HitPaw Image & Video Enhancer Skill

A powerful OpenClaw skill that integrates HitPaw's state-of-the-art AI enhancement technology for both images and videos. Enhance, upscale, restore, and denoise with multiple AI models.

🎯 Features

Based on the official HitPaw API Documentation, this skill leverages industrial-grade AI models developed in-house by HitPaw's expert R&D team.

Core Strengths

• Quality: Industry-defining quality fit for professional use cases, from commercial photography to archival restoration
• Fidelity: Preserves the original details and identities in the source images, ensuring the output remains true to the input
• Efficiency: Optimized for low latency and high throughput, capable of processing distinct enhancement tasks at scale

📸 Image Enhancement

According to the Image API Introduction, our image processing services offer world-class capabilities designed to handle a wide variety of restoration scenarios:

Key Capabilities

• Upscale: Output high-resolution images from low-resolution input files using standard or high-fidelity models
• Face Recovery: Ensure high-quality facial details, offering both "Clear" (soft/beauty) and "Natural" (textured/realistic) restoration options
• Sharpen & Denoise: Bring images into focus by removing blur and sensor noise while preserving the original structure
• Generative Restoration: Leverage Diffusion technology to reconstruct details in severely degraded portraits or general images

Model Classes

The Image API offers two classes of AI models to suit different needs:

• Standard Models: Fast and efficient, prioritizing preserving original fidelity and details. Recommended for most professional and general restoration use cases
• Generative Models: Utilize Stable Diffusion to produce the highest quality outputs, capable of "imagining" missing details. Ideal for extremely low-quality inputs where traditional upscaling fails

Standard Models

As detailed in the Available Models documentation:

| Model | Multiplier | Description | Best For | |-------|------------|-------------|----------| | general_2x / general_4x | 2x / 4x | General Enhance Model | General photos, landscapes | | face_2x / face_4x | 2x / 4x | Portrait Model (Clear) | Soft/beauty style portrait enhancement | | face_v2_2x / face_v2_4x | 2x / 4x | Portrait Model (Natural) | Natural/realistic portrait enhancement | | high_fidelity_2x / high_fidelity_4x | 2x / 4x | High Fidelity Model | Professional photography, conservatively upscaling high-quality sources | | sharpen_denoise_1x | 1x | Sharp Denoise Model | Aggressive denoising with sharpening | | detail_denoise_1x | 1x | Detail Denoise Model | Gentle denoising with texture preservation |

Generative Models

Powered by Stable Diffusion technology:

| Model | Multiplier | Description | Best For | |-------|------------|-------------|----------| | generative_portrait_1x/2x/4x | 1x/2x/4x | Generative Portrait Model | Extremely low-quality portraits, "re-imagines" details | | generative_general_1x/2x/4x | 1x/2x/4x | Generative Enhance Model | Heavily compressed or very low-resolution general images |

Technical Highlights:

• Generative models excel at texture generation and sharpening
• They can fill in missing details that traditional upscalers cannot recover
• Ideal for restoration tasks where source data is severely degraded

Example Image Use Cases

bash
# General photo upscaling (landscape, architecture)
enhance-image -u landscape.jpg -m general_4x -o hd_landscape.jpg

# Portrait beautification (soft skin)
enhance-image -u selfie.jpg -m face_4x -o portrait_beautified.jpg

# Professional archival restoration (natural look)
enhance-image -u old_photo.png -m face_v2_2x -o restored.png --keep-exif

# Denoise grainy low-light photo
enhance-image -u night_photo.jpg -m sharpen_denoise_1x -o clean.jpg

# Generative reconstruction for severely degraded image
enhance-image -u blurry_face.jpg -m generative_portrait_2x -o ai_face.jpg

🎬 Video Enhancement

According to the Video API Introduction, our video processing services provide industrial-grade solutions for restoring and upscaling video content:

