--- title: LTX-2.5-FP8 canonical_url: "https://www.modelscope.cn/models/unsloth/LTX-2.5-FP8" md_url: "https://www.modelscope.cn/models/unsloth/LTX-2.5-FP8.md" repository: unsloth/LTX-2.5-FP8 last_updated: 2026-10-16 license: other pipeline_tag: text-to-video-synthesis tasks: - text-to-video-synthesis base_model: - Lightricks/LTX-2.5-Diffusers base_model_relation: finetune parameters: 92.5B tensor_type: - BF16 - F32 - I8 - F8_E4M3 library_name: - pytorch - diffusers - safetensors frameworks: - pytorch language: - en downloads: 555 stars: 1 tags: - fp8 - int8 - unsloth - quantized - diffusers - ltx-2.5 - text-to-video - image-to-video - video-generation --- # LTX-2.5-FP8 > LTX-2.5-FP8 - unsloth 在 ModelScope 开源的模型。This is an FP8 / INT8 quantized version of the LTX-2.5 distilled video DiT. Each file replaces the 38.0 GB bf16 transformer of Lightricks/LTX-2.5-Diffusers with a 19.1 to 25.1 GB one. The text encoder, connectors,… unsloth/LTX-2.5-FP8 是 ModelScope 魔搭社区上的 92.5B 参数text-to-video-synthesis模型，采用 other 许可，基于 Lightricks/LTX-2.5-Diffusers 构建。 - **Repository**: unsloth/LTX-2.5-FP8 - **License**: other - **Tasks**: text-to-video-synthesis - **Parameters**: 92.5B - **Base model**: Lightricks/LTX-2.5-Diffusers - **Tags**: fp8, int8, unsloth, quantized, diffusers, ltx-2.5, text-to-video, image-to-video, video-generation - **Downloads**: 555 - **Stars**: 1 - **Last updated**: 2026-10-16 Source: https://www.modelscope.cn/models/unsloth/LTX-2.5-FP8 --- This is an FP8 / INT8 quantized version of the [LTX-2.5](https://huggingface.co/Lightricks/LTX-2.5-Diffusers) distilled video DiT.
- Each file replaces the 38.0 GB bf16 `transformer` of [Lightricks/LTX-2.5-Diffusers](https://huggingface.co/Lightricks/LTX-2.5-Diffusers) with a 19.1 to 25.1 GB one. The text encoder, connectors, VAEs, vocoder and latent upsampler are unchanged and come from the Diffusers repo. - Same-seed LPIPS vs the bf16 DiT, averaged over all 121 frames of 4 clips (lower is better): 0.265 INT8-ConvRot, 0.366 FP8, 0.385 INT8. INT8-ConvRot is the recommended file. - FP8 and INT8 are bit-identical to quantizing the bf16 DiT at load with the same recipe, so they add no error over on-the-fly quantization; they save the download and the bf16 staging.

| file | size | scheme | LPIPS vs bf16 (mean / max frame) | PSNR vs bf16 (mean / min frame) | |---|---|---|---|---| | `LTX-2.5-INT8-ConvRot.safetensors` | 25.11 GB | INT8 W8A8, group-256 Hadamard rotation | 0.265 / 0.449 | 20.2 / 16.8 dB | | `LTX-2.5-FP8.safetensors` | 19.06 GB | FP8 e4m3, per-row scales | 0.366 / 0.518 | 17.6 / 11.6 dB | | `LTX-2.5-INT8.safetensors` | 25.11 GB | INT8 W8A8, no rotation | 0.385 / 0.632 | 17.4 / 13.9 dB | Built from the `transformer/` of `Lightricks/LTX-2.5-Diffusers` at revision `da0b690960026eab794b8ceaf841233cf7064478` (tensor-identical to `ltx-2.5-22b-distilled-transformer-bf16.safetensors` in [Lightricks/LTX-2.5](https://huggingface.co/Lightricks/LTX-2.5)). FP8 quantizes every linear with at least 512 features. INT8 keeps the audio stream, the audio-video cross attention, the adaLN and timestep projections and the norms in bf16. INT8-ConvRot rotates the inputs of all 480 quantized video linears with a block Hadamard (group 256) before quantizing; the rotation is listed in the file's metadata and applied to the activations at run time. Scores are against the bf16 DiT at the reference distilled settings (1280x704, 121 frames, 24 fps, 8 + 3 two-stage steps, CFG 1), text-to-video and image-to-video, seeds 42 and 7, with the same bf16 text encoder in every run; a repeated bf16 render is bit-identical (LPIPS 0.000). The distilled sampler injects 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, as the samples show. Every file is safetensors in torchao's layout (`tensor_names` plus a per-tensor entry in the header, with the three root-level parameters stored as `unsloth_root::`), so `torchao.prototype.safetensors.safetensors_support.unflatten_tensor_state_dict` rebuilds it and the stock diffusers `LTX2VideoTransformer3DModel` loads it strictly. Loading runs no pickled code. Checked with torch 2.13 and torchao 0.18. --- ### Samples Frame 60 of each clip.

bf16	INT8-ConvRot	FP8

---

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} } ```