mirror of https://github.com/Comfy-Org/ComfyUI_frontend.git synced 2026-04-20 14:30:41 +00:00

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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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FlashVSR

FlashVSR is a diffusion-based streaming video super-resolution framework that achieves near real-time 4× upscaling through one-step inference with locality-constrained sparse attention.

Model Variants

FlashVSR v1

Initial release of the one-step streaming VSR model
Built on Wan2.1 1.3B video diffusion backbone
4× super-resolution optimized

FlashVSR v1.1

Enhanced stability and fidelity over v1
Improved artifact handling across different aspect ratios
Recommended for production use

Key Features

One-step diffusion inference (no multi-step denoising required)
Streaming architecture with KV cache for sequential frame processing
Locality-Constrained Sparse Attention (LCSA) prevents artifacts at high resolutions
Tiny Conditional Decoder (TC Decoder) achieves 7× faster decoding than standard WanVAE
Three-stage distillation pipeline from multi-step to single-step inference
Runs at ~17 FPS for 768×1408 videos on a single A100 GPU
Up to 12× speedup over prior one-step diffusion VSR models
Scales reliably to ultra-high resolutions

Hardware Requirements

Minimum: 24GB VRAM (A100 or similar recommended)
Optimized for NVIDIA A100 GPUs
Significant VRAM required for high-resolution video processing
Multi-GPU inference not required but beneficial for throughput

Common Use Cases

Real-world video upscaling to 4K
AI-generated video enhancement and artifact removal
Long video super-resolution with temporal consistency
Streaming video quality improvement
Restoring compressed or low-resolution video footage

Key Parameters

scale: Upscaling factor (4× recommended for best results)
tile_size: Spatial tiling for memory management (0 = auto)
input_resolution: Source video resolution (outputs 4× larger)
model_version: v1 or v1.1 checkpoint selection

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