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--- |
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tags: |
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- ltx-video |
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- text-to-video |
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- image-to-video |
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pinned: true |
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language: |
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- en |
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license: other |
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--- |
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This is a special fork of LTX-Video designed to facilitate deployment to the Inference Endpoints, using diffusers + varnish. |
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If you want to know how to use it once you've deployed it, please refer to this Python demo: |
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https://huggingface.co/jbilcke-hf/LTX-Video-for-InferenceEndpoints/blob/main/example.py |
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## Why? |
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Using the Hugging Face Inference Endpoints gives you with reliable and controllable availability. |
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The goal of this wrapper is to offer flexibility and low-level settings, to add things such as upscaling, interpolation, film grain, and compression controls. |
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## Setup on Hugging Face Inference Endpoints |
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### Pick a large machine |
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It is recommended to use at least a NVIDIA L40S with 48 Gb of VRAM. |
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### Downloads all assets |
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Make sure to select "Download everything" when selecting the model. |
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Otherwise some files ending in `.pth` won't be downloaded. |
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### Select Text-to-Video or Image-to-Video |
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By default the handler will do Text-to-Video. |
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To do Image-Text-To-Video, you need to set the environment variable `SUPPORT_INPUT_IMAGE_PROMPT` to a trueish value (eg `1`, `True`). |
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It is possible to support both pipelines at the same time if you modify the `handler.py`. |
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But if you keep both pipelines active in parallel, this will consume more memory. |
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### Using private LTX-Video LoRAs |
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If you plan on using private LoRA models you will have to set the `HF_API_TOKEN` environment variable. |
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## Credits |
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For more information about this model, please see the [original HF repository here](https://huggingface.co/Lightricks/LTX-Video/). |
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