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.gitignore

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+**.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

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models/newdream-sdxl-20/.gitignore

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+step_*
+epoch_*

models/newdream-sdxl-20/README.md

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+---
+license: creativeml-openrail-m
+tags:
+  - stablediffusionapi.com
+  - stable-diffusion-api
+  - text-to-image
+  - ultra-realistic
+pinned: true
+---
+
+# NewDream-SDXL 2.0 API Inference
+
+![generated from stablediffusionapi.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/8478583971702167737.png)
+## Get API Key
+
+Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed. 
+
+Replace Key in below code, change **model_id**  to "newdream-sdxl-20"
+
+Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs)
+
+Try model for free: [Generate Images](https://stablediffusionapi.com/models/newdream-sdxl-20)
+
+Model link: [View model](https://stablediffusionapi.com/models/newdream-sdxl-20)
+
+Credits: [View credits](https://civitai.com/?query=NewDream-SDXL%202.0)
+
+View all models: [View Models](https://stablediffusionapi.com/models)
+
+    import requests  
+    import json  
+      
+    url =  "https://stablediffusionapi.com/api/v4/dreambooth"  
+      
+    payload = json.dumps({  
+    "key":  "your_api_key",  
+    "model_id":  "newdream-sdxl-20",  
+    "prompt":  "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",  
+    "negative_prompt":  "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",  
+    "width":  "512",  
+    "height":  "512",  
+    "samples":  "1",  
+    "num_inference_steps":  "30",  
+    "safety_checker":  "no",  
+    "enhance_prompt":  "yes",  
+    "seed":  None,  
+    "guidance_scale":  7.5,  
+    "multi_lingual":  "no",  
+    "panorama":  "no",  
+    "self_attention":  "no",  
+    "upscale":  "no",  
+    "embeddings":  "embeddings_model_id",  
+    "lora":  "lora_model_id",  
+    "webhook":  None,  
+    "track_id":  None  
+    })  
+      
+    headers =  {  
+    'Content-Type':  'application/json'  
+    }  
+      
+    response = requests.request("POST", url, headers=headers, data=payload)  
+      
+    print(response.text)
+
+> Use this coupon code to get 25% off **DMGG0RBN** 

models/newdream-sdxl-20/model_index.json

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+{
+  "_class_name": "StableDiffusionXLPipeline",
+  "_diffusers_version": "0.21.2",
+  "force_zeros_for_empty_prompt": true,
+  "scheduler": [
+    "diffusers",
+    "EulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "text_encoder_2": [
+    "transformers",
+    "CLIPTextModelWithProjection"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "tokenizer_2": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

models/newdream-sdxl-20/scheduler/scheduler_config.json

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+{
+  "_class_name": "EulerDiscreteScheduler",
+  "_diffusers_version": "0.21.2",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "interpolation_type": "linear",
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": false,
+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "timestep_spacing": "leading",
+  "trained_betas": null,
+  "use_karras_sigmas": false
+}

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+{
+  "architectures": [
+    "CLIPTextModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
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+  "hidden_act": "quick_gelu",
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+  "initializer_factor": 1.0,
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+  "layer_norm_eps": 1e-05,
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+  "model_type": "clip_text_model",
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+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "projection_dim": 768,
+  "torch_dtype": "float16",
+  "transformers_version": "4.33.1",
+  "vocab_size": 49408
+}

