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Summary

Add Z-Image Turbo and related models to the starter models list for easy installation via the Model Manager:

  • Z-Image Turbo - Full precision Diffusers format (~13GB)
  • Z-Image Turbo (quantized) - GGUF Q4_K format (~4GB)
  • Z-Image Qwen3 Text Encoder - Full precision (~8GB)
  • Z-Image Qwen3 Text Encoder (quantized) - GGUF Q6_K format (~3.3GB)
  • Z-Image ControlNet Union - Unified ControlNet supporting Canny, HED, Depth, Pose, MLSD, and Inpainting modes

The quantized Turbo model includes the quantized Qwen3 encoder as a dependency for automatic installation.

Related Issues / Discussions

Builds on the Z-Image Turbo support added in main.

QA Instructions

  1. Open Model Manager → Starter Models
  2. Search for "Z-Image"
  3. Verify all 5 models appear with correct descriptions
  4. Install the quantized version and confirm the Qwen3 encoder dependency is also installed

Merge Plan

Standard merge, no special considerations.

Checklist

  • The PR has a short but descriptive title, suitable for a changelog
  • Tests added / updated (if applicable)
  • ❗Changes to a redux slice have a corresponding migration
  • Documentation added / updated (if applicable)
  • Updated What's New copy (if doing a release after this PR)

Add Z-Image Turbo and related models to the starter models list:
- Z-Image Turbo (full precision, ~13GB)
- Z-Image Turbo quantized (GGUF Q4_K, ~4GB)
- Z-Image Qwen3 Text Encoder (full precision, ~8GB)
- Z-Image Qwen3 Text Encoder quantized (GGUF Q6_K, ~3.3GB)
- Z-Image ControlNet Union (Canny, HED, Depth, Pose, MLSD, Inpainting)

The quantized Turbo model includes the quantized Qwen3 encoder as a
dependency for automatic installation.
@github-actions github-actions bot added python PRs that change python files backend PRs that change backend files labels Dec 22, 2025
@blessedcoolant
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I suggest adding the Q6_K_M or the Q8 quant for the base model also as one of the starter models along with the new controlnet tile model. Rest seems good. Covers all VRAM bases. The rest can be manual installs.

Pfannkuchensack and others added 2 commits December 23, 2025 03:27
Add higher quality Q8_0 quantization option for Z-Image Turbo (~6.6GB)
to complement existing Q4_K variant, providing better quality for users
with more VRAM.

Add dedicated Z-Image ControlNet Tile model (~6.7GB) for upscaling and
detail enhancement workflows.
@blessedcoolant blessedcoolant merged commit 5a0b227 into invoke-ai:main Dec 23, 2025
13 checks passed
@Pfannkuchensack Pfannkuchensack deleted the feat/z-image-starter-models branch December 23, 2025 12:24
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2 participants