feat(starter-models): add Z-Image Turbo starter models (#8689)

## 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

- [x] _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)_

Author: blessedcoolant

invokeai/backend/model_manager/starter_models.py

@@ -690,6 +690,69 @@ class StarterModelBundle(BaseModel):
 )
 # endregion
 
+# region Z-Image
+z_image_qwen3_encoder = StarterModel(
+    name="Z-Image Qwen3 Text Encoder",
+    base=BaseModelType.Any,
+    source="Tongyi-MAI/Z-Image-Turbo::text_encoder",
+    description="Qwen3 4B text encoder for Z-Image (full precision). ~8GB",
+    type=ModelType.Qwen3Encoder,
+)
+
+z_image_qwen3_encoder_quantized = StarterModel(
+    name="Z-Image Qwen3 Text Encoder (quantized)",
+    base=BaseModelType.Any,
+    source="https://huggingface.co/worstplayer/Z-Image_Qwen_3_4b_text_encoder_GGUF/resolve/main/Qwen_3_4b-Q6_K.gguf",
+    description="Qwen3 4B text encoder for Z-Image quantized to GGUF Q6_K format. ~3.3GB",
+    type=ModelType.Qwen3Encoder,
+    format=ModelFormat.GGUFQuantized,
+)
+
+z_image_turbo = StarterModel(
+    name="Z-Image Turbo",
+    base=BaseModelType.ZImage,
+    source="Tongyi-MAI/Z-Image-Turbo",
+    description="Z-Image Turbo - fast 6B parameter text-to-image model with 8 inference steps. Supports bilingual prompts (English & Chinese). ~13GB",
+    type=ModelType.Main,
+)
+
+z_image_turbo_quantized = StarterModel(
+    name="Z-Image Turbo (quantized)",
+    base=BaseModelType.ZImage,
+    source="https://huggingface.co/leejet/Z-Image-Turbo-GGUF/resolve/main/z_image_turbo-Q4_K.gguf",
+    description="Z-Image Turbo quantized to GGUF Q4_K format. Requires separate Qwen3 text encoder. ~4GB",
+    type=ModelType.Main,
+    format=ModelFormat.GGUFQuantized,
+    dependencies=[z_image_qwen3_encoder_quantized],
+)
+
+z_image_turbo_q8 = StarterModel(
+    name="Z-Image Turbo (Q8)",
+    base=BaseModelType.ZImage,
+    source="https://huggingface.co/leejet/Z-Image-Turbo-GGUF/resolve/main/z_image_turbo-Q8_0.gguf",
+    description="Z-Image Turbo quantized to GGUF Q8_0 format. Higher quality, larger size. Requires separate Qwen3 text encoder. ~6.6GB",
+    type=ModelType.Main,
+    format=ModelFormat.GGUFQuantized,
+    dependencies=[z_image_qwen3_encoder_quantized],
+)
+
+z_image_controlnet_union = StarterModel(
+    name="Z-Image ControlNet Union",
+    base=BaseModelType.ZImage,
+    source="https://huggingface.co/alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.1/resolve/main/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.safetensors",
+    description="Unified ControlNet for Z-Image Turbo supporting Canny, HED, Depth, Pose, MLSD, and Inpainting modes.",
+    type=ModelType.ControlNet,
+)
+
+z_image_controlnet_tile = StarterModel(
+    name="Z-Image ControlNet Tile",
+    base=BaseModelType.ZImage,
+    source="https://huggingface.co/alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union-2.1/resolve/main/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.safetensors",
+    description="Dedicated Tile ControlNet for Z-Image Turbo. Useful for upscaling and adding detail. ~6.7GB",
+    type=ModelType.ControlNet,
+)
+# endregion
+
 # List of starter models, displayed on the frontend.
 # The order/sort of this list is not changed by the frontend - set it how you want it here.
 STARTER_MODELS: list[StarterModel] = [
@@ -766,6 +829,13 @@ class StarterModelBundle(BaseModel):
     cogview4,
     flux_krea,
     flux_krea_quantized,
+    z_image_turbo,
+    z_image_turbo_quantized,
+    z_image_turbo_q8,
+    z_image_qwen3_encoder,
+    z_image_qwen3_encoder_quantized,
+    z_image_controlnet_union,
+    z_image_controlnet_tile,
 ]
 
 sd1_bundle: list[StarterModel] = [