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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
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+    "lstrip": false,
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+    "rstrip": false,
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+  },
+  "errors": "replace",
+  "model_max_length": 77,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": {
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+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "!",
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

models/newdream-sdxl-20/tokenizer_2/tokenizer_config.json

@@ -0,0 +1,33 @@
+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "errors": "replace",
+  "model_max_length": 77,
+  "pad_token": "!",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

models/newdream-sdxl-20/unet/config.json

@@ -0,0 +1,71 @@
+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.21.2",
+  "act_fn": "silu",
+  "addition_embed_type": "text_time",
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": 256,
+  "attention_head_dim": [
+    5,
+    10,
+    20
+  ],
+  "attention_type": "default",
+  "block_out_channels": [
+    320,
+    640,
+    1280
+  ],
+  "center_input_sample": false,
+  "class_embed_type": null,
+  "class_embeddings_concat": false,
+  "conv_in_kernel": 3,
+  "conv_out_kernel": 3,
+  "cross_attention_dim": 2048,
+  "cross_attention_norm": null,
+  "down_block_types": [
+    "DownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "dropout": 0.0,
+  "dual_cross_attention": false,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_only_cross_attention": null,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "out_channels": 4,
+  "projection_class_embeddings_input_dim": 2816,
+  "resnet_out_scale_factor": 1.0,
+  "resnet_skip_time_act": false,
+  "resnet_time_scale_shift": "default",
+  "sample_size": 128,
+  "time_cond_proj_dim": null,
+  "time_embedding_act_fn": null,
+  "time_embedding_dim": null,
+  "time_embedding_type": "positional",
+  "timestep_post_act": null,
+  "transformer_layers_per_block": [
+    1,
+    2,
+    10
+  ],
+  "up_block_types": [
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "UpBlock2D"
+  ],
+  "upcast_attention": false,
+  "use_linear_projection": true
+}

models/newdream-sdxl-20/unet/diffusion_pytorch_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9715fb3c6d0d81f46fbe6be46ddce05d91806aaf041504a52a44e1ec9e4f660f
+size 5135149760

models/newdream-sdxl-20/vae/config.json

@@ -0,0 +1,31 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.21.2",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": true,
+  "in_channels": 3,
+  "latent_channels": 4,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 1024,
+  "scaling_factor": 0.13025,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

models/newdream-sdxl-20/vae/diffusion_pytorch_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:915b909d0eeef5985462226b2c9950ca9da42b5a6ec8c296c2e73f6419ae465c
+size 167335342

pyproject.toml

@@ -0,0 +1,28 @@
+[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 = "6"
+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",
+]
+
+[project.scripts]
+start_inference = "main:main"

src/cache_diffusion/cachify.py

@@ -0,0 +1,144 @@
+# 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

@@ -0,0 +1,55 @@
+# 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

@@ -0,0 +1,45 @@
+_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/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 trt_pipeline.deploy import load_unet_trt
+from loss import SchedulerWrapper
+
+generator = Generator(torch.device("cuda")).manual_seed(69)
+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(
+        "models/newdream-sdxl-20", torch_dtype=torch.float16, use_safetensors=True, local_files_only=True
+    ).to("cuda") 
+    load_unet_trt(
+        pipe.unet,
+        engine_path=Path("./engine"),
+        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(5):
+            pipe(prompt=prompt, num_inference_steps=20)
+    cachify.disable(pipe)
+    pipe.scheduler.prepare_loss()
+    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.prepare(pipeline, SDXL_DEFAULT_CONFIG)
+    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=15,
+            ).images[0]
+    return image

src/trt_pipeline/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/trt_pipeline/deploy.py

@@ -0,0 +1,144 @@
+# 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 trt_pipeline.config import ONNX_CONFIG
+from trt_pipeline.models.sd3 import sd3_forward
+from trt_pipeline.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 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 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 load_unet_trt(unet, engine_path: Path, batch_size: int = 1):
+    backbone = unet 
+    engine_path.mkdir(parents=True, exist_ok=True)
+    replace_new_forward(backbone) 
+    load_engines(backbone, engine_path, batch_size)
+    warm_up(backbone, batch_size)
+    backbone.use_trt_infer = True

src/trt_pipeline/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/trt_pipeline/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/trt_pipeline/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

uv.lock

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+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "psutil" },
+    { name = "pyyaml" },
+    { name = "safetensors" },
+    { name = "torch" },
+]
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