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name: tiled-vae description: "Configure and troubleshoot the extension's Tiled VAE panel for high-resolution VAE encode/decode, tile-size tuning, fast modes, attention backend compatibility, NaN, and OOM recovery." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Tiled VAE

Use this sub-skill when the user's task concerns Tiled VAE: decoding/encoding very large images, VAE out-of-memory failures, fp16 NaNs, encoder/decoder tile sizes, fast encoder/decoder modes, color fix, or WebUI attention optimization compatibility.

When to read

Read this for:

• CUDA OOM during VAE encode/decode rather than during UNet denoising;
• 4K/8K image decode or img2img encode where regular VAE fails;
• selecting encoder and decoder tile sizes based on VRAM;
• deciding whether to move VAE to GPU;
• interpreting fast encoder, fast decoder, and fast encoder color fix;
• NaNs with half VAE or unknown attention optimization warnings.

For tiled denoising, region prompts, or img2img upscale settings, read tiled-diffusion. For DemoFusion staged upscale, read demofusion.

Core operating facts

• The panel is AlwaysVisible in both txt2img and img2img.
• Enabling Tiled VAE replaces the active VAE encoder/decoder forward methods with tiled hooks for the duration of processing, then restores them in postprocess.
• The encoder uses padding of 32 pixels; the decoder uses padding of 11 latent pixels.
• If an input is small enough for the selected tile size, the hook prints that tiling is unnecessary and falls back to the original VAE forward.
• Recommended tile sizes are computed from CUDA device memory when available; CPU defaults are conservative.
• The source comments warn that fp16 VAE can produce NaNs for giant images and recommends --no-half-vae for those cases.

References

• Workflows: setup, tile-size defaults, fast mode semantics, and validation steps.
• Troubleshooting: OOM, NaN, attention backend, stale hook, and performance recovery.