From fc4ad862a9721dbee1f773c2b53cd99edf2d55cf Mon Sep 17 00:00:00 2001 From: changwangss Date: Thu, 20 Jun 2024 20:01:11 -0700 Subject: [PATCH] restore deployment Signed-off-by: changwangss --- ..._StableDiffusionInstructPix2PixPipeline.py | 738 +++++++++++++++++ .../deployment/stable_diffusion/README.md | 290 +++++++ .../stable_diffusion/diffusion_utils.py | 696 ++++++++++++++++ .../diffusion_utils_img2img.py | 755 ++++++++++++++++++ .../deployment/stable_diffusion/export_ir.py | 311 ++++++++ .../stable_diffusion/export_model.sh | 77 ++ .../images/astronaut_rides_horse.png | Bin 0 -> 464344 bytes .../astronaut_rides_horse_from_engine_1.png | Bin 0 -> 256718 bytes .../stable_diffusion/images/mountain.png | Bin 0 -> 399205 bytes .../images/mountain_cartoonized.png | Bin 0 -> 381908 bytes .../stable_diffusion/prepare_model.py | 337 ++++++++ .../stable_diffusion/quantization_modules.py | 146 ++++ .../stable_diffusion/requirements.txt | 12 + .../stable_diffusion/run_executor.py | 236 ++++++ .../squad/bert_large/run_bert_large.sh | 2 +- .../deployment/squad/bert_large/run_qa.py | 56 +- .../squad/ipex/bert_large/run_bert_large.sh | 2 +- .../squad/ipex/bert_large/run_qa.py | 25 +- .../distilbert_base_uncased/run_distilbert.sh | 2 +- .../ipex/distilbert_base_uncased/run_qa.py | 26 +- .../run_distilbert_sparse.sh | 2 +- .../distilbert_base_uncased_sparse/run_qa.py | 25 +- .../length_adaptive_transformer/README.md | 2 +- .../length_adaptive_transformer/run_LAT.sh | 2 +- .../length_adaptive_transformer/run_qa.py | 23 +- .../distilbert_base_uncased/run_emotion.py | 48 +- .../distilbert_base_uncased/run_emotion.sh | 2 +- .../mrpc/bert_base/run_bert_base.sh | 2 +- .../deployment/mrpc/bert_base/run_glue.py | 48 +- .../bert_base_cased/run_bert_base_cased.sh | 2 +- .../mrpc/bert_base_cased/run_glue.py | 48 +- .../mrpc/bert_mini/run_bert_mini.sh | 2 +- .../deployment/mrpc/bert_mini/run_glue.py | 49 +- .../run_distilbert_base.sh | 2 +- .../mrpc/distilbert_base_uncased/run_glue.py | 49 +- .../deployment/mrpc/roberta_base/run_glue.py | 49 +- .../mrpc/roberta_base/run_roberta_base.sh | 2 +- .../sparse/bert_mini/run_bert_mini.sh | 2 +- .../deployment/sparse/bert_mini/run_glue.py | 47 +- .../distilbert_base_uncased/run_distilbert.sh | 2 +- .../sparse/distilbert_base_uncased/run_qa.py | 47 +- .../sst2/bert_mini/run_bert_mini.sh | 2 +- .../deployment/sst2/bert_mini/run_glue.py | 49 +- .../run_distilbert_base.sh | 2 +- .../sst2/distilbert_base_uncased/run_glue.py | 49 +- .../sst2/minilm_l6_h384_uncased/run_glue.py | 48 +- .../sst2/minilm_l6_h384_uncased/run_minilm.sh | 2 +- .../deployment/mteb/bge/run_bge.sh | 2 +- .../deployment/mteb/bge/run_mteb.py | 50 +- 49 files changed, 3887 insertions(+), 483 deletions(-) create mode 100644 examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/ITREX_StableDiffusionInstructPix2PixPipeline.py create mode 100644 examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/README.md create mode 100644 examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/diffusion_utils.py create mode 100644 examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/diffusion_utils_img2img.py create mode 100644 examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/export_ir.py create mode 100644 examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/export_model.sh create mode 100644 examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/images/astronaut_rides_horse.png create mode 100644 examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/images/astronaut_rides_horse_from_engine_1.png create mode 100644 examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/images/mountain.png create mode 100644 examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/images/mountain_cartoonized.png create mode 100644 examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/prepare_model.py create mode 100644 examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/quantization_modules.py create mode 100644 examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/requirements.txt create mode 100644 examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/run_executor.py diff --git a/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/ITREX_StableDiffusionInstructPix2PixPipeline.py b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/ITREX_StableDiffusionInstructPix2PixPipeline.py new file mode 100644 index 00000000000..a00ccb896cb --- /dev/null +++ b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/ITREX_StableDiffusionInstructPix2PixPipeline.py @@ -0,0 +1,738 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# Copyright (c) 2023 Intel Corporation +# +# 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. +"""Pipeline Modificaiton based from the diffusers 0.12.1 StableDiffusionInstructPix2PixPipeline""" + +import inspect +from typing import Callable, List, Optional, Union + +import numpy as np +import torch +import copy +import PIL +from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer + +from diffusers.models import AutoencoderKL, UNet2DConditionModel +from diffusers.schedulers import KarrasDiffusionSchedulers +from diffusers.utils import ( + PIL_INTERPOLATION, + deprecate, + is_accelerate_available, + logging, + randn_tensor, + replace_example_docstring, +) +from diffusers.pipeline_utils import DiffusionPipeline +from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput +from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess +def preprocess(image): + if isinstance(image, torch.Tensor): + return image + elif isinstance(image, PIL.Image.Image): + image = [image] + + if isinstance(image[0], PIL.Image.Image): + w, h = image[0].size + w, h = map(lambda x: x - x % 8, (w, h)) # resize to integer multiple of 8 + + image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] + image = np.concatenate(image, axis=0) + image = np.array(image).astype(np.float32) / 255.0 + image = image.transpose(0, 3, 1, 2) + image = 2.0 * image - 1.0 + image = torch.from_numpy(image) + elif isinstance(image[0], torch.Tensor): + image = torch.cat(image, dim=0) + return image + + +class StableDiffusionInstructPix2PixPipeline(DiffusionPipeline): + r""" + Pipeline for pixel-level image editing by following text instructions. Based on Stable Diffusion. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the + library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + text_encoder ([`CLIPTextModel`]): + Frozen text-encoder. Stable Diffusion uses the text portion of + [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically + the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. + tokenizer (`CLIPTokenizer`): + Tokenizer of class + [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). + unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of + [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. + safety_checker ([`StableDiffusionSafetyChecker`]): + Classification module that estimates whether generated images could be considered offensive or harmful. + Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. + feature_extractor ([`CLIPFeatureExtractor`]): + Model that extracts features from generated images to be used as inputs for the `safety_checker`. + """ + _optional_components = ["safety_checker", "feature_extractor"] + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + tokenizer: CLIPTokenizer, + unet: UNet2DConditionModel, + scheduler: KarrasDiffusionSchedulers, + safety_checker: StableDiffusionSafetyChecker, + feature_extractor: CLIPFeatureExtractor, + requires_safety_checker: bool = True, + ): + super().__init__() + + 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." + ) + + self.register_modules( + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + unet=unet, + scheduler=scheduler, + safety_checker=safety_checker, + feature_extractor=feature_extractor, + ) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.register_to_config(requires_safety_checker=requires_safety_checker) + + @torch.no_grad() + def __call__( + self, + prompt: Union[str, List[str]] = None, + image: Union[torch.FloatTensor, PIL.Image.Image] = None, + num_inference_steps: int = 100, + guidance_scale: float = 7.5, + image_guidance_scale: float = 1.5, + negative_prompt: Optional[Union[str, List[str]]] = None, + num_images_per_prompt: Optional[int] = 1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: Optional[int] = 1, + engine_graph: Optional[list] = [], + ): + r""" + Function invoked when calling the pipeline for generation. + + Args: + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. + instead. + image (`PIL.Image.Image`): + `Image`, or tensor representing an image batch which will be repainted according to `prompt`. + num_inference_steps (`int`, *optional*, defaults to 100): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + guidance_scale (`float`, *optional*, defaults to 7.5): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. This pipeline requires a value of at least `1`. + image_guidance_scale (`float`, *optional*, defaults to 1.5): + Image guidance scale is to push the generated image towards the initial image `image`. Image guidance + scale is enabled by setting `image_guidance_scale > 1`. Higher image guidance scale encourages to + generate images that are closely linked to the source image `image`, usually at the expense of lower + image quality. This pipeline requires a value of at least `1`. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` + is less than `1`). + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator`, *optional*): + One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) + to make generation deterministic. + latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor will ge generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + + Examples: + + ```py + >>> import PIL + >>> import requests + >>> import torch + >>> from io import BytesIO + + >>> from diffusers import StableDiffusionInstructPix2PixPipeline + + + >>> def download_image(url): + ... response = requests.get(url) + ... return PIL.Image.open(BytesIO(response.content)).convert("RGB") + + + >>> img_url = "https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png" + + >>> image = download_image(img_url).resize((512, 512)) + + >>> pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( + ... "timbrooks/instruct-pix2pix", torch_dtype=torch.float16 + ... ) + >>> pipe = pipe.to("cuda") + + >>> prompt = "make the mountains snowy" + >>> image = pipe(prompt=prompt, image=image).images[0] + ``` + + Returns: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. + When returning a tuple, the first element is a list with the generated images, and the second element is a + list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" + (nsfw) content, according to the `safety_checker`. + """ + # 0. Check inputs + self.check_inputs(prompt, callback_steps) + + if image is None: + raise ValueError("`image` input cannot be undefined.") + + # 1. Define call parameters + batch_size = 1 if isinstance(prompt, str) else len(prompt) + device = self._execution_device + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 and image_guidance_scale >= 1.0 + # check if scheduler is in sigmas space + scheduler_is_in_sigma_space = hasattr(self.scheduler, "sigmas") + + # 2. Encode input prompt + prompt_embeds = self._encode_prompt( + engine_graph[0], + prompt, + device, + num_images_per_prompt, + do_classifier_free_guidance, + negative_prompt, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + ) + + # 3. Preprocess image + image = preprocess(image) + height, width = image.shape[-2:] + + # 4. set timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + # 5. Prepare Image latents + image_latents = self.prepare_image_latents( + image, + batch_size, + num_images_per_prompt, + prompt_embeds.dtype, + device, + do_classifier_free_guidance, + generator, + ) + + # 6. Prepare latent variables + num_channels_latents = self.vae.config.latent_channels + latents = self.prepare_latents( + batch_size * num_images_per_prompt, + num_channels_latents, + height, + width, + prompt_embeds.dtype, + device, + generator, + latents, + ) + + # 7. Check that shapes of latents and image match the UNet channels + num_channels_image = image_latents.shape[1] + if num_channels_latents + num_channels_image != self.unet.config.in_channels: + raise ValueError( + f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects" + f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +" + f" `num_channels_image`: {num_channels_image} " + f" = {num_channels_latents+num_channels_image}. Please verify the config of" + " `pipeline.unet` or your `image` input." + ) + + # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # 9. Denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + # Expand the latents if we are doing classifier free guidance. + # The latents are expanded 3 times because for pix2pix the guidance\ + # is applied for both the text and the input image. + latent_model_input = torch.cat([latents] * 3) if do_classifier_free_guidance else latents + + # concat latents, image_latents in the channel dimension + scaled_latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + scaled_latent_model_input = torch.cat([scaled_latent_model_input, image_latents], dim=1) + + # Original Pytorch Diffuser Unet Code: predict the noise residual + #noise_pred = self.unet(scaled_latent_model_input, t, encoder_hidden_states=prompt_embeds).sample + + # The ITREX Unet Code + scaled_latent_model_input = scaled_latent_model_input.contiguous() + prompt_embeds = prompt_embeds.contiguous() + t_1d = torch.tensor([t], dtype=torch.float32).contiguous() + engine_output = engine_graph[1].inference([scaled_latent_model_input, t_1d, prompt_embeds]) + noise_pred = torch.from_numpy(engine_output['out_sample:0']) + + # Hack: + # For karras style schedulers the model does classifier free guidance using the + # predicted_original_sample instead of the noise_pred. So we need to compute the + # predicted_original_sample here if we are using a karras style scheduler. + if scheduler_is_in_sigma_space: + step_index = (self.scheduler.timesteps == t).nonzero().item() + sigma = self.scheduler.sigmas[step_index] + noise_pred = latent_model_input - sigma * noise_pred + + # perform guidance + if do_classifier_free_guidance: + noise_pred_text, noise_pred_image, noise_pred_uncond = noise_pred.chunk(3) + noise_pred = ( + noise_pred_uncond + + guidance_scale * (noise_pred_text - noise_pred_image) + + image_guidance_scale * (noise_pred_image - noise_pred_uncond) + ) + + # Hack: + # For karras style schedulers the model does classifier free guidance using the + # predicted_original_sample instead of the noise_pred. But the scheduler.step function + # expects the noise_pred and computes the predicted_original_sample internally. So we + # need to overwrite the noise_pred here such that the value of the computed + # predicted_original_sample is correct. + if scheduler_is_in_sigma_space: + noise_pred = (noise_pred - latents) / (-sigma) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + if callback is not None and i % callback_steps == 0: + callback(i, t, latents) + + # 10. Post-processing + image = self.decode_latents(latents, engine_graph[2]) + + # 11. Run safety checker + image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) + + # 12. Convert to PIL + if output_type == "pil": + image = self.numpy_to_pil(image) + + if not return_dict: + return (image, has_nsfw_concept) + + return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload + def enable_sequential_cpu_offload(self, gpu_id=0): + r""" + Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, + text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a + `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. + """ + if is_accelerate_available(): + from accelerate import cpu_offload + else: + raise ImportError("Please install accelerate via `pip install accelerate`") + + device = torch.device(f"cuda:{gpu_id}") + + for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: + cpu_offload(cpu_offloaded_model, device) + + if self.safety_checker is not None: + cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) + + @property + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device + def _execution_device(self): + r""" + Returns the device on which the pipeline's models will be executed. After calling + `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module + hooks. + """ + if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"): + return self.device + for module in self.unet.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + def _encode_prompt( + self, + text_encoder_graph, + prompt, + device, + num_images_per_prompt, + do_classifier_free_guidance, + negative_prompt=None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + device: (`torch.device`): + torch device + num_images_per_prompt (`int`): + number of images that should be generated per prompt + do_classifier_free_guidance (`bool`): + whether to use classifier free guidance or not + negative_ prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. + Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + """ + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + if prompt_embeds is None: + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=self.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = self.tokenizer.batch_decode( + untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] + ) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {self.tokenizer.model_max_length} tokens: {removed_text}" + ) + + if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = text_inputs.attention_mask.to(device) + else: + attention_mask = None + + # The ITREX Text Encdoer Code + # prompt_embeds = text_encoder_graph.inference([text_input_ids]) + # bsz, seq_length = text_input_ids.shape + # encoder_hidden_state = prompt_embeds['last_hidden_state:0'] + # if encoder_hidden_state.dtype == np.int16: + # prompt_embeds['last_hidden_state:0'] = bf16_to_fp32(encoder_hidden_state) + # prompt_embeds = torch.from_numpy(prompt_embeds['last_hidden_state:0']).reshape( + # bsz, seq_length, -1) + + #Original Pytorch Diffuser Text Encoder Code + prompt_embeds = self.text_encoder( + text_input_ids.to(device), + attention_mask=attention_mask, + ) + prompt_embeds = prompt_embeds[0] + + prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) + + bs_embed, seq_len, _ = prompt_embeds.shape + # duplicate text embeddings for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) + prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance and negative_prompt_embeds is None: + uncond_tokens: List[str] + if negative_prompt is None: + uncond_tokens = [""] * batch_size + elif type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = negative_prompt + + max_length = prompt_embeds.shape[1] + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = uncond_input.attention_mask.to(device) + else: + attention_mask = None + + # The ITREX Text Encdoer Code + negative_prompt_embeds = text_encoder_graph.inference([uncond_input.input_ids]) + bsz, seq_length = uncond_input.input_ids.shape + encoder_hidden_state = negative_prompt_embeds['last_hidden_state:0'] + if encoder_hidden_state.dtype == np.int16: + negative_prompt_embeds['last_hidden_state:0'] = bf16_to_fp32(encoder_hidden_state) + negative_prompt_embeds = torch.from_numpy(negative_prompt_embeds['last_hidden_state:0']).reshape( + bsz, seq_length, -1) + + # Original Pytorch Diffuser Text Encoder Code + # negative_prompt_embeds = self.text_encoder( + # uncond_input.input_ids.to(device), + # attention_mask=attention_mask, + # ) + # negative_prompt_embeds = negative_prompt_embeds[0] + + if do_classifier_free_guidance: + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + + negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) + + negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + # pix2pix has two negative embeddings, and unlike in other pipelines latents are ordered [prompt_embeds, negative_prompt_embeds, negative_prompt_embeds] + prompt_embeds = torch.cat([prompt_embeds, negative_prompt_embeds, negative_prompt_embeds]) + + return prompt_embeds + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker + def run_safety_checker(self, image, device, dtype): + if self.safety_checker is not None: + safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device) + image, has_nsfw_concept = self.safety_checker( + images=image, clip_input=safety_checker_input.pixel_values.to(dtype) + ) + else: + has_nsfw_concept = None + return image, has_nsfw_concept + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents + def decode_latents(self, latents, vae_decoder_graph): + latents = 1 / 0.18215 * latents + + # Original Pytorch Diffuser Vae Code + image = self.vae.decode(latents).sample + + # The ITREX Vae Codes + latents = latents.contiguous() + output = vae_decoder_graph.inference([latents]) + image = torch.from_numpy(output['sample:0']) + + image = (image / 2 + 0.5).clamp(0, 1) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 + image = image.cpu().permute(0, 2, 3, 1).float().numpy() + return image + + def check_inputs(self, prompt, callback_steps): + if not isinstance(prompt, str) and not isinstance(prompt, list): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + + if (callback_steps is None) or ( + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents + def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): + shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def prepare_image_latents( + self, image, batch_size, num_images_per_prompt, dtype, device, do_classifier_free_guidance, generator=None + ): + if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): + raise ValueError( + f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" + ) + + image = image.to(device=device, dtype=dtype) + + batch_size = batch_size * num_images_per_prompt + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if isinstance(generator, list): + image_latents = [self.vae.encode(image[i : i + 1]).latent_dist.mode() for i in range(batch_size)] + image_latents = torch.cat(image_latents, dim=0) + else: + image_latents = self.vae.encode(image).latent_dist.mode() + + if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0: + # expand image_latents for batch_size + deprecation_message = ( + f"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial" + " images (`image`). Initial images are now duplicating to match the number of text prompts. Note" + " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update" + " your script to pass as many initial images as text prompts to suppress this warning." + ) + deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False) + additional_image_per_prompt = batch_size // image_latents.shape[0] + image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0) + elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0: + raise ValueError( + f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts." + ) + else: + image_latents = torch.cat([image_latents], dim=0) + + if do_classifier_free_guidance: + uncond_image_latents = torch.zeros_like(image_latents) + image_latents = torch.cat([image_latents, image_latents, uncond_image_latents], dim=0) + + return image_latents + +def fp32_to_bf16(fp32_np): + assert(fp32_np.dtype==np.float32) + temp = copy.deepcopy(fp32_np) + int32_np = temp.view(dtype=np.int32) + int32_np = int32_np >> 16 + bf16_np = int32_np.astype(np.uint16) + return bf16_np + +def bf16_to_fp32(bf16_np): + assert(bf16_np.dtype==np.int16) + temp = copy.deepcopy(bf16_np) + int32_np = temp.astype(dtype=np.int32) + int32_np = int32_np << 16 + fp32_np = int32_np.view(np.float32) + return fp32_np diff --git a/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/README.md b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/README.md new file mode 100644 index 00000000000..ade21d9086b --- /dev/null +++ b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/README.md @@ -0,0 +1,290 @@ +Step-by-Step +========= +This document describes the end-to-end workflow for Text-to-image generative AI models across the Neural Engine backend. + +Supported Text-to-image Generative AI models: +1. [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) +2. [runwayml/stable-diffusion-v1-5](https://github.com/runwayml/stable-diffusion) +3. [stabilityai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1) +4. [instruction-tuning-sd](https://huggingface.co/instruction-tuning-sd) + * [scratch-low-level-img-proc](https://huggingface.co/instruction-tuning-sd/scratch-low-level-img-proc) + * [scratch-cartoonizer](https://huggingface.co/instruction-tuning-sd/scratch-cartoonizer) + * [cartoonizer](https://huggingface.co/instruction-tuning-sd/cartoonizer) + * [low-level-img-proc](https://huggingface.co/instruction-tuning-sd/low-level-img-proc) + +The inference and accuracy of the above pretrained models are verified in the default configs. + +# Prerequisite + +## Prepare Python Environment +Create a python environment, optionally with autoconf for jemalloc support. +```shell +conda create -n python=3.10 [autoconf] +conda activate +``` +>**Note**: Make sure pip <=23.2.2 + +Check that `gcc` version is higher than 9.0. +```shell +gcc -v +``` + +Install Intel® Extension for Transformers, please refer to [installation](/docs/installation.md). +```shell +# Install from pypi +pip install intel-extension-for-transformers + +# Or, install from source code +cd +pip install -r requirements.txt +pip install -v . +``` + +Install required dependencies for this example +```shell +cd /examples/huggingface/pytorch/text-to-image/deployment/stable_diffusion + +pip install -r requirements.txt +pip install transformers==4.34.1 +pip install diffusers==0.12.1 +``` +>**Note**: Please use transformers no higher than 4.34.1 + +## Environment Variables (Optional) +```shell +# Preload libjemalloc.so may improve the performance when inference under multi instance. +conda install jemalloc==5.2.1 -c conda-forge -y +export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libjemalloc.so + +# Using weight sharing can save memory and may improve the performance when multi instances. +export WEIGHT_SHARING=1 +export INST_NUM= +``` +>**Note**: This step is optional. +# End-to-End Workflow +## 1. Prepare Models + +The stable diffusion mainly includes three sub models: +1. Text Encoder +2. Unet +3. Vae Decoder. + +Here we take the [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) as an example. + +### 1.1 Download Models +Export FP32 ONNX models from the hugginface diffusers module, command as follows: + +```python +python prepare_model.py --input_model=CompVis/stable-diffusion-v1-4 --output_path=./model +``` + +By setting --bf16 to export FP32 and BF16 models. +```python +python prepare_model.py --input_model=CompVis/stable-diffusion-v1-4 --output_path=./model --bf16 +``` + +For INT8 quantized mode, we **only support runwayml/stable-diffusion-v1-5** for now. +You need to get a quantized INT8 model first through QAT, Please refer the [link](https://github.com/intel/intel-extension-for-transformers/blob/main/examples/huggingface/pytorch/text-to-image/quantization/qat/README.md). +Then by setting --qat_int8 to export INT8 models, to export INT8 model. +```python +python prepare_model.py --input_model=runwayml/stable-diffusion-v1-5 --output_path=./model --qat_int8 +``` + +### 1.2 Compile Models + +Export three FP32 onnx sub models of the stable diffusion to Nerual Engine IR. + +```bash +# running the follow bash command to get all IR. +bash export_model.sh --input_model=model --precision=fp32 +``` + +Export three BF16 onnx sub models of the stable diffusion to Nerual Engine IR. + +```bash +# running the follow bash command to get all IR. +bash export_model.sh --input_model=model --precision=bf16 +``` + +Export mixed FP32 & dynamic quantized Int8 IR. + +```bash +bash export_model.sh --input_model=model --precision=fp32 --cast_type=dynamic_int8 +``` + +Export mixed BF16 & QAT quantized Int8 IR. +```bash +bash export_model.sh --input_model=model --precision=qat_int8 +``` + +## 2. Performance + +Python API command as follows: +```python +# FP32 IR +python run_executor.py --ir_path=./fp32_ir --mode=latency --input_model=CompVis/stable-diffusion-v1-4 + +# Mixed FP32 & dynamic quantized Int8 IR. +python run_executor.py --ir_path=./fp32_dynamic_int8_ir --mode=latency --input_model=CompVis/stable-diffusion-v1-4 + +# BF16 IR +python run_executor.py --ir_path=./bf16_ir --mode=latency --input_model=CompVis/stable-diffusion-v1-4 + +# QAT INT8 IR +python run_executor.py --ir_path=./qat_int8_ir --mode=latency --input_model=runwayml/stable-diffusion-v1-5 +``` + +## 3. Accuracy +Frechet Inception Distance(FID) metric is used to evaluate the accuracy. This case we check the FID scores between the pytorch image and engine image. + +By setting --accuracy to check FID socre. +Python API command as follows: +```python +# FP32 IR +python run_executor.py --ir_path=./fp32_ir --mode=accuracy --input_model=CompVis/stable-diffusion-v1-4 + +# Mixed FP32 & dynamic quantized Int8 IR +python run_executor.py --ir_path=./fp32_dynamic_int8_ir --mode=accuracy --input_model=CompVis/stable-diffusion-v1-4 + +# BF16 IR +python run_executor.py --ir_path=./bf16_ir --mode=accuracy --input_model=CompVis/stable-diffusion-v1-4 + +# QAT INT8 IR +python run_executor.py --ir_path=./qat_int8_ir --mode=accuracy --input_model=runwayml/stable-diffusion-v1-5 +``` + +## 4. Try Text to Image + +### 4.1 Text2Img + +Try using one sentence to create a picture! + +```python +# Running FP32 models or BF16 models, just import different IR. +# FP32 models +# Note: +# 1. Using --image to set the path of your image, here we use the default download link. +# 2. The default image is "https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png". +# 3. The default prompt is "Cartoonize the following image". + +python run_executor.py --ir_path=./fp32_ir --input_model=CompVis/stable-diffusion-v1-4 +``` +![picture1](./images/astronaut_rides_horse.png) + +```python +# BF16 models +python run_executor.py --ir_path=./bf16_ir --input_model=CompVis/stable-diffusion-v1-4 +``` +![picture2](./images/astronaut_rides_horse_from_engine_1.png) + + +### 4.2 Img2Img: instruction-tuning-sd + +Try using one image and prompts to create a new picture! + +```python +# Running FP32 models or BF16 models, just import different IR. +# BF16 models +python run_executor.py --ir_path=./bf16_ir --input_model=instruction-tuning-sd/cartoonizer --pipeline=instruction-tuning-sd --prompts="Cartoonize the following image" --steps=100 +``` + +Original image: + +![picture3](./images/mountain.png) + +Cartoonized image: + +![picture4](./images/mountain_cartoonized.png) + + +## 5. Validated Result + + +### 5.1 Latency (s) + + +Input: a photo of an astronaut riding a horse on mars + +Batch Size: 1 + + +| Model | FP32 | BF16 | +|---------------------|:----------------------:|-----------------------| +| [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) | 10.33 (s) | 3.02 (s) | + +> Note: Performance results test on ​​06/09/2023 with Intel(R) Xeon(R) Platinum 8480+. +Performance varies by use, configuration and other factors. See platform configuration for configuration details. For more complete information about performance and benchmark results, visit www.intel.com/benchmarks + + + +### 5.2 Platform Configuration + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ManufacturerQuanta Cloud Technology Inc
Product NameQuantaGrid D54Q-2U
OSCentOS Stream 8
Kernel5.16.0-rc1-intel-next-00543-g5867b0a2a125
Microcode0x2b000111
IRQ BalanceEnabled
CPU ModelIntel(R) Xeon(R) Platinum 8480+
Base Frequency2.0GHz
Maximum Frequency3.8GHz
CPU(s)224
Thread(s) per Core2
Core(s) per Socket56
Socket(s)2
NUMA Node(s)2
TurboEnabled
FrequencyGovernerPerformance
diff --git a/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/diffusion_utils.py b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/diffusion_utils.py new file mode 100644 index 00000000000..f7e7b0ddd6d --- /dev/null +++ b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/diffusion_utils.py @@ -0,0 +1,696 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# Copyright (c) 2023 Intel Corporation +# +# 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. + +import inspect +from typing import Callable, List, Optional, Union +import torch +import numpy as np +import copy + +from packaging import version +from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer + +from intel_extension_for_transformers.transformers.runtime.compile.graph import Graph + +from diffusers.configuration_utils import FrozenDict +from diffusers.models import AutoencoderKL, UNet2DConditionModel +from diffusers.schedulers import KarrasDiffusionSchedulers +from diffusers.utils import deprecate, is_accelerate_available, logging, randn_tensor, replace_example_docstring +from diffusers.pipeline_utils import DiffusionPipeline +from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput +from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +EXAMPLE_DOC_STRING = """ + Examples: + ```py + >>> import torch + >>> from diffusers import StableDiffusionPipeline + + >>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) + >>> pipe = pipe.to("cuda") + + >>> prompt = "a photo of an astronaut riding a horse on mars" + >>> image = pipe(prompt).images[0] + ``` +""" + + +class StableDiffusionPipeline(DiffusionPipeline): + r""" + Pipeline for text-to-image generation using Stable Diffusion. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the + library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + text_encoder ([`CLIPTextModel`]): + Frozen text-encoder. Stable Diffusion uses the text portion of + [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically + the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. + tokenizer (`CLIPTokenizer`): + Tokenizer of class + [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). + unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of + [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. + safety_checker ([`StableDiffusionSafetyChecker`]): + Classification module that estimates whether generated images could be considered offensive or harmful. + Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. + feature_extractor ([`CLIPFeatureExtractor`]): + Model that extracts features from generated images to be used as inputs for the `safety_checker`. + """ + _optional_components = ["safety_checker", "feature_extractor"] + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + tokenizer: CLIPTokenizer, + unet: UNet2DConditionModel, + scheduler: KarrasDiffusionSchedulers, + safety_checker: StableDiffusionSafetyChecker, + feature_extractor: CLIPFeatureExtractor, + requires_safety_checker: bool = True, + ): + 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, + ) + self.vae_scale_factor = 2**(len(self.vae.config.block_out_channels) - 1) + self.register_to_config(requires_safety_checker=requires_safety_checker) + + def enable_vae_slicing(self): + r""" + Enable sliced VAE decoding. + + When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several + steps. This is useful to save some memory and allow larger batch sizes. + """ + self.vae.enable_slicing() + + def disable_vae_slicing(self): + r""" + Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to + computing decoding in one step. + """ + self.vae.disable_slicing() + + def enable_sequential_cpu_offload(self, gpu_id=0): + r""" + Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, + text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a + `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. + """ + if is_accelerate_available(): + from accelerate import cpu_offload + else: + raise ImportError("Please install accelerate via `pip install accelerate`") + + device = torch.device(f"cuda:{gpu_id}") + + for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: + cpu_offload(cpu_offloaded_model, device) + + if self.safety_checker is not None: + cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) + + @property + def _execution_device(self): + r""" + Returns the device on which the pipeline's models will be executed. After calling + `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module + hooks. + """ + if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"): + return self.device + for module in self.unet.modules(): + if (hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None): + return torch.device(module._hf_hook.execution_device) + return self.device + + def _encode_prompt( + self, + text_encoder_graph, + prompt, + device, + num_images_per_prompt, + do_classifier_free_guidance, + negative_prompt=None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + device: (`torch.device`): + torch device + num_images_per_prompt (`int`): + number of images that should be generated per prompt + do_classifier_free_guidance (`bool`): + whether to use classifier free guidance or not + negative_ prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. + Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + """ + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + if prompt_embeds is None: + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=self.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids): + removed_text = self.tokenizer.batch_decode( + untruncated_ids[:, self.tokenizer.model_max_length - 1:-1]) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {self.tokenizer.model_max_length} tokens: {removed_text}") + + if hasattr(self.text_encoder.config, + "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = text_inputs.attention_mask.to(device) + else: + attention_mask = None + + # The ITREX Text Encdoer Code + prompt_embeds = text_encoder_graph.inference([text_input_ids]) + bsz, seq_length = text_input_ids.shape + encoder_hidden_state = prompt_embeds['last_hidden_state:0'] + if encoder_hidden_state.dtype == np.int16: + prompt_embeds['last_hidden_state:0'] = bf16_to_fp32(encoder_hidden_state) + prompt_embeds = torch.from_numpy(prompt_embeds['last_hidden_state:0']).reshape( + bsz, seq_length, -1) + + # Original Pytorch Diffuser Text Encoder Code + # prompt_embeds = self.text_encoder( + # text_input_ids.to(device), + # attention_mask=attention_mask, + # ) + # prompt_embeds = prompt_embeds[0] + + prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) + + bs_embed, seq_len, _ = prompt_embeds.shape + # duplicate text embeddings for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) + prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance and negative_prompt_embeds is None: + uncond_tokens: List[str] + if negative_prompt is None: + uncond_tokens = [""] * batch_size + elif type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}.") + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`.") + else: + uncond_tokens = negative_prompt + + max_length = prompt_embeds.shape[1] + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + if hasattr(self.text_encoder.config, + "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = uncond_input.attention_mask.to(device) + else: + attention_mask = None + + # The ITREX Text Encdoer Code + negative_prompt_embeds = text_encoder_graph.inference([uncond_input.input_ids]) + bsz, seq_length = uncond_input.input_ids.shape + encoder_hidden_state = negative_prompt_embeds['last_hidden_state:0'] + if encoder_hidden_state.dtype == np.int16: + negative_prompt_embeds['last_hidden_state:0'] = bf16_to_fp32(encoder_hidden_state) + negative_prompt_embeds = torch.from_numpy(negative_prompt_embeds['last_hidden_state:0']).reshape( + bsz, seq_length, -1) + + if do_classifier_free_guidance: + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + + negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) + + negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, + -1) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) + + return prompt_embeds + + def run_safety_checker(self, image, device, dtype): + if self.safety_checker is not None: + safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), + return_tensors="pt").to(device) + image, has_nsfw_concept = self.safety_checker( + images=image, clip_input=safety_checker_input.pixel_values.to(dtype)) + else: + has_nsfw_concept = None + return image, has_nsfw_concept + + def decode_latents(self, latents, vae_decoder_graph): + latents = 1 / 0.18215 * latents + # Original Pytorch Diffuser Vae Code + # image = self.vae.decode(latents).sample + + # The ITREX Vae Codes + output = vae_decoder_graph.inference([latents]) + image = torch.from_numpy(output['sample:0']) + + image = (image / 2 + 0.5).clamp(0, 1) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 + image = image.cpu().permute(0, 2, 3, 1).float().numpy() + return image + + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs( + self, + prompt, + height, + width, + callback_steps, + negative_prompt=None, + prompt_embeds=None, + negative_prompt_embeds=None, + ): + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + + if (callback_steps is None) or (callback_steps is not None and + (not isinstance(callback_steps, int) or callback_steps <= 0)): + raise ValueError(f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}.") + + if prompt is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two.") + elif prompt is None and prompt_embeds is None: + raise ValueError( + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." + ) + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + + if negative_prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two.") + + if prompt_embeds is not None and negative_prompt_embeds is not None: + if prompt_embeds.shape != negative_prompt_embeds.shape: + raise ValueError( + "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" + f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" + f" {negative_prompt_embeds.shape}.") + + def prepare_latents(self, + batch_size, + num_channels_latents, + height, + width, + dtype, + device, + generator, + latents=None): + shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, + width // self.vae_scale_factor) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators.") + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + prompt: Union[str, List[str]] = None, + height: Optional[int] = None, + width: Optional[int] = None, + num_inference_steps: int = 50, + guidance_scale: float = 7.5, + negative_prompt: Optional[Union[str, List[str]]] = None, + num_images_per_prompt: Optional[int] = 1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: Optional[int] = 1, + engine_graph: Optional[list] = [], + ): + r""" + Function invoked when calling the pipeline for generation. + + Args: + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. + instead. + height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The width in pixels of the generated image. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + guidance_scale (`float`, *optional*, defaults to 7.5): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. + Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) + to make generation deterministic. + latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor will ge generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + + Examples: + + Returns: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. + When returning a tuple, the first element is a list with the generated images, and the second element is a + list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" + (nsfw) content, according to the `safety_checker`. + """ + # 0. Default height and width to unet + height = height or self.unet.config.sample_size * self.vae_scale_factor + width = width or self.unet.config.sample_size * self.vae_scale_factor + + # 1. Check inputs. Raise error if not correct + self.check_inputs(prompt, height, width, callback_steps, negative_prompt, prompt_embeds, + negative_prompt_embeds) + + # 2. Define call parameters + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + device = self._execution_device + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # 3. Encode input prompt + prompt_embeds = self._encode_prompt( + engine_graph[0], + prompt, + device, + num_images_per_prompt, + do_classifier_free_guidance, + negative_prompt, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + ) + + # 4. Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + # 5. Prepare latent variables + num_channels_latents = self.unet.in_channels + latents = self.prepare_latents( + batch_size * num_images_per_prompt, + num_channels_latents, + height, + width, + prompt_embeds.dtype, + device, + generator, + latents, + ) + + # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # 7. Denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + # Original Pytorch Diffuser Unet Code: predict the noise residual + # noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample + + # The ITREX Unet Code + t_1d = torch.tensor([t], dtype=torch.float32) + engine_output = engine_graph[1].inference([latent_model_input, t_1d, prompt_embeds]) + noise_pred = torch.from_numpy(engine_output['out_sample:0']) + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and + (i + 1) % self.scheduler.order == 0): + progress_bar.update() + if callback is not None and i % callback_steps == 0: + callback(i, t, latents) + + # 8. Post-processing + image = self.decode_latents(latents, engine_graph[2]) + + # 9. Run safety checker + image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) + + # 10. Convert to PIL + if output_type == "pil": + image = self.numpy_to_pil(image) + + if not return_dict: + return (image, has_nsfw_concept) + + return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) + + +def neural_engine_init(ir_path): + text_encoder_graph = Graph() + text_encoder_path = ir_path + '/text_encoder/' + text_encoder_conf = text_encoder_path + 'conf.yaml' + text_encoder_bin = text_encoder_path + 'model.bin' + text_encoder_graph.graph_init(text_encoder_conf, text_encoder_bin) + + unet_graph = Graph() + uent_path = ir_path + '/unet/' + unet_conf = uent_path + 'conf.yaml' + unet_bin = uent_path + 'model.bin' + unet_graph.graph_init(unet_conf, unet_bin, True) + + vae_decoder_graph = Graph() + vae_decoder_path = ir_path + '/vae_decoder/' + vae_decoder_conf = vae_decoder_path + 'conf.yaml' + vae_decoder_bin = vae_decoder_path + 'model.bin' + vae_decoder_graph.graph_init(vae_decoder_conf, vae_decoder_bin) + + return [text_encoder_graph, unet_graph, vae_decoder_graph] + +def fp32_to_bf16(fp32_np): + assert(fp32_np.dtype==np.float32) + temp = copy.deepcopy(fp32_np) + int32_np = temp.view(dtype=np.int32) + int32_np = int32_np >> 16 + bf16_np = int32_np.astype(np.uint16) + return bf16_np + +def bf16_to_fp32(bf16_np): + assert(bf16_np.dtype==np.int16) + temp = copy.deepcopy(bf16_np) + int32_np = temp.astype(dtype=np.int32) + int32_np = int32_np << 16 + fp32_np = int32_np.view(np.float32) + return fp32_np diff --git a/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/diffusion_utils_img2img.py b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/diffusion_utils_img2img.py new file mode 100644 index 00000000000..e464b07acef --- /dev/null +++ b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/diffusion_utils_img2img.py @@ -0,0 +1,755 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# Copyright (c) 2023 Intel Corporation +# +# 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. +"""Pipeline Modificaiton based from the diffusers 0.12.1 StableDiffusionImg2ImgPipeline""" + +import inspect +from typing import Callable, List, Optional, Union + +import numpy as np +import torch +import copy +import PIL +from packaging import version +from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer + +from diffusers.configuration_utils import FrozenDict +from diffusers.models import AutoencoderKL, UNet2DConditionModel +from diffusers.schedulers import KarrasDiffusionSchedulers +from diffusers.utils import ( + PIL_INTERPOLATION, + deprecate, + is_accelerate_available, + logging, + randn_tensor, + replace_example_docstring, +) +from diffusers.pipeline_utils import DiffusionPipeline +from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput +from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +EXAMPLE_DOC_STRING = """ + Examples: + ```py + >>> import requests + >>> import torch + >>> from PIL import Image + >>> from io import BytesIO + + >>> from diffusers import StableDiffusionImg2ImgPipeline + + >>> device = "cuda" + >>> model_id_or_path = "runwayml/stable-diffusion-v1-5" + >>> pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16) + >>> pipe = pipe.to(device) + + >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" + + >>> response = requests.get(url) + >>> init_image = Image.open(BytesIO(response.content)).convert("RGB") + >>> init_image = init_image.resize((768, 512)) + + >>> prompt = "A fantasy landscape, trending on artstation" + + >>> images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images + >>> images[0].save("fantasy_landscape.png") + ``` +""" + + +def preprocess(image): + if isinstance(image, torch.Tensor): + return image + elif isinstance(image, PIL.Image.Image): + image = [image] + + if isinstance(image[0], PIL.Image.Image): + w, h = image[0].size + w, h = map(lambda x: x - x % 8, (w, h)) # resize to integer multiple of 8 + + image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] + image = np.concatenate(image, axis=0) + image = np.array(image).astype(np.float32) / 255.0 + image = image.transpose(0, 3, 1, 2) + image = 2.0 * image - 1.0 + image = torch.from_numpy(image) + elif isinstance(image[0], torch.Tensor): + image = torch.cat(image, dim=0) + return image + + +class StableDiffusionImg2ImgPipeline(DiffusionPipeline): + r""" + Pipeline for text-guided image to image generation using Stable Diffusion. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the + library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + text_encoder ([`CLIPTextModel`]): + Frozen text-encoder. Stable Diffusion uses the text portion of + [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically + the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. + tokenizer (`CLIPTokenizer`): + Tokenizer of class + [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). + unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of + [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. + safety_checker ([`StableDiffusionSafetyChecker`]): + Classification module that estimates whether generated images could be considered offensive or harmful. + Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. + feature_extractor ([`CLIPFeatureExtractor`]): + Model that extracts features from generated images to be used as inputs for the `safety_checker`. + """ + _optional_components = ["safety_checker", "feature_extractor"] + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.__init__ + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + tokenizer: CLIPTokenizer, + unet: UNet2DConditionModel, + scheduler: KarrasDiffusionSchedulers, + safety_checker: StableDiffusionSafetyChecker, + feature_extractor: CLIPFeatureExtractor, + requires_safety_checker: bool = True, + ): + 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, + ) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.register_to_config(requires_safety_checker=requires_safety_checker) + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload + def enable_sequential_cpu_offload(self, gpu_id=0): + r""" + Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, + text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a + `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. + """ + if is_accelerate_available(): + from accelerate import cpu_offload + else: + raise ImportError("Please install accelerate via `pip install accelerate`") + + device = torch.device(f"cuda:{gpu_id}") + + for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: + cpu_offload(cpu_offloaded_model, device) + + if self.safety_checker is not None: + cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True) + + @property + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device + def _execution_device(self): + r""" + Returns the device on which the pipeline's models will be executed. After calling + `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module + hooks. + """ + if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"): + return self.device + for module in self.unet.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt + def _encode_prompt( + self, + text_encoder_graph, + prompt, + device, + num_images_per_prompt, + do_classifier_free_guidance, + negative_prompt=None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + device: (`torch.device`): + torch device + num_images_per_prompt (`int`): + number of images that should be generated per prompt + do_classifier_free_guidance (`bool`): + whether to use classifier free guidance or not + negative_ prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. + Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + """ + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + if prompt_embeds is None: + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=self.