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.gitignore

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+**/__pycache__
+**.egg-info

.gitmodules

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+[submodule "models/newdream-sdxl-20"]
+	path = models/newdream-sdxl-20
+	url = https://huggingface.co/stablediffusionapi/newdream-sdxl-20

README.md

@@ -0,0 +1,2 @@
+Heban olla vogola
+

loss_params.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bf0b76d04764575883fcd146c7ae8e0edb9e049ad55db61640e37deffa652fdb
+size 3120

pyproject.toml

@@ -0,0 +1,33 @@
+[build-system]
+requires = ["setuptools >= 61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "edge-maxxing-4090-newdream"
+description = "An edge-maxxing model submission for the 4090 newdream contest"
+requires-python = ">=3.10,<3.11"
+version = "7"
+dependencies = [
+    "wheel",
+    "diffusers==0.30.2",
+    "transformers==4.41.2",
+    "accelerate==0.31.0",
+    "omegaconf==2.3.0",
+    "torch==2.4.1",
+    "edge-maxxing-pipelines @ git+https://github.com/womboai/edge-maxxing@8d8ff45863416484b5b4bc547782591bbdfc696a#subdirectory=pipelines",
+    "polygraphy",
+    "onnx",
+    "tensorrt>=10.5.0",
+    "tensorrt-cu12-libs>=10.5.0",
+    "tensorrt-cu12-bindings>=10.5.0",
+    "cuda-python>=12.6.0",
+    "setuptools>=75.2.0",
+]
+[tool.edge-maxxing]
+models = [
+    "stablediffusionapi/newdream-sdxl-20",
+    "RobertML/edge-onnx",
+    "RobertML/edge-engine",
+]
+[project.scripts]
+start_inference = "main:main"

src/cache_diffusion/cachify.py

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+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: MIT
+#
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+
+import fnmatch
+from contextlib import contextmanager
+
+from diffusers.models.attention import BasicTransformerBlock, JointTransformerBlock
+from diffusers.models.transformers.pixart_transformer_2d import PixArtTransformer2DModel
+from diffusers.models.transformers.transformer_sd3 import SD3Transformer2DModel
+from diffusers.models.unets.unet_2d_blocks import (
+    CrossAttnDownBlock2D,
+    CrossAttnUpBlock2D,
+    DownBlock2D,
+    UNetMidBlock2DCrossAttn,
+    UpBlock2D,
+)
+from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
+from diffusers.models.unets.unet_3d_blocks import (
+    CrossAttnDownBlockSpatioTemporal,
+    CrossAttnUpBlockSpatioTemporal,
+    DownBlockSpatioTemporal,
+    UNetMidBlockSpatioTemporal,
+    UpBlockSpatioTemporal,
+)
+from diffusers.models.unets.unet_spatio_temporal_condition import UNetSpatioTemporalConditionModel
+
+from .module import CachedModule
+from .utils import replace_module
+
+CACHED_PIPE = {
+    UNet2DConditionModel: (
+        DownBlock2D,
+        CrossAttnDownBlock2D,
+        UNetMidBlock2DCrossAttn,
+        CrossAttnUpBlock2D,
+        UpBlock2D,
+    ),
+    PixArtTransformer2DModel: (BasicTransformerBlock),
+    UNetSpatioTemporalConditionModel: (
+        CrossAttnDownBlockSpatioTemporal,
+        DownBlockSpatioTemporal,
+        UpBlockSpatioTemporal,
+        CrossAttnUpBlockSpatioTemporal,
+        UNetMidBlockSpatioTemporal,
+    ),
+    SD3Transformer2DModel: (JointTransformerBlock),
+}
+
+
+def _apply_to_modules(model, action, modules=None, config_list=None):
+    if hasattr(model, "use_trt_infer") and model.use_trt_infer:
+        for key, module in model.engines.items():
+            if isinstance(module, CachedModule):
+                action(module)
+            elif config_list:
+                for config in config_list:
+                    if _pass(key, config["wildcard_or_filter_func"]):
+                        model.engines[key] = CachedModule(module, config["select_cache_step_func"])
+    else:
+        for name, module in model.named_modules():
+            if isinstance(module, CachedModule):
+                action(module)
+            elif modules and config_list:
+                for config in config_list:
+                    if _pass(name, config["wildcard_or_filter_func"]) and isinstance(
+                        module, modules
+                    ):
+                        replace_module(
+                            model,
+                            name,
+                            CachedModule(module, config["select_cache_step_func"]),
+                        )
+
+
+def cachify(model, config_list, modules):
+    def cache_action(module):
+        pass  # No action needed, caching is handled in the loop itself
+
+    _apply_to_modules(model, cache_action, modules, config_list)
+
+
+def disable(pipe):
+    model = get_model(pipe)
+    _apply_to_modules(model, lambda module: module.disable_cache())
+
+
+def enable(pipe):
+    model = get_model(pipe)
+    _apply_to_modules(model, lambda module: module.enable_cache())
+
+
+def reset_status(pipe):
+    model = get_model(pipe)
+    _apply_to_modules(model, lambda module: setattr(module, "cur_step", 0))
+
+
+def _pass(name, wildcard_or_filter_func):
+    if isinstance(wildcard_or_filter_func, str):
+        return fnmatch.fnmatch(name, wildcard_or_filter_func)
+    elif callable(wildcard_or_filter_func):
+        return wildcard_or_filter_func(name)
+    else:
+        raise NotImplementedError(f"Unsupported type {type(wildcard_or_filter_func)}")
+
+
+def get_model(pipe):
+    if hasattr(pipe, "unet"):
+        return pipe.unet
+    elif hasattr(pipe, "transformer"):
+        return pipe.transformer
+    else:
+        raise KeyError
+
+
+@contextmanager
+def infer(pipe):
+    try:
+        yield pipe
+    finally:
+        reset_status(pipe)
+
+
+def prepare(pipe, config_list):
+    model = get_model(pipe)
+    assert model.__class__ in CACHED_PIPE.keys(), f"{model.__class__} is not supported!"
+    cachify(model, config_list, CACHED_PIPE[model.__class__])

