LTX.io
comfyui
quantization
int4
convrot
diffusion
image-generation
video-generation
upscaling
krea
seedvr2
Instructions to use milo01/INT4-Convrot-Comfy-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LTX.io
How to use milo01/INT4-Convrot-Comfy-Models with LTX.io:
# Install the LTX-2 pipelines git clone https://github.com/Lightricks/LTX-2.git cd LTX-2 uv sync --frozen
# Download the weights from this repo, plus the Gemma text encoder hf download milo01/INT4-Convrot-Comfy-Models --local-dir models/INT4-Convrot-Comfy-Models hf download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b
# Fast pipeline (distilled model, no distilled LoRA needed) uv run python -m ltx_pipelines.distilled \ --distilled-checkpoint-path models/INT4-Convrot-Comfy-Models/<distilled-checkpoint>.safetensors \ --spatial-upsampler-path models/INT4-Convrot-Comfy-Models/<spatial-upsampler>.safetensors \ --gemma-root models/gemma-3-12b \ --prompt "A beautiful sunset over the ocean" \ --output-path output.mp4 # For image-to-video, add: --image path/to/image.jpg 0 0.8# HQ pipeline (two-stage, higher quality) uv run python -m ltx_pipelines.ti2vid_two_stages_hq \ --checkpoint-path models/INT4-Convrot-Comfy-Models/<checkpoint>.safetensors \ --distilled-lora models/INT4-Convrot-Comfy-Models/<distilled-lora>.safetensors 0.8 \ --spatial-upsampler-path models/INT4-Convrot-Comfy-Models/<spatial-upsampler>.safetensors \ --gemma-root models/gemma-3-12b \ --prompt "A beautiful sunset over the ocean" \ --output-path output.mp4 # For image-to-video, add: --image path/to/image.jpg 0 0.8 - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 87b04712df77090093a40ddda1dd23c478ecaa596519b97a2fff477a26051928
- Size of remote file:
- 2.82 MB
- SHA256:
- 74411d1a922867cc9886965c64ecd2a0229fe35286a3764078a1fbd0b8ecc785
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