--- title: LTX-2.5-GGUF canonical_url: "https://www.modelscope.cn/models/unsloth/LTX-2.5-GGUF" md_url: "https://www.modelscope.cn/models/unsloth/LTX-2.5-GGUF.md" repository: unsloth/LTX-2.5-GGUF last_updated: 2026-10-16 license: other pipeline_tag: image-to-video tasks: - image-to-video base_model: - Lightricks/LTX-2.5 base_model_relation: quantized parameters: 56.6B tensor_type: - BF16 - F32 - U8 library_name: - pytorch - safetensors - gguf frameworks: - pytorch language: - en - de - es - fr - ja - ko - zh - it - pt downloads: 3095 stars: 0 tags: - gguf - unsloth - comfyui - text-to-video - image-to-video - text-to-audio-video - image-to-audio-video - ltx-2 - ltx-2.5 - ltx-video - ltxv - lightricks --- # LTX-2.5-GGUF > LTX-2.5-GGUF - unsloth 在 ModelScope 开源的模型。This is a GGUF quantized version of LTX-2.5 by Lightricks, the 22B audio-video DiT, both the distilled (8 + 3 steps, CFG 1) and the dev (full, 30 steps, CFG 3) checkpoint. unsloth/LTX-2.5-GGUF uses Unsloth Dynamic… unsloth/LTX-2.5-GGUF 是 ModelScope 魔搭社区上的 56.6B 参数image-to-video模型，采用 other 许可，基于 Lightricks/LTX-2.5 构建。 - **Repository**: unsloth/LTX-2.5-GGUF - **License**: other - **Tasks**: image-to-video - **Parameters**: 56.6B - **Base model**: Lightricks/LTX-2.5 - **Tags**: gguf, unsloth, comfyui, text-to-video, image-to-video, text-to-audio-video, image-to-audio-video, ltx-2, ltx-2.5, ltx-video, ltxv, lightricks - **Downloads**: 3095 - **Stars**: 0 - **Last updated**: 2026-10-16 Source: https://www.modelscope.cn/models/unsloth/LTX-2.5-GGUF --- This is a GGUF quantized version of [LTX-2.5](https://huggingface.co/Lightricks/LTX-2.5) by Lightricks, the 22B audio-video DiT, both the **distilled** (8 + 3 steps, CFG 1) and the **dev** (full, 30 steps, CFG 3) checkpoint.
unsloth/LTX-2.5-GGUF uses [Unsloth Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) methodology: the `UD-` files are mixed precision, with every layer's type chosen from a measured sensitivity scan, and they beat the uniform files at the same size. - Loads in [Unsloth Studio](https://github.com/unslothai/unsloth/) and in ComfyUI through [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) (`Unet Loader (GGUF)`), both tested on every file here. - Same layout as [unsloth/LTX-2.3-GGUF](https://huggingface.co/unsloth/LTX-2.3-GGUF): dev files at the root, distilled files in `distilled/`. The text encoder, VAEs and latent upscaler are unchanged; take them from [Lightricks/LTX-2.5](https://huggingface.co/Lightricks/LTX-2.5).
**Recommended:** `UD-Q4_K_XL` (14.0 GB) for most GPUs: clearly better than `Q4_K_M` and on par with `Q5_K_M`, which is 1.6 GB larger. For output closest to bf16 use `Q8_0`; on small cards use `UD-Q2_K_XL` (9.5 GB), which is far cleaner than `Q2_K` and close to `Q3_K_M` at 1.8 GB less. ## Samples `distilled/ltx-2.5-22b-distilled-UD-Q4_K_XL.gguf`, 1280x704, 121 frames, 24 fps, seed 42, with audio: [text to video](samples/ltx25_distilled_UD-Q4_K_XL_t2v.mp4), [image to video](samples/ltx25_distilled_UD-Q4_K_XL_i2v.mp4). Frame 60 of the same text-to-video render from each file: And of the image to video render: ## Files and accuracy LPIPS (lower is better) and PSNR against the bf16 DiT, same prompt, seed, text encoder and settings, mean over every one of the 121 frames. Distilled: 1280x704, 121 frames, 8 + 3 two-stage steps, CFG 1, text to video and image to video, 4 seeds (8 clips). Dev: 1280x704, 121 frames, 30 steps, CFG 3, text to video and image to video, 2 seeds (4 clips). Both run deterministically, so a repeated bf16 render scores 0 and the numbers measure only the quantization. The samplers re-inject noise every step, so small weight errors move the motion more than the look of a frame: every file keeps the subject, scene and prompt. For scale: the hosted [unsloth/LTX-2.5-FP8](https://huggingface.co/unsloth/LTX-2.5-FP8) INT8-ConvRot and FP8 DiTs score 