diff --git a/CHANGELOG.md b/CHANGELOG.md
index 31c6fead0..5fc27ab65 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -16,6 +16,8 @@
   - **Z-Image** support loading transformer file-tunes in safetensors format  
     as with any transformers/unet finetunes, place them then `models/unet`  
     and use **UNET Model** to load safetensors file as they are not complete models  
+  - **Z-Image** support for **ControlNet Union**  
+    includes 1.0, 2.0 and 2.1 variants  
   - **Detailer** support for segmentation models  
     some detection models can produce exact segmentation mask and not just box  
     to enable, set `use segmentation` option  
diff --git a/modules/control/units/controlnet.py b/modules/control/units/controlnet.py
index 571491987..e5db65221 100644
--- a/modules/control/units/controlnet.py
+++ b/modules/control/units/controlnet.py
@@ -111,6 +111,11 @@ predefined_hunyuandit = {
     "HunyuanDiT Pose": 'Tencent-Hunyuan/HunyuanDiT-v1.2-ControlNet-Diffusers-Pose',
     "HunyuanDiT Depth": 'Tencent-Hunyuan/HunyuanDiT-v1.2-ControlNet-Diffusers-Depth',
 }
+predefined_zimage = {
+    "Z-Image-Turbo Union 1.0": 'hlky/Z-Image-Turbo-Fun-Controlnet-Union',
+    "Z-Image-Turbo Union 2.0": 'hlky/Z-Image-Turbo-Fun-Controlnet-Union-2.0',
+    "Z-Image-Turbo Union 2.1": 'hlky/Z-Image-Turbo-Fun-Controlnet-Union-2.1',
+}
 
 variants = {
     'NoobAI Canny XL': 'fp16',
@@ -137,6 +142,7 @@ all_models.update(predefined_f1)
 all_models.update(predefined_sd3)
 all_models.update(predefined_qwen)
 all_models.update(predefined_hunyuandit)
+all_models.update(predefined_zimage)
 cache_dir = 'models/control/controlnet'
 load_lock = threading.Lock()
 
@@ -175,6 +181,8 @@ def api_list_models(model_type: str = None):
         model_list += list(predefined_qwen)
     if model_type == 'hunyuandit' or model_type == 'all':
         model_list += list(predefined_hunyuandit)
+    if model_type == 'z_image':
+        model_list == list(predefined_zimage)
     model_list += sorted(find_models())
     return model_list
 
@@ -199,9 +207,11 @@ def list_models(refresh=False):
         models = ['None'] + list(predefined_qwen) + sorted(find_models())
     elif modules.shared.sd_model_type == 'hunyuandit':
         models = ['None'] + list(predefined_hunyuandit) + sorted(find_models())
+    elif modules.shared.sd_model_type == 'z_image':
+        models = ['None'] + list(predefined_zimage) + sorted(find_models())
     else:
         log.warning(f'Control {what} model list failed: unknown model type')
-        models = ['None'] + sorted(predefined_sd15) + sorted(predefined_sdxl) + sorted(predefined_f1) + sorted(predefined_sd3) + sorted(find_models())
+        models = ['None'] + list(all_models) + sorted(find_models())
     debug_log(f'Control list {what}: path={cache_dir} models={models}')
     return models
 
@@ -263,6 +273,14 @@ class ControlNet():
         elif shared.sd_model_type == 'hunyuandit':
             from diffusers import HunyuanDiT2DControlNetModel as cls
             config = 'Tencent-Hunyuan/HunyuanDiT-v1.2-ControlNet-Diffusers-Canny'
+        elif shared.sd_model_type == 'z_image':
+            from diffusers import ZImageControlNetModel as cls
+            if '2.0' in model_id:
+                config = 'hlky/Z-Image-Turbo-Fun-Controlnet-Union-2.0'
+            elif '2.1' in model_id:
+                config = 'hlky/Z-Image-Turbo-Fun-Controlnet-Union-2.1'
+            else:
+                config = 'hlky/Z-Image-Turbo-Fun-Controlnet-Union'
         else:
             log.error(f'Control {what}: type={shared.sd_model_type} unsupported model')
             return None, None
@@ -508,6 +526,16 @@ class ControlNetPipeline():
                 feature_extractor=None,
                 controlnet=controlnets[0] if isinstance(controlnets, list) else controlnets, # can be a list
             )
+        elif detect.is_zimage(pipeline) and len(controlnets) > 0:
+            from diffusers import ZImageControlNetPipeline
+            self.pipeline = ZImageControlNetPipeline(
+                vae=pipeline.vae,
+                text_encoder=pipeline.text_encoder,
+                tokenizer=pipeline.tokenizer,
+                transformer=pipeline.transformer,
+                scheduler=pipeline.scheduler,
+                controlnet=controlnets[0] if isinstance(controlnets, list) else controlnets, # can be a list
+            )
         elif len(loras) > 0:
             self.pipeline = pipeline
             for lora in loras:
diff --git a/modules/control/units/detect.py b/modules/control/units/detect.py
index 8d836015f..83be1093f 100644
--- a/modules/control/units/detect.py
+++ b/modules/control/units/detect.py
@@ -28,3 +28,6 @@ def is_qwen(model):
 
 def is_hunyuandit(model):
     return is_compatible(model, pattern='HunyuanDiT')
+
+def is_zimage(model):
+    return is_compatible(model, pattern='ZImage')
diff --git a/modules/sd_vae_taesd.py b/modules/sd_vae_taesd.py
index 1ec5b15cb..00294d560 100644
--- a/modules/sd_vae_taesd.py
+++ b/modules/sd_vae_taesd.py
@@ -156,8 +156,8 @@ def decode(latents):
                     image = vae.decode(tensor, return_dict=False)[0]
                     image = (image / 2.0 + 0.5).clamp(0, 1).detach()
                 t1 = time.time()
-                if (t1 - t0) > 1.0 and not first_run:
-                    shared.log.warning(f'Decode: type="taesd" variant="{variant}" time{t1 - t0:.2f}')
+                if (t1 - t0) > 3.0 and not first_run:
+                    shared.log.warning(f'Decode: type="taesd" variant="{variant}" long decode time={t1 - t0:.2f}')
                 first_run = False
                 return image
         except Exception as e: