Revids

REST API for generating video from images using LTX-2.3 distilled pipeline.

Architecture

• FastAPI REST server with auto-generated OpenAPI docs
• LTX-2.3 DistilledPipeline — two-stage video generation (8 steps stage 1, 4 steps stage 2)
• Async task queue — submit job, poll status, download when ready
• SQLite — job persistence via aiosqlite
• Single GPU lock — jobs serialized to avoid VRAM contention

Quick Start

# 1. Clone the repo (including LTX-2 submodule)
git clone --recursive <repo-url> revids
cd revids

# 2. Create venv and install LTX packages (editable, from submodule)
python -m venv .venv && source .venv/bin/activate
pip install -e libs/LTX-2/packages/ltx-core -e libs/LTX-2/packages/ltx-pipelines

# 3. Install API dependencies
pip install -r requirements.txt

# 4. Download model weights from HuggingFace
## Distilled model checkpoint
huggingface-cli download Lightricks/LTX-2.3 \
  --include "ltx-2.3-22b-distilled-1.1.safetensors" \
  --local-dir models/

## Spatial upsampler (required for two-stage pipeline)
huggingface-cli download Lightricks/LTX-2.3 \
  --include "ltx-2.3-spatial-upscaler-x2-1.1.safetensors" \
  --local-dir models/

## Gemma text encoder
huggingface-cli download google/gemma-3-12b-it-qat-q4_0-unquantized \
  --local-dir models/gemma-3-12b-it-qat-q4_0-unquantized

# 5. Optimize memory (optional but recommended)
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True

# 6. Run
uvicorn app.main:app --reload

The API is available at http://localhost:8000. Swagger docs at http://localhost:8000/docs.

API Endpoints

Submit Job — POST /generate

Upload an image and generation parameters. Returns a job ID immediately.

curl -X POST http://localhost:8000/generate \
  -F "image=@photo.jpg" \
  -F "prompt=A cinematic pan right across a misty mountain range at dawn, golden light breaking through clouds" \
  -F "width=768" \
  -F "height=512" \
  -F "num_frames=97" \
  -F "fps=24.0" \
  -F "seed=42"

Parameters:

Param	Type	Default	Description
image	file (required)	—	Input image (PNG, JPG, WebP, etc.)
prompt	string	""	Optional text prompt to guide generation
width	int	768	Output width (must be divisible by 32)
height	int	512	Output height (must be divisible by 32)
num_frames	int	65	Frame count (must be 8n+1: 9, 17, …, 65, 97, 121, 161, 257)
fps	float	24.0	Frames per second
seed	int	random	Random seed for reproducibility

Response:

{
  "job_id": "abc123def456",
  "status": "pending",
  "message": "Job submitted. Poll GET /jobs/{job_id} for status."
}

Check Job Status — GET /jobs/{job_id}

curl http://localhost:8000/jobs/abc123def456

Response:

{
  "job_id": "abc123def456",
  "status": "completed",
  "prompt": "A cinematic pan right...",
  "params": {"width": 768, "height": 512, "num_frames": 97, "fps": 24.0, "seed": 42},
  "error": null,
  "created_at": "2025-01-01T12:00:00+00:00",
  "completed_at": "2025-01-01T12:01:30+00:00"
}

Status values: pending → processing → completed or failed

Download Video — GET /jobs/{job_id}/download

Returns the .mp4 video file when the job is completed. Returns 404 if not ready.

curl http://localhost:8000/jobs/abc123def456/download -o output.mp4

Delete Job — DELETE /jobs/{job_id}

Removes the job record and associated video file.

curl -X DELETE http://localhost:8000/jobs/abc123def456

Health Check — GET /health

Returns GPU availability and device info.

curl http://localhost:8000/health

{"status": "ok", "gpu": true, "gpu_name": "NVIDIA A100-SXM4-80GB"}

Frame Count Reference

Num_frames must follow 8n+1 pattern. Common values:

Frames	Duration (24 fps)	Duration (25 fps)
65	~2.7s	~2.6s
97	~4.0s	~3.9s
121	~5.0s	~4.8s
161	~6.7s	~6.4s
257	~10.7s	~10.3s

Resolution Reference

Width and height must be divisible by 32 (min 256, max 2048).

Resolution	Aspect Ratio
768×512	3:2 landscape
512×768	2:3 portrait
704×512	4:3 standard
640×640	1:1 square

Configuration

All settings can be overridden via .env file (placed in project root) or environment variables prefixed with REVIDS_. See app/config.py for all available options.

Env Variable	Default	Description
REVIDS_LTX_DISTILLED_CHECKPOINT	models/ltx-2.3-22b-distilled-1.1.safetensors	Distilled model checkpoint path
REVIDS_LTX_GEMMA_ROOT	models/gemma-3-12b-it-qat-q4_0-unquantized	Gemma text encoder path
REVIDS_LTX_SPATIAL_UPSAMPLER	models/ltx-2.3-spatial-upscaler-x2-1.1.safetensors	Spatial upsampler checkpoint
REVIDS_LTX_QUANTIZATION	fp8_cast	Quantization: fp8_cast, fp8_scaled_mm, or "" (bfloat16)
REVIDS_LTX_LORAS	(empty)	LoRA config: "path1:1.0,path2:0.5"
REVIDS_MAX_CONCURRENT_JOBS	1	Concurrent GPU jobs
REVIDS_PORT	8000	Server port
REVIDS_RELOAD	true	uvicorn auto-reload

LoRA Support

To load custom or Lightricks LoRAs:

# Single LoRA at full strength
export REVIDS_LTX_LORAS="models/my-style.safetensors:1.0"

# Multiple LoRAs
export REVIDS_LTX_LORAS="models/style.safetensors:1.0,models/motion.safetensors:0.5"

Lightricks-published LoRAs:

• IC-LoRA Union Control
• IC-LoRA Motion Track Control
• IC-LoRA LipDub
• Camera Control LoRAs (dolly in/out, jib up/down, static, etc.)

Memory Optimization

# Improve CUDA memory allocation fragmentation
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True

# Use FP8 quantization (~40% VRAM reduction, minimal quality loss)
export REVIDS_LTX_QUANTIZATION=fp8_cast

# For Hopper GPUs (H100) with TensorRT-LLM:
# export REVIDS_LTX_QUANTIZATION=fp8_scaled_mm

Hardware Requirements

• GPU: NVIDIA with CUDA support
• VRAM: ≥ 24 GB (bfloat16), ~14 GB with FP8 quantization
• Disk: ~30 GB for all model weights (distilled + upsampler + Gemma encoder)

Project Structure

revids/
  app/
    __init__.py
    main.py            # FastAPI app + HTTP routes
    config.py          # Settings (pydantic-settings, env var support)
    models.py          # Request/response Pydantic models
    service.py         # LTX pipeline wrapper, job processing, video encoding
    database.py        # SQLite job store (aiosqlite)
  libs/
    LTX-2/             # LTX-2 git submodule (source for ltx-core/ltx-pipelines)
  videos/              # Generated .mp4 output (gitignored)
  requirements.txt
  .gitignore

Prompting Guide

For best results with LTX-2.3, write detailed, chronological motion descriptions:

1. Start with the main action in one sentence
2. Add specific movement details and gestures
3. Describe appearances precisely
4. Include background and environment
5. Specify camera angle and movement
6. Describe lighting and color palette
7. Keep under 200 words

Full guide: https://ltx.video/blog/how-to-prompt-for-ltx-2

License

See LTX-2 LICENSE for model usage terms.