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = self.tokenizer.batch_decode( + untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] + ) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {self.tokenizer.model_max_length} tokens: {removed_text}" + ) + + if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = text_inputs.attention_mask.to(device) + else: + attention_mask = None + + # The ITREX Text Encdoer Code + prompt_embeds = text_encoder_graph.inference([text_input_ids]) + bsz, seq_length = text_input_ids.shape + encoder_hidden_state = prompt_embeds['last_hidden_state:0'] + if encoder_hidden_state.dtype == np.int16: + prompt_embeds['last_hidden_state:0'] = bf16_to_fp32(encoder_hidden_state) + prompt_embeds = torch.from_numpy(prompt_embeds['last_hidden_state:0']).reshape( + bsz, seq_length, -1) + + # Original Pytorch Diffuser Text Encoder Code + # prompt_embeds = self.text_encoder( + # text_input_ids.to(device), + # attention_mask=attention_mask, + # ) + # prompt_embeds = prompt_embeds[0] + + prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) + + bs_embed, seq_len, _ = prompt_embeds.shape + # duplicate text embeddings for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) + prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance and negative_prompt_embeds is None: + uncond_tokens: List[str] + if negative_prompt is None: + uncond_tokens = [""] * batch_size + elif type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = negative_prompt + + max_length = prompt_embeds.shape[1] + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ) + + if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = uncond_input.attention_mask.to(device) + else: + attention_mask = None + + # The ITREX Text Encdoer Code + negative_prompt_embeds = text_encoder_graph.inference([uncond_input.input_ids]) + bsz, seq_length = uncond_input.input_ids.shape + encoder_hidden_state = negative_prompt_embeds['last_hidden_state:0'] + if encoder_hidden_state.dtype == np.int16: + negative_prompt_embeds['last_hidden_state:0'] = bf16_to_fp32(encoder_hidden_state) + negative_prompt_embeds = torch.from_numpy(negative_prompt_embeds['last_hidden_state:0']).reshape( + bsz, seq_length, -1) + + # Original Pytorch Diffuser Text Encoder Code + # negative_prompt_embeds = self.text_encoder( + # uncond_input.input_ids.to(device), + # attention_mask=attention_mask, + # ) + # negative_prompt_embeds = negative_prompt_embeds[0] + + if do_classifier_free_guidance: + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + + negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) + + negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) + + return prompt_embeds + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker + def run_safety_checker(self, image, device, dtype): + if self.safety_checker is not None: + safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device) + image, has_nsfw_concept = self.safety_checker( + images=image, clip_input=safety_checker_input.pixel_values.to(dtype) + ) + else: + has_nsfw_concept = None + return image, has_nsfw_concept + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents + def decode_latents(self, latents, vae_decoder_graph): + latents = 1 / 0.18215 * latents + # Original Pytorch Diffuser Vae Code + # image = self.vae.decode(latents).sample + + # The ITREX Vae Codes + latents = latents.contiguous() + output = vae_decoder_graph.inference([latents]) + image = torch.from_numpy(output['sample:0']) + + image = (image / 2 + 0.5).clamp(0, 1) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 + image = image.cpu().permute(0, 2, 3, 1).float().numpy() + return image + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs( + self, prompt, strength, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None + ): + if not isinstance(prompt, str) and not isinstance(prompt, list): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + + if strength < 0 or strength > 1: + raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") + + if (callback_steps is None) or ( + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + + if prompt is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt is None and prompt_embeds is None: + raise ValueError( + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." + ) + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + + if negative_prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if prompt_embeds is not None and negative_prompt_embeds is not None: + if prompt_embeds.shape != negative_prompt_embeds.shape: + raise ValueError( + "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" + f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" + f" {negative_prompt_embeds.shape}." + ) + + def get_timesteps(self, num_inference_steps, strength, device): + # get the original timestep using init_timestep + init_timestep = min(int(num_inference_steps * strength), num_inference_steps) + + t_start = max(num_inference_steps - init_timestep, 0) + timesteps = self.scheduler.timesteps[t_start:] + + return timesteps, num_inference_steps - t_start + + def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None): + if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): + raise ValueError( + f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" + ) + + image = image.to(device=device, dtype=dtype) + + batch_size = batch_size * num_images_per_prompt + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if isinstance(generator, list): + init_latents = [ + self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size) + ] + init_latents = torch.cat(init_latents, dim=0) + else: + init_latents = self.vae.encode(image).latent_dist.sample(generator) + + init_latents = 0.18215 * init_latents + + if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0: + # expand init_latents for batch_size + deprecation_message = ( + f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial" + " images (`image`). Initial images are now duplicating to match the number of text prompts. Note" + " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update" + " your script to pass as many initial images as text prompts to suppress this warning." + ) + deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False) + additional_image_per_prompt = batch_size // init_latents.shape[0] + init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0) + elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0: + raise ValueError( + f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts." + ) + else: + init_latents = torch.cat([init_latents], dim=0) + + shape = init_latents.shape + noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + + # get latents + init_latents = self.scheduler.add_noise(init_latents, noise, timestep) + latents = init_latents + + return latents + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + prompt: Union[str, List[str]] = None, + image: Union[torch.FloatTensor, PIL.Image.Image] = None, + strength: float = 0.8, + num_inference_steps: Optional[int] = 50, + guidance_scale: Optional[float] = 7.5, + negative_prompt: Optional[Union[str, List[str]]] = None, + num_images_per_prompt: Optional[int] = 1, + eta: Optional[float] = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: Optional[int] = 1, + engine_graph: Optional[list] = [], + **kwargs, + ): + r""" + Function invoked when calling the pipeline for generation. + + Args: + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. + instead. + image (`torch.FloatTensor` or `PIL.Image.Image`): + `Image`, or tensor representing an image batch, that will be used as the starting point for the + process. + strength (`float`, *optional*, defaults to 0.8): + Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image` + will be used as a starting point, adding more noise to it the larger the `strength`. The number of + denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will + be maximum and the denoising process will run for the full number of iterations specified in + `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. This parameter will be modulated by `strength`. + guidance_scale (`float`, *optional*, defaults to 7.5): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` + is less than `1`). + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator`, *optional*): + One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) + to make generation deterministic. + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + Examples: + + Returns: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. + When returning a tuple, the first element is a list with the generated images, and the second element is a + list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" + (nsfw) content, according to the `safety_checker`. + """ + message = "Please use `image` instead of `init_image`." + init_image = deprecate("init_image", "0.13.0", message, take_from=kwargs) + image = init_image or image + + # 1. Check inputs. Raise error if not correct + self.check_inputs(prompt, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds) + + # 2. Define call parameters + batch_size = 1 if isinstance(prompt, str) else len(prompt) + device = self._execution_device + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # 3. Encode input prompt + prompt_embeds = self._encode_prompt( + engine_graph[0], + prompt, + device, + num_images_per_prompt, + do_classifier_free_guidance, + negative_prompt, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + ) + + # 4. Preprocess image + image = preprocess(image) + + # 5. set timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) + latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) + + # 6. Prepare latent variables + latents = self.prepare_latents( + image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator + ) + + # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # 8. Denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + # Original Pytorch Diffuser Unet Code: predict the noise residual + # noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample + + # The ITREX Unet Code + latent_model_input = latent_model_input.contiguous() + prompt_embeds = prompt_embeds.contiguous() + t_1d = torch.tensor([t], dtype=torch.float32).contiguous() + engine_output = engine_graph[1].inference([latent_model_input, t_1d, prompt_embeds]) + noise_pred = torch.from_numpy(engine_output['out_sample:0']) + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + if callback is not None and i % callback_steps == 0: + callback(i, t, latents) + + # 9. Post-processing + image = self.decode_latents(latents, engine_graph[2]) + + # 10. Run safety checker + image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) + + # 11. Convert to PIL + if output_type == "pil": + image = self.numpy_to_pil(image) + + if not return_dict: + return (image, has_nsfw_concept) + + return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) + +def fp32_to_bf16(fp32_np): + assert(fp32_np.dtype==np.float32) + temp = copy.deepcopy(fp32_np) + int32_np = temp.view(dtype=np.int32) + int32_np = int32_np >> 16 + bf16_np = int32_np.astype(np.uint16) + return bf16_np + +def bf16_to_fp32(bf16_np): + assert(bf16_np.dtype==np.int16) + temp = copy.deepcopy(bf16_np) + int32_np = temp.astype(dtype=np.int32) + int32_np = int32_np << 16 + fp32_np = int32_np.view(np.float32) + return fp32_np diff --git a/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/export_ir.py b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/export_ir.py new file mode 100644 index 00000000000..35cf29d247d --- /dev/null +++ b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/export_ir.py @@ -0,0 +1,311 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# Copyright (c) 2023 Intel Corporation +# +# 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. + +import argparse +from intel_extension_for_transformers.transformers.runtime.compile import compile, autocast + +text_encoder_pattern_config = { + 'pattern_switch': { + # General Pattern + 'PaddingSequence': False, + 'AttentionReshape': False, + 'QKVReshape': False, + 'ReshapeFusion': False, + 'InsertBF16Node': False, + 'OperatorAdaptor': False, + + # transpose_int8 + 'QKVMerge': False, + + # 'TextEncoder + 'TextEncoder_WordEmbedding': True, + 'TextEncoder_QReshape': True, + 'TextEncoder_KVReshape': True, + 'TextEncoder_AttentionMaskAddReshape': True, + 'TextEncoder_SoftmaxReshape': True, + 'TextEncoder_MulReshape': True, + 'TextEncoder_AttentionReshape': True, + 'TextEncoder_CasualAttentionMask': True, + + # for unet and vae decoder + 'GroupNorm': False, + + # vae decoder & Transformer2Dmodel + 'AttentionBlock_Resize2Gather': False, + 'AttentionBlock_QKVPreReshape': False, + 'AttentionBlock_AttentionMaskAddReshape': False, + 'AttentionBlock_ConstantOfShapeWithMul': False, + + 'Transformer2Dmodel_GetSampleBatch': False, + 'Transformer2Dmodel_SampleSlice': False, + 'Transformer2Dmodel_EncoderHiddenStatesReshape': False, + 'Transformer2Dmodel_ConstantOfShapeWithMul': False, + 'Transformer2Dmodel_QKVPreReshape': False, + 'Transformer2Dmodel_QKVReshape': False, + 'AttentionBlock_QKVReshape': False, + 'Transformer2Dmodel_QKVReshapeTo4D': False, + 'Transformer2Dmodel_AttentionMaskAddReshape': False, + 'Transformer2Dmodel_FFNInputSlice': False, + 'Transformer2Dmodel_FFNInputSlice_1': False, + 'Transformer2DModel_UpBlockResize': False, + + # for all stable diffusion models + 'StableDiffusion_bf16Convert': True, + 'StableDiffusion_ReshapeFusion': True, + + # MHA + 'TorchInsertBF16Node': False, + 'StableDiffusion_MHAReshape': True, + 'StableDiffusion_MHA': False, + 'ExplicitNHWCTransposeForConv': True, + 'ExplicitNHWCTransposeForConvQAT': False, + 'MultiHeadAttention': False, + + # Channel_last + 'ConvReshape': False + } +} + +unet_pattern_config = { + 'pattern_switch': { + # General Pattern + 'PaddingSequence': False, + 'AttentionReshape': False, + 'QKVReshape': False, + 'ReshapeFusion': False, + 'InsertBF16Node': False, + 'OperatorAdaptor': False, + + # transpose_int8 + 'QKVMerge': False, + + # 'TextEncoder + 'TextEncoder_WordEmbedding': False, + 'TextEncoder_QReshape': False, + 'TextEncoder_KVReshape': False, + 'TextEncoder_AttentionMaskAddReshape': False, + 'TextEncoder_SoftmaxReshape': False, + 'TextEncoder_MulReshape': False, + 'TextEncoder_AttentionReshape': False, + 'TextEncoder_CasualAttentionMask': False, + + # for unet and vae decoder + 'GroupNorm': True, + + # vae decoder & Transformer2Dmodel + 'AttentionBlock_Resize2Gather': True, + 'AttentionBlock_QKVPreReshape': True, + 'AttentionBlock_AttentionMaskAddReshape': True, + 'AttentionBlock_ConstantOfShapeWithMul': True, + + 'Transformer2Dmodel_GetSampleBatch': True, + 'Transformer2Dmodel_SampleSlice': True, + 'Transformer2Dmodel_EncoderHiddenStatesReshape': True, + 'Transformer2Dmodel_ConstantOfShapeWithMul': True, + 'Transformer2Dmodel_QKVPreReshape': True, + 'Transformer2Dmodel_QKVReshape': True, + 'AttentionBlock_QKVReshape': False, + 'Transformer2Dmodel_QKVReshapeTo4D': True, + 'Transformer2Dmodel_AttentionMaskAddReshape': True, + 'Transformer2Dmodel_FFNInputSlice': True, + 'Transformer2Dmodel_FFNInputSlice_1': True, + 'Transformer2DModel_UpBlockResize': True, + + # for all stable diffusion models + 'StableDiffusion_bf16Convert': True, + 'StableDiffusion_ReshapeFusion': True, + + # MHA + 'TorchInsertBF16Node': False, + 'StableDiffusion_MHAReshape': True, + 'StableDiffusion_MHA': False, + 'ExplicitNHWCTransposeForConv': True, + 'ExplicitNHWCTransposeForConvQAT': False, + 'MultiHeadAttention': False, + + # Channel_last + 'ConvReshape': False + } +} + +qat_unet_pattern_config = { + 'pattern_switch': { + # General Pattern + 'PaddingSequence': False, + 'AttentionReshape': False, + 'QKVReshape': False, + 'ReshapeFusion': False, + 'InsertBF16Node': False, + 'OperatorAdaptor': False, + + # transpose_int8 + 'QKVMerge': False, + + # 'TextEncoder + 'TextEncoder_WordEmbedding': False, + 'TextEncoder_QReshape': False, + 'TextEncoder_KVReshape': False, + 'TextEncoder_AttentionMaskAddReshape': False, + 'TextEncoder_SoftmaxReshape': False, + 'TextEncoder_MulReshape': False, + 'TextEncoder_AttentionReshape': False, + 'TextEncoder_CasualAttentionMask': False, + + # for unet and vae decoder + 'GroupNorm': True, + + # vae decoder & Transformer2Dmodel + 'AttentionBlock_Resize2Gather': True, + 'AttentionBlock_QKVPreReshape': True, + 'AttentionBlock_AttentionMaskAddReshape': True, + 'AttentionBlock_ConstantOfShapeWithMul': True, + + 'Transformer2Dmodel_GetSampleBatch': True, + 'Transformer2Dmodel_SampleSlice': True, + 'Transformer2Dmodel_EncoderHiddenStatesReshape': True, + 'Transformer2Dmodel_ConstantOfShapeWithMul': True, + 'Transformer2Dmodel_QKVPreReshape': True, + 'Transformer2Dmodel_QKVReshape': True, + 'AttentionBlock_QKVReshape': False, + 'Transformer2Dmodel_QKVReshapeTo4D': True, + 'Transformer2Dmodel_AttentionMaskAddReshape': True, + 'Transformer2Dmodel_FFNInputSlice': True, + 'Transformer2Dmodel_FFNInputSlice_1': True, + 'Transformer2DModel_UpBlockResize': True, + + # for all stable diffusion models + 'StableDiffusion_bf16Convert': True, + 'StableDiffusion_ReshapeFusion': True, + + # MHA + 'TorchInsertBF16Node': False, + 'StableDiffusion_MHAReshape': True, + 'StableDiffusion_MHA': True, + 'ExplicitNHWCTransposeForConv': False, + 'ExplicitNHWCTransposeForConvQAT': True, + 'MultiHeadAttention': False, + + # QAT for the stable diffusion + 'StableDiffusion_InsertQuantNode': True, + 'StableDiffusion_CollectQuantInfo': True, + 'CollectQuantInfo': False, + 'InsertQuantNode': False, + 'QuantizeFusion': False, + 'StableDiffusion_QuantizeFusion': True, + + # Channel_last + 'ConvReshape': False + } +} + +vae_decoder_pattern_config = { + 'pattern_switch': { + # General Pattern + 'PaddingSequence': False, + 'AttentionReshape': False, + 'QKVReshape': False, + 'ReshapeFusion': False, + 'InsertBF16Node': False, + 'OperatorAdaptor': False, + + # transpose_int8 + 'QKVMerge': False, + + # 'TextEncoder + 'TextEncoder_WordEmbedding': False, + 'TextEncoder_QReshape': False, + 'TextEncoder_KVReshape': False, + 'TextEncoder_AttentionMaskAddReshape': False, + 'TextEncoder_SoftmaxReshape': False, + 'TextEncoder_MulReshape': False, + 'TextEncoder_AttentionReshape': False, + 'TextEncoder_CasualAttentionMask': False, + + # for unet and vae decoder + 'GroupNorm': True, + + # vae decoder & Transformer2Dmodel + 'AttentionBlock_Resize2Gather': True, + 'AttentionBlock_QKVPreReshape': True, + 'AttentionBlock_AttentionMaskAddReshape': True, + 'AttentionBlock_ConstantOfShapeWithMul': True, + + 'Transformer2Dmodel_GetSampleBatch': True, + 'Transformer2Dmodel_SampleSlice': True, + 'Transformer2Dmodel_EncoderHiddenStatesReshape': True, + 'Transformer2Dmodel_ConstantOfShapeWithMul': True, + 'Transformer2Dmodel_QKVPreReshape': True, + 'Transformer2Dmodel_QKVReshape': True, + 'AttentionBlock_QKVReshape': True, + 'Transformer2Dmodel_QKVReshapeTo4D': False, + 'Transformer2Dmodel_AttentionMaskAddReshape': True, + 'Transformer2Dmodel_FFNInputSlice': True, + 'Transformer2Dmodel_FFNInputSlice_1': True, + 'Transformer2DModel_UpBlockResize': True, + + # for all stable diffusion models + 'StableDiffusion_bf16Convert': True, + 'StableDiffusion_ReshapeFusion': True, + + # MHA + 'TorchInsertBF16Node': False, + 'StableDiffusion_MHAReshape': True, + 'StableDiffusion_MHA': False, + 'ExplicitNHWCTransposeForConv': True, + 'ExplicitNHWCTransposeForConvQAT': False, + 'MultiHeadAttention': False, + + # Channel_last + 'ConvReshape': False + } +} + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--onnx_model", default="./model", + type=str, help="onnx model path.") + parser.add_argument("--pattern_config", default="./", + type=str, help="pattern graph path.") + parser.add_argument("--output_path", default="./ir", + type=str, help="pattern graph path.") + parser.add_argument("--dtype", default="fp32", type=str) + args = parser.parse_args() + + if args.pattern_config == 'text_encoder': + args.pattern_config = text_encoder_pattern_config + if args.pattern_config == 'unet': + args.pattern_config = unet_pattern_config + if args.pattern_config == 'vae_decoder': + args.pattern_config = vae_decoder_pattern_config + + if args.dtype == "bf16": + args.pattern_config['pattern_switch']['StableDiffusion_MHA'] = True + with autocast(args.dtype): + graph = compile(args.onnx_model, args.pattern_config) + graph.save(args.output_path) + elif args.dtype == "dynamic_int8": + with autocast(args.dtype): + graph = compile(args.onnx_model, args.pattern_config) + graph.save(args.output_path) + elif args.dtype == "qat_int8": + args.pattern_config = qat_unet_pattern_config + with autocast(args.dtype): + graph = compile(args.onnx_model, args.pattern_config) + graph.save(args.output_path) + else: + graph = compile(args.onnx_model, args.pattern_config) + graph.save(args.output_path) diff --git a/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/export_model.sh b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/export_model.sh new file mode 100644 index 00000000000..d6b74e204d9 --- /dev/null +++ b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/export_model.sh @@ -0,0 +1,77 @@ +#!/bin/bash +# -*- coding: utf-8 -*- +# +# Copyright (c) 2023 Intel Corporation +# +# 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. + +for var in "$@" + do + case $var in + --input_model=*) + input_model=$(echo $var |cut -f2 -d=) + ;; + --precision=*) + precision=$(echo $var |cut -f2 -d=) + ;; + --cast_type=*) + cast_type=$(echo $var |cut -f2 -d=) + ;; + esac + done + +if [[ ${cast_type} == 'dynamic_int8' ]]; then + echo "[INFO] cast_type is dynamic int8 and model will be dynamic quantized based on $precision" +# 1. text encoder +echo "[INFO] Text encoder ir will be $precision ..." +echo "[INFO] Start to export text encoder ir..." +python export_ir.py --onnx_model=${input_model}/text_encoder_fp32/model.onnx --pattern_config=text_encoder --output_path=./${precision}_${cast_type}_ir/text_encoder/ --dtype=${precision} + +# 2. unet +echo "[INFO] Start to export unet ir..." +python export_ir.py --onnx_model=${input_model}/unet_fp32/model.onnx --pattern_config=unet --output_path=./${precision}_${cast_type}_ir/unet/ --dtype=${cast_type} + +# 3. vae_decoder +echo "[INFO] start to export vae_decoder ir..." +python export_ir.py --onnx_model=${input_model}/vae_decoder_fp32/model.onnx --pattern_config=vae_decoder --output_path=./${precision}_${cast_type}_ir/vae_decoder/ --dtype=${cast_type} +exit +fi + +if [[ ${precision} == 'qat_int8' ]]; then +cast_type=qat_int8 +echo "[INFO] cast_type is qat int8" +# 1. text encoder +echo "[INFO] Start to export text encoder bf16 ir..." +python export_ir.py --onnx_model=${input_model}/text_encoder_bf16/model.onnx --pattern_config=text_encoder --output_path=./${cast_type}_ir/text_encoder/ --dtype=bf16 + +# 2. unet +echo "[INFO] Start to export unet qat int8 ir..." +python export_ir.py --onnx_model=${input_model}/unet_${cast_type}/model.onnx --pattern_config=unet --output_path=./${cast_type}_ir/unet/ --dtype=${cast_type} + +# 3. vae_decoder +echo "[INFO] start to export vae_decoder bf16 ir..." +python export_ir.py --onnx_model=${input_model}/vae_decoder_bf16/model.onnx --pattern_config=vae_decoder --output_path=./${cast_type}_ir/vae_decoder/ --dtype=bf16 +exit +fi + +# 1. text encoder +echo "[INFO] Start to export text encoder ir..." +python export_ir.py --onnx_model=${input_model}/text_encoder_${precision}/model.onnx --pattern_config=text_encoder --output_path=./${precision}_ir/text_encoder/ --dtype=${precision} + +# 2. unet +echo "[INFO] Start to export unet ir..." +python export_ir.py --onnx_model=${input_model}/unet_${precision}/model.onnx --pattern_config=unet --output_path=./${precision}_ir/unet/ --dtype=${precision} + +# 3. vae_decoder +echo "[INFO] start to export vae_decoder ir..." +python export_ir.py --onnx_model=${input_model}/vae_decoder_${precision}/model.onnx --pattern_config=vae_decoder --output_path=./${precision}_ir/vae_decoder/ --dtype=${precision} diff --git a/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/images/astronaut_rides_horse.png b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/images/astronaut_rides_horse.png new file mode 100644 index 0000000000000000000000000000000000000000..0374ac3d663fc49e2d6b466389fc7bbcf16b9a58 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diff --git a/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/images/mountain.png b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/images/mountain.png
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diff --git a/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/prepare_model.py b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/prepare_model.py
new file mode 100644
index 00000000000..5977c186e01
--- /dev/null
+++ b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/prepare_model.py
@@ -0,0 +1,337 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2023 Intel Corporation
+#
+# 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.
+
+import argparse
+import os
+import shutil
+from pathlib import Path
+
+import torch
+import onnx
+from diffusers import StableDiffusionPipeline
+import shlex
+
+
+@torch.no_grad()
+def _export_bf16_onnx_model(fp32_model_path, bf16_model_path):
+    model = onnx.load(fp32_model_path)
+    bf16_type_list = ['MatMul', 'Gemm', 'Conv']
+    bf16_tensor_name_list = []
+    for node in model.graph.node:
+        if node.op_type in bf16_type_list:
+            for inp in node.input:
+                bf16_tensor_name_list.append(inp)
+    import numpy as np
+    from onnx import TensorProto, numpy_helper
+    for tensor in model.graph.initializer:
+        if tensor.name in bf16_tensor_name_list:
+
+            def fp32_to_bf16(fp32_np):
+                assert (fp32_np.dtype == np.float32)
+                int32_np = fp32_np.view(dtype=np.int32)
+                int32_np = int32_np >> 16
+                bf16_np = int32_np.astype(np.int16)
+                return bf16_np
+
+            fp16_data = fp32_to_bf16(numpy_helper.to_array(tensor))
+            tensor.raw_data = fp16_data.tobytes()
+            tensor.data_type = TensorProto.BFLOAT16
+    onnx.save(model, bf16_model_path)
+
+
+def prepare_model(
+    model_name: str,
+    output_path: Path,
+    opset: int,
+    expected_dtype: str,
+    fake_quant_model_qinit_path: str,
+    fake_quant_model_qinit_name: str
+):
+    device = 'cpu'
+    dtype = torch.float32
+    output_path = Path(output_path)
+    pipeline = StableDiffusionPipeline.from_pretrained(model_name, torch_dtype=dtype).to(device)
+
+    # TEXT ENCODER
+    num_tokens = pipeline.text_encoder.config.max_position_embeddings
+    text_hidden_size = pipeline.text_encoder.config.hidden_size
+    text_input = pipeline.tokenizer(
+        "A sample prompt",
+        padding="max_length",
+        max_length=pipeline.tokenizer.model_max_length,
+        truncation=True,
+        return_tensors="pt",
+    )
+
+    text_encoder = output_path / "text_encoder_fp32" / "model.onnx"
+    text_encoder.parent.mkdir(parents=True, exist_ok=True)
+
+    torch.onnx.export(
+        pipeline.text_encoder,
+        args=(text_input.input_ids.to(device=device, dtype=torch.int32)),
+        f=text_encoder.as_posix(),
+        input_names=["input_ids"],
+        output_names=["last_hidden_state", "pooler_output"],
+        dynamic_axes={
+            "input_ids": {
+                0: "batch",
+                1: "sequence"
+            },
+        },
+        do_constant_folding=True,
+        opset_version=opset,
+    )
+
+    if expected_dtype == 'bf16' or expected_dtype == 'qat_int8':
+        text_encoder_bf16 = output_path / "text_encoder_bf16" / "model.onnx"
+        text_encoder_bf16_dir = output_path / "text_encoder_bf16"
+        if os.path.exists(text_encoder_bf16_dir):
+            shutil.rmtree(text_encoder_bf16_dir)
+        os.mkdir(shlex.quote(text_encoder_bf16_dir.as_posix()))
+        _export_bf16_onnx_model(text_encoder.as_posix(), text_encoder_bf16.as_posix())
+
+    del pipeline.text_encoder
+
+    # UNET
+    if expected_dtype == 'qat_int8':
+        prepare_qat_model(model_name, output_path, fake_quant_model_qinit_path, fake_quant_model_qinit_name)
+
+    unet_in_channels = pipeline.unet.config.in_channels
+    unet_sample_size = pipeline.unet.config.sample_size
+    unet_path = output_path / "unet_fp32" / "model.onnx"
+    unet_path.parent.mkdir(parents=True, exist_ok=True)
+    torch.onnx.export(
+        pipeline.unet,
+        args=(
+            torch.randn(2, unet_in_channels, unet_sample_size, unet_sample_size).to(device=device,
+                                                                                    dtype=dtype),
+            torch.randn(2).to(device=device, dtype=dtype),
+            torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype),
+            False,
+        ),
+        f=unet_path.as_posix(),
+        input_names=["sample", "timestep", "encoder_hidden_states", "return_dict"],
+        output_names=["out_sample"],  # has to be different from "sample" for correct tracing
+        dynamic_axes={
+            "sample": {
+                0: "batch",
+                1: "channels",
+                2: "height",
+                3: "width"
+            },
+            "timestep": {
+                0: "batch"
+            },
+            "encoder_hidden_states": {
+                0: "batch",
+                1: "sequence"
+            },
+        },
+        do_constant_folding=True,
+        opset_version=opset,
+    )
+
+    unet_model_path = str(unet_path.absolute().as_posix())
+
+    if expected_dtype == 'bf16' or expected_dtype == 'qat_int8':
+        unet_bf16_model_path = output_path / "unet_bf16" / "model.onnx"
+        unet_bf16_dir = output_path / "unet_bf16"
+        if os.path.exists(unet_bf16_dir):
+            shutil.rmtree(unet_bf16_dir)
+        os.mkdir(shlex.quote(unet_bf16_dir.as_posix()))
+        _export_bf16_onnx_model(unet_path.as_posix(), unet_bf16_model_path.as_posix())
+        unet_bf16_model = onnx.load(unet_bf16_model_path)
+
+    unet_dir = os.path.dirname(unet_model_path)
+    unet = onnx.load(unet_model_path)
+    # clean up existing tensor files
+    shutil.rmtree(unet_dir)
+    os.mkdir(shlex.quote(unet_dir))
+    # collate external tensor files into one
+    onnx.save_model(
+        unet,
+        unet_model_path,
+        save_as_external_data=True,
+        all_tensors_to_one_file=True,
+        location="weights.pb",
+        convert_attribute=False,
+    )
+    if expected_dtype == 'bf16' or expected_dtype == 'qat_int8':
+        unet_bf16_model_path = str(unet_bf16_model_path.absolute().as_posix())
+        onnx.save_model(
+            unet_bf16_model,
+            unet_bf16_model_path,
+            save_as_external_data=True,
+            all_tensors_to_one_file=True,
+            location="weights.pb",
+        )
+    del pipeline.unet
+
+    # VAE DECODER
+    vae_decoder = pipeline.vae
+    vae_latent_channels = vae_decoder.config.latent_channels
+    # forward only through the decoder part
+    vae_decoder.forward = pipeline.vae.decode
+
+    vae_decoder_path = output_path / "vae_decoder_fp32" / "model.onnx"
+    vae_decoder_path.parent.mkdir(parents=True, exist_ok=True)
+
+    torch.onnx.export(
+        vae_decoder,
+        args=(
+            torch.randn(1, vae_latent_channels, unet_sample_size, unet_sample_size).to(device=device,
+                                                                                       dtype=dtype),
+            False,
+        ),
+        f=vae_decoder_path.as_posix(),
+        input_names=["latent_sample", "return_dict"],
+        output_names=["sample"],
+        dynamic_axes={
+            "latent_sample": {
+                0: "batch",
+                1: "channels",
+                2: "height",
+                3: "width"
+            },
+        },
+        do_constant_folding=True,
+        opset_version=opset,
+    )
+
+    if expected_dtype == 'bf16' or expected_dtype == 'qat_int8':
+        vae_decoder_bf16_model = output_path / "vae_decoder_bf16" / "model.onnx"
+        vae_decoder_bf16_dir = output_path / "vae_decoder_bf16"
+        if os.path.exists(vae_decoder_bf16_dir):
+            shutil.rmtree(vae_decoder_bf16_dir)
+        os.mkdir(shlex.quote(vae_decoder_bf16_dir.as_posix()))
+        _export_bf16_onnx_model(vae_decoder_path.as_posix(), vae_decoder_bf16_model.as_posix())
+    del pipeline.vae
+
+
+def prepare_qat_model(
+    model_name: str,
+    output_path: Path,
+    fake_quant_model_qinit_path: str = "./",
+    fake_quant_model_qinit_name: str = "fake_quant_model_qinit.pt"
+):
+    device = 'cpu'
+    output_path = Path(output_path)
+    pipeline = StableDiffusionPipeline.from_pretrained(model_name).to(device)
+    unet = pipeline.unet
+
+    from quantization_modules import find_and_replace, convert2quantized_model
+    find_and_replace(unet)
+    unet.load_state_dict(torch.load(os.path.join(fake_quant_model_qinit_path, fake_quant_model_qinit_name)))
+    unet = convert2quantized_model(unet)
+    unet.eval()
+    setattr(pipeline, "unet", unet)
+
+    onnx_model_path = output_path / "unet_qat_int8" / "model.onnx"
+    os.makedirs(os.path.dirname(onnx_model_path), exist_ok=True)
+    if os.path.exists(os.path.dirname(onnx_model_path)):
+        def model_wrapper(model_fn):
+        # export doesn't support a dictionary output, so manually turn it into a tuple
+        # refer to https://discuss.tvm.apache.org/t/how-to-deal-with-prim-dictconstruct/11978
+            def wrapper(*args, **kwargs):
+                output = model_fn(*args, **kwargs)
+                if isinstance(output, dict):
+                    return tuple(v for v in output.values() if v is not None)
+                else:
+                    return output
+            return wrapper
+        unet.forward = model_wrapper(unet.forward)
+        
+        torch.onnx.export(
+            unet,
+            args=(
+                torch.randn(2, 4, 64, 64).to(device=device,dtype=torch.float32),
+                torch.randn(2).to(device=device, dtype=torch.float32),
+                torch.randn(2, 77, 768).to(device=device, dtype=torch.float32),
+            ),
+            f=onnx_model_path,
+            input_names=["sample", "timestep", "encoder_hidden_states"],# "return_dict"],
+            output_names=["out_sample"],  # has to be different from "sample" for correct tracing
+            dynamic_axes={
+                "sample": {
+                    0: "batch",
+                    1: "channels",
+                    2: "height",
+                    3: "width"
+                },
+                "timestep": {
+                    0: "batch"
+                },
+                "encoder_hidden_states": {
+                    0: "batch",
+                    1: "sequence"
+                },
+            },
+            do_constant_folding=True,
+            opset_version=14,
+        )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--input_model",
+        type=str,
+        required=True,
+        help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
+    )
+    parser.add_argument('--pattern_config',
+                        default="./pattern_config",
+                        type=str,
+                        help="The fusion pattern config path for the nerual engine.")
+    parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
+    parser.add_argument(
+        "--opset",
+        default=14,
+        type=int,
+        help="The version of the ONNX operator set to use.",
+    )
+    parser.add_argument("--bf16", action="store_true", help="Export the models in `bfloat16` mode")
+    parser.add_argument("--qat_int8", action="store_true", help="Export the models in `bfloat16` mode")
+    parser.add_argument(
+        "--fake_quant_model_qinit_path",
+        type=str,
+        default="./",
+        help="Path to the fake_quant_model_qinit",
+    )
+    parser.add_argument(
+        "--fake_quant_model_qinit_name",
+        type=str,
+        default="fake_quant_model_qinit.pt",
+        help="Name of the fake_quant_model_qinit",
+    )
+
+    args = parser.parse_args()
+
+    expected_dtype = 'fp32'
+    if args.bf16:
+        expected_dtype = 'bf16'
+    elif args.qat_int8:
+        expected_dtype = 'qat_int8'
+
+    prepare_model(
+        args.input_model,
+        args.output_path,
+        args.opset,
+        expected_dtype,
+        args.fake_quant_model_qinit_path,
+        args.fake_quant_model_qinit_name
+    )
diff --git a/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/quantization_modules.py b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/quantization_modules.py
new file mode 100644
index 00000000000..c50b20b12b3
--- /dev/null
+++ b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/quantization_modules.py
@@ -0,0 +1,146 @@
+import torch
+import torch.nn.functional as F
+from torch.ao.quantization import FakeQuantize, FakeQuantizeBase, default_fake_quant, default_per_channel_weight_fake_quant, default_fused_act_fake_quant, default_fused_per_channel_wt_fake_quant
+from torch.ao.nn.quantized import Linear, Conv2d, Quantize
+try:
+    from peft.tuners.lora import Linear as LoRALinear
+    from peft.utils import transpose
+    peft_available = True
+except:
+    peft_available = False
+
+ACT_REDUCE_RANGE = False
+WEIGHT_REDUCE_RANGE = False
+
+
+class QuantizedLinear(Linear):
+    def forward(self, x):
+        return super().forward(
+            self.input_quant(x)
+        ).dequantize()
+
+
+class FakeQuantLinear(torch.nn.Linear):
+    def __init__(self, module: torch.nn.Linear):
+        self.__dict__.update(module.__dict__.copy())
+        self.add_module('activation_pre_process', default_fake_quant(reduce_range=ACT_REDUCE_RANGE))
+        self.add_module('weight_fake_quant', default_per_channel_weight_fake_quant(reduce_range=WEIGHT_REDUCE_RANGE))
+        self.add_module('activation_post_process', default_fake_quant(reduce_range=ACT_REDUCE_RANGE))
+        self.is_lora_layer = True if peft_available and isinstance(module, LoRALinear) else False
+
+    def forward(self, x):
+        x = self.activation_pre_process(x)
+        weight = self.weight
+        if self.is_lora_layer and not self.disable_adapters and self.r[self.active_adapter] > 0:
+            lora_weight = transpose(
+                    self.lora_B[self.active_adapter].weight @ self.lora_A[self.active_adapter].weight,
+                    self.fan_in_fan_out,
+                ) * self.scaling[self.active_adapter]
+            weight = weight + lora_weight
+        x = F.linear(x, self.weight_fake_quant(weight), self.bias)
+        x = self.activation_post_process(x)
+        return x
+
+    def convert(self):
+        if self.is_lora_layer and not self.disable_adapters and self.r[self.active_adapter] > 0:
+            lora_weight = transpose(
+                    self.lora_B[self.active_adapter].weight @ self.lora_A[self.active_adapter].weight,
+                    self.fan_in_fan_out,
+                ) * self.scaling[self.active_adapter]
+            self.weight.data += lora_weight.data
+        module = QuantizedLinear.from_float(self)
+        input_quant = torch.quantization.QuantStub()
+        input_quant.add_module('activation_post_process', self.activation_pre_process)
+        input_quant = Quantize.from_float(input_quant)
+        module.add_module('input_quant', input_quant)
+        return module
+
+
+class QuantizedConv2d(Conv2d):
+    def forward(self, x):
+        return super().forward(
+            self.input_quant(x)
+        ).dequantize()
+
+
+class FakeQuantConv2d(torch.nn.Conv2d):
+    def __init__(self, module: torch.nn.Conv2d):
+        self.__dict__.update(module.__dict__.copy())
+        self.add_module('activation_pre_process', default_fake_quant(reduce_range=ACT_REDUCE_RANGE))
+        self.add_module('weight_fake_quant', default_per_channel_weight_fake_quant(reduce_range=WEIGHT_REDUCE_RANGE))
+        self.add_module('activation_post_process', default_fake_quant(reduce_range=ACT_REDUCE_RANGE))
+
+    def forward(self, x):
+        x = self.activation_pre_process(x)
+        x = self._conv_forward(x, self.weight_fake_quant(self.weight), self.bias)
+        x = self.activation_post_process(x)
+        return x
+
+    def convert(self):
+        module = QuantizedConv2d.from_float(self)
+        input_quant = torch.quantization.QuantStub()
+        input_quant.add_module('activation_post_process', self.activation_pre_process)
+        input_quant = Quantize.from_float(input_quant)
+        module.add_module('input_quant', input_quant)
+        return module
+
+
+def get_submodules(model, key):
+    parent = model.get_submodule(".".join(key.split(".")[:-1]))
+    target_name = key.split(".")[-1]
+    target = model.get_submodule(key)
+    return parent, target, target_name
+
+def find_and_replace(model, fake_quant=True):
+    assert isinstance(model, torch.nn.Module), "Only support torch Module."
+    key_list = [key for key, _ in model.named_modules()]
+    for key in key_list:
+        try:
+            parent, target, target_name = get_submodules(model, key)
+        except:
+            continue
+        if fake_quant:
+            if isinstance(target, torch.nn.Linear):
+                setattr(parent, target_name, FakeQuantLinear(target))
+            elif isinstance(target, torch.nn.Conv2d):
+                setattr(parent, target_name, FakeQuantConv2d(target))
+        else:
+            if isinstance(target, FakeQuantLinear):
+                setattr(parent, target_name, target.convert())
+            elif isinstance(target, FakeQuantConv2d):
+                setattr(parent, target_name, target.convert())
+
+def convert2quantized_model(model):
+    model.to(torch.device("cpu"))
+    find_and_replace(model, fake_quant=False)
+    return model
+
+def disable_all_observers(model):
+    assert isinstance(model, torch.nn.Module), "Only support torch Module."
+    for name, module in model.named_modules():
+        if isinstance(module, FakeQuantizeBase):
+            module.disable_observer()
+
+def sync_all_observers(model):
+    assert isinstance(model, torch.nn.Module), "Only support torch Module."
+    for name, module in model.named_modules():
+        if isinstance(module, FakeQuantize):
+            _scale, _zero_point = module.calculate_qparams()
+            _scale, _zero_point = _scale.to(module.scale.device), _zero_point.to(module.zero_point.device)
+            if module.scale.shape != _scale.shape:
+                module.scale.resize_(_scale.shape)
+                module.zero_point.resize_(_zero_point.shape)
+            module.scale.copy_(_scale)
+            module.zero_point.copy_(_zero_point)
+
+def load_int8_model(fp32_model, int8_model_path, fake_quantize_model=False):
+    find_and_replace(fp32_model)
+    if fake_quantize_model:
+        fp32_model.load_state_dict(torch.load(int8_model_path))
+        disable_all_observers(fp32_model)
+        sync_all_observers(fp32_model)
+    int8_model = convert2quantized_model(fp32_model)
+    print('Converted to quantized model.')
+    if not fake_quantize_model:
+        int8_model.load_state_dict(torch.load(int8_model_path))
+    return int8_model
\ No newline at end of file
diff --git a/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/requirements.txt b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/requirements.txt
new file mode 100644
index 00000000000..7295f511f4f
--- /dev/null
+++ b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/requirements.txt
@@ -0,0 +1,12 @@
+neural-compressor
+transformers
+accelerate
+datasets >= 1.8.0
+sentencepiece != 0.1.92
+protobuf
+torch==2.3.0
+onnx>=1.12
+onnxruntime==1.13.1
+diffusers==0.12.1
+pytorch_fid
+optimum
\ No newline at end of file
diff --git a/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/run_executor.py b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/run_executor.py
new file mode 100644
index 00000000000..dbb7fd99e41
--- /dev/null
+++ b/examples/huggingface/pytorch/image-classification/deployment/stable_diffusion/run_executor.py
@@ -0,0 +1,236 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2023 Intel Corporation
+#
+# 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.
+
+import argparse
+
+import diffusion_utils
+import torch
+import time
+from pytorch_fid import fid_score
+from diffusers import DPMSolverMultistepScheduler
+import os
+
+
+def benchmark(pipe, neural_engine_graph, generator, steps=20):
+    print('Benchmark start...')
+    warmup = 4
+    total = 8
+    total_time = 0
+    with torch.no_grad():
+        prompt = "a photo of an astronaut riding a horse on mars"
+        for i in range(total):
+            start2 = time.time()
+            pipe(prompt,
+                 engine_graph=neural_engine_graph,
+                 num_inference_steps=steps,
+                 generator=generator).images[0]
+            end2 = time.time()
+            if i >= warmup:
+                total_time += end2 - start2
+            print("Total inference latency: ", str(end2 - start2) + "s")
+    print("Average Latency: ", (total_time) / (total - warmup), "s")
+    print("Average Throughput: {:.5f} samples/sec".format(
+        (total - warmup) / (total_time)))
+
+
+def accuracy(pipe, original_pipe, neural_engine_graph, generator):
+    with torch.no_grad():
+        prompt = "a photo of an astronaut riding a horse on mars"
+
+        save_time = time.strftime("_%H_%M_%S")
+        # Engine
+        engine_image = pipe(prompt,
+                            engine_graph=neural_engine_graph,
+                            generator=generator).images[0]
+        engine_image.save("astronaut_rides_horse_from_engine" + save_time +
+                          '.png')
+
+        engine_image_dir = "engine_image"
+        os.makedirs(engine_image_dir, exist_ok=True)
+        if os.path.isfile(
+                os.path.join(engine_image_dir, "astronaut_rides_horse.png")):
+            os.remove(
+                os.path.join(engine_image_dir, "astronaut_rides_horse.png"))
+        engine_image.save(engine_image_dir + "/astronaut_rides_horse.png")
+
+        # Pytorch
+        pytorch_image = original_pipe(prompt, generator=generator).images[0]
+        pytorch_image.save("astronaut_rides_horse_from_pytorch" + save_time +
+                           '.png')
+
+        pytorch_image_dir = "pytorch_image"
+        os.makedirs(pytorch_image_dir, exist_ok=True)
+        if os.path.isfile(
+                os.path.join(pytorch_image_dir, "astronaut_rides_horse.png")):
+            os.remove(
+                os.path.join(pytorch_image_dir, "astronaut_rides_horse.png"))
+        pytorch_image.save(pytorch_image_dir + "/astronaut_rides_horse.png")
+
+        fid = fid_score.calculate_fid_given_paths(
+            (pytorch_image_dir, engine_image_dir), 1, "cpu", 2048, 2)
+        print("Finally FID score Accuracy: {}".format(fid))
+        return fid
+
+
+def executor(pipe, neural_engine_graph, prompt, name, size, generator):
+    print('Executor start...')
+    for i in range(size):
+        save_time = time.strftime("_%H_%M_%S")
+        image = pipe(prompt,
+                     engine_graph=neural_engine_graph,
+                     generator=generator).images[0]
+        image.save(name + str(i) + save_time + '.png')
+    return
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--input_model",
+        default="runwayml/stable-diffusion-v1-5",
+        type=str,
+        help=
+        "Path to pretrained model or model identifier from huggingface.co/models."
+    )
+    parser.add_argument(
+        "--prompt",
+        default="a photo of an astronaut riding a horse on mars",
+        type=str,
+        help=
+        "The input of the model, like: 'a photo of an astronaut riding a horse on mars'."
+    )
+    parser.add_argument("--ir_path",
+                        default="./ir",
+                        type=str,
+                        help="Neural engine IR path.")
+    parser.add_argument("--name",
+                        default="output_image",
+                        type=str,
+                        help="output image name.")
+    parser.add_argument("--mode",
+                        type=str,
+                        help="Benchmark mode of latency or accuracy.")
+    parser.add_argument("--seed", type=int, default=666, help="random seed")
+    parser.add_argument("--steps",
+                        type=int,
+                        default=20,
+                        help="denoising steps")
+    parser.add_argument("--size",
+                        type=int,
+                        default=1,
+                        help="the number of output images per prompt")
+
+    # for img2img
+    parser.add_argument("--pipeline",
+                        default="text2img",
+                        type=str,
+                        help="text2img or img2img pipeline.")
+    parser.add_argument("--prompts",
+                        type=str,
+                        default="Cartoonize the following image",
+                        help="prompts for img2img")
+    parser.add_argument(
+        "--image",
+        type=str,
+        default=
+        "https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png",
+        help="the original image for the img2img pipeline")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    neural_engine_graph = diffusion_utils.neural_engine_init(args.ir_path)
+    if args.pipeline == "text2img":
+        dpm = DPMSolverMultistepScheduler.from_pretrained(
+            args.input_model, subfolder="scheduler")
+        pipe = diffusion_utils.StableDiffusionPipeline.from_pretrained(
+            args.input_model, scheduler=dpm)
+        pipe.safety_checker = lambda images, clip_input: (images, False)
+        generator = torch.Generator("cpu").manual_seed(args.seed)
+        if args.mode == "latency":
+            benchmark(pipe, neural_engine_graph, generator, args.steps)
+            return
+
+        if args.mode == "accuracy":
+            from diffusers import StableDiffusionPipeline
+            original_pipe = StableDiffusionPipeline.from_pretrained(
+                args.input_model)
+            accuracy(pipe, original_pipe, neural_engine_graph, generator)
+            return
+
+        executor(pipe, neural_engine_graph, args.prompt, args.name, args.size,
+                 generator)
+
+    if args.pipeline == "img2img":
+        from diffusion_utils_img2img import StableDiffusionImg2ImgPipeline
+        import requests
+        from PIL import Image
+        from io import BytesIO
+        pipe = StableDiffusionImg2ImgPipeline.from_pretrained(args.input_model)
+        url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
+        response = requests.get(url)
+        init_image = Image.open(BytesIO(response.content)).convert("RGB")
+        init_image = init_image.resize((768, 512))
+
+        prompt = "A fantasy landscape, trending on artstation"
+        images = pipe(prompt=prompt,
+                      image=init_image,
+                      engine_graph=neural_engine_graph,
+                      strength=0.75,
+                      guidance_scale=7.5).images
+        images[0].save("fantasy_landscape.png")
+
+    if args.pipeline == "instruction-tuning-sd":
+        """
+            # officical Example: https://huggingface.co/instruction-tuning-sd/cartoonizer
+            import torch
+            from diffusers import StableDiffusionInstructPix2PixPipeline
+            from diffusers.utils import load_image
+
+            model_id = "instruction-tuning-sd/cartoonizer"
+            pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(
+                model_id, torch_dtype=torch.float16, use_auth_token=True
+            ).to("cuda")
+
+            image_path = "https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png"
+            image = load_image(image_path)
+
+            image = pipeline("Cartoonize the following image", image=image).images[0]
+            image.save("image.png")
+        """
+        from ITREX_StableDiffusionInstructPix2PixPipeline import StableDiffusionInstructPix2PixPipeline
+        from diffusers.utils import load_image
+        pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(
+            args.input_model, torch_dtype=torch.float32, use_auth_token=True)
+
+        image_path = args.image
+        image = load_image(image_path)
+        image = image.resize((512, 512))
+
+        image = pipeline(args.prompts,
+                         image=image,
+                         engine_graph=neural_engine_graph,
+                         num_inference_steps=args.steps).images[0]
+        save_time = time.strftime("_%H_%M_%S")
+        image.save("image" + save_time + '.png')
+
+    return
+
+
+if __name__ == '__main__':
+    main()
diff --git a/examples/huggingface/pytorch/question-answering/deployment/squad/bert_large/run_bert_large.sh b/examples/huggingface/pytorch/question-answering/deployment/squad/bert_large/run_bert_large.sh
index 017b7a2efd3..5c65ca50e0b 100644
--- a/examples/huggingface/pytorch/question-answering/deployment/squad/bert_large/run_bert_large.sh
+++ b/examples/huggingface/pytorch/question-answering/deployment/squad/bert_large/run_bert_large.sh
@@ -123,7 +123,7 @@ else
     echo "==========    Prepare Model ${MODEL_NAME_OR_PATH} with Precision ${PRECISION} ========"
     mode_cmd=""
     if [[ ${PRECISION} = 'int8' ]]; then
-        mode_cmd=$mode_cmd" --tune --quantization_approach static"
+        mode_cmd=$mode_cmd" --tune --quantization_approach PostTrainingStatic"
     elif [[ ${PRECISION} = 'bf16' ]]; then
         mode_cmd=$mode_cmd" --enable_bf16"
     fi
diff --git a/examples/huggingface/pytorch/question-answering/deployment/squad/bert_large/run_qa.py b/examples/huggingface/pytorch/question-answering/deployment/squad/bert_large/run_qa.py
index 39bb8c074ac..4ce35df134d 100644
--- a/examples/huggingface/pytorch/question-answering/deployment/squad/bert_large/run_qa.py
+++ b/examples/huggingface/pytorch/question-answering/deployment/squad/bert_large/run_qa.py
@@ -26,13 +26,7 @@
 import transformers
 from dataclasses import dataclass, field
 from datasets import load_dataset, load_metric
-from intel_extension_for_transformers.transformers import metrics , OptimizedModel
-from neural_compressor.config import (
-    PostTrainingQuantConfig,
-    QuantizationAwareTrainingConfig,
-    TuningCriterion,
-    AccuracyCriterion
-)
+from intel_extension_for_transformers.transformers import metrics , OptimizedModel, QuantizationConfig
 from trainer_qa import QuestionAnsweringTrainer
 from transformers import (
     AutoConfig,
@@ -217,9 +211,9 @@ class OptimizationArguments:
         metadata={"help": "Whether or not to apply quantization."},
     )
     quantization_approach: Optional[str] = field(
-        default="static",
-        metadata={"help": "Quantization approach. Supported approach are static, "
-                  "dynamic and qat."},
+        default="PostTrainingStatic",
+        metadata={"help": "Quantization approach. Supported approach are PostTrainingStatic, "
+                  "PostTrainingDynamic and QuantizationAwareTraining."},
     )
     metric_name: Optional[str] = field(
         default="eval_f1",
@@ -652,43 +646,25 @@ def compute_metrics(p: EvalPrediction):
         if not training_args.do_eval:
             raise ValueError("do_eval must be set to True for quantization.")
 
