Video Generation

+++ disableToc = false title = "Video Generation" weight = 51 url = "/features/video-generation/" aliases = ["/features/longcat-video/"] +++

LocalAI can generate videos from text prompts and optional image or audio conditioning via the /video endpoint. Supported backends include diffusers, stablediffusion, vllm-omni, vllm-cpp (MiniMax-H3, which generates video and audio together), and the dedicated longcat-video backend.

API

• Method: POST
• Endpoint: /video

Request

The request body is JSON with the following fields:

Response

Returns an OpenAI-compatible JSON response:

Usage

First install a video-generation model from the gallery (the examples below use longcat-video):

local-ai run longcat-video

Generate a video from a text prompt

curl http://localhost:8080/video \
  -H "Content-Type: application/json" \
  -d '{
    "model": "longcat-video",
    "prompt": "A cat playing in a garden on a sunny day",
    "width": 512,
    "height": 512,
    "num_frames": 16,
    "fps": 8
  }'

Example response

{
  "created": 1709900000,
  "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "data": [
    {
      "url": "/generated-videos/abc123.mp4"
    }
  ]
}

Generate with a starting image

curl http://localhost:8080/video \
  -H "Content-Type: application/json" \
  -d '{
    "model": "longcat-video",
    "prompt": "A timelapse of flowers blooming",
    "start_image": "https://example.com/flowers.jpg",
    "num_frames": 24,
    "fps": 12,
    "seed": 42,
    "cfg_scale": 7.5,
    "step": 30
  }'

Get base64-encoded output

curl http://localhost:8080/video \
  -H "Content-Type: application/json" \
  -d '{
    "model": "longcat-video",
    "prompt": "Ocean waves on a beach",
    "response_format": "b64_json"
  }'

LongCat-Video and Avatar 1.5

LocalAI's longcat-video backend serves Meituan's official LongCat video-generation models through the /video API and the Studio Video page.

The base checkpoint supports text-to-video and image-to-video. Avatar 1.5 adds audio-driven character animation, optional portrait conditioning, and continuation segments for longer speech.

{{% notice warning %}} LongCat is a large, CUDA-only model family. LocalAI publishes this backend for Linux with NVIDIA CUDA 12 or CUDA 13 on x86_64 and CUDA 13 on ARM64. CPU, ROCm, and macOS images are not available. Avatar 1.5 also loads components from the base checkpoint, so reserve substantial disk and GPU or unified memory. {{% /notice %}}

Install from the Model Gallery

Install one or both recipes from Models in the web UI, or use the CLI:

local-ai models install longcat-video
local-ai models install longcat-video-avatar-1.5

You can also import either official Hugging Face URL. The importer recognizes the two repositories and writes a longcat-video model config with the appropriate use case and input/output modalities.

The required OCI backend is installed automatically when LocalAI first loads the model. The hardware detector selects the CUDA 12, CUDA 13, or CUDA 13 ARM64 variant.

DGX Spark and NVIDIA ARM64

Use a LocalAI CUDA 13 ARM64 image as described in [GPU acceleration]({{%relref "features/GPU-acceleration" %}}). The backend defaults to PyTorch SDPA, avoiding the FlashAttention dependency that is commonly unavailable on Blackwell ARM64 systems.

For unified-memory systems, start with BF16 (use_int8:false, the default). INT8 lowers steady-state DiT memory but can have a higher load-time peak because the full model is materialized before the quantized weights are applied.

Generate in Studio

1. Open Studio, then choose Video.
2. Select longcat-video or longcat-video-avatar-1.5.
3. Enter a prompt and choose 832x480 or 1280x720.
4. Expand Reference media to upload a start image. For Avatar 1.5, upload or record the speech under Avatar audio.
5. Select Generate.

The base model can run without a reference image for text-to-video. Avatar 1.5 requires audio; the portrait is optional.

