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

Prompt

Fill the green spaces according to the image

Prompt

Fill the green spaces according to the image

Prompt

Fill the green spaces according to the image

Prompt

Fill the green spaces according to the image

🖼️ Flux Green Screen Outpainting LoRA

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

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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

Input (with green border)	Output (extended image)

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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

File	Use Case
flux-outpaint-lora.safetensors	fal.ai
LyNiaZ53Tudg0J6sT8Xbx_pytorch_lora_weights_comfy_converted.safetensors	ComfyUI

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🎓 Training Details

Click to expand

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

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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