copilot-swe-agent[bot]andhsliuustc0106
d89dc0ad58
Co-authored-by: hsliuustc0106 <222337142+hsliuustc0106@users.noreply.github.com>
43 lines
1.5 KiB
Markdown
43 lines
1.5 KiB
Markdown
# LTX-2 Text-To-Video
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Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/ltx2>.
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The `Lightricks/LTX-2` pipeline generates high-quality videos from text prompts using a 19B parameter DiT architecture. It supports up to 4K video generation with synchronized audio.
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## Local CLI Usage
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```bash
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python text_to_video.py \
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--prompt "A panda riding a bicycle through a forest, cinematic lighting" \
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--height 512 \
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--width 768 \
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--num_frames 121 \
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--num_inference_steps 40 \
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--guidance_scale 4.0 \
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--output ltx2_output.mp4
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```
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Key arguments:
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- `--prompt`: Text description of the video to generate (string).
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- `--height/--width`: Output resolution (defaults 512x768). Dimensions must be divisible by 32.
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- `--num_frames`: Number of frames (should be 8*n+1, e.g., 25, 81, 121). Default is 121.
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- `--guidance_scale`: Classifier-free guidance scale (default: 4.0). Range 3.0-5.0 recommended.
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- `--negative_prompt`: Optional text describing what to avoid in the video.
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- `--num_inference_steps`: Number of denoising steps (default: 40).
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- `--seed`: Random seed for reproducibility (default: 42).
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- `--fps`: Frames per second for the saved MP4 (default: 24).
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- `--output`: Path to save the generated video.
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## Example materials
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??? abstract "text_to_video.py"
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``````py
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--8<-- "examples/offline_inference/ltx2/text_to_video.py"
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``````
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??? abstract "text_to_video.md"
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``````md
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--8<-- "examples/offline_inference/ltx2/text_to_video.md"
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``````
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