Support Torch Compile (#1368)

* support simple compile

* add support for compile

* minor fix

* minor fix

* minor fix

README.md

@@ -32,7 +32,7 @@ We believe that a well-developed open-source code framework can lower the thresh
 > DiffSynth-Studio has undergone major version updates, and some old features are no longer maintained. If you need to use old features, please switch to the [last historical version](https://github.com/modelscope/DiffSynth-Studio/tree/afd101f3452c9ecae0c87b79adfa2e22d65ffdc3) before the major version update.
 
 > Currently, the development personnel of this project are limited, with most of the work handled by [Artiprocher](https://github.com/Artiprocher) and [mi804](https://github.com/mi804). Therefore, the progress of new feature development will be relatively slow, and the speed of responding to and resolving issues is limited. We apologize for this and ask developers to understand.
-- **January 19, 2026**: Added support for [openmoss/MOVA-720p](https://modelscope.cn/models/openmoss/MOVA-720p) and [openmoss/MOVA-360p](https://modelscope.cn/models/openmoss/MOVA-360p) models, including training and inference capabilities. [Documentation](/docs/en/Model_Details/Wan.md) and [example code](/examples/mova/) are now available.
+- **March 19, 2026**: Added support for [openmoss/MOVA-720p](https://modelscope.cn/models/openmoss/MOVA-720p) and [openmoss/MOVA-360p](https://modelscope.cn/models/openmoss/MOVA-360p) models, including training and inference capabilities. [Documentation](/docs/en/Model_Details/Wan.md) and [example code](/examples/mova/) are now available.
 
 - **March 12, 2026**: We have added support for the [LTX-2.3](https://modelscope.cn/models/Lightricks/LTX-2.3) audio-video generation model. The features includes text-to-audio/video, image-to-audio/video, IC-LoRA control, audio-to-video, and audio-video inpainting. We have supported the complete inference and training functionalities. For details, please refer to the [documentation](/docs/en/Model_Details/LTX-2.md) and [code](/examples/ltx2/).
 

README_zh.md

@@ -33,7 +33,7 @@ DiffSynth 目前包括两个开源项目：
 
 > 目前本项目的开发人员有限，大部分工作由 [Artiprocher](https://github.com/Artiprocher) 和 [mi804](https://github.com/mi804) 负责，因此新功能的开发进展会比较缓慢，issue 的回复和解决速度有限，我们对此感到非常抱歉，请各位开发者理解。
 
-- **2026年1月19日** 新增对 [openmoss/MOVA-720p](https://modelscope.cn/models/openmoss/MOVA-720p) 和 [openmoss/MOVA-360p](https://modelscope.cn/models/openmoss/MOVA-360p) 模型的支持，包括完整的训练和推理功能。[文档](/docs/zh/Model_Details/Wan.md)和[示例代码](/examples/mova/)现已可用。
+- **2026年3月19日** 新增对 [openmoss/MOVA-720p](https://modelscope.cn/models/openmoss/MOVA-720p) 和 [openmoss/MOVA-360p](https://modelscope.cn/models/openmoss/MOVA-360p) 模型的支持，包括完整的训练和推理功能。[文档](/docs/zh/Model_Details/Wan.md)和[示例代码](/examples/mova/)现已可用。
 
 - **2026年3月12日** 我们新增了 [LTX-2.3](https://modelscope.cn/models/Lightricks/LTX-2.3) 音视频生成模型的支持，模型支持的功能包括文生音视频、图生音视频、IC-LoRA控制、音频生视频、音视频局部Inpainting，框架支持完整的推理和训练功能。详细信息请参考 [文档](/docs/zh/Model_Details/LTX-2.md) 和 [示例代码](/examples/ltx2/)。
 

diffsynth/diffusion/base_pipeline.py

@@ -339,6 +339,38 @@ class BasePipeline(torch.nn.Module):
             noise_pred = noise_pred_posi
         return noise_pred
 
