[bugfix]: fix kandinsky5 registry detectors and distilled repo name

The i2v detector matched any path containing 'i2v' without excluding 't2v',
misrouting T2V checkpoints stored under i2v-containing directories. The
variant detectors all require lite/pro tokens, so the model_index _class_name
fallback ('kandinsky5t2vpipeline') could never match and I2V-Lite-distilled
checkpoints had no detector at all; add base T2V/I2V fallback registrations
after the variants. Also fix the registered Lite distilled repo to the real
kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers (the old
name 404s) and correct the example comments.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

examples/inference/basic/basic_kandinsky5_t2v.py

@@ -4,10 +4,10 @@ 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"
+        # "kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers"
+        # "kandinskylab/Kandinsky-5.0-T2V-Pro-distilled-5s-Diffusers"
         num_gpus=1,
         use_fsdp_inference=False,
         dit_cpu_offload=False,

fastvideo/registry.py

@@ -507,8 +507,12 @@ def _register_configs() -> None:
         return _is_kandinsky5(path_lower) and "t2v" in path_lower and "i2v" not in path_lower
 
     def _is_kandinsky5_i2v(path: str) -> bool:
+        # Exclude "t2v" so a T2V checkpoint stored under an i2v-containing
+        # directory (e.g. ~/i2v_experiments/kandinsky5-t2v-ft) is not
+        # misrouted; ambiguous paths fall through to the model_index
+        # _class_name fallback detectors below.
         path_lower = path.lower()
-        return _is_kandinsky5(path_lower) and "i2v" in path_lower
+        return _is_kandinsky5(path_lower) and "i2v" in path_lower and "t2v" not in path_lower
 
     def _is_kandinsky5_t2v_lite(path: str) -> bool:
         path_lower = path.lower()
@@ -575,7 +579,7 @@ def _register_configs() -> None:
         pipeline_config_cls=Kandinsky5T2VConfig,
         workload_types=(WorkloadType.T2V, ),
         hf_model_paths=[
-            "kandinskylab/Kandinsky-5.0-T2V-Lite-distilled-5s-Diffusers",
+            "kandinskylab/Kandinsky-5.0-T2V-Lite-distilled16steps-5s-Diffusers",
         ],
         model_detectors=[
             _is_kandinsky5_t2v_lite_distilled,
@@ -639,6 +643,35 @@ def _register_configs() -> None:
         default_preset="kandinsky5_i2v_pro_distilled_5s",
     )
 
+    # Kandinsky5 fallbacks — registered AFTER the variant detectors so those
+    # win first-match. Catch checkpoints the variant detectors cannot resolve:
+    # token-less local paths matched via the model_index _class_name
+    # ("kandinsky5t2vpipeline" carries no lite/pro marker), variant combos
+    # without a dedicated entry (e.g. I2V Lite distilled), and t2v+i2v
+    # ambiguous paths resolved by the checkpoint's _class_name.
+    register_configs(
+        sampling_param_cls=None,
+        pipeline_config_cls=Kandinsky5T2VConfig,
+        workload_types=(),
+        model_detectors=[
+            _is_kandinsky5_t2v,
+        ],
+        model_family="kandinsky5",
+        default_preset="kandinsky5_t2v_lite_5s",
+        pipeline_cls_name="Kandinsky5T2VPipeline",
+    )
+    register_configs(
+        sampling_param_cls=None,
+        pipeline_config_cls=Kandinsky5I2VConfig,
+        workload_types=(),
+        model_detectors=[
+            _is_kandinsky5_i2v,
+        ],
+        model_family="kandinsky5",
+        default_preset="kandinsky5_i2v_lite_5s",
+        pipeline_cls_name="Kandinsky5I2VPipeline",
+    )
+
     # LongCat (T2V, I2V, VC use same config; workload varies by path)
     register_configs(
         sampling_param_cls=None,