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`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.
**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
```python
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.
- `WanGPSession.submit(source) -> SessionJob`
- Starts a job from a settings dict, a manifest list, or a saved `.json` / `.zip` file.
- `WanGPSession.submit_task(settings) -> SessionJob`
- Preferred single-task entrypoint.
- `WanGPSession.submit_manifest(settings_list) -> 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.
## `init(...)` Parameters
```python
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
)
```
- `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.
## 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"`.
### Single Task
For single-task use, the intended input is the task settings dictionary itself:
```python
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:
```python
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:
```python
job = session.submit(Path(r"C:\WanGP\my_queue.zip"))
```
## Streaming Events
Each job exposes `job.events`, a `SessionStream`.
The stream yields `SessionEvent` objects:
```python
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()`:
```python
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`:
```python
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(...)`:
```python
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(...)`:
```python
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(...)`:
```python
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:
```python
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(...)` or `WanGPSession(...)`.
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:
```python
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:
```python
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
```python
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`.
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