-        if optim_args.quantization_approach != "dynamic":
+        if optim_args.quantization_approach != "PostTrainingDynamic":
             if not training_args.do_train:
                 raise ValueError(
                     "do_train must be set to True for static and aware training quantization."
                 )
+        if optim_args.quantization_approach == "QuantizationAwareTraining":
+            early_stopping_patience = 6
+            early_stopping_threshold = 0.001 # optional
+            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience,
+                                                                    early_stopping_threshold))
 
         tune_metric = metrics.Metric(
             name=metric_name, is_relative=optim_args.is_relative, criterion=optim_args.perf_tol
         )
-        trainer.metrics = tune_metric
-        if optim_args.quantization_approach != "qat":
-            tuning_criterion = TuningCriterion(max_trials=600)
-            accuracy_criterion = AccuracyCriterion(
-                higher_is_better=False,  # optional.
-                criterion="relative" if optim_args.is_relative else "absolute",  # optional. Available values are "relative" and "absolute".
-                tolerable_loss=optim_args.perf_tol,  # optional.
-            )
-            quantization_config = PostTrainingQuantConfig(
-                approach=optim_args.quantization_approach,
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )
-        else:
-            tuning_criterion = TuningCriterion(max_trials=600)
-            accuracy_criterion = AccuracyCriterion(
-                higher_is_better=False,  # optional.
-                criterion="relative" if optim_args.is_relative else "absolute",  # optional. Available values are "relative" and "absolute".
-                tolerable_loss=optim_args.perf_tol,  # optional.
-            )
-            quantization_config = QuantizationAwareTrainingConfig(
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )
-            early_stopping_patience = 2
-            early_stopping_threshold = 0.001 # optional
-            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience, \
-                                                                    early_stopping_threshold))
+        quantization_config = QuantizationConfig(
+            approach=optim_args.quantization_approach,
+            max_trials=200,
+            metrics=[tune_metric],
+        )
         model = trainer.quantize(quant_config=quantization_config)
 
