Lightricks/LTX-2.5 · Hugging Face

Source: https://huggingface.co/Lightricks/LTX-2.5

LTX-2.5is an open world model with open weights, built for local execution and fine-tuning. Its established use is generating synchronized, high-fidelity video and audio from text, image, and video inputs; applicability to emerging domains such as robotics and physical AI is developing.

Full control and customization— self-host on your own infrastructure. No per-generation billing, no per-seat lock-in, no forced API dependency. Revenue is measured across the whole entity, including subsidiaries and affiliates under common control. The full, binding terms live inLICENSE.

https://huggingface.co/Lightricks/LTX-2.5#whats-new-in-ltx-25What’s new in LTX-2.5

• Native multishot generation— generate connected scenes in a single pass: multiple shots that hold character identity, environment, lighting, voice, and visual style across cuts (previous versions produced a single continuous shot).
• Diffusion fidelity rendering— Instead of locking every scene to one compression rate, our model dynamically allocates compute by scene complexity and budget, rendering flawless detail where it matters, efficient everywhere else.
• New diffusion video decoder— replaces the VAE reconstruction stage; sharper faces, textures, and on-screen text, better motion, and fewer artifacts in demanding scenes.
• Custom Gemma 4 12B text encoder— holds complex prompts together (multiple characters, camera moves, lighting, actions) instead of dropping details across a longer sequence.
• Prompt enhancer— expands a short prompt into richer cinematic instructions at minimal extra compute.
• Duration predictor (optional)— an opt-in node predicts a clip’s length from the prompt and sets the frame count for you, instead of relying on a fixed-duration parameter.
• Substantially improved distilled model— retains much more of the full model’s visual quality, prompt adherence, and motion consistency in a smaller, faster checkpoint.

https://huggingface.co/Lightricks/LTX-2.5#model-family–checkpointsModel family & checkpoints

LTX-2.5 ships as asplit, Comfy-aligned pack(one\.safetensorsper component) rather than a single monolith. Point each CLI flag / loader at the file below.

https://huggingface.co/Lightricks/LTX-2.5#transformers-ditTransformers (DiT)

https://huggingface.co/Lightricks/LTX-2.5#other-componentsOther components

https://huggingface.co/Lightricks/LTX-2.5#usageUsage

https://huggingface.co/Lightricks/LTX-2.5#online-demoOnline demo

Try LTX-2.5 in theAPI Playgroundwithout installing anything locally.

https://huggingface.co/Lightricks/LTX-2.5#option-a–python-ltx-pipelinesOption A — Python (ltx\-pipelines)

Weights on this repo aresplit(Comfy-aligned): one safetensors file per component. TheLTX-2ltx\-pipelinespackage loads them via\-\-transformer\-path,\-\-text\-encoder\-path, etc.

https://huggingface.co/Lightricks/LTX-2.5#installInstall

git clone https://github.com/Lightricks/LTX-2.git
cd LTX-2
uv sync
source .venv/bin/activate

Python >= 3.12, CUDA >= 12.7, PyTorch ~= 2.7 recommended. See therepo READMEfor attention backends and optional extras.

https://huggingface.co/Lightricks/LTX-2.5#download-weightsDownload weights

hf auth login

# LTX-2.5 distilled split pack
hf download Lightricks/LTX-2.5 \
  diffusion_models/ltx-2.5-22b-distilled-transformer-bf16.safetensors \
  text_encoders/gemma4-12b-with-proj-ltx-2.5-bf16.safetensors \
  vae/ltx-2.5-video-vae-bf16.safetensors \
  vae/ltx-2.5-audio-vae-bf16.safetensors \
  model_patches/ltx-2.5-duration-head-bf16.safetensors \
  --local-dir models/ltx-2.5

# Spatial upscaler (still hosted on LTX-2.3; required by the distilled pipeline)
hf download Lightricks/LTX-2.3 \
  ltx-2.3-spatial-upscaler-x2-1.1.safetensors \
  --local-dir models/ltx-2.3

https://huggingface.co/Lightricks/LTX-2.5#distilled-text-to-videoDistilled text-to-video

uv run python -m ltx_pipelines.distilled \
  --transformer-path     models/ltx-2.5/diffusion_models/ltx-2.5-22b-distilled-transformer-bf16.safetensors \
  --text-encoder-path    models/ltx-2.5/text_encoders/gemma4-12b-with-proj-ltx-2.5-bf16.safetensors \
  --video-vae-path       models/ltx-2.5/vae/ltx-2.5-video-vae-bf16.safetensors \
  --audio-vae-path       models/ltx-2.5/vae/ltx-2.5-audio-vae-bf16.safetensors \
  --duration-head-path   models/ltx-2.5/model_patches/ltx-2.5-duration-head-bf16.safetensors \
  --spatial-upsampler-path models/ltx-2.3/ltx-2.3-spatial-upscaler-x2-1.1.safetensors \
  --prompt "A golden retriever running through a sunny meadow, cinematic lighting" \
  --seed 42 \
  --output-path output_distilled.mp4

