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# 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.

<Tip>

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.

</Tip>

## 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,
    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