src/cache_diffusion/module.py

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+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: MIT
+#
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+
+from torch import nn
+
+
+class CachedModule(nn.Module):
+    def __init__(self, block, select_cache_step_func) -> None:
+        super().__init__()
+        self.block = block
+        self.select_cache_step_func = select_cache_step_func
+        self.cur_step = 0
+        self.cached_results = None
+        self.enabled = True
+
+    def __getattr__(self, name):
+        try:
+            return super().__getattr__(name)
+        except AttributeError:
+            return getattr(self.block, name)
+
+    def if_cache(self):
+        return self.select_cache_step_func(self.cur_step) and self.enabled
+
+    def enable_cache(self):
+        self.enabled = True
+
+    def disable_cache(self):
+        self.enabled = False
+        self.cur_step = 0
+
+    def forward(self, *args, **kwargs):
+        if not self.if_cache():
+            self.cached_results = self.block(*args, **kwargs)
+        if self.enabled:
+            self.cur_step += 1
+        return self.cached_results

src/cache_diffusion/utils.py

@@ -0,0 +1,61 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: MIT
+#
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+
+import re
+
+SDXL_DEFAULT_CONFIG = [
+    {
+        "wildcard_or_filter_func": lambda name: "up_blocks.2" not in name,
+        "select_cache_step_func": lambda step: (step % 2) != 0,
+    }
+]
+
+PIXART_DEFAULT_CONFIG = [
+    {
+        "wildcard_or_filter_func": lambda name: not re.search(
+            r"transformer_blocks\.(2[1-7])\.", name
+        ),
+        "select_cache_step_func": lambda step: (step % 3) != 0,
+    }
+]
+
+SVD_DEFAULT_CONFIG = [
+    {
+        "wildcard_or_filter_func": lambda name: "up_blocks.3" not in name,
+        "select_cache_step_func": lambda step: (step % 2) != 0,
+    }
+]
+
+SD3_DEFAULT_CONFIG = [
+    {
+        "wildcard_or_filter_func": lambda name: re.search(
+            r"^((?!transformer_blocks\.(1[6-9]|2[0-3])).)*$", name
+        ),
+        "select_cache_step_func": lambda step: (step % 2) != 0,
+    }
+]
+
+
+def replace_module(parent, name_path, new_module):
+    path_parts = name_path.split(".")
+    for part in path_parts[:-1]:
+        parent = getattr(parent, part)
+    setattr(parent, path_parts[-1], new_module)

src/loss.py

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+_A=None
+import torch
+from tqdm import tqdm
+class LossSchedulerModel(torch.nn.Module):
+	def __init__(A,wx,we):super(LossSchedulerModel,A).__init__();assert len(wx.shape)==1 and len(we.shape)==2;B=wx.shape[0];assert B==we.shape[0]and B==we.shape[1];A.register_parameter('wx',torch.nn.Parameter(wx));A.register_parameter('we',torch.nn.Parameter(we))
+	def forward(A,t,xT,e_prev):
+		B=e_prev;assert t-len(B)+1==0;C=xT*A.wx[t]
+		for(D,E)in zip(B,A.we[t]):C+=D*E
+		return C.to(xT.dtype)
+class LossScheduler:
+	def __init__(A,timesteps,model):A.timesteps=timesteps;A.model=model;A.init_noise_sigma=1.;A.order=1
+	@staticmethod
+	def load(path):A,B,C=torch.load(path,map_location='cpu');D=LossSchedulerModel(B,C);return LossScheduler(A,D)
+	def save(A,path):B,C,D=A.timesteps,A.model.wx,A.model.we;torch.save((B,C,D),path)
+	def set_timesteps(A,num_inference_steps,device='cuda'):B=device;A.xT=_A;A.e_prev=[];A.t_prev=-1;A.model=A.model.to(B);A.timesteps=A.timesteps.to(B)
+	def scale_model_input(A,sample,*B,**C):return sample
+	@torch.no_grad()
+	def step(self,model_output,timestep,sample,*D,**E):
+		A=self;B=A.timesteps.tolist().index(timestep);assert A.t_prev==-1 or B==A.t_prev+1
+		if A.t_prev==-1:A.xT=sample
+		A.e_prev.append(model_output);C=A.model(B,A.xT,A.e_prev)
+		if B+1==len(A.timesteps):A.xT=_A;A.e_prev=[];A.t_prev=-1
+		else:A.t_prev=B
+		return C,
+class SchedulerWrapper:
+	def __init__(A,scheduler,loss_params_path='loss_params.pth'):A.scheduler=scheduler;A.catch_x,A.catch_e,A.catch_x_={},{},{};A.loss_scheduler=_A;A.loss_params_path=loss_params_path
+	def set_timesteps(A,num_inference_steps,**C):
+		D=num_inference_steps
+		if A.loss_scheduler is _A:B=A.scheduler.set_timesteps(D,**C);A.timesteps=A.scheduler.timesteps;A.init_noise_sigma=A.scheduler.init_noise_sigma;A.order=A.scheduler.order;return B
+		else:B=A.loss_scheduler.set_timesteps(D,**C);A.timesteps=A.loss_scheduler.timesteps;A.init_noise_sigma=A.scheduler.init_noise_sigma;A.order=A.scheduler.order;return B
+	def step(B,model_output,timestep,sample,**F):
+		D=sample;E=model_output;A=timestep
+		if B.loss_scheduler is _A:
+			C=B.scheduler.step(E,A,D,**F);A=A.tolist()
+			if A not in B.catch_x:B.catch_x[A]=[];B.catch_e[A]=[];B.catch_x_[A]=[]
+			B.catch_x[A].append(D.clone().detach().cpu());B.catch_e[A].append(E.clone().detach().cpu());B.catch_x_[A].append(C[0].clone().detach().cpu());return C
+		else:C=B.loss_scheduler.step(E,A,D,**F);return C
+	def scale_model_input(A,sample,timestep):return sample
+	def add_noise(A,original_samples,noise,timesteps):B=A.scheduler.add_noise(original_samples,noise,timesteps);return B
+	def get_path(C):
+		A=sorted([A for A in C.catch_x],reverse=True);B,D=[],[]
+		for E in A:F=torch.cat(C.catch_x[E],dim=0);B.append(F);G=torch.cat(C.catch_e[E],dim=0);D.append(G)
+		H=A[-1];I=torch.cat(C.catch_x_[H],dim=0);B.append(I);A=torch.tensor(A,dtype=torch.int32);B=torch.stack(B);D=torch.stack(D);return A,B,D
+	def load_loss_params(A):B,C,D=torch.load(A.loss_params_path,map_location='cpu');A.loss_model=LossSchedulerModel(C,D);A.loss_scheduler=LossScheduler(B,A.loss_model)
+	def prepare_loss(A,num_accelerate_steps=15):A.load_loss_params()

