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import inspect
from typing import List, Optional, Union, Dict, Tuple
import numpy as np
from pathlib import Path
from diffusers import AutoPipelineForText2Image
from transformers import CLIPVisionModelWithProjection
from diffusers.utils import load_image
from diffusers import LCMScheduler
import PIL
import cv2
import torch
import openvino as ov
from transformers import CLIPTokenizer, CLIPImageProcessor
from diffusers import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.pipeline_output import (
StableDiffusionPipelineOutput,
)
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from resampler import Resampler
def scale_fit_to_window(dst_width: int, dst_height: int, image_width: int, image_height: int):
"""
Preprocessing helper function for calculating image size for resize with peserving original aspect ratio
and fitting image to specific window size
Parameters:
dst_width (int): destination window width
dst_height (int): destination window height
image_width (int): source image width
image_height (int): source image height
Returns:
result_width (int): calculated width for resize
result_height (int): calculated height for resize
"""
im_scale = min(dst_height / image_height, dst_width / image_width)
return int(im_scale * image_width), int(im_scale * image_height)
def randn_tensor(
shape: Union[Tuple, List],
generator: Optional[Union[List["torch.Generator"], "torch.Generator"]] = None,
dtype: Optional["torch.dtype"] = None,
):
"""A helper function to create random tensors on the desired `device` with the desired `dtype`. When
passing a list of generators, you can seed each batch size individually.
"""
batch_size = shape[0]
rand_device = torch.device("cpu")
# make sure generator list of length 1 is treated like a non-list
if isinstance(generator, list) and len(generator) == 1:
generator = generator[0]
if isinstance(generator, list):
shape = (1,) + shape[1:]
latents = [torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype) for i in range(batch_size)]
latents = torch.cat(latents, dim=0)
else:
latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype)
return latents
def preprocess(image: PIL.Image.Image, height, width):
"""
Image preprocessing function. Takes image in PIL.Image format, resizes it to keep aspect ration and fits to model input window 512x512,
then converts it to np.ndarray and adds padding with zeros on right or bottom side of image (depends from aspect ratio), after that
converts data to float32 data type and change range of values from [0, 255] to [-1, 1], finally, converts data layout from planar NHWC to NCHW.
The function returns preprocessed input tensor and padding size, which can be used in postprocessing.
Parameters:
image (PIL.Image.Image): input image
Returns:
image (np.ndarray): preprocessed image tensor
meta (Dict): dictionary with preprocessing metadata info
"""
src_width, src_height = image.size
dst_width, dst_height = scale_fit_to_window(height, width, src_width, src_height)
image = np.array(image.resize((dst_width, dst_height), resample=PIL.Image.Resampling.LANCZOS))[None, :]
print(image.shape)
pad_width = width - dst_width
pad_height = height - dst_height
pad = ((0, 0), (0, pad_height), (0, pad_width), (0, 0))
image = np.pad(image, pad, mode="constant")
image = image.astype(np.float32) / 255.0
#image = image.astype(np.float16) / 255.0
image = 2.0 * image - 1.0
image = image.transpose(0, 3, 1, 2)
print(image.shape)
return image, {"padding": pad, "src_width": src_width, "src_height": src_height}
class OVStableDiffusionPipeline(DiffusionPipeline):
def __init__(
self,
vae_decoder: ov.Model,
text_encoder: ov.Model,
tokenizer: CLIPTokenizer,
unet: ov.Model,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
image_encoder: ov.Model,
feature_extractor: CLIPImageProcessor,
vae_encoder: ov.Model,
):
"""
Pipeline for text-to-image generation using Stable Diffusion and IP-Adapter with OpenVINO
Parameters:
vae_decoder (ov.Model):
Variational Auto-Encoder (VAE) Model to decode images to and from latent representations.
text_encoder (ov.Model):CLIPImageProcessor
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 (ov.Model): 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
image_encoder (ov.Model):
IP-Adapter image encoder for embedding input image as input prompt for generation
feature_extractor :
"""
super().__init__()
self.scheduler = scheduler
self.vae_decoder = vae_decoder
self.image_encoder = image_encoder
self.text_encoder = text_encoder
self.unet = unet
self.height = 512
self.width = 512
self.vae_scale_factor = 8
self.tokenizer = tokenizer
self.vae_encoder = vae_encoder
self.feature_extractor = feature_extractor
def __call__(
self,
prompt: Union[str, List[str]],
ip_adapter_image: PIL.Image.Image,
image: PIL.Image.Image = None,
num_inference_steps: Optional[int] = 4,
negative_prompt: Union[str, List[str]] = None,
guidance_scale: Optional[float] = 0.5,
eta: Optional[float] = 0.0,
output_type: Optional[str] = "pil",
height: Optional[int] = None,
width: Optional[int] = None,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
strength: float = 1.0,
**kwargs,
):
"""
Function invoked when calling the pipeline for generation.
