diff --git a/examples/inference/basic/basic_kandinsky5_i2v.py b/examples/inference/basic/basic_kandinsky5_i2v.py
index 665dd19d5..c30ca2f48 100644
--- a/examples/inference/basic/basic_kandinsky5_i2v.py
+++ b/examples/inference/basic/basic_kandinsky5_i2v.py
@@ -1,13 +1,15 @@
 from fastvideo import VideoGenerator
 
-OUTPUT_PATH = "video_samples_kandinsky5_2B_i2v"
+OUTPUT_PATH = "video_samples_kandinsky5_i2v"
 
 IMAGE_PATH = "assets/girl.png"
 
 
 def main():
     generator = VideoGenerator.from_pretrained(
-        "kandinskylab/Kandinsky-5.0-I2V-Lite-sft-5s-Diffusers",
+        "kandinskylab/Kandinsky-5.0-I2V-Pro-distilled-5s-Diffusers", 
+        # "kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers"
+        # "kandinskylab/Kandinsky-5.0-I2V-Lite-5s-Diffusers"
         num_gpus=1,
         use_fsdp_inference=False,
         dit_cpu_offload=False,
@@ -18,18 +20,15 @@ def main():
     )
 
     prompt = (
-        "A woman sits at a wooden table by the window in a cozy café. She reaches out "
-        "with her right hand, picks up the white coffee cup from the saucer, and gently "
-        "brings it to her lips to take a sip. After drinking, she places the cup back on "
-        "the table and looks out the window, enjoying the peaceful atmosphere."
+        "A woman stands up and walks away"
     )
     _ = generator.generate_video(
         prompt,
         image_path=IMAGE_PATH,
         output_path=OUTPUT_PATH,
         save_video=True,
-        height=512,
-        width=768,
+        height=1024,
+        width=1024,
         num_frames=121,
     )
 
diff --git a/examples/inference/basic/basic_kandinsky5_t2v.py b/examples/inference/basic/basic_kandinsky5_t2v.py
index b79edb6fe..a5da476d8 100644
--- a/examples/inference/basic/basic_kandinsky5_t2v.py
+++ b/examples/inference/basic/basic_kandinsky5_t2v.py
@@ -1,10 +1,13 @@
 from fastvideo import VideoGenerator
 
-OUTPUT_PATH = "video_samples_kandinsky5_2B_t2v"
+OUTPUT_PATH = "video_samples_kandinsky5_t2v"
 
 def main():
     generator = VideoGenerator.from_pretrained(
-        "kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers",
+        "kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers", 
+        # "kandinskylab/Kandinsky-5.0-T2V-Pro-sft-5s-Diffusers"
+        # "kandinskylab/Kandinsky-5.0-T2V-Lite-Distilled-5s-Diffusers"
+        # "kandinskylab/Kandinsky-5.0-T2V-Pro-Distilled-5s-Diffusers"
         num_gpus=1,
         use_fsdp_inference=False,
         dit_cpu_offload=False,
diff --git a/fastvideo/pipelines/basic/kandinsky5/presets.py b/fastvideo/pipelines/basic/kandinsky5/presets.py
index f75db1760..8dbd70d71 100644
--- a/fastvideo/pipelines/basic/kandinsky5/presets.py
+++ b/fastvideo/pipelines/basic/kandinsky5/presets.py
@@ -34,6 +34,60 @@ KANDINSKY5_T2V_LITE_5S = InferencePreset(
     },
 )
 