LongCat API examples

Text-to-video

curl http://localhost:8080/video \
  -H "Content-Type: application/json" \
  -d '{
    "model": "longcat-video",
    "prompt": "A cinematic tracking shot through a misty redwood forest",
    "width": 832,
    "height": 480,
    "num_frames": 93,
    "fps": 15
  }'

Image-to-video

start_image accepts raw base64, a browser-style data URI, or a public HTTP(S) URL:

curl http://localhost:8080/video \
  -H "Content-Type: application/json" \
  -d "{
    \"model\": \"longcat-video\",
    \"prompt\": \"The subject turns toward the camera as leaves move in the breeze\",
    \"start_image\": \"$(base64 --wrap=0 portrait.png)\",
    \"params\": {
      \"resolution\": \"480p\"
    }
  }"

Avatar from speech and a portrait

audio accepts raw base64, a data URI, or a public HTTP(S) URL. Each staged image or audio input is limited to 128 MiB.

curl http://localhost:8080/video \
  -H "Content-Type: application/json" \
  -d "{
    \"model\": \"longcat-video-avatar-1.5\",
    \"prompt\": \"A friendly presenter speaking naturally to camera\",
    \"start_image\": \"$(base64 --wrap=0 portrait.png)\",
    \"audio\": \"$(base64 --wrap=0 speech.wav)\",
    \"width\": 832,
    \"height\": 480,
    \"params\": {
      \"offload_kv_cache\": \"true\"
    }
  }"

Avatar output is generated at 25 FPS and is muxed with the submitted audio. When neither num_frames nor params.num_segments is provided, LocalAI derives the continuation count from the audio duration, up to the model's max_segments setting.

LongCat model configuration

The gallery and importer make each model self-describing. A manual Avatar 1.5 config looks like this:

name: longcat-video-avatar-1.5
backend: longcat-video
known_usecases:
  - video
known_input_modalities:
  - text
  - image
  - audio
known_output_modalities:
  - video
options:
  - attention_backend:sdpa
  - use_distill:true
  - max_segments:8
parameters:
  model: meituan-longcat/LongCat-Video-Avatar-1.5

The explicit modality declarations are used by GET /v1/models/capabilities and attachment-aware clients. They avoid inferring model behavior from backend or checkpoint names.

Load options

Model load options use key:value entries in options:

The initial backend supports one GPU per process. Tensor or context parallel sizes above one are rejected.

Per-request parameters

The /video request's params object accepts string values:

With distillation enabled, Avatar uses eight inference steps and fixed text/audio guidance of 1.0. Disable use_distill in the model config before tuning step, cfg_scale, or audio_guidance_scale.

LongCat troubleshooting

• HTTP 400, audio is required: Avatar 1.5 was selected without audio.
• HTTP 400, request needs too many segments: trim the audio or raise max_segments in the model options.
• HTTP 412: the installed LocalAI runtime cannot select a compatible NVIDIA backend image.
• Out of memory while loading: use BF16 on unified-memory hardware, close other GPU workloads, or reduce model concurrency. INT8 is not guaranteed to reduce peak load memory.
• Slow first request: the backend and checkpoints are downloaded and loaded on demand; subsequent requests reuse the loaded pipeline.

MiniMax-H3 (vllm.cpp)

The vllm-cpp backend — LocalAI's own C++ port of vLLM — also serves MiniMax-H3, which generates video and audio jointly. The clip comes back as an MP4 with a real AAC track rather than a silent render.

local-ai models install minimax-h3-fl2va-q4

{{% notice warning %}} This is a large, slow model. The five weight files total roughly 40 GB, and generation was measured at about 176 seconds per denoise step at the default 1344x768 canvas on a 20-SM device — so the 50-step default is a multi-hour request, not a multi-second one. Nothing in the path imposes a deadline, but plan for a long-running HTTP call, and use a CUDA host. {{% /notice %}}

Ask for the sound

The model generates picture and sound from the same prompt, so a prompt that only describes what is seen produces room tone and ambience. To get speech, say that the character talks and put the words in the prompt:

It is TALKING to the camera: its mouth moves clearly in sync with its speech,
in a dry, deadpan tone.

It says, clearly and audibly: "Michael scheduled another all-hands.
It is about the printer. Again."

Audio: a single clear voice, close-miked, with quiet room tone underneath.

Geometry and clip length

The trained canvas is 1344x768 at 124 frames and 24 fps, about 5.2 seconds, and that is what the gallery entry defaults to. Two rules the engine enforces:

• The canvas is truncated onto a 32-pixel grid.
• The frame count sits on a 17n+5 grid (…, 90, 107, 124, 141, …). A count off the grid is rounded up, and LocalAI logs the value it actually rendered.