src/main.py

@@ -0,0 +1,59 @@
+import atexit
+from io import BytesIO
+from multiprocessing.connection import Listener
+from os import chmod, remove
+from os.path import abspath, exists
+from pathlib import Path
+
+import torch
+
+from PIL.JpegImagePlugin import JpegImageFile
+from pipelines.models import TextToImageRequest
+
+from pipeline import load_pipeline, infer
+
+SOCKET = abspath(Path(__file__).parent.parent / "inferences.sock")
+
+
+def at_exit():
+    torch.cuda.empty_cache()
+
+
+def main():
+    atexit.register(at_exit)
+
+    print(f"Loading pipeline")
+    pipeline = load_pipeline()
+
+    print(f"Pipeline loaded, creating socket at '{SOCKET}'")
+
+    if exists(SOCKET):
+        remove(SOCKET)
+
+    with Listener(SOCKET) as listener:
+        chmod(SOCKET, 0o777)
+
+        print(f"Awaiting connections")
+        with listener.accept() as connection:
+            print(f"Connected")
+
+            while True:
+                try:
+                    request = TextToImageRequest.model_validate_json(connection.recv_bytes().decode("utf-8"))
+                except EOFError:
+                    print(f"Inference socket exiting")
+
+                    return
+
+                image = infer(request, pipeline)
+
+                data = BytesIO()
+                image.save(data, format=JpegImageFile.format)
+
+                packet = data.getvalue()
+
+                connection.send_bytes(packet)
+
+
+if __name__ == '__main__':
+    main()

src/pipe/config.py

@@ -0,0 +1,162 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: MIT
+#
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+from diffusers.models.transformers.transformer_sd3 import SD3Transformer2DModel
+from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
+
+sd3_common_transformer_block_config = {
+    "dummy_input": {
+        "hidden_states": (2, 4096, 1536),
+        "encoder_hidden_states": (2, 333, 1536),
+        "temb": (2, 1536),
+    },
+    "output_names": ["encoder_hidden_states_out", "hidden_states_out"],
+    "dynamic_axes": {
+        "hidden_states": {0: "batch_size"},
+        "encoder_hidden_states": {0: "batch_size"},
+        "temb": {0: "steps"},
+    },
+}
+
+ONNX_CONFIG = {
+    UNet2DConditionModel: {
+        "down_blocks.0": {
+            "dummy_input": {
+                "hidden_states": (2, 320, 128, 128),
+                "temb": (2, 1280),
+            },
+            "output_names": ["sample", "res_samples_0", "res_samples_1", "res_samples_2"],
+            "dynamic_axes": {
+                "hidden_states": {0: "batch_size"},
+                "temb": {0: "steps"},
+            },
+        },
+        "down_blocks.1": {
+            "dummy_input": {
+                "hidden_states": (2, 320, 64, 64),
+                "temb": (2, 1280),
+                "encoder_hidden_states": (2, 77, 2048),
+            },
+            "output_names": ["sample", "res_samples_0", "res_samples_1", "res_samples_2"],
+            "dynamic_axes": {
+                "hidden_states": {0: "batch_size"},
+                "temb": {0: "steps"},
+                "encoder_hidden_states": {0: "batch_size"},
+            },
+        },
+        "down_blocks.2": {
+            "dummy_input": {
+                "hidden_states": (2, 640, 32, 32),
+                "temb": (2, 1280),
+                "encoder_hidden_states": (2, 77, 2048),
+            },
+            "output_names": ["sample", "res_samples_0", "res_samples_1"],
+            "dynamic_axes": {
+                "hidden_states": {0: "batch_size"},
+                "temb": {0: "steps"},
+                "encoder_hidden_states": {0: "batch_size"},
+            },
+        },
+        "mid_block": {
+            "dummy_input": {
+                "hidden_states": (2, 1280, 32, 32),
+                "temb": (2, 1280),
+                "encoder_hidden_states": (2, 77, 2048),
+            },
+            "output_names": ["sample"],
+            "dynamic_axes": {
+                "hidden_states": {0: "batch_size"},
+                "temb": {0: "steps"},
+                "encoder_hidden_states": {0: "batch_size"},
+            },
+        },
+        "up_blocks.0": {
+            "dummy_input": {
+                "hidden_states": (2, 1280, 32, 32),
+                "res_hidden_states_0": (2, 640, 32, 32),
+                "res_hidden_states_1": (2, 1280, 32, 32),
+                "res_hidden_states_2": (2, 1280, 32, 32),
+                "temb": (2, 1280),
+                "encoder_hidden_states": (2, 77, 2048),
+            },
+            "output_names": ["sample"],
+            "dynamic_axes": {
+                "hidden_states": {0: "batch_size"},
+                "temb": {0: "steps"},
+                "encoder_hidden_states": {0: "batch_size"},
+                "res_hidden_states_0": {0: "batch_size"},
+                "res_hidden_states_1": {0: "batch_size"},
+                "res_hidden_states_2": {0: "batch_size"},
+            },
+        },
+        "up_blocks.1": {
+            "dummy_input": {
+                "hidden_states": (2, 1280, 64, 64),
+                "res_hidden_states_0": (2, 320, 64, 64),
+                "res_hidden_states_1": (2, 640, 64, 64),
+                "res_hidden_states_2": (2, 640, 64, 64),
+                "temb": (2, 1280),
+                "encoder_hidden_states": (2, 77, 2048),
+            },
+            "output_names": ["sample"],
+            "dynamic_axes": {
+                "hidden_states": {0: "batch_size"},
+                "temb": {0: "steps"},
+                "encoder_hidden_states": {0: "batch_size"},
+                "res_hidden_states_0": {0: "batch_size"},
+                "res_hidden_states_1": {0: "batch_size"},
+                "res_hidden_states_2": {0: "batch_size"},
+            },
+        },
+        "up_blocks.2": {
+            "dummy_input": {
+                "hidden_states": (2, 640, 128, 128),
+                "res_hidden_states_0": (2, 320, 128, 128),
+                "res_hidden_states_1": (2, 320, 128, 128),
+                "res_hidden_states_2": (2, 320, 128, 128),
+                "temb": (2, 1280),
+            },
+            "output_names": ["sample"],
+            "dynamic_axes": {
+                "hidden_states": {0: "batch_size"},
+                "temb": {0: "steps"},
+                "res_hidden_states_0": {0: "batch_size"},
+                "res_hidden_states_1": {0: "batch_size"},
+                "res_hidden_states_2": {0: "batch_size"},
+            },
+        },
+    },
+    SD3Transformer2DModel: {
+        **{f"transformer_blocks.{i}": sd3_common_transformer_block_config for i in range(23)},
+        "transformer_blocks.23": {
+            "dummy_input": {
+                "hidden_states": (2, 4096, 1536),
+                "encoder_hidden_states": (2, 333, 1536),
+                "temb": (2, 1536),
+            },
+            "output_names": ["hidden_states_out"],
+            "dynamic_axes": {
+                "hidden_states": {0: "batch_size"},
+                "encoder_hidden_states": {0: "batch_size"},
+                "temb": {0: "steps"},
+            },
+        },
+    },
+}

