LTX-2.5 serving bundle

A mirror, not a new model. Every weight file here is copied byte-for-byte (server-side, via huggingface_hub.copy_files) from Lightricks/LTX-2.5 and is governed by the LTX-2.x Community License linked above. All credit to Lightricks.

It exists because RunPod Serverless' model-caching feature allows exactly one cached repo per endpoint and cannot filter which files it pulls โ€” naming the upstream repo directly would pre-provision all ~299 GB of it, including ComfyUI-only int8 variants this deployment never loads. This repo is the subset ltx-pipelines actually needs, and nothing else.

Contents (140.75 GB)

File GB Used by
diffusion_models/ltx-2.5-22b-distilled-transformer-bf16.safetensors 42.02 DistilledPipeline (image-to-video, fast path)
diffusion_models/ltx-2.5-22b-distilled-transformer-nvfp4.safetensors 18.72 same, --quantization nvfp4-prequant (Blackwell SM>=10)
diffusion_models/ltx-2.5-22b-dev-transformer-bf16.safetensors 42.02 KeyframeInterpolationPipeline, TI2VidTwoStages*Pipeline
loras/ltx-2.5-22b-distilled-lora-450-bf16.safetensors 8.90 required by both dev-transformer pipelines above
text_encoders/gemma4-12b-with-proj-ltx-2.5-bf16.safetensors 26.26 all pipelines
vae/ltx-2.5-video-vae-bf16.safetensors 1.47 all pipelines
vae/ltx-2.5-audio-vae-bf16.safetensors 0.36 all pipelines (LTX generates audio natively)
latent_upscale_models/ltx-2.5-latent-spatial-upscaler-x2-bf16-1.0.safetensors 1.00 stage-2 spatial upsampler

Deliberately excluded: *-comfy-int8-convrot.safetensors (ComfyUI-only, ltx-pipelines cannot read them), vae/ltx-2.5-video-vae-conv-bf16 (faster, lower-quality alternative), latent_upscale_models/*temporal* and model_patches/ltx-2.5-duration-head-bf16 (auto-duration is unwanted where billing is per requested duration).

Notes

  • Resolution must be divisible by 64 for two-stage pipelines (assert_resolution(..., is_two_stage=True)) โ€” the model card's "divisible by 32" is the one-stage rule.
  • Frame count must satisfy num_frames % 8 == 1.
  • LTX-2.3 files are not interchangeable with these, and a LoRA only works with the model it was trained on.
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