SolitaryThinker
825c6365e0
A bundle's training variant (header-declared `variant`, filename token as
fallback) now selects its sampling preset: distilled -> ltx2_distilled
(8 steps, cfg 1.0, distilled sigmas engage at <=8 steps), sft/base ->
ltx2_base. The bundle table also pins the pipeline class, so
VideoGenerator.from_pretrained(<bundle>) resolves without an override.
Adds basic_ltx2_5{,_distilled}.py examples (--model-path takes the FILE,
--gemma-root declares the paired encoder root), a README section, and CPU
tests for variant/preset resolution, table integrity, and the examples.
86 lines
3.1 KiB
Markdown
86 lines
3.1 KiB
Markdown
# Basic Video Generation Tutorial
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The `VideoGenerator` class provides the primary Python interface for doing offline video generation, which is interacting with a diffusion pipeline without using a separate inference api server.
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## Requirements
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- At least a single NVIDIA GPU with CUDA 12.4.
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- Python 3.10-3.12
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## Installation
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If you have not installed FastVideo, please following these [instructions](https://hao-ai-lab.github.io/FastVideo/getting_started/installation) first.
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## Usage
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The first script in this example shows the most basic usage of FastVideo. If you are new to Python and FastVideo, you should start here.
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```bash
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# if you have not cloned the directory:
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git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
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python examples/inference/basic/basic.py
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```
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For an example on Apple silicon:
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```
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python examples/inference/basic/basic_mps.py
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```
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For an example running DMD+VSA inference:
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```
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python examples/inference/basic/basic_dmd.py
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```
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For the typed config/request path added during the inference API refactor:
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```
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python examples/inference/basic/basic_dmd_new_api.py
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```
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## LTX-2.5 single-file bundles
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These examples load LTX-2.5 from a single-file bundle: one `.safetensors`
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file carrying every component (transformer, video VAE, audio VAE, vocoder,
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text projection). (The Hugging Face release ships as a split pack — one
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file per component — which loads through the standard repo path instead.)
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Pass the bundle FILE path directly:
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```bash
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python examples/inference/basic/basic_ltx2_5_distilled.py \
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--model-path /path/to/bundle.safetensors \
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--gemma-root /path/to/gemma_root
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```
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| bundle variant | preset | sampling defaults |
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|---|---|---|
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| distilled | `ltx2_distilled` | one stage, 8 steps, CFG 1.0 (no CFG/STG, no spatial upscaler) |
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| sft / base | `ltx2_base` | 40 steps, CFG 3.0 with STG |
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- The variant is read from the bundle header when declared; otherwise the
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`distilled` token in the file name decides.
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- The Gemma text encoder is NOT in the bundle. Declare its root with
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`--gemma-root` (or `FASTVIDEO_LTX_ENCODER_ROOT`), and use the root shipped
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with the same transformer variant — the roots differ in prompt templating.
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- Decoder caveat: bundles that declare a diffusion VAE decoder
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(`CausalDiffusionVAE`) are not supported yet; loading them currently
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fails at VAE build time. Bundles with the classic `CausalVideoAutoencoder`
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run end to end.
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## Basic Walkthrough
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All you need to generate videos using multi-gpus from state-of-the-art diffusion pipelines is the following few lines!
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```python
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from fastvideo import VideoGenerator
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def main():
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generator = VideoGenerator.from_pretrained(
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"Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
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num_gpus=1,
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)
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prompt = ("A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
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"wide with interest. The playful yet serene atmosphere is complemented by soft "
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"natural light filtering through the petals. Mid-shot, warm and cheerful tones.")
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video = generator.generate_video(prompt)
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if __name__ == "__main__":
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main()
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```
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