backend/lcmdiffusion/pipelines/openvino/lcm_ov_pipeline.py

# https://huggingface.co/deinferno/LCM_Dreamshaper_v7-openvino

import inspect

from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional, Tuple, Union, Dict, Any, Callable, OrderedDict

import numpy as np
import openvino
import torch

from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from optimum.intel.openvino.modeling_diffusion import OVStableDiffusionPipeline, OVModelUnet, OVModelVaeDecoder, OVModelTextEncoder, OVModelVaeEncoder, VaeImageProcessor
from optimum.utils import (
    DIFFUSION_MODEL_TEXT_ENCODER_2_SUBFOLDER,
    DIFFUSION_MODEL_TEXT_ENCODER_SUBFOLDER,
    DIFFUSION_MODEL_UNET_SUBFOLDER,
    DIFFUSION_MODEL_VAE_DECODER_SUBFOLDER,
    DIFFUSION_MODEL_VAE_ENCODER_SUBFOLDER,
)


from diffusers import logging
logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

class LCMOVModelUnet(OVModelUnet):
    def __call__(
        self,
        sample: np.ndarray,
        timestep: np.ndarray,
        encoder_hidden_states: np.ndarray,
        timestep_cond: Optional[np.ndarray] = None,
        text_embeds: Optional[np.ndarray] = None,
        time_ids: Optional[np.ndarray] = None,
    ):
        self._compile()

        inputs = {
            "sample": sample,
            "timestep": timestep,
            "encoder_hidden_states": encoder_hidden_states,
        }

        if timestep_cond is not None:
            inputs["timestep_cond"] = timestep_cond
        if text_embeds is not None:
            inputs["text_embeds"] = text_embeds
        if time_ids is not None:
            inputs["time_ids"] = time_ids

        outputs = self.request(inputs, shared_memory=True)
        return list(outputs.values())

class OVLatentConsistencyModelPipeline(OVStableDiffusionPipeline):

    def __init__(
        self,
        vae_decoder: openvino.runtime.Model,
        text_encoder: openvino.runtime.Model,
        unet: openvino.runtime.Model,
        config: Dict[str, Any],
        tokenizer: "CLIPTokenizer",
        scheduler: Union["DDIMScheduler", "PNDMScheduler", "LMSDiscreteScheduler"],
        feature_extractor: Optional["CLIPFeatureExtractor"] = None,
        vae_encoder: Optional[openvino.runtime.Model] = None,
        text_encoder_2: Optional[openvino.runtime.Model] = None,
        tokenizer_2: Optional["CLIPTokenizer"] = None,
        device: str = "CPU",
        dynamic_shapes: bool = True,
        compile: bool = True,
        ov_config: Optional[Dict[str, str]] = None,
        model_save_dir: Optional[Union[str, Path, TemporaryDirectory]] = None,
        **kwargs,
    ):
        self._internal_dict = config
        self._device = device.upper()
        self.is_dynamic = dynamic_shapes
        self.ov_config = ov_config if ov_config is not None else {}
        self._model_save_dir = (
            Path(model_save_dir.name) if isinstance(model_save_dir, TemporaryDirectory) else model_save_dir
        )
        self.vae_decoder = OVModelVaeDecoder(vae_decoder, self)
        self.unet = LCMOVModelUnet(unet, self)
        self.text_encoder = OVModelTextEncoder(text_encoder, self) if text_encoder is not None else None
        self.text_encoder_2 = (
            OVModelTextEncoder(text_encoder_2, self, model_name=DIFFUSION_MODEL_TEXT_ENCODER_2_SUBFOLDER)
            if text_encoder_2 is not None
            else None
        )
        self.vae_encoder = OVModelVaeEncoder(vae_encoder, self) if vae_encoder is not None else None

        if "block_out_channels" in self.vae_decoder.config:
            self.vae_scale_factor = 2 ** (len(self.vae_decoder.config["block_out_channels"]) - 1)
        else:
            self.vae_scale_factor = 8

