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bbd0a6b201
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)
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Depth Anything V2
Depth Anything V2 is a monocular depth estimation model trained on 595K synthetic labeled images and 62M+ real unlabeled images, providing robust relative depth maps from single images.
Model Variants
Depth-Anything-V2-Small
- Lightweight variant for fast inference
- ViT-S (Small) encoder backbone
- Suitable for real-time applications
Depth-Anything-V2-Base
- Mid-range variant balancing speed and accuracy
- ViT-B (Base) encoder backbone
Depth-Anything-V2-Large
- High-accuracy variant for detailed depth maps
- ViT-L (Large) encoder backbone with 256 output features
- Recommended for most production use cases
Depth-Anything-V2-Giant
- Maximum accuracy variant
- ViT-G (Giant) encoder backbone
- Highest computational requirements
Key Features
- More fine-grained depth detail than Depth Anything V1
- More robust than V1 and Stable Diffusion-based alternatives (Marigold, Geowizard)
- 10× faster than SD-based depth estimation models
- Trained on large-scale synthetic + real data mixture
- Produces relative (not metric) depth maps by default
- DPT (Dense Prediction Transformer) decoder architecture
Hardware Requirements
- Small: 2GB VRAM minimum
- Base: 4GB VRAM minimum
- Large: 6GB VRAM recommended
- Giant: 12GB+ VRAM recommended
- CPU inference supported for smaller variants
Common Use Cases
- Depth map generation for compositing and VFX
- ControlNet depth conditioning for image generation
- 3D scene understanding and reconstruction
- Foreground/background separation
- Augmented reality occlusion
- Video depth estimation for parallax effects
Key Parameters
- encoder: Model size variant (vits, vitb, vitl, vitg)
- input_size: Processing resolution (higher = more detail, more VRAM)
- output_type: Raw depth array or normalized visualization