Omit\-\-num\-framesto let the duration head pick a length from the prompt (LTX-2.5+). Or set e.g.\-\-num\-frames 121(must satisfyframes % 8 == 1). Width/height must be divisible by 32.

https://huggingface.co/Lightricks/LTX-2.5#image-to-videoImage-to-video

Add one or more\-\-image PATH FRAME\_IDX STRENGTHflags (frame 0 = first frame conditioning):

uv run python -m ltx_pipelines.distilled \
  --transformer-path     models/ltx-2.5/diffusion_models/ltx-2.5-22b-distilled-transformer-bf16.safetensors \
  --text-encoder-path    models/ltx-2.5/text_encoders/gemma4-12b-with-proj-ltx-2.5-bf16.safetensors \
  --video-vae-path       models/ltx-2.5/vae/ltx-2.5-video-vae-bf16.safetensors \
  --audio-vae-path       models/ltx-2.5/vae/ltx-2.5-audio-vae-bf16.safetensors \
  --duration-head-path   models/ltx-2.5/model_patches/ltx-2.5-duration-head-bf16.safetensors \
  --spatial-upsampler-path models/ltx-2.3/ltx-2.3-spatial-upscaler-x2-1.1.safetensors \
  --image path/to/first_frame.jpg 0 1.0 \
  --prompt "The camera slowly dollies out as wind moves through the grass" \
  --seed 42 \
  --output-path output_i2v.mp4

https://huggingface.co/Lightricks/LTX-2.5#low-vram-tipsLow-VRAM tips

# Downcast bf16 transformer on the fly + CPU offload
  ...existing flags... \
  --quantization fp8-cast \
  --offload cpu

Use thebf16checkpoints withltx\-pipelines. The\*\-comfy\-int8\-convrot\.safetensorsfiles are ComfyUI-only and are not loaded by this PyTorch path.

https://huggingface.co/Lightricks/LTX-2.5#python-api-same-split-pathsPython API (same split paths)

from ltx_pipelines.distilled import DistilledPipeline
from ltx_pipelines.utils.model_paths import ModelPaths

model_paths = ModelPaths.from_split(
    transformer_path="models/ltx-2.5/diffusion_models/ltx-2.5-22b-distilled-transformer-bf16.safetensors",
    text_encoder_path="models/ltx-2.5/text_encoders/gemma4-12b-with-proj-ltx-2.5-bf16.safetensors",
    video_vae_path="models/ltx-2.5/vae/ltx-2.5-video-vae-bf16.safetensors",
    audio_vae_path="models/ltx-2.5/vae/ltx-2.5-audio-vae-bf16.safetensors",
    duration_head_path="models/ltx-2.5/model_patches/ltx-2.5-duration-head-bf16.safetensors",
)

pipe = DistilledPipeline(
    model_paths=model_paths,
    spatial_upsampler_path="models/ltx-2.3/ltx-2.3-spatial-upscaler-x2-1.1.safetensors",
)
# See packages/ltx-pipelines for __call__ args (prompt, seed, num_frames, images, ...).

uv run python -m ltx_pipelines.distilled --help

Full docs:ltx-pipelines installation.

https://huggingface.co/Lightricks/LTX-2.5#option-b–comfyuiOption B — ComfyUI

Official LTX-2.5 workflow templates ship in ComfyUI. Full instructions:ComfyUI integration.

https://huggingface.co/Lightricks/LTX-2.5#option-c–diffusersOption C — Diffusers

A Diffusers-compatible pack lives atLightricks/LTX\-2\.5\-Diffusers— same model, Diffusers-friendly packaging.

https://huggingface.co/Lightricks/LTX-2.5#install-1Install

LTX-2.5 support is not in adiffusersrelease yet, so install from main:

pip install git+https://github.com/huggingface/diffusers

https://huggingface.co/Lightricks/LTX-2.5#image-to-video-two-stagesImage-to-video, two stages

import torch
from diffusers import LTX2ImageToVideoPipeline, LTX2LatentUpsamplePipeline
from diffusers.pipelines.ltx2.latent_upsampler import LTX2LatentUpsamplerModel
from diffusers.pipelines.ltx2.utils import (
    DEFAULT_NEGATIVE_PROMPT,
    DISTILLED_SIGMA_VALUES,
    STAGE_2_DISTILLED_SIGMA_VALUES,
)
from diffusers.utils import encode_video, load_image

