diff --git a/README.md b/README.md index 9efa77c1..c156b80e 100644 --- a/README.md +++ b/README.md @@ -40,6 +40,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy - [How to Make a Ransomware in Python](https://www.thepythoncode.com/article/make-a-ransomware-in-python). ([code](ethical-hacking/ransomware)) - [How to Perform DNS Enumeration in Python](https://www.thepythoncode.com/article/dns-enumeration-with-python). ([code](ethical-hacking/dns-enumeration)) - [How to Geolocate IP addresses in Python](https://www.thepythoncode.com/article/geolocate-ip-addresses-with-ipinfo-in-python). ([code](ethical-hacking/geolocating-ip-addresses)) + - [How to Crack Hashes in Python](https://thepythoncode.com/article/crack-hashes-in-python). ([code](ethical-hacking/hash-cracker)) - ### [Machine Learning](https://www.thepythoncode.com/topic/machine-learning) - ### [Natural Language Processing](https://www.thepythoncode.com/topic/nlp) @@ -61,6 +62,8 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy - [How to Fine Tune BERT for Semantic Textual Similarity using Transformers in Python](https://www.thepythoncode.com/article/finetune-bert-for-semantic-textual-similarity-in-python). ([code](machine-learning/nlp/semantic-textual-similarity)) - [How to Calculate the BLEU Score in Python](https://www.thepythoncode.com/article/bleu-score-in-python). ([code](machine-learning/nlp/bleu-score)) - [Word Error Rate in Python](https://www.thepythoncode.com/article/calculate-word-error-rate-in-python). ([code](machine-learning/nlp/wer-score)) + - [How to Calculate ROUGE Score in Python](https://www.thepythoncode.com/article/calculate-rouge-score-in-python). ([code](machine-learning/nlp/rouge-score)) + - [Visual Question Answering with Transformers](https://www.thepythoncode.com/article/visual-question-answering-with-transformers-in-python). ([code](machine-learning/visual-question-answering)) - ### [Computer Vision](https://www.thepythoncode.com/topic/computer-vision) - [How to Detect Human Faces in Python using OpenCV](https://www.thepythoncode.com/article/detect-faces-opencv-python). ([code](machine-learning/face_detection)) - [How to Make an Image Classifier in Python using TensorFlow and Keras](https://www.thepythoncode.com/article/image-classification-keras-python). ([code](machine-learning/image-classifier)) @@ -86,6 +89,10 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy - [How to Generate Images from Text using Stable Diffusion in Python](https://www.thepythoncode.com/article/generate-images-from-text-stable-diffusion-python). ([code](machine-learning/stable-diffusion-models)) - [How to Perform Image to Image Generation with Stable Diffusion in Python](https://www.thepythoncode.com/article/generate-images-using-depth-to-image-huggingface-python). ([code](machine-learning/depth2image-stable-diffusion)) - [Real-time Object Tracking with OpenCV and YOLOv8 in Python](https://www.thepythoncode.com/article/real-time-object-tracking-with-yolov8-opencv). ([code](https://github.com/python-dontrepeatyourself/Real-Time-Object-Tracking-with-DeepSORT-and-YOLOv8)) + - [How to Control the Generated Images by diffusion models via ControlNet in Python](https://www.thepythoncode.com/article/control-generated-images-with-controlnet-with-huggingface). ([code](machine-learning/control-image-generation-with-controlnet)) + - [How to Edit Images using InstructPix2Pix in Python](https://www.thepythoncode.com/article/edit-images-using-instruct-pix2pix-with-huggingface). ([code](machine-learning/edit-images-instruct-pix2pix)) + - [How to Upscale Images using Stable Diffusion in Python](https://www.thepythoncode.com/article/upscale-images-using-stable-diffusion-x4-upscaler-huggingface). ([code](machine-learning/stable-diffusion-upscaler)) + - [Real-Time Vehicle Detection, Tracking and Counting in Python](https://thepythoncode.com/article/real-time-vehicle-tracking-and-counting-with-yolov8-opencv). ([code](https://github.com/python-dontrepeatyourself/Real-Time-Vehicle-Detection-Tracking-and-Counting-in-Python/)) - [Building a Speech Emotion Recognizer using Scikit-learn](https://www.thepythoncode.com/article/building-a-speech-emotion-recognizer-using-sklearn). ([code](machine-learning/speech-emotion-recognition)) - [How to Convert Speech to Text in Python](https://www.thepythoncode.com/article/using-speech-recognition-to-convert-speech-to-text-python). ([code](machine-learning/speech-recognition)) - [Top 8 Python Libraries For Data Scientists and Machine Learning Engineers](https://www.thepythoncode.com/article/top-python-libraries-for-data-scientists). @@ -253,6 +260,9 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy - [How to Make an Accounting App with Django in Python](https://www.thepythoncode.com/article/make-an-accounting-app-with-django-in-python). ([code](web-programming/accounting-app)) - [How to Build a News Site API with Django Rest Framework in Python](https://www.thepythoncode.com/article/a-news-site-api-with-django-python). ([code](web-programming/news_project)) - [How to Create a RESTful API with Flask in Python](https://www.thepythoncode.com/article/create-a-restful-api-with-flask-in-python). ([code](web-programming/restful-api-flask)) + - [How to Build a GraphQL API in Python](https://www.thepythoncode.com/article/build-a-graphql-api-with-fastapi-strawberry-and-postgres-python). ([code](https://github.com/menard-codes/PythonGQLArticle)) + - [How to Build a Chat App using Flask in Python](https://thepythoncode.com/article/how-to-build-a-chat-app-in-python-using-flask-and-flasksocketio). ([code](https://github.com/menard-codes/FlaskChatApp)) + - [How to Build a Full-Stack Web App in Python using FastAPI and React.js](https://thepythoncode.com/article/fullstack-notes-app-with-fastapi-and-reactjs) ([Backend](https://github.com/menard-codes/NotesAppBackend-FastAPI-React), [Frontend](https://github.com/menard-codes/NotesAppFrontend-FastAPI-React)) - ### [GUI Programming](https://www.thepythoncode.com/topic/gui-programming) - [How to Make a Text Editor using Tkinter in Python](https://www.thepythoncode.com/article/text-editor-using-tkinter-python). ([code](gui-programming/text-editor)) @@ -288,6 +298,9 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy - [How to Make a Checkers Game with Pygame in Python](https://www.thepythoncode.com/article/make-a-checkers-game-with-pygame-in-python). ([code](gui-programming/checkers-game)) - [How to Make a Snake Game in Python](https://www.thepythoncode.com/article/make-a-snake-game-with-pygame-in-python). ([code](gui-programming/snake-game)) - [How to Create a Slide Puzzle Game in Python](https://www.thepythoncode.com/article/slide-puzzle-game-in-python). ([code](gui-programming/slide-puzzle)) + - [How to Make a Maze Game in Python](https://www.thepythoncode.com/article/build-a-maze-game-in-python). ([code](gui-programming/maze-game)) + - [How to Create a Platformer Game in Python](https://www.thepythoncode.com/article/platformer-game-with-pygame-in-python). ([code](gui-programming/platformer-game)) + - [How to Make a Flappy Bird Game in Python](https://thepythoncode.com/article/make-a-flappy-bird-game-python). ([code](gui-programming/flappy-bird-game)) For any feedback, please consider pulling requests. diff --git a/ethical-hacking/hash-cracker/README.md b/ethical-hacking/hash-cracker/README.md new file mode 100644 index 00000000..7f59fab6 --- /dev/null +++ b/ethical-hacking/hash-cracker/README.md @@ -0,0 +1,14 @@ +# [How to Crack Hashes in Python](https://thepythoncode.com/article/crack-hashes-in-python) +To run this: +- `pip install -r requirements.txt` +- Get usage: `python crack_hashes.py --help` +- Crack a SHA-256 hash using `wordlist.txt`: + ```bash + $ python crack_hashes.py 6ca13d52ca70c883e0f0bb101e425a89e8624de51db2d2392593af6a84118090 wordlist.txt --hash-type sha256 + ``` + **Output:** + ``` + [*] Cracking hash 6ca13d52ca70c883e0f0bb101e425a89e8624de51db2d2392593af6a84118090 using sha256 with a list of 14344394 words. + Cracking hash: 96%|███████████████████████████████████████████████████████████████████████████████████████████▉ | 13735317/14344394 [00:20<00:00, 664400.58it/s] + [+] Found password: abc123 + ``` \ No newline at end of file diff --git a/ethical-hacking/hash-cracker/crack_hashes.py b/ethical-hacking/hash-cracker/crack_hashes.py new file mode 100644 index 00000000..5e98db51 --- /dev/null +++ b/ethical-hacking/hash-cracker/crack_hashes.py @@ -0,0 +1,53 @@ +import hashlib +from tqdm import tqdm + +# List of supported hash types +hash_names = [ + 'blake2b', + 'blake2s', + 'md5', + 'sha1', + 'sha224', + 'sha256', + 'sha384', + 'sha3_224', + 'sha3_256', + 'sha3_384', + 'sha3_512', + 'sha512', +] + +def crack_hash(hash, wordlist, hash_type=None): + """Crack a hash using a wordlist. + + Args: + hash (str): The hash to crack. + wordlist (str): The path to the wordlist. + + Returns: + str: The cracked hash. + """ + hash_fn = getattr(hashlib, hash_type, None) + if hash_fn is None or hash_type not in hash_names: + # not supported hash type + raise ValueError(f'[!] Invalid hash type: {hash_type}, supported are {hash_names}') + # Count the number of lines in the wordlist to set the total + total_lines = sum(1 for line in open(wordlist, 'r')) + print(f"[*] Cracking hash {hash} using {hash_type} with a list of {total_lines} words.") + # open the wordlist + with open(wordlist, 'r') as f: + # iterate over each line + for line in tqdm(f, desc='Cracking hash', total=total_lines): + if hash_fn(line.strip().encode()).hexdigest() == hash: + return line + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser(description='Crack a hash using a wordlist.') + parser.add_argument('hash', help='The hash to crack.') + parser.add_argument('wordlist', help='The path to the wordlist.') + parser.add_argument('--hash-type', help='The hash type to use.', default='md5') + args = parser.parse_args() + print() + print("[+] Found password:", crack_hash(args.hash, args.wordlist, args.hash_type)) diff --git a/ethical-hacking/hash-cracker/requirements.txt b/ethical-hacking/hash-cracker/requirements.txt new file mode 100644 index 00000000..fa9cf064 --- /dev/null +++ b/ethical-hacking/hash-cracker/requirements.txt @@ -0,0 +1 @@ +tqdm \ No newline at end of file diff --git a/ethical-hacking/hash-cracker/wordlist.txt b/ethical-hacking/hash-cracker/wordlist.txt new file mode 100644 index 00000000..554c961b --- /dev/null +++ b/ethical-hacking/hash-cracker/wordlist.txt @@ -0,0 +1,5000 @@ +123456 +12345 +123456789 +password +iloveyou +princess +12345678 +1234567 +nicole +daniel +monkey +babygirl +qwerty +lovely +654321 +michael +jessica +111111 +ashley +000000 +iloveu +michelle +tigger +sunshine +chocolate +password1 +soccer +anthony +friends +purple +angel +butterfly +jordan +fuckyou +123123 +justin +liverpool +football +loveme +secret +andrea +jennifer +joshua +carlos +superman +bubbles +hannah +1234567890 +amanda +andrew +loveyou +pretty +basketball +angels +flower +tweety +hello +playboy +charlie +elizabeth +samantha +hottie +chelsea +tinkerbell +shadow +barbie +666666 +jasmine +lovers +brandon +teamo +matthew 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+princess01 +parrot +ducky +rasmus +inlove1 +kookie +biteme1 +karen1 +fernandes +zipper +smoking +brujita +toledo +abcdef12345 \ No newline at end of file diff --git a/gui-programming/flappy-bird-game/README.md b/gui-programming/flappy-bird-game/README.md new file mode 100644 index 00000000..5bcb2419 --- /dev/null +++ b/gui-programming/flappy-bird-game/README.md @@ -0,0 +1 @@ +# [How to Make a Flappy Bird Game in Python](https://thepythoncode.com/article/make-a-flappy-bird-game-python) \ No newline at end of file diff --git a/gui-programming/flappy-bird-game/assets/bird/0.png b/gui-programming/flappy-bird-game/assets/bird/0.png new file mode 100644 index 00000000..cc0c1f20 Binary files /dev/null and b/gui-programming/flappy-bird-game/assets/bird/0.png differ diff --git a/gui-programming/flappy-bird-game/assets/bird/1.png b/gui-programming/flappy-bird-game/assets/bird/1.png new file mode 100644 index 00000000..73e9592a Binary files /dev/null and b/gui-programming/flappy-bird-game/assets/bird/1.png differ diff --git a/gui-programming/flappy-bird-game/assets/bird/2.png b/gui-programming/flappy-bird-game/assets/bird/2.png new file mode 100644 index 00000000..d89bb79b Binary files /dev/null and b/gui-programming/flappy-bird-game/assets/bird/2.png differ diff --git a/gui-programming/flappy-bird-game/assets/terrain/bg.png b/gui-programming/flappy-bird-game/assets/terrain/bg.png new file mode 100644 index 00000000..0c6b138c Binary files /dev/null and b/gui-programming/flappy-bird-game/assets/terrain/bg.png differ diff --git a/gui-programming/flappy-bird-game/assets/terrain/ground.png b/gui-programming/flappy-bird-game/assets/terrain/ground.png new file mode 100644 index 00000000..70105cb5 Binary files /dev/null and b/gui-programming/flappy-bird-game/assets/terrain/ground.png differ diff --git a/gui-programming/flappy-bird-game/assets/terrain/pipe.png b/gui-programming/flappy-bird-game/assets/terrain/pipe.png new file mode 100644 index 00000000..03e3e82c Binary files /dev/null and b/gui-programming/flappy-bird-game/assets/terrain/pipe.png differ diff --git a/gui-programming/flappy-bird-game/bird.py b/gui-programming/flappy-bird-game/bird.py new file mode 100644 index 00000000..c5ad5e89 --- /dev/null +++ b/gui-programming/flappy-bird-game/bird.py @@ -0,0 +1,42 @@ +import pygame +from settings import import_sprite + +class Bird(pygame.sprite.Sprite): + def __init__(self, pos, size): + super().__init__() + # bird basic info + self.frame_index = 0 + self.animation_delay = 3 + self.jump_move = -9 + + # bird animation + self.bird_img = import_sprite("assets/bird") + self.image = self.bird_img[self.frame_index] + self.image = pygame.transform.scale(self.image, (size, size)) + self.rect = self.image.get_rect(topleft = pos) + self.mask = pygame.mask.from_surface(self.image) + + # bird status + self.direction = pygame.math.Vector2(0, 0) + self.score = 0 + + # for bird's flying animation + def _animate(self): + sprites = self.bird_img + sprite_index = (self.frame_index // self.animation_delay) % len(sprites) + self.image = sprites[sprite_index] + self.frame_index += 1 + self.rect = self.image.get_rect(topleft=(self.rect.x, self.rect.y)) + self.mask = pygame.mask.from_surface(self.image) + if self.frame_index // self.animation_delay > len(sprites): + self.frame_index = 0 + + # to make the bird fly higher + def _jump(self): + self.direction.y = self.jump_move + + # updates the bird's overall state + def update(self, is_jump): + if is_jump: + self._jump() + self._animate() \ No newline at end of file diff --git a/gui-programming/flappy-bird-game/game.py b/gui-programming/flappy-bird-game/game.py new file mode 100644 index 00000000..d2d7e622 --- /dev/null +++ b/gui-programming/flappy-bird-game/game.py @@ -0,0 +1,25 @@ +import pygame +from settings import WIDTH, HEIGHT + +pygame.font.init() + +class GameIndicator: + def __init__(self, screen): + self.screen = screen + self.font = pygame.font.SysFont('Bauhaus 93', 60) + self.inst_font = pygame.font.SysFont('Bauhaus 93', 30) + self.color = pygame.Color("white") + self.inst_color = pygame.Color("black") + + def show_score(self, int_score): + bird_score = str(int_score) + score = self.font.render(bird_score, True, self.color) + self.screen.blit(score, (WIDTH // 2, 50)) + + def instructions(self): + inst_text1 = "Press SPACE button to Jump," + inst_text2 = "Press \"R\" Button to Restart Game." + ins1 = self.inst_font.render(inst_text1, True, self.inst_color) + ins2 = self.inst_font.render(inst_text2, True, self.inst_color) + self.screen.blit(ins1, (95, 400)) + self.screen.blit(ins2, (70, 450)) diff --git a/gui-programming/flappy-bird-game/main.py b/gui-programming/flappy-bird-game/main.py new file mode 100644 index 00000000..61e68c60 --- /dev/null +++ b/gui-programming/flappy-bird-game/main.py @@ -0,0 +1,53 @@ +import pygame, sys +from settings import WIDTH, HEIGHT, ground_space +from world import World + +pygame.init() + +screen = pygame.display.set_mode((WIDTH, HEIGHT + ground_space)) +pygame.display.set_caption("Flappy Bird") + +class Main: + def __init__(self, screen): + self.screen = screen + self.bg_img = pygame.image.load('assets/terrain/bg.png') + self.bg_img = pygame.transform.scale(self.bg_img, (WIDTH, HEIGHT)) + self.ground_img = pygame.image.load('assets/terrain/ground.png') + self.ground_scroll = 0 + self.scroll_speed = -6 + self.FPS = pygame.time.Clock() + self.stop_ground_scroll = False + + def main(self): + world = World(screen) + while True: + self.stop_ground_scroll = world.game_over + self.screen.blit(self.bg_img, (0, 0)) + + for event in pygame.event.get(): + if event.type == pygame.QUIT: + pygame.quit() + sys.exit() + + elif event.type == pygame.KEYDOWN: + if not world.playing and not world.game_over: + world.playing = True + if event.key == pygame.K_SPACE: + world.update("jump") + if event.key == pygame.K_r: + world.update("restart") + + world.update() + + self.screen.blit(self.ground_img, (self.ground_scroll, HEIGHT)) + if not self.stop_ground_scroll: + self.ground_scroll += self.scroll_speed + if abs(self.ground_scroll) > 35: + self.ground_scroll = 0 + + pygame.display.update() + self.FPS.tick(60) + +if __name__ == "__main__": + play = Main(screen) + play.main() diff --git a/gui-programming/flappy-bird-game/pipe.py b/gui-programming/flappy-bird-game/pipe.py new file mode 100644 index 00000000..a66a959b --- /dev/null +++ b/gui-programming/flappy-bird-game/pipe.py @@ -0,0 +1,21 @@ +import pygame + +class Pipe(pygame.sprite.Sprite): + def __init__(self, pos, width, height, flip): + super().