+    def compile_pipeline(self, mode: str = "default", dynamic: bool = True, fullgraph: bool = False, compile_models: list = None, **kwargs):
+        """
+        compile the pipeline with torch.compile. The models that will be compiled are determined by the `compilable_models` attribute of the pipeline.
+        If a model has `_repeated_blocks` attribute, we will compile these blocks with regional compilation. Otherwise, we will compile the whole model.
+        See https://docs.pytorch.org/docs/stable/generated/torch.compile.html#torch.compile for details about compilation arguments.
+        Args:
+            mode: The compilation mode, which will be passed to `torch.compile`, options are "default", "reduce-overhead", "max-autotune" and "max-autotune-no-cudagraphs. Default to "default".
+            dynamic: Whether to enable dynamic graph compilation to support dynamic input shapes, which will be passed to `torch.compile`. Default to True (recommended).
+            fullgraph: Whether to use full graph compilation, which will be passed to `torch.compile`. Default to False (recommended).
+            compile_models: The list of model names to be compiled. If None, we will compile the models in `pipeline.compilable_models`. Default to None.
+            **kwargs: Other arguments for `torch.compile`.
+        """
+        compile_models = compile_models or getattr(self, "compilable_models", [])
+        if len(compile_models) == 0:
+            print("No compilable models in the pipeline. Skip compilation.")
+            return
+        for name in compile_models:
+            model = getattr(self, name, None)
+            if model is None:
+                print(f"Model '{name}' not found in the pipeline.")
+                continue
+            repeated_blocks = getattr(model, "_repeated_blocks", None)
+            # regional compilation for repeated blocks.
+            if repeated_blocks is not None:
+                for submod in model.modules():
+                    if submod.__class__.__name__ in repeated_blocks:
+                        submod.compile(mode=mode, dynamic=dynamic, fullgraph=fullgraph, **kwargs)
+            # compile the whole model.
+            else:
+                model.compile(mode=mode, dynamic=dynamic, fullgraph=fullgraph, **kwargs)
+            print(f"{name} is compiled with mode={mode}, dynamic={dynamic}, fullgraph={fullgraph}.")
+
 
 class PipelineUnitGraph:
     def __init__(self):

diffsynth/models/anima_dit.py

@@ -1270,6 +1270,9 @@ class LLMAdapter(nn.Module):
 
 
 class AnimaDiT(MiniTrainDIT):
+
+    _repeated_blocks = ["Block"]
+
     def __init__(self):
         kwargs = {'image_model': 'anima', 'max_img_h': 240, 'max_img_w': 240, 'max_frames': 128, 'in_channels': 16, 'out_channels': 16, 'patch_spatial': 2, 'patch_temporal': 1, 'model_channels': 2048, 'concat_padding_mask': True, 'crossattn_emb_channels': 1024, 'pos_emb_cls': 'rope3d', 'pos_emb_learnable': True, 'pos_emb_interpolation': 'crop', 'min_fps': 1, 'max_fps': 30, 'use_adaln_lora': True, 'adaln_lora_dim': 256, 'num_blocks': 28, 'num_heads': 16, 'extra_per_block_abs_pos_emb': False, 'rope_h_extrapolation_ratio': 4.0, 'rope_w_extrapolation_ratio': 4.0, 'rope_t_extrapolation_ratio': 1.0, 'extra_h_extrapolation_ratio': 1.0, 'extra_w_extrapolation_ratio': 1.0, 'extra_t_extrapolation_ratio': 1.0, 'rope_enable_fps_modulation': False, 'dtype': torch.bfloat16, 'device': None, 'operations': torch.nn}
         super().__init__(**kwargs)

diffsynth/models/flux2_dit.py

@@ -879,6 +879,9 @@ class Flux2Modulation(nn.Module):
 
 
 class Flux2DiT(torch.nn.Module):
+
+    _repeated_blocks = ["Flux2TransformerBlock", "Flux2SingleTransformerBlock"]
+
     def __init__(
         self,
         patch_size: int = 1,

diffsynth/models/flux_dit.py

@@ -275,6 +275,9 @@ class AdaLayerNormContinuous(torch.nn.Module):
 
 
 class FluxDiT(torch.nn.Module):
+
+    _repeated_blocks = ["FluxJointTransformerBlock", "FluxSingleTransformerBlock"]
+
     def __init__(self, disable_guidance_embedder=False, input_dim=64, num_blocks=19):
         super().__init__()
         self.pos_embedder = RoPEEmbedding(3072, 10000, [16, 56, 56])