0.265 and 0.366 on the same clips in Studio's default (compiled) mode, where compiled versus uncompiled bf16 alone already differs by 0.265. Distilled (`distilled/`): | File | Size | Bits per weight | LPIPS vs bf16 | PSNR vs bf16 | |---|---|---|---|---| | `distilled/ltx-2.5-22b-distilled-Q2_K.gguf` | 8.46 GB | 3.22 | 0.567 | 14.8 dB | | `distilled/ltx-2.5-22b-distilled-UD-Q2_K_XL.gguf` | 9.50 GB | 3.62 | 0.416 | 17.1 dB | | `distilled/ltx-2.5-22b-distilled-Q3_K_M.gguf` | 11.30 GB | 4.30 | 0.390 | 17.3 dB | | `distilled/ltx-2.5-22b-distilled-UD-Q3_K_XL.gguf` | 12.00 GB | 4.57 | 0.310 | 19.1 dB | | `distilled/ltx-2.5-22b-distilled-Q4_K_M.gguf` | 13.58 GB | 5.17 | 0.352 | 18.1 dB | | `distilled/ltx-2.5-22b-distilled-UD-Q4_K_XL.gguf` | 14.00 GB | 5.33 | 0.320 | 19.4 dB | | `distilled/ltx-2.5-22b-distilled-Q5_K_M.gguf` | 15.63 GB | 5.95 | 0.316 | 18.9 dB | | `distilled/ltx-2.5-22b-distilled-UD-Q5_K_XL.gguf` | 16.00 GB | 6.09 | 0.296 | 20.3 dB | | `distilled/ltx-2.5-22b-distilled-Q6_K.gguf` | 17.81 GB | 6.78 | 0.274 | 20.4 dB | | `distilled/ltx-2.5-22b-distilled-Q8_0.gguf` | 22.78 GB | 8.68 | 0.242 | 21.6 dB | | `distilled/ltx-2.5-22b-distilled-BF16.gguf` | 42.03 GB | 16.01 | 0 (bit-identical) | lossless | Dev (repo root): | File | Size | Bits per weight | LPIPS vs bf16 | PSNR vs bf16 | |---|---|---|---|---| | `ltx-2.5-22b-dev-Q2_K.gguf` | 8.46 GB | 3.22 | 0.592 | 14.3 dB | | `ltx-2.5-22b-dev-UD-Q2_K_XL.gguf` | 9.50 GB | 3.62 | 0.354 | 16.6 dB | | `ltx-2.5-22b-dev-Q3_K_M.gguf` | 11.30 GB | 4.30 | 0.427 | 15.4 dB | | `ltx-2.5-22b-dev-UD-Q3_K_XL.gguf` | 12.00 GB | 4.57 | 0.246 | 20.0 dB | | `ltx-2.5-22b-dev-Q4_K_M.gguf` | 13.58 GB | 5.17 | 0.330 | 16.8 dB | | `ltx-2.5-22b-dev-UD-Q4_K_XL.gguf` | 14.00 GB | 5.33 | 0.183 | 21.4 dB | | `ltx-2.5-22b-dev-Q5_K_M.gguf` | 15.63 GB | 5.95 | 0.208 | 20.1 dB | | `ltx-2.5-22b-dev-UD-Q5_K_XL.gguf` | 16.00 GB | 6.09 | 0.162 | 22.1 dB | | `ltx-2.5-22b-dev-Q6_K.gguf` | 17.81 GB | 6.78 | 0.180 | 21.2 dB | | `ltx-2.5-22b-dev-Q8_0.gguf` | 22.78 GB | 8.68 | 0.169 | 23.4 dB | | `ltx-2.5-22b-dev-BF16.gguf` | 42.03 GB | 16.01 | reference | reference | `BF16` is a lossless repack: rendered through the GGUF path it is bit-identical to the original bf16 safetensors. ## Run in Unsloth Studio Pick LTX-2.5 on the Video page and choose one of these GGUF files. Studio loads the DiT from the GGUF and the text encoder, VAEs, connectors and upsampler from [Lightricks/LTX-2.5-Diffusers](https://huggingface.co/Lightricks/LTX-2.5-Diffusers), and runs the distilled files with the two-stage 8 + 3 step recipe and the dev files at 30 steps, CFG 3. ## Run in ComfyUI Use an up to date ComfyUI (with LTX-2.5 support) and the [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) custom node. ```bash cd ComfyUI/custom_nodes git clone https://github.com/city96/ComfyUI-GGUF.git pip install -r ComfyUI-GGUF/requirements.txt cd ../models ln -s "$(hf download unsloth/LTX-2.5-GGUF distilled/ltx-2.5-22b-distilled-UD-Q4_K_XL.gguf --quiet)" unet/. ln -s "$(hf download Lightricks/LTX-2.5 text_encoders/gemma4-12b-with-proj-ltx-2.5-bf16.safetensors --quiet)" text_encoders/. ln -s "$(hf download Lightricks/LTX-2.5 vae/ltx-2.5-video-vae-bf16.safetensors --quiet)" vae/. ln -s "$(hf download Lightricks/LTX-2.5 vae/ltx-2.5-audio-vae-bf16.safetensors --quiet)" vae/. ln -s "$(hf download Lightricks/LTX-2.5 latent_upscale_models/ltx-2.5-latent-spatial-upscaler-x2-bf16-1.0.safetensors --quiet)" latent_upscale_models/. ``` Open ComfyUI's built-in **Text to Video (LTX-2.5)** template and replace its `Load Diffusion Model` node with `Unet Loader (GGUF)` pointing at the GGUF file. Everything else in the template stays as it is. ## Licence LTX-2.x Community License Agreement, from Lightricks/LTX-2.5. Full text in [`LICENSE`](LICENSE). These files are quantized derivatives of the original weights; not an official Lightricks release. ---
LTX-2.5 — Video, Audio & World Simulation

LTX-2.5 — Video, Audio & World Simulation

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--- **LTX-2.5** is 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 in [`LICENSE`](https://github.com/Lightricks/LTX-2/blob/main/LICENSE-2_x). ## What'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. --- # Model family & checkpoints LTX-2.5 ships as a **split, Comfy-aligned pack** (one `.safetensors` per component) rather than a single monolith. Point each CLI flag / loader at the file below. ## Transformers (DiT) | File | Notes | |------|-------| | [`diffusion_models/ltx-2.5-22b-distilled-transformer-bf16.safetensors`](https://huggingface.co/Lightricks/LTX-2.5/blob/main/diffusion_models/ltx-2.5-22b-distilled-transformer-bf16.safetensors) | Distilled DiT (bf16). Fixed 8-step schedule, CFG=1. | | [`diffusion_models/ltx-2.5-22b-dev-transformer-bf16.safetensors`](https://huggingface.co/Lightricks/LTX-2.5/blob/main/diffusion_models/ltx-2.5-22b-dev-transformer-bf16.safetensors) | Full / trainable DiT (bf16). | | [`diffusion_models/ltx-2.5-22b-distilled-transformer-comfy-int8-convrot.safetensors`](https://huggingface.co/Lightricks/LTX-2.5/blob/main/diffusion_models/ltx-2.5-22b-distilled-transformer-comfy-int8-convrot.safetensors) | Distilled DiT (Comfy int8 + convrot). **ComfyUI only** — not for `ltx-pipelines` / PyTorch. | | [`diffusion_models/ltx-2.5-22b-dev-transformer-comfy-int8-convrot.safetensors`](https://huggingface.co/Lightricks/LTX-2.5/blob/main/diffusion_models/ltx-2.5-22b-dev-transformer-comfy-int8-convrot.safetensors) | Full DiT (Comfy int8 + convrot). **ComfyUI only** — not for `ltx-pipelines` / PyTorch. | | [`diffusion_models/ltx-2.5-22b-distilled-transformer-nvfp4.safetensors`](https://huggingface.co/Lightricks/LTX-2.5/blob/main/diffusion_models/ltx-2.5-22b-distilled-transformer-nvfp4.safetensors) | Distilled DiT (NVFP4). ComfyUI, or `ltx-pipelines` with `--quantization nvfp4-prequant` (Blackwell / `ltx-kernels`). | ## Other components | File | Notes | |------|-------| | [`text_encoders/gemma4-12b-with-proj-ltx-2.5-bf16.safetensors`](https://huggingface.co/Lightricks/LTX-2.5/blob/main/text_encoders/gemma4-12b-with-proj-ltx-2.5-bf16.safetensors) | Gemma4 TE + projections (bf16) | | [`text_encoders/gemma4-12b-with-proj-ltx-2.5-comfy-int8-convrot.safetensors`](https://huggingface.co/Lightricks/LTX-2.5/blob/main/text_encoders/gemma4-12b-with-proj-ltx-2.5-comfy-int8-convrot.safetensors) | Same TE, Comfy int8 — **ComfyUI only** | | [`vae/ltx-2.5-video-vae-bf16.safetensors`](https://huggingface.co/Lightricks/LTX-2.5/blob/main/vae/ltx-2.5-video-vae-bf16.safetensors) | DiffVAE — higher quality, heavier | | [`vae/ltx-2.5-video-vae-conv-bf16.safetensors`](https://huggingface.co/Lightricks/LTX-2.5/blob/main/vae/ltx-2.5-video-vae-conv-bf16.safetensors) | Conv VAE — faster, lighter | | [`vae/ltx-2.5-audio-vae-bf16.safetensors`](https://huggingface.co/Lightricks/LTX-2.5/blob/main/vae/ltx-2.5-audio-vae-bf16.safetensors) | Audio VAE + vocoder | | [`loras/ltx-2.5-22b-distilled-lora-450-bf16.safetensors`](https://huggingface.co/Lightricks/LTX-2.5/blob/main/loras/ltx-2.5-22b-distilled-lora-450-bf16.safetensors) | Distilled LoRA (dev-transformer workflows) | | [`model_patches/ltx-2.5-duration-head-bf16.safetensors`](https://huggingface.co/Lightricks/LTX-2.5/blob/main/model_patches/ltx-2.5-duration-head-bf16.safetensors) | Auto duration when `--num-frames` omitted | | [`latent_upscale_models/ltx-2.5-latent-spatial-upscaler-x2-bf16-1.0.safetensors`](https://huggingface.co/Lightricks/LTX-2.5/blob/main/latent_upscale_models/ltx-2.5-latent-spatial-upscaler-x2-bf16-1.0.safetensors) | x2 spatial upscaler required for multi-stage pipeline | | [`latent_upscale_models/ltx-2.5-latent-temporal-upscaler-x2-bf16-1.0.safetensors`](https://huggingface.co/Lightricks/LTX-2.5/blob/main/latent_upscale_models/ltx-2.5-latent-temporal-upscaler-x2-bf16-1.0.safetensors) | x2 