The trained clip range is roughly 124 to 362 frames (about 5 to 15 seconds).

Text-to-video with sound

curl http://localhost:8080/video \
  -H "Content-Type: application/json" \
  -d '{
    "model": "minimax-h3-fl2va-q4",
    "prompt": "A cyan llama mascot in a grey office chair, talking to the camera. It says, clearly and audibly: \"the printer is down again\". Audio: one clear close-miked voice.",
    "num_frames": 124,
    "step": 50,
    "seed": 42
  }'

First-frame conditioning

start_image pins the supplied image as frame 0 (H3's fl2va task); end_image pins the last frame. LocalAI converts the upload to the binary PPM at the exact output canvas that the engine requires, using ffmpeg, so PNG and JPEG uploads work. When no width/height is given, the canvas is derived from the image's aspect on a 768-pixel short edge.

curl http://localhost:8080/video \
  -H "Content-Type: application/json" \
  -d "{
    \"model\": \"minimax-h3-fl2va-q4\",
    \"prompt\": \"the subject turns toward the camera and starts speaking\",
    \"start_image\": \"$(base64 --wrap=0 portrait.png)\"
  }"

Partitions: what this checkpoint will and will not do

MiniMax-H3 ships as two DiT partitions, and the gallery entry installs FL2VA, which serves t2va (text only) and fl2va (first/last frame). Reference conditioning — a whole reference image, a reference clip, or reference audio — belongs to the separate Ref2VA checkpoint.

This matters because the mismatch does not fail cleanly upstream: a reference passed to an FL2VA DiT renders for hours and returns a coloured lattice over the frame. The backend refuses the combination up front instead, naming the partition. The community quantisations strip the release metadata and the two DiTs are byte-structurally identical, so the partition is declared in the model config (video_partition) rather than detected.

ffmpeg is required on the host

The engine writes frames and a WAV and composes the ffmpeg command line; LocalAI runs it. That process boundary is deliberate upstream, so the backend image ships no ffmpeg: install one on the host, or point options: [ffmpeg:/path/to/ffmpeg] at a binary. Without it, generation succeeds and the mux fails with a message saying so.

MiniMax-H3 model configuration

name: minimax-h3-fl2va-q4
backend: vllm-cpp
cuda: true
known_usecases:
  - video
known_input_modalities:
  - text
  - image
known_output_modalities:
  - video
options:
  - video_encoder:minimax-h3/qwen3vl-32B-MiniMax-H3-Q4_K_M.gguf
  - video_tokenizer:minimax-h3/tokenizer.json
  - video_vae:minimax-h3/video_vae.safetensors
  - video_vae_config:minimax-h3/video_vae_config.json
  - audio_vae:minimax-h3/audio_vae.safetensors
  - audio_vae_config:minimax-h3/audio_vae_config.json
  - video_partition:fl2va
  - video_device:cuda
  - video_dequant_bf16:true
  - video_width:1344
  - video_height:768
  - video_num_frames:124
parameters:
  model: minimax-h3/MiniMax-H3-FL2VA-Q4_K_M.gguf

parameters.model is the DiT. H3 is a checkpoint set rather than one model directory, so the encoder, the tokenizer and the two VAEs are named in options. Relative paths resolve against the models directory.

Load options

Per-request parameters

The /video request's params object accepts string values. Unknown keys are rejected rather than ignored, so a typo does not cost you a multi-hour render of the wrong thing.

negative_prompt, cfg_scale and fps have no MiniMax-H3 equivalent: H3 has no negative prompt or CFG scale, and it renders at a fixed frame rate that the audio track is synchronised to. Setting them is logged and ignored rather than silently honoured.

MiniMax-H3 troubleshooting

• ffmpeg not found: install ffmpeg on the host or set options: [ffmpeg:<path>]. The frames and WAV are already rendered; only the mux failed.
• the FL2VA checkpoint serves t2va and fl2va only: you passed a reference image, clip or audio to the FL2VA DiT. Use start_image for first-frame conditioning, or install a Ref2VA checkpoint.
• video_partition must be "fl2va" or "ref2va": the config declares something else.
• unknown params key: params accepts only the four keys above.
• Text inside the frame comes out malformed: this is the model's weakest area. Composite logos and signage in afterwards.

Error Responses