        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)

        self.tokenizer = tokenizer
        self.tokenizer_2 = tokenizer_2
        self.scheduler = scheduler
        self.feature_extractor = feature_extractor
        self.safety_checker = None
        self.preprocessors = []

        if self.is_dynamic:
            self.reshape(batch_size=-1, height=-1, width=-1, num_images_per_prompt=-1)

        if compile:
            self.compile()

        sub_models = {
            DIFFUSION_MODEL_TEXT_ENCODER_SUBFOLDER: self.text_encoder,
            DIFFUSION_MODEL_UNET_SUBFOLDER: self.unet,
            DIFFUSION_MODEL_VAE_DECODER_SUBFOLDER: self.vae_decoder,
            DIFFUSION_MODEL_VAE_ENCODER_SUBFOLDER: self.vae_encoder,
            DIFFUSION_MODEL_TEXT_ENCODER_2_SUBFOLDER: self.text_encoder_2,
        }
        for name in sub_models.keys():
            self._internal_dict[name] = (
                ("optimum", sub_models[name].__class__.__name__) if sub_models[name] is not None else (None, None)
            )

        self._internal_dict.pop("vae", None)

    def _reshape_unet(
        self,
        model: openvino.runtime.Model,
        batch_size: int = -1,
        height: int = -1,
        width: int = -1,
        num_images_per_prompt: int = -1,
        tokenizer_max_length: int = -1,
    ):  
        if batch_size == -1 or num_images_per_prompt == -1:
            batch_size = -1
        else:
            batch_size = batch_size * num_images_per_prompt

        height = height // self.vae_scale_factor if height > 0 else height
        width = width // self.vae_scale_factor if width > 0 else width
        shapes = {}
        for inputs in model.inputs:
            shapes[inputs] = inputs.get_partial_shape()
            if inputs.get_any_name() == "timestep":
                shapes[inputs][0] = 1
            elif inputs.get_any_name() == "sample":
                in_channels = self.unet.config.get("in_channels", None)
                if in_channels is None:
                    in_channels = shapes[inputs][1]
                    if in_channels.is_dynamic:
                        logger.warning(
                            "Could not identify `in_channels` from the unet configuration, to statically reshape the unet please provide a configuration."
                        ) 
                        self.is_dynamic = True
    
                shapes[inputs] = [batch_size, in_channels, height, width]
            elif inputs.get_any_name() == "timestep_cond":
                shapes[inputs] = [batch_size, inputs.get_partial_shape()[1]]
            elif inputs.get_any_name() == "text_embeds":
                shapes[inputs] = [batch_size, self.text_encoder_2.config["projection_dim"]]
            elif inputs.get_any_name() == "time_ids":
                shapes[inputs] = [batch_size, inputs.get_partial_shape()[1]]
            else:
                shapes[inputs][0] = batch_size
                shapes[inputs][1] = tokenizer_max_length
        model.reshape(shapes)
        return model

    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=np.float32):
        """
        see https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
        Args:
        timesteps: np.array: generate embedding vectors at these timesteps
        embedding_dim: int: dimension of the embeddings to generate
        dtype: data type of the generated embeddings

        Returns:
        embedding vectors with shape `(len(timesteps), embedding_dim)`
        """
        assert len(w.shape) == 1
        w = w * 1000.

        half_dim = embedding_dim // 2
        emb = np.log(np.array(10000.)) / (half_dim - 1)
        emb = np.exp(np.arange(half_dim, dtype=dtype) * -emb)
        emb = w.astype(dtype)[:, None] * emb[None, :]
        emb = np.concatenate([np.sin(emb), np.cos(emb)], axis=1)
        if embedding_dim % 2 == 1:  # zero pad
            emb = np.pad(emb, (0, 1))
        assert emb.shape == (w.shape[0], embedding_dim)
        return emb

