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from huggingface_hub import notebook_login
import cv2
from google.colab.patches import cv2_imshow
import tempfile
notebook_login()
import inspect
from typing import List, Optional, Union
import numpy as np
import torch
import PIL
from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, PNDMScheduler, UNet2DConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
def preprocess_image(image):
w, h = image.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.0 * image - 1.0
def preprocess_mask(mask):
mask=mask.convert("L")
w, h = mask.size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
mask = mask.resize((w//8, h//8), resample=PIL.Image.NEAREST)
mask = np.array(mask).astype(np.float32) / 255.0
mask = np.tile(mask,(4,1,1))
mask = mask[None].transpose(0, 1, 2, 3)#what does this step do?
mask = 1 - mask #repaint white, keep black
mask = torch.from_numpy(mask)
return mask
class StableDiffusionInpaintingPipeline(DiffusionPipeline):
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[DDIMScheduler, PNDMScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPFeatureExtractor,
):
super().__init__()
scheduler = scheduler.set_format("pt")
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
init_image: torch.FloatTensor,
mask_image: torch.FloatTensor,
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
guidance_scale: Optional[float] = 7.5,
eta: Optional[float] = 0.0,
generator: Optional[torch.Generator] = None,
output_type: Optional[str] = "pil",
):
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
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}")
# set timesteps
accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
extra_set_kwargs = {}
offset = 0
if accepts_offset:
offset = 1
extra_set_kwargs["offset"] = 1
self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
#preprocess image
init_image = preprocess_image(init_image).to(self.device)
# encode the init image into latents and scale the latents
init_latents = self.vae.encode(init_image).sample()
init_latents = 0.18215 * init_latents
# prepare init_latents noise to latents
init_latents = torch.cat([init_latents] * batch_size)
init_latents_orig = init_latents
# preprocess mask
mask = preprocess_mask(mask_image).to(self.device)
mask = torch.cat([mask] * batch_size)
#check sizes
if not mask.shape == init_latents.shape:
raise ValueError(f"The mask and init_image should be the same size!")
# get the original timestep using init_timestep
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
timesteps = self.scheduler.timesteps[-init_timestep]
timesteps = torch.tensor([timesteps] * batch_size, dtype=torch.long, device=self.device)
# add noise to latents using the timesteps
noise = torch.randn(init_latents.shape, generator=generator, device=self.device)
init_latents = self.scheduler.add_noise(init_latents, noise, timesteps)
# get prompt text embeddings
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
# 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
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
max_length = text_input.input_ids.shape[-1]
uncond_input = self.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
# 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 = torch.cat([uncond_embeddings, text_embeddings])
# 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
latents = init_latents
t_start = max(num_inference_steps - init_timestep + offset, 0)
for i, t in tqdm(enumerate(self.scheduler.timesteps[t_start:])):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]
# 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"]
#masking
init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, t)
latents = ( init_latents_proper * mask ) + ( latents * (1-mask) )
# scale and decode the image latents with vae
latents = 1 / 0.18215 * latents
image = self.vae.decode(latents)
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
# run safety checker
safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
if output_type == "pil":
image = self.numpy_to_pil(image)
return {"sample": image, "nsfw_content_detected": has_nsfw_concept}
device = "cuda"
model_path = "CompVis/stable-diffusion-v1-4"
pipe = StableDiffusionInpaintingPipeline.from_pretrained(
model_path,
revision="fp16",
torch_dtype=torch.float16,
use_auth_token=True
).to(device)
import gdown
def download_gdrive_url():
url = 'https://drive.google.com/u/0/uc?id=1PPO2MCttsmSqyB-vKh5C7SumwFKuhgyj&export=download'
output = 'haarcascade_frontalface_default.xml'
gdown.download(url, output, quiet=False)
from torch import autocast
def inpaint(p, init_image, mask_image=None, strength=0.75, guidance_scale=7.5, generator=None, num_samples=1, n_iter=1):
all_images = []
for _ in range(n_iter):
with autocast("cuda"):
images = pipe(
prompt=[p] * num_samples,
init_image=init_image,
mask_image=mask_image,
strength=strength,
guidance_scale=guidance_scale,
generator=generator,
num_inference_steps=75
)["sample"]
all_images.extend(images)
print(len(all_images))
return all_images[0]
def identify_face(user_image):
img = cv2.imread(user_image.name) # read the resized image in cv2
print(img.shape)
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # convert to grayscale
download_gdrive_url() #download the haarcascade face recognition stuff
haar_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
faces_rect = haar_cascade.detectMultiScale(gray_img, scaleFactor=1.1, minNeighbors=9)
for (x, y, w, h) in faces_rect[:1]:
mask = np.zeros(img.shape[:2], dtype="uint8")
print(mask.shape)
cv2.rectangle(mask, (x, y), (x+w, y+h), 255, -1)
print(mask.shape)
inverted_image = cv2.bitwise_not(mask)
return inverted_image
def sample_images(init_image, mask_image):
p = "4K UHD professional profile picture of a person wearing a suit for work"
strength=0.65
guidance_scale=10
num_samples = 1
n_iter = 1
generator = torch.Generator(device="cuda").manual_seed(random.randint(0, 1000000)) # change the seed to get different results
all_images = inpaint(p, init_image, mask_image, strength=strength, guidance_scale=guidance_scale, generator=generator, num_samples=num_samples, n_iter=n_iter)
return all_images
import gradio as gr
import random
# accept an image input
# trigger the set of functions to occur => identify face, generate mask, save the inverted face mask, sample for the inverted images
# output the sampled images
def main(user_image):
# accept the image as input
init_image = PIL.Image.open(user_image).convert("RGB")
# # resize the image to be (512, 512)
newsize = (512, 512)
init_image = init_image.resize(newsize)
init_image.save(user_image.name) # save the resized image
## identify the face + save the inverted mask
inverted_mask = identify_face(user_image)
fp = tempfile.NamedTemporaryFile(mode='wb', suffix=".png")
cv2.imwrite(fp.name, inverted_mask) # save the inverted image mask
pil_inverted_mask = PIL.Image.open(fp.name).convert("RGB")
print("type(init_image): ", type(init_image))
print("type(pil_inverted_mask): ", type(pil_inverted_mask))
# sample the new images
return sample_images(init_image, pil_inverted_mask)
demo = gr.Interface(main, gr.Image(type="file"), "image")
demo.launch(debug=True) |