     if optim_args.benchmark or optim_args.accuracy_only:
@@ -698,7 +674,7 @@ def compute_metrics(p: EvalPrediction):
         max_eval_samples = data_args.max_eval_samples \
             if data_args.max_eval_samples is not None else len(eval_dataset)
         eval_samples = min(max_eval_samples, len(eval_dataset))
-        samples = eval_samples - (eval_samples % training_args.per_device_eval_batch_size) \
+        samples = eval_samples - (eval_samples % batch_size) \
             if training_args.dataloader_drop_last else eval_samples
         logger.info("metrics keys: {}".format(results.keys()))
         bert_task_acc_keys = ['eval_f1', 'eval_accuracy', 'eval_matthews_correlation',
diff --git a/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/bert_large/run_bert_large.sh b/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/bert_large/run_bert_large.sh
index 0d93e12079b..7f3253746ab 100644
--- a/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/bert_large/run_bert_large.sh
+++ b/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/bert_large/run_bert_large.sh
@@ -121,7 +121,7 @@ else
     echo "==========    Prepare Model ${MODEL_NAME_OR_PATH} with Precision ${PRECISION} ========"
     mode_cmd=""
     if [[ ${PRECISION} = 'int8' ]]; then
-        mode_cmd=$mode_cmd" --tune --quantization_approach static"
+        mode_cmd=$mode_cmd" --tune --quantization_approach PostTrainingStatic"
     elif [[ ${PRECISION} = 'fp32' ]]; then
         mode_cmd=$mode_cmd" --fp32"
     fi
diff --git a/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/bert_large/run_qa.py b/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/bert_large/run_qa.py
index 46122402c6e..d34418e0dd8 100644
--- a/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/bert_large/run_qa.py
+++ b/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/bert_large/run_qa.py
@@ -26,8 +26,7 @@
 import transformers
 from dataclasses import dataclass, field
 from datasets import load_dataset, load_metric
-from intel_extension_for_transformers.transformers import metrics , OptimizedModel
-from neural_compressor.config import PostTrainingQuantConfig
+from intel_extension_for_transformers.transformers import metrics , OptimizedModel, QuantizationConfig
 from trainer_qa import QuestionAnsweringTrainer
 from transformers import (
     AutoConfig,
@@ -212,9 +211,9 @@ class OptimizationArguments:
         metadata={"help": "Whether or not to apply quantization."},
     )
     quantization_approach: Optional[str] = field(
-        default="static",
-        metadata={"help": "Quantization approach. Supported approach are static, "
-                  "dynamic."},
+        default="PostTrainingStatic",
+        metadata={"help": "Quantization approach. Supported approach are PostTrainingStatic, "
+                  "PostTrainingDynamic and QuantizationAwareTraining."},
     )
     metric_name: Optional[str] = field(
         default="eval_f1",
@@ -641,21 +640,27 @@ def compute_metrics(p: EvalPrediction):
 
