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Automated PR - 2026-01-08

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2026-01-08 15:29:32 +00:00
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packages/ltx-pipelines/README.md
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@@ -43,7 +43,7 @@ All pipelines can be run directly from the command line. Each pipeline module is
# Run a pipeline (example: two-stage text-to-video)
python -m ltx_pipelines.ti2vid_two_stages \
--checkpoint-path path/to/checkpoint.safetensors \
--distilled-lora-path path/to/distilled_lora.safetensors \
--distilled-lora path/to/distilled_lora.safetensors 0.8 \
--spatial-upsampler-path path/to/upsampler.safetensors \
--gemma-root path/to/gemma \
--prompt "A beautiful sunset over the ocean" \
@@ -55,11 +55,11 @@ python -m ltx_pipelines.ti2vid_two_stages --help
Available pipeline modules:
- `ltx_pipelines.ti2vid_two_stages` - Two-stage text-to-video (recommended)
- `ltx_pipelines.ti2vid_one_stage` - Single-stage text-to-video
- `ltx_pipelines.distilled` - Fast distilled pipeline
- `ltx_pipelines.ic_lora` - Video-to-video with IC-LoRA
- `ltx_pipelines.keyframe_interpolation` - Keyframe interpolation
- `ltx_pipelines.ti2vid_two_stages` - Two-stage text/image-to-video (recommended).
- `ltx_pipelines.ti2vid_one_stage` - Single-stage text/image-to-video.
- `ltx_pipelines.distilled` - Fast text/image-to-video pipeline using only the distilled model.
- `ltx_pipelines.ic_lora` - Video-to-video with IC-LoRA.
- `ltx_pipelines.keyframe_interpolation` - Keyframe interpolation.
Use `--help` with any pipeline module to see all available options and parameters.
@@ -73,9 +73,9 @@ Use `--help` with any pipeline module to see all available options and parameter
Do you need to condition on existing images/videos?
├─ YES → Do you have reference videos for video-to-video?
│ ├─ YES → Use ICLoraPipeline
│ └─ NO → Do you have keyframe images to interpolate?
│ └─ NO → Do you have multiple keyframe images to interpolate?
│ ├─ YES → Use KeyframeInterpolationPipeline
│ └─ NO → Use ICLoraPipeline (image conditioning only)
│ └─ NO → Use TI2VidTwoStagesPipeline (image conditioning only)
│
└─ NO → Text-to-video only
├─ Do you need best quality?
@@ -103,7 +103,7 @@ Do you need to condition on existing images/videos?
### 1. TI2VidTwoStagesPipeline
**Best for:** High-quality text-to-video generation with upsampling. **Recommended for production use.**
**Best for:** High-quality text/image-to-video generation with upsampling. **Recommended for production use.**
**Source**: [`src/ltx_pipelines/ti2vid_two_stages.py`](src/ltx_pipelines/ti2vid_two_stages.py)
@@ -212,8 +212,7 @@ When authoring custom scripts, pass the `fp8transformer` flag to pipeline classe
```python
pipeline = TI2VidTwoStagesPipeline(
checkpoint_path=ltx_model_path,
distilled_lora_path=distilled_lora_path,
distilled_lora_strength=0.6,
distilled_lora=distilled_lora,
spatial_upsampler_path=upsampler_path,
gemma_root=gemma_root_path,
loras=[],
@@ -275,11 +274,20 @@ This allows you to use **20-30 steps instead of 40** while maintaining quality.
## 📖 Example: Image-to-Video
```python
from ltx_core.loader import LTXV_LORA_COMFY_RENAMING_MAP, LoraPathStrengthAndSDOps
from ltx_pipelines.ti2vid_two_stages import TI2VidTwoStagesPipeline
distilled_lora = [
LoraPathStrengthAndSDOps(
"/path/to/distilled_lora.safetensors",
0.6,
LTXV_LORA_COMFY_RENAMING_MAP
),
]
pipeline = TI2VidTwoStagesPipeline(
checkpoint_path="/path/to/checkpoint.safetensors",
distilled_lora_path="/path/to/distilled_lora.safetensors",
distilled_lora=distilled_lora,
spatial_upsampler_path="/path/to/upsampler.safetensors",
gemma_root="/path/to/gemma",
loras=[],