LCM_Dreamshaper_v7-openvino / lcm_ov_pipeline.py

Update scheduler and pipeline code to be closer to diffusers version

8943a6e 8 months ago

No virus

18.5 kB

	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)