Wan2GP/docs/API.md

WanGP Python API

shared/api.py provides a lightweight in-process wrapper over WanGP's existing generation path.

The main goal is to let third-party code call WanGP directly, keep the last loaded model alive across requests, receive structured progress updates, and still capture the same stdout/stderr output that would normally go to the console.

This same API can be used directly from a WanGP PlugIn and uses WanGP Web Queue to process the requested Jobs of called from a Third Party App (for instance https://github.com/deepbeepmeep/LTX-Desktop-WanGP).

Please note that use of the WanGP API is subject to the WanGP Terms and Conditions. Any product that integrates WanGP should clearly disclose that it uses WanGP in both its user interface and its documentation.

Quick Start

The WanGP API consumes mostly WanGP Settings. In order to get the format of the settings, just launch the Web interface of WanGP, pick the model you want to use and fill the UI with settings you want to use, then click the Export Settings button at the bottom.

from pathlib import Path

from shared.api import init

session = init(
    root=Path(r"C:\WanGP"),
    cli_args=["--attention", "sdpa", "--profile", "4"],
)

settings = {
    "model_type": "ltx2_22B_distilled",
    "prompt": "Cinematic shot of a neon train entering a rainy station",
    "resolution": "1280x704",
    "num_inference_steps": 8,
    "video_length": 97,
    "duration_seconds": 4,
    "force_fps": 24,
}

job = session.submit_task(settings)

for event in job.events.iter(timeout=0.2):
    if event.kind == "progress":
        progress = event.data
        print(progress.phase, progress.progress, progress.current_step, progress.total_steps)
    elif event.kind == "preview":
        preview = event.data
        if preview.image is not None:
            preview.image.save("preview.png")
    elif event.kind == "stream":
        line = event.data
        print(f"[{line.stream}] {line.text}")

result = job.result()
if result.success:
    print(result.generated_files)
else:
    for error in result.errors:
        print(error.message)

Main Entry Points

• init(...) -> WanGPSession
  • Creates a reusable session and eagerly loads the runtime.
  • This is the normal entrypoint for standalone / CLI-style third-party integrations.
• WanGPSession.submit(source, callbacks=None) -> SessionJob
  • Starts a job from a settings dict, a manifest list, or a saved .json / .zip file.
• WanGPSession.submit_task(settings, callbacks=None) -> SessionJob
  • Preferred single-task entrypoint.
• WanGPSession.submit_manifest(settings_list, callbacks=None) -> SessionJob
  • Batch entrypoint for multiple tasks.
• SessionJob.result() -> GenerationResult
  • Waits for completion and returns a structured result object.
• SessionJob.cancel()
  • Requests cancellation of the active generation.

When init(...) Is Needed

• Standalone / CLI-style third-party app or Python script:
  • yes, call init(...)
• WanGP plugin tab receiving an injected api_session in create_config_ui(...):
  • no, do not call init(...)
  • use the injected api_session directly
• Low-level in-process integration inside WanGP with direct access to the live Gradio state:
  • possible with init(webui_state=state, ...)
  • this is an advanced/internal path, not the normal plugin entrypoint

init(...) Parameters

session = init(
    root=Path(r"C:\WanGP"),
    config_path=Path(r"C:\WanGP\wgp_config.json"),  # optional
    output_dir=Path(r"C:\WanGP\outputs_override"),  # optional
    callbacks=MyCallbacks(),                        # optional
    cli_args=["--attention", "sdpa"],              # optional
    console_output=True,                           # optional, default=True
    console_isatty=True,                           # optional, default=True
    webui_state=None,                              # optional
)

• root

  • Path to the WanGP installation folder.
  • Example: C:\WanGP

• config_path

  • Optional path to wgp_config.json.
  • If omitted, WanGP uses C:\WanGP\wgp_config.json.
  • This must point to a file named wgp_config.json.

• output_dir

  • Optional override for generated outputs.
  • If omitted, WanGP uses the output paths defined in the config file.

• callbacks

  • Optional callback object. See the callback section below.