__init__() + self.width = width + img_path = 'assets/terrain/pipe.png' + self.image = pygame.image.load(img_path) + self.image = pygame.transform.scale(self.image, (width, height)) + if flip: + flipped_image = pygame.transform.flip(self.image, False, True) + self.image = flipped_image + self.rect = self.image.get_rect(topleft = pos) + + # update object position due to world scroll + def update(self, x_shift): + self.rect.x += x_shift + + # removes the pipe in the game screen once it is not shown in the screen anymore + if self.rect.right < (-self.width): + self.kill() \ No newline at end of file diff --git a/gui-programming/flappy-bird-game/requirements.txt b/gui-programming/flappy-bird-game/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/flappy-bird-game/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/flappy-bird-game/settings.py b/gui-programming/flappy-bird-game/settings.py new file mode 100644 index 00000000..2c5eb460 --- /dev/null +++ b/gui-programming/flappy-bird-game/settings.py @@ -0,0 +1,26 @@ +from os import walk +import pygame + +WIDTH, HEIGHT = 600, 650 + +pipe_pair_sizes = [ + (1, 7), + (2, 6), + (3, 5), + (4, 4), + (5, 3), + (6, 2), + (7, 1) +] +pipe_size = HEIGHT // 10 +pipe_gap = (pipe_size * 2) + (pipe_size // 2) +ground_space = 50 + +def import_sprite(path): + surface_list = [] + for _, __, img_file in walk(path): + for image in img_file: + full_path = f"{path}/{image}" + img_surface = pygame.image.load(full_path).convert_alpha() + surface_list.append(img_surface) + return surface_list \ No newline at end of file diff --git a/gui-programming/flappy-bird-game/world.py b/gui-programming/flappy-bird-game/world.py new file mode 100644 index 00000000..36bf131f --- /dev/null +++ b/gui-programming/flappy-bird-game/world.py @@ -0,0 +1,101 @@ +import pygame +from pipe import Pipe +from bird import Bird +from game import GameIndicator +from settings import WIDTH, HEIGHT, pipe_size, pipe_gap, pipe_pair_sizes +import random + +class World: + def __init__(self, screen): + self.screen = screen + self.world_shift = 0 + self.current_x = 0 + self.gravity = 0.5 + self.current_pipe = None + self.pipes = pygame.sprite.Group() + self.player = pygame.sprite.GroupSingle() + self._generate_world() + self.playing = False + self.game_over = False + self.passed = True + self.game = GameIndicator(screen) + + # creates the player and the obstacle + def _generate_world(self): + self._add_pipe() + bird = Bird((WIDTH//2 - pipe_size, HEIGHT//2 - pipe_size), 30) + self.player.add(bird) + + # adds pipe once the last pipe added reached the desired pipe horizontal spaces + def _add_pipe(self): + pipe_pair_size = random.choice(pipe_pair_sizes) + top_pipe_height, bottom_pipe_height = pipe_pair_size[0] * pipe_size, pipe_pair_size[1] * pipe_size + + pipe_top = Pipe((WIDTH, 0 - (bottom_pipe_height + pipe_gap)), pipe_size, HEIGHT, True) + pipe_bottom = Pipe((WIDTH, top_pipe_height + pipe_gap), pipe_size, HEIGHT, False) + self.pipes.add(pipe_top) + self.pipes.add(pipe_bottom) + self.current_pipe = pipe_top + + # for moving background/obstacle + def _scroll_x(self): + if self.playing: + self.world_shift = -6 + else: + self.world_shift = 0 + + # add gravity to bird for falling + def _apply_gravity(self, player): + if self.playing or self.game_over: + player.direction.y += self.gravity + player.rect.y += player.direction.y + + # handles scoring and collision + def _handle_collisions(self): + bird = self.player.sprite + # for collision checking + if pygame.sprite.groupcollide(self.player, self.pipes, False, False) or bird.rect.bottom >= HEIGHT or bird.rect.top <= 0: + self.playing = False + self.game_over = True + else: + # if player pass through the pipe gaps + bird = self.player.sprite + if bird.rect.x >= self.current_pipe.rect.centerx: + bird.score += 1 + self.passed = True + + # updates the bird's overall state + def update(self, player_event = None): + # new pipe adder + if self.current_pipe.rect.centerx <= (WIDTH // 2) - pipe_size: + self._add_pipe() + + # updates, draws pipes + self.pipes.update(self.world_shift) + self.pipes.draw(self.screen) + + # applying game physics + self._apply_gravity(self.player.sprite) + self._scroll_x() + self._handle_collisions() + + # configuring player actions + if player_event == "jump" and not self.game_over: + player_event = True + elif player_event == "restart": + self.game_over = False + self.pipes.empty() + self.player.empty() + self.player.score = 0 + self._generate_world() + else: + player_event = False + + if not self.playing: + self.game.instructions() + + # updates, draws pipes + self.player.update(player_event) + self.player.draw(self.screen) + + self.game.show_score(self.player.sprite.score) \ No newline at end of file diff --git a/gui-programming/maze-game/README.md b/gui-programming/maze-game/README.md new file mode 100644 index 00000000..5c6a2cd8 --- /dev/null +++ b/gui-programming/maze-game/README.md @@ -0,0 +1 @@ +# [How to Make a Maze Game in Python](https://www.thepythoncode.com/article/build-a-maze-game-in-python) \ No newline at end of file diff --git a/gui-programming/maze-game/cell.py b/gui-programming/maze-game/cell.py new file mode 100644 index 00000000..5cbf4963 --- /dev/null +++ b/gui-programming/maze-game/cell.py @@ -0,0 +1,45 @@ +import pygame +from random import choice + +class Cell: + def __init__(self, x, y, thickness): + self.x, self.y = x, y + self.thickness = thickness + self.walls = {'top': True, 'right': True, 'bottom': True, 'left': True} + self.visited = False + + # draw grid cell walls + def draw(self, sc, tile): + x, y = self.x * tile, self.y * tile + if self.walls['top']: + pygame.draw.line(sc, pygame.Color('darkgreen'), (x, y), (x + tile, y), self.thickness) + if self.walls['right']: + pygame.draw.line(sc, pygame.Color('darkgreen'), (x + tile, y), (x + tile, y + tile), self.thickness) + if self.walls['bottom']: + pygame.draw.line(sc, pygame.Color('darkgreen'), (x + tile, y + tile), (x , y + tile), self.thickness) + if self.walls['left']: + pygame.draw.line(sc, pygame.Color('darkgreen'), (x, y + tile), (x, y), self.thickness) + + # checks if cell does exist and returns it if it does + def check_cell(self, x, y, cols, rows, grid_cells): + find_index = lambda x, y: x + y * cols + if x < 0 or x > cols - 1 or y < 0 or y > rows - 1: + return False + return grid_cells[find_index(x, y)] + + # checking cell neighbors of current cell if visited (carved) or not + def check_neighbors(self, cols, rows, grid_cells): + neighbors = [] + top = self.check_cell(self.x, self.y - 1, cols, rows, grid_cells) + right = self.check_cell(self.x + 1, self.y, cols, rows, grid_cells) + bottom = self.check_cell(self.x, self.y + 1, cols, rows, grid_cells) + left = self.check_cell(self.x - 1, self.y, cols, rows, grid_cells) + if top and not top.visited: + neighbors.append(top) + if right and not right.visited: + neighbors.append(right) + if bottom and not bottom.visited: + neighbors.append(bottom) + if left and not left.visited: + neighbors.append(left) + return choice(neighbors) if neighbors else False diff --git a/gui-programming/maze-game/clock.py b/gui-programming/maze-game/clock.py new file mode 100644 index 00000000..1686aa7f --- /dev/null +++ b/gui-programming/maze-game/clock.py @@ -0,0 +1,30 @@ +import pygame, time + +pygame.font.init() + +class Clock: + def __init__(self): + self.start_time = None + self.elapsed_time = 0 + self.font = pygame.font.SysFont("monospace", 35) + self.message_color = pygame.Color("yellow") + + # Start the timer + def start_timer(self): + self.start_time = time.time() + + # Update the timer + def update_timer(self): + if self.start_time is not None: + self.elapsed_time = time.time() - self.start_time + + # Display the timer + def display_timer(self): + secs = int(self.elapsed_time % 60) + mins = int(self.elapsed_time / 60) + my_time = self.font.render(f"{mins:02}:{secs:02}", True, self.message_color) + return my_time + + # Stop the timer + def stop_timer(self): + self.start_time = None diff --git a/gui-programming/maze-game/game.py b/gui-programming/maze-game/game.py new file mode 100644 index 00000000..13815ad5 --- /dev/null +++ b/gui-programming/maze-game/game.py @@ -0,0 +1,31 @@ +import pygame + +pygame.font.init() + +class Game: + def __init__(self, goal_cell, tile): + self.font = pygame.font.SysFont("impact", 35) + self.message_color = pygame.Color("darkorange") + self.goal_cell = goal_cell + self.tile = tile + + # add goal point for player to reach + def add_goal_point(self, screen): + # adding gate for the goal point + img_path = 'img/gate.png' + img = pygame.image.load(img_path) + img = pygame.transform.scale(img, (self.tile, self.tile)) + screen.blit(img, (self.goal_cell.x * self.tile, self.goal_cell.y * self.tile)) + + # winning message + def message(self): + msg = self.font.render('You Win!!', True, self.message_color) + return msg + + # checks if player reached the goal point + def is_game_over(self, player): + goal_cell_abs_x, goal_cell_abs_y = self.goal_cell.x * self.tile, self.goal_cell.y * self.tile + if player.x >= goal_cell_abs_x and player.y >= goal_cell_abs_y: + return True + else: + return False \ No newline at end of file diff --git a/gui-programming/maze-game/img/gate.png b/gui-programming/maze-game/img/gate.png new file mode 100644 index 00000000..c088b640 Binary files /dev/null and b/gui-programming/maze-game/img/gate.png differ diff --git a/gui-programming/maze-game/main.py b/gui-programming/maze-game/main.py new file mode 100644 index 00000000..8ef89a63 --- /dev/null +++ b/gui-programming/maze-game/main.py @@ -0,0 +1,114 @@ +import pygame, sys +from maze import Maze +from player import Player +from game import Game +from clock import Clock + +pygame.init() +pygame.font.init() + +class Main(): + def __init__(self, screen): + self.screen = screen + self.font = pygame.font.SysFont("impact", 30) + self.message_color = pygame.Color("cyan") + self.running = True + self.game_over = False + self.FPS = pygame.time.Clock() + + def instructions(self): + instructions1 = self.font.render('Use', True, self.message_color) + instructions2 = self.font.render('Arrow Keys', True, self.message_color) + instructions3 = self.font.render('to Move', True, self.message_color) + self.screen.blit(instructions1,(655,300)) + self.screen.blit(instructions2,(610,331)) + self.screen.blit(instructions3,(630,362)) + + # draws all configs; maze, player, instructions, and time + def _draw(self, maze, tile, player, game, clock): + # draw maze + [cell.draw(self.screen, tile) for cell in maze.grid_cells] + + # add a goal point to reach + game.add_goal_point(self.screen) + + # draw every player movement + player.draw(self.screen) + player.update() + + # instructions, clock, winning message + self.instructions() + if self.game_over: + clock.stop_timer() + self.screen.blit(game.message(),(610,120)) + else: + clock.update_timer() + self.screen.blit(clock.display_timer(), (625,200)) + + pygame.display.flip() + + # main game loop + def main(self, frame_size, tile): + cols, rows = frame_size[0] // tile, frame_size[-1] // tile + maze = Maze(cols, rows) + game = Game(maze.grid_cells[-1], tile) + player = Player(tile // 3, tile // 3) + clock = Clock() + + maze.generate_maze() + clock.start_timer() + while self.running: + self.screen.fill("gray") + self.screen.fill( pygame.Color("darkslategray"), (603, 0, 752, 752)) + + for event in pygame.event.get(): + if event.type == pygame.QUIT: + pygame.quit() + sys.exit() + + # if keys were pressed still + if event.type == pygame.KEYDOWN: + if not self.game_over: + if event.key == pygame.K_LEFT: + player.left_pressed = True + if event.key == pygame.K_RIGHT: + player.right_pressed = True + if event.key == pygame.K_UP: + player.up_pressed = True + if event.key == pygame.K_DOWN: + player.down_pressed = True + player.check_move(tile, maze.grid_cells, maze.thickness) + + # if pressed key released + if event.type == pygame.KEYUP: + if not self.game_over: + if event.key == pygame.K_LEFT: + player.left_pressed = False + if event.key == pygame.K_RIGHT: + player.right_pressed = False + if event.key == pygame.K_UP: + player.up_pressed = False + if event.key == pygame.K_DOWN: + player.down_pressed = False + player.check_move(tile, maze.grid_cells, maze.thickness) + + if game.is_game_over(player): + self.game_over = True + player.left_pressed = False + player.right_pressed = False + player.up_pressed = False + player.down_pressed = False + + self._draw(maze, tile, player, game, clock) + self.FPS.tick(60) + + +if __name__ == "__main__": + window_size = (602, 602) + screen = (window_size[0] + 150, window_size[-1]) + tile_size = 30 + screen = pygame.display.set_mode(screen) + pygame.display.set_caption("Maze") + + game = Main(screen) + game.main(window_size, tile_size) diff --git a/gui-programming/maze-game/maze.py b/gui-programming/maze-game/maze.py new file mode 100644 index 00000000..69384699 --- /dev/null +++ b/gui-programming/maze-game/maze.py @@ -0,0 +1,44 @@ +import pygame +from cell import Cell + +class Maze: + def __init__(self, cols, rows): + self.cols = cols + self.rows = rows + self.thickness = 4 + self.grid_cells = [Cell(col, row, self.thickness) for row in range(self.rows) for col in range(self.cols)] + + # carve grid cell walls + def remove_walls(self, current, next): + dx = current.x - next.x + if dx == 1: + current.walls['left'] = False + next.walls['right'] = False + elif dx == -1: + current.walls['right'] = False + next.walls['left'] = False + dy = current.y - next.y + if dy == 1: + current.walls['top'] = False + next.walls['bottom'] = False + elif dy == -1: + current.walls['bottom'] = False + next.walls['top'] = False + + # generates maze + def generate_maze(self): + current_cell = self.grid_cells[0] + array = [] + break_count = 1 + while break_count != len(self.grid_cells): + current_cell.visited = True + next_cell = current_cell.check_neighbors(self.cols, self.rows, self.grid_cells) + if next_cell: + next_cell.visited = True + break_count += 1 + array.append(current_cell) + self.remove_walls(current_cell, next_cell) + current_cell = next_cell + elif array: + current_cell = array.pop() + return self.grid_cells diff --git a/gui-programming/maze-game/player.py b/gui-programming/maze-game/player.py new file mode 100644 index 00000000..0a671268 --- /dev/null +++ b/gui-programming/maze-game/player.py @@ -0,0 +1,66 @@ +import pygame + +class Player: + def __init__(self, x, y): + self.x = int(x) + self.y = int(y) + self.player_size = 10 + self.rect = pygame.Rect(self.x, self.y, self.player_size, self.player_size) + self.color = (250, 120, 60) + self.velX = 0 + self.velY = 0 + self.left_pressed = False + self.right_pressed = False + self.up_pressed = False + self.down_pressed = False + self.speed = 4 + + # get current cell position of the player + def get_current_cell(self, x, y, grid_cells): + for cell in grid_cells: + if cell.x == x and cell.y == y: + return cell + + # stops player to pass through walls + def