black-forest-labs/flux-3/edit-video

video-to-video

BLACKFORESTLABS FLUX 3 Edit Video API by Black Forest Labs

black-forest-labs/flux-3/edit-video

Edit-video

Re-render an input video according to the prompt while preserving its motion, timing, and framing, using FLUX 3.

BLACKFORESTLABS FLUX 3 Edit Video is developed by Black Forest Labs. Atlas Cloud (operated by Atlas Cloud AI LLC) provides access to it and does not own it. All trademarks belong to their respective owners.

INPUT

Prompt *

Video *

You can drag and drop a file or click to upload

MAX:1

Safety tolerance

OUTPUT

Idle

Your generated videos will appear here

Configure your settings and click Run to get started

Your request will cost $0.03 per run. For $10 you can run this model approximately 333 times.

Here's what you can do next:

Seedance 2.0 Kling v3 Vidu Wan2.7

Parameters

Code Example
import requests
import time

# Step 1: Start video generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "black-forest-labs/flux-3/edit-video",  # Required. Model name. options: black-forest-labs/flux-3/edit-video
    "prompt": "A beautiful sunset over the ocean with gentle waves",  # Required. The text prompt describing how to change the input video
    "video_url": "example_value",  # Required. URL of the input video to edit
    "safety_tolerance": 2,  # Safety tolerance level. (min: 0, max: 4)
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] in ["completed", "succeeded"]:
            print("Generated video:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

video_url = check_status()

Install

Install the required package for your language.

pip install requests

Authentication

All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.

export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP Headers

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Keep your API key secure

Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.

Submit a request

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "prompt": "A beautiful landscape"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

Submit a Request

Submit an asynchronous generation request. The API returns a prediction ID that you can use to check the status and retrieve the result.

POST/api/v1/model/generateVideo

Request Body

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "black-forest-labs/flux-3/edit-video",
    "prompt": "A beautiful sunset over the ocean with gentle waves"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['data']['id']}")
print(f"Status: {result['data']['status']}")

Response

{
  "code": 200,
  "data": {
    "id": "pred_abc123",
    "status": "processing",
    "model": "model-name",
    "created_at": "2025-01-01T00:00:00Z"
  }
}

Check Status

Poll the prediction endpoint to check the current status of your request.

GET/api/v1/model/prediction/{prediction_id}

Polling Example

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

Status Values

processingThe request is still being processed.

completedGeneration is complete. Outputs are available.

succeededGeneration succeeded. Outputs are available.

failedGeneration failed. Check the error field.

Completed Response

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.mp4"
    ],
    "metrics": {
      "predict_time": 45.2
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

Upload Files

Upload files to Atlas Cloud storage and get a URL you can use in your API requests. Use multipart/form-data to upload.

POST/api/v1/model/uploadMedia

Upload Example

import requests

url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

with open("image.png", "rb") as f:
    files = {"file": ("image.png", f, "image/png")}
    response = requests.post(url, headers=headers, files=files)

result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")

Response

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

The following parameters are accepted in the request body.

Total: 4Required: 3Optional: 1

modelstringrequired

Model name.

Default: "black-forest-labs/flux-3/edit-video"

black-forest-labs/flux-3/edit-video

promptstringrequired

The text prompt describing how to change the input video. The clip is re-rendered preserving motion, timing, and framing.

video_urlstringrequired

URL of the input video to edit. MP4, under 50 MB and under 15 seconds. Output duration and resolution follow the input clip.

Format: uri

safety_toleranceinteger

Safety tolerance level. 0 is the strictest and 4 is the most permissive.

Default: 2Min: 0Max: 4

Example Request Body

{
  "model": "black-forest-labs/flux-3/edit-video",
  "prompt": "A beautiful landscape",
  "video_url": "example_video_url",
  "safety_tolerance": 2
}

Output Schema

The API returns a prediction response with the generated output URLs.

codeinteger

messagestring

dataunknown

Example Response

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.mp4"
  ],
  "metrics": {
    "predict_time": 45.2
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills integrates 400+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.

Supported Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ supported clients

Install

npx skills add AtlasCloudAI/atlas-cloud-skills

Setup API Key

Get your API key from the Atlas Cloud dashboard and set it as an environment variable.

export ATLASCLOUD_API_KEY="your-api-key-here"

Capabilities

Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.

Image GenerationGenerate images with models like Nano Banana 2, Z-Image, and more.

Video CreationCreate videos from text or images with Kling, Vidu, Veo, etc.

LLM ChatChat with Qwen, DeepSeek, and other large language models.

