Inference Shell Mask Image
Combines two images—a main image and a semi-transparent mask—to selectively hide or reveal parts of the main image, creating a partially transparent result.
api reference
about
combines two images—a main image and a semi-transparent mask—to selectively hide or reveal parts of the main image, creating a partially transparent result.
1. calling the api
install the client
the client provides a convenient way to interact with the api.
1pip install inferenceshsetup your api key
set INFERENCE_API_KEY as an environment variable. get your key from settings → api keys.
1export INFERENCE_API_KEY="inf_your_key"run and get result
submit a request and wait for the final result. best for batch processing or when you don't need progress updates.
1import os2from inferencesh import inference34client = inference(api_key=os.environ["INFERENCE_API_KEY"])567result = client.run({8 "app": "infsh/mask-image",9 "input": {10 "input_image": {},11 "mask_image": {}12 }13 })1415print(result["output"])stream live updates
get real-time progress updates as the task runs. ideal for showing progress bars, partial results, or long-running tasks.
1import os2from inferencesh import inference34client = inference(api_key=os.environ["INFERENCE_API_KEY"])567# stream=True yields updates as they arrive8for update in client.run({9 "app": "infsh/mask-image",10 "input": {11 "input_image": {},12 "mask_image": {}13 }14 }, stream=True):15 if update.get("progress"):16 print(f"progress: {update['progress']}%")17 if update.get("output"):18 print(f"output: {update['output']}")2. authentication
the api uses api keys for authentication. see the authentication docs for detailed setup instructions.
3. files
file inputs are automatically handled by the sdk. you can pass local paths, urls, or base64 data.
automatic upload
the python sdk automatically detects local file paths and uploads them. urls are passed through as-is.
1# local file paths are automatically uploaded2result = client.run({3 "app": "infsh/mask-image",4 "input": {5 "image": "/path/to/local/image.png", # detected & uploaded6 "audio": "https://example.com/audio.mp3", # url passed through7 }8})4. webhooks
get notified when a task completes by providing a webhook url. when the task reaches a terminal state (completed, failed, or cancelled), a POST request is sent to your url with the task result.
1result = client.run({2 "app": "infsh/mask-image",3 "input": {4 "input_image": {},5 "mask_image": {}6 },7 "webhook": "https://your-server.com/webhook"8}, wait=False)webhook payload
your endpoint receives a JSON POST with the task result:
1{2 "event": "task.completed",3 "timestamp": "2024-01-15T10:30:05Z",4 "data": {5 "id": "task_abc123",6 "short_id": "abc123",7 "status": 10,8 "status_text": "completed",9 "output": { ... },10 "created_at": "2024-01-15T10:30:00Z",11 "updated_at": "2024-01-15T10:30:05Z"12 }13}5. schema
new models
VACE
pruna/vace
video$0.40/video
P-Video-Animate
pruna/p-video-animate
video$0.03/sec 720p, $0.06/sec 1080p
P-Video-Upscale
pruna/p-video-upscale
videoStd: $0.01/sec 720p, $0.02 1080p, $0.04 2k, $0.08 4k; high fps 2x
P-Video-2-Pro-Avatar
pruna/p-video-2-pro-avatar
video$0.02/sec 480p, $0.04/sec 768p, $0.06/sec 1080p, $0.08/sec 2k, $0.12/sec 4k
P-Video-2-Pro-Reference
pruna/p-video-2-pro-reference
videoOutput: cost $0.015-$0.105/sec, speed $0.02-$0.115/sec. Ref video: $0.03-$0.06/sec. Ref img: $0.005/img. Ref audio: $0.0015-$0.0025/sec
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