Parameters:
prompt (str or List[str]):
The prompt or prompts to guide the image generation.
image (PIL.Image.Image, *optional*, None):
Intinal image for generation.
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.
negative_prompt (str or List[str]):https://user-images.githubusercontent.com/29454499/258651862-28b63016-c5ff-4263-9da8-73ca31100165.jpeg
The negative prompt or prompts to guide the image generation.
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.
Higher guidance scale encourages to generate images that are closely linked to the text prompt,
usually at the expense of lower image quality.
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
[DDIMScheduler], will be ignored for others.
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.
height (int, *optional*, 512):
Generated image height
width (int, *optional*, 512):
Generated image width
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](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 is generated by sampling using the supplied random `generator`.
Returns:
Dictionary with keys:
sample - the last generated image PIL.Image.Image or np.arrayhttps://huggingface.co/latent-consistency/lcm-lora-sdv1-5
iterations - *optional* (if gif=True) images for all diffusion steps, List of PIL.Image.Image or np.array.
"""
do_classifier_free_guidance = guidance_scale > 1.0
# get prompt text embeddings
text_embeddings = self._encode_prompt(
prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
)
# get ip-adapter image embeddings
image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image)
if do_classifier_free_guidance:
image_embeds = np.concatenate([negative_image_embeds, image_embeds])
# set timesteps
accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
extra_set_kwargs = {}
if accepts_offset:
extra_set_kwargs["offset"] = 1
self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
latent_timestep = timesteps[:1]
print(num_inference_steps,timesteps)
# get the initial random noise unless the user supplied it
latents, meta = self.prepare_latents(
1,
4,
height or self.height,
width or self.width,
generator=generator,
latents=latents,
image=image,
latent_timestep=latent_timestep,
)
# 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
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if you are doing classifier free guidance
latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet([latent_model_input, t, text_embeddings, image_embeds])[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred[0], noise_pred[1]
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(
torch.from_numpy(noise_pred),
t,
torch.from_numpy(latents),
**extra_step_kwargs,
)["prev_sample"].numpy()
# scale and decode the image latents with vae
image = self.vae_decoder(latents * (1 / 0.18215))[0]
image = self.postprocess_image(image, meta, output_type)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=False)
def _encode_prompt(
self,
prompt: Union[str, List[str]],
num_images_per_prompt: int = 1,
do_classifier_free_guidance: bool = True,
negative_prompt: Union[str, List[str]] = None,
):
"""
Encodes the prompt into text encoder hidden states.
Parameters:
prompt (str or list(str)): prompt to be encoded
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)): negative prompt to be encoded.
Returns:
text_embeddings (np.ndarray): text encoder hidden states
"""
batch_size = len(prompt) if isinstance(prompt, list) else 1
# tokenize input prompts
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
text_input_ids = text_inputs.input_ids
text_embeddings = self.text_encoder(text_input_ids)[0]
# duplicate text embeddings for each generation per prompt
if num_images_per_prompt != 1:
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = np.tile(text_embeddings, (1, num_images_per_prompt, 1))
text_embeddings = np.reshape(text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
max_length = text_input_ids.shape[-1]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
else:
uncond_tokens = negative_prompt
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="np",
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids)[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = np.tile(uncond_embeddings, (1, num_images_per_prompt, 1))
uncond_embeddings = np.reshape(uncond_embeddings, (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
text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
return text_embeddings
def prepare_latents(
self,
batch_size,
num_channels_latents,
height,
width,
dtype=torch.float16,
generator=None,
latents=None,
image=None,