         trainer.save_model(training_args.output_dir)
         trainer.calib_dataloader = trainer.get_eval_dataloader()
-        if optim_args.quantization_approach != "dynamic":
+        if optim_args.quantization_approach != "PostTrainingDynamic":
             if not training_args.do_train:
                 raise ValueError(
                     "do_train must be set to True for static and aware training quantization."
                 )
+        if optim_args.quantization_approach == "QuantizationAwareTraining":
+            early_stopping_patience = 6
+            early_stopping_threshold = 0.001 # optional
+            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience,
+                                                                    early_stopping_threshold))
 
         tune_metric = metrics.Metric(
             name=metric_name, is_relative=optim_args.is_relative, criterion=optim_args.perf_tol
         )
-        trainer.metrics = tune_metric
-        quantization_config = PostTrainingQuantConfig(
-            backend="ipex",
+        quantization_config = QuantizationConfig(
             approach=optim_args.quantization_approach,
-            excluded_precisions=["bf16"]
+            max_trials=200,
+            metrics=[tune_metric],
+            use_bf16=False
         )
+        quantization_config.framework = "pytorch_ipex"
         model = trainer.quantize(quant_config=quantization_config)
 
     if optim_args.benchmark or optim_args.accuracy_only:
diff --git a/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/distilbert_base_uncased/run_distilbert.sh b/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/distilbert_base_uncased/run_distilbert.sh
index d0d2bbb9db7..1c0ca172eba 100644
--- a/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/distilbert_base_uncased/run_distilbert.sh
+++ b/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/distilbert_base_uncased/run_distilbert.sh
@@ -121,7 +121,7 @@ else
     echo "==========    Prepare Model ${MODEL_NAME_OR_PATH} with Precision ${PRECISION} ========"
     mode_cmd=""
     if [[ ${PRECISION} = 'int8' ]]; then
-        mode_cmd=$mode_cmd" --tune --quantization_approach static"
+        mode_cmd=$mode_cmd" --tune --quantization_approach PostTrainingStatic"
     elif [[ ${PRECISION} = 'fp32' ]]; then
         mode_cmd=$mode_cmd" --fp32"
     fi
diff --git a/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/distilbert_base_uncased/run_qa.py b/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/distilbert_base_uncased/run_qa.py
index 46122402c6e..9417cf4f65f 100644
--- a/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/distilbert_base_uncased/run_qa.py
+++ b/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/distilbert_base_uncased/run_qa.py
@@ -26,8 +26,7 @@
 import transformers
 from dataclasses import dataclass, field
 from datasets import load_dataset, load_metric
-from intel_extension_for_transformers.transformers import metrics , OptimizedModel
-from neural_compressor.config import PostTrainingQuantConfig
+from intel_extension_for_transformers.transformers import metrics , OptimizedModel, QuantizationConfig
 from trainer_qa import QuestionAnsweringTrainer
 from transformers import (
     AutoConfig,
@@ -212,9 +211,9 @@ class OptimizationArguments:
         metadata={"help": "Whether or not to apply quantization."},
     )
     quantization_approach: Optional[str] = field(
-        default="static",
-        metadata={"help": "Quantization approach. Supported approach are static, "
-                  "dynamic."},
+        default="PostTrainingStatic",
+        metadata={"help": "Quantization approach. Supported approach are PostTrainingStatic, "
+                  "PostTrainingDynamic and QuantizationAwareTraining."},
     )
     metric_name: Optional[str] = field(
         default="eval_f1",
@@ -641,21 +640,28 @@ def compute_metrics(p: EvalPrediction):
 