    # Adapted from https://github.com/huggingface/optimum/blob/15b8d1eed4d83c5004d3b60f6b6f13744b358f01/optimum/pipelines/diffusers/pipeline_stable_diffusion.py#L201
    def __call__(
        self,
        prompt: Optional[Union[str, List[str]]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 4,
        original_inference_steps: int = None,
        guidance_scale: float = 7.5,
        num_images_per_prompt: int = 1,
        eta: float = 0.0,
        generator: Optional[np.random.RandomState] = None,
        latents: Optional[np.ndarray] = None,
        prompt_embeds: Optional[np.ndarray] = None,
        output_type: str = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
        callback_steps: int = 1,
        guidance_rescale: float = 0.0,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`Optional[Union[str, List[str]]]`, defaults to None):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            height (`Optional[int]`, defaults to None):
                The height in pixels of the generated image.
            width (`Optional[int]`, defaults to None):
                The width in pixels of the generated image.
            num_inference_steps (`int`, defaults to 4):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            original_inference_steps (`int`, *optional*):
                The original number of inference steps use to generate a linearly-spaced timestep schedule, from which
                we will draw `num_inference_steps` evenly spaced timesteps from as our final timestep schedule,
                following the Skipping-Step method in the paper (see Section 4.3). If not set this will default to the
                scheduler's `original_inference_steps` attribute.
            guidance_scale (`float`, 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.
            num_images_per_prompt (`int`, defaults to 1):
                The number of images to generate per prompt.
            eta (`float`, 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 (`Optional[np.random.RandomState]`, defaults to `None`)::
                A np.random.RandomState to make generation deterministic.
            latents (`Optional[np.ndarray]`, defaults to `None`):
                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 (`Optional[np.ndarray]`, defaults to `None`):
                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.
            output_type (`str`, 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`, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (Optional[Callable], defaults to `None`):
                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`, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.
            guidance_rescale (`float`, defaults to 0.0):
                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
                Guidance rescale factor should fix overexposure when using zero terminal SNR.

        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`.
        """
        height = height or self.unet.config.get("sample_size", 64) * self.vae_scale_factor
        width = width or self.unet.config.get("sample_size", 64) * self.vae_scale_factor

        # check inputs. Raise error if not correct
        self.check_inputs(
            prompt, height, width, callback_steps, None, prompt_embeds, None
        )

        # define call parameters
        if isinstance(prompt, str):
            batch_size = 1
        elif isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        if generator is None:
            generator = np.random

        # Create torch.Generator instance with same state as np.random.RandomState
        torch_generator = torch.Generator().manual_seed(int(generator.get_state()[1][0]))

        #do_classifier_free_guidance = guidance_scale > 1.0

        # NOTE: when a LCM is distilled from an LDM via latent consistency distillation (Algorithm 1) with guided
        # distillation, the forward pass of the LCM learns to approximate sampling from the LDM using CFG with the
        # unconditional prompt "" (the empty string). Due to this, LCMs currently do not support negative prompts.
        prompt_embeds = self._encode_prompt(
            prompt,
            num_images_per_prompt,
            False,
            negative_prompt=None,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=None,
        )

        # set timesteps
        self.scheduler.set_timesteps(num_inference_steps, "cpu", original_inference_steps=original_inference_steps)
        timesteps = self.scheduler.timesteps

        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            self.unet.config.get("in_channels", 4),
            height,
            width,
            prompt_embeds.dtype,
            generator,
            latents,
        )

        # Get Guidance Scale Embedding
        w = np.tile(guidance_scale - 1, batch_size * num_images_per_prompt)
        w_embedding = self.get_guidance_scale_embedding(w, embedding_dim=self.unet.config.get("time_cond_proj_dim", 256))

        # Adapted from diffusers to extend it for other runtimes than ORT
        timestep_dtype = self.unet.input_dtype.get("timestep", np.float32)

        # 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

        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = torch_generator

        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        for i, t in enumerate(self.progress_bar(timesteps)):

            # predict the noise residual
            timestep = np.array([t], dtype=timestep_dtype)
            
            noise_pred = self.unet(sample=latents, timestep=timestep, timestep_cond = w_embedding, encoder_hidden_states=prompt_embeds)[0]

            # compute the previous noisy sample x_t -> x_t-1
            latents, denoised = self.scheduler.step(
                torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs, return_dict = False
            )

            latents, denoised = latents.numpy(), denoised.numpy()

            # call the callback, if provided
            if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                if callback is not None and i % callback_steps == 0:
                    callback(i, t, latents)

        if output_type == "latent":
            image = latents
            has_nsfw_concept = None
        else:
            denoised /= self.vae_decoder.config.get("scaling_factor", 0.18215)
            # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
            image = np.concatenate(
                [self.vae_decoder(latent_sample=denoised[i : i + 1])[0] for i in range(latents.shape[0])]
            )
            image, has_nsfw_concept = self.run_safety_checker(image)

        if has_nsfw_concept is None:
            do_denormalize = [True] * image.shape[0]
        else:
            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]

        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)