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main/README.md

@@ -1641,18 +1641,18 @@ from io import BytesIO
 from PIL import Image
 import torch
 from diffusers import DDIMScheduler
-from diffusers.pipelines.stable_diffusion import StableDiffusionImg2ImgPipeline
+from diffusers import DiffusionPipeline
 
 # Use the DDIMScheduler scheduler here instead
 scheduler = DDIMScheduler.from_pretrained("stabilityai/stable-diffusion-2-1",
                                             subfolder="scheduler")
 
 
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-1",
-                                                custom_pipeline="stable_diffusion_tensorrt_img2img",
-                                                variant='fp16',
-                                                torch_dtype=torch.float16,
-                                                scheduler=scheduler,)
+pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1",
+                                            custom_pipeline="stable_diffusion_tensorrt_img2img",
+                                            variant='fp16',
+                                            torch_dtype=torch.float16,
+                                            scheduler=scheduler,)
 
 # re-use cached folder to save ONNX models and TensorRT Engines
 pipe.set_cached_folder("stabilityai/stable-diffusion-2-1", variant='fp16',)

main/stable_diffusion_tensorrt_img2img.py

@@ -18,8 +18,7 @@
 import gc
 import os
 from collections import OrderedDict
-from copy import copy
-from typing import List, Optional, Union
+from typing import List, Optional, Tuple, Union
 
 import numpy as np
 import onnx
@@ -27,9 +26,11 @@ import onnx_graphsurgeon as gs
 import PIL.Image
 import tensorrt as trt
 import torch
+from cuda import cudart
 from huggingface_hub import snapshot_download
 from huggingface_hub.utils import validate_hf_hub_args
 from onnx import shape_inference
+from packaging import version
 from polygraphy import cuda
 from polygraphy.backend.common import bytes_from_path
 from polygraphy.backend.onnx.loader import fold_constants
@@ -41,12 +42,13 @@ from polygraphy.backend.trt import (
     network_from_onnx_path,
     save_engine,
 )
-from polygraphy.backend.trt import util as trt_util
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
 
+from diffusers import DiffusionPipeline
+from diffusers.configuration_utils import FrozenDict, deprecate
+from diffusers.image_processor import VaeImageProcessor
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.stable_diffusion import (
-    StableDiffusionImg2ImgPipeline,
     StableDiffusionPipelineOutput,
     StableDiffusionSafetyChecker,
 )
@@ -58,7 +60,7 @@ from diffusers.utils import logging
 """
 Installation instructions
 python3 -m pip install --upgrade transformers diffusers>=0.16.0
-python3 -m pip install --upgrade tensorrt>=8.6.1
+python3 -m pip install --upgrade tensorrt-cu12==10.2.0
 python3 -m pip install --upgrade polygraphy>=0.47.0 onnx-graphsurgeon --extra-index-url https://pypi.ngc.nvidia.com
 python3 -m pip install onnxruntime
 """
@@ -88,10 +90,6 @@ else:
 torch_to_numpy_dtype_dict = {value: key for (key, value) in numpy_to_torch_dtype_dict.items()}
 
 
-def device_view(t):
-    return cuda.DeviceView(ptr=t.data_ptr(), shape=t.shape, dtype=torch_to_numpy_dtype_dict[t.dtype])
-
-
 def preprocess_image(image):
     """
     image: torch.Tensor
@@ -125,10 +123,8 @@ class Engine:
         onnx_path,
         fp16,
         input_profile=None,
-        enable_preview=False,
         enable_all_tactics=False,
         timing_cache=None,
-        workspace_size=0,
     ):
         logger.warning(f"Building TensorRT engine for {onnx_path}: {self.engine_path}")
         p = Profile()
@@ -137,20 +133,13 @@ class Engine:
                 assert len(dims) == 3
                 p.add(name, min=dims[0], opt=dims[1], max=dims[2])
 