check_move(self, tile, grid_cells, thickness): + current_cell_x, current_cell_y = self.x // tile, self.y // tile + current_cell = self.get_current_cell(current_cell_x, current_cell_y, grid_cells) + current_cell_abs_x, current_cell_abs_y = current_cell_x * tile, current_cell_y * tile + if self.left_pressed: + if current_cell.walls['left']: + if self.x <= current_cell_abs_x + thickness: + self.left_pressed = False + if self.right_pressed: + if current_cell.walls['right']: + if self.x >= current_cell_abs_x + tile - (self.player_size + thickness): + self.right_pressed = False + if self.up_pressed: + if current_cell.walls['top']: + if self.y <= current_cell_abs_y + thickness: + self.up_pressed = False + if self.down_pressed: + if current_cell.walls['bottom']: + if self.y >= current_cell_abs_y + tile - (self.player_size + thickness): + self.down_pressed = False + + # drawing player to the screen + def draw(self, screen): + pygame.draw.rect(screen, self.color, self.rect) + + # updates player position while moving + def update(self): + self.velX = 0 + self.velY = 0 + if self.left_pressed and not self.right_pressed: + self.velX = -self.speed + if self.right_pressed and not self.left_pressed: + self.velX = self.speed + if self.up_pressed and not self.down_pressed: + self.velY = -self.speed + if self.down_pressed and not self.up_pressed: + self.velY = self.speed + + self.x += self.velX + self.y += self.velY + + self.rect = pygame.Rect(int(self.x), int(self.y), self.player_size, self.player_size) \ No newline at end of file diff --git a/gui-programming/maze-game/requirements.txt b/gui-programming/maze-game/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/maze-game/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/platformer-game/README.Md b/gui-programming/platformer-game/README.Md new file mode 100644 index 00000000..fb346961 --- /dev/null +++ b/gui-programming/platformer-game/README.Md @@ -0,0 +1 @@ +# [How to Create a Platformer Game in Python](https://www.thepythoncode.com/article/platformer-game-with-pygame-in-python) \ No newline at end of file diff --git a/gui-programming/platformer-game/assets/goal/gate.png b/gui-programming/platformer-game/assets/goal/gate.png new file mode 100644 index 00000000..c088b640 Binary files /dev/null and b/gui-programming/platformer-game/assets/goal/gate.png differ diff --git a/gui-programming/platformer-game/assets/life/life.png b/gui-programming/platformer-game/assets/life/life.png new file mode 100644 index 00000000..da08d810 Binary files /dev/null and b/gui-programming/platformer-game/assets/life/life.png differ diff --git a/gui-programming/platformer-game/assets/player/fall/0.png b/gui-programming/platformer-game/assets/player/fall/0.png new file mode 100644 index 00000000..1beedac8 Binary files /dev/null and b/gui-programming/platformer-game/assets/player/fall/0.png differ diff --git a/gui-programming/platformer-game/assets/player/idle/0.png b/gui-programming/platformer-game/assets/player/idle/0.png new file mode 100644 index 00000000..e5cc59e7 Binary files /dev/null and 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--- /dev/null +++ b/gui-programming/platformer-game/game.py @@ -0,0 +1,43 @@ +import pygame +from settings import HEIGHT, WIDTH + +pygame.font.init() + +class Game: + def __init__(self, screen): + self.screen = screen + self.font = pygame.font.SysFont("impact", 70) + self.message_color = pygame.Color("darkorange") + + # if player ran out of life or fell below the platform + def _game_lose(self, player): + player.game_over = True + message = self.font.render('You Lose...', True, self.message_color) + self.screen.blit(message,(WIDTH // 3 + 70, 70)) + + # if player reach the goal + def _game_win(self, player): + player.game_over = True + player.win = True + message = self.font.render('You Win!!', True, self.message_color) + self.screen.blit(message,(WIDTH // 3, 70)) + + # checks if the game is over or not, and if win or lose + def game_state(self, player, goal): + if player.life <= 0 or player.rect.y >= HEIGHT: + self._game_lose(player) + elif player.rect.colliderect(goal.rect): + self._game_win(player) + else: + None + + def show_life(self, player): + life_size = 30 + img_path = "assets/life/life.png" + life_image = pygame.image.load(img_path) + life_image = pygame.transform.scale(life_image, (life_size, life_size)) + # life_rect = life_image.get_rect(topleft = pos) + indent = 0 + for life in range(player.life): + indent += life_size + self.screen.blit(life_image, (indent, life_size)) \ No newline at end of file diff --git a/gui-programming/platformer-game/goal.py b/gui-programming/platformer-game/goal.py new file mode 100644 index 00000000..419e66a4 --- /dev/null +++ b/gui-programming/platformer-game/goal.py @@ -0,0 +1,13 @@ +import pygame + +class Goal(pygame.sprite.Sprite): + def __init__(self, pos, size): + super().__init__() + img_path = 'assets/goal/gate.png' + self.image = pygame.image.load(img_path) + self.image = pygame.transform.scale(self.image, (size, size)) + self.rect = self.image.get_rect(topleft = pos) + + # update object position due to world scroll + def update(self, x_shift): + self.rect.x += x_shift \ No newline at end of file diff --git a/gui-programming/platformer-game/main.py b/gui-programming/platformer-game/main.py new file mode 100644 index 00000000..ac01a83c --- /dev/null +++ b/gui-programming/platformer-game/main.py @@ -0,0 +1,46 @@ +import pygame, sys +from settings import * +from world import World + +pygame.init() + +screen = pygame.display.set_mode((WIDTH, HEIGHT)) +pygame.display.set_caption("Platformer") + +class Platformer: + def __init__(self, screen, width, height): + self.screen = screen + self.clock = pygame.time.Clock() + self.player_event = False + + self.bg_img = pygame.image.load('assets/terrain/bg.jpg') + self.bg_img = pygame.transform.scale(self.bg_img, (width, height)) + + def main(self): + world = World(world_map, self.screen) + while True: + self.screen.blit(self.bg_img, (0, 0)) + + for event in pygame.event.get(): + if event.type == pygame.QUIT: + pygame.quit() + sys.exit() + + elif event.type == pygame.KEYDOWN: + if event.key == pygame.K_LEFT: + self.player_event = "left" + if event.key == pygame.K_RIGHT: + self.player_event = "right" + if event.key == pygame.K_SPACE: + self.player_event = "space" + elif event.type == pygame.KEYUP: + self.player_event = False + + world.update(self.player_event) + pygame.display.update() + self.clock.tick(60) + + +if __name__ == "__main__": + play = Platformer(screen, WIDTH, HEIGHT) + play.main() \ No newline at end of file diff --git a/gui-programming/platformer-game/player.py b/gui-programming/platformer-game/player.py new file mode 100644 index 00000000..8f614ca3 --- /dev/null +++ b/gui-programming/platformer-game/player.py @@ -0,0 +1,115 @@ +import pygame +from support import import_sprite + +class Player(pygame.sprite.Sprite): + def __init__(self, pos): + super().__init__() + self._import_character_assets() + self.frame_index = 0 + self.animation_speed = 0.15 + self.image = self.animations["idle"][self.frame_index] + self.rect = self.image.get_rect(topleft = pos) + self.mask = pygame.mask.from_surface(self.image) + + # player movement + self.direction = pygame.math.Vector2(0, 0) + self.speed = 5 + self.jump_move = -16 + + # player status + self.life = 5 + self.game_over = False + self.win = False + self.status = "idle" + self.facing_right = True + self.on_ground = False + self.on_ceiling = False + self.on_left = False + self.on_right = False + + # gets all the image needed for animating specific player action + def _import_character_assets(self): + character_path = "assets/player/" + self.animations = { + "idle": [], + "walk": [], + "jump": [], + "fall": [], + "lose": [], + "win": [] + } + for animation in self.animations.keys(): + full_path = character_path + animation + self.animations[animation] = import_sprite(full_path) + + # animates the player actions + def _animate(self): + animation = self.animations[self.status] + + # loop over frame index + self.frame_index += self.animation_speed + if self.frame_index >= len(animation): + self.frame_index = 0 + image = animation[int(self.frame_index)] + image = pygame.transform.scale(image, (35, 50)) + if self.facing_right: + self.image = image + else: + flipped_image = pygame.transform.flip(image, True, False) + self.image = flipped_image + + # set the rect + if self.on_ground and self.on_right: + self.rect = self.image.get_rect(bottomright = self.rect.bottomright) + elif self.on_ground and self.on_left: + self.rect = self.image.get_rect(bottomleft = self.rect.bottomleft) + elif self.on_ground: + self.rect = self.image.get_rect(midbottom = self.rect.midbottom) + elif self.on_ceiling and self.on_right: + self.rect = self.image.get_rect(topright = self.rect.topright) + elif self.on_ceiling and self.on_left: + self.rect = self.image.get_rect(bottomleft = self.rect.topleft) + elif self.on_ceiling: + self.rect = self.image.get_rect(midtop = self.rect.midtop) + + # checks if the player is moving towards left or right or not moving + def _get_input(self, player_event): + if player_event != False: + if player_event == "right": + self.direction.x = 1 + self.facing_right = True + elif player_event == "left": + self.direction.x = -1 + self.facing_right = False + else: + self.direction.x = 0 + + def _jump(self): + self.direction.y = self.jump_move + + # identifies player action + def _get_status(self): + if self.direction.y < 0: + self.status = "jump" + elif self.direction.y > 1: + self.status = "fall" + elif self.direction.x != 0: + self.status = "walk" + else: + self.status = "idle" + + # update the player's state + def update(self, player_event): + self._get_status() + if self.life > 0 and not self.game_over: + if player_event == "space" and self.on_ground: + self._jump() + else: + self._get_input(player_event) + elif self.game_over and self.win: + self.direction.x = 0 + self.status = "win" + else: + self.direction.x = 0 + self.status = "lose" + self._animate() diff --git a/gui-programming/platformer-game/requirements.txt b/gui-programming/platformer-game/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/platformer-game/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/platformer-game/settings.py b/gui-programming/platformer-game/settings.py new file mode 100644 index 00000000..dd578461 --- /dev/null +++ b/gui-programming/platformer-game/settings.py @@ -0,0 +1,15 @@ +world_map = [ + ' ', + ' ', + ' t t ', + ' X XXXXXXXXXs XX X ', + ' tXXXt XX XX XXXX tt XX ', + ' XX XX XXXXX ', + ' Xt t t t X G ', + ' XXXXXX XXXXs XXXXXXXXXXX XX tt t XXX', + ' P XX X XX X X XXXt X XX XX XXX XXXXXXXXs XXXXXX ', + 'XXXXXXX X X X X XXXXXXXXX XX XX XXX XX XX XXXXXXX X ', +] + +tile_size = 50 +WIDTH, HEIGHT = 1000, len(world_map) * tile_size \ No newline at end of file diff --git a/gui-programming/platformer-game/support.py b/gui-programming/platformer-game/support.py new file mode 100644 index 00000000..d0e297b5 --- /dev/null +++ b/gui-programming/platformer-game/support.py @@ -0,0 +1,11 @@ +from os import walk +import pygame + +def import_sprite(path): + surface_list = [] + for _, __, img_file in walk(path): + for image in img_file: + full_path = f"{path}/{image}" + img_surface = pygame.image.load(full_path).convert_alpha() + surface_list.append(img_surface) + return surface_list \ No newline at end of file diff --git a/gui-programming/platformer-game/tile.py b/gui-programming/platformer-game/tile.py new file mode 100644 index 00000000..7e3ec1ec --- /dev/null +++ b/gui-programming/platformer-game/tile.py @@ -0,0 +1,13 @@ +import pygame + +class Tile(pygame.sprite.Sprite): + def __init__(self, pos, size): + super().__init__() + img_path = 'assets/terrain/stone.jpg' + self.image = pygame.image.load(img_path) + self.image = pygame.transform.scale(self.image, (size, size)) + self.rect = self.image.get_rect(topleft = pos) + + # update object position due to world scroll + def update(self, x_shift): + self.rect.x += x_shift \ No newline at end of file diff --git a/gui-programming/platformer-game/trap.py b/gui-programming/platformer-game/trap.py new file mode 100644 index 00000000..45034c1f --- /dev/null +++ b/gui-programming/platformer-game/trap.py @@ -0,0 +1,29 @@ +import pygame +from support import import_sprite + +class Trap(pygame.sprite.Sprite): + def __init__(self, pos, size): + super().__init__() + self.blade_img = import_sprite("assets/trap/blade") + self.frame_index = 0 + self.animation_delay = 3 + self.image = self.blade_img[self.frame_index] + self.image = pygame.transform.scale(self.image, (size, size)) + self.mask = pygame.mask.from_surface(self.image) + self.rect = self.image.get_rect(topleft = pos) + + # adds the spinning effect to the Blade trap + def _animate(self): + sprites = self.blade_img + sprite_index = (self.frame_index // self.animation_delay) % len(sprites) + self.image = sprites[sprite_index] + self.frame_index += 1 + self.rect = self.image.get_rect(topleft=(self.rect.x, self.rect.y)) + self.mask = pygame.mask.from_surface(self.image) + if self.frame_index // self.animation_delay > len(sprites): + self.frame_index = 0 + + # update object position due to world scroll + def update(self, x_shift): + self._animate() + self.rect.x += x_shift diff --git a/gui-programming/platformer-game/world.py b/gui-programming/platformer-game/world.py new file mode 100644 index 00000000..3d78bc0d --- /dev/null +++ b/gui-programming/platformer-game/world.py @@ -0,0 +1,143 @@ +import pygame +from settings import tile_size, WIDTH +from tile import Tile +from trap import Trap +from goal import Goal +from player import Player +from game import Game + +class World: + def __init__(self, world_data, screen): + self.screen = screen + self.world_data = world_data + self._setup_world(world_data) + self.world_shift = 0 + self.current_x = 0 + self.gravity = 0.7 + self.game = Game(self.screen) + + # generates the world + def _setup_world(self, layout): + self.tiles = pygame.sprite.Group() + self.traps = pygame.sprite.Group() + self.player = pygame.sprite.GroupSingle() + self.goal = pygame.sprite.GroupSingle() + + for row_index, row in enumerate(layout): + for col_index, cell in enumerate(row): + x, y = col_index * tile_size, row_index * tile_size + if cell == "X": + tile = Tile((x, y), tile_size) + self.tiles.add(tile) + elif cell == "t": + tile = Trap((x + (tile_size // 4), y + (tile_size // 4)), tile_size // 2) + self.traps.add(tile) + elif cell == "P": + player_sprite = Player((x, y)) + self.player.add(player_sprite) + elif cell == "G": + goal_sprite = Goal((x, y), tile_size) + self.goal.add(goal_sprite) + + # world scroll when the player is walking towards left/right + def _scroll_x(self): + player = self.player.sprite + player_x = player.rect.centerx + direction_x = player.direction.x + + if player_x < WIDTH // 3 and direction_x < 0: + self.world_shift = 8 + player.speed = 0 + elif player_x > WIDTH - (WIDTH // 3) and direction_x > 0: + self.world_shift = -8 + player.speed = 0 + else: + self.world_shift = 0 + player.speed = 3 + + # add gravity for player to fall + def _apply_gravity(self, player): + player.direction.y += self.gravity + player.rect.y += player.direction.y + + # prevents player to pass through objects horizontally + def _horizontal_movement_collision(self): + player = self.player.sprite + player.rect.x += player.direction.x * player.speed + + for sprite in self.tiles.sprites(): + if sprite.rect.colliderect(player.rect): + # checks if moving towards left + if player.direction.x < 0: + player.rect.left = sprite.rect.right + player.on_left = True + self.current_x = player.rect.left + # checks if moving towards right + elif player.direction.x > 0: + player.rect.right = sprite.rect.left + player.on_right = True + self.current_x = player.rect.right + if player.on_left and (player.rect.left < self.current_x or player.direction.x >= 0): + player.on_left = False + if player.on_right and (player.rect.right > self.current_x or player.direction.x <= 0): + player.on_right = False + + # prevents player to pass through objects vertically + def _vertical_movement_collision(self): + player = self.player.sprite + self._apply_gravity(player) + + for sprite in self.tiles.sprites(): + if sprite.rect.colliderect(player.rect): + # checks if moving towards bottom + if player.direction.y > 0: + player.rect.bottom = sprite.rect.top + player.direction.y = 0 + player.on_ground = True + # checks if moving towards up + elif player.direction.y < 0: + player.rect.top = sprite.rect.bottom + player.direction.y = 0 + player.on_ceiling = True + if player.on_ground and player.direction.y < 0 or player.direction.y > 1: + player.on_ground = False + if player.on_ceiling and player.direction.y > 0: + player.on_ceiling = False + + # add consequences when player run through traps + def _handle_traps(self): + player = self.player.sprite + + for sprite in self.traps.sprites(): + if sprite.rect.colliderect(player.rect): + if player.direction.x < 0 or player.direction.y > 0: + player.rect.x += tile_size + elif player.direction.x > 0 or player.direction.y > 0: + player.rect.x -= tile_size + player.life -= 1 + + # updating the game world from all changes commited + def update(self, player_event): + # for tile + self.tiles.update(self.world_shift) + self.tiles.draw(self.screen) + + # for trap + self.traps.update(self.world_shift) + self.traps.draw(self.screen) + + # for goal + self.goal.update(self.world_shift) + self.goal.draw(self.screen) + + self._scroll_x() + + # for player + self._horizontal_movement_collision() + self._vertical_movement_collision() + self._handle_traps() + self.player.update(player_event) + self.game.show_life(self.player.sprite) + self.player.draw(self.screen) + + self.game.game_state(self.player.sprite, self.goal.sprite) diff --git a/gui-programming/slide-puzzle/frame.py b/gui-programming/slide-puzzle/frame.py index 833844e7..04647307 100644 --- a/gui-programming/slide-puzzle/frame.py +++ b/gui-programming/slide-puzzle/frame.py @@ -15,14 +15,17 @@ def __init__(self, frame_size): self.pieces = self._generate_piece() self._setup() + self.randomize_puzzle() def _generate_cell(self): cells = [] c_id = 0 for col in range(self.grid_size): + new_row = [] for row in range(self.grid_size): - cells.append(Cell(row, col, self.cell_size, c_id)) + new_row.append(Cell(row, col, self.cell_size, c_id)) c_id += 1 + cells.append(new_row) return cells def _generate_piece(self): @@ -35,40 +38,58 @@ def _generate_piece(self): return puzzle_pieces def _setup(self): - for cell in self.grid: - piece_choice = random.choice(self.pieces) - cell.occupying_piece = piece_choice - self.pieces.remove(piece_choice) + for row in self.grid: + for cell in row: + tile_piece = self.pieces[-1] + cell.occupying_piece = tile_piece + self.pieces.remove(tile_piece) - def _get_cell_from_id(self, given_id): - for cell in self.grid: - if cell.c_id == given_id: - return cell + def randomize_puzzle(self): + moves = [(0, 1),(0, -1),(1, 0),(-1, 0)] + for i in range(30): + shuffle_move = random.choice(moves) + for row in self.grid: + for cell in row: + tile_x = self.grid.index(row) + shuffle_move[0] + tile_y = row.index(cell) + shuffle_move[1] + if tile_x >= 0 and tile_x <= 2 and tile_y >= 0 and tile_y <= 2: + new_cell = self.grid[tile_x][tile_y] + if new_cell.occupying_piece.img == None: + c = (cell, new_cell) + try: + c[0].occupying_piece, c[1].occupying_piece = c[1].occupying_piece, c[0].occupying_piece + except: + return False + else: + continue def _is_move_valid(self, click): moves = { - 79: 1, - 80: -1, - 81: 3, - 82: -3 + 79: (0, 1), + 80: (0, -1), + 81: (1, 0), + 82: (-1, 0) } - for cell in self.grid: - move_id = cell.c_id + moves[click.scancode] - if move_id >= 0 and move_id <= 8: - new_cell = self._get_cell_from_id(move_id) - if new_cell.occupying_piece.img == None: - return (cell, new_cell) - else: - continue + for row in self.grid: + for cell in row: + move = moves[click.scancode] + tile_x = self.grid.index(row) + move[0] + tile_y = row.index(cell) + move[1] + if tile_x >= 0 and tile_x <= 2 and tile_y >= 0 and tile_y <= 2: + new_cell = self.grid[tile_x][tile_y] + if new_cell.occupying_piece.img == None: + return (cell, new_cell) + else: + continue def handle_click(self, click): c = self._is_move_valid(click) try: - # print(c[0].c_id, c[1].c_id) c[0].occupying_piece, c[1].occupying_piece = c[1].occupying_piece, c[0].occupying_piece except: return False def draw(self, display): - for cell in self.grid: - cell.draw(display) + for row in self.grid: + for cell in row: + cell.draw(display) diff --git a/gui-programming/slide-puzzle/game.py b/gui-programming/slide-puzzle/game.py index ca47dafb..7a51e13c 100644 --- a/gui-programming/slide-puzzle/game.py +++ b/gui-programming/slide-puzzle/game.py @@ -16,14 +16,14 @@ def arrow_key_clicked(self, click): return(False) def is_game_over(self, frame): - for cell in frame.grid: - piece_id = cell.occupying_piece.p_id - # print(piece_id, cell.c_id) - if cell.c_id == piece_id: - is_arranged = True - else: - is_arranged = False - break + for row in frame.grid: + for cell in row: + piece_id = cell.occupying_piece.p_id + if cell.c_id == piece_id: + is_arranged = True + else: + is_arranged = False + break return is_arranged def message(self, screen): diff --git a/machine-learning/control-image-generation-with-controlnet/ControlNet_PythonCodeTutorial.ipynb b/machine-learning/control-image-generation-with-controlnet/ControlNet_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..e2500451 --- /dev/null +++ b/machine-learning/control-image-generation-with-controlnet/ControlNet_PythonCodeTutorial.ipynb @@ -0,0 +1,20363 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8_55MSKhrtss", + "outputId": "44fe5aac-1542-4f19-b926-6fee990835e9" + }, + "outputs": [], + "source": [ + "!pip install -qU xformers diffusers transformers accelerate" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Xrl7OL6BrtpU", + "outputId": "9525d6ad-4af6-4a9f-98b0-6d76308fdf13" + }, + "outputs": [], + "source": [ + "!pip install -qU controlnet_aux\n", + "!pip install opencv-contrib-python" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "h6XPbq0FrPQG" + }, + "source": [ + "# Open Pose" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0wEufS0RrObO", + "outputId": "77977e89-4b72-4a5d-b39a-801953031247" + }, + "outputs": [], + "source": [ + "from PIL import Image\n", + "from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\n", + "import torch\n", + "from controlnet_aux import OpenposeDetector\n", + "from diffusers.utils import load_image\n", + "from tqdm import tqdm\n", + "from torch import autocast" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 662, + "referenced_widgets": [ + "a74467c946df4fe99fba1b2751a0cad1", + "48dabed3f2ce465ab16a704e0c6b12f7", + "810aba56f12342fcbcb7344d08235ec8", + "0dc0896c330d417a8b452d27699f86bf", + "9074425e4bf04c428926f5504cd3a8de", + "962917ac2b044e5989e88100ceb45296", + "a212be07369b404eb9f04a9cde6543d4", + "bf52593db97e4b5f876d22d5c27e8c0d", + "ed79049f4f3a41899ce7744c6733bf27", + "6fbeaf60da4746b6a1c02a211657886b", + "9d1f8ee81b994f94882d776f6e401f02", + "d14a6ce3bc334df5a2f319932fd9a338", + "05ba62113d1840c38f0bcf8f291f7439", + "117ca4fd09bb4ebeb8eae3ba5c3112f5", + "b2fad15effb84bce8aac9b9d2bf94b19", + "78493887a0f5466cbfe8dff7f5fbd801", + "5850294bc56d489cb8eb90483f77254e", + "9d2061e3643940f496528dc7ad6e6e2e", + 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"d2a03fa596c34b12a1aed264e8d08077", + "74702e35e09048eabfb470dc85fb5be4", + "4276b44c60b64716ab7ad52d8354bc1f", + "87fcb7d5949d440dbe3c18e7059e65ad", + "173d85b54dd4419883fc83cfa2beacc5", + "cd4f33b831ff4bd4afabbdec87b9bc8d", + "79b1b7bd05714b528d9a617b1c875dfe", + "d6a728463cce43d2a0c8573e5faf2dde", + "e59d5e76445943c39cdb32721727d3e8" + ] + }, + "id": "ts8fiPLordOD", + "outputId": "08a7450f-8e53-48a4-9c2d-2994353c3140" + }, + "outputs": [], + "source": [ + "# load the openpose model\n", + "openpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')\n", + "\n", + "# load the controlnet for openpose\n", + "controlnet = ControlNetModel.from_pretrained(\n", + " \"lllyasviel/sd-controlnet-openpose\", torch_dtype=torch.float16\n", + ")\n", + "\n", + "# define stable diffusion pipeline with controlnet\n", + "pipe = StableDiffusionControlNetPipeline.from_pretrained(\n", + " \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n", + ")\n", + "pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LPdQiPkXri1y" + }, + "outputs": [], + "source": [ + "# Remove if you do not have xformers installed\n", + "# see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers\n", + "# for installation instructions\n", + "pipe.enable_xformers_memory_efficient_attention()\n", + "pipe.enable_model_cpu_offload()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 657 + }, + "id": "f_HH_n8WrdQN", + "outputId": "2432a1d8-cdec-4b0e-ca8e-d62ce008136e" + }, + "outputs": [], + "source": [ + "image_input = load_image(\"https://cdn.pixabay.com/photo/2016/05/17/22/19/fashion-1399344_640.jpg\")\n", + "image_input" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 785 + }, + "id": "AUJikHhlrdSX", + "outputId": "398bd929-ff3e-4f05-cad6-a3079b911d75" + }, + "outputs": [], + "source": [ + "image_pose = openpose(image_input)\n", + "image_pose" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "b5a39dc5eccf4747ade601d25681c2b2", + "1ce482eed7dd4a62b5c97b7fae38f392", + "f566fa59f127470087bb68beea6ee8fb", + "6c0b26e65230468cb5ef11d3d0bf1bd3", + "aa4097be74c3412eb9571ed6ef250828", + "f02818f8f3d54f72961c51e8fe526dc2", + "8d2da5062f414a7f8c98facdd7d2bad0", + "2fa71efbdefa4607b37d985b272fcffa", + "e20f4ef3c100484cae5ba0a570be604c", + "f48bc2b8666b4e48a6f8cad4cda2530d", + "29e96ee99a714e2c86a0d68be1a31055" + ] + }, + "id": "0MfsiN_Jri4G", + "outputId": "5286a2ac-13a3-4c35-80ab-a9968c9c8602" + }, + "outputs": [], + "source": [ + "image_output = pipe(\"A professional photograph of a male fashion model\", image_pose, num_inference_steps=20).images[0]\n", + "image_output" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "HEiobo68Kzso" + }, + "source": [ + "# Custom implementation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0y9iWI9cK17f" + }, + "outputs": [], + "source": [ + "class ControlNetDiffusionPipelineCustom:\n", + " \"\"\"custom implementation of the ControlNet Diffusion Pipeline\"\"\"\n", + "\n", + " def __init__(self,\n", + " vae,\n", + " tokenizer,\n", + " text_encoder,\n", + " unet,\n", + " controlnet,\n", + " scheduler,\n", + " image_processor,\n", + " control_image_processor):\n", + "\n", + " self.vae = vae\n", + " self.tokenizer = tokenizer\n", + " self.text_encoder = text_encoder\n", + " self.unet = unet\n", + " self.scheduler = scheduler\n", + " self.controlnet = controlnet\n", + " self.image_processor = image_processor\n", + " self.control_image_processor = control_image_processor\n", + " self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + "\n", + "\n", + "\n", + " def get_text_embeds(self, text):\n", + " \"\"\"returns embeddings for the given `text`\"\"\"\n", + "\n", + " # tokenize the text\n", + " text_input = self.tokenizer(text,\n", + " padding='max_length',\n", + " max_length=tokenizer.model_max_length,\n", + " truncation=True,\n", + " return_tensors='pt')\n", + " # embed the text\n", + " with torch.no_grad():\n", + " text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0]\n", + " return text_embeds\n", + "\n", + "\n", + "\n", + " def get_prompt_embeds(self, prompt):\n", + " \"\"\"returns prompt embeddings based on classifier free guidance\"\"\"\n", + "\n", + " if isinstance(prompt, str):\n", + " prompt = [prompt]\n", + " # get conditional prompt embeddings\n", + " cond_embeds = self.get_text_embeds(prompt)\n", + " # get unconditional prompt embeddings\n", + " uncond_embeds = self.get_text_embeds([''] * len(prompt))\n", + " # concatenate the above 2 embeds\n", + " prompt_embeds = torch.cat([uncond_embeds, cond_embeds])\n", + " return prompt_embeds\n", + "\n", + "\n", + " def transform_image(self, image):\n", + " \"\"\"convert image from pytorch tensor to PIL format\"\"\"\n", + "\n", + " image = self.image_processor.postprocess(image, output_type='pil')\n", + " return image\n", + "\n", + "\n", + "\n", + " def get_initial_latents(self, height, width, num_channels_latents, batch_size):\n", + " \"\"\"returns noise latent tensor of relevant shape scaled by the scheduler\"\"\"\n", + "\n", + " image_latents = torch.randn((batch_size,\n", + " num_channels_latents,\n", + " height // 8,\n", + " width // 8)).to(self.device)\n", + " # scale the initial noise by the standard deviation required by the scheduler\n", + " image_latents = image_latents * self.scheduler.init_noise_sigma\n", + " return image_latents\n", + "\n", + "\n", + "\n", + " def denoise_latents(self,\n", + " prompt_embeds,\n", + " controlnet_image,\n", + " timesteps,\n", + " latents,\n", + " guidance_scale=7.5):\n", + " \"\"\"denoises latents from noisy latent to a meaningful latent as conditioned by controlnet\"\"\"\n", + "\n", + " # use autocast for automatic mixed precision (AMP) inference\n", + " with autocast('cuda'):\n", + " for i, t in tqdm(enumerate(timesteps)):\n", + " # duplicate image latents to do classifier free guidance\n", + " latent_model_input = torch.cat([latents] * 2)\n", + " latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n", + "\n", + " control_model_input = latents\n", + " controlnet_prompt_embeds = prompt_embeds\n", + "\n", + " # get output from the control net blocks\n", + " down_block_res_samples, mid_block_res_sample = self.controlnet(\n", + " control_model_input,\n", + " t,\n", + " encoder_hidden_states=controlnet_prompt_embeds,\n", + " controlnet_cond=controlnet_image,\n", + " conditioning_scale=1.0,\n", + " return_dict=False,\n", + " )\n", + "\n", + " # predict noise residuals\n", + " with torch.no_grad():\n", + " noise_pred = self.unet(\n", + " latent_model_input,\n", + " t,\n", + " encoder_hidden_states=prompt_embeds,\n", + " down_block_additional_residuals=down_block_res_samples,\n", + " mid_block_additional_residual=mid_block_res_sample,\n", + " )['sample']\n", + "\n", + " # separate predictions for unconditional and conditional outputs\n", + " noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n", + "\n", + " # perform guidance\n", + " noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n", + "\n", + " # remove the noise from the current sample i.e. go from x_t to x_{t-1}\n", + " latents = self.scheduler.step(noise_pred, t, latents)['prev_sample']\n", + "\n", + " return latents\n", + "\n", + "\n", + "\n", + " def prepare_controlnet_image(self,\n", + " image,\n", + " height,\n", + " width):\n", + " \"\"\"preprocesses the controlnet image\"\"\"\n", + "\n", + " # process the image\n", + " image = self.control_image_processor.preprocess(image, height, width).to(dtype=torch.float32)\n", + " # send image to CUDA\n", + " image = image.to(self.device)\n", + " # repeat the image for classifier free guidance\n", + " image = torch.cat([image] * 2)\n", + " return image\n", + "\n", + "\n", + "\n", + " def __call__(self,\n", + " prompt,\n", + " image,\n", + " num_inference_steps=20,\n", + " guidance_scale=7.5,\n", + " height=512, width=512):\n", + " \"\"\"generates new image based on the `prompt` and the `image`\"\"\"\n", + "\n", + " # encode input prompt\n", + " prompt_embeds = self.get_prompt_embeds(prompt)\n", + "\n", + " # prepare image for controlnet\n", + " controlnet_image = self.prepare_controlnet_image(image, height, width)\n", + " height, width = controlnet_image.shape[-2:]\n", + "\n", + " # prepare timesteps\n", + " self.scheduler.set_timesteps(num_inference_steps)\n", + " timesteps = self.scheduler.timesteps\n", + "\n", + " # prepare the initial image in the latent space (noise on which we will do reverse diffusion)\n", + " num_channels_latents = self.unet.config.in_channels\n", + " batch_size = prompt_embeds.shape[0] // 2\n", + " latents = self.get_initial_latents(height, width, num_channels_latents, batch_size)\n", + "\n", + " # denoise latents\n", + " latents = self.denoise_latents(prompt_embeds,\n", + " controlnet_image,\n", + " timesteps,\n", + " latents,\n", + " guidance_scale)\n", + "\n", + " # decode latents to get the image into pixel space\n", + " latents = latents.to(torch.float16) # change dtype of latents since\n", + " image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n", + "\n", + " # convert to PIL Image format\n", + " image = image.detach() # detach to remove any computed gradients\n", + " image = self.transform_image(image)\n", + "\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kugjwBC3K4JK" + }, + "outputs": [], + "source": [ + "# We