src/pipe/deploy.py

@@ -0,0 +1,210 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: MIT
+#
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+
+import types
+from pathlib import Path
+
+import tensorrt as trt
+import torch
+from cache_diffusion.cachify import CACHED_PIPE, get_model
+from cuda import cudart
+from diffusers.models.transformers.transformer_sd3 import SD3Transformer2DModel
+from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
+from pipe.config import ONNX_CONFIG
+from pipe.models.sd3 import sd3_forward
+from pipe.models.sdxl import (
+    cachecrossattnupblock2d_forward,
+    cacheunet_forward,
+    cacheupblock2d_forward,
+)
+from polygraphy.backend.trt import (
+    CreateConfig,
+    Profile,
+    engine_from_network,
+    network_from_onnx_path,
+    save_engine,
+)
+from torch.onnx import export as onnx_export
+
+from .utils import Engine
+
+
+def replace_new_forward(backbone):
+    if backbone.__class__ == UNet2DConditionModel:
+        backbone.forward = types.MethodType(cacheunet_forward, backbone)
+        for upsample_block in backbone.up_blocks:
+            if (
+                hasattr(upsample_block, "has_cross_attention")
+                and upsample_block.has_cross_attention
+            ):
+                upsample_block.forward = types.MethodType(
+                    cachecrossattnupblock2d_forward, upsample_block
+                )
+            else:
+                upsample_block.forward = types.MethodType(cacheupblock2d_forward, upsample_block)
+    elif backbone.__class__ == SD3Transformer2DModel:
+        backbone.forward = types.MethodType(sd3_forward, backbone)
+
+
+def get_input_info(dummy_dict, info: str = None, batch_size: int = 1):
+    return_val = [] if info == "profile_shapes" or info == "input_names" else {}
+
+    def collect_leaf_keys(d):
+        for key, value in d.items():
+            if isinstance(value, dict):
+                collect_leaf_keys(value)
+            else:
+                value = (value[0] * batch_size,) + value[1:]
+                if info == "profile_shapes":
+                    return_val.append((key, value))  # type: ignore
+                elif info == "profile_shapes_dict":
+                    return_val[key] = value  # type: ignore
+                elif info == "dummy_input":
+                    return_val[key] = torch.ones(value).half().cuda()  # type: ignore
+                elif info == "input_names":
+                    return_val.append(key)  # type: ignore
+
+    collect_leaf_keys(dummy_dict)
+    return return_val
+
+
+def complie2trt(cls, onnx_path: Path, engine_path: Path, batch_size: int = 1):
+    subdirs = [f for f in onnx_path.iterdir() if f.is_dir()]
+    for subdir in subdirs:
+        if subdir.name not in ONNX_CONFIG[cls].keys():
+            continue
+        model_path = subdir / "model.onnx"
+        plan_path = engine_path / f"{subdir.name}.plan"
+        if not plan_path.exists():
+            print(f"Building {str(model_path)}")
+            build_profile = Profile()
+            profile_shapes = get_input_info(
+                ONNX_CONFIG[cls][subdir.name]["dummy_input"], "profile_shapes", batch_size
+            )
+            for input_name, input_shape in profile_shapes:
+                min_input_shape = (2,) + input_shape[1:]
+                build_profile.add(input_name, min_input_shape, input_shape, input_shape)
+            block_network = network_from_onnx_path(
+                str(model_path), flags=[trt.OnnxParserFlag.NATIVE_INSTANCENORM], strongly_typed=True
+            )
+            build_config = CreateConfig(
+                builder_optimization_level=6,
+                tf32=True,
+                #bf16=True,
+                profiles=[build_profile],
+            )
+            engine = engine_from_network(
+                block_network,
+                config=build_config,
+            )
+            save_engine(engine, path=plan_path)
+        else:
+            print(f"{str(model_path)} already exists!")
+
+
+def get_total_device_memory(backbone):
+    max_device_memory = 0
+    for _, engine in backbone.engines.items():
+        max_device_memory = max(max_device_memory, engine.engine.device_memory_size)
+    return max_device_memory
+
+
+def load_engines(backbone, engine_path: Path, batch_size: int = 1):
+    backbone.engines = {}
+    for f in engine_path.iterdir():
+        if f.is_file():
+            eng = Engine()
+            eng.load(str(f))
+            backbone.engines[f"{f.stem}"] = eng
+    _, shared_device_memory = cudart.cudaMalloc(get_total_device_memory(backbone))
+    for engine in backbone.engines.values():
+        engine.activate(shared_device_memory)
+    backbone.cuda_stream = cudart.cudaStreamCreate()[1]
+    for block_name in backbone.engines.keys():
+        backbone.engines[block_name].allocate_buffers(
+            shape_dict=get_input_info(
+                ONNX_CONFIG[backbone.__class__][block_name]["dummy_input"],
+                "profile_shapes_dict",
+                batch_size,
+            ),
+            device=backbone.device,
+            batch_size=batch_size,
+        )
+    # TODO: Free and clean up the origin pytorch cuda memory
+
+
+def export_onnx(backbone, onnx_path: Path):
+    for name, module in backbone.named_modules():
+        if isinstance(module, CACHED_PIPE[backbone.__class__]):
+            _onnx_dir = onnx_path.joinpath(f"{name}")
+            _onnx_file = _onnx_dir.joinpath("model.onnx")
+            if not _onnx_file.exists():
+                _onnx_dir.mkdir(parents=True, exist_ok=True)
+                dummy_input = get_input_info(
+                    ONNX_CONFIG[backbone.__class__][f"{name}"]["dummy_input"], "dummy_input"
+                )
+                input_names = get_input_info(
+                    ONNX_CONFIG[backbone.__class__][f"{name}"]["dummy_input"], "input_names"
+                )
+                output_names = ONNX_CONFIG[backbone.__class__][f"{name}"]["output_names"]
+                onnx_export(
+                    module,
+                    args=dummy_input,
+                    f=_onnx_file.as_posix(),
+                    input_names=input_names,
+                    output_names=output_names,
+                    dynamic_axes=ONNX_CONFIG[backbone.__class__][f"{name}"]["dynamic_axes"],
+                    do_constant_folding=True,
+                    opset_version=17,
+                )
+            else:
+                print(f"{str(_onnx_file)} alread exists!")
+
+
+def warm_up(backbone, batch_size: int = 1):
+    print("Warming-up TensorRT engines...")
+    for name, engine in backbone.engines.items():
+        dummy_input = get_input_info(
+            ONNX_CONFIG[backbone.__class__][name]["dummy_input"], "dummy_input", batch_size
+        )
+        _ = engine(dummy_input, backbone.cuda_stream)
+
+
+def teardown(pipe):
+    backbone = get_model(pipe)
+    for engine in backbone.engines.values():
+        del engine
+
+    cudart.cudaStreamDestroy(backbone.cuda_stream)
+    del backbone.cuda_stream
+
+
+def compile(pipe, onnx_path: Path, engine_path: Path, batch_size: int = 1):
+    backbone = get_model(pipe)
+    onnx_path.mkdir(parents=True, exist_ok=True)
+    engine_path.mkdir(parents=True, exist_ok=True)
+
+    replace_new_forward(backbone)
+    export_onnx(backbone, onnx_path)
+    complie2trt(backbone.__class__, onnx_path, engine_path, batch_size)
+    load_engines(backbone, engine_path, batch_size)
+    warm_up(backbone, batch_size)
+    backbone.use_trt_infer = True