-        config_kwargs = {}
-
-        config_kwargs["preview_features"] = [trt.PreviewFeature.DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805]
-        if enable_preview:
-            # Faster dynamic shapes made optional since it increases engine build time.
-            config_kwargs["preview_features"].append(trt.PreviewFeature.FASTER_DYNAMIC_SHAPES_0805)
-        if workspace_size > 0:
-            config_kwargs["memory_pool_limits"] = {trt.MemoryPoolType.WORKSPACE: workspace_size}
+        extra_build_args = {}
         if not enable_all_tactics:
-            config_kwargs["tactic_sources"] = []
+            extra_build_args["tactic_sources"] = []
 
         engine = engine_from_network(
             network_from_onnx_path(onnx_path, flags=[trt.OnnxParserFlag.NATIVE_INSTANCENORM]),
-            config=CreateConfig(fp16=fp16, profiles=[p], load_timing_cache=timing_cache, **config_kwargs),
+            config=CreateConfig(fp16=fp16, profiles=[p], load_timing_cache=timing_cache, **extra_build_args),
             save_timing_cache=timing_cache,
         )
         save_engine(engine, path=self.engine_path)
@@ -163,28 +152,24 @@ class Engine:
         self.context = self.engine.create_execution_context()
 
     def allocate_buffers(self, shape_dict=None, device="cuda"):
-        for idx in range(trt_util.get_bindings_per_profile(self.engine)):
-            binding = self.engine[idx]
-            if shape_dict and binding in shape_dict:
-                shape = shape_dict[binding]
+        for binding in range(self.engine.num_io_tensors):
+            name = self.engine.get_tensor_name(binding)
+            if shape_dict and name in shape_dict:
+                shape = shape_dict[name]
             else:
-                shape = self.engine.get_binding_shape(binding)
-            dtype = trt.nptype(self.engine.get_binding_dtype(binding))
-            if self.engine.binding_is_input(binding):
-                self.context.set_binding_shape(idx, shape)
+                shape = self.engine.get_tensor_shape(name)
+            dtype = trt.nptype(self.engine.get_tensor_dtype(name))
+            if self.engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT:
+                self.context.set_input_shape(name, shape)
             tensor = torch.empty(tuple(shape), dtype=numpy_to_torch_dtype_dict[dtype]).to(device=device)
-            self.tensors[binding] = tensor
-            self.buffers[binding] = cuda.DeviceView(ptr=tensor.data_ptr(), shape=shape, dtype=dtype)
+            self.tensors[name] = tensor
 
     def infer(self, feed_dict, stream):
-        start_binding, end_binding = trt_util.get_active_profile_bindings(self.context)
-        # shallow copy of ordered dict
-        device_buffers = copy(self.buffers)
         for name, buf in feed_dict.items():
-            assert isinstance(buf, cuda.DeviceView)
-            device_buffers[name] = buf
-        bindings = [0] * start_binding + [buf.ptr for buf in device_buffers.values()]
-        noerror = self.context.execute_async_v2(bindings=bindings, stream_handle=stream.ptr)
+            self.tensors[name].copy_(buf)
+        for name, tensor in self.tensors.items():
+            self.context.set_tensor_address(name, tensor.data_ptr())
+        noerror = self.context.execute_async_v3(stream)
         if not noerror:
             raise ValueError("ERROR: inference failed.")
 
@@ -325,10 +310,8 @@ def build_engines(
     force_engine_rebuild=False,
     static_batch=False,
     static_shape=True,
-    enable_preview=False,
     enable_all_tactics=False,
     timing_cache=None,
-    max_workspace_size=0,
 ):
     built_engines = {}
     if not os.path.isdir(onnx_dir):
@@ -393,9 +376,7 @@ def build_engines(
                     static_batch=static_batch,
                     static_shape=static_shape,
                 ),
-                enable_preview=enable_preview,
                 timing_cache=timing_cache,
-                workspace_size=max_workspace_size,
             )
         built_engines[model_name] = engine
 
@@ -674,7 +655,7 @@ def make_VAEEncoder(model, device, max_batch_size, embedding_dim, inpaint=False)
     return VAEEncoder(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)
 
 
-class TensorRTStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
+class TensorRTStableDiffusionImg2ImgPipeline(DiffusionPipeline):
     r"""
     Pipeline for image-to-image generation using TensorRT accelerated Stable Diffusion.
 