Media UploadUpload local files for image editing and image-to-video workflows.

Learn more

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server connects your IDE with 400+ AI models via the Model Context Protocol. Works with any MCP-compatible client.

Supported Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ supported clients

Install

npx -y atlascloud-mcp

Configuration

Add the following configuration to your IDE's MCP settings file.

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Available Tools

atlas_generate_imageGenerate images from text prompts.

atlas_generate_videoCreate videos from text or images.

atlas_chatChat with large language models.

atlas_list_modelsBrowse 400+ available AI models.

atlas_quick_generateOne-step content creation with auto model selection.

atlas_upload_mediaUpload local files for API workflows.

Learn more

github.com/AtlasCloudAI/mcp-server

API Schema

{
  "info": {
    "title": "AtlasCloud API",
    "version": "1.0.0",
    "description": "The AtlasCloud API."
  },
  "openapi": "3.0.0",
  "paths": {
    "/api/v1/model/generateVideo": {
      "post": {
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          },
          "required": true
        },
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        }
      },
      "x-api-name": "model_run"
    },
    "/api/v1/model/prediction/{request_id}": {
      "get": {
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "required": true,
            "schema": {
              "description": "Request ID",
              "type": "string"
            }
          }
        ],
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        }
      },
      "x-api-name": "model_result"
    }
  },
  "components": {
    "schemas": {
      "Input": {
        "type": "object",
        "properties": {
          "model": {
            "type": "string",
            "description": "Model name.",
            "default": "black-forest-labs/flux-3/edit-video",
            "enum": [
              "black-forest-labs/flux-3/edit-video"
            ]
          },
          "prompt": {
            "description": "The text prompt describing how to change the input video. The clip is re-rendered preserving motion, timing, and framing.",
            "type": "string"
          },
          "video_url": {
            "description": "URL of the input video to edit. MP4, under 50 MB and under 15 seconds. Output duration and resolution follow the input clip.",
            "type": "string",
            "format": "uri",
            "x-format": "video",
            "x-ui-component": "video-upload"
          },
          "safety_tolerance": {
            "description": "Safety tolerance level. 0 is the strictest and 4 is the most permissive.",
            "type": "integer",
            "default": 2,
            "minimum": 0,
            "maximum": 4,
            "x-ui-component": "slider"
          }
        },
        "required": [
          "model",
          "prompt",
          "video_url"
        ],
        "x-order-properties": [
          "model",
          "prompt",
          "video_url",
          "safety_tolerance"
        ]
      },
      "PredictionResponse": {
        "type": "object",
        "properties": {
          "code": {
            "type": "integer"
          },
          "message": {
            "type": "string"
          },
          "data": {
            "$ref": "#/components/schemas/PredictionData"
          }
        }
      },
      "PredictionData": {
        "type": "object",
        "properties": {
          "id": {
            "type": "string",
            "description": "Unique ID of the generation task."
          },
          "model": {
            "type": "string",
            "description": "Model used for the generation."
          },
          "outputs": {
            "type": "array",
            "description": "Generated video results. Each item is a video URL. Null or empty until the task completes.",
            "items": {
              "type": "string"
            }
          },
          "urls": {
            "type": "object",
            "properties": {
              "get": {
                "type": "string"
              }
            }
          },
          "status": {
            "type": "string",
            "description": "Current task status, such as created, processing, completed, or failed."
          },
          "created_at": {
            "type": "string",
            "format": "date-time"
          },
          "error": {
            "type": "string",
            "description": "Error message returned when the task fails."
          }
        }
      }
    }
  },
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ]
}

LLM-Friendly Prompt Template

# black-forest-labs/flux-3/edit-video

> Re-render an input video according to the prompt while preserving its motion, timing, and framing, using FLUX 3.


## Overview

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateVideo` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `black-forest-labs/flux-3/edit-video`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  Model name.
  - Default: `"black-forest-labs/flux-3/edit-video"`
  - Options: "black-forest-labs/flux-3/edit-video"

- **`prompt`** (`string`, _required_):
  The text prompt describing how to change the input video. The clip is re-rendered preserving motion, timing, and framing.

- **`video_url`** (`string`, _required_):
  URL of the input video to edit. MP4, under 50 MB and under 15 seconds. Output duration and resolution follow the input clip.