         trainer.save_model(training_args.output_dir)
         trainer.calib_dataloader = trainer.get_eval_dataloader()
-        if optim_args.quantization_approach != "dynamic":
+        if optim_args.quantization_approach != "PostTrainingDynamic":
             if not training_args.do_train:
                 raise ValueError(
                     "do_train must be set to True for static and aware training quantization."
                 )
+            
+        elif optim_args.quantization_approach == "QuantizationAwareTraining":
+            early_stopping_patience = 6
+            early_stopping_threshold = 0.001 # optional
+            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience,
+                                                                    early_stopping_threshold))
 
         tune_metric = metrics.Metric(
             name=metric_name, is_relative=optim_args.is_relative, criterion=optim_args.perf_tol
         )
-        trainer.metrics = tune_metric
-        quantization_config = PostTrainingQuantConfig(
-            backend="ipex",
+        quantization_config = QuantizationConfig(
             approach=optim_args.quantization_approach,
-            excluded_precisions=["bf16"]
+            max_trials=200,
+            metrics=[tune_metric],
+            use_bf16=False
         )
+        quantization_config.framework = "pytorch_ipex"
         model = trainer.quantize(quant_config=quantization_config)
 
     if optim_args.benchmark or optim_args.accuracy_only:
diff --git a/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/distilbert_base_uncased_sparse/run_distilbert_sparse.sh b/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/distilbert_base_uncased_sparse/run_distilbert_sparse.sh
index 7621827eef9..3e82836a0d2 100644
--- a/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/distilbert_base_uncased_sparse/run_distilbert_sparse.sh
+++ b/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/distilbert_base_uncased_sparse/run_distilbert_sparse.sh
@@ -121,7 +121,7 @@ else
     echo "==========    Prepare Model ${MODEL_NAME_OR_PATH} with Precision ${PRECISION} ========"
     mode_cmd=""
     if [[ ${PRECISION} = 'int8' ]]; then
-        mode_cmd=$mode_cmd" --tune --quantization_approach static"
+        mode_cmd=$mode_cmd" --tune --quantization_approach PostTrainingStatic"
     elif [[ ${PRECISION} = 'fp32' ]]; then
         mode_cmd=$mode_cmd" --fp32"
     fi
diff --git a/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/distilbert_base_uncased_sparse/run_qa.py b/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/distilbert_base_uncased_sparse/run_qa.py
index 1c250201c9d..25c51b01192 100644
--- a/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/distilbert_base_uncased_sparse/run_qa.py
+++ b/examples/huggingface/pytorch/question-answering/deployment/squad/ipex/distilbert_base_uncased_sparse/run_qa.py
@@ -26,8 +26,7 @@
 import transformers
 from dataclasses import dataclass, field
 from datasets import load_dataset, load_metric
-from intel_extension_for_transformers.transformers import metrics , OptimizedModel
-from neural_compressor.config import PostTrainingQuantConfig
+from intel_extension_for_transformers.transformers import metrics , OptimizedModel, QuantizationConfig
 from trainer_qa import QuestionAnsweringTrainer
 from transformers import (
     AutoConfig,
@@ -212,9 +211,9 @@ class OptimizationArguments:
         metadata={"help": "Whether or not to apply quantization."},
     )
     quantization_approach: Optional[str] = field(
-        default="static",
-        metadata={"help": "Quantization approach. Supported approach are static, "
-                  "dynamic."},
+        default="PostTrainingStatic",
+        metadata={"help": "Quantization approach. Supported approach are PostTrainingStatic, "
+                  "PostTrainingDynamic and QuantizationAwareTraining."},
     )
     metric_name: Optional[str] = field(
         default="eval_f1",
@@ -641,21 +640,27 @@ def compute_metrics(p: EvalPrediction):
 
         trainer.save_model(training_args.output_dir)
         trainer.calib_dataloader = trainer.get_eval_dataloader()
-        if optim_args.quantization_approach != "dynamic":
+        if optim_args.quantization_approach != "PostTrainingDynamic":
             if not training_args.do_train:
                 raise ValueError(
                     "do_train must be set to True for static and aware training quantization."
                 )
+        elif optim_args.quantization_approach == "QuantizationAwareTraining":
+            early_stopping_patience = 6
+            early_stopping_threshold = 0.001 # optional
+            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience,
+                                                                    early_stopping_threshold))
 
         tune_metric = metrics.Metric(
             name=metric_name, is_relative=optim_args.is_relative, criterion=optim_args.perf_tol
         )
-        trainer.metrics = tune_metric
-        quantization_config = PostTrainingQuantConfig(
-            backend="ipex",
+        quantization_config = QuantizationConfig(
             approach=optim_args.quantization_approach,
-            excluded_precisions=["bf16"],
+            max_trials=200,
+            metrics=[tune_metric],
+            use_bf16=False
         )
+        quantization_config.framework = "pytorch_ipex"
         model = trainer.quantize(quant_config=quantization_config)
 
     if optim_args.benchmark or optim_args.accuracy_only:
diff --git a/examples/huggingface/pytorch/question-answering/deployment/squad/length_adaptive_transformer/README.md b/examples/huggingface/pytorch/question-answering/deployment/squad/length_adaptive_transformer/README.md
index 98f9050cdc5..d7082e1194a 100644
--- a/examples/huggingface/pytorch/question-answering/deployment/squad/length_adaptive_transformer/README.md
+++ b/examples/huggingface/pytorch/question-answering/deployment/squad/length_adaptive_transformer/README.md
@@ -76,7 +76,7 @@ python run_qa.py --model_name_or_path "sguskin/dynamic-minilmv2-L6-H384-squad1.1
 
 For INT8:
 ```shell
-python run_qa.py --model_name_or_path "sguskin/dynamic-minilmv2-L6-H384-squad1.1" --dataset_name squad --do_train --do_eval --output_dir model_and_tokenizer --overwrite_output_dir --length_config "(269, 253, 252, 202, 104, 34)" --overwrite_cache --to_onnx --tune --quantization_approach static
+python run_qa.py --model_name_or_path "sguskin/dynamic-minilmv2-L6-H384-squad1.1" --dataset_name squad --do_train --do_eval --output_dir model_and_tokenizer --overwrite_output_dir --length_config "(269, 253, 252, 202, 104, 34)" --overwrite_cache --to_onnx --tune --quantization_approach PostTrainingStatic
 ```
 
 For BF16:
diff --git a/examples/huggingface/pytorch/question-answering/deployment/squad/length_adaptive_transformer/run_LAT.sh b/examples/huggingface/pytorch/question-answering/deployment/squad/length_adaptive_transformer/run_LAT.sh
index a2119fd1dc3..163c8894cf7 100644
--- a/examples/huggingface/pytorch/question-answering/deployment/squad/length_adaptive_transformer/run_LAT.sh
+++ b/examples/huggingface/pytorch/question-answering/deployment/squad/length_adaptive_transformer/run_LAT.sh
@@ -123,7 +123,7 @@ else
     echo "==========    Prepare Model ${MODEL_NAME_OR_PATH} with Precision ${PRECISION} ========"
     mode_cmd=""
     if [[ ${PRECISION} = 'int8' ]]; then
-        mode_cmd=$mode_cmd" --tune --quantization_approach static"
+        mode_cmd=$mode_cmd" --tune --quantization_approach PostTrainingStatic"
     elif [[ ${PRECISION} = 'bf16' ]]; then
         mode_cmd=$mode_cmd" --enable_bf16"
     fi
diff --git a/examples/huggingface/pytorch/question-answering/deployment/squad/length_adaptive_transformer/run_qa.py b/examples/huggingface/pytorch/question-answering/deployment/squad/length_adaptive_transformer/run_qa.py
index 81209b7d4f7..eb41bf85a32 100644
--- a/examples/huggingface/pytorch/question-answering/deployment/squad/length_adaptive_transformer/run_qa.py
+++ b/examples/huggingface/pytorch/question-answering/deployment/squad/length_adaptive_transformer/run_qa.py
@@ -29,8 +29,7 @@
 import transformers
 from dataclasses import dataclass, field
 from datasets import load_dataset, load_metric
-from intel_extension_for_transformers.transformers import metrics , OptimizedModel, DynamicLengthConfig
-from neural_compressor.config import PostTrainingQuantConfig
+from intel_extension_for_transformers.transformers import metrics , OptimizedModel, QuantizationConfig, DynamicLengthConfig
 from trainer_qa import QuestionAnsweringTrainer
 
 from intel_extension_for_transformers.transformers.modeling.modeling_roberta_dynamic import RobertaForQuestionAnswering
@@ -222,9 +221,9 @@ class OptimizationArguments:
         metadata={"help": "Whether or not to apply quantization."},
     )
     quantization_approach: Optional[str] = field(
-        default="static",
-        metadata={"help": "Quantization approach. Supported approach are static, "
-                  "dynamic."},
+        default="PostTrainingStatic",
+        metadata={"help": "Quantization approach. Supported approach are PostTrainingStatic, "
+                  "PostTrainingDynamic and QuantizationAwareTraining."},
     )
     metric_name: Optional[str] = field(
         default="eval_f1",
@@ -781,20 +780,24 @@ def compute_metrics(p: EvalPrediction):
 
         trainer.save_model(training_args.output_dir)
         trainer.calib_dataloader = trainer.get_eval_dataloader()
-        if optim_args.quantization_approach != "dynamic":
+        if optim_args.quantization_approach != "PostTrainingDynamic":
             if not training_args.do_train:
                 raise ValueError(
                     "do_train must be set to True for static and aware training quantization."
                 )
+        if optim_args.quantization_approach == "QuantizationAwareTraining":
+            early_stopping_patience = 6
+            early_stopping_threshold = 0.001 # optional
+            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience,
+                                                                    early_stopping_threshold))
 
         tune_metric = metrics.Metric(
             name=metric_name, is_relative=optim_args.is_relative, criterion=optim_args.perf_tol
         )
-        trainer.metrics = tune_metric
-        quantization_config = PostTrainingQuantConfig(
-            backend="ipex",
+        quantization_config = QuantizationConfig(
             approach=optim_args.quantization_approach,
-            excluded_precision=["bf16"]
+            max_trials=200,
+            metrics=[tune_metric],
         )
         model = trainer.quantize(quant_config=quantization_config)
 
diff --git a/examples/huggingface/pytorch/text-classification/deployment/emotion/distilbert_base_uncased/run_emotion.py b/examples/huggingface/pytorch/text-classification/deployment/emotion/distilbert_base_uncased/run_emotion.py
index fc8b047c58d..720e4109657 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/emotion/distilbert_base_uncased/run_emotion.py
+++ b/examples/huggingface/pytorch/text-classification/deployment/emotion/distilbert_base_uncased/run_emotion.py
@@ -26,13 +26,7 @@
 import transformers
 from dataclasses import dataclass, field
 from datasets import load_dataset, load_metric
-from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel
-from neural_compressor.config import (
-    PostTrainingQuantConfig,
-    QuantizationAwareTrainingConfig,
-    TuningCriterion,
-    AccuracyCriterion
-)
+from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel, QuantizationConfig
 from intel_extension_for_transformers.transformers.trainer import NLPTrainer
 from transformers import (
     AutoConfig,
@@ -204,9 +198,9 @@ class OptimizationArguments:
         metadata={"help": "Whether or not to apply quantization."},
     )
     quantization_approach: Optional[str] = field(
-        default="static",
-        metadata={"help": "Quantization approach. Supported approach are static, "
-                  "dynamic and qat."},
+        default="PostTrainingStatic",
+        metadata={"help": "Quantization approach. Supported approach are PostTrainingStatic, "
+                  "PostTrainingDynamic and QuantizationAwareTraining."},
     )
     metric_name: Optional[str] = field(
         default=None,
@@ -527,37 +521,27 @@ def compute_metrics(p: EvalPrediction):
         if not training_args.do_eval:
             raise ValueError("do_eval must be set to True for quantization.")
 
-        if optim_args.quantization_approach != "dynamic":
+        if optim_args.quantization_approach != "PostTrainingDynamic":
             if not training_args.do_train:
                 raise ValueError(
                     "do_train must be set to True for static and aware training quantization."
                 )
+        if optim_args.quantization_approach == "QuantizationAwareTraining":
+            early_stopping_patience = 6
+            early_stopping_threshold = 0.001 # optional
+            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience,
+                                                                    early_stopping_threshold))
 
         tune_metric = metrics.Metric(
             name=metric_name, is_relative=optim_args.is_relative, criterion=optim_args.perf_tol
         )
-        trainer.metrics = tune_metric
-        tuning_criterion = TuningCriterion(max_trials=600)
-        accuracy_criterion = AccuracyCriterion(
-            higher_is_better=False,  # optional.
-            criterion="relative" if optim_args.is_relative else "absolute",  # optional. Available values are "relative" and "absolute".
-            tolerable_loss=optim_args.perf_tol,  # optional.
+        objective = objectives.performance
+        quantization_config = QuantizationConfig(
+            approach=optim_args.quantization_approach,
+            max_trials=600,
+            metrics=[tune_metric],
+            objectives=[objective]
         )
-        if optim_args.quantization_approach != "qat":
-            quantization_config = PostTrainingQuantConfig(
-                approach=optim_args.quantization_approach,
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )
-        else:
-            quantization_config = QuantizationAwareTrainingConfig(
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )            
-            early_stopping_patience = 2
-            early_stopping_threshold = 0.001 # optional
-            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience, \
-                                                                    early_stopping_threshold))
         model = trainer.quantize(quant_config=quantization_config)
 
     if optim_args.benchmark or optim_args.accuracy_only:
diff --git a/examples/huggingface/pytorch/text-classification/deployment/emotion/distilbert_base_uncased/run_emotion.sh b/examples/huggingface/pytorch/text-classification/deployment/emotion/distilbert_base_uncased/run_emotion.sh
index 79c91ee5b18..57cee9c4494 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/emotion/distilbert_base_uncased/run_emotion.sh
+++ b/examples/huggingface/pytorch/text-classification/deployment/emotion/distilbert_base_uncased/run_emotion.sh
@@ -121,7 +121,7 @@ else
     echo "==========    Prepare Model ${MODEL_NAME_OR_PATH} with Precision ${PRECISION} ========"
     mode_cmd=""
     if [[ ${PRECISION} = 'int8' ]]; then
-        mode_cmd=$mode_cmd" --tune --quantization_approach static"
+        mode_cmd=$mode_cmd" --tune --quantization_approach PostTrainingStatic"
     elif [[ ${PRECISION} = 'bf16' ]]; then
         mode_cmd=$mode_cmd" --enable_bf16"
     fi
diff --git a/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_base/run_bert_base.sh b/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_base/run_bert_base.sh
index d1648a0c661..f2e133f1d9e 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_base/run_bert_base.sh
+++ b/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_base/run_bert_base.sh
@@ -123,7 +123,7 @@ else
     echo "==========    Prepare Model ${MODEL_NAME_OR_PATH} with Precision ${PRECISION} ========"
     mode_cmd=""
     if [[ ${PRECISION} = 'int8' ]]; then
-        mode_cmd=$mode_cmd" --tune --quantization_approach static"
+        mode_cmd=$mode_cmd" --tune --quantization_approach PostTrainingStatic"
     elif [[ ${PRECISION} = 'bf16' ]]; then
         mode_cmd=$mode_cmd" --enable_bf16"
     fi
diff --git a/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_base/run_glue.py b/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_base/run_glue.py
index 7267b845f98..eb8e47583e0 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_base/run_glue.py
+++ b/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_base/run_glue.py
@@ -26,13 +26,7 @@
 import transformers
 from dataclasses import dataclass, field
 from datasets import load_dataset, load_metric
-from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel
-from neural_compressor.config import (
-    PostTrainingQuantConfig,
-    QuantizationAwareTrainingConfig,
-    TuningCriterion,
-    AccuracyCriterion
-)
+from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel, QuantizationConfig
 from intel_extension_for_transformers.transformers.trainer import NLPTrainer
 from transformers import (
     AutoConfig,
@@ -203,9 +197,9 @@ class OptimizationArguments:
         metadata={"help": "Whether or not to apply quantization."},
     )
     quantization_approach: Optional[str] = field(
-        default="static",
-        metadata={"help": "Quantization approach. Supported approach are static, "
-                  "dynamic and qat."},
+        default="PostTrainingStatic",
+        metadata={"help": "Quantization approach. Supported approach are PostTrainingStatic, "
+                  "PostTrainingDynamic and QuantizationAwareTraining."},
     )
     metric_name: Optional[str] = field(
         default=None,
@@ -530,37 +524,27 @@ def compute_metrics(p: EvalPrediction):
         if not training_args.do_eval:
             raise ValueError("do_eval must be set to True for quantization.")
 
-        if optim_args.quantization_approach != "dynamic":
+        if optim_args.quantization_approach != "PostTrainingDynamic":
             if not training_args.do_train:
                 raise ValueError(
                     "do_train must be set to True for static and aware training quantization."
                 )
+        if optim_args.quantization_approach == "QuantizationAwareTraining":
+            early_stopping_patience = 6
+            early_stopping_threshold = 0.001 # optional
+            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience,
+                                                                    early_stopping_threshold))
 
         tune_metric = metrics.Metric(
             name=metric_name, is_relative=optim_args.is_relative, criterion=optim_args.perf_tol
         )
-        trainer.metrics = tune_metric
-        tuning_criterion = TuningCriterion(max_trials=600)
-        accuracy_criterion = AccuracyCriterion(
-            higher_is_better=False,  # optional.
-            criterion="relative" if optim_args.is_relative else "absolute",  # optional. Available values are "relative" and "absolute".
-            tolerable_loss=optim_args.perf_tol,  # optional.
+        objective = objectives.performance
+        quantization_config = QuantizationConfig(
+            approach=optim_args.quantization_approach,
+            max_trials=600,
+            metrics=[tune_metric],
+            objectives=[objective]
         )
-        if optim_args.quantization_approach != "qat":
-            quantization_config = PostTrainingQuantConfig(
-                approach=optim_args.quantization_approach,
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )
-        else:
-            quantization_config = QuantizationAwareTrainingConfig(
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )            
-            early_stopping_patience = 2
-            early_stopping_threshold = 0.001 # optional
-            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience, \
-                                                                    early_stopping_threshold))
         model = trainer.quantize(quant_config=quantization_config)
 
     if optim_args.benchmark or optim_args.accuracy_only:
diff --git a/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_base_cased/run_bert_base_cased.sh b/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_base_cased/run_bert_base_cased.sh
index cca1268e646..4daabbfe41a 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_base_cased/run_bert_base_cased.sh
+++ b/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_base_cased/run_bert_base_cased.sh
@@ -123,7 +123,7 @@ else
     echo "==========    Prepare Model ${MODEL_NAME_OR_PATH} with Precision ${PRECISION} ========"
     mode_cmd=""
     if [[ ${PRECISION} = 'int8' ]]; then
-        mode_cmd=$mode_cmd" --tune --quantization_approach static"
+        mode_cmd=$mode_cmd" --tune --quantization_approach PostTrainingStatic"
     elif [[ ${PRECISION} = 'bf16' ]]; then
         mode_cmd=$mode_cmd" --enable_bf16"
     fi
diff --git a/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_base_cased/run_glue.py b/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_base_cased/run_glue.py
index c68d3527abc..9374620302a 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_base_cased/run_glue.py
+++ b/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_base_cased/run_glue.py
@@ -26,13 +26,7 @@
 import transformers
 from dataclasses import dataclass, field
 from datasets import load_dataset, load_metric
-from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel
-from neural_compressor.config import (
-    PostTrainingQuantConfig,
-    QuantizationAwareTrainingConfig,
-    TuningCriterion,
-    AccuracyCriterion
-)
+from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel, QuantizationConfig
 from intel_extension_for_transformers.transformers.trainer import NLPTrainer
 from transformers import (
     AutoConfig,
@@ -203,9 +197,9 @@ class OptimizationArguments:
         metadata={"help": "Whether or not to apply quantization."},
     )
     quantization_approach: Optional[str] = field(
-        default="static",
-        metadata={"help": "Quantization approach. Supported approach are static, "
-                  "dynamic and qat."},
+        default="PostTrainingStatic",
+        metadata={"help": "Quantization approach. Supported approach are PostTrainingStatic, "
+                  "PostTrainingDynamic and QuantizationAwareTraining."},
     )
     metric_name: Optional[str] = field(
         default=None,
@@ -530,37 +524,27 @@ def compute_metrics(p: EvalPrediction):
         if not training_args.do_eval:
             raise ValueError("do_eval must be set to True for quantization.")
 
-        if optim_args.quantization_approach != "dynamic":
+        if optim_args.quantization_approach != "PostTrainingDynamic":
             if not training_args.do_train:
                 raise ValueError(
                     "do_train must be set to True for static and aware training quantization."
                 )
+        if optim_args.quantization_approach == "QuantizationAwareTraining":
+            early_stopping_patience = 6
+            early_stopping_threshold = 0.001 # optional
+            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience,
+                                                                    early_stopping_threshold))
 
         tune_metric = metrics.Metric(
             name=metric_name, is_relative=optim_args.is_relative, criterion=optim_args.perf_tol
         )
-        trainer.metrics = tune_metric
-        tuning_criterion = TuningCriterion(max_trials=600)
-        accuracy_criterion = AccuracyCriterion(
-            higher_is_better=False,  # optional.
-            criterion="relative" if optim_args.is_relative else "absolute",  # optional. Available values are "relative" and "absolute".
-            tolerable_loss=optim_args.perf_tol,  # optional.
+        objective = objectives.performance
+        quantization_config = QuantizationConfig(
+            approach=optim_args.quantization_approach,
+            max_trials=600,
+            metrics=[tune_metric],
+            objectives=[objective]
         )
-        if optim_args.quantization_approach != "qat":
-            quantization_config = PostTrainingQuantConfig(
-                approach=optim_args.quantization_approach,
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )
-        else:
-            quantization_config = QuantizationAwareTrainingConfig(
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )            
-            early_stopping_patience = 2
-            early_stopping_threshold = 0.001 # optional
-            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience, \
-                                                                    early_stopping_threshold))
         model = trainer.quantize(quant_config=quantization_config)
 
     if optim_args.benchmark or optim_args.accuracy_only:
diff --git a/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_mini/run_bert_mini.sh b/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_mini/run_bert_mini.sh
index fa9f005fa54..6e9db50fddd 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_mini/run_bert_mini.sh
+++ b/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_mini/run_bert_mini.sh
@@ -123,7 +123,7 @@ else
     echo "==========    Prepare Model ${MODEL_NAME_OR_PATH} with Precision ${PRECISION} ========"
     mode_cmd=""
     if [[ ${PRECISION} = 'int8' ]]; then
-        mode_cmd=$mode_cmd" --tune --quantization_approach static"
+        mode_cmd=$mode_cmd" --tune --quantization_approach PostTrainingStatic"
     elif [[ ${PRECISION} = 'bf16' ]]; then
         mode_cmd=$mode_cmd" --enable_bf16"
     fi
diff --git a/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_mini/run_glue.py b/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_mini/run_glue.py
index 6e3bc04cd0d..eb8e47583e0 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_mini/run_glue.py
+++ b/examples/huggingface/pytorch/text-classification/deployment/mrpc/bert_mini/run_glue.py
@@ -26,13 +26,7 @@
 import transformers
 from dataclasses import dataclass, field
 from datasets import load_dataset, load_metric
-from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel
-from neural_compressor.config import (
-    PostTrainingQuantConfig,
-    QuantizationAwareTrainingConfig,
-    TuningCriterion,
-    AccuracyCriterion
-)
+from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel, QuantizationConfig
 from intel_extension_for_transformers.transformers.trainer import NLPTrainer
 from transformers import (
     AutoConfig,
@@ -203,9 +197,9 @@ class OptimizationArguments:
         metadata={"help": "Whether or not to apply quantization."},
     )
     quantization_approach: Optional[str] = field(
-        default="static",
-        metadata={"help": "Quantization approach. Supported approach are static, "
-                  "dynamic and qat."},
+        default="PostTrainingStatic",
+        metadata={"help": "Quantization approach. Supported approach are PostTrainingStatic, "
+                  "PostTrainingDynamic and QuantizationAwareTraining."},
     )
     metric_name: Optional[str] = field(
         default=None,
@@ -530,36 +524,27 @@ def compute_metrics(p: EvalPrediction):
         if not training_args.do_eval:
             raise ValueError("do_eval must be set to True for quantization.")
 
-        if optim_args.quantization_approach != "dynamic":
+        if optim_args.quantization_approach != "PostTrainingDynamic":
             if not training_args.do_train:
                 raise ValueError(
                     "do_train must be set to True for static and aware training quantization."
                 )
+        if optim_args.quantization_approach == "QuantizationAwareTraining":
+            early_stopping_patience = 6
+            early_stopping_threshold = 0.001 # optional
+            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience,
+                                                                    early_stopping_threshold))
+
         tune_metric = metrics.Metric(
             name=metric_name, is_relative=optim_args.is_relative, criterion=optim_args.perf_tol
         )
-        trainer.metrics = tune_metric
-        tuning_criterion = TuningCriterion(max_trials=600)
-        accuracy_criterion = AccuracyCriterion(
-            higher_is_better=False,  # optional.
-            criterion="relative" if optim_args.is_relative else "absolute",  # optional. Available values are "relative" and "absolute".
-            tolerable_loss=optim_args.perf_tol,  # optional.
+        objective = objectives.performance
+        quantization_config = QuantizationConfig(
+            approach=optim_args.quantization_approach,
+            max_trials=600,
+            metrics=[tune_metric],
+            objectives=[objective]
         )
-        if optim_args.quantization_approach != "qat":
-            quantization_config = PostTrainingQuantConfig(
-                approach=optim_args.quantization_approach,
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )
-        else:
-            quantization_config = QuantizationAwareTrainingConfig(
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )            
-            early_stopping_patience = 2
-            early_stopping_threshold = 0.001 # optional
-            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience, \
-                                                                    early_stopping_threshold))
         model = trainer.quantize(quant_config=quantization_config)
 
     if optim_args.benchmark or optim_args.accuracy_only:
diff --git a/examples/huggingface/pytorch/text-classification/deployment/mrpc/distilbert_base_uncased/run_distilbert_base.sh b/examples/huggingface/pytorch/text-classification/deployment/mrpc/distilbert_base_uncased/run_distilbert_base.sh
index b20cffed3b5..8c75385ea5b 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/mrpc/distilbert_base_uncased/run_distilbert_base.sh
+++ b/examples/huggingface/pytorch/text-classification/deployment/mrpc/distilbert_base_uncased/run_distilbert_base.sh
@@ -123,7 +123,7 @@ else
     echo "==========    Prepare Model ${MODEL_NAME_OR_PATH} with Precision ${PRECISION} ========"
     mode_cmd=""
     if [[ ${PRECISION} = 'int8' ]]; then
-        mode_cmd=$mode_cmd" --tune --quantization_approach static"
+        mode_cmd=$mode_cmd" --tune --quantization_approach PostTrainingStatic"
     elif [[ ${PRECISION} = 'bf16' ]]; then
         mode_cmd=$mode_cmd" --enable_bf16"
     fi
diff --git a/examples/huggingface/pytorch/text-classification/deployment/mrpc/distilbert_base_uncased/run_glue.py b/examples/huggingface/pytorch/text-classification/deployment/mrpc/distilbert_base_uncased/run_glue.py
index 6e3bc04cd0d..eb8e47583e0 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/mrpc/distilbert_base_uncased/run_glue.py
+++ b/examples/huggingface/pytorch/text-classification/deployment/mrpc/distilbert_base_uncased/run_glue.py
@@ -26,13 +26,7 @@
 import transformers
 from dataclasses import dataclass, field
 from datasets import load_dataset, load_metric
-from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel
-from neural_compressor.config import (
-    PostTrainingQuantConfig,
-    QuantizationAwareTrainingConfig,
-    TuningCriterion,
-    AccuracyCriterion
-)
+from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel, QuantizationConfig
 from intel_extension_for_transformers.transformers.trainer import NLPTrainer
 from transformers import (
     AutoConfig,
@@ -203,9 +197,9 @@ class OptimizationArguments:
         metadata={"help": "Whether or not to apply quantization."},
     )
     quantization_approach: Optional[str] = field(
-        default="static",
-        metadata={"help": "Quantization approach. Supported approach are static, "
-                  "dynamic and qat."},
+        default="PostTrainingStatic",
+        metadata={"help": "Quantization approach. Supported approach are PostTrainingStatic, "
+                  "PostTrainingDynamic and QuantizationAwareTraining."},
     )
     metric_name: Optional[str] = field(
         default=None,
@@ -530,36 +524,27 @@ def compute_metrics(p: EvalPrediction):
         if not training_args.do_eval:
             raise ValueError("do_eval must be set to True for quantization.")
 
-        if optim_args.quantization_approach != "dynamic":
+        if optim_args.quantization_approach != "PostTrainingDynamic":
             if not training_args.do_train:
                 raise ValueError(
                     "do_train must be set to True for static and aware training quantization."
                 )
+        if optim_args.quantization_approach == "QuantizationAwareTraining":
+            early_stopping_patience = 6
+            early_stopping_threshold = 0.001 # optional
+            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience,
+                                                                    early_stopping_threshold))
+
         tune_metric = metrics.Metric(
             name=metric_name, is_relative=optim_args.is_relative, criterion=optim_args.perf_tol
         )
-        trainer.metrics = tune_metric
-        tuning_criterion = TuningCriterion(max_trials=600)
-        accuracy_criterion = AccuracyCriterion(
-            higher_is_better=False,  # optional.
-            criterion="relative" if optim_args.is_relative else "absolute",  # optional. Available values are "relative" and "absolute".
-            tolerable_loss=optim_args.perf_tol,  # optional.
+        objective = objectives.performance
+        quantization_config = QuantizationConfig(
+            approach=optim_args.quantization_approach,
+            max_trials=600,
+            metrics=[tune_metric],
+            objectives=[objective]
         )
-        if optim_args.quantization_approach != "qat":
-            quantization_config = PostTrainingQuantConfig(
-                approach=optim_args.quantization_approach,
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )
-        else:
-            quantization_config = QuantizationAwareTrainingConfig(
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )            
-            early_stopping_patience = 2
-            early_stopping_threshold = 0.001 # optional
-            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience, \
-                                                                    early_stopping_threshold))
         model = trainer.quantize(quant_config=quantization_config)
 
     if optim_args.benchmark or optim_args.accuracy_only:
diff --git a/examples/huggingface/pytorch/text-classification/deployment/mrpc/roberta_base/run_glue.py b/examples/huggingface/pytorch/text-classification/deployment/mrpc/roberta_base/run_glue.py
index 6f899f98ed4..efc762b5c59 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/mrpc/roberta_base/run_glue.py
+++ b/examples/huggingface/pytorch/text-classification/deployment/mrpc/roberta_base/run_glue.py
@@ -26,13 +26,7 @@
 import transformers
 from dataclasses import dataclass, field
 from datasets import load_dataset, load_metric
-from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel
-from neural_compressor.config import (
-    PostTrainingQuantConfig,
-    QuantizationAwareTrainingConfig,
-    TuningCriterion,
-    AccuracyCriterion
-)
+from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel, QuantizationConfig
 from intel_extension_for_transformers.transformers.trainer import NLPTrainer
 from transformers import (
     AutoConfig,
@@ -203,9 +197,9 @@ class OptimizationArguments:
         metadata={"help": "Whether or not to apply quantization."},
     )
     quantization_approach: Optional[str] = field(
-        default="static",
-        metadata={"help": "Quantization approach. Supported approach are static, "
-                  "dynamic and qat."},
+        default="PostTrainingStatic",
+        metadata={"help": "Quantization approach. Supported approach are PostTrainingStatic, "
+                  "PostTrainingDynamic and QuantizationAwareTraining."},
     )
     metric_name: Optional[str] = field(
         default=None,
@@ -530,36 +524,27 @@ def compute_metrics(p: EvalPrediction):
         if not training_args.do_eval:
             raise ValueError("do_eval must be set to True for quantization.")
 
-        if optim_args.quantization_approach != "dynamic":
+        if optim_args.quantization_approach != "PostTrainingDynamic":
             if not training_args.do_train:
                 raise ValueError(
                     "do_train must be set to True for static and aware training quantization."
                 )
+        if optim_args.quantization_approach == "QuantizationAwareTraining":
+            early_stopping_patience = 6
+            early_stopping_threshold = 0.001 # optional
+            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience,
+                                                                    early_stopping_threshold))
+
         tune_metric = metrics.Metric(
             name=metric_name, is_relative=optim_args.is_relative, criterion=optim_args.perf_tol
         )
-        trainer.metrics = tune_metric
-        tuning_criterion = TuningCriterion(max_trials=600)
-        accuracy_criterion = AccuracyCriterion(
-            higher_is_better=False,  # optional.
-            criterion="relative" if optim_args.is_relative else "absolute",  # optional. Available values are "relative" and "absolute".
-            tolerable_loss=optim_args.perf_tol,  # optional.
+        objective = objectives.performance
+        quantization_config = QuantizationConfig(
+            approach=optim_args.quantization_approach,
+            max_trials=600,
+            metrics=[tune_metric],
+            objectives=[objective]
         )
-        if optim_args.quantization_approach != "qat":
-            quantization_config = PostTrainingQuantConfig(
-                approach=optim_args.quantization_approach,
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )
-        else:
-            quantization_config = QuantizationAwareTrainingConfig(
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )            
-            early_stopping_patience = 2
-            early_stopping_threshold = 0.001 # optional
-            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience, \
-                                                                    early_stopping_threshold))
         model = trainer.quantize(quant_config=quantization_config)
 
     if optim_args.benchmark or optim_args.accuracy_only:
diff --git a/examples/huggingface/pytorch/text-classification/deployment/mrpc/roberta_base/run_roberta_base.sh b/examples/huggingface/pytorch/text-classification/deployment/mrpc/roberta_base/run_roberta_base.sh
index 4228e299003..37110ee356c 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/mrpc/roberta_base/run_roberta_base.sh
+++ b/examples/huggingface/pytorch/text-classification/deployment/mrpc/roberta_base/run_roberta_base.sh
@@ -123,7 +123,7 @@ else
     echo "==========    Prepare Model ${MODEL_NAME_OR_PATH} with Precision ${PRECISION} ========"
     mode_cmd=""
     if [[ ${PRECISION} = 'int8' ]]; then
-        mode_cmd=$mode_cmd" --tune --quantization_approach static"
+        mode_cmd=$mode_cmd" --tune --quantization_approach PostTrainingStatic"
     elif [[ ${PRECISION} = 'bf16' ]]; then
         mode_cmd=$mode_cmd" --enable_bf16"
     fi
diff --git a/examples/huggingface/pytorch/text-classification/deployment/sparse/bert_mini/run_bert_mini.sh b/examples/huggingface/pytorch/text-classification/deployment/sparse/bert_mini/run_bert_mini.sh
index 4e36c1330a1..48b24477a34 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/sparse/bert_mini/run_bert_mini.sh
+++ b/examples/huggingface/pytorch/text-classification/deployment/sparse/bert_mini/run_bert_mini.sh
@@ -128,7 +128,7 @@ else
     echo "==========    Prepare Model ${MODEL_NAME_OR_PATH} with Precision ${PRECISION} ========"
     mode_cmd=""
     if [[ ${PRECISION} = 'int8' ]]; then
-        mode_cmd=$mode_cmd" --tune --quantization_approach static"
+        mode_cmd=$mode_cmd" --tune --quantization_approach PostTrainingStatic"
     elif [[ ${PRECISION} = 'bf16' ]]; then
         mode_cmd=$mode_cmd" --enable_bf16"
     fi
diff --git a/examples/huggingface/pytorch/text-classification/deployment/sparse/bert_mini/run_glue.py b/examples/huggingface/pytorch/text-classification/deployment/sparse/bert_mini/run_glue.py
index b5c05350e84..4400593f0c2 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/sparse/bert_mini/run_glue.py
+++ b/examples/huggingface/pytorch/text-classification/deployment/sparse/bert_mini/run_glue.py
@@ -26,13 +26,7 @@
 import transformers
 from dataclasses import dataclass, field
 from datasets import load_dataset, load_metric
-from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel
-from neural_compressor.config import (
-    PostTrainingQuantConfig,
-    QuantizationAwareTrainingConfig,
-    TuningCriterion,
-    AccuracyCriterion
-)
+from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel, QuantizationConfig
 from intel_extension_for_transformers.transformers.trainer import NLPTrainer
 from transformers import (
     AutoConfig,
@@ -203,9 +197,9 @@ class OptimizationArguments:
         metadata={"help": "Whether or not to apply quantization."},
     )
     quantization_approach: Optional[str] = field(
-        default="static",
-        metadata={"help": "Quantization approach. Supported approach are static, "
-                  "dynamic and qat."},
+        default="PostTrainingStatic",
+        metadata={"help": "Quantization approach. Supported approach are PostTrainingStatic, "
+                  "PostTrainingDynamic and QuantizationAwareTraining."},
     )
     metric_name: Optional[str] = field(
         default=None,
@@ -530,38 +524,27 @@ def compute_metrics(p: EvalPrediction):
         if not training_args.do_eval:
             raise ValueError("do_eval must be set to True for quantization.")
 
-        if optim_args.quantization_approach != "dynamic":
+        if optim_args.quantization_approach != "PostTrainingDynamic":
             if not training_args.do_train:
                 raise ValueError(
                     "do_train must be set to True for static and aware training quantization."
                 )
+        if optim_args.quantization_approach == "QuantizationAwareTraining":
+            early_stopping_patience = 6
+            early_stopping_threshold = 0.001 # optional
+            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience,
+                                                                    early_stopping_threshold))
 
         tune_metric = metrics.Metric(
             name=metric_name, is_relative=optim_args.is_relative, criterion=optim_args.perf_tol
         )
-        trainer.metrics = tune_metric
         objective = objectives.performance
-        tuning_criterion = TuningCriterion(max_trials=600, objective=[objective.name])
-        accuracy_criterion = AccuracyCriterion(
-            higher_is_better=True,  # optional.
-            criterion="relative" if optim_args.is_relative else "absolute",  # optional. Available values are "relative" and "absolute".
-            tolerable_loss=optim_args.perf_tol,  # optional.
+        quantization_config = QuantizationConfig(
+            approach=optim_args.quantization_approach,
+            max_trials=600,
+            metrics=[tune_metric],
+            objectives=[objective]
         )
-        if optim_args.quantization_approach != "qat":
-            quantization_config = PostTrainingQuantConfig(
-                approach=optim_args.quantization_approach,
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )
-        else:
-            quantization_config = QuantizationAwareTrainingConfig(
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )            
-            early_stopping_patience = 2
-            early_stopping_threshold = 0.001 # optional
-            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience, \
-                                                                    early_stopping_threshold))
         model = trainer.quantize(quant_config=quantization_config)
 
     if optim_args.benchmark or optim_args.accuracy_only:
diff --git a/examples/huggingface/pytorch/text-classification/deployment/sparse/distilbert_base_uncased/run_distilbert.sh b/examples/huggingface/pytorch/text-classification/deployment/sparse/distilbert_base_uncased/run_distilbert.sh
index 43d99aa3419..53c694f0a89 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/sparse/distilbert_base_uncased/run_distilbert.sh
+++ b/examples/huggingface/pytorch/text-classification/deployment/sparse/distilbert_base_uncased/run_distilbert.sh
@@ -128,7 +128,7 @@ else
     echo "==========    Prepare Model ${MODEL_NAME_OR_PATH} with Precision ${PRECISION} ========"
     mode_cmd=""
     if [[ ${PRECISION} = 'int8' ]]; then
-        mode_cmd=$mode_cmd" --tune --quantization_approach static"
+        mode_cmd=$mode_cmd" --tune --quantization_approach PostTrainingStatic"
     elif [[ ${PRECISION} = 'bf16' ]]; then
         mode_cmd=$mode_cmd" --enable_bf16"
     fi
diff --git a/examples/huggingface/pytorch/text-classification/deployment/sparse/distilbert_base_uncased/run_qa.py b/examples/huggingface/pytorch/text-classification/deployment/sparse/distilbert_base_uncased/run_qa.py
index af1e10149f3..373669fb338 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/sparse/distilbert_base_uncased/run_qa.py
+++ b/examples/huggingface/pytorch/text-classification/deployment/sparse/distilbert_base_uncased/run_qa.py
@@ -26,13 +26,7 @@
 import transformers
 from dataclasses import dataclass, field
 from datasets import load_dataset, load_metric
-from intel_extension_for_transformers.transformers import metrics , OptimizedModel
-from neural_compressor.config import (
-    PostTrainingQuantConfig,
-    QuantizationAwareTrainingConfig,
-    TuningCriterion,
-    AccuracyCriterion
-)
+from intel_extension_for_transformers.transformers import metrics , OptimizedModel, QuantizationConfig
 from trainer_qa import QuestionAnsweringTrainer
 from transformers import (
     AutoConfig,
@@ -217,9 +211,9 @@ class OptimizationArguments:
         metadata={"help": "Whether or not to apply quantization."},
     )
     quantization_approach: Optional[str] = field(
-        default="static",
-        metadata={"help": "Quantization approach. Supported approach are static, "
-                  "dynamic and qat."},
+        default="PostTrainingStatic",
+        metadata={"help": "Quantization approach. Supported approach are PostTrainingStatic, "
+                  "PostTrainingDynamic and QuantizationAwareTraining."},
     )
     metric_name: Optional[str] = field(
         default="eval_f1",
@@ -652,38 +646,25 @@ def compute_metrics(p: EvalPrediction):
         if not training_args.do_eval:
             raise ValueError("do_eval must be set to True for quantization.")
 
