Create new file

app.py

@@ -0,0 +1,286 @@
+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)