[BugFix] Respect configured precision in Qwen layered path

python/sglang/multimodal_gen/runtime/pipelines/qwen_image.py

@@ -13,6 +13,7 @@
 )
 from sglang.multimodal_gen.runtime.server_args import ServerArgs
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
+from sglang.multimodal_gen.utils import PRECISION_TO_TYPE
 
 # TODO(will): move PRECISION_TO_TYPE to better place
 
@@ -122,6 +123,10 @@ def create_pipeline_stages(self, server_args: ServerArgs):
                 transformer=self.get_module("transformer"),
                 scheduler=self.get_module("scheduler"),
                 model_path=self.model_path,
+                vae_dtype=PRECISION_TO_TYPE[server_args.pipeline_config.vae_precision],
+                text_encoder_dtype=PRECISION_TO_TYPE[
+                    server_args.pipeline_config.text_encoder_precisions[0]
+                ],
             )
         )
 

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

@@ -111,18 +111,28 @@ def retrieve_timesteps(
 
 class QwenImageLayeredBeforeDenoisingStage(PipelineStage):
     def __init__(
-        self, vae, tokenizer, processor, transformer, scheduler, model_path
+        self,
+        vae,
+        tokenizer,
+        processor,
+        transformer,
+        scheduler,
+        model_path,
+        vae_dtype: torch.dtype,
+        text_encoder_dtype: torch.dtype,
     ) -> None:
         super().__init__()
-        self.vae = vae.to(torch.bfloat16)
+        self.vae = vae.to(dtype=vae_dtype)
+        self.vae_dtype = vae_dtype
+        self.text_encoder_dtype = text_encoder_dtype
         from transformers import Qwen2_5_VLForConditionalGeneration
 
         self.text_encoder = (
             Qwen2_5_VLForConditionalGeneration.from_pretrained(
                 model_path, subfolder="text_encoder"
             )
             .to(get_local_torch_device())
-            .to(torch.bfloat16)
+            .to(dtype=self.text_encoder_dtype)
         )
         self.tokenizer = tokenizer
         self.processor = processor
@@ -441,7 +451,7 @@ def forward(
             image, calculated_height, calculated_width
         )
         image = image.unsqueeze(2)
-        image = image.to(dtype=torch.bfloat16)
+        image = image.to(dtype=self.vae_dtype)
 
         prompt = self.get_image_caption(
             prompt_image, use_en_prompt=use_en_prompt, device=device