- **`safety_tolerance`** (`integer`, _optional_):
  Safety tolerance level. 0 is the strictest and 4 is the most permissive.
  - Default: `2`
  - Min: 0
  - Max: 4



**Required Parameters Example**:

```json
{
  "model": "black-forest-labs/flux-3/edit-video",
  "prompt": "",
  "video_url": ""
}
```


**Full Example**:

```json
{
  "model": "black-forest-labs/flux-3/edit-video",
  "prompt": "",
  "video_url": "",
  "safety_tolerance": 2
}
```


### Output Schema

The API returns the following output format:


- **`code`** (`integer`, _optional_):

- **`message`** (`string`, _optional_):

- **`data`** (`string`, _optional_):



**Example Response**:

```json
{
  "code": 0,
  "message": "",
  "data": null
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateVideo" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "black-forest-labs/flux-3/edit-video",
  "prompt": "",
  "video_url": "",
  "safety_tolerance": 2
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/black-forest-labs/flux-3/edit-video)

Adjust the video style to a comic book style.

FLUX 3 Edit Video

FLUX 3 Edit Video re-renders an existing clip from a natural-language instruction. Describe the change you want — a season, a material, a colour grade, an object swap — and the model applies it while preserving the original motion, timing, and framing.

Why Choose This?

Instruction-driven editing — no masks, no keyframes; one sentence describes the edit.
Motion and timing preserved — the source clip's camera movement, pacing, and composition survive the edit.
Minimal input — only a video and a prompt are required.
Output follows the source — duration and framing match the input clip, so edits drop straight back into your timeline.

Parameters

Parameter	Required	Description
`model`	Yes	`black-forest-labs/flux-3/edit-video`
`prompt`	Yes	How to change the input video. The clip is re-rendered preserving motion, timing, and framing.
`video_url`	Yes	URL of the input video. MP4, under 50 MB and under 15 seconds.
`safety_tolerance`	No	Safety level `0` (strictest) to `4` (most permissive). Default: `2`

There is no duration, resolution, or aspect_ratio input — the output follows the input clip.

How to Use

curl -X POST "https://api.atlascloud.ai/api/v1/model/generateVideo" \
  -H "Authorization: Bearer $AIP_API_KEY" \
  -H "Content-Type: application/json" \
  --data-raw '{
    "model": "black-forest-labs/flux-3/edit-video",
    "prompt": "Make it snow heavily; give the scene a cold winter palette.",
    "video_url": "https://static.atlascloud.ai/media/videos/af3ce4416487e61f8b299c5dc42642e3.mp4",
    "safety_tolerance": 2
  }'

Generation is asynchronous — poll /api/v1/model/prediction/{request_id} until status is completed, then read the video URL from outputs.

Best Use Cases

Look development — try a different season, time of day, or colour grade on a shot you already like.
Product and set dressing — swap an object or material without reshooting.
Style passes — apply a consistent treatment across a set of clips.
Continuity fixes — adjust a detail that is wrong in an otherwise usable take.

Pro Tips

Name what changes and what stays: "replace the sushi with a steak, keep the same plate and lighting".
Keep the edit within one idea per request; chain requests for compound changes.
Shorter clips return faster and cost less — trim before uploading rather than after.
The input must be reachable over public HTTPS; the URL is fetched at submit time for both billing and generation.

Notes

Source video constraints: MP4, under 50 MB, under 15 seconds.
Output duration and framing follow the input clip; they cannot be set per request.
Generated videos are re-hosted on AtlasCloud storage, so the returned URL is ours, not the upstream provider's.

FLUX 3 Extend Video — Continue a clip beyond its final frames.
FLUX 3 Text-to-Video — Generate video from a text prompt alone.
FLUX 3 Image-to-Video — Animate a starting image.
FLUX 3 First & Last Frame to Video — Interpolate between two images.
FLUX 3 Keyframes to Video — Pin multiple images to exact frame positions.

BLACKFORESTLABS FLUX 3 Edit Video API by Black Forest Labs

INPUT

OUTPUT

Parameters

Code Example

Install

Authentication

HTTP Headers

Submit a request

Submit a Request

Request Body

Response

Check Status

Polling Example

Status Values

Completed Response

Upload Files

Upload Example

Response

Input Schema

Example Request Body

Output Schema

Example Response

Atlas Cloud Skills

Supported Clients

Install

Setup API Key

Capabilities

MCP Server

Supported Clients

Install

Configuration

Available Tools

API Schema

LLM-Friendly Prompt Template

FLUX 3 Edit Video

Why Choose This?

Parameters

How to Use

Best Use Cases

Pro Tips

Notes

Related Models

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INPUT

OUTPUT

Parameters

Code Example

Install

Authentication

HTTP Headers

Submit a request

Submit a Request

Request Body

Response

Check Status

Polling Example

Status Values

Completed Response

Upload Files

Upload Example

Response

Input Schema