Key Capabilities

• Video Upscale: Convert SD or HD footage to 4K Ultra HD clarity using deep convolution and feature learning technologies
• Portrait Restoration: Specialized models to detect, stabilize, and enhance faces in video streams, removing motion blur and noise while maintaining identity
• General Restoration: A comprehensive solution based on GAN technology to de-noise, de-blur, and enhance details in general video content
• Generative Reconstruction: Utilizing Stable Diffusion for video to reconstruct textures and details in extremely low-quality footage

Core Pillars

• Temporal Stability: Unlike image-only models, our video engines ensure smooth transitions between frames, eliminating flickering and jitter
• Clarity: Recovering fine details and removing compression artifacts common in streaming or legacy media
• Performance: Optimized inference times to handle heavy video processing workloads efficiently

Model Classes

• Restoration & Upscale (Standard): Models like Ultra HD and General Restore focus on cleaning up the footage and increasing resolution without altering the fundamental content. They rely on pixel-perfect accuracy and temporal consistency
• Generative Video: Uses advanced logic-based reconstruction. Designed for "impossible" restoration tasks where the source video lacks sufficient data, generating realistic textures and details to fill the gaps

Available Video Models

From the Video Models Documentation:

| Model | Description | Use Case | |-------|-------------|----------| | ultrahd_restore_2x | Ultra HD Model | High-definition upscale; natural-looking 1080p→4K | | general_restore_1x / 2x / 4x | General Restore Model | General video restoration, de-noising, de-blurring | | portrait_restore_1x / 2x | Portrait Restore Model | Multi-face restoration with temporal stability | | face_soft_2x | Video Face Soft Model | Facial beautification with consistent appearance | | generative_1x | Generative Video Model | Extreme restoration of heavily degraded footage |

Technical Highlights:

• Generates realistic textures and eliminates flickering via multi-frame SD architecture
• Handles heavy compression, high-ISO noise, and complex motion blur
• Maintains identity consistency across frames

Example Video Use Cases

bash
# Convert old 720p footage to 4K
enhance-video -u old_clip.mp4 -m ultrahd_restore_2x -r 3840x2160 -o 4k_remastered.mp4

# Restore grainy, noisy home video
enhance-video -u home_movie.avi -m general_restore_2x -r 1920x1080 -o cleaned.mp4

# Beautify faces in vlog/interview
enhance-video -u interview.mp4 -m face_soft_2x -r 1920x1080 -o soft_faces.mp4

# Stabilize and restore old family footage with multiple faces
enhance-video -u family_reunion.mov -m portrait_restore_2x -r 1920x1080 -o restored.mp4

# Generative AI restoration for severely degraded source
enhance-video -u heavily_compressed.mp4 -m generative_1x -r 1920x1080 -o regenerated.mp4

🚀 Why Choose HitPaw API?

Industry-Leading Quality: Professional-grade output suitable for commercial photography, archival restoration, and broadcast-quality video remastering

Unparalleled Fidelity: Strictly retains original details and subject identity, ensuring outputs remain true to inputs

Comprehensive Model Catalog: 16 specialized models covering virtually every restoration scenario

Scalable Performance: Optimized for low-latency, high-throughput workloads

📊 Quick Reference

Image Model Selection Guide

| Scenario | Recommended Model | |----------|-------------------| | General photo upscale | general_2x or general_4x | | Portrait beautification | face_2x or face_4x | | Portrait natural look | face_v2_2x or face_v2_4x | | Professional archival | high_fidelity_2x / high_fidelity_4x | | Grainy low-light | sharpen_denoise_1x | | Subtle denoise | detail_denoise_1x | | Severely degraded | generative_portrait_* or generative_general_* |

Video Model Selection Guide

| Scenario | Recommended Model | |----------|-------------------| | SD → 4K upscale | ultrahd_restore_2x | | General cleanup | general_restore_2x | | Interview/vlog beautification | face_soft_2x | | Old home movies (multiple faces) | portrait_restore_2x | | Severely compressed/ degraded | generative_1x |

Installation

bash
clawhub install hitpaw-image-enhancer

Configuration

Set your HitPaw API key:

bash
export HITPAW_API_KEY="your_api_key_here"

Or create a .env file in your OpenClaw workspace:

HITPAW_API_KEY=your_api_key_here

Get your API key at: https://playground.hitpaw.com/

Test the API directly in the browser: HitPaw Playground →

📸 Examples & Gallery

> Note: Place actual before/after screenshots in the images/ folder. See images/README.md for guidelines.

Image Enhancement Examples

| Scenario | Before | After | |----------|--------|-------| | General Upscale (2x) | !Before | !After | | Face Enhancement | !Before | !After | | Generative Portrait | !Before | !After |

Video Enhancement Examples

| Scenario | Original Frame | Enhanced Frame | |----------|----------------|----------------| | General Restoration | !Original | !Enhanced | | Portrait Restoration | !Before | !After |

IMAGE COMMAND

Usage: enhance-image

Command Line Options

| Option | Type | Default | Description | |--------|------|---------|-------------| | --url, -u | string | required | URL of the image to enhance | | --output, -o | string | output.jpg | Output file path | | --model, -m | string | general_2x | Image model (see below) | | --extension, -e | string | .jpg | Output extension (.jpg, .png, .webp) | | --dpi | number | original | Target DPI for metadata | | --keep-exif | boolean | false | Preserve EXIF data from original | | --poll-interval | number | 5 | Polling interval in seconds | | --timeout | number | 300 | Maximum wait time in seconds |

Available Image Models

| Model | Multiplier | Best For | DPI Support | |-------|------------|----------|-------------| | general_2x / general_4x | 2x / 4x | General photos, landscapes | ✅ | | face_2x / face_4x | 2x / 4x | Portrait & face enhancement | ✅ | | face_v2_2x / face_v2_4x | 2x / 4x | Improved face model | ✅ | | high_fidelity_2x / high_fidelity_4x | 2x / 4x | High quality preservation | ✅ | | sharpen_denoise_1x | 1x | Denoise & sharpen | ✅ | | detail_denoise_1x | 1x | Detail preservation | ✅ | | generative_* (1x/2x/4x) | — | AI generative fill | ❌ |

Examples

bash
# Simple 2x upscale with general model
enhance-image -u photo.jpg -o enhanced.jpg -m general_2x

# Face enhancement 4x
enhance-image -u portrait.jpg -m face_4x -o portrait_4x.jpg --keep-exif

# High fidelity with custom DPI
enhance-image -u old-photo.png -m high_fidelity_2x -dpi 300 -o hd.png

# Batch processing
for img in *.jpg; do
  enhance-image -u "$img" -o "upscaled/$img" -m general_4x
done

VIDEO COMMAND

Usage: enhance-video

⚠️ Important Notes

• Resolution is required (--resolution or -r). Must be in WIDTHxHEIGHT format (e.g., 1920x1080).
• Ensure target resolution does not exceed max output resolution (36 MP total pixels) per API limits.
• Video processing can take minutes to hours depending on length. Use --timeout to extend if needed.
• Input video must be at a publicly accessible URL (local files not directly supported).

Command Line Options

| Option | Type | Default | Description | |--------|------|---------|-------------| | --url, -u | string | required | URL of the video to enhance | | --output, -o | string | output.mp4 | Output file path | | --model, -m | string | general_restore_2x | Video model (see below) | | --resolution, -r | string | required | Target resolution in WxH (e.g., 1920x1080) | | --original-resolution | string | — | Original resolution (e.g., 1280x720) - optional | | --extension, -e | string | .mp4 | Output extension (.mp4, .mov, .avi) | | --fps | number | — | Target FPS (preserves original if omitted) | | --keep-audio | boolean | true | Preserve audio track | | --poll-interval | number | 10 | Polling interval in seconds | | --timeout | number | 600 | Maximum wait time in seconds |

