AGENTS.md

Setup (order matters)

# 1. Clone with submodule (libs/LTX-2 is a git submodule, mandatory)
git clone --recursive <repo> revids && cd revids

# 2. Create venv and install LTX packages from submodule in editable mode
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 ~30 GB of model weights (required before first run)
huggingface-cli download Lightricks/LTX-2.3 --include "ltx-2.3-22b-distilled-1.1.safetensors" --local-dir models/
huggingface-cli download Lightricks/LTX-2.3 --include "ltx-2.3-spatial-upscaler-x2-1.1.safetensors" --local-dir models/
huggingface-cli download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b-it-qat-q4_0-unquantized

# 5. Set env (must be set before every run to avoid GPU OOM)
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True

Run the server

# Development (auto-reload, sets CUDA alloc conf)
./run-dev.sh

# Production (no reload)
./run.sh

# Manual (equivalent)
.venv/bin/uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload

API is at http://localhost:8000, Swagger docs at /docs.

Configuration

All settings via .env file or REVIDS_-prefixed env vars. See app/config.py and .env.example. Key settings:

REVIDS_LTX_DISTILLED_CHECKPOINT — distilled model .safetensors path
REVIDS_LTX_GEMMA_ROOT — Gemma text encoder directory
REVIDS_LTX_SPATIAL_UPSAMPLER — upsampler .safetensors path
REVIDS_LTX_QUANTIZATION — fp8_cast (default, ~40% VRAM savings), fp8_scaled_mm, or "" (bf16)
REVIDS_LTX_GEMMA_QUANTIZATION — nf4 for NF4-quantized Gemma (requires bitsandbytes), or ""/unset for bf16
REVIDS_LTX_LORAS — comma-separated path:scale pairs for LoRA loading

Architecture

Single-service FastAPI app. All code lives in app/:

main.py — FastAPI routes, HTTP handlers
service.py — LTX pipeline wrapper, job lifecycle, video encoding
database.py — SQLite job store (aiosqlite, auto-creates schema)
models.py — Pydantic request/response validators
config.py — pydantic-settings with REVIDS_ env prefix
text_encoder.py — NF4 quantized Gemma encoder; patches ltx_pipelines at runtime
cli.py — CLI client (revids entry point in pyproject.toml)

Runtime behavior

Pipeline loads lazily on first job submission, not at server start
Single GPU lock: jobs are serialized via asyncio.Semaphore(1) (configurable)
Jobs persist in app/jobs.db (SQLite, gitignored). DB is created on first server start
Video output: videos/ directory (gitignored), files named <job_id>.mp4
Job IDs: 12 lowercase hex chars (uuid4().hex[:12])

Pipeline patching

service.py:_patch_ltx_pipelines() monkeypatches DistilledPipeline.__init__ and ltx_pipelines.utils.blocks.PromptEncoder at runtime to inject the project's Nf4PromptEncoder and wire gemma_quantization support. These patches run once, before the first pipeline load.

Submodule is read-only

libs/LTX-2/ is a read-only git submodule. Never modify files in ltx-core or ltx-pipelines. If upstream behavior needs changing, work around it in app/ (e.g., via monkeypatching, wrappers, or config).

Validation constraints (enforced at API layer)

num_frames must be 8n+1: 9, 17, 25…, 65, 97, 121, 161, 257
width and height must be divisible by 32, range [256, 2048]
fps must be in (0, 60]
Uploaded image must be under 10 MB

No tests, no lint config for the app itself

The app/ code has no test suite, no ruff/pyright config, and no CI workflows. The libs/LTX-2/ submodule has its own tooling (ruff, pytest) managed by uv — but that's separate from this project.

Hardware

Requires NVIDIA GPU with CUDA. Minimum ~24 GB VRAM (bf16), ~14 GB with FP8 quantization.

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