src/pipe/models/sd3.py

@@ -0,0 +1,159 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: MIT
+#
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+
+from typing import Any, Dict, List, Optional, Union
+
+import torch
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    is_torch_version,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+
+
+def sd3_forward(
+    self,
+    hidden_states: torch.FloatTensor,
+    encoder_hidden_states: torch.FloatTensor = None,
+    pooled_projections: torch.FloatTensor = None,
+    timestep: torch.LongTensor = None,
+    block_controlnet_hidden_states: List = None,
+    joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+    return_dict: bool = True,
+) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
+    """
+    The [`SD3Transformer2DModel`] forward method.
+
+    Args:
+        hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
+            Input `hidden_states`.
+        encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
+            Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
+        pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
+            from the embeddings of input conditions.
+        timestep ( `torch.LongTensor`):
+            Used to indicate denoising step.
+        block_controlnet_hidden_states: (`list` of `torch.Tensor`):
+            A list of tensors that if specified are added to the residuals of transformer blocks.
+        joint_attention_kwargs (`dict`, *optional*):
+            A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+            `self.processor` in
+            [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+        return_dict (`bool`, *optional*, defaults to `True`):
+            Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
+            tuple.
+
+    Returns:
+        If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+        `tuple` where the first element is the sample tensor.
+    """
+    if joint_attention_kwargs is not None:
+        joint_attention_kwargs = joint_attention_kwargs.copy()
+        lora_scale = joint_attention_kwargs.pop("scale", 1.0)
+    else:
+        lora_scale = 1.0
+
+    if USE_PEFT_BACKEND:
+        # weight the lora layers by setting `lora_scale` for each PEFT layer
+        scale_lora_layers(self, lora_scale)
+
+    height, width = hidden_states.shape[-2:]
+
+    hidden_states = self.pos_embed(hidden_states)  # takes care of adding positional embeddings too.
+    temb = self.time_text_embed(timestep, pooled_projections)
+    encoder_hidden_states = self.context_embedder(encoder_hidden_states)
+
+    for index_block, block in enumerate(self.transformer_blocks):
+        if self.training and self.gradient_checkpointing:
+
+            def create_custom_forward(module, return_dict=None):
+                def custom_forward(*inputs):
+                    if return_dict is not None:
+                        return module(*inputs, return_dict=return_dict)
+                    else:
+                        return module(*inputs)
+
+                return custom_forward
+
+            ckpt_kwargs: Dict[str, Any] = (
+                {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+            )
+            encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(block),
+                hidden_states,
+                encoder_hidden_states,
+                temb,
+                **ckpt_kwargs,
+            )
+
+        else:
+            if hasattr(self, "use_trt_infer") and self.use_trt_infer:
+                feed_dict = {
+                    "hidden_states": hidden_states,
+                    "encoder_hidden_states": encoder_hidden_states,
+                    "temb": temb,
+                }
+                _results = self.engines[f"transformer_blocks.{index_block}"](
+                    feed_dict, self.cuda_stream
+                )
+                if index_block != 23:
+                    encoder_hidden_states = _results["encoder_hidden_states_out"]
+                hidden_states = _results["hidden_states_out"]
+            else:
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                )
+
+        # controlnet residual
+        if block_controlnet_hidden_states is not None and block.context_pre_only is False:
+            interval_control = len(self.transformer_blocks) // len(block_controlnet_hidden_states)
+            hidden_states = (
+                hidden_states + block_controlnet_hidden_states[index_block // interval_control]
+            )
+
+    hidden_states = self.norm_out(hidden_states, temb)
+    hidden_states = self.proj_out(hidden_states)
+
+    # unpatchify
+    patch_size = self.config.patch_size
+    height = height // patch_size
+    width = width // patch_size
+
+    hidden_states = hidden_states.reshape(
+        shape=(hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels)
+    )
+    hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
+    output = hidden_states.reshape(
+        shape=(hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size)
+    )
+
+    if USE_PEFT_BACKEND:
+        # remove `lora_scale` from each PEFT layer
+        unscale_lora_layers(self, lora_scale)
+
+    if not return_dict:
+        return (output,)
+
+    return Transformer2DModelOutput(sample=output)