• cli_args

  • Optional WanGP startup flags.
  • Example: ["--attention", "sdpa", "--profile", "4"]

• console_output

  • Enables or disables writing WanGP stdout/stderr to the real console.
  • Default: True
  • The stream object always receives a copy of stdout/stderr, regardless of this setting.

• console_isatty

  • Controls the TTY capability reported by the API's console capture wrapper.
  • Default: True
  • Keep this enabled if you want tqdm or other terminal-style progress output to behave like a live console stream even when WanGP is called from another Python process.

• webui_state

  • Optional live WanGP WebUI state dictionary.
  • When provided, submitted tasks target the existing WanGP Gradio queue instead of calling the headless generation path directly.
  • This is mainly a low-level internal / advanced integration hook.
  • Normal WanGP plugins should use the injected api_session instead of calling init(...) themselves.
  • In this mode, output_dir is ignored and on_stream(...) is not expected because the WebUI queue path is probe-based rather than stdout-capture based.

WebUI Queue Mode

This is the WebUI-backed queue mode used by WanGP plugin integrations.

If you are implementing a low-level in-process integration and explicitly need to bind the API to WanGP's live Gradio queue yourself, pass the current state dict to init(...):

from shared.api import init

session = init(webui_state=state, console_output=False)
job = session.submit_task(
    {
        "model_type": "ltx2_22B_distilled",
        "prompt": "A cinematic rainy street at night",
        "resolution": "1280x720",
        "num_inference_steps": 8,
        "video_length": 241,
    }
)

This mode reuses WanGP's existing WebUI queue loader server-side, then probes the live queue by client_id until the task is admitted, completed, fails, or is cancelled.

Differences versus standalone / CLI-style mode:

• third-party apps normally use plain init(...) without webui_state
• this WebUI-backed mode is the WanGP-side queue path
• low-level direct use still calls init(...), but with webui_state=state
• jobs run through WanGP's existing Gradio queue instead of the headless direct generation path
• output_dir is ignored
• on_stream(...) / job.events console lines are not the primary signal because this path is probe-based
• the API banner is intentionally not printed in this mode

Plugin Tab API Object

For WanGP plugins, a WebUI-backed session can be injected directly into the tab constructor.

If a plugin tab constructor accepts one extra positional argument, WanGP now passes a session-shaped API object there:

def create_config_ui(self, api_session):
    ...

That object behaves like a normal WanGPSession. In a WanGP plugin tab, the WebUI queue integration stays inside the injected session object, so plugin code can keep using the same submit_task(...), job.result(), and job.cancel() pattern as CLI code.

Plugin-specific difference:

• in a plugin tab, you normally do not call init(...)
• WanGP creates and injects the WebUI-backed session object for you
• plugin code should treat api_session as the API entrypoint
• this is the normal WebUI/API path for WanGP plugins

Minimal plugin pattern:

def create_config_ui(self, api_session):
    active_job = {"job": None}

    def start_demo(progress=gr.Progress(track_tqdm=False)):
        class DemoCallbacks:
            ratio = 0.0

            def on_status(self, status):
                status = str(status or "").strip()
                if status:
                    progress(self.ratio, desc=status)

            def on_progress(self, update):
                self.ratio = max(0.0, min(1.0, float(getattr(update, "progress", 0)) / 100.0))
                progress(self.ratio, desc=str(getattr(update, "status", "") or "Generating..."))

        job = api_session.submit_task(settings, callbacks=DemoCallbacks())
        active_job["job"] = job
        result = job.result()
        return result.generated_files[0] if result.success and result.generated_files else gr.update()

    def cancel_demo():
        job = active_job.get("job")
        if job is not None and not job.done:
            job.cancel()

In a plugin tab, callback methods like on_status(...) and on_progress(...) can safely update a local gr.Progress(...) while the job itself still runs through WanGP's main WebUI queue.

Useful SessionJob handles for plugin authors:

• job.result(timeout=None) -> GenerationResult
  • Waits for completion and returns generated files, errors, and any requested returned media artifacts.
• job.cancel()
  • Requests cancellation of the current job.
• job.done
  • True once the job has fully finished.
• job.events
  • Event stream for progress, preview, status, output, and completion events.
• job.cancel_requested
  • True after cancellation was requested.