MODEL_ID = "Lightricks/LTX-2.5-Diffusers"
# Stage 1 resolution; stage 2 runs at 2x this.
HEIGHT, WIDTH, NUM_FRAMES, FRAME_RATE = 544, 960, 121, 24.0

pipe = LTX2ImageToVideoPipeline.from_pretrained(MODEL_ID, dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
pipe.vae.enable_tiling()  # stage 2 decodes at 2x

latent_upsampler = LTX2LatentUpsamplerModel.from_pretrained(
    MODEL_ID, subfolder="latent_upsampler", dtype=torch.bfloat16
).to("cuda")
upsample_pipe = LTX2LatentUpsamplePipeline(vae=pipe.vae, latent_upsampler=latent_upsampler)

generator = torch.Generator("cuda").manual_seed(42)
shared = dict(
    image=load_image("path/to/first_frame.jpg"),
    prompt="The camera slowly dollies out as wind moves through the grass",
    negative_prompt=DEFAULT_NEGATIVE_PROMPT,
    frame_rate=FRAME_RATE,
    guidance_scale=1.0,
    audio_guidance_scale=1.0,
    stg_scale=0.0,
    audio_stg_scale=0.0,
    modality_scale=1.0,
    audio_modality_scale=1.0,
    generator=generator,
    return_dict=False,
)

stage_1_latents, audio_latents = pipe(
    height=HEIGHT, width=WIDTH, num_frames=NUM_FRAMES,
    sigmas=DISTILLED_SIGMA_VALUES, output_type="latent", **shared,
)

upsampled_latents = upsample_pipe(
    latents=stage_1_latents, output_type="latent", return_dict=False
)[0]

# Stage 2 takes its size from the upsampled latents, so pass no height/width.
video, audio = pipe(
    num_frames=NUM_FRAMES,
    sigmas=STAGE_2_DISTILLED_SIGMA_VALUES,
    latents=upsampled_latents,
    audio_latents=audio_latents,
    noise_scale=STAGE_2_DISTILLED_SIGMA_VALUES[0],
    output_type="np",
    **shared,
)

encode_video(
    video[0],
    fps=int(FRAME_RATE),
    output_path="output_i2v_two_stage.mp4",
    audio=audio[0].float().cpu(),
    audio_sample_rate=pipe.vocoder.config.output_sampling_rate,
)

https://huggingface.co/Lightricks/LTX-2.5#constraintsConstraints

• Frame count:num\_frames % 8 == 1(1, 9, 17, …, 121, …)
• Width and height divisible by 32

https://huggingface.co/Lightricks/LTX-2.5#promptingPrompting

Well-structured, detailed prompts materially improve results. For multishot prompting and a full guide, seeHow to prompt LTX-2.

https://huggingface.co/Lightricks/LTX-2.5#training–fine-tuningTraining & fine-tuning

Thedevtransformer is fully trainable. Reproduce published LoRAs and IC-LoRAs with theLTX-2 Trainer.

Based on our testing, the large majority of LoRAs and IC-LoRAs trained on LTX-2.3 run on LTX-2.5 without changes. A small number of exceptions exist — validate your adapters before production use.

https://huggingface.co/Lightricks/LTX-2.5#limitationsLimitations

• This model is not intended or able to provide factual information.
• As a statistical model, this checkpoint may amplify existing societal biases.
• Prompt following is heavily influenced by prompting style.
• The model may fail to generate videos that match the prompt perfectly.
• The model may generate content that is inappropriate or offensive.

https://huggingface.co/Lightricks/LTX-2.5#citationCitation

@article{hacohen2025ltx2,
  title={LTX-2: Efficient Joint Audio-Visual Foundation Model},
  author={HaCohen, Yoav and Brazowski, Benny and Chiprut, Nisan and Bitterman, Yaki and Kvochko, Andrew and Berkowitz, Avishai and Shalem, Daniel and Lifschitz, Daphna and Moshe, Dudu and Porat, Eitan and Richardson, Eitan and Guy Shiran and Itay Chachy and Jonathan Chetboun and Michael Finkelson and Michael Kupchick and Nir Zabari and Nitzan Guetta and Noa Kotler and Ofir Bibi and Ori Gordon and Poriya Panet and Roi Benita and Shahar Armon and Victor Kulikov and Yaron Inger and Yonatan Shiftan and Zeev Melumian and Zeev Farbman},
  journal={arXiv preprint arXiv:2601.03233},
  year={2026}
}