# LTX [LTX Video](https://huggingface.co/Lightricks/LTX-Video) is the first DiT-based video generation model capable of generating high-quality videos in real-time. It produces 24 FPS videos at a 768x512 resolution faster than they can be watched. Trained on a large-scale dataset of diverse videos, the model generates high-resolution videos with realistic and varied content. We provide a model for both text-to-video as well as image + text-to-video usecases. Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.md) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. ## Loading Single Files Loading the original LTX Video checkpoints is also possible with [`~ModelMixin.from_single_file`]. ```python import torch from diffusers import AutoencoderKLLTXVideo, LTXImageToVideoPipeline, LTXVideoTransformer3DModel single_file_url = "https://huggingface.co/Lightricks/LTX-Video/ltx-video-2b-v0.9.safetensors" transformer = LTXVideoTransformer3DModel.from_single_file( single_file_url, torch_dtype=torch.bfloat16 ) vae = AutoencoderKLLTXVideo.from_single_file(single_file_url, torch_dtype=torch.bfloat16) pipe = LTXImageToVideoPipeline.from_pretrained( "Lightricks/LTX-Video", transformer=transformer, vae=vae, torch_dtype=torch.bfloat16 ) # ... inference code ... ``` Alternatively, the pipeline can be used to load the weights with [`~FromSingleFileMixin.from_single_file`]. ```python import torch from diffusers import LTXImageToVideoPipeline from transformers import T5EncoderModel, T5Tokenizer single_file_url = "https://huggingface.co/Lightricks/LTX-Video/ltx-video-2b-v0.9.safetensors" text_encoder = T5EncoderModel.from_pretrained( "Lightricks/LTX-Video", subfolder="text_encoder", torch_dtype=torch.bfloat16 ) tokenizer = T5Tokenizer.from_pretrained( "Lightricks/LTX-Video", subfolder="tokenizer", torch_dtype=torch.bfloat16 ) pipe = LTXImageToVideoPipeline.from_single_file( single_file_url, text_encoder=text_encoder, tokenizer=tokenizer, torch_dtype=torch.bfloat16 ) ``` Loading [LTX GGUF checkpoints](https://huggingface.co/city96/LTX-Video-gguf) are also supported: ```py import torch from diffusers.utils import export_to_video from diffusers import LTXPipeline, LTXVideoTransformer3DModel, GGUFQuantizationConfig ckpt_path = ( "https://huggingface.co/city96/LTX-Video-gguf/blob/main/ltx-video-2b-v0.9-Q3_K_S.gguf" ) transformer = LTXVideoTransformer3DModel.from_single_file( ckpt_path, quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16), torch_dtype=torch.bfloat16, ) pipe = LTXPipeline.from_pretrained( "Lightricks/LTX-Video", transformer=transformer, generator=torch.manual_seed(0), torch_dtype=torch.bfloat16, ) pipe.enable_model_cpu_offload() prompt = "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" negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted" video = pipe( prompt=prompt, negative_prompt=negative_prompt, width=704, height=480, num_frames=161, num_inference_steps=50, ).frames[0] export_to_video(video, "output_gguf_ltx.mp4", fps=24) ``` Make sure to read the [documentation on GGUF](../../quantization/gguf) to learn more about our GGUF support. Refer to [this section](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogvideox#memory-optimization) to learn more about optimizing memory consumption. ## LTXPipeline [[autodoc]] LTXPipeline - all - __call__ ## LTXImageToVideoPipeline [[autodoc]] LTXImageToVideoPipeline - all - __call__ ## LTXPipelineOutput [[autodoc]] pipelines.ltx.pipeline_output.LTXPipelineOutput