text-to-video and image-to-video with LongCat-Video;
audio + text-to-avatar and portrait + audio-to-avatar with Avatar 1.5;
multi-segment avatar continuation for speech longer than one segment;
PyTorch SDPA when FlashAttention is unavailable, including CUDA 13 ARM64 systems such as NVIDIA DGX Spark.

Install the longcat-video or longcat-video-avatar-1.5 recipe from the LocalAI Model Gallery. See the LongCat user guide for Studio and API examples, hardware requirements, and manual configuration.

The upstream source is pinned in Makefile and patched at build time. The patch adds only the missing SDPA attention branches; model and source licenses remain MIT.

Model options

Option	Default	Description
`attention_backend`	`sdpa`	`sdpa`, `auto`, `flash2`, `flash3`, or `xformers`. The packaged backend guarantees only `sdpa`.
`use_distill`	`true` for Avatar, `false` for base	Loads the checkpoint's fast distillation LoRA.
`use_int8`	`false`	Loads Avatar 1.5's INT8 DiT. BF16 has a lower load-time peak on unified-memory systems.
`base_model`	`meituan-longcat/LongCat-Video`	Base components used by Avatar 1.5.
`max_segments`	`8`	Maximum avatar continuation segments accepted per request.
`resolution`	`480p`	Image-conditioned generation resolution (`480p` or `720p`).

Per-request params may set num_segments, audio_guidance_scale, offload_kv_cache, ref_img_index, mask_frame_range, and resolution.

Gallery and imported configs declare known_input_modalities and known_output_modalities. Keep those declarations in manual configs as well; they let model discovery distinguish base image-conditioned video from Avatar audio conditioning without inspecting the backend or checkpoint name.

LongCat is CUDA-only and very large. Avatar 1.5 also loads tokenizer, text-encoder, and VAE components from the base checkpoint. Keep ample unified memory and storage available; no CPU or macOS backend image is published. The initial backend supports one GPU per process; tensor parallel sizes above one are rejected explicitly.