can get all the components from the ControlNet Diffusion Pipeline (the one implemented by Hugging Face as well)\n", + "vae = pipe.vae\n", + "tokenizer = pipe.tokenizer\n", + "text_encoder = pipe.text_encoder\n", + "unet = pipe.unet\n", + "controlnet = pipe.controlnet\n", + "scheduler = pipe.scheduler\n", + "image_processor = pipe.image_processor\n", + "control_image_processor = pipe.control_image_processor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6ooKnlkVK4LV" + }, + "outputs": [], + "source": [ + "custom_pipe = ControlNetDiffusionPipelineCustom(vae, tokenizer, text_encoder, unet, controlnet, scheduler, image_processor, control_image_processor)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + }, + "id": "BYW8mEH2K4NY", + "outputId": "6e610454-e4c4-4c9d-c091-23cf9cbecec9" + }, + "outputs": [], + "source": [ + "# sample image 1\n", + "images_custom = custom_pipe(\"a fashion model wearing a beautiful dress\", image_pose, num_inference_steps=20)\n", + "images_custom[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + }, + "id": "w4XGMCsUK4RA", + "outputId": "59e49ba7-d3c3-4fd7-bf0c-ed9ac23695dd" + }, + "outputs": [], + "source": [ + "# sample image 2\n", + "images_custom = custom_pipe(\"A male fashion model posing in a museum\", image_pose, num_inference_steps=20)\n", + "images_custom[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + }, + "id": "GU9P7QMacZTW", + "outputId": "b3c7e3aa-04b1-47c9-e29d-a8b68457b8e8" + }, + "outputs": [], + "source": [ + "# sample image with a different prompt\n", + "images_custom = custom_pipe(\"A professional ice skater wearing a dark blue jacket around sunset, realistic, UHD\", image_pose, num_inference_steps=20)\n", + "images_custom[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tXShSB0Fd7qd" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9dW5PCk0d7t7" + }, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "fwnnyHq3oi7O" + }, + "source": [ + "# Canny" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1L96sjJ_oi7P" + }, + "outputs": [], + "source": [ + "import cv2\n", + "from PIL import Image\n", + "from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\n", + "import torch\n", + "import numpy as np\n", + "from diffusers.utils import load_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 566, + 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"ca9e4db991b54eb986375b30bb367afa", + "4c8fa5fd3d224ce694243ea790de8986", + "b2efd56d5e834196818f46a7b287c2c3", + "3097e4c3b53e4343a9ed6860b8d3d033", + "a3fc1ea7dc724184ae564edeb2e755ba", + "28baa1ec6f3c46309c04f2c8462b1798", + "9cc4d939cc454a0da5eb7bdd234f69b5", + "058220c3342b49e0bcc1f0973566ac72", + "7db99c4ea5e14e9bb5180a5beb4cc142", + "344e28ed2fc344f4ad5ab6f62a042631", + "dc495adaeb53477d903ce952060e4f3f", + "d95e3f841d4f4f278fb3adae0b11d4c3", + "ee9448f0b9964d8fad850501e08e3c84", + "485caac1aa1641a3b31827620ddfa3e4", + "55fe364a57aa4eaa93cb93b889d241c5", + "3deaa7c9f0c940c3af3b9a7c5d0c8de4", + "93df8bff634740a29f3820042d69347f", + "373d310d37454a409b53c30e7d7d1de5", + "bcd48b3b056c4c14b6034ef32fdde5d5", + "948f2f29870b4ee7974ddad6ba18397d", + "978a0bae9c5f4ab7ac45f1f4f870fef0", + "233120cc0399483ea17284432feb16d3", + "183551cc7517479db8e0fffe634f2e6c", + "90398133563a48288ebf11cf0e0e242d", + "b6c5549bd95644ad9e553c2e59199f04", + "48ec35653f99446db9cefabe146bb2d8", + "84e8bc8164b948d8897a8637c226ef1c", + "89abbaee27c441b3ab1c656806a7e67d", + "9a46a59ffb8d44c89b067519101f7a74", + "c0771d4d9b7242c4a1b6ea8954e9e96e", + "682a337dc14e48908be4cf40f7320d1f", + "6d699038776e4fad8bb74d2381109b65", + "38dd90648c1949b196ebf27fb21436ba", + "dde9a4c3dd824b6f8345b29f2a28ef5f", + "6da0c63935ac4ee4bfe6a6efbedfa93d", + "42109c92a03c45cabd7fbb0cc7cf1c4a", + "db6ac9519e6a438ebca23446b0f2a4aa", + "8adb835bb80941dfa3e21c9dac93d793", + "e57b840c4d3c4abcae1b69b189701c72", + "304e7a7a38834b3093002b6694c7cb67", + "5056e439bb4b4bad8b0694c79138424f", + "7d6d17a2798c41c6b27c25e1057869a3", + "52a201ace1f647059d93e5d9406afee5", + "43820ed672c04bbf80155b25c4e4de15", + "b8a38cb7b914460caf5f190692f5b935", + "633f3db9f5dd4f6984aa821a1717c03b", + "c184aabb31504cbd8361a9ab06899438", + "5c428d2de97f4bc28835ead1f6331e9e", + "229a1efdf3df4fe9aac07562774b084b", + "697d6a7fa4ee4d37bb80d624ffedf526", + "c87c97a9ed4b493c8d6df7d11b1684ca", + "9ee7a0fe9750457abaf427983e5866fe", + "103ffa0973364618b4ce1d5528078062", + "b08e7b6e25c24054ac5b2b18bcb6f2ae", + "5532a6b6181a4aecbfca636813b661fd", + "4f425c415dac44b3b86aa4f481254196", + "97ffad3c1b6e4c9c9621966c028c12aa", + "3c2af8d5617b4b878b2e68f3fc7ebff6", + "8d27414ca68a4494ab3febc1c8a03025", + "10f4d1976dc04c3bb651b45c08298fab", + "eb72384f0e50444d9db03c12aca4b899", + "e7ddc52526db41e4907a2ede8dcef7c7", + "9f4a6f9a897c4db0a8d92bf01f3f820c", + "88e4029063f44ce5a7e89e8f17664eeb", + "77a73421b7ae4bfe9fcb87353a121402", + "7bee130b66754bbc85e8454455ece425", + "f61abe945b124e31a72254333f26c785", + "b1da7b0efaaf4db4a7660bc5457f2bfa", + "3e55680d33db47099479572d0c8915f2", + "c48ca8b557154fac885edaebc4879321", + "d4c261dab25d405ba560d4626683bccb", + "1daa130f38a84c6d9a4149536b21927e", + "bf14652b43264c2b884e2d19786d6f5e", + "a94d5d8411844431bb022813d14461c0", + "161da25b295b4afa8f8b88507818d052", + "fd444832eb3b4399946317b54b495d52", + "629f9b2f0b79420ba0baeb5961df73da", + "869c22e5399145dab7e36633b910abca", + "6e89b30bfcd04f1ebc77492eae365847", + "7f27d4dadc2a4c4783f35193cf085593", + "fa4ce3277cce4064aaf40849870966c1", + "d2324949415f4df2a964046b326231a4", + "4a3c28369fc14bbebdc749d0b3a5747b", + "be72ef0e4b8840608fd9160a6b43075d", + "6d8e99610d624a09a53ef0c77b3f0c25", + "b9df58ad481f414f8240ce076cf53363", + "3b2265f2c01844fb9c99e18c61344d69", + "e060cf453034447dba6af95a918e3092", + "7f49c5a346894250ad0587fa3420635a", + "c318de49fe3a455992738cf20f7099ad", + "3ff34cbabc564abebd12a9e5e19786b0", + "611bde262a924746a253ab5481b9b212", + "3ae595d0044944ea959d949d8c68d509", + "5494938bff7d4c6da3d82f1034404362", + "f05ad5aa13a040609336f0d6b2580be2", + "02648abd8847434496722b0c5bb69858", + "e3b2deb57c0644dcbf97297abf22f91c", + "7267e3d447ef4dfa97c3fc6a560ea797", + "2bc90ab8db7b4f979ff0cb5eb01284a6" + ] + }, + "id": "Dhg4rTN3oi7P", + "outputId": "f2114cf6-bbc5-4e5e-827e-e5fc0a92351e" + }, + "outputs": [], + "source": [ + "# load the controlnet model for canny edge detection\n", + "controlnet = ControlNetModel.from_pretrained(\n", + " \"lllyasviel/sd-controlnet-canny\", torch_dtype=torch.float16\n", + ")\n", + "\n", + "# load the stable diffusion pipeline with controlnet\n", + "pipe = StableDiffusionControlNetPipeline.from_pretrained(\n", + " \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n", + ")\n", + "pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "H-m6uNFCoi7P" + }, + "outputs": [], + "source": [ + "# enable efficient implementations using xformers for faster inference\n", + "pipe.enable_xformers_memory_efficient_attention()\n", + "pipe.enable_model_cpu_offload()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 441 + }, + "id": "ATEwEV-6oi7Q", + "outputId": "055658c9-5ab9-4496-a407-f4a213f84bd8" + }, + "outputs": [], + "source": [ + "image_input = load_image(\"https://cdn.pixabay.com/photo/2023/06/03/16/05/spotted-laughingtrush-8037974_640.png\")\n", + "image_input = np.array(image_input)\n", + "\n", + "Image.fromarray(image_input)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 441 + }, + "id": "K2c64abboi7Q", + "outputId": "fd9de66a-d340-4b1e-bc51-7e9c96f1f80a" + }, + "outputs": [], + "source": [ + "# define parameters from canny edge detection\n", + "low_threshold = 100\n", + "high_threshold = 200\n", + "\n", + "# do canny edge detection\n", + "image_canny = cv2.Canny(image_input, low_threshold, high_threshold)\n", + "\n", + "# convert to PIL image format\n", + "image_canny = image_canny[:, :, None]\n", + "image_canny = np.concatenate([image_canny, image_canny, image_canny], axis=2)\n", + "image_canny = Image.fromarray(image_canny)\n", + "\n", + "image_canny" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 473, + "referenced_widgets": [ + "27d1ad377e6c45d7b4b1c49786438015", + "475c331703bb43adb142f6c9cc41a3bf", + "10be4b9595c84f36aeab9593e63440e6", + "645ebe129fd544a4b9383dfc1414cf0a", + "51605e182e344a94a99c50fcaaef17b1", + "c45aec98de234d3982ff83924dce8d7a", + "942ace43ed1d48aeafc6c5e17f14e034", + "51890b032fbb48139e11d69850a6db57", + "30408dcf6dd14e27bab9cc06cc664c4c", + "b539338d7bf84f50a8c57f90986a8d9a", + "d16de73052ac42e8a8065d4dc54ebe51" + ] + }, + "id": "zac6FpNwoi7Q", + "outputId": "8bf84b99-fe11-43d2-82e2-f2b35ae99d5a" + }, + "outputs": [], + "source": [ + "image_output = pipe(\"bird\", image_canny, num_inference_steps=20).images[0]\n", + "image_output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 473, + "referenced_widgets": [ + "48a26564f08f43dd962464feb762c232", + "c6965bc561db4c519b751c5b7bff96a8", + "032471548f5c45ed89d8a62db800c7fc", + "0af102aac55747fcb8854b8b5ed2dd27", + "a086d1c6722547d5be5939cf4284a1ef", + "ba3cf4ae2c4e45e58abb82d2491fb7ba", + "c64f35616d8e4b4bb129bc7aaa4ae889", + "01fc82bb9bb84c628890b9a2349a6e6e", + "f66573653707458f80b29a40e1193d31", + "7b0ece2c75614540a942214d1f527f91", + "0d3e1914b85047c7af6b6bd4fd94e197" + ] + }, + "id": "DXrdLeZrplMW", + "outputId": "31bb0440-493a-4ee8-edc0-23c090f679c4" + }, + "outputs": [], + "source": [ + "image_output = pipe(\"a cute blue bird with colorful aesthetic feathers\", image_canny, num_inference_steps=20).images[0]\n", + "image_output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-Yti9Dg8ofxd" + }, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "-o-9jxdtpvgi" + }, + "source": [ + "# Depth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rgwvAu2xpvgj" + }, + "outputs": [], + "source": [ + "from transformers import pipeline\n", + "from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\n", + "from PIL import Image\n", + "import numpy as np\n", + "import torch\n", + "from diffusers.utils import load_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IcxFcjHspvgj", + "outputId": "79bed3a2-27b2-411e-a748-22949d5cac62" + }, + "outputs": [], + "source": [ + "# load the depth estimator model\n", + "depth_estimator = pipeline('depth-estimation')\n", + "\n", + "# load the controlnet model for depth estimation\n", + "controlnet = ControlNetModel.from_pretrained(\n", + " \"lllyasviel/sd-controlnet-depth\", torch_dtype=torch.float16\n", + ")\n", + "\n", + "# load the stable diffusion pipeline with controlnet\n", + "pipe = StableDiffusionControlNetPipeline.from_pretrained(\n", + " \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n", + ")\n", + "pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QfZNDXHgpvgj" + }, + "outputs": [], + "source": [ + "# enable efficient implementations using xformers for faster inference\n", + "pipe.enable_xformers_memory_efficient_attention()\n", + "pipe.enable_model_cpu_offload()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 475 + }, + "id": "t0_6mNOgpvgj", + "outputId": "8dd080e3-9fbb-4bc5-bd4c-375a4e1f18ee" + }, + "outputs": [], + "source": [ + "image_input = load_image(\"https://huggingface.co/lllyasviel/sd-controlnet-depth/resolve/main/images/stormtrooper.png\")\n", + "image_input" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 475 + }, + "id": "oTim6FQrpvgk", + "outputId": "4cc296a7-0822-4012-a07c-232f14bb039e" + }, + "outputs": [], + "source": [ + "# get depth estimates\n", + "image_depth = depth_estimator(image_input)['depth']\n", + "\n", + "# convert to PIL image format\n", + "image_depth = np.array(image_depth)\n", + "image_depth = image_depth[:, :, None]\n", + "image_depth = np.concatenate([image_depth, image_depth, image_depth], axis=2)\n", + "image_depth = Image.fromarray(image_depth)\n", + "\n", + "image_depth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 505, + "referenced_widgets": [ + "173d24393b854ad0a03fd02d56c7d037", + "a33e6193984b46d4bb9b4187d726009e", + "230461cc201f4535a9f9bcf7a3ae7669", + "4900e42f3bfa4a6096b16d47d38687dc", + "2112261a320b4b9297aa3edc85fa2316", + "1c65960b291c4d92bced5bca619e8256", + "194f16cb7fbb42a78d9237e4e6654b58", + "b87077da3f824478ac88369582d77e8e", + "6f9f95e2d503403f83be05364b4199cd", + "c39f69cd4e8b4bca8e95bda3b06a74f1", + "f8dbc4c64a0d440e9660b0d7ef083b5c" + ] + }, + "id": "zK5HpMNdpvgq", + "outputId": "2b2e889d-8920-404f-e8de-e2753caebf05" + }, + "outputs": [], + "source": [ + "image_output = pipe(\"Darth Vader giving lecture\", image_depth, num_inference_steps=20).images[0]\n", + "image_output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 505, + "referenced_widgets": [ + "083aa6ce3274445c830c4157dc9ea4be", + "be56ad8a63884a33a22bfb3b39da1cc0", + "83b4c17fa697445f86f7f034fe670934", + "c3dc31d7d2be42be88fe03fa33e5f20d", + "2c38cd148fe74a5897c3319be42b7d7e", + "c44f196498724b1b80b25bda832dc310", + "069b424c2d3846b8ba69a455dbda1f88", + "707ce79ce0da4f35ad6b8f636d2901f8", + "261aabd2415d43809b669b49f3899504", + "7427e45c55994a6fa304d6d58ffeb1fb", + "2df0162802aa488dbddb9a7376954865" + ] + }, + "id": "ulgn37vmrUS8", + "outputId": "aba588b6-5de1-44c5-8d96-79ac14a89f9a" + }, + "outputs": [], + "source": [ + "image_output = pipe(\"A realistic, aesthetic portrait style photograph of Darth Vader giving lecture, 8k, unreal engine\", image_depth, num_inference_steps=20).images[0]\n", + "image_output" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "-uc3OBwGryX9" + }, + "source": [ + "# Normal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vZ8NidcHryYF" + }, + "outputs": [], + "source": [ + "from PIL import Image\n", + "from transformers import pipeline\n", + "import numpy as np\n", + "import cv2\n", + "from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\n", + "import torch\n", + "from diffusers.utils import load_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 214, + "referenced_widgets": [ + "f0ddc614bd9a40468c083db3a457e1c1", + "a27de7fc1bfa486f9ff8829d4d4198f6", + "47507d2cf4e04f2f927ee369c6d7b4d4", + "e11151541738459ba16b582a9edefd2c", + "33c23b5b6adf403dba45e9f8e64e37dc", + "a9a5528792504d12bf153d2b02fd3847", + "439ee7b8de924bf6ba5ee4ef22e0e659", + "b4e865be02fb4290849b676f4dd18ed0", + "87d4c1e6a2a047f887704f6b0a148d2e", + "b74532a8bd8e49f2a8286a1eed72a0d3", + "dd66e37aec9449de944edfa6dacdfc5f", + "57216de8dfcc4ab78bfb8c41fae856b4", + "c85b3e0dd20f414181d768cac5c9ab4e", + "53774d0251ea4e4a884fc265a5ce561b", + "30cdb95dd5d746b1adacaaf823b21b89", + "bfd0a68276654b4997b802bc6d47f684", + "dd81ef8c35384eadb1572400c775f789", + "de80e8cbbd25433c88267a1f997772e7", + "e116ebfa368d424ca64eea23a82ef959", + "2b92efb0492b4af6b50e2d5754212b5c", + "2fee0c2fcb3b4705ad4cc212d6138ec9", + "5f012cede7a0405fbb12550072bccbc2", + "a999af1084134026904611113b3feac6", + "814f29773b6b40d99d03320a3f5fe7d4", + "9f7e4357439944e392d0c9e93068c898", + "8e31c3cc06bd4e9b92c3b1e6d5a9d61f", + "9dc6d26cdb7945ffb31b2c381412a383", + "36de2037f4b841afb366f27a7eb08396", + "cb805d94485c4ff5a0aaec67e6cdb827", + "d923cb2715554512a7faf5b5d73a4ed8", + "4399ee6304e94797ac6f36318165d4cc", + "7c2b45b1cf2a485b93e52b2dee04a278", + "851c4973cb0c4fbe91536a09f006ad43", + "7d10f1e6258943dd86d9c75aa17553c8", + "193fa11998404439b90913f5f5b4619e", + "0383c8a7e3384eec9205dc8f37654ee4", + "4615b3c5ce674070840fbb7bcb54f262", + "b57f5b94be66435fab8e8aa9b877848c", + "8b7c3bcf27644ac09871f733662c4d8b", + "42cabc32ffc34ddd924fde45571ada38", + "58c63ae0605a4f1192c52e4306602fd7", + "55e0a986be424043846c0e863158a1bd", + "23a9e406e89041f8ab9a15eae4f9c61b", + "f18d3449a23c485a85e38f0ced810c99", + "1d4352761751419282c93eb85c0954c0", + "b84453116ba14690b8ae8a8e723f8510", + "6e14135b00e447c18a0a693d47f5c92b", + "9a540d6ed00a424da320fd7a12c31ac5", + "13f07d79c96146aaa1d7731526aaec29", + "b54312c4603b400b97884eba33ad1095", + "b93ed8d01dbd4e0a84ff01b2e15f68ec", + "8119e3c1840d4e0b975055cb0c255208", + "ad785ec6b7924f2ebcdf0957a16c656d", + "d44b6d406224497d9db5c903c6972323", + "d3010a25994f4f12b95922f2863f4f3b" + ] + }, + "id": "Rt4ecMkaryYG", + "outputId": "f8ab6e1a-2257-48bd-9fa6-c7fe19188404" + }, + "outputs": [], + "source": [ + "# load the Dense Prediction Transformer (DPT) model for getting normal maps\n", + "depth_estimator = pipeline(\"depth-estimation\", model =\"Intel/dpt-hybrid-midas\")\n", + "\n", + "# load the controlnet model for normal maps\n", + "controlnet = ControlNetModel.from_pretrained(\n", + " \"fusing/stable-diffusion-v1-5-controlnet-normal\", torch_dtype=torch.float16\n", + ")\n", + "\n", + "# load