temporal upscaler | --- # Usage ### Online demo Try LTX-2.5 in the [API Playground](https://console.ltx.video/playground/) without installing anything locally. ### Option A — Python (`ltx-pipelines`) Weights on this repo are **split** (Comfy-aligned): one safetensors file per component. The [LTX-2](https://github.com/Lightricks/LTX-2) `ltx-pipelines` package loads them via `--transformer-path`, `--text-encoder-path`, etc. #### Install ```bash 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 the [repo README](https://github.com/Lightricks/LTX-2) for attention backends and optional extras. #### Download weights ```bash 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 \ latent_upscale_models/ltx-2.5-latent-spatial-upscaler-x2-bf16-1.0.safetensors \ --local-dir models/ltx-2.5 ``` #### Distilled text-to-video ```bash 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-2.5/latent_upscale_models/ltx-2.5-latent-spatial-upscaler-x2-bf16-1.0.safetensors \ --prompt "A golden retriever running through a sunny meadow, cinematic lighting" \ --seed 42 \ --output-path output_distilled.mp4 ``` Omit `--num-frames` to let the duration head pick a length from the prompt (LTX-2.5+). Or set e.g. `--num-frames 121` (must satisfy `frames % 8 == 1`). Width/height must be divisible by 32. #### Image-to-video Add one or more `--image PATH FRAME_IDX STRENGTH` flags (frame 0 = first frame conditioning): ```bash 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-2.5/latent_upscale_models/ltx-2.5-latent-spatial-upscaler-x2-bf16-1.0.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 ``` #### Low-VRAM tips ```bash # Downcast bf16 transformer on the fly + CPU offload ...existing flags... \ --quantization fp8-cast \ --offload cpu ``` Use the **bf16** checkpoints with `ltx-pipelines`. The `*-comfy-int8-convrot.safetensors` files are ComfyUI-only and are not loaded by this PyTorch path. #### Python API (same split paths) ```python 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-2.5/latent_upscale_models/ltx-2.5-latent-spatial-upscaler-x2-bf16-1.0.safetensors", ) # See packages/ltx-pipelines for __call__ args (prompt, seed, num_frames, images, ...). ``` ```bash uv run python -m ltx_pipelines.distilled --help ``` Full docs: [ltx-pipelines installation](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx-pipelines/docs/installation.md). ### Option B — ComfyUI Official LTX-2.5 workflow templates ship in ComfyUI. Full instructions: [ComfyUI integration](https://docs.ltx.video/open-source-model/integration-tools/comfy-ui). ### Option C — Diffusers A Diffusers-compatible pack lives at [`Lightricks/LTX-2.5-Diffusers`](https://huggingface.co/Lightricks/LTX-2.5-Diffusers) — same model, Diffusers-friendly packaging. #### Install LTX-2.5 support is not in a `diffusers` release yet, so install from main: ```bash pip install git+https://github.com/huggingface/diffusers ``` #### Image-to-video, two stages ```python 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, ) ``` --- ### Constraints - Frame count: `num_frames % 8 == 1` (1, 9, 17, …, 121, …) - Width and height divisible by 32 ### Prompting Well-structured, detailed prompts materially improve results. For multishot prompting and a full guide, see [How to prompt LTX-2](https://docs.ltx.video/open-source-model/usage-guides/prompting-guide). --- # Training & fine-tuning The **dev** transformer is fully trainable. Reproduce published LoRAs and IC-LoRAs with the [LTX-2 Trainer](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx-trainer/README.md). 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. --- # Limitations - 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. --- ## Citation ```bibtex @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} } ```