src/pipe/models/sdxl.py

@@ -0,0 +1,275 @@
+# Adapted from
+# https://github.com/huggingface/diffusers/blob/73acebb8cfbd1d2954cabe1af4185f9994e61917/src/diffusers/models/unets/unet_2d_condition.py#L1039-L1312
+# https://github.com/huggingface/diffusers/blob/73acebb8cfbd1d2954cabe1af4185f9994e61917/src/diffusers/models/unets/unet_2d_blocks.py#L2482-L2564
+# https://github.com/huggingface/diffusers/blob/73acebb8cfbd1d2954cabe1af4185f9994e61917/src/diffusers/models/unets/unet_2d_blocks.py#L2617-L2679
+
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Not a contribution
+# Changes made by NVIDIA CORPORATION & AFFILIATES or otherwise documented as
+# NVIDIA-proprietary are not a contribution and subject to the following terms and conditions:
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+
+from typing import Any, Dict, Optional, Tuple, Union
+
+import torch
+from diffusers.models.unets.unet_2d_condition import UNet2DConditionOutput
+
+
+def cachecrossattnupblock2d_forward(
+    self,
+    hidden_states: torch.FloatTensor,
+    res_hidden_states_0: torch.FloatTensor,
+    res_hidden_states_1: torch.FloatTensor,
+    res_hidden_states_2: torch.FloatTensor,
+    temb: Optional[torch.FloatTensor] = None,
+    encoder_hidden_states: Optional[torch.FloatTensor] = None,
+    cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+    upsample_size: Optional[int] = None,
+    attention_mask: Optional[torch.FloatTensor] = None,
+    encoder_attention_mask: Optional[torch.FloatTensor] = None,
+) -> torch.FloatTensor:
+    res_hidden_states_tuple = (res_hidden_states_0, res_hidden_states_1, res_hidden_states_2)
+    for resnet, attn in zip(self.resnets, self.attentions):
+        # pop res hidden states
+        res_hidden_states = res_hidden_states_tuple[-1]
+        res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+
+        hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+        hidden_states = resnet(hidden_states, temb)
+        hidden_states = attn(
+            hidden_states,
+            encoder_hidden_states=encoder_hidden_states,
+            cross_attention_kwargs=cross_attention_kwargs,
+            attention_mask=attention_mask,
+            encoder_attention_mask=encoder_attention_mask,
+            return_dict=False,
+        )[0]
+
+    if self.upsamplers is not None:
+        for upsampler in self.upsamplers:
+            hidden_states = upsampler(hidden_states, upsample_size)
+
+    return hidden_states
+
+
+def cacheupblock2d_forward(
+    self,
+    hidden_states: torch.FloatTensor,
+    res_hidden_states_0: torch.FloatTensor,
+    res_hidden_states_1: torch.FloatTensor,
+    res_hidden_states_2: torch.FloatTensor,
+    temb: Optional[torch.FloatTensor] = None,
+    upsample_size: Optional[int] = None,
+) -> torch.FloatTensor:
+    res_hidden_states_tuple = (res_hidden_states_0, res_hidden_states_1, res_hidden_states_2)
+    for resnet in self.resnets:
+        # pop res hidden states
+        res_hidden_states = res_hidden_states_tuple[-1]
+        res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+
+        hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+        hidden_states = resnet(hidden_states, temb)
+
+    if self.upsamplers is not None:
+        for upsampler in self.upsamplers:
+            hidden_states = upsampler(hidden_states, upsample_size)
+
+    return hidden_states
+
+
+def cacheunet_forward(
+    self,
+    sample: torch.FloatTensor,
+    timestep: Union[torch.Tensor, float, int],
+    encoder_hidden_states: torch.Tensor,
+    class_labels: Optional[torch.Tensor] = None,
+    timestep_cond: Optional[torch.Tensor] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+    added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+    down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+    mid_block_additional_residual: Optional[torch.Tensor] = None,
+    down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+    encoder_attention_mask: Optional[torch.Tensor] = None,
+    return_dict: bool = True,
+) -> Union[UNet2DConditionOutput, Tuple]:
+    # 1. time
+    t_emb = self.get_time_embed(sample=sample, timestep=timestep)
+    emb = self.time_embedding(t_emb, timestep_cond)
+    aug_emb = None
+
+    aug_emb = self.get_aug_embed(
+        emb=emb,
+        encoder_hidden_states=encoder_hidden_states,
+        added_cond_kwargs=added_cond_kwargs,
+    )
+
+    emb = emb + aug_emb if aug_emb is not None else emb
+
+    encoder_hidden_states = self.process_encoder_hidden_states(
+        encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
+    )
+
+    # 2. pre-process
+    sample = self.conv_in(sample)
+
+    if hasattr(self, "_export_precess_onnx") and self._export_precess_onnx:
+        return (
+            sample,
+            encoder_hidden_states,
+            emb,
+        )
+
+    down_block_res_samples = (sample,)
+    for i, downsample_block in enumerate(self.down_blocks):
+        if (
+            hasattr(downsample_block, "has_cross_attention")
+            and downsample_block.has_cross_attention
+        ):
+            if hasattr(self, "use_trt_infer") and self.use_trt_infer:
+                feed_dict = {
+                    "hidden_states": sample,
+                    "temb": emb,
+                    "encoder_hidden_states": encoder_hidden_states,
+                }
+                down_results = self.engines[f"down_blocks.{i}"](feed_dict, self.cuda_stream)
+                sample = down_results["sample"]
+                res_samples_0 = down_results["res_samples_0"]
+                res_samples_1 = down_results["res_samples_1"]
+                if "res_samples_2" in down_results.keys():
+                    res_samples_2 = down_results["res_samples_2"]
+            else:
+                # For t2i-adapter CrossAttnDownBlock2D
+                additional_residuals = {}
+
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    encoder_attention_mask=encoder_attention_mask,
+                    **additional_residuals,
+                )
+        else:
+            if hasattr(self, "use_trt_infer") and self.use_trt_infer:
+                feed_dict = {"hidden_states": sample, "temb": emb}
+                down_results = self.engines[f"down_blocks.{i}"](feed_dict, self.cuda_stream)
+                sample = down_results["sample"]
+                res_samples_0 = down_results["res_samples_0"]
+                res_samples_1 = down_results["res_samples_1"]
+                if "res_samples_2" in down_results.keys():
+                    res_samples_2 = down_results["res_samples_2"]
+            else:
+                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
+
+        if hasattr(self, "use_trt_infer") and self.use_trt_infer:
+            down_block_res_samples += (
+                res_samples_0,
+                res_samples_1,
+            )
+            if "res_samples_2" in down_results.keys():
+                down_block_res_samples += (res_samples_2,)
+        else:
+            down_block_res_samples += res_samples
+
+    if hasattr(self, "use_trt_infer") and self.use_trt_infer:
+        feed_dict = {
+            "hidden_states": sample,
+            "temb": emb,
+            "encoder_hidden_states": encoder_hidden_states,
+        }
+        mid_results = self.engines["mid_block"](feed_dict, self.cuda_stream)
+        sample = mid_results["sample"]
+    else:
+        sample = self.mid_block(
+            sample,
+            emb,
+            encoder_hidden_states=encoder_hidden_states,
+            attention_mask=attention_mask,
+            cross_attention_kwargs=cross_attention_kwargs,
+            encoder_attention_mask=encoder_attention_mask,
+        )
+
+    # 5. up
+    for i, upsample_block in enumerate(self.up_blocks):
+        res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+        down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+
+        if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
+            if hasattr(self, "use_trt_infer") and self.use_trt_infer:
+                feed_dict = {
+                    "hidden_states": sample,
+                    "res_hidden_states_0": res_samples[0],
+                    "res_hidden_states_1": res_samples[1],
+                    "res_hidden_states_2": res_samples[2],
+                    "temb": emb,
+                    "encoder_hidden_states": encoder_hidden_states,
+                }
+                up_results = self.engines[f"up_blocks.{i}"](feed_dict, self.cuda_stream)
+                sample = up_results["sample"]
+            else:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_0=res_samples[0],
+                    res_hidden_states_1=res_samples[1],
+                    res_hidden_states_2=res_samples[2],
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                )
+        else:
+            if hasattr(self, "use_trt_infer") and self.use_trt_infer:
+                feed_dict = {
+                    "hidden_states": sample,
+                    "res_hidden_states_0": res_samples[0],
+                    "res_hidden_states_1": res_samples[1],
+                    "res_hidden_states_2": res_samples[2],
+                    "temb": emb,
+                }
+                up_results = self.engines[f"up_blocks.{i}"](feed_dict, self.cuda_stream)
+                sample = up_results["sample"]
+            else:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_0=res_samples[0],
+                    res_hidden_states_1=res_samples[1],
+                    res_hidden_states_2=res_samples[2],
+                )
+
+    # 6. post-process
+    if self.conv_norm_out:
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+    sample = self.conv_out(sample)
+
+    if not return_dict:
+        return (sample,)
+
+    return UNet2DConditionOutput(sample=sample)