+KANDINSKY5_T2V_LITE_DISTILLED_5S = InferencePreset(
+    name="kandinsky5_t2v_lite_distilled_5s",
+    version=1,
+    model_family="kandinsky5",
+    description="Kandinsky-5.0 Lite T2V Distilled 5s",
+    workload_type="t2v",
+    stage_schemas=(_DENOISE_STAGE, ),
+    defaults={
+        "height": 512,
+        "width": 768,
+        "num_frames": 121,
+        "fps": 24,
+        "guidance_scale": 1.0,
+        "num_inference_steps": 16,
+        "negative_prompt": _NEGATIVE_PROMPT,
+    },
+)
+
+KANDINSKY5_T2V_PRO_5S = InferencePreset(
+    name="kandinsky5_t2v_pro_5s",
+    version=1,
+    model_family="kandinsky5",
+    description="Kandinsky-5.0 Pro T2V 5s",
+    workload_type="t2v",
+    stage_schemas=(_DENOISE_STAGE, ),
+    defaults={
+        "height": 512,
+        "width": 768,
+        "num_frames": 121,
+        "fps": 24,
+        "guidance_scale": 5.0,
+        "num_inference_steps": 50,
+        "negative_prompt": _NEGATIVE_PROMPT,
+    },
+)
+
+KANDINSKY5_T2V_PRO_DISTILLED_5S = InferencePreset(
+    name="kandinsky5_t2v_pro_distilled_5s",
+    version=1,
+    model_family="kandinsky5",
+    description="Kandinsky-5.0 Pro T2V Distilled 5s",
+    workload_type="t2v",
+    stage_schemas=(_DENOISE_STAGE, ),
+    defaults={
+        "height": 512,
+        "width": 768,
+        "num_frames": 121,
+        "fps": 24,
+        "guidance_scale": 1.0,
+        "num_inference_steps": 16,
+        "negative_prompt": _NEGATIVE_PROMPT,
+    },
+)
+
 KANDINSKY5_I2V_LITE_5S = InferencePreset(
     name="kandinsky5_i2v_lite_5s",
     version=1,
@@ -52,4 +106,40 @@ KANDINSKY5_I2V_LITE_5S = InferencePreset(
     },
 )
 
-ALL_PRESETS = (KANDINSKY5_T2V_LITE_5S, KANDINSKY5_I2V_LITE_5S)
+KANDINSKY5_I2V_PRO_5S = InferencePreset(
+    name="kandinsky5_i2v_pro_5s",
+    version=1,
+    model_family="kandinsky5",
+    description="Kandinsky-5.0 Pro I2V 5s",
+    workload_type="i2v",
+    stage_schemas=(_DENOISE_STAGE, ),
+    defaults={
+        "height": 512,
+        "width": 768,
+        "num_frames": 121,
+        "fps": 24,
+        "guidance_scale": 5.0,
+        "num_inference_steps": 50,
+        "negative_prompt": _NEGATIVE_PROMPT,
+    },
+)
+
+KANDINSKY5_I2V_PRO_DISTILLED_5S = InferencePreset(
+    name="kandinsky5_i2v_pro_distilled_5s",
+    version=1,
+    model_family="kandinsky5",
+    description="Kandinsky-5.0 Pro I2V Distilled 5s",
+    workload_type="i2v",
+    stage_schemas=(_DENOISE_STAGE, ),
+    defaults={
+        "height": 512,
+        "width": 768,
+        "num_frames": 121,
+        "fps": 24,
+        "guidance_scale": 1.0,
+        "num_inference_steps": 16,
+        "negative_prompt": _NEGATIVE_PROMPT,
+    },
+)
+
+ALL_PRESETS = (KANDINSKY5_T2V_LITE_5S, KANDINSKY5_T2V_LITE_DISTILLED_5S, KANDINSKY5_T2V_PRO_5S, KANDINSKY5_T2V_PRO_DISTILLED_5S, KANDINSKY5_I2V_LITE_5S, KANDINSKY5_I2V_PRO_5S, KANDINSKY5_I2V_PRO_DISTILLED_5S)
diff --git a/fastvideo/registry.py b/fastvideo/registry.py
index af78894ce..7da30bffd 100644
--- a/fastvideo/registry.py
+++ b/fastvideo/registry.py
@@ -504,6 +504,7 @@ def _register_configs() -> None:
         workload_types=(WorkloadType.T2V, ),
         hf_model_paths=[
             "kandinskylab/Kandinsky-5.0-T2V-Lite-sft-5s-Diffusers",
+            
         ],
         model_detectors=[
             lambda path: any(token in path.lower() for token in ("kandinsky5", "kandinsky-5"))
@@ -514,14 +515,65 @@ def _register_configs() -> None:
         pipeline_cls_name="Kandinsky5T2VPipeline",
     )
 