Available Video Models

| Model | Description | Use Case | |-------|-------------|----------| | general_restore_1x / 2x / 4x | General video restoration | General upscaling | | face_soft_2x | Face-softening enhancement | Portrait videos | | portrait_restore_1x / 2x | Portrait restoration | Face-focused content | | ultrahd_restore_2x | Ultra HD upscaling | Highest quality upscale | | generative_1x | Generative fill | AI-powered restoration |

Examples

bash
# Upscale to 1080p using general_restore_2x
enhance-video -u input.mp4 -o output_1080p.mp4 -m general_restore_2x -r 1920x1080

# Upscale to 4K with specific original resolution
enhance-video -u clip.mov -o 4k.mov -m general_restore_4x -r 3840x2160 --original-resolution 1920x1080

# Denoise with portrait model
enhance-video -u portrait_video.avi -m portrait_restore_2x -r 1920x1080 -o clean_portrait.mp4

# Add color to B&W (if generative model supports)
enhance-video -u bw_vintage.mp4 -m generative_1x -r 1920x1080 -o colorized.mp4

Coin Consumption

Image Enhancement

• 2x/4x models: ~75 coins per image
• 1x models: ~50 coins per image
• Generative models: ~100+ coins per image

Video Enhancement

Coin costs depend on video length, model, and resolution. Approximate rates:

• Upscale models: ~200-400 coins per minute
• Restoration models: ~150-300 coins per minute

Always check current rates at: https://playground.hitpaw.com/

Error Handling

Common errors and solutions:

| Error | Cause | Fix | |-------|-------|-----| | Invalid API key | Wrong or expired key | Update HITPAW_API_KEY | | Insufficient coins | Account balance too low | Top up at HitPaw Playground | | Unsupported model | Model name typo or not available | Check model table above | | Invalid extension | Output format not supported | Use .jpg/.png/.webp for images; .mp4/.mov/.avi for videos | | Invalid video URL | URL not publicly accessible | Ensure video is reachable via HTTPS | | Input/target resolution over limit | Exceeds 36 MP total pixels (e.g., 7680x4320 = ~33 MP) | Reduce resolution | | Video duration over limit | Video longer than 1 hour | Trim video first | | Rate limit exceeded | Too many requests | Wait and retry with exponential backoff | | Video processing failed | Corrupt video or unsupported codec | Try different input format or re-encode |

Technical Details

API Compatibility

This skill implements the official HitPaw API as documented:

• Base URL: https://api-base.hitpaw.com
• Image endpoint: POST /api/photo-enhancer
• Video endpoint: POST /api/video-enhancer
• Status endpoint: POST /api/task-status

Both endpoints return a job_id. Use the status endpoint to poll until COMPLETED, then download from res_url.

Polling Strategy

• Images: default poll every 5s, timeout 300s (5 min)
• Videos: default poll every 10s, timeout 600s (10 min)

For longer videos, increase --timeout as needed (e.g., --timeout 3600 for 1 hour).

Resolution Handling

For videos, resolution is required. Choose based on your needs:

• Keep original size? Set resolution to original dimensions (use --original-resolution for better quality).
• Upscale? Multiply original width/height by factor (2x, 4x).
• Downscale? Rare but possible; just specify smaller dimensions.

Max output: 36 megapixels total (width × height ≤ 36,000,000 pixels). Examples: 3840×2160 = 8.3 MP ✅, 7680×4320 = 33.2 MP ✅, 8192×4608 = 37.7 MP ❌.

Audio Preservation

By default, enhance-video keeps the audio track (--keep-audio, default true). Use --no-keep-audio to strip audio.

Support

• Image API Docs: https://developer.hitpaw.com/image/API-reference
• Video API Docs: https://developer.hitpaw.com/video/API-reference
• Playground: https://playground.hitpaw.com/
• Contact: support@hitpaw.com

This skill is an unofficial integration with HitPaw API. You must have a valid API key and comply with HitPaw's terms. The skill author is not responsible for any charges incurred.

License

MIT © HitPaw-Official