[Feat] Distrifusion Acceleration Support for Diffusion Inference (#5895)

* Distrifusion Support source

* comp comm overlap optimization

* sd3 benchmark

* pixart distrifusion bug fix

* sd3 bug fix and benchmark

* generation bug fix

* naming fix

* add docstring, fix counter and shape error

* add reference

* readme and requirement

colossalai/inference/modeling/layers/diffusion.py

@@ -0,0 +1,54 @@
+import inspect
+import types
+
+import torch
+from torch import nn
+
+
+class DiffusionPipe(nn.Module):
+    """
+    This Class convert a class of `DiffusionPipeline` into `nn.Module` and reserve most of origin attr,function and property.
+    """
+
+    def __init__(self, source_obj) -> None:
+        super(DiffusionPipe, self).__init__()
+
+        for k, v in source_obj.__dict__.items():
+            if isinstance(v, nn.Module):
+                self.add_module(k, v)
+            else:
+                setattr(self, k, v)
+
+        skip_list = ["_execution_device", "to", "device"]  # this
+
+        for name, member in inspect.getmembers(source_obj.__class__):
+            if name in skip_list:
+                continue
+            if not name.startswith("__") and not name.endswith("__"):
+                if isinstance(member, property):
+                    setattr(self.__class__, name, member)
+                elif inspect.isfunction(member) or inspect.ismethod(member):
+                    bound_method = types.MethodType(member, self)
+                    setattr(self, name, bound_method)
+                elif not callable(member) and not isinstance(member, property):
+                    setattr(self, name, member)
+            elif name == "__call__":
+                bound_method = types.MethodType(member, self)
+                setattr(self, "_forward", bound_method)
+
+    @property
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        [`~DiffusionPipeline.enable_sequential_cpu_offload`] the execution device can only be inferred from
+        Accelerate's module hooks.
+        """
+        # return self.device
+        return torch.device("cuda")
+
+    @property
+    def device(self):
+        next(self.parameters()).device
+
+    def forward(self, *args, **kwargs):
+        return self._forward(*args, **kwargs)