mirror of
https://github.com/Comfy-Org/ComfyUI_frontend.git
synced 2026-04-20 14:30:41 +00:00
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)
1.7 KiB
1.7 KiB
Upscaling
Upscaling increases image resolution while adding detail, turning a small generated image into a large, sharp result. In ComfyUI, there are two main approaches: model-based upscaling, which uses trained AI models (like RealESRGAN or 4x-UltraSharp) to intelligently enlarge an image in one pass, and latent-based upscaling, which works in latent space with a KSampler to add new detail during the enlargement process. Model-based is faster, while latent-based offers more creative control.
How It Works in ComfyUI
- Key nodes involved:
UpscaleModelLoader,ImageUpscaleWithModel,ImageScaleBy,LatentUpscale,VAEDecodeTiled - Typical workflow pattern: Generate image → Upscale model loader → ImageUpscaleWithModel → Save image (model-based), or Generate latent → LatentUpscale → KSampler (lower denoise) → VAEDecode → Save image (latent-based)
Key Settings
- Upscale model: The AI model used for model-based upscaling.
RealESRGAN_x4plusis a reliable general-purpose choice;4x-UltraSharpexcels at photo-realistic detail. - Scale factor: How much to enlarge — 2x and 4x are typical. Higher factors increase VRAM usage significantly.
- tile_size: For tiled decoding/encoding of very large images. Range 512–1024; smaller tiles use less VRAM but take longer.
Tips
- Model-based upscaling is faster but less creative; latent upscaling paired with a KSampler adds genuinely new detail.
- Use
VAEDecodeTiledfor very large images to avoid out-of-memory errors. - Chain two 2x upscales instead of one 4x for better overall quality.
- When using latent upscaling, set KSampler denoise to 0.3–0.5 to add detail without changing the composition.