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dante01yoon bbd0a6b201 feat: migrate workflow template site as apps/hub

Migrate workflow_templates/site into the frontend monorepo as apps/hub
so the hub can use @comfyorg/design-system and shared packages.

Changes to existing files:
- pnpm-workspace.yaml: add @astrojs/sitemap, @astrojs/vercel, lucide-vue-next
- eslint.config.ts: add hub ignores and i18n/import rule overrides
- .oxlintrc.json: add hub scripts to ignore patterns
- knip.config.ts: add hub workspace config

apps/hub adaptations from source:
- Replace local cn() with @comfyorg/tailwind-utils (19 files)
- Integrate @comfyorg/design-system/css/base.css in global.css
- Make TEMPLATES_DIR configurable via HUB_TEMPLATES_DIR env var
- Add HUB_SKIP_SYNC flag for builds without template data
- Remove Vite 8-incompatible rollupOptions.output.manualChunks
- Fix stylelint violations (modern color notation, number precision)
- Gitignore generated content (thumbnails, synced templates, AI cache)

2026-04-06 20:53:13 +09:00

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Real-ESRGAN

Real-ESRGAN is a practical image and video super-resolution model that extends ESRGAN with improved training on pure synthetic data for real-world restoration.

Model Variants

RealESRGAN_x4plus

General-purpose 4× upscaling model for real-world images
RRDB (Residual-in-Residual Dense Block) architecture
Handles noise, blur, JPEG compression artifacts

RealESRGAN_x4plus_anime_6B

Optimized for anime and illustration images
Smaller 6-block model for faster inference
Better edge preservation for line art

RealESRGAN_x2plus

2× upscaling variant for moderate enlargement
Lower risk of hallucinated details

realesr-animevideov3

Lightweight model designed for anime video frames
Temporal consistency for video processing

Key Features

Trained entirely on synthetic degradation data (no paired real-world data needed)
Second-order degradation modeling simulates real-world compression chains
GFPGAN integration for face enhancement during upscaling
Tiling support for processing large images with limited VRAM
FP16 (half precision) inference for faster processing
NCNN Vulkan portable executables for cross-platform GPU support (Intel/AMD/NVIDIA)
Supports 2×, 3×, and 4× upscaling with arbitrary output scale via LANCZOS4 resize

Hardware Requirements

Minimum: 2GB VRAM with tiling enabled
Recommended: 4GB+ VRAM for comfortable use
NCNN Vulkan build runs on any GPU with Vulkan support
CPU inference supported but significantly slower

Common Use Cases

Upscaling old or low-resolution photographs
Enhancing compressed web images
Anime and manga image upscaling
Video frame super-resolution
Restoring degraded historical images
Pre-processing for print from low-resolution sources

Key Parameters

outscale: Final upsampling scale factor (default: 4)
tile: Tile size for memory management (0 = no tiling)
face_enhance: Enable GFPGAN face enhancement (default: false)
model_name: Select model variant (RealESRGAN_x4plus, anime_6B, etc.)
denoise_strength: Balance noise removal vs detail preservation (realesr-general-x4v3)

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