@@ -702,6 +683,8 @@ class TensorRTStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
 
+    _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
+
     def __init__(
         self,
         vae: AutoencoderKL,
@@ -722,24 +705,86 @@ class TensorRTStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
         onnx_dir: str = "onnx",
         # TensorRT engine build parameters
         engine_dir: str = "engine",
-        build_preview_features: bool = True,
         force_engine_rebuild: bool = False,
         timing_cache: str = "timing_cache",
     ):
-        super().__init__(
-            vae,
-            text_encoder,
-            tokenizer,
-            unet,
-            scheduler,
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
+                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
+                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
+            )
+            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        if safety_checker is not None and feature_extractor is None:
+            raise ValueError(
+                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
+                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
+            )
+
+        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse("0.9.0.dev0")
+        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
+            deprecation_message = (
+                "The configuration file of the unet has set the default `sample_size` to smaller than"
+                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
+                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
+                " in the config might lead to incorrect results in future versions. If you have downloaded this"
+                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
+                " the `unet/config.json` file"
+            )
+            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(unet.config)
+            new_config["sample_size"] = 64
+            unet._internal_dict = FrozenDict(new_config)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
             safety_checker=safety_checker,
             feature_extractor=feature_extractor,
             image_encoder=image_encoder,
-            requires_safety_checker=requires_safety_checker,
         )
 
-        self.vae.forward = self.vae.decode
-
         self.stages = stages
         self.image_height, self.image_width = image_height, image_width
         self.inpaint = False
@@ -750,7 +795,6 @@ class TensorRTStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
         self.timing_cache = timing_cache
         self.build_static_batch = False
         self.build_dynamic_shape = False
-        self.build_preview_features = build_preview_features
 
         self.max_batch_size = max_batch_size
         # TODO: Restrict batch size to 4 for larger image dimensions as a WAR for TensorRT limitation.
@@ -761,6 +805,11 @@ class TensorRTStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
         self.models = {}  # loaded in __loadModels()
         self.engine = {}  # loaded in build_engines()
 
+        self.vae.forward = self.vae.decode
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.register_to_config(requires_safety_checker=requires_safety_checker)
+
     def __loadModels(self):
         # Load pipeline models
         self.embedding_dim = self.text_encoder.config.hidden_size
@@ -779,6 +828,33 @@ class TensorRTStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
         if "vae_encoder" in self.stages:
             self.models["vae_encoder"] = make_VAEEncoder(self.vae, **models_args)
 
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
+    def run_safety_checker(
+        self, image: Union[torch.Tensor, PIL.Image.Image], device: torch.device, dtype: torch.dtype
+    ) -> Tuple[Union[torch.Tensor, PIL.Image.Image], Optional[bool]]:
+        r"""
+        Runs the safety checker on the given image.
+        Args:
+            image (Union[torch.Tensor, PIL.Image.Image]): The input image to be checked.
+            device (torch.device): The device to run the safety checker on.
+            dtype (torch.dtype): The data type of the input image.
+        Returns:
+            (image, has_nsfw_concept) Tuple[Union[torch.Tensor, PIL.Image.Image], Optional[bool]]: A tuple containing the processed image and
+            a boolean indicating whether the image has a NSFW (Not Safe for Work) concept.
+        """
+        if self.safety_checker is None:
+            has_nsfw_concept = None
+        else:
+            if torch.is_tensor(image):
+                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
+            else:
+                feature_extractor_input = self.image_processor.numpy_to_pil(image)
+            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        return image, has_nsfw_concept
+
     @classmethod
     @validate_hf_hub_args
     def set_cached_folder(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
@@ -826,7 +902,6 @@ class TensorRTStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
             force_engine_rebuild=self.force_engine_rebuild,
             static_batch=self.build_static_batch,
             static_shape=not self.build_dynamic_shape,
-            enable_preview=self.build_preview_features,
             timing_cache=self.timing_cache,
         )
 
