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[Model] Minimize LTX-2.5 adapter changes

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Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>
This commit is contained in:
Hongsheng Liu committed 2026-08-12 12:33:36 +08:00
1 parent 30c72afdda
commit 40284854bb
5 files changed
+97 -158
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vllm_omni/diffusion/models/diffusers_adapter/pipeline_diffusers_adapter.py
+8 -66
View File
@@ -116,16 +116,15 @@ class DiffusersAdapterPipeline(nn.Module, DiffusionPipelineProfilerMixin):
dtype = self.od_config.dtype
validate_model_compatibility(self.od_config)
pipeline_class = self.od_config.diffusers_pipeline_cls
pipeline_class_name = pipeline_class.__name__ if pipeline_class is not None else None
self._pipeline_utils = get_pipeline_utils(pipeline_class_name)
load_kwargs = {
"torch_dtype": dtype,
**self.od_config.diffusers_load_kwargs,
}
convert_diffusers_quantization_config(load_kwargs)
pipeline_class = self.od_config.diffusers_pipeline_cls
pipeline_class_name = pipeline_class.__name__ if pipeline_class is not None else None
self._pipeline_utils = get_pipeline_utils(pipeline_class_name)
self._pipeline_utils.update_load_kwargs(self.od_config, load_kwargs)
component_names = (
self._load_diffusers_component_names(model_id, load_kwargs)
@@ -136,9 +135,6 @@ class DiffusersAdapterPipeline(nn.Module, DiffusionPipelineProfilerMixin):
logger.debug(f"Loading diffusers pipeline with kwargs: {load_kwargs}")
self._pipeline = DiffusionPipeline.from_pretrained(model_id, **load_kwargs)
# Gated repositories may be loadable with an explicit token in
# diffusers_load_kwargs even when config enrichment could not resolve
# their pipeline class. In that case the loaded class is authoritative.
if pipeline_class_name is None:
self._pipeline_utils = get_pipeline_utils(self._pipeline.__class__.__name__)
self._pipeline_utils.apply_post_load_updates(self._pipeline, self.od_config)
@@ -157,14 +153,14 @@ class DiffusersAdapterPipeline(nn.Module, DiffusionPipelineProfilerMixin):
# VAE slicing and tiling: try-catch because not all models have VAE
if self.od_config.vae_use_slicing:
try:
self._enable_vae_optimization("slicing")
self._pipeline_utils.enable_vae_optimization(self._pipeline, "slicing")
except Exception as e:
logger.warning(
f"Failed to enable VAE slicing for diffusers pipeline {self._pipeline.__class__.__name__}: {e}"
)
if self.od_config.vae_use_tiling:
try:
self._enable_vae_optimization("tiling")
self._pipeline_utils.enable_vae_optimization(self._pipeline, "tiling")
except Exception as e:
logger.warning(
f"Failed to enable VAE tiling for diffusers pipeline {self._pipeline.__class__.__name__}: {e}"
@@ -173,18 +169,6 @@ class DiffusersAdapterPipeline(nn.Module, DiffusionPipelineProfilerMixin):
# Attention backend
self._set_attention_backend()
def _enable_vae_optimization(self, optimization: str) -> None:
"""Enable a VAE optimization through the pipeline or VAE component API."""
pipeline_method = getattr(self._pipeline, f"enable_vae_{optimization}", None)
if callable(pipeline_method):
pipeline_method()
return
vae_method = getattr(getattr(self._pipeline, "vae", None), f"enable_{optimization}", None)
if not callable(vae_method):
raise AttributeError(f"Neither the pipeline nor its VAE supports {optimization}")
vae_method()
# ------------------------------------------------------------------
# Step-wise execution — explicitly rejected
# ------------------------------------------------------------------
@@ -393,19 +377,8 @@ class DiffusersAdapterPipeline(nn.Module, DiffusionPipelineProfilerMixin):
)
# Request-time sampling params
for key, value in sampling.extra_args.items():
if value is None:
continue
if self._accept_call_kwargs is None or key in self._accept_call_kwargs:
kwargs[key] = value
else:
logger.warning(
f"Skipping unsupported diffusers pipeline __call__ argument `{key}` from extra_args. "
f"Check out the documentation of {self._pipeline.__class__.__name__}."
)
for key, value in sampling.__dict__.items():
if key == "extra_args" or value is None:
if value is None:
continue
if self._accept_call_kwargs is None or key in self._accept_call_kwargs:
kwargs[key] = value
@@ -533,26 +506,13 @@ class DiffusersAdapterPipeline(nn.Module, DiffusionPipelineProfilerMixin):
"""
from vllm_omni.diffusion.data import DiffusionOutput
output = self._pipeline_utils.normalize_output(self._pipeline, self.od_config, output)
if hasattr(output, "images"):
# Preserve diffusers image format (`output_type`)
return DiffusionOutput(output=output.images)
if hasattr(output, "frames"):
audio = getattr(output, "audio", None)
if audio is not None:
metadata: dict[str, Any] = {}
if (sample_rate := self._get_output_audio_sample_rate()) is not None:
metadata["audio"] = {"sample_rate": sample_rate}
envelope: dict[str, Any] = {
"payload": {
"video": output.frames,
"audio": audio,
}
}
if metadata:
envelope["metadata"] = metadata
return DiffusionOutput(output=envelope)
# Preserve diffusers video format (`output_type`)
return DiffusionOutput(output=output.frames)
@@ -561,24 +521,6 @@ class DiffusersAdapterPipeline(nn.Module, DiffusionPipelineProfilerMixin):
return DiffusionOutput(output=output)
def _get_output_audio_sample_rate(self) -> int | None:
vocoder = getattr(self._pipeline, "vocoder", None)
config = getattr(vocoder, "config", None)
if config is None:
return None
if hasattr(config, "get"):
value = config.get("output_sampling_rate")
else:
value = getattr(config, "output_sampling_rate", None)
if isinstance(value, bool):
return None
try:
sample_rate = int(value)
except (TypeError, ValueError):
return None
return sample_rate if sample_rate > 0 else None
@staticmethod
def _summarize_call_kwargs_value(value: Any) -> Any:
"""Return a sanitized summary of diffusers call kwargs for logging."""