Useful GenerationResult fields:

• result.generated_files
  • Output paths collected from WanGP's normal gallery / save path handling.
• result.errors
  • Structured generation errors. Runtime failures do not raise from submit_task(...); they appear here.
• result.artifacts
  • Optional returned media payloads requested through _api.
• result.cancelled
  • Optional returned if job was cancelled / aborted.

Useful GeneratedArtifact fields:

• artifact.path
  • Saved output path for that artifact when WanGP produced one.
• artifact.media_type
  • "video", "image", or "audio".
• artifact.video_tensor_uint8
  • Optional returned video tensor in WanGP's native post-decode layout: [C, F, H, W], uint8.
• artifact.audio_tensor
  • Optional returned audio tensor / array when requested.
• artifact.audio_sampling_rate
  • Sampling rate associated with artifact.audio_tensor when present.
• artifact.fps
  • Output FPS associated with artifact.video_tensor_uint8 when present.

Getting Outputs In Memory

By default, the API gives you output file paths in result.generated_files.

If you also want the generated media directly in memory, request it explicitly:

• pass _api={"return_media": True} in the submitted task settings
• then read the returned payloads from result.artifacts

Important notes:

• result.generated_files and result.artifacts are not mutually exclusive
  • WanGP still saves normal output files
  • the same completed task can also return in-memory media through result.artifacts
• result.artifacts is ordered like the submitted tasks for the tasks that actually returned media
• for video, the returned tensor layout is [C, F, H, W] with dtype=uint8
• for audio, use artifact.audio_tensor together with artifact.audio_sampling_rate

Minimal example:

job = session.submit_task(
    {
        "model_type": "ltx2_22B_distilled",
        "prompt": "generate a video",
        "resolution": "1280x720",
        "num_inference_steps": 8,
        "video_length": 241,
        "_api": {"return_media": True},
    }
)

result = job.result()

artifact = result.artifacts[0]
video_tensor = artifact.video_tensor_uint8   # [C, F, H, W], uint8
audio_tensor = artifact.audio_tensor         # optional
audio_sr = artifact.audio_sampling_rate      # optional
saved_path = artifact.path                   # optional saved file path

If you only care about the in-memory result, you can ignore result.generated_files and work directly from result.artifacts.

Accepted Input Shapes

Relative attachment paths are normalized to absolute paths when the job is submitted.

• For direct settings dictionaries and .json settings files, the base is the API caller's current working directory at submit time.
• For .zip queue files, WanGP keeps the queue bundle behavior and resolves bundled media from the extracted queue contents.
• A few WanGP string-like fields are normalized for convenience. For example, force_fps may be passed as 24 or "24".

Targeting A Frame Range Inside A Video File

For media inputs that accept a file path, WanGP also supports a virtual-media suffix so you can target only part of a source file without extracting an intermediate clip first.

Syntax:

• path/to/file.ext|start_frame=123,end_frame=456

Notes:

• start_frame is zero-based
• end_frame is inclusive
• the underlying source file stays the same; WanGP just decodes the requested frame range
• this is especially useful for video_guide, video_mask, and similar video-input fields

Optional audio-track targeting is also supported:

• path/to/file.ext|start_frame=123,end_frame=456,audio_track_no=2

Example:

job = session.submit_task(
    {
        "model_type": "ltx2_22B_distilled",
        "prompt": "generate a video",
        "video_prompt_type": "VG",
        "video_guide": r"F:\ALIENS_t01.mkv|start_frame=57542,end_frame=57782",
        "resolution": "1280x720",
        "num_inference_steps": 8,
        "video_length": 241,
    }
)

If you want to build that string programmatically, use shared.utils.virtual_media.build_virtual_media_path(...).

Optional API Meta Settings

submit_task(settings, ...) also accepts a reserved _api dictionary inside settings. This is API metadata, not a normal WanGP generation setting.

Current keys:

• _api={"return_media": True}
  • Requests returned media artifacts in result.artifacts.
  • Video outputs return artifact.video_tensor_uint8 when WanGP can expose a contiguous uint8 tensor.
  • Audio outputs return artifact.audio_tensor and artifact.audio_sampling_rate when available.

Example:

job = session.submit_task(
    {
        "model_type": "ltx2_22B_distilled",
        "prompt": "generate a video",
        "resolution": "1280x720",
        "num_inference_steps": 8,
        "video_length": 241,
        "_api": {"return_media": True},
    }
)

result = job.result()
artifact = result.artifacts[0]
video_tensor = artifact.video_tensor_uint8

Single Task

For single-task use, the intended input is the task settings dictionary itself:

settings = {
    "model_type": "qwen_image_20B",
    "prompt": "A red bicycle parked in front of a bakery",
    "resolution": "1024x1024",
    "num_inference_steps": 4,
    "image_mode": 1,
}

job = session.submit_task(settings)

Manifest

submit_manifest(...) accepts a list of settings dictionaries:

settings_list = [
    {
        "model_type": "qwen_image_20B",
        "prompt": "A quiet library at sunrise",
        "resolution": "1024x1024",
        "num_inference_steps": 4,
        "image_mode": 1,
    },
    {
        "model_type": "qwen_image_20B",
        "prompt": "A rainy alley with neon signs",
        "resolution": "1024x1024",
        "num_inference_steps": 4,
        "image_mode": 1,
    },
]

job = session.submit_manifest(settings_list)

Saved Queue / Settings File

submit(...) also accepts:

• a .json settings file path
• a .zip saved queue path

Example:

job = session.submit(Path(r"C:\WanGP\my_queue.zip"))

Streaming Events

Each job exposes job.events, a SessionStream.

The stream yields SessionEvent objects:

SessionEvent(
    kind="progress",
    data=ProgressUpdate(...),
    timestamp=1710000000.0,
)

Known kind values:

• started
  • Job accepted and session processing started.
• progress
  • Structured progress update.
• preview
  • RGB preview update.
• stream
  • One stdout/stderr line.
• status
  • WanGP status message.
• info
  • WanGP informational message.
• output
  • Raw output refresh event from WanGP.
• refresh_models
  • Raw model-refresh event from WanGP.
• completed
  • Final GenerationResult.
• error
  • One GenerationError record.

Returned Objects

GenerationResult

Returned by job.result():

GenerationResult(
    success=False,
    generated_files=[
        r"C:\WanGP\outputs\clip_001.mp4",
    ],
    errors=[
        GenerationError(
            message="Task 2 failed validation",
            task_index=2,
            task_id=2,
            stage="validation",
        ),
    ],
    total_tasks=3,
    successful_tasks=2,
    failed_tasks=1,
)

Fields:

• success: bool
  • True only when every submitted task completed without error.
• generated_files: list[str]
  • Absolute paths to every file generated by the job, including partial-success runs.
• errors: list[GenerationError]
  • Structured error records collected during the run.
• total_tasks: int
  • Number of tasks submitted in the job.
• successful_tasks: int
  • Number of tasks that completed successfully.
• failed_tasks: int
  • Number of tasks that failed or were cancelled.

job.result() does not raise generation-task failures. Instead, inspect result.success and result.errors.

GenerationError

Delivered through error events, on_error(...), and GenerationResult.errors:

GenerationError(
    message="Task 2 did not complete successfully",
    task_index=2,
    task_id=2,
    stage="generation",
)

Fields:

• message: str
  • Human-readable error message.
• task_index: int | None
  • One-based task index when the error is associated with a specific task.
• task_id: Any
  • Task identifier from the manifest when available.
• stage: str | None
  • Error stage such as validation, generation, cancelled, or runtime.

ProgressUpdate

Delivered through progress events and on_progress(...):

ProgressUpdate(
    phase="inference",
    status="Prompt 1/1 | Denoising | 7.2s",
    progress=54,
    current_step=4,
    total_steps=8,
    raw_phase="Denoising",
    unit=None,
)

Fields:

• phase: str
  • Normalized phase. Typical values:
  • loading_model
  • encoding_text
  • inference
  • decoding
  • downloading_output
  • cancelled
• status: str
  • Human-readable status string produced by WanGP.
• progress: int
  • Estimated percentage from 0 to 100.
• current_step: int | None
  • Current inference step when available.
• total_steps: int | None
  • Total inference steps when available.
• raw_phase: str | None
  • Original WanGP phase label before normalization.
• unit: str | None
  • Optional progress unit if WanGP provides one.