the stable diffusion pipeline with controlnet\n", + "pipe = StableDiffusionControlNetPipeline.from_pretrained(\n", + " \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n", + ")\n", + "pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UoGdz3FkryYG" + }, + "outputs": [], + "source": [ + "# enable efficient implementations using xformers for faster inference\n", + "pipe.enable_xformers_memory_efficient_attention()\n", + "pipe.enable_model_cpu_offload()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 870 + }, + "id": "0WLxPD8fryYG", + "outputId": "d605305f-4c8e-40dd-e238-13131f64c961" + }, + "outputs": [], + "source": [ + "image_input = load_image(\"https://cdn.pixabay.com/photo/2023/06/07/13/02/butterfly-8047187_1280.jpg\")\n", + "image_input" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 401 + }, + "id": "Uqhw6NR4ryYG", + "outputId": "2f3c8930-541b-49d7-fb87-374972c078e5" + }, + "outputs": [], + "source": [ + "# do all the preprocessing to get the normal image\n", + "image = depth_estimator(image_input)['predicted_depth'][0]\n", + "\n", + "image = image.numpy()\n", + "\n", + "image_depth = image.copy()\n", + "image_depth -= np.min(image_depth)\n", + "image_depth /= np.max(image_depth)\n", + "\n", + "bg_threhold = 0.4\n", + "\n", + "x = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3)\n", + "x[image_depth < bg_threhold] = 0\n", + "\n", + "y = cv2.Sobel(image, cv2.CV_32F, 0, 1, ksize=3)\n", + "y[image_depth < bg_threhold] = 0\n", + "\n", + "z = np.ones_like(x) * np.pi * 2.0\n", + "\n", + "image = np.stack([x, y, z], axis=2)\n", + "image /= np.sum(image ** 2.0, axis=2, keepdims=True) ** 0.5\n", + "image = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8)\n", + "image_normal = Image.fromarray(image)\n", + "\n", + "image_normal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 433, + "referenced_widgets": [ + "7aed2e247f5e488991be8b97ab6bae6a", + "f8b042b72ea1403284bc45b55cbfcdcf", + "9dcbc2386e3c4d6e820fd6baed91f0df", + "67768915a19a448c94620204f861d005", + "a218d578aded48ba8acca854622c14a9", + "5cbe59686c0d475a8e2274ffcb64d992", + "3bf3dd9f53354c758187674b5b1eac19", + "a1ed5abc44e94c1ea19fa959fc95b91a", + "7669fb4960904becb83cbdc4169a121b", + "7699034aeb584f12864aaa30a7f2e6b5", + "0762e7a5efb84a4b9bf60c5cf3235671" + ] + }, + "id": "_BLefbf1ryYG", + "outputId": "be063e2c-4e78-4c1e-cbe3-98fd0dcdad72" + }, + "outputs": [], + "source": [ + "image_output = pipe(\"A colorful butterfly sitting on apples\", image_normal, num_inference_steps=20).images[0]\n", + "image_output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 433, + "referenced_widgets": [ + "ad63323795ae48679bb40de0056219d3", + "4cb413851dc3469a8ea04e40373b11de", + "21549aef7c6f4c64b8eb676c6dd105e2", + "7cd3b5a0ce274ae5b5954a3c7d9d3bb4", + "4a3a0d14e22f4a6e9dbb5fb35bc33e8d", + "8d2f2b219308442db07d23b057f8e7cf", + "7b9508943bee4f76b996ba561d4bbe9b", + "94cc8073f13f45b085e1f23d3d4bbbc9", + "cd502f2ef8814082979d147f08177ff6", + "f357992a28754d7689721ebda28f0b0f", + "2f8938d9a9254868869767a332bdf84f" + ] + }, + "id": "c-iOJPe1ryYH", + "outputId": "ee338887-1753-47f0-aed3-838749415d3a" + }, + "outputs": [], + "source": [ + "image_output = pipe(\"A beautiful design\", image_normal, num_inference_steps=20).images[0]\n", + "image_output" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "_zlU00SCzecq" + }, + "source": [ + "# Segmentation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iLEetydkzec_" + }, + "outputs": [], + "source": [ + "from transformers import AutoImageProcessor, UperNetForSemanticSegmentation\n", + "from PIL import Image\n", + "import numpy as np\n", + "import torch\n", + "from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\n", + "from diffusers.utils import load_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 214, + "referenced_widgets": [ + "cbbf80dd8dc9471197c49732ae4d08ee", + "0a8a88ac007a41e4b0096fb114fe8a47", + "d4c98d8a36eb4f769e23aa3f36535a0f", + "20f315f7aae840d19ee8a068c68ff63c", + "c420c12b2a76457c9ec7bb9db8eac9b3", + "e38139b6898e4897b67aac89ee982c7a", + "5e144bb789054a88a43db4329bb2df05", + "15de6a583c5e4525a559749577fd4331", + "4af5ce87c2a243238375d7edf93d08b2", + "56cc25e58ea3445f9135817143224d2c", + "6c6f19d716d2410b813e32f3b051619a", + "7f1b3b31059e4abbbe62bd88da98627d", + "0ee14dea902c4efe81c616f34dbc7562", + "e43b78d4e4844565ad39e979861285a8", + "9ba3483798e8439aab7d5560c1a5bac1", + "993b867eff394cfe843eb18ee194b8d6", + "b1659a9d5e294f9bbc1b20e892e16326", + "308c9dc88e034f4bbcb4ee24628694ba", + "0e38cf52fb3d40d5a87ec975499ef648", + "a37c136cb6c34c1ea5a66bd78c5750ae", + "60ebb0e8c1174c839a26182b38d4ae9b", + "f3be39d3186448afb9d851b9732df52b", + "7d9df7ed952144dfacc3d2908497ce94", + "7010861902ab409e9a4f65efad33683c", + "978ecf658d464bc999bc8a5c540e3ad0", + "198e8a1603fc40218d805d46da1cc603", + "ee7d2281e6964da8a3e610343a7de76d", + "c30088b9866c429fa513397b03d2e3c8", + "f48a988402a947a098ed14bfd0dc9a53", + "f0a064bb91524f01972e941a3bd7354d", + "7efd39ed94f94ecd9292069da0697a5f", + "54746d0ef2e7453faddcc3b6066def51", + "ff535db7c20f4719820b0b483a3a41ff", + "3282a8068816447285333083b4c8fe96", + "2c260ce7a89840e3857942d9294f9321", + "eba07e4598814b6cb22ba58d16587cf3", + "cfe9f9f05cb8493f804cd521a3e3c1c1", + "b3418378805c4029aa6e6b939ae3c84b", + "2cd604429546460d8e79a7ac4980d4cd", + "1cf1b7e84b744edf907f94aa3d1983bc", + "ab44913ac7c44a0f89a963fddf9dba39", + "c9678e8106544428a9f155e2da9f2693", + "15ce5867e9ba470b94f0408cfa8236ad", + "ee848051813f401c958c169f8b77a323", + "71ee3795a28a4776bdb13f68f11ffaaa", + "af113f9b9b1749f0a856b3f371f03a2d", + "83cb84140f504cdaa6d5ed308baaa9bf", + "f81e6ba8c0ee4d19afc1691be3650a94", + "8bc53de7028e4a05bc683a6f47548d62", + "72fc016402374ad996a292470fa75906", + "4ec20db88a97441982a914517888a8b4", + "75837fa1f8ed4a7b8734f0c49e3bb6d0", + "527478832a8e451ea056c50ee2b241ab", + "e1877e0c82a1464597250ef0572d2679", + "3005ddef183645cbb3039da2a621ebf4" + ] + }, + "id": "BRZqE7YLzedA", + "outputId": "b9f4d2e9-2667-4992-f3d5-f574108640ec" + }, + "outputs": [], + "source": [ + "# load the image processor and the model for doing segmentation\n", + "image_processor = AutoImageProcessor.from_pretrained(\"openmmlab/upernet-convnext-small\")\n", + "image_segmentor = UperNetForSemanticSegmentation.from_pretrained(\"openmmlab/upernet-convnext-small\")\n", + "\n", + "# load the controlnet model for semantic segmentation\n", + "controlnet = ControlNetModel.from_pretrained(\n", + " \"lllyasviel/sd-controlnet-seg\", torch_dtype=torch.float16\n", + ")\n", + "\n", + "# load the stable diffusion pipeline with controlnet\n", + "pipe = StableDiffusionControlNetPipeline.from_pretrained(\n", + " \"runwayml/stable-diffusion-v1-5\", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\n", + ")\n", + "pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZzEaHeM2zedA" + }, + "outputs": [], + "source": [ + "# enable efficient implementations using xformers for faster inference\n", + "pipe.enable_xformers_memory_efficient_attention()\n", + "pipe.enable_model_cpu_offload()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3kZI0eD2zkfm" + }, + "outputs": [], + "source": [ + "# define color palette that is used by the semantic segmentation models\n", + "\n", + "palette = np.asarray([\n", + " [0, 0, 0],\n", + " [120, 120, 120],\n", + " [180, 120, 120],\n", + " [6, 230, 230],\n", + " [80, 50, 50],\n", + " [4, 200, 3],\n", + " [120, 120, 80],\n", + " [140, 140, 140],\n", + " [204, 5, 255],\n", + " [230, 230, 230],\n", + " [4, 250, 7],\n", + " [224, 5, 255],\n", + " [235, 255, 7],\n", + " [150, 5, 61],\n", + " [120, 120, 70],\n", + " [8, 255, 51],\n", + " [255, 6, 82],\n", + " [143, 255, 140],\n", + " [204, 255, 4],\n", + " [255, 51, 7],\n", + " [204, 70, 3],\n", + " [0, 102, 200],\n", + " [61, 230, 250],\n", + " [255, 6, 51],\n", + " [11, 102, 255],\n", + " [255, 7, 71],\n", + " [255, 9, 224],\n", + " [9, 7, 230],\n", + " [220, 220, 220],\n", + " [255, 9, 92],\n", + " [112, 9, 255],\n", + " [8, 255, 214],\n", + " [7, 255, 224],\n", + " [255, 184, 6],\n", + " [10, 255, 71],\n", + " [255, 41, 10],\n", + " [7, 255, 255],\n", + " [224, 255, 8],\n", + " [102, 8, 255],\n", + " [255, 61, 6],\n", + " [255, 194, 7],\n", + " [255, 122, 8],\n", + " [0, 255, 20],\n", + " [255, 8, 41],\n", + " [255, 5, 153],\n", + " [6, 51, 255],\n", + " [235, 12, 255],\n", + " [160, 150, 20],\n", + " [0, 163, 255],\n", + " [140, 140, 140],\n", + " [250, 10, 15],\n", + " [20, 255, 0],\n", + " [31, 255, 0],\n", + " [255, 31, 0],\n", + " [255, 224, 0],\n", + " [153, 255, 0],\n", + " [0, 0, 255],\n", + " [255, 71, 0],\n", + " [0, 235, 255],\n", + " [0, 173, 255],\n", + " [31, 0, 255],\n", + " [11, 200, 200],\n", + " [255, 82, 0],\n", + " [0, 255, 245],\n", + " [0, 61, 255],\n", + " [0, 255, 112],\n", + " [0, 255, 133],\n", + " [255, 0, 0],\n", + " [255, 163, 0],\n", + " [255, 102, 0],\n", + " [194, 255, 0],\n", + " [0, 143, 255],\n", + " [51, 255, 0],\n", + " [0, 82, 255],\n", + " [0, 255, 41],\n", + " [0, 255, 173],\n", + " [10, 0, 255],\n", + " [173, 255, 0],\n", + " [0, 255, 153],\n", + " [255, 92, 0],\n", + " [255, 0, 255],\n", + " [255, 0, 245],\n", + " [255, 0, 102],\n", + " [255, 173, 0],\n", + " [255, 0, 20],\n", + " [255, 184, 184],\n", + " [0, 31, 255],\n", + " [0, 255, 61],\n", + " [0, 71, 255],\n", + " [255, 0, 204],\n", + " [0, 255, 194],\n", + " [0, 255, 82],\n", + " [0, 10, 255],\n", + " [0, 112, 255],\n", + " [51, 0, 255],\n", + " [0, 194, 255],\n", + " [0, 122, 255],\n", + " [0, 255, 163],\n", + " [255, 153, 0],\n", + " [0, 255, 10],\n", + " [255, 112, 0],\n", + " [143, 255, 0],\n", + " [82, 0, 255],\n", + " [163, 255, 0],\n", + " [255, 235, 0],\n", + " [8, 184, 170],\n", + " [133, 0, 255],\n", + " [0, 255, 92],\n", + " [184, 0, 255],\n", + " [255, 0, 31],\n", + " [0, 184, 255],\n", + " [0, 214, 255],\n", + " [255, 0, 112],\n", + " [92, 255, 0],\n", + " [0, 224, 255],\n", + " [112, 224, 255],\n", + " [70, 184, 160],\n", + " [163, 0, 255],\n", + " [153, 0, 255],\n", + " [71, 255, 0],\n", + " [255, 0, 163],\n", + " [255, 204, 0],\n", + " [255, 0, 143],\n", + " [0, 255, 235],\n", + " [133, 255, 0],\n", + " [255, 0, 235],\n", + " [245, 0, 255],\n", + " [255, 0, 122],\n", + " [255, 245, 0],\n", + " [10, 190, 212],\n", + " [214, 255, 0],\n", + " [0, 204, 255],\n", + " [20, 0, 255],\n", + " [255, 255, 0],\n", + " [0, 153, 255],\n", + " [0, 41, 255],\n", + " [0, 255, 204],\n", + " [41, 0, 255],\n", + " [41, 255, 0],\n", + " [173, 0, 255],\n", + " [0, 245, 255],\n", + " [71, 0, 255],\n", + " [122, 0, 255],\n", + " [0, 255, 184],\n", + " [0, 92, 255],\n", + " [184, 255, 0],\n", + " [0, 133, 255],\n", + " [255, 214, 0],\n", + " [25, 194, 194],\n", + " [102, 255, 0],\n", + " [92, 0, 255],\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 870 + }, + "id": "GjsCwREqzedB", + "outputId": "6ac8fcaf-a89b-4bef-bd9b-31fd753568d7" + }, + "outputs": [], + "source": [ + "image_input = load_image(\"https://cdn.pixabay.com/photo/2023/02/24/07/14/crowd-7810353_1280.jpg\")\n", + "image_input" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 870 + }, + "id": "qK5gkfCIzedB", + "outputId": "71e92874-04d1-459a-8cc6-89bc31796b2f" + }, + "outputs": [], + "source": [ + "# get the pixel values\n", + "pixel_values = image_processor(image_input, return_tensors=\"pt\").pixel_values\n", + "\n", + "# do semantic segmentation\n", + "with torch.no_grad():\n", + " outputs = image_segmentor(pixel_values)\n", + "\n", + "# post process the semantic segmentation\n", + "seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image_input.size[::-1]])[0]\n", + "\n", + "# add colors to the different identified classes\n", + "color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3\n", + "for label, color in enumerate(palette):\n", + " color_seg[seg == label, :] = color\n", + "\n", + "# convert into PIL image format\n", + "color_seg = color_seg.astype(np.uint8)\n", + "image_seg = Image.fromarray(color_seg)\n", + "\n", + "image_seg" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 897, + "referenced_widgets": [ + "09441b43551f4e5f9ff77512f59d2f9e", + "955af4f359254125801ec776793397e7", + "27f8dc0ef76448be90b004529fa562cf", + "a3f598a48fa448dbb44a40c8c7af85e6", + "6c353682d9d64022a11f31357816f14d", + "8c0d69533b674629b69a64345fc4d67e", + "f2241131e8d54c1f9e8976b94b46bc67", + "0120420c938f4e588ac496f37bcc42eb", + "89a159fc68ef4972a0368c72306cd8c4", + "c8ce8fd6e04e416381d892fbf75a0b75", + "8d62ab081ff94e7abe3cc911d3376bbd" + ] + }, + "id": "z0eWMTU0zedB", + "outputId": "9502f927-18cb-4baa-dc24-9c18ec4ce150" + }, + "outputs": [], + "source": [ + "image_output = pipe(\"A crowd of people staring at a glorious painting\", image_seg, num_inference_steps=20).images[0]\n", + "image_output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 897, + "referenced_widgets": [ + "312f1ce28d144f8e828d5d33c0783a58", + "36aae82533a5470abfa287df328448fc", + "7afd5032c0964fc5823189e2fc8599ed", + "944390d866cf430fbd562fca7781a9d9", + "66f7c6f267864c929727fd96a83e6d06", + "2e3313ac79794a18a38a6b98c0f4f28a", + "77ad5edb54bb4e30bdd82186e80367c8", + "c9311122d93c495faa2a8e0f4b8f7662", + "58657e2570434d73be122eafb42f490a", + "ef977ff25ba842c9a0475befc7b47b89", + "c8da6be1960c4f89a18d49205cbaa0df" + ] + }, + "id": "LbWbrzpszedB", + "outputId": "4bdd8997-e047-4fbb-ef58-6162ad40a4be" + }, + "outputs": [], + "source": [ + "image_output = pipe(\"Aliens looking at earth from inside their spaceship from a window, not creepy, not scary, not gross, octane render, smooth\", image_seg, num_inference_steps=20).images[0]\n", + "image_output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kXmBKD8G7WRU" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [ + "h6XPbq0FrPQG", + 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b/machine-learning/control-image-generation-with-controlnet/README.md new file mode 100644 index 00000000..d7e237d6 --- /dev/null +++ b/machine-learning/control-image-generation-with-controlnet/README.md @@ -0,0 +1 @@ +# [How to Control the Generated Images by diffusion models via ControlNet in Python](https://www.thepythoncode.com/article/control-generated-images-with-controlnet-with-huggingface) \ No newline at end of file diff --git a/machine-learning/control-image-generation-with-controlnet/controlnet_pythoncodetutorial.py b/machine-learning/control-image-generation-with-controlnet/controlnet_pythoncodetutorial.py new file mode 100644 index 00000000..55397665 --- /dev/null +++ b/machine-learning/control-image-generation-with-controlnet/controlnet_pythoncodetutorial.py @@ -0,0 +1,689 @@ +# %% +!pip install -qU xformers diffusers transformers accelerate + +# %% +!pip install -qU controlnet_aux +!pip install opencv-contrib-python + +# %% [markdown] +# # Open Pose + +# %% +from PIL import Image +from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler +import torch +from controlnet_aux import OpenposeDetector +from diffusers.utils import load_image +from tqdm import tqdm +from torch import autocast + +# %% +# load the openpose model +openpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet') + +# load the controlnet for openpose +controlnet = ControlNetModel.from_pretrained( + "lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16 +) + +# define stable diffusion pipeline with controlnet +pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 +) +pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) + +# %% +# Remove if you do not have xformers installed +# see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers +# for installation instructions +pipe.enable_xformers_memory_efficient_attention() +pipe.enable_model_cpu_offload() + +# %% +image_input = load_image("https://cdn.pixabay.com/photo/2016/05/17/22/19/fashion-1399344_640.jpg") +image_input + +# %% +image_pose = openpose(image_input) +image_pose + +# %% +image_output = pipe("A professional photograph of a male fashion model", image_pose, num_inference_steps=20).images[0] +image_output + +# %% [markdown] +# # Custom implementation + +# %% +class ControlNetDiffusionPipelineCustom: + """custom implementation of the ControlNet Diffusion Pipeline""" + + def __init__(self, + vae, + tokenizer, + text_encoder, + unet, + controlnet, + scheduler, + image_processor, + control_image_processor): + + self.vae = vae + self.tokenizer = tokenizer + self.text_encoder = text_encoder + self.unet = unet + self.scheduler = scheduler + self.controlnet = controlnet + self.image_processor = image_processor + self.control_image_processor = control_image_processor + self.device = 'cuda' if torch.cuda.is_available() else 'cpu' + + + + def get_text_embeds(self, text): + """returns embeddings for the given `text`""" + + # tokenize the text + text_input = self.tokenizer(text, + padding='max_length', + max_length=tokenizer.model_max_length, + truncation=True, + return_tensors='pt') + # embed the text + with torch.no_grad(): + text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0] + return text_embeds + + + + def get_prompt_embeds(self, prompt): + """returns prompt embeddings based on classifier free guidance""" + + if isinstance(prompt, str): + prompt = [prompt] + # get conditional prompt embeddings + cond_embeds = self.get_text_embeds(prompt) + # get unconditional prompt embeddings + uncond_embeds = self.get_text_embeds([''] * len(prompt)) + # concatenate the above 2 embeds + prompt_embeds = torch.cat([uncond_embeds, cond_embeds]) + return prompt_embeds + + + def transform_image(self, image): + """convert image from pytorch tensor to PIL format""" + + image = self.image_processor.postprocess(image, output_type='pil') + return image + + + + def get_initial_latents(self, height, width, num_channels_latents, batch_size): + """returns noise latent tensor of relevant shape scaled by the scheduler""" + + image_latents = torch.randn((batch_size, + num_channels_latents, + height // 8, + width // 8)).to(self.device) + # scale the initial noise by the standard deviation required by the scheduler + image_latents = image_latents * self.scheduler.init_noise_sigma + return image_latents + + + + def denoise_latents(self, + prompt_embeds, + controlnet_image, + timesteps, + latents, + guidance_scale=7.5): + """denoises latents from noisy latent to a meaningful latent as conditioned by controlnet""" + + # use autocast for automatic mixed precision (AMP) inference + with autocast('cuda'): + for i, t in tqdm(enumerate(timesteps)): + # duplicate image latents to do classifier free guidance + latent_model_input = torch.cat([latents] * 2) + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + control_model_input = latents + controlnet_prompt_embeds = prompt_embeds + + # get output from the control net blocks + down_block_res_samples, mid_block_res_sample = self.controlnet( + control_model_input, + t, + encoder_hidden_states=controlnet_prompt_embeds, + controlnet_cond=controlnet_image, + conditioning_scale=1.0, + return_dict=False, + ) + + # predict noise residuals + with torch.no_grad(): + noise_pred = self.unet( + latent_model_input, + t, + encoder_hidden_states=prompt_embeds, + down_block_additional_residuals=down_block_res_samples, + mid_block_additional_residual=mid_block_res_sample, + )['sample'] + + # separate predictions for unconditional and conditional outputs + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + + # perform guidance + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # remove the noise from the current sample i.e. go from x_t to x_{t-1} + latents = self.scheduler.step(noise_pred, t, latents)['prev_sample'] + + return latents + + + + def prepare_controlnet_image(self, + image, + height, + width): + """preprocesses the controlnet image""" + + # process the image + image = self.control_image_processor.preprocess(image, height, width).to(dtype=torch.float32) + # send image to CUDA + image = image.to(self.device) + # repeat the image for classifier free guidance + image = torch.cat([image] * 2) + return image + + + + def __call__(self, + prompt, + image, + num_inference_steps=20, + guidance_scale=7.5, + height=512, width=512): + """generates new image based on the `prompt` and the `image`""" + + # encode input prompt + prompt_embeds = self.get_prompt_embeds(prompt) + + # prepare image for controlnet + controlnet_image = self.prepare_controlnet_image(image, height, width) + height, width = controlnet_image.shape[-2:] + + # prepare timesteps + self.scheduler.set_timesteps(num_inference_steps) + timesteps = self.scheduler.timesteps + + # prepare the initial image in the latent space (noise on which we will do reverse diffusion) + num_channels_latents = self.unet.config.in_channels + batch_size = prompt_embeds.shape[0] // 2 + latents = self.get_initial_latents(height, width, num_channels_latents, batch_size) + + # denoise latents + latents = self.denoise_latents(prompt_embeds, + controlnet_image, + timesteps, + latents, + guidance_scale) + + # decode latents to get the image into pixel space + latents = latents.to(torch.float16) # change dtype of latents since + image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] + + # convert to PIL Image format + image = image.detach() # detach to remove any computed gradients + image = self.transform_image(image) + + return image + +# %% +# We can get all the components from the ControlNet Diffusion Pipeline (the one implemented by Hugging Face as well) +vae = pipe.vae +tokenizer = pipe.tokenizer +text_encoder = pipe.text_encoder +unet = pipe.unet +controlnet = pipe.controlnet +scheduler = pipe.scheduler +image_processor = pipe.image_processor +control_image_processor = pipe.control_image_processor + +# %% +custom_pipe = ControlNetDiffusionPipelineCustom(vae, tokenizer, text_encoder, unet, controlnet, scheduler, image_processor, control_image_processor) + +# %% +# sample image 1 +images_custom = custom_pipe("a fashion model wearing a beautiful dress", image_pose, num_inference_steps=20) +images_custom[0] + +# %% +# sample image 2 +images_custom = custom_pipe("A male fashion model posing in a museum", image_pose, num_inference_steps=20) +images_custom[0] + +# %% +# sample image with a different prompt +images_custom = custom_pipe("A professional ice skater wearing a dark blue jacket around sunset, realistic, UHD", image_pose, num_inference_steps=20) +images_custom[0] + +# %% + + +# %% + + +# %% [markdown] +# # Canny + +# %% +import cv2 +from PIL import Image +from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler +import torch +import numpy as np +from diffusers.utils import load_image + +# %% +# load the controlnet model for canny edge detection +controlnet = ControlNetModel.from_pretrained( + "lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16 +) + +# load the stable diffusion pipeline with controlnet +pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 +) +pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) + +# %% +# enable efficient implementations using xformers for faster inference +pipe.enable_xformers_memory_efficient_attention() +pipe.enable_model_cpu_offload() + +# %% +image_input = load_image("https://cdn.pixabay.com/photo/2023/06/03/16/05/spotted-laughingtrush-8037974_640.png") +image_input = np.array(image_input) + +Image.fromarray(image_input) + +# %% +# define parameters from canny edge detection +low_threshold = 100 +high_threshold = 200 + +# do canny edge detection +image_canny = cv2.Canny(image_input, low_threshold, high_threshold) + +# convert to PIL image format +image_canny = image_canny[:, :, None] +image_canny = np.concatenate([image_canny, image_canny, image_canny], axis=2) +image_canny = Image.fromarray(image_canny) + +image_canny + +# %% +image_output = pipe("bird", image_canny, num_inference_steps=20).images[0] +image_output + +# %% +image_output = pipe("a cute blue bird with colorful aesthetic feathers", image_canny, num_inference_steps=20).images[0] +image_output + +# %% + + +# %% [markdown] +# # Depth + +# %% +from transformers import pipeline +from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler +from PIL import Image +import numpy as np +import torch +from diffusers.utils import load_image + +# %% +# load the depth estimator model +depth_estimator = pipeline('depth-estimation') + +# load the controlnet model for depth estimation +controlnet = ControlNetModel.from_pretrained( + "lllyasviel/sd-controlnet-depth", torch_dtype=torch.float16 +) + +# load the stable diffusion pipeline with controlnet +pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 +) +pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) + +# %% +# enable efficient implementations using xformers for faster inference +pipe.enable_xformers_memory_efficient_attention() +pipe.enable_model_cpu_offload() + +# %% +image_input = load_image("https://huggingface.co/lllyasviel/sd-controlnet-depth/resolve/main/images/stormtrooper.png") +image_input + +# %% +# get depth estimates +image_depth = depth_estimator(image_input)['depth'] + +# convert to PIL image format +image_depth = np.array(image_depth) +image_depth = image_depth[:, :, None] +image_depth = np.concatenate([image_depth, image_depth, image_depth], axis=2) +image_depth = Image.fromarray(image_depth) + +image_depth + +# %% +image_output = pipe("Darth Vader giving lecture", image_depth, num_inference_steps=20).images[0] +image_output + +# %% +image_output = pipe("A realistic, aesthetic portrait style photograph of Darth Vader giving lecture, 8k, unreal engine", image_depth, num_inference_steps=20).images[0] +image_output + +# %% [markdown] +# # Normal + +# %% +from PIL import Image +from transformers import pipeline +import numpy as np +import cv2 +from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler +import torch +from diffusers.utils import load_image + +# %% +# load the Dense Prediction Transformer (DPT) model for getting normal maps +depth_estimator = pipeline("depth-estimation", model ="Intel/dpt-hybrid-midas") + +# load the controlnet model for normal maps +controlnet = ControlNetModel.from_pretrained( + "fusing/stable-diffusion-v1-5-controlnet-normal", torch_dtype=torch.float16 +) + +# load the stable diffusion pipeline with controlnet +pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 +) +pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) + +# %% +# enable efficient implementations using xformers for faster inference +pipe.enable_xformers_memory_efficient_attention() +pipe.enable_model_cpu_offload() + +# %% +image_input = load_image("https://cdn.pixabay.com/photo/2023/06/07/13/02/butterfly-8047187_1280.jpg") +image_input + +# %% +# do all the preprocessing to get the normal image +image = depth_estimator(image_input)['predicted_depth'][0] + +image = image.numpy() + +image_depth = image.copy() +image_depth -= np.min(image_depth) +image_depth /= np.max(image_depth) + +bg_threhold = 0.4 + +x = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3) +x[image_depth < bg_threhold] = 0 + +y = cv2.Sobel(image, cv2.CV_32F, 0, 1, ksize=3) +y[image_depth < bg_threhold] = 0 + +z = np.ones_like(x) * np.pi * 2.0 + +image = np.stack([x, y, z], axis=2) +image /= np.sum(image ** 2.0, axis=2, keepdims=True) ** 0.5 +image = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8) +image_normal = Image.fromarray(image) + +image_normal + +# %% +image_output = pipe("A colorful butterfly sitting on apples", image_normal, num_inference_steps=20).images[0] +image_output + +# %% +image_output = pipe("A beautiful design", image_normal, num_inference_steps=20).images[0] +image_output + +# %% [markdown] +# # Segmentation + +# %% +from transformers import AutoImageProcessor, UperNetForSemanticSegmentation +from PIL import Image +import numpy as np +import torch +from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler +from diffusers.utils import load_image + +# %% +# load the image processor and the model for doing segmentation +image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small") +image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small") + +# load the controlnet model for semantic segmentation +controlnet = ControlNetModel.from_pretrained( + "lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16 +) + +# load the stable diffusion pipeline with controlnet +pipe = StableDiffusionControlNetPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 +) +pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) + +# %% +# enable efficient implementations using xformers for faster inference +pipe.enable_xformers_memory_efficient_attention() +pipe.enable_model_cpu_offload() + +# %% +# define color palette that is used by the semantic segmentation models + +palette = np.asarray([ + [0, 0, 0], + [120, 120, 120], + [180, 120, 120], + [6, 230, 230], + [80, 50, 50], + [4, 200, 3], + [120, 120, 80], + [140, 140, 140], + [204, 5, 255], + [230, 230, 230], + [4, 250, 7], + [224, 5, 255], + [235, 255, 7], + [150, 5, 61], + [120, 120, 70], + [8, 255, 51], + [255, 6, 82], + [143, 255, 140], + [204, 255, 4], + [255, 51, 7], + [204, 70, 3], + [0, 102, 200], + [61, 230, 250], + [255, 6, 51], + [11, 102, 255], + [255, 7, 71], + [255, 9, 224], + [9, 7, 230], + [220, 220, 220], + [255, 9, 92], + [112, 9, 255], + [8, 255, 214], + [7, 255, 224], + [255, 184, 6], + [10, 255, 71], + [255, 41, 10], + [7, 255, 255], + [224, 255, 8], + [102, 8, 255], + [255, 61, 6], + [255, 194, 7], + [255, 122, 8], + [0, 255, 20], + [255, 8, 41], + [255, 5, 153], + [6, 51, 255], + [235, 12, 255], + [160, 150, 20], + [0, 163, 255], + [140, 140, 140], + [250, 10, 15], + [20, 255, 0], + [31, 255, 0], + [255, 31, 0], + [255, 224, 0], + [153, 255, 0], + [0, 0, 255], + [255, 71, 0], + [0, 235, 255], + [0, 173, 255], + [31, 0, 255], + [11, 200, 200], + [255, 82, 0], + [0, 255, 245], + [0, 61, 255], + [0, 255, 112], + [0, 255, 133], + [255, 0, 0], + [255, 163, 0], + [255, 102, 0], + [194, 255, 0], + [0, 143, 255], + [51, 255, 0], + [0, 82, 255], + [0, 255, 41], + [0, 255, 173], + [10, 0, 255], + [173, 255, 0], + [0, 255, 153], + [255, 92, 0], + [255, 0, 255], + [255, 0, 245], + [255, 0, 102], + [255, 173, 0], + [255, 0, 20], + [255, 184, 184], + [0, 31, 255], + [0, 255, 61], + [0, 71, 255], + [255, 0, 204], + [0, 255, 194], + [0, 255, 82], + [0, 10, 255], + [0, 112, 255], + [51, 0, 255], + [0, 194, 255], + [0, 122, 255], + [0, 255, 163], + [255, 153, 0], + [0, 255, 10], + [255, 112, 0], + [143, 255, 0], + [82, 0, 255], + [163, 255, 0], + [255, 235, 0], + [8, 184, 170], + [133, 0, 255], + [0, 255, 92], + [184, 0, 255], + [255, 0, 31], + [0, 184, 255], + [0, 214, 255], + [255, 0, 112], + [92, 255, 0], + [0, 224, 255], + [112, 224, 255], + [70, 184, 160], + [163, 0, 255], + [153, 0, 255], + [71, 255, 0], + [255, 0, 163], + [255, 204, 0], + [255, 0, 143], + [0, 255, 235], + [133, 255, 0], + [255, 0, 235], + [245, 0, 255], + [255, 0, 122], + [255, 245, 0], + [10, 190, 212], + [214, 255, 0], + [0, 204, 255], + [20, 0, 255], + [255, 255, 0], + [0, 153, 255], + [0, 41, 255], + [0, 255, 204], + [41, 0, 255], + [41, 255, 0], + [173, 0, 255], + [0, 245, 255], + [71, 0, 255], + [122, 0, 255], + [0, 255, 184], + [0, 92, 255], + [184, 255, 0], + [0, 133, 255], + [255, 214, 0], + [25, 194, 194], + [102, 255, 0], + [92, 0, 255], +]) + +# %% +image_input = load_image("https://cdn.pixabay.com/photo/2023/02/24/07/14/crowd-7810353_1280.jpg") +image_input + +# %% +# get the pixel values +pixel_values = image_processor(image_input, return_tensors="pt").pixel_values + +# do semantic segmentation +with torch.no_grad(): + outputs = image_segmentor(pixel_values) + +# post process the semantic segmentation +seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image_input.size[::-1]])[0] + +# add colors to the different identified classes +color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3 +for label, color in enumerate(palette): + color_seg[seg == label, :] = color + +# convert into PIL image format +color_seg = color_seg.astype(np.uint8) +image_seg = Image.fromarray(color_seg) + +image_seg + +# %% +image_output = pipe("A crowd of people staring at a glorious painting", image_seg, num_inference_steps=20).images[0] +image_output + +# %% +image_output = pipe("Aliens looking at earth from inside their spaceship from a window, not creepy, not scary, not gross, octane render, smooth", image_seg, num_inference_steps=20).images[0] +image_output + +# %% + + + diff --git a/machine-learning/control-image-generation-with-controlnet/requirements.txt b/machine-learning/control-image-generation-with-controlnet/requirements.txt new file mode 100644 index 00000000..6efce1d9 --- /dev/null +++ b/machine-learning/control-image-generation-with-controlnet/requirements.txt @@ -0,0 +1,6 @@ +xformers +diffusers +transformers +accelerate +controlnet_aux +opencv-contrib-python \ No