-    # Kandinsky5 I2V
+    # Kandinsky5 Pro T2V
+    register_configs(
+        sampling_param_cls=None,
+        pipeline_config_cls=Kandinsky5T2VConfig,
+        workload_types=(WorkloadType.T2V, ),
+        hf_model_paths=[
+            "kandinskylab/Kandinsky-5.0-T2V-Pro-sft-5s-Diffusers",
+            
+        ],
+        model_detectors=[
+            lambda path: any(token in path.lower() for token in ("kandinsky5", "kandinsky-5"))
+            and "i2v" not in path.lower(),
+        ],
+        model_family="kandinsky5",
+        default_preset="kandinsky5_t2v_pro_5s",
+    )
+
+    # Kandinsky5 Lite T2V Distilled
+    register_configs(
+        sampling_param_cls=None,
+        pipeline_config_cls=Kandinsky5T2VConfig,
+        workload_types=(WorkloadType.T2V, ),
+        hf_model_paths=[
+            "kandinskylab/Kandinsky-5.0-T2V-Lite-distilled-5s-Diffusers",
+            
+        ],
+        model_detectors=[
+            lambda path: any(token in path.lower() for token in ("kandinsky5", "kandinsky-5"))
+            and "i2v" not in path.lower(),
+        ],
+        model_family="kandinsky5",
+        default_preset="kandinsky5_t2v_lite_distilled_5s",
+    )
+
+    # Kandinsky5 Pro T2V Distilled
+    register_configs(
+        sampling_param_cls=None,
+        pipeline_config_cls=Kandinsky5T2VConfig,
+        workload_types=(WorkloadType.T2V, ),
+        hf_model_paths=[
+            "kandinskylab/Kandinsky-5.0-T2V-Pro-distilled-5s-Diffusers",
+            
+        ],
+        model_detectors=[
+            lambda path: any(token in path.lower() for token in ("kandinsky5", "kandinsky-5"))
+            and "i2v" not in path.lower(),
+        ],
+        model_family="kandinsky5",
+        default_preset="kandinsky5_t2v_pro_distilled_5s",
+    )
+
+
+    # Kandinsky5 Lite I2V 
     register_configs(
         sampling_param_cls=None,
         pipeline_config_cls=Kandinsky5I2VConfig,
         workload_types=(WorkloadType.I2V, ),
         hf_model_paths=[
-            "kandinskylab/Kandinsky-5.0-I2V-Lite-sft-5s-Diffusers",
-            "kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers",
+            "kandinskylab/Kandinsky-5.0-I2V-Lite-5s-Diffusers"
         ],
         model_detectors=[
             lambda path: any(token in path.lower() for token in ("kandinsky5", "kandinsky-5"))
@@ -532,6 +584,38 @@ def _register_configs() -> None:
         pipeline_cls_name="Kandinsky5I2VPipeline",
     )
 
+    # Kandinsky5 Pro I2V
+    register_configs(
+        sampling_param_cls=None,
+        pipeline_config_cls=Kandinsky5I2VConfig,
+        workload_types=(WorkloadType.I2V, ),
+        hf_model_paths=[
+            "kandinskylab/Kandinsky-5.0-I2V-Pro-sft-5s-Diffusers"
+        ],
+        model_detectors=[
+            lambda path: any(token in path.lower() for token in ("kandinsky5", "kandinsky-5"))
+            and "i2v" in path.lower(),
+        ],
+        model_family="kandinsky5",
+        default_preset="kandinsky5_i2v_pro_5s",
+    )
+
+    # Kandinsky5 Pro I2V Distilled
+    register_configs(
+        sampling_param_cls=None,
+        pipeline_config_cls=Kandinsky5I2VConfig,
+        workload_types=(WorkloadType.I2V, ),
+        hf_model_paths=[
+            "kandinskylab/Kandinsky-5.0-I2V-Pro-distilled-5s-Diffusers"
+        ],
+        model_detectors=[
+            lambda path: any(token in path.lower() for token in ("kandinsky5", "kandinsky-5"))
+            and "i2v" in path.lower(),
+        ],
+        model_family="kandinsky5",
+        default_preset="kandinsky5_i2v_pro_distilled_5s",
+    )
+
     # LongCat (T2V, I2V, VC use same config; workload varies by path)
     register_configs(
         sampling_param_cls=None,