diffsynth/models/ltx2_dit.py

@@ -1280,6 +1280,7 @@ class LTXModel(torch.nn.Module):
     LTX model transformer implementation.
     This class implements the transformer blocks for the LTX model.
     """
+    _repeated_blocks = ["BasicAVTransformerBlock"]
 
     def __init__(  # noqa: PLR0913
         self,

diffsynth/models/qwen_image_dit.py

@@ -549,6 +549,9 @@ class QwenImageTransformerBlock(nn.Module):
 
 
 class QwenImageDiT(torch.nn.Module):
+
+    _repeated_blocks = ["QwenImageTransformerBlock"]
+
     def __init__(
         self,
         num_layers: int = 60,

diffsynth/models/wan_video_dit.py

@@ -336,6 +336,9 @@ class WanToDanceInjector(nn.Module):
 
 
 class WanModel(torch.nn.Module):
+
+    _repeated_blocks = ["DiTBlock"]
+
     def __init__(
         self,
         dim: int,

diffsynth/models/z_image_dit.py

@@ -326,6 +326,7 @@ class RopeEmbedder:
 class ZImageDiT(nn.Module):
     _supports_gradient_checkpointing = True
     _no_split_modules = ["ZImageTransformerBlock"]
+    _repeated_blocks = ["ZImageTransformerBlock"]
 
     def __init__(
         self,

diffsynth/pipelines/anima_image.py

@@ -39,6 +39,7 @@ class AnimaImagePipeline(BasePipeline):
             AnimaUnit_PromptEmbedder(),
         ]
         self.model_fn = model_fn_anima
+        self.compilable_models = ["dit"]
     
     
     @staticmethod

diffsynth/pipelines/flux2_image.py

@@ -42,6 +42,7 @@ class Flux2ImagePipeline(BasePipeline):
             Flux2Unit_ImageIDs(),
         ]
         self.model_fn = model_fn_flux2
+        self.compilable_models = ["dit"]
     
     
     @staticmethod

diffsynth/pipelines/flux_image.py

@@ -103,6 +103,7 @@ class FluxImagePipeline(BasePipeline):
             FluxImageUnit_LoRAEncode(),
         ]
         self.model_fn = model_fn_flux_image
+        self.compilable_models = ["dit"]
         self.lora_loader = FluxLoRALoader
 
     def enable_lora_merger(self):

diffsynth/pipelines/ltx2_audio_video.py

@@ -76,6 +76,7 @@ class LTX2AudioVideoPipeline(BasePipeline):
             LTX2AudioVideoUnit_SetScheduleStage2(),
         ]
         self.model_fn = model_fn_ltx2
+        self.compilable_models = ["dit"]
 
         self.default_negative_prompt = {
             "LTX-2": (

diffsynth/pipelines/mova_audio_video.py

@@ -52,6 +52,7 @@ class MovaAudioVideoPipeline(BasePipeline):
             MovaAudioVideoUnit_UnifiedSequenceParallel(),
         ]
         self.model_fn = model_fn_mova_audio_video
+        self.compilable_models = ["video_dit", "video_dit2", "audio_dit"]
 
     def enable_usp(self):
         from ..utils.xfuser import get_sequence_parallel_world_size, usp_attn_forward

diffsynth/pipelines/qwen_image.py

@@ -56,6 +56,7 @@ class QwenImagePipeline(BasePipeline):
             QwenImageUnit_BlockwiseControlNet(),
         ]
         self.model_fn = model_fn_qwen_image
+        self.compilable_models = ["dit"]
     
     
     @staticmethod

diffsynth/pipelines/wan_video.py

@@ -83,6 +83,7 @@ class WanVideoPipeline(BasePipeline):
             WanVideoPostUnit_S2V(),
         ]
         self.model_fn = model_fn_wan_video
+        self.compilable_models = ["dit", "dit2"]
 
 
     def enable_usp(self):

diffsynth/pipelines/z_image.py

@@ -54,6 +54,7 @@ class ZImagePipeline(BasePipeline):
             ZImageUnit_PAIControlNet(),
         ]
         self.model_fn = model_fn_z_image
+        self.compilable_models = ["dit"]
     
     
     @staticmethod