@@ -850,9 +925,7 @@ class TensorRTStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
         return tuple(init_images)
 
     def __encode_image(self, init_image):
-        init_latents = runEngine(self.engine["vae_encoder"], {"images": device_view(init_image)}, self.stream)[
-            "latent"
-        ]
+        init_latents = runEngine(self.engine["vae_encoder"], {"images": init_image}, self.stream)["latent"]
         init_latents = 0.18215 * init_latents
         return init_latents
 
@@ -881,9 +954,8 @@ class TensorRTStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
             .to(self.torch_device)
         )
 
-        text_input_ids_inp = device_view(text_input_ids)
         # NOTE: output tensor for CLIP must be cloned because it will be overwritten when called again for negative prompt
-        text_embeddings = runEngine(self.engine["clip"], {"input_ids": text_input_ids_inp}, self.stream)[
+        text_embeddings = runEngine(self.engine["clip"], {"input_ids": text_input_ids}, self.stream)[
             "text_embeddings"
         ].clone()
 
@@ -899,8 +971,7 @@ class TensorRTStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
             .input_ids.type(torch.int32)
             .to(self.torch_device)
         )
-        uncond_input_ids_inp = device_view(uncond_input_ids)
-        uncond_embeddings = runEngine(self.engine["clip"], {"input_ids": uncond_input_ids_inp}, self.stream)[
+        uncond_embeddings = runEngine(self.engine["clip"], {"input_ids": uncond_input_ids}, self.stream)[
             "text_embeddings"
         ]
 
@@ -924,18 +995,15 @@ class TensorRTStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
             # Predict the noise residual
             timestep_float = timestep.float() if timestep.dtype != torch.float32 else timestep
 
-            sample_inp = device_view(latent_model_input)
-            timestep_inp = device_view(timestep_float)
-            embeddings_inp = device_view(text_embeddings)
             noise_pred = runEngine(
                 self.engine["unet"],
-                {"sample": sample_inp, "timestep": timestep_inp, "encoder_hidden_states": embeddings_inp},
+                {"sample": latent_model_input, "timestep": timestep_float, "encoder_hidden_states": text_embeddings},
                 self.stream,
             )["latent"]
 
             # Perform guidance
             noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-            noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+            noise_pred = noise_pred_uncond + self._guidance_scale * (noise_pred_text - noise_pred_uncond)
 
             latents = self.scheduler.step(noise_pred, timestep, latents).prev_sample
 
@@ -943,12 +1011,12 @@ class TensorRTStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
         return latents
 
     def __decode_latent(self, latents):
-        images = runEngine(self.engine["vae"], {"latent": device_view(latents)}, self.stream)["images"]
+        images = runEngine(self.engine["vae"], {"latent": latents}, self.stream)["images"]
         images = (images / 2 + 0.5).clamp(0, 1)
         return images.cpu().permute(0, 2, 3, 1).float().numpy()
 
     def __loadResources(self, image_height, image_width, batch_size):
-        self.stream = cuda.Stream()
+        self.stream = cudart.cudaStreamCreate()[1]
 
         # Allocate buffers for TensorRT engine bindings
         for model_name, obj in self.models.items():
@@ -1061,5 +1129,6 @@ class TensorRTStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
             # VAE decode latent
             images = self.__decode_latent(latents)
 
+        images, has_nsfw_concept = self.run_safety_checker(images, self.torch_device, text_embeddings.dtype)
         images = self.numpy_to_pil(images)
-        return StableDiffusionPipelineOutput(images=images, nsfw_content_detected=None)
+        return StableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)