# FLUX.1 Fill

> Run FLUX.1 Fill by infsh online or via API. GPU time, billed per millisecond, no setup.

**URL:** https://inference.sh/apps/infsh/flux-1-fill
**Last updated:** 2026-11-03

---

Fills designated areas in existing images based on a descriptive text input.

- app: `infsh/flux-1-fill`
- maker: [infsh](https://inference.sh/apps/infsh.md)
- category: [image](https://inference.sh/apps/category/image.md)
- tasks: [image editing](https://inference.sh/apps/tag/image-editing.md)
- price: GPU time, billed per millisecond
- runs via: api, javascript and python sdk, belt cli, mcp

## flux-1-fill API Guide

### About
fills designated areas in existing images based on a descriptive text input.

### Get an API Key
Create one in [settings → api keys](https://app.inference.sh/settings/keys). The examples read it from `INFERENCE_API_KEY`.

```bash
export INFERENCE_API_KEY="inf_your_key"
```

### 1. Calling the API

#### Install the Client

```bash
pip install inferencesh
```

#### Run and Get Result
Submit a request and wait for the final result.

```python
import os
from inferencesh import inference

client = inference(api_key=os.environ["INFERENCE_API_KEY"])

## version @3r41wwn2 pinned
result = client.run({
        "app": "infsh/flux-1-fill@3r41wwn2",
        "input": {
            "prompt": "prompt",
            "image": "https://example.com/file.png",
            "mask": "https://example.com/file.png"
        }
    })

print(result["output"])
```

#### Stream Live Updates
Get real-time progress updates as the task runs.

```python
import os
from inferencesh import inference

client = inference(api_key=os.environ["INFERENCE_API_KEY"])

## version @3r41wwn2 pinned
## stream=True yields updates as they arrive
for update in client.run({
        "app": "infsh/flux-1-fill@3r41wwn2",
        "input": {
            "prompt": "prompt",
            "image": "https://example.com/file.png",
            "mask": "https://example.com/file.png"
        }
    }, stream=True):
    if update.get("progress"):
        print(f"progress: {update['progress']}%")
    if update.get("output"):
        print(f"output: {update['output']}")
```

#### Run in the Background
Start the task and get its id back right away, then check on it with `GET /tasks/{id}`. It is done when `status` is 10 (completed), 11 (failed) or 12 (cancelled).

```python
import os
from inferencesh import inference, TaskStatus

client = inference(api_key=os.environ["INFERENCE_API_KEY"])

## returns as soon as the task is created
task = client.run({
        "app": "infsh/flux-1-fill@3r41wwn2",
        "input": {
            "prompt": "prompt",
            "image": "https://example.com/file.png",
            "mask": "https://example.com/file.png"
        }
    }, wait=False)

## later: read its current state
state = client.tasks.get(task["id"]).data
if state["status"] == TaskStatus.COMPLETED:
    print(state["output"])
```

### 2. Files

#### Automatic Upload

```python
## local file paths are automatically uploaded
result = client.run({
    "app": "infsh/flux-1-fill@3r41wwn2",
    "input": {
        "image": "/path/to/local/image.png",  # detected & uploaded
        "audio": "https://example.com/audio.mp3",  # url passed through
    }
})
```

#### Manual Upload

```python
## upload and get a hosted URL
file = client.files.upload("/path/to/file.png")
print(file.uri)  # https://cloud.inference.sh/...
```

### 3. Schema

#### Input
- **prompt** (string) *required*: Text prompt describing what to fill the masked area with
- **image** (string) *required*: Input image file to be filled
- **mask** (string) *required*: Mask image file indicating the area to be filled
- **guidance_scale** (number): Guidance scale for the generation process
- **num_inference_steps** (integer): Number of denoising steps
- **max_sequence_length** (integer): Maximum sequence length for the text encoder
- **seed** (integer): Random seed for reproducible results

#### Output
- **result** (string): Generated image with filled area

## other image apps

- [Gemini Nano Banana 2.1](https://inference.sh/apps/google/gemini-nano-banana-2-1.md): $1.50/M input, $0.0336–$0.1134/image (1K–4K), +$0.014/query Google Search
- [Grok Imagine Image 2.0](https://inference.sh/apps/xai/grok-imagine-image-2-0.md): $0.04/img low 1k, $0.06/img low 2k, $0.06/img med 1k, $0.08/img med 2k; $0.01/source img (edits)
- [GPT Image 2.5 Sunburst](https://inference.sh/apps/openai/gpt-image-2-5-sunburst.md): $0.006-$0.21 per 1024x1024 image: low $0.006, medium $0.013, high $0.053, xhigh $0.094, max $0.21. Larger sizes cost more; edits add ~$0.008 per reference image.
- [GPT Image 2.5 Flare](https://inference.sh/apps/openai/gpt-image-2-5-flare.md): $0.006-$0.21/image at 1024x1024: low $0.0059, medium $0.0132, high $0.0527, xhigh $0.0937, max $0.2107. Larger sizes cost more; edits add ~$0.0083 per reference image.
- [Image Resize & Crop](https://inference.sh/apps/eval/resize.md): from $0.00000277/sec

## faq

### how much does FLUX.1 Fill cost?

GPU time, billed per millisecond. you pay per run from your inference shell credits. no subscription is required.

### how do I run FLUX.1 Fill via api?

call infsh/flux-1-fill with the javascript or python sdk, the rest api, or `belt app run infsh/flux-1-fill` from a terminal. the api reference on this page lists every input.

### who makes FLUX.1 Fill?

FLUX.1 Fill is made by infsh and runs on inference shell. every infsh app is listed at https://inference.sh/apps/infsh.

### can my agent use FLUX.1 Fill?

yes. agents call it as a tool through the inference shell mcp server (https://api.inference.sh/mcp), the belt cli or the sdk, and pay per run like you do.

### what are the alternatives to FLUX.1 Fill?

other image apps on inference shell include Gemini Nano Banana 2.1, Grok Imagine Image 2.0, GPT Image 2.5 Sunburst, GPT Image 2.5 Flare. they run through the same api, so switching is a one-line change.