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Ming3D/
flux-2-klein-4B-outpaint-lora

Name: flux-2-klein-4B-outpaint-lora
Availability: InStock

black-forest-labs/FLUX.2-klein-4B

adapter

apache-2.0

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🖼️ Flux Green Screen Outpainting LoRA

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

🎯 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.

🖼️ Examples

Input (with green border)	Output (extended image)

🚀 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

💻 Usage

Try it Live on fal.ai

▶️ Open Playground

With fal.ai SDK

python

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)

📦 Model Files

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

🎓 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

🎮 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

⚠️ Limitations

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

📄 License

Apache 2.0

Ming3D/flux-2-klein-4B-outpaint-lora