PreviewUpdate

Delivered through preview events and on_preview(...):

PreviewUpdate(
    image=<PIL.Image.Image image mode=RGB size=800x200>,
    phase="inference",
    status="Prompt 1/1 | Denoising",
    progress=54,
    current_step=4,
    total_steps=8,
)

Fields:

• image: PIL.Image.Image | None
  • RGB preview image generated from WanGP's latent preview payload.
• phase, status, progress, current_step, total_steps
  • Same interpretation as ProgressUpdate.

StreamMessage

Delivered through stream events and on_stream(...):

StreamMessage(
    stream="stdout",
    text="New video saved to Path: C:\\WanGP\\outputs\\clip_001.mp4",
)

Fields:

• stream: str
  • Usually stdout or stderr.
• text: str
  • One redirected line of console output.

SessionEvent

Generic event wrapper:

SessionEvent(
    kind="stream",
    data=StreamMessage(stream="stdout", text="Model loaded"),
    timestamp=1710000000.0,
)

Fields:

• kind: str
  • Event type.
• data: Any
  • Payload object for that event.
• timestamp: float
  • Event creation time.

Callback Object

You can pass a callback object to init(...) / WanGPSession(...) as the session default, or pass one directly to submit(...), submit_task(...), or submit_manifest(...) for a specific job.

Supported callback methods:

• on_progress(progress_update)

  • Called when WanGP emits a structured progress update.
  • Use this for progress bars, step counters, and status text.

• on_preview(preview_update)

  • Called when a preview image is available.
  • Use this when you want live RGB preview frames during inference.

• on_stream(stream_message)

  • Called for every redirected stdout/stderr line.
  • This is the programmatic equivalent of watching the terminal output.

• on_status(text)

  • Called for WanGP status messages.
  • Use this if you want coarse status without parsing full progress objects.

• on_info(text)

  • Called for informational messages.

• on_output(data)

  • Called for raw WanGP output refresh events.
  • This is a low-level hook and is usually not needed by third-party integrations.

• on_complete(result)

  • Called when the job finishes.
  • Receives a GenerationResult.

• on_error(error)

  • Called each time WanGP reports a task or runtime error.
  • Receives a GenerationError.

• on_event(session_event)

  • Generic catch-all event hook.
  • Called alongside the specific callback above, not instead of it.

Example:

class Callbacks:
    def on_progress(self, progress):
        print("progress:", progress.progress, progress.phase)

    def on_preview(self, preview):
        if preview.image is not None:
            preview.image.save("latest_preview.png")

    def on_stream(self, line):
        print(f"[{line.stream}] {line.text}")

    def on_complete(self, result):
        print("success:", result.success)
        print("generated:", result.generated_files)

    def on_error(self, error):
        print("error:", error.message)

Full signature example:

from shared.api import GenerationError, GenerationResult, PreviewUpdate, ProgressUpdate, SessionEvent, StreamMessage


class VerboseCallbacks:
    def on_progress(self, progress: ProgressUpdate) -> None:
        print("progress", progress.progress, progress.current_step, progress.total_steps)

    def on_preview(self, preview: PreviewUpdate) -> None:
        print("preview", preview.phase, preview.image.size if preview.image is not None else None)

    def on_stream(self, line: StreamMessage) -> None:
        print(line.stream, line.text)

    def on_status(self, text: str) -> None:
        print("status", text)

    def on_info(self, text: str) -> None:
        print("info", text)

    def on_output(self, data: object) -> None:
        print("output", data)

    def on_complete(self, result: GenerationResult) -> None:
        print("success", result.success)
        print("files", result.generated_files)

    def on_error(self, error: GenerationError) -> None:
        print("error", error.stage, error.task_index, error.message)

    def on_event(self, event: SessionEvent) -> None:
        print("event", event.kind)

Cancellation

job = session.submit_task(settings)
job.cancel()

Cancellation is cooperative and forwards WanGP's normal abort signal to the active model. A cancelled run completes with result.success == False and a cancellation entry in result.errors.