latent_timestep=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, dtype=dtype)
if image is None:
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents.numpy(), {}
input_image, meta = preprocess(image, height, width)
print(input_image.shape)
image_latents = self.vae_encoder(input_image)[0]
image_latents = image_latents * 0.18215
latents = self.scheduler.add_noise(torch.from_numpy(image_latents), latents, latent_timestep).numpy()
return latents, meta
def postprocess_image(self, image: np.ndarray, meta: Dict, output_type: str = "pil"):
"""
Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initial image size (if required),
normalize and convert to [0, 255] pixels range. Optionally, converts it from np.ndarray to PIL.Image format
Parameters:
image (np.ndarray):
Generated image
meta (Dict):
Metadata obtained on the latents preparing step can be empty
output_type (str, *optional*, pil):
Output format for result, can be pil or numpy
Returns:
image (List of np.ndarray or PIL.Image.Image):
Post-processed images
"""
if "padding" in meta:
pad = meta["padding"]
(_, end_h), (_, end_w) = pad[1:3]
h, w = image.shape[2:]
unpad_h = h - end_h
unpad_w = w - end_w
image = image[:, :, :unpad_h, :unpad_w]
image = np.clip(image / 2 + 0.5, 0, 1)
image = np.transpose(image, (0, 2, 3, 1))
# 9. Convert to PIL
if output_type == "pil":
image = self.numpy_to_pil(image)
if "src_height" in meta:
orig_height, orig_width = meta["src_height"], meta["src_width"]
image = [img.resize((orig_width, orig_height), PIL.Image.Resampling.LANCZOS) for img in image]
else:
if "src_height" in meta:
orig_height, orig_width = meta["src_height"], meta["src_width"]
image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
return image
def encode_image(self, image, num_images_per_prompt=1):
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image_embeds = self.image_encoder(image)[0]
"""
print(1,image_embeds)
image_proj_model = Resampler(
dim=1024,
depth=2,
dim_head=64,
heads=16,
num_queries=8,
embedding_dim=1280,
output_dim=1280,
ff_mult=2,
max_seq_len=257,
apply_pos_emb=True,
num_latents_mean_pooled=4,
)
image_embeds = image_proj_model(image_embeds)
print(2,image_embeds)
"""
if num_images_per_prompt > 1:
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = np.zeros(image_embeds.shape)
return image_embeds, uncond_image_embeds
def get_timesteps(self, num_inference_steps: int, strength: float):
"""
Helper function for getting scheduler timesteps for generation
In case of image-to-image generation, it updates number of steps according to strength
Parameters:
num_inference_steps (int):
number of inference steps for generation
strength (float):
value between 0.0 and 1.0, that controls the amount of noise that is added to the input image.
Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input.
"""
# 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
core = ov.Core()
device = "CPU"
models_dir = Path('on-canvers-disney-v3.9.1-ov-face') #'on-canvers-real-ov-ref-v3.9.1')
IMAGE_ENCODER_PATH = models_dir / "image_encoder.xml"
UNET_PATH = models_dir / "unet.xml"
VAE_DECODER_PATH = models_dir / "vae_decoder.xml"
VAE_ENCODER_PATH = models_dir / "vae_encoder.xml"
TEXT_ENCODER_PATH = models_dir / "text_encoder.xml"
from transformers import AutoTokenizer
from PIL import Image
ov_config = {}# {"INFERENCE_PRECISION_HINT": "fp16"}
vae_decoder = core.compile_model(VAE_DECODER_PATH, device, ov_config)
vae_encoder = core.compile_model(VAE_ENCODER_PATH, device, ov_config)
text_encoder = core.compile_model(TEXT_ENCODER_PATH, device)
image_encoder = core.compile_model(IMAGE_ENCODER_PATH, device)
unet = core.compile_model(UNET_PATH, device)
scheduler = LCMScheduler.from_pretrained(models_dir / "scheduler")
tokenizer = AutoTokenizer.from_pretrained(models_dir / "tokenizer")
feature_extractor = CLIPImageProcessor.from_pretrained(models_dir / "feature_extractor")
ov_pipe = OVStableDiffusionPipeline(
vae_decoder,
text_encoder,
tokenizer,
unet,
scheduler,
image_encoder,
feature_extractor,
vae_encoder,
)
generator = torch.Generator(device="cpu").manual_seed(576)
ip_image = load_image("./input.jpg")
#ip_image.resize((512, 512))
image = Image.open("ai_face.png").convert('RGB')
image.resize((512, 512))
#image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/vermeer.jpg")
#ip_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/river.png")
result = ov_pipe(
prompt="best quality, high quality, beautiful korean woman is wearing glasses",
#image=image,
ip_adapter_image=image,
height=512,
width=512,
guidance_scale=1,
generator=generator,
#strength=0.7,
num_inference_steps=4,
).images[0]
result.save("test7.png") |