newline at end of file diff --git a/machine-learning/edit-images-instruct-pix2pix/InstructPix2Pix_PythonCodeTutorial.ipynb b/machine-learning/edit-images-instruct-pix2pix/InstructPix2Pix_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..5d744500 --- /dev/null +++ b/machine-learning/edit-images-instruct-pix2pix/InstructPix2Pix_PythonCodeTutorial.ipynb @@ -0,0 +1,12948 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ifD7o7YOTehV", + "outputId": "8b42a9b5-d180-4986-b868-900a349b01bc" + }, + "outputs": [], + "source": [ + "!pip install -qU diffusers accelerate safetensors transformers" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "CqAnOnnOcp4W" + }, + "source": [ + "# Hugging Face" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "B2QCQqM9ckWd" + }, + "outputs": [], + "source": [ + "import PIL\n", + "import requests\n", + "import torch\n", + "from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yzLP1VRHdBY7" + }, + "outputs": [], + "source": [ + "def download_image(url):\n", + " image = PIL.Image.open(requests.get(url, stream=True).raw)\n", + " image = PIL.ImageOps.exif_transpose(image)\n", + " image = image.convert(\"RGB\")\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 829, + "referenced_widgets": [ + "baf9ec5ebd604db9b1f5dfa948742aa2", + "b9a0167274eb44b7be65b17130d3ade6", + "44638b1824bc48de85029440f2f4a612", + "b9f1fb9570e148e7852f4f569ae82852", + "9f75675721ae47eb99c80fedb78ea2b7", + 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"a8983f58e5c44385864ba164e3d00083", + "d11ce4de43984beab4937652c55419cb", + "422cdb3a6aed4177aba5f6e9ac77e1bf", + "14fe6c5fa5274bde8d90be608d3a6814", + "65947bf28d8c47059e1dae1f1d2f97b2", + "93b5852ebf584c3e95ca3275b2c9b698", + "1d631802aab44912a6097e97d2675102", + "c772f5d3ffca49a2a4c0d16a4469105c", + "eb33b9af3ec14698bec89ff8b66bae06", + "b00687d7649d45af882cdd6d0f97591d", + "62905ea2ea4045aeb5ec9a8426247100", + "9eb63a9230ba472d87cf106e8abb5dff", + "fcc8de512f89491ab8b7a50cc37f83f5", + "48d6f53c34b84be4a219cc30ac539aeb", + "6e4877900597413e96d01faf5f76b700", + "6923960e3752477590c9c9ebc862163c", + "b286d3656c404762b3aaff97f4a5892d", + "8811223ffe8547e99b837842150396f4", + "4e55fede968943718a3c10962f5f708b" + ] + }, + "id": "YUXSNFWQckY6", + "outputId": "68b5d0d1-14e0-440d-8fd1-ffa021810b7a" + }, + "outputs": [], + "source": [ + "model_id = \"timbrooks/instruct-pix2pix\"\n", + "pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None)\n", + "pipe.to(\"cuda\")\n", + "pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 657 + }, + "id": "8TsGblmidHew", + "outputId": "2f023570-99cc-4a79-aca4-bbee89c22965" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2013/01/05/21/02/art-74050_640.jpg\"\n", + "image = download_image(url)\n", + "image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 689, + "referenced_widgets": [ + "039beb2fc4c6412d9e05d9d50884e940", + "0cf59b40f6304ec38bdaaee06943173f", + "63ecaa7f41ce4289aa768b77e9ceebe7", + "2a8a4ed0b2854b558efd5acce8dcf5c2", + "19469649ab6b44f8b1d6408ac2646579", + "1cbc65f4245e4c25a64827f0e2beb08d", + "59e8c90ecdf04c2b89f9a539e7f7fdbb", + "7ede80a1f6544f2a8dfd631cb6d4eaca", + "3bcb60cbd7ab48e2884460a1e539bd12", + "ee1a66e0f3ab4c67beef305317124a0f", + "b390338978084bd19f7e7e6ce8ea7e44" + ] + }, + "id": "sAoN5eGgdEku", + "outputId": "12f7ab74-6115-45ed-9092-936916fe6d69" + }, + "outputs": [], + "source": [ + "prompt = \"convert the lady into a highly detailed marble statue\"\n", + "images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 689, + "referenced_widgets": [ + "b4c47f72d4204e2abbe9781707b754ee", + "8e4655cdbd0348bb86523938a0d95367", + "b99c852bd2f54c79afac0eb0049c5dd0", + "5fd422b13c324a8baf619da5d1ad0b8e", + "dc16acab929344cab2be08d297182843", + "a613c20dd06d4754b7a890d802476395", + "7342611c130c4e3da540d7ae0ac95b52", + "d1d635fff656454e9b0cdba740067c07", + "e535c42b82cb4e1fa4fbf492768e5762", + "8f10179341c44eb192f865feb143c57e", + "7f58916aaee04e5b89978d0111acf83d" + ] + }, + "id": "frOPmxYejP1g", + "outputId": "c0558f66-35ea-4a3e-f698-93d94894631c" + }, + "outputs": [], + "source": [ + "prompt = \"convert the lady into a highly detailed marble statue\"\n", + "images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1.5).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 444 + }, + "id": "oIzmKWERckbG", + "outputId": "2470148e-11be-4a08-c685-bb9eb5cf7f82" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2017/02/07/16/47/kingfisher-2046453_640.jpg\"\n", + "image = download_image(url)\n", + "image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 473, + "referenced_widgets": [ + "3593ebaab56e4bca9706c5775ae87b75", + "dc8d5cf759be4ab08d801e1d4758b34a", + "ef3472cacb4d426cbacec25777507432", + "d5a6cb28917b48d4910903e016319aec", + "ba1422edddc447b78ba7fbf4ba11e364", + "8bb4488a1c1c4defafb072afa695918f", + "2dc8bda3ec0046b09c6422b5248f1312", + "50569e1467f7411e862553111c97afb3", + "72effdc24eb84648b6838729c11fed72", + "ecc2c348430c471791a449784f5df44c", + "dd94c326988a41cd8b57f4dd9a26f499" + ] + }, + "id": "p2rDwOJ2cken", + "outputId": "4ba88e9a-9da1-4ad4-9f76-ee8bddd3e85e" + }, + "outputs": [], + "source": [ + "prompt = \"turn the bird to red\"\n", + "images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 445 + }, + "id": "Ka85jznIggXl", + "outputId": "9e11a102-9fb1-49ab-e6e6-5bc69740af5e" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2018/05/08/06/52/vacation-3382400_640.jpg\"\n", + "image = download_image(url)\n", + "image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 473, + "referenced_widgets": [ + "c3514684a02e496a875cb8fa0e86b449", + "0ace66c377a14034a594774ec024e074", + "865a15f101944359bbf30226e4cc1c90", + "5caac903dab34c119294cd4bfb65725c", + "cf013b91c09f493b94c050f224449336", + "9d28ea1de5494de5b911c1f120185b7a", + "9887290953b6414eacb4396a5b240a2e", + "5a00f85a6e9e44fcaa25dd0d982f2d0f", + "14a49cf00e9744dc8f2ac8ec3f19c90c", + "eed1d1e67a1d4a20ba49586148ca5a2a", + "147c95feace0447ca58cc4079cb2cbe5" + ] + }, + "id": "HQ6tutGNggZf", + "outputId": "f84857ef-4a50-4305-dfd7-05840d893ae2" + }, + "outputs": [], + "source": [ + "prompt = \"turn the suitcase yellow\"\n", + "images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.7).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "mXejIqrcggbu" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "CgINC4vJggfD" + }, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "mPGPiJhXcsr4" + }, + "source": [ + "# Custom implementation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5aBPcPxqePIB" + }, + "outputs": [], + "source": [ + "from tqdm import tqdm\n", + "from torch import autocast" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "F2gPSPPyZP0_" + }, + "outputs": [], + "source": [ + "class InstructPix2PixPipelineCustom:\n", + " \"\"\"custom implementation of the InstructPix2Pix Pipeline\"\"\"\n", + "\n", + " def __init__(self,\n", + " vae,\n", + " tokenizer,\n", + " text_encoder,\n", + " unet,\n", + " scheduler,\n", + " image_processor):\n", + "\n", + " self.vae = vae\n", + " self.tokenizer = tokenizer\n", + " self.text_encoder = text_encoder\n", + " self.unet = unet\n", + " self.scheduler = scheduler\n", + " self.image_processor = image_processor\n", + " self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + "\n", + "\n", + "\n", + " def get_text_embeds(self, text):\n", + " \"\"\"returns embeddings for the given `text`\"\"\"\n", + "\n", + " # tokenize the text\n", + " text_input = self.tokenizer(text,\n", + " padding='max_length',\n", + " max_length=tokenizer.model_max_length,\n", + " truncation=True,\n", + " return_tensors='pt')\n", + " # embed the text\n", + " with torch.no_grad():\n", + " text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0]\n", + " return text_embeds\n", + "\n", + "\n", + " def get_prompt_embeds(self, prompt, prompt_negative=None):\n", + " \"\"\"returns prompt embeddings based on classifier free guidance\"\"\"\n", + "\n", + " if isinstance(prompt, str):\n", + " prompt = [prompt]\n", + "\n", + " if prompt_negative is None:\n", + " prompt_negative = ['']\n", + " elif isinstance(prompt_negative, str):\n", + " prompt_negative = [prompt_negative]\n", + "\n", + " # get conditional prompt embeddings\n", + " cond_embeds = self.get_text_embeds(prompt)\n", + " # get unconditional prompt embeddings\n", + " uncond_embeds = self.get_text_embeds(prompt_negative)\n", + "\n", + " # instructpix2pix takes conditional embeds first, followed by unconditional embeds twice\n", + " # this is different from other diffusion pipelines\n", + " prompt_embeds = torch.cat([cond_embeds, uncond_embeds, uncond_embeds])\n", + " return prompt_embeds\n", + "\n", + "\n", + " def transform_image(self, image):\n", + " \"\"\"transform image from pytorch tensor to PIL format\"\"\"\n", + " image = self.image_processor.postprocess(image, output_type='pil')\n", + " return image\n", + "\n", + "\n", + "\n", + " def get_image_latents(self, image):\n", + " \"\"\"get image latents to be used with classifier free guidance\"\"\"\n", + "\n", + " # get conditional image embeds\n", + " image = image.to(self.device)\n", + " image_latents_cond = self.vae.encode(image).latent_dist.mode()\n", + "\n", + " # get unconditional image embeds\n", + " image_latents_uncond = torch.zeros_like(image_latents_cond)\n", + " image_latents = torch.cat([image_latents_cond, image_latents_cond, image_latents_uncond])\n", + "\n", + " return image_latents\n", + "\n", + "\n", + "\n", + " def get_initial_latents(self, height, width, num_channels_latents, batch_size):\n", + " \"\"\"returns noise latent tensor of relevant shape scaled by the scheduler\"\"\"\n", + "\n", + " image_latents = torch.randn((batch_size, num_channels_latents, height, width))\n", + " image_latents = image_latents.to(self.device)\n", + "\n", + " # scale the initial noise by the standard deviation required by the scheduler\n", + " image_latents = image_latents * self.scheduler.init_noise_sigma\n", + " return image_latents\n", + "\n", + "\n", + "\n", + " def denoise_latents(self,\n", + " prompt_embeds,\n", + " image_latents,\n", + " timesteps,\n", + " latents,\n", + " guidance_scale,\n", + " image_guidance_scale):\n", + " \"\"\"denoises latents from noisy latent to a meaningful latent as conditioned by image_latents\"\"\"\n", + "\n", + " # use autocast for automatic mixed precision (AMP) inference\n", + " with autocast('cuda'):\n", + " for i, t in tqdm(enumerate(timesteps)):\n", + " # duplicate image latents *thrice* to do classifier free guidance\n", + " latent_model_input = torch.cat([latents] * 3)\n", + " latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n", + "\n", + " latent_model_input = torch.cat([latent_model_input, image_latents], dim=1)\n", + "\n", + "\n", + " # predict noise residuals\n", + " with torch.no_grad():\n", + " noise_pred = self.unet(latent_model_input, t,\n", + " encoder_hidden_states=prompt_embeds)['sample']\n", + "\n", + " # separate predictions into conditional (on text), conditional (on image) and unconditional outputs\n", + " noise_pred_text, noise_pred_image, noise_pred_uncond = noise_pred.chunk(3)\n", + " # perform guidance\n", + " noise_pred = (\n", + " noise_pred_uncond\n", + " + guidance_scale * (noise_pred_text - noise_pred_image)\n", + " + image_guidance_scale * (noise_pred_image - noise_pred_uncond)\n", + " )\n", + "\n", + " # remove the noise from the current sample i.e. go from x_t to x_{t-1}\n", + " latents = self.scheduler.step(noise_pred, t, latents)['prev_sample']\n", + "\n", + " return latents\n", + "\n", + "\n", + "\n", + " def __call__(self,\n", + " prompt,\n", + " image,\n", + " prompt_negative=None,\n", + " num_inference_steps=20,\n", + " guidance_scale=7.5,\n", + " image_guidance_scale=1.5):\n", + " \"\"\"generates new image based on the `prompt` and the `image`\"\"\"\n", + "\n", + " # encode input prompt\n", + " prompt_embeds = self.get_prompt_embeds(prompt, prompt_negative)\n", + "\n", + " # preprocess image\n", + " image = self.image_processor.preprocess(image)\n", + "\n", + " # prepare image latents\n", + " image = image.half()\n", + " image_latents = self.get_image_latents(image)\n", + "\n", + " # prepare timesteps\n", + " self.scheduler.set_timesteps(num_inference_steps)\n", + " timesteps = self.scheduler.timesteps\n", + "\n", + " height_latents, width_latents = image_latents.shape[-2:]\n", + "\n", + " # prepare the initial image in the latent space (noise on which we will do reverse diffusion)\n", + " num_channels_latents = self.vae.config.latent_channels\n", + " batch_size = prompt_embeds.shape[0] // 2\n", + " latents = self.get_initial_latents(height_latents, width_latents, num_channels_latents, batch_size)\n", + "\n", + " # denoise latents\n", + " latents = self.denoise_latents(prompt_embeds,\n", + " image_latents,\n", + " timesteps,\n", + " latents,\n", + " guidance_scale,\n", + " image_guidance_scale)\n", + "\n", + " # decode latents to get the image into pixel space\n", + " latents = latents.to(torch.float16) # change dtype of latents since\n", + " image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n", + "\n", + " # convert to PIL Image format\n", + " image = image.detach() # detach to remove any computed gradients\n", + " image = self.transform_image(image)\n", + "\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "itugpKfQZP3S" + }, + "outputs": [], + "source": [ + "# We can get all the components from the InstructPix2Pix Pipeline\n", + "vae = pipe.vae\n", + "tokenizer = pipe.tokenizer\n", + "text_encoder = pipe.text_encoder\n", + "unet = pipe.unet\n", + "scheduler = pipe.scheduler\n", + "image_processor = pipe.image_processor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Z4mM2_z_ZP5f" + }, + "outputs": [], + "source": [ + "custom_pipe = InstructPix2PixPipelineCustom(vae, tokenizer, text_encoder, unet, scheduler, image_processor)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 657 + }, + "id": "NjkhgTz0mIPi", + "outputId": "56a214f8-02c5-4157-8648-6165bd4f2ec7" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2013/01/05/21/02/art-74050_640.jpg\"\n", + "image = download_image(url)\n", + "image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 674 + }, + "id": "pcG55mdtZP8N", + "outputId": "8c91332c-2b17-49b5-c3aa-416b141a035e" + }, + "outputs": [], + "source": [ + "# sample image 1\n", + "prompt = \"convert the lady into a highly detailed marble statue\"\n", + "images_custom = custom_pipe(prompt, image, num_inference_steps=20)\n", + "images_custom[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 657 + }, + "id": "l0m6vIq4ZP-K", + "outputId": "a97e7c0d-22a5-490a-c476-22d27aebd76a" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2023/03/22/01/41/little-girl-7868485_640.jpg\"\n", + "image = download_image(url)\n", + "image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 674 + }, + "id": "3eDhpvlHZQAo", + "outputId": "4d93878c-1a89-44ff-a823-4f8ca97c5e88" + }, + "outputs": [], + "source": [ + "# sample image 2\n", + "prompt = \"turn into 8k anime\"\n", + "images_custom = custom_pipe(prompt, image, num_inference_steps=20)\n", + "images_custom[0]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "6rAfgSFK3s-B" + }, + "source": [ + "# Limitations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 674 + }, + "id": "hKs8yic0RQFi", + "outputId": "68fd4162-3c98-488c-d3b1-164efbdc80ad" + }, + "outputs": [], + "source": [ + "prompt = \"turn entire pic into anime frame\"\n", + "images_custom = custom_pipe(prompt, image, num_inference_steps=20)\n", + "images_custom[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "61YH9qpyRXiQ" + }, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "OU9zLHTR2H_j" + }, + "source": [ + "# Rough\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 689, + "referenced_widgets": [ + "b1ac65dd59c94a269c3660d4ebd8fa16", + "76732cd3aa5745058c70bb1aa44b1667", + "09bc80852b334a6f9905c6d93eada57c", + "c1ee4f8f667743ed94a939a9fe749e39", + "ff7c3d43d89a40a9941f2b9baf8e9fd9", + "1494c31d3d114a13a3ebbe85df2316ba", + "9604b8f7a8ef4fa6b488ee1892e138d2", + "7bcb49396cf84ea6878d5ceddd480326", + "8c8b4b87df9242318f1ceaf5fc00b216", + "25c92f3c188e4009ba806c6d56cc66c4", + "1da8ad0bdb2547cdb4edc5bc5df7a8ea" + ] + }, + "id": "i5lucCESjxyr", + "outputId": "19b61940-802c-4e44-a2b2-c63ffba8faa6" + }, + "outputs": [], + "source": [ + "prompt = \"convert the lady into a highly detailed marble statue\"\