-        if optim_args.quantization_approach != "dynamic":
+        if optim_args.quantization_approach != "PostTrainingDynamic":
             if not training_args.do_train:
                 raise ValueError(
                     "do_train must be set to True for static and aware training quantization."
                 )
+        if optim_args.quantization_approach == "QuantizationAwareTraining":
+            early_stopping_patience = 6
+            early_stopping_threshold = 0.001 # optional
+            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience,
+                                                                    early_stopping_threshold))
 
         tune_metric = metrics.Metric(
             name=metric_name, is_relative=optim_args.is_relative, criterion=optim_args.perf_tol
         )
-        trainer.metrics = tune_metric
-        objective = objectives.performance
-        tuning_criterion = TuningCriterion(max_trials=600, objective=[objective.name])
-        accuracy_criterion = AccuracyCriterion(
-            higher_is_better=True,  # optional.
-            criterion="relative" if optim_args.is_relative else "absolute",  # optional. Available values are "relative" and "absolute".
-            tolerable_loss=optim_args.perf_tol,  # optional.
+        quantization_config = QuantizationConfig(
+            approach=optim_args.quantization_approach,
+            max_trials=200,
+            metrics=[tune_metric],
         )
-        if optim_args.quantization_approach != "qat":
-            quantization_config = PostTrainingQuantConfig(
-                approach=optim_args.quantization_approach,
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )
-        else:
-            quantization_config = QuantizationAwareTrainingConfig(
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )            
-            early_stopping_patience = 2
-            early_stopping_threshold = 0.001 # optional
-            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience, \
-                                                                    early_stopping_threshold))
         model = trainer.quantize(quant_config=quantization_config)
 
     if optim_args.benchmark or optim_args.accuracy_only:
diff --git a/examples/huggingface/pytorch/text-classification/deployment/sst2/bert_mini/run_bert_mini.sh b/examples/huggingface/pytorch/text-classification/deployment/sst2/bert_mini/run_bert_mini.sh
index d756a945cf6..4a5a986fdc1 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/sst2/bert_mini/run_bert_mini.sh
+++ b/examples/huggingface/pytorch/text-classification/deployment/sst2/bert_mini/run_bert_mini.sh
@@ -123,7 +123,7 @@ else
     echo "==========    Prepare Model ${MODEL_NAME_OR_PATH} with Precision ${PRECISION} ========"
     mode_cmd=""
     if [[ ${PRECISION} = 'int8' ]]; then
-        mode_cmd=$mode_cmd" --tune --quantization_approach static"
+        mode_cmd=$mode_cmd" --tune --quantization_approach PostTrainingStatic"
     elif [[ ${PRECISION} = 'bf16' ]]; then
         mode_cmd=$mode_cmd" --enable_bf16"
     fi
diff --git a/examples/huggingface/pytorch/text-classification/deployment/sst2/bert_mini/run_glue.py b/examples/huggingface/pytorch/text-classification/deployment/sst2/bert_mini/run_glue.py
index 6f899f98ed4..efc762b5c59 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/sst2/bert_mini/run_glue.py
+++ b/examples/huggingface/pytorch/text-classification/deployment/sst2/bert_mini/run_glue.py
@@ -26,13 +26,7 @@
 import transformers
 from dataclasses import dataclass, field
 from datasets import load_dataset, load_metric
-from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel
-from neural_compressor.config import (
-    PostTrainingQuantConfig,
-    QuantizationAwareTrainingConfig,
-    TuningCriterion,
-    AccuracyCriterion
-)
+from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel, QuantizationConfig
 from intel_extension_for_transformers.transformers.trainer import NLPTrainer
 from transformers import (
     AutoConfig,
@@ -203,9 +197,9 @@ class OptimizationArguments:
         metadata={"help": "Whether or not to apply quantization."},
     )
     quantization_approach: Optional[str] = field(
-        default="static",
-        metadata={"help": "Quantization approach. Supported approach are static, "
-                  "dynamic and qat."},
+        default="PostTrainingStatic",
+        metadata={"help": "Quantization approach. Supported approach are PostTrainingStatic, "
+                  "PostTrainingDynamic and QuantizationAwareTraining."},
     )
     metric_name: Optional[str] = field(
         default=None,
@@ -530,36 +524,27 @@ def compute_metrics(p: EvalPrediction):
         if not training_args.do_eval:
             raise ValueError("do_eval must be set to True for quantization.")
 
-        if optim_args.quantization_approach != "dynamic":
+        if optim_args.quantization_approach != "PostTrainingDynamic":
             if not training_args.do_train:
                 raise ValueError(
                     "do_train must be set to True for static and aware training quantization."
                 )
+        if optim_args.quantization_approach == "QuantizationAwareTraining":
+            early_stopping_patience = 6
+            early_stopping_threshold = 0.001 # optional
+            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience,
+                                                                    early_stopping_threshold))
+
         tune_metric = metrics.Metric(
             name=metric_name, is_relative=optim_args.is_relative, criterion=optim_args.perf_tol
         )
-        trainer.metrics = tune_metric
-        tuning_criterion = TuningCriterion(max_trials=600)
-        accuracy_criterion = AccuracyCriterion(
-            higher_is_better=False,  # optional.
-            criterion="relative" if optim_args.is_relative else "absolute",  # optional. Available values are "relative" and "absolute".
-            tolerable_loss=optim_args.perf_tol,  # optional.
+        objective = objectives.performance
+        quantization_config = QuantizationConfig(
+            approach=optim_args.quantization_approach,
+            max_trials=600,
+            metrics=[tune_metric],
+            objectives=[objective]
         )
-        if optim_args.quantization_approach != "qat":
-            quantization_config = PostTrainingQuantConfig(
-                approach=optim_args.quantization_approach,
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )
-        else:
-            quantization_config = QuantizationAwareTrainingConfig(
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )            
-            early_stopping_patience = 2
-            early_stopping_threshold = 0.001 # optional
-            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience, \
-                                                                    early_stopping_threshold))
         model = trainer.quantize(quant_config=quantization_config)
 
     if optim_args.benchmark or optim_args.accuracy_only:
diff --git a/examples/huggingface/pytorch/text-classification/deployment/sst2/distilbert_base_uncased/run_distilbert_base.sh b/examples/huggingface/pytorch/text-classification/deployment/sst2/distilbert_base_uncased/run_distilbert_base.sh
index 962a0044ca0..d2be2d43c50 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/sst2/distilbert_base_uncased/run_distilbert_base.sh
+++ b/examples/huggingface/pytorch/text-classification/deployment/sst2/distilbert_base_uncased/run_distilbert_base.sh
@@ -123,7 +123,7 @@ else
     echo "==========    Prepare Model ${MODEL_NAME_OR_PATH} with Precision ${PRECISION} ========"
     mode_cmd=""
     if [[ ${PRECISION} = 'int8' ]]; then
-        mode_cmd=$mode_cmd" --tune --quantization_approach static"
+        mode_cmd=$mode_cmd" --tune --quantization_approach PostTrainingStatic"
     elif [[ ${PRECISION} = 'bf16' ]]; then
         mode_cmd=$mode_cmd" --enable_bf16"
     fi
diff --git a/examples/huggingface/pytorch/text-classification/deployment/sst2/distilbert_base_uncased/run_glue.py b/examples/huggingface/pytorch/text-classification/deployment/sst2/distilbert_base_uncased/run_glue.py
index 6e3bc04cd0d..eb8e47583e0 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/sst2/distilbert_base_uncased/run_glue.py
+++ b/examples/huggingface/pytorch/text-classification/deployment/sst2/distilbert_base_uncased/run_glue.py
@@ -26,13 +26,7 @@
 import transformers
 from dataclasses import dataclass, field
 from datasets import load_dataset, load_metric
-from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel
-from neural_compressor.config import (
-    PostTrainingQuantConfig,
-    QuantizationAwareTrainingConfig,
-    TuningCriterion,
-    AccuracyCriterion
-)
+from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel, QuantizationConfig
 from intel_extension_for_transformers.transformers.trainer import NLPTrainer
 from transformers import (
     AutoConfig,
@@ -203,9 +197,9 @@ class OptimizationArguments:
         metadata={"help": "Whether or not to apply quantization."},
     )
     quantization_approach: Optional[str] = field(
-        default="static",
-        metadata={"help": "Quantization approach. Supported approach are static, "
-                  "dynamic and qat."},
+        default="PostTrainingStatic",
+        metadata={"help": "Quantization approach. Supported approach are PostTrainingStatic, "
+                  "PostTrainingDynamic and QuantizationAwareTraining."},
     )
     metric_name: Optional[str] = field(
         default=None,
@@ -530,36 +524,27 @@ def compute_metrics(p: EvalPrediction):
         if not training_args.do_eval:
             raise ValueError("do_eval must be set to True for quantization.")
 
-        if optim_args.quantization_approach != "dynamic":
+        if optim_args.quantization_approach != "PostTrainingDynamic":
             if not training_args.do_train:
                 raise ValueError(
                     "do_train must be set to True for static and aware training quantization."
                 )
+        if optim_args.quantization_approach == "QuantizationAwareTraining":
+            early_stopping_patience = 6
+            early_stopping_threshold = 0.001 # optional
+            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience,
+                                                                    early_stopping_threshold))
+
         tune_metric = metrics.Metric(
             name=metric_name, is_relative=optim_args.is_relative, criterion=optim_args.perf_tol
         )
-        trainer.metrics = tune_metric
-        tuning_criterion = TuningCriterion(max_trials=600)
-        accuracy_criterion = AccuracyCriterion(
-            higher_is_better=False,  # optional.
-            criterion="relative" if optim_args.is_relative else "absolute",  # optional. Available values are "relative" and "absolute".
-            tolerable_loss=optim_args.perf_tol,  # optional.
+        objective = objectives.performance
+        quantization_config = QuantizationConfig(
+            approach=optim_args.quantization_approach,
+            max_trials=600,
+            metrics=[tune_metric],
+            objectives=[objective]
         )
-        if optim_args.quantization_approach != "qat":
-            quantization_config = PostTrainingQuantConfig(
-                approach=optim_args.quantization_approach,
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )
-        else:
-            quantization_config = QuantizationAwareTrainingConfig(
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )            
-            early_stopping_patience = 2
-            early_stopping_threshold = 0.001 # optional
-            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience, \
-                                                                    early_stopping_threshold))
         model = trainer.quantize(quant_config=quantization_config)
 
     if optim_args.benchmark or optim_args.accuracy_only:
diff --git a/examples/huggingface/pytorch/text-classification/deployment/sst2/minilm_l6_h384_uncased/run_glue.py b/examples/huggingface/pytorch/text-classification/deployment/sst2/minilm_l6_h384_uncased/run_glue.py
index 9eaa0cb0ecd..4400593f0c2 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/sst2/minilm_l6_h384_uncased/run_glue.py
+++ b/examples/huggingface/pytorch/text-classification/deployment/sst2/minilm_l6_h384_uncased/run_glue.py
@@ -26,13 +26,7 @@
 import transformers
 from dataclasses import dataclass, field
 from datasets import load_dataset, load_metric
-from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel
-from neural_compressor.config import (
-    PostTrainingQuantConfig,
-    QuantizationAwareTrainingConfig,
-    TuningCriterion,
-    AccuracyCriterion
-)
+from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel, QuantizationConfig
 from intel_extension_for_transformers.transformers.trainer import NLPTrainer
 from transformers import (
     AutoConfig,
@@ -203,9 +197,9 @@ class OptimizationArguments:
         metadata={"help": "Whether or not to apply quantization."},
     )
     quantization_approach: Optional[str] = field(
-        default="static",
-        metadata={"help": "Quantization approach. Supported approach are static, "
-                  "dynamic and qat."},
+        default="PostTrainingStatic",
+        metadata={"help": "Quantization approach. Supported approach are PostTrainingStatic, "
+                  "PostTrainingDynamic and QuantizationAwareTraining."},
     )
     metric_name: Optional[str] = field(
         default=None,
@@ -530,37 +524,27 @@ def compute_metrics(p: EvalPrediction):
         if not training_args.do_eval:
             raise ValueError("do_eval must be set to True for quantization.")
 
-        if optim_args.quantization_approach != "dynamic":
+        if optim_args.quantization_approach != "PostTrainingDynamic":
             if not training_args.do_train:
                 raise ValueError(
                     "do_train must be set to True for static and aware training quantization."
                 )
+        if optim_args.quantization_approach == "QuantizationAwareTraining":
+            early_stopping_patience = 6
+            early_stopping_threshold = 0.001 # optional
+            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience,
+                                                                    early_stopping_threshold))
 
         tune_metric = metrics.Metric(
             name=metric_name, is_relative=optim_args.is_relative, criterion=optim_args.perf_tol
         )
-        trainer.metrics = tune_metric
-        tuning_criterion = TuningCriterion(max_trials=600)
-        accuracy_criterion = AccuracyCriterion(
-            higher_is_better=False,  # optional.
-            criterion="relative" if optim_args.is_relative else "absolute",  # optional. Available values are "relative" and "absolute".
-            tolerable_loss=optim_args.perf_tol,  # optional.
+        objective = objectives.performance
+        quantization_config = QuantizationConfig(
+            approach=optim_args.quantization_approach,
+            max_trials=600,
+            metrics=[tune_metric],
+            objectives=[objective]
         )
-        if optim_args.quantization_approach != "qat":
-            quantization_config = PostTrainingQuantConfig(
-                approach=optim_args.quantization_approach,
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )
-        else:
-            quantization_config = QuantizationAwareTrainingConfig(
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )
-            early_stopping_patience = 2
-            early_stopping_threshold = 0.001 # optional
-            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience, \
-                                                                    early_stopping_threshold))
         model = trainer.quantize(quant_config=quantization_config)
 
     if optim_args.benchmark or optim_args.accuracy_only:
diff --git a/examples/huggingface/pytorch/text-classification/deployment/sst2/minilm_l6_h384_uncased/run_minilm.sh b/examples/huggingface/pytorch/text-classification/deployment/sst2/minilm_l6_h384_uncased/run_minilm.sh
index 31f6033ce6e..20417a96738 100644
--- a/examples/huggingface/pytorch/text-classification/deployment/sst2/minilm_l6_h384_uncased/run_minilm.sh
+++ b/examples/huggingface/pytorch/text-classification/deployment/sst2/minilm_l6_h384_uncased/run_minilm.sh
@@ -123,7 +123,7 @@ else
     echo "==========    Prepare Model ${MODEL_NAME_OR_PATH} with Precision ${PRECISION} ========"
     mode_cmd=""
     if [[ ${PRECISION} = 'int8' ]]; then
-        mode_cmd=$mode_cmd" --tune --quantization_approach static"
+        mode_cmd=$mode_cmd" --tune --quantization_approach PostTrainingStatic"
     elif [[ ${PRECISION} = 'bf16' ]]; then
         mode_cmd=$mode_cmd" --enable_bf16"
     fi
diff --git a/examples/huggingface/pytorch/text-embedding/deployment/mteb/bge/run_bge.sh b/examples/huggingface/pytorch/text-embedding/deployment/mteb/bge/run_bge.sh
index 5b62734d906..8c24dbcffef 100644
--- a/examples/huggingface/pytorch/text-embedding/deployment/mteb/bge/run_bge.sh
+++ b/examples/huggingface/pytorch/text-embedding/deployment/mteb/bge/run_bge.sh
@@ -123,7 +123,7 @@ else
     echo "==========    Prepare Model ${MODEL_NAME_OR_PATH} with Precision ${PRECISION} ========"
     mode_cmd=""
     if [[ ${PRECISION} = 'int8' ]]; then
-        mode_cmd=$mode_cmd" --tune --quantization_approach static"
+        mode_cmd=$mode_cmd" --tune --quantization_approach PostTrainingStatic"
     elif [[ ${PRECISION} = 'bf16' ]]; then
         mode_cmd=$mode_cmd" --enable_bf16"
     fi
diff --git a/examples/huggingface/pytorch/text-embedding/deployment/mteb/bge/run_mteb.py b/examples/huggingface/pytorch/text-embedding/deployment/mteb/bge/run_mteb.py
index 225c8012b34..557524c6590 100644
--- a/examples/huggingface/pytorch/text-embedding/deployment/mteb/bge/run_mteb.py
+++ b/examples/huggingface/pytorch/text-embedding/deployment/mteb/bge/run_mteb.py
@@ -28,13 +28,7 @@
 import transformers
 from dataclasses import dataclass, field
 from datasets import load_dataset, load_metric
-from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel
-from neural_compressor.config import (
-    PostTrainingQuantConfig,
-    QuantizationAwareTrainingConfig,
-    TuningCriterion,
-    AccuracyCriterion
-)
+from intel_extension_for_transformers.transformers import metrics, objectives, OptimizedModel, QuantizationConfig
 from intel_extension_for_transformers.transformers.trainer import NLPTrainer
 
 from transformers import (
@@ -207,9 +201,9 @@ class OptimizationArguments:
         metadata={"help": "Whether or not to apply quantization."},
     )
     quantization_approach: Optional[str] = field(
-        default="static",
-        metadata={"help": "Quantization approach. Supported approach are static, "
-                  "dynamic and qat."},
+        default="PostTrainingStatic",
+        metadata={"help": "Quantization approach. Supported approach are PostTrainingStatic, "
+                  "PostTrainingDynamic and QuantizationAwareTraining."},
     )
     metric_name: Optional[str] = field(
         default=None,
@@ -577,36 +571,28 @@ def preprocess_function(example):
         if not training_args.do_eval:
             raise ValueError("do_eval must be set to True for quantization.")
 
-        if optim_args.quantization_approach != "dynamic":
+        if optim_args.quantization_approach != "PostTrainingDynamic":
             if not training_args.do_train:
                 raise ValueError(
                     "do_train must be set to True for static and aware training quantization."
                 )
+        if optim_args.quantization_approach == "QuantizationAwareTraining":
+            early_stopping_patience = 6
+            early_stopping_threshold = 0.001 # optional
+            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience,
+                                                                    early_stopping_threshold))
+
         tune_metric = metrics.Metric(
             name=metric_name, is_relative=optim_args.is_relative, criterion=optim_args.perf_tol
         )
-        trainer.metrics = tune_metric
-        tuning_criterion = TuningCriterion(max_trials=600)
-        accuracy_criterion = AccuracyCriterion(
-            higher_is_better=False,  # optional.
-            criterion="relative" if optim_args.is_relative else "absolute",  # optional. Available values are "relative" and "absolute".
-            tolerable_loss=optim_args.perf_tol,  # optional.
+        objective = objectives.performance
+        quantization_config = QuantizationConfig(
+            approach=optim_args.quantization_approach,
+            max_trials=600,
+            metrics=[tune_metric],
+            objectives=[objective],
+	    sampling_size = len(train_dataset)//20
         )
-        if optim_args.quantization_approach != "qat":
-            quantization_config = PostTrainingQuantConfig(
-                approach=optim_args.quantization_approach,
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )
-        else:
-            quantization_config = QuantizationAwareTrainingConfig(
-                tuning_criterion=tuning_criterion,
-                accuracy_criterion=accuracy_criterion
-            )            
-            early_stopping_patience = 2
-            early_stopping_threshold = 0.001 # optional
-            trainer.add_callback(transformers.EarlyStoppingCallback(early_stopping_patience, \
-                                                                    early_stopping_threshold))
 	
         stmodel = SentenceTransformer(model_args.model_name_or_path)
         def eval_func(model):

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