A resident generation server that runs as an AI Toolkit job and serves every registered model arch (image, video, audio). It hot-swaps models per request: components the new model shares with the previous one (a text encoder, a VAE) stay resident, the rest is freed and loaded. Progress and the raw per-step latents stream back over the request's HTTP connection.

Design notes and status: PLANNING.md in this folder.

Running

From the UI: Generate in the sidebar, pick a GPU, Start engine. The engine shows up in the Queue as an inference job and runs until stopped.

Headless:

# inference_engine.yaml
job: extension
config:
  name: inference_engine
  process:
    - type: InferenceEngine
      device: cuda
      engine:
        job_folder: output/inference_engine   # engine.json + outputs/ + assets/
        # port: 8866         # fixed port for external clients (default: ephemeral)
        # token: secret      # request token (default: random per launch)
        # default_model: {arch: zimage}   # warm load

python run.py inference_engine.yaml

The server binds 127.0.0.1 on an ephemeral port and writes <job_folder>/engine.json (host, port, token, pid). The UI proxies /api/inference/* to it; nothing else is exposed.

HTTP API

All routes except /health need the token (X-Engine-Token: <token> or Authorization: Bearer <token>).

route
`GET /health`	busy/queue, active model, VRAM, pool residents + hit/miss stats
`GET /models`	every arch with modality, defaults, control-image requirement (`toolkit/models/registry.py`)
`POST /generate`	body `{model, sample, stream}` → binary frame stream (below). `{"wait": true}` returns JSON when done instead
`POST /cancel/{request_id}`	cancel a queued/running request
`GET /queue`	queued + recent requests
`POST /unload`	drop the holder and every pooled component
`POST /assets?name=x.png`	raw-body upload; returns `{path}` usable as `sample.ctrl_img`
`GET /outputs/{relpath}`	fetch a produced file

model = ModelConfig kwargs (arch required; name_or_path, quantize, qtype, quantize_te, qtype_te, low_vram, layer_offloading, lora_path, extras_name_or_path, ...). Registry defaults fill in what is omitted. sample = GenerateImageConfig kwargs (prompt, negative_prompt, width, height, num_inference_steps, guidance_scale, seed, num_frames, fps, ctrl_img, output_ext). stream = {latents: "raw"|"none", every_n_steps, max_frames}.

Frame stream

u32 header_len | json header | u64 payload_len | payload (little endian). Header always has type and request_id:

start — resolved model/sample, modality, preview (latent→RGB factors)
status — holder stage lines (loading, quantizing, ...)
progress — {step, total, elapsed}
latent — raw fp16 tensor payload; shape, dtype, layout (BCHW, BCFHW, BLC). Video latents are subsampled to max_frames frames.
result — {path, kind, ext, seed, width, height, ...} per produced file
error — {message, cancelled}
end — {status}

Python reader: protocol.FrameReader; browser reader: ui/src/lib/engineStream.ts (also does the latent→RGB projection from the preview block, so the client needs no per-arch code).

Per-step latents

BaseModel.generate_images wraps scheduler.step on the sampling scheduler while sample_step_hook is set, so every diffusers-style pipeline reports its latents without per-arch code. Holders with a hand-rolled loop call self._emit_sample_step(latents, i, n) (ace_step does).

Component pool

toolkit/models/v2/pool.py. While a pool is active, OstrisModelMixin. load_model returns the resident module for an identical source request and aitk_post_load is a no-op for a module already carrying that policy. After each holder build the engine frees every pooled component the new model did not touch. On CUDA OOM during a load it frees the untouched components and retries once. Components loaded outside the mixin's load_model (custom holder paths, legacy monolith archs) are not pooled and reload each time.

Tests

python testing/test_inference_engine.py --arch zimage --quantize
python testing/test_inference_engine.py --arch zimage --cancel
python testing/test_inference_engine.py --swap zimage,zimage_l2p,zimage --quantize
python testing/test_inference_engine.py --all

--quantize forces convrot8 on transformer + TE for small GPUs; --allow-download permits hub downloads (default offline → SKIP).