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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)
```
## LTXPipeline
[[autodoc]] LTXPipeline
- all
- __call__
## LTXImageToVideoPipeline
[[autodoc]] LTXImageToVideoPipeline
- all
- __call__
## LTXPipelineOutput
[[autodoc]] pipelines.ltx.pipeline_output.LTXPipelineOutput
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