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[diffusion] fix: fix Diffusers backend ignores model-specific sampling parameter (#20080)

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Co-authored-by: Mick <mickjagger19@icloud.com>
This commit is contained in:
Junhao Liu
2026-03-17 16:10:46 +08:00
committed by GitHub
co-authored by Mick
parent 9a697ceabb
commit ee106757df
2 changed files with 13 additions and 11 deletions
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python/sglang/multimodal_gen/registry.py
+3 -1
View File
@@ -360,12 +360,14 @@ def _get_diffusers_model_info(
DiffusersPipeline, DiffusersPipeline,
) )
sampling_param_cls = DiffusersGenericSamplingParams
pipeline_config_cls = DiffusersGenericPipelineConfig pipeline_config_cls = DiffusersGenericPipelineConfig
# If there is a registered native config for this model, inherit its task_type # If there is a registered native config for this model, inherit its task_type
if model_path is not None: if model_path is not None:
config_info = _get_config_info(model_path, model_id=model_id) config_info = _get_config_info(model_path, model_id=model_id)
if config_info is not None: if config_info is not None:
sampling_param_cls = config_info.sampling_param_cls
native_task_type = config_info.pipeline_config_cls.task_type native_task_type = config_info.pipeline_config_cls.task_type
if native_task_type != DiffusersGenericPipelineConfig.task_type: if native_task_type != DiffusersGenericPipelineConfig.task_type:
pipeline_config_cls = dataclasses.make_dataclass( pipeline_config_cls = dataclasses.make_dataclass(
@@ -386,7 +388,7 @@ def _get_diffusers_model_info(
return ModelInfo( return ModelInfo(
pipeline_cls=DiffusersPipeline, pipeline_cls=DiffusersPipeline,
sampling_param_cls=DiffusersGenericSamplingParams, sampling_param_cls=sampling_param_cls,
pipeline_config_cls=pipeline_config_cls, pipeline_config_cls=pipeline_config_cls,
) )
python/sglang/multimodal_gen/runtime/pipelines/diffusers_pipeline.py
+10 -10
View File
@@ -288,7 +288,7 @@ class DiffusersExecutionStage(PipelineStage):
if batch.generator is not None: if batch.generator is not None:
kwargs["generator"] = batch.generator kwargs["generator"] = batch.generator
elif batch.seed is not None: elif batch.seed is not None:
device = self._get_pipeline_device() device = self._get_generator_device(batch)
kwargs["generator"] = torch.Generator(device=device).manual_seed(batch.seed) kwargs["generator"] = torch.Generator(device=device).manual_seed(batch.seed)
# Image input for img2img or inpainting # Image input for img2img or inpainting
@@ -307,15 +307,15 @@ class DiffusersExecutionStage(PipelineStage):
return kwargs return kwargs
def _get_pipeline_device(self) -> str: def _get_generator_device(self, batch: Req) -> str:
"""Get the device the pipeline is running on.""" """Resolve RNG device consistently with the non-diffusers path.
for attr in ["unet", "transformer", "vae"]:
component = getattr(self.diffusers_pipe, attr, None) Diffusers CPU offload can temporarily park modules on CPU, but that
if component is not None: should not silently switch a CUDA request to CPU RNG, otherwise the
try: same seed produces different outputs depending on runtime placement.
return str(next(component.parameters()).device) """
except StopIteration: if batch.generator_device == "cpu":
pass return "cpu"
return current_platform.device_type return current_platform.device_type
def _load_input_image(self, batch: Req) -> Image.Image | None: def _load_input_image(self, batch: Req) -> Image.Image | None: