[diffusion] fix: fix Qwen-Image-Layered latent shape (#28832)

python/sglang/multimodal_gen/configs/pipeline_configs/qwen_image.py

@@ -783,22 +783,31 @@ class QwenImageLayeredPipelineConfig(QwenImageEditPipelineConfig):
         return cond_kwargs
 
     def _unpad_and_unpack_latents(self, latents, batch):
-        vae_scale_factor = self.get_vae_scale_factor()
         channels = self.dit_config.arch_config.in_channels
         batch_size = latents.shape[0]
-        layers = batch.num_frames
 
-        height = 2 * (int(batch.height) // (vae_scale_factor * 2))
-        width = 2 * (int(batch.width) // (vae_scale_factor * 2))
+        img_shapes = batch.img_shapes
+        generated_shapes = img_shapes[0][:-1] if img_shapes and img_shapes[0] else []
+        if not generated_shapes:
+            raise ValueError("Qwen-Image-Layered requires generated latent shapes.")
+        if len({tuple(shape) for shape in generated_shapes}) != 1:
+            raise ValueError(
+                "Qwen-Image-Layered generated latent shapes must match, got "
+                f"{generated_shapes}."
+            )
+        layers = len(generated_shapes)
+        _, latent_height, latent_width = generated_shapes[0]
+        height = 2 * int(latent_height)
+        width = 2 * int(latent_width)
 
         latents = maybe_unpad_latents(latents, batch)
         latents = latents.view(
-            batch_size, layers + 1, height // 2, width // 2, channels // 4, 2, 2
+            batch_size, layers, height // 2, width // 2, channels // 4, 2, 2
         )
         latents = latents.permute(0, 1, 4, 2, 5, 3, 6)
 
         latents = latents.reshape(
-            batch_size, layers + 1, channels // (2 * 2), height, width
+            batch_size, layers, channels // (2 * 2), height, width
         )
         latents = latents.permute(0, 2, 1, 3, 4)  # (b, c, f, h, w)
         return latents, batch_size, channels, height, width

python/sglang/multimodal_gen/runtime/loader/component_loaders/component_loader.py

@@ -112,6 +112,11 @@ class ComponentLoader(ABC):
     ) -> dict[str, Any]:
         return {}
 
+    def should_raise_customized_load_error(
+        self, _server_args: ServerArgs, _component_name: str
+    ) -> bool:
+        return False
+
     @staticmethod
     def _is_component_set_as_layerwise_load(
         server_args: ServerArgs, component_name: str
@@ -219,6 +224,12 @@ class ComponentLoader(ABC):
                 )
             source = "sgl-diffusion"
         except Exception as e:
+            if self.should_raise_customized_load_error(server_args, component_name):
+                traceback.print_exc()
+                raise RuntimeError(
+                    f"Failed to load customized {component_name}; native fallback "
+                    "is disabled for this component configuration."
+                ) from e
             if "Unsupported model architecture" in str(e):
                 logger.info(
                     f"Component: {component_name} doesn't have a customized version yet, using native version"

python/sglang/multimodal_gen/runtime/loader/component_loaders/transformer_loader.py

@@ -79,6 +79,14 @@ class TransformerLoader(ComponentLoader):
     ]
     expected_library = "diffusers"
 
+    def should_raise_customized_load_error(
+        self, server_args: ServerArgs, component_name: str
+    ) -> bool:
+        component_server_args = _server_args_for_transformer_component(
+            server_args, component_name
+        )
+        return component_server_args.transformer_weights_path is not None
+
     def load_customized(
         self, component_model_path: str, server_args: ServerArgs, component_name: str
     ):

python/sglang/multimodal_gen/runtime/loader/transformer_load_utils.py

@@ -22,7 +22,10 @@ from sglang.multimodal_gen.runtime.layers.quantization.configs.nunchaku_config i
 )
 from sglang.multimodal_gen.runtime.loader.utils import _list_safetensors_files
 from sglang.multimodal_gen.runtime.server_args import ServerArgs
-from sglang.multimodal_gen.runtime.utils.hf_diffusers_utils import maybe_download_model
+from sglang.multimodal_gen.runtime.utils.hf_diffusers_utils import (
+    maybe_download_model,
+    snapshot_download,
+)
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 from sglang.multimodal_gen.runtime.utils.precision import resolve_precision
 from sglang.multimodal_gen.runtime.utils.quantization_utils import (
@@ -317,12 +320,31 @@ def resolve_transformer_safetensors_to_load(
     quantized_path = server_args.transformer_weights_path
 
     if quantized_path:
-        quantized_path = maybe_download_model(quantized_path)
+        original_quantized_path = quantized_path
+        quantized_path = maybe_download_model(original_quantized_path)
         logger.info("using quantized transformer weights from: %s", quantized_path)
         if os.path.isfile(quantized_path) and quantized_path.endswith(".safetensors"):
             safetensors_list = [quantized_path]
         else:
             safetensors_list = _list_safetensors_files(quantized_path)
+            if not safetensors_list and not os.path.exists(original_quantized_path):
+                logger.warning(
+                    "No safetensors files found in cached transformer weights path "
+                    "%s; refreshing snapshot for %s",
+                    quantized_path,
+                    original_quantized_path,
+                )
+                quantized_path = snapshot_download(
+                    repo_id=original_quantized_path,
+                    ignore_patterns=["*.onnx", "*.msgpack"],
+                    allow_patterns=[
+                        "*.json",
+                        "*.safetensors",
+                        "*.safetensors.index.json",
+                    ],
+                    max_workers=8,
+                )
+                safetensors_list = _list_safetensors_files(quantized_path)
     else:
         safetensors_list = _list_safetensors_files(component_model_path)
 

python/sglang/multimodal_gen/runtime/pipelines_core/stages/model_specific_stages/qwen_image_layered.py

@@ -512,6 +512,8 @@ the image\n<|vision_start|><|image_pad|><|vision_end|><|im_end|>\n<|im_start|>as
         multiple_of = self.vae_scale_factor * 2
         width = width // multiple_of * multiple_of
         height = height // multiple_of * multiple_of
+        batch.width = width
+        batch.height = height
 
         # if image is not None and not (isinstance(image, torch.Tensor) and image.size(1) == self.latent_channels):
         image = self.image_processor.resize(image, calculated_height, calculated_width)

python/sglang/multimodal_gen/test/unit/test_qwen_image_layered.py

@@ -0,0 +1,47 @@
+import unittest
+from types import SimpleNamespace
+
+import torch
+
+from sglang.multimodal_gen.configs.pipeline_configs.qwen_image import (
+    QwenImageLayeredPipelineConfig,
+)
+
+
+class TestQwenImageLayeredPipelineConfig(unittest.TestCase):
+    def test_unpack_uses_layered_img_shapes_not_stale_request_size(self):
+        config = QwenImageLayeredPipelineConfig()
+        channels = config.dit_config.arch_config.in_channels
+        generated_layers = 2
+        latent_height = 40
+        latent_width = 40
+        latents = torch.empty(
+            1,
+            generated_layers * latent_height * latent_width,
+            channels,
+        )
+        batch = SimpleNamespace(
+            height=512,
+            width=512,
+            raw_latent_shape=latents.shape,
+            img_shapes=[
+                [
+                    (1, latent_height, latent_width),
+                    (1, latent_height, latent_width),
+                    (1, latent_height, latent_width),
+                ]
+            ],
+        )
+
+        unpacked, batch_size, unpacked_channels, height, width = (
+            config._unpad_and_unpack_latents(latents, batch)
+        )
+
+        self.assertEqual(batch_size, 1)
+        self.assertEqual(unpacked_channels, channels)
+        self.assertEqual((height, width), (80, 80))
+        self.assertEqual(unpacked.shape, (1, channels // 4, generated_layers, 80, 80))
+
+
+if __name__ == "__main__":
+    unittest.main()

python/sglang/multimodal_gen/test/unit/test_transformer_quant.py

@@ -3,6 +3,7 @@ This unittest is introduced in #22360, preventing duplicate transformer safetens
 """
 
 import json
+import os
 import sys
 import tempfile
 import types
@@ -126,6 +127,34 @@ class TestTransformerQuantHelpers(unittest.TestCase):
 
         self.assertEqual(resolved, [mixed])
 
+    @patch(
+        "sglang.multimodal_gen.runtime.loader.transformer_load_utils.snapshot_download",
+    )
+    @patch(
+        "sglang.multimodal_gen.runtime.loader.transformer_load_utils.maybe_download_model",
+    )
+    def test_resolve_transformer_safetensors_to_load_refreshes_empty_cached_repo(
+        self, mock_download_model, mock_snapshot_download
+    ):
+        with tempfile.TemporaryDirectory() as cached_dir:
+            repo_id = "black-forest-labs/FLUX.2-dev-NVFP4"
+            mixed = os.path.join(cached_dir, "flux2-dev-nvfp4-mixed.safetensors")
+            mock_download_model.return_value = cached_dir
+
+            def _snapshot_download(**_kwargs):
+                open(mixed, "a").close()
+                return cached_dir
+
+            mock_snapshot_download.side_effect = _snapshot_download
+
+            server_args = self._make_server_args(transformer_weights_path=repo_id)
+            resolved = resolve_transformer_safetensors_to_load(
+                server_args, "/unused/component/path"
+            )
+
+        self.assertEqual(resolved, [mixed])
+        mock_snapshot_download.assert_called_once()
+
     def test_filter_transformer_precision_variants_prefers_canonical_file(self):
         files = [
             "/tmp/transformer/diffusion_pytorch_model.fp16.safetensors",