src/pipe/utils.py

@@ -0,0 +1,129 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: MIT
+#
+# Permission is hereby granted, free of charge, to any person obtaining a
+# copy of this software and associated documentation files (the "Software"),
+# to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense,
+# and/or sell copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+# DEALINGS IN THE SOFTWARE.
+
+from collections import OrderedDict
+
+import numpy as np
+import tensorrt as trt
+import torch
+from cuda import cudart
+from polygraphy.backend.common import bytes_from_path
+from polygraphy.backend.trt import engine_from_bytes
+
+numpy_to_torch_dtype_dict = {
+    np.uint8: torch.uint8,
+    np.int8: torch.int8,
+    np.int16: torch.int16,
+    np.int32: torch.int32,
+    np.int64: torch.int64,
+    np.float16: torch.float16,
+    np.float32: torch.float32,
+    np.float64: torch.float64,
+    np.complex64: torch.complex64,
+    np.complex128: torch.complex128,
+}
+
+
+class Engine:
+    def __init__(
+        self,
+    ):
+        self.engine = None
+        self.context = None
+        self.buffers = OrderedDict()
+        self.tensors = OrderedDict()
+        self.cuda_graph_instance = None  # cuda graph
+        self.has_cross_attention = False
+
+    def __del__(self):
+        del self.engine
+        del self.context
+        del self.buffers
+        del self.tensors
+
+    def load(self, engine_path):
+        self.engine = engine_from_bytes(bytes_from_path(engine_path))
+
+    def activate(self, reuse_device_memory=None):
+        if reuse_device_memory:
+            self.context = self.engine.create_execution_context_without_device_memory()  # type: ignore
+            self.context.device_memory = reuse_device_memory
+        else:
+            self.context = self.engine.create_execution_context()  # type: ignore
+
+    def allocate_buffers(self, shape_dict=None, device="cuda", batch_size=1):
+        for binding in range(self.engine.num_io_tensors):  # type: ignore
+            name = self.engine.get_tensor_name(binding)  # type: ignore
+            if shape_dict and name in shape_dict:
+                shape = shape_dict[name]
+            else:
+                shape = self.engine.get_tensor_shape(name)  # type: ignore
+                shape = (batch_size * 2,) + shape[1:]
+            dtype = trt.nptype(self.engine.get_tensor_dtype(name))  # type: ignore
+            if self.engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT:  # type: ignore
+                self.context.set_input_shape(name, shape)  # type: ignore
+            tensor = torch.empty(tuple(shape), dtype=numpy_to_torch_dtype_dict[dtype]).to(
+                device=device
+            )
+            self.tensors[name] = tensor
+
+    def __call__(self, feed_dict, stream, use_cuda_graph=False):
+        for name, buf in feed_dict.items():
+            self.tensors[name].copy_(buf)
+
+        for name, tensor in self.tensors.items():
+            self.context.set_tensor_address(name, tensor.data_ptr())  # type: ignore
+
+        if use_cuda_graph:
+            if self.cuda_graph_instance is not None:
+                cuassert(cudart.cudaGraphLaunch(self.cuda_graph_instance, stream))
+                cuassert(cudart.cudaStreamSynchronize(stream))
+            else:
+                # do inference before CUDA graph capture
+                noerror = self.context.execute_async_v3(stream)  # type: ignore
+                if not noerror:
+                    raise ValueError("ERROR: inference failed.")
+                # capture cuda graph
+                cuassert(
+                    cudart.cudaStreamBeginCapture(
+                        stream, cudart.cudaStreamCaptureMode.cudaStreamCaptureModeGlobal
+                    )
+                )
+                self.context.execute_async_v3(stream)  # type: ignore
+                self.graph = cuassert(cudart.cudaStreamEndCapture(stream))
+                self.cuda_graph_instance = cuassert(cudart.cudaGraphInstantiate(self.graph, 0))
+        else:
+            noerror = self.context.execute_async_v3(stream)  # type: ignore
+            if not noerror:
+                raise ValueError("ERROR: inference failed.")
+
+        return self.tensors
+
+
+def cuassert(cuda_ret):
+    err = cuda_ret[0]
+    if err != cudart.cudaError_t.cudaSuccess:
+        raise RuntimeError(
+            f"CUDA ERROR: {err}, error code reference: https://nvidia.github.io/cuda-python/module/cudart.html#cuda.cudart.cudaError_t"
+        )
+    if len(cuda_ret) > 1:
+        return cuda_ret[1]
+    return None

src/pipeline.py

@@ -0,0 +1,54 @@
+import torch
+from pathlib import Path
+from PIL.Image import Image
+from diffusers import StableDiffusionXLPipeline, DDIMScheduler
+from pipelines.models import TextToImageRequest
+from torch import Generator
+from cache_diffusion import cachify
+from pipe.deploy import compile
+from loss import SchedulerWrapper
+
+generator = Generator(torch.device("cuda")).manual_seed(6969)
+prompt = "Make submissions great again"
+SDXL_DEFAULT_CONFIG = [
+            {
+                "wildcard_or_filter_func": lambda name: "down_blocks.2" not in name and"down_blocks.3" not in name and "up_blocks.2" not in name,
+                "select_cache_step_func": lambda step: (step % 2 != 0) and (step >= 10),
+                }]
+def load_pipeline() -> StableDiffusionXLPipeline:
+    pipe = StableDiffusionXLPipeline.from_pretrained(
+        "stablediffusionapi/newdream-sdxl-20",torch_dtype=torch.float16, use_safetensors=True
+    ).to("cuda") 
+    compile(
+        pipe,
+        onnx_path=Path("/home/sandbox/.cache/huggingface/hub/models--RobertML--edge-onnx/snapshots/d56fa8ea1dc675b87de08eece735bc5ec80a247f"),
+        engine_path=Path("/home/sandbox/.cache/huggingface/hub/models--RobertML--edge-engine/snapshots/e0dd02be0c58057947801857c41839f76df2fc88"),
+        batch_size=1,
+    )
+    cachify.prepare(pipe, SDXL_DEFAULT_CONFIG)
+    cachify.enable(pipe)
+    pipe.scheduler = SchedulerWrapper(DDIMScheduler.from_config(pipe.scheduler.config))
+    with cachify.infer(pipe) as cached_pipe:
+        for _ in range(4):
+            pipe(prompt=prompt, num_inference_steps=20)
+        pipe.scheduler.prepare_loss()
+    cachify.disable(pipe)
+    return pipe
+
+def infer(request: TextToImageRequest, pipeline: StableDiffusionXLPipeline) -> Image:
+
+    if request.seed is None:
+        generator = None
+    else:
+        generator = Generator(pipeline.device).manual_seed(request.seed)
+    cachify.enable(pipeline)
+    with cachify.infer(pipeline) as cached_pipe:
+        image = cached_pipe(
+            prompt=request.prompt,
+            negative_prompt=request.negative_prompt,
+            width=request.width,
+            height=request.height,
+            generator=generator,
+            num_inference_steps=13,
+            ).images[0]
+    return image

uv.lock

@@ -0,0 +1,816 @@
+version = 1
+requires-python = "==3.10.*"
+
+[[package]]
+name = "accelerate"
+version = "0.31.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "huggingface-hub" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "psutil" },
+    { name = "pyyaml" },
+    { name = "safetensors" },
+    { name = "torch" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/89/e2/94937840162a87baa6b56c82247bbb06690b290ad3da0f083192d7b539a9/accelerate-0.31.0.tar.gz", hash = "sha256:b5199865b26106ccf9205acacbe8e4b3b428ad585e7c472d6a46f6fb75b6c176", size = 307110 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f0/62/9ebaf1fdd3d3c737a8814f9ae409d4ac04bc93b26a46a7dab456bb7e16f8/accelerate-0.31.0-py3-none-any.whl", hash = "sha256:0fc608dc49584f64d04711a39711d73cb0ad4ef3d21cddee7ef2216e29471144", size = 309428 },
+]
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
+]
+
+[[package]]
+name = "antlr4-python3-runtime"
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