skillfed

fal-image-to-image

fal-image-to-image provides unified access to fal.ai's image transformation suite, including FLUX and SDXL models for style transfer, ControlNet-guided generation with canny/depth/pose control, mask-based inpainting, upscaling via ESRGAN and Clarity, background removal, and face restoration. Configure strength parameters and control types to fine-tune how much the original image influences the output.

fal-image-to-image transforms images through style transfer, inpainting, ControlNet guidance, and enhancement using fal.ai models.

AI-generated summary based on this skill's SKILL.md

49 10 MIT updated by JosiahSiegel

Install

JosiahSiegel/claude-plugin-marketplace/fal-image-to-image · repository language: Shell

CLI (skillfed)coming soon
git clone https://github.com/JosiahSiegel/claude-plugin-marketplace
cp -r claude-plugin-marketplace/plugins/fal-ai-master/skills/fal-image-to-image ~/.claude/skills/fal-image-to-image

Frequently asked questions

AI-generated answers based on this skill's SKILL.md and metadata

What is fal-image-to-image and what can it do?

fal-image-to-image provides unified access to fal.ai's image transformation suite. It supports style transfer using FLUX and SDXL models, ControlNet-guided generation with canny edge, depth, and pose control, mask-based inpainting for editing specific regions, upscaling via ESRGAN and Clarity AI, background removal, and face restoration. You can configure strength parameters and control types to fine-tune how much the original image influences the output.

How do I do style transfer on images with fal-image-to-image?

fal-image-to-image enables style transfer by transforming images while preserving structure using FLUX and SDXL models. You can apply IP-Adapter style transfer to reimagine your image in a new artistic style. The strength parameter lets you control how much stylization is applied—adjust it to balance between maintaining the original content and achieving your desired visual transformation.

Can fal-image-to-image remove backgrounds from photos?

Yes, fal-image-to-image includes background removal powered by BiRefNet. This feature automatically detects and removes the background from your photo, leaving the subject intact. It's useful for product photography, portrait editing, and creating transparent backgrounds for further composition work.

What inpainting with mask capabilities does fal-image-to-image offer?

fal-image-to-image supports mask-based inpainting to edit specific regions of your image. You provide a mask defining the area to modify, and the model regenerates that region while preserving the masked-out portions. This enables precise, localized edits without affecting the rest of your image.

How does fal-image-to-image upscale low resolution images?

fal-image-to-image upscales low-resolution images using ESRGAN and Clarity AI upscalers. These AI-powered enhancement tools intelligently reconstruct detail and improve image quality. This is ideal for restoring old photos, enlarging thumbnails, or preparing images for print without losing visual fidelity.

What ControlNet options are available in fal-image-to-image?

fal-image-to-image offers ControlNet-guided generation with multiple control types: canny edge detection for structure, depth maps for spatial control, and pose-based guidance for human figure positioning. FLUX ControlNet union combines these modes for precise, layered control over image generation, letting you direct composition and structure exactly as needed.

SKILL.md

rendered from the published skill — quoted content, verbatim

Quick Reference

Task Endpoint Key Parameter
Transform fal-ai/flux/dev/image-to-image strength: 0-1
Inpaint fal-ai/flux/dev/inpainting mask_url
ControlNet fal-ai/flux-controlnet-union control_type
Upscale fal-ai/clarity-upscaler scale_factor: 2-4
Remove BG fal-ai/birefnet -
Face fix fal-ai/codeformer fidelity: 0-1
Strength Value Effect
0.2-0.3 Subtle style hint
0.5-0.7 Moderate transform
0.8-1.0 Full regeneration

| ControlNet Type | Use Case

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Related skills

Tags

guided-generation structural-control region-editing resolution-enhancement face-restoration style-reference mask-based-editing multi-model-pipeline conditioning-scale