D) for more information.
+
+# Acknowledgement
+
+We are grateful for the following awesome projects when implementing LTX-Video:
+* [DiT](https://github.com/facebookresearch/DiT) and [PixArt-alpha](https://github.com/PixArt-alpha/PixArt-alpha): vision transformers for image generation.
+
+
+## Citation
+
+📄 Our tech report is out! If you find our work helpful, please ⭐️ star the repository and cite our paper.
+
+```
+@article{HaCohen2024LTXVideo,
+  title={LTX-Video: Realtime Video Latent Diffusion},
+  author={HaCohen, Yoav and Chiprut, Nisan and Brazowski, Benny and Shalem, Daniel and Moshe, Dudu and Richardson, Eitan and Levin, Eran and Shiran, Guy and Zabari, Nir and Gordon, Ori and Panet, Poriya and Weissbuch, Sapir and Kulikov, Victor and Bitterman, Yaki and Melumian, Zeev and Bibi, Ofir},
+  journal={arXiv preprint arXiv:2501.00103},
+  year={2024}
+}
+```