flux-2-klein-4B-outpaint-lora

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🖼️ Flux Green Screen Outpainting LoRA

Extend images by filling green border areas with AI-generated content

<img src="images/banner.jpg" alt="Outpaint LoRA Banner" width="100%"> </div>

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🎯 What does this model do?

This LoRA fills green border areas around an image with contextually appropriate AI-generated content. Simply add a green border to any image, and the model will extend the scene naturally.

┌─────────────────────────────────────┐
│ ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ │  Green areas
│ ░░░┌───────────────────────┐░░░░░░░ │  get filled
│ ░░░│                       │░░░░░░░ │  with matching
│ ░░░│    Original Image     │░░░░░░░ │  content
│ ░░░│                       │░░░░░░░ │
│ ░░░└───────────────────────┘░░░░░░░ │
│ ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ │
└─────────────────────────────────────┘

Perfect for extending photos, creating wider compositions, and generating seamless backgrounds.

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🖼️ Examples

<table> <tr> <th>Input (with green border)</th> <th>Output (extended image)</th> </tr> <tr> <td><img src="examples/example1_input.jpg" width="300"></td> <td><img src="examples/example1_output.jpg" width="300"></td> </tr> <tr> <td><img src="examples/example2_input.jpg" width="300"></td> <td><img src="examples/example2_output.jpg" width="300"></td> </tr> <tr> <td><img src="examples/example3_input.jpg" width="300"></td> <td><img src="examples/example3_output.jpg" width="300"></td> </tr> <tr> <td><img src="examples/example4_input.jpg" width="300"></td> <td><img src="examples/example4_output.jpg" width="300"></td> </tr> </table>

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🚀 Quick Start

Prompt

Fill the green spaces according to the image

Green Border Requirements

• Use pure green: RGB(0, 255, 0) or #00FF00
• Border can be any thickness (5%-25% of image dimension works well)
• Each side can have different border widths

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💻 Usage

Try it Live on fal.ai

▶️ Open Playground

With fal.ai SDK

import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "fal-ai/flux-2/klein/4b/base/edit/lora",
    arguments={
        "prompt": "Fill the green spaces according to the image",
        "model_name": None,
        "loras": [{
            "path": "https://huggingface.co/fal/flux-2-klein-4B-outpaint-lora/resolve/main/flux-outpaint-lora.safetensors",
            "scale": 1.1
        }],
        "embeddings": [],
        "image_urls": ["https://your-image-with-green-border.png"]
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

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📦 Model Files

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🎓 Training Details

<details> <summary>Click to expand</summary>

Dataset

• Size: 1000 image pairs
• Content: Diverse subjects including:
  • Landscapes: mountains, beaches, forests, deserts, cities
  • People: portraits, groups, activities
  • Animals: pets, wildlife, birds
  • Food: dishes, ingredients, table settings
  • Architecture: buildings, interiors, landmarks
  • Art styles: paintings, illustrations, abstract
  • Objects: vehicles, electronics, furniture
  • Nature: weather, space, underwater
• Border variation: Random green borders (5%-25% of each side)
• Aspect ratios: Various (1:1, 16:9, 9:16, 4:3, 3:4, etc.)

Training

• Base Model: FLUX.2-Klein 4B
• Platform: fal.ai
• Method: LoRA training

</details>

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🎮 Use Cases

• Photo Extension: Expand cropped photos to wider compositions
• Background Generation: Create seamless backgrounds for subjects
• Aspect Ratio Conversion: Convert portrait to landscape or vice versa
• Creative Composition: Add environmental context to isolated subjects

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⚠️ Limitations

• Works best with pure green (#00FF00) borders
• Complex scenes may require multiple passes
• Very large border areas may produce less coherent results

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📄 License

Apache 2.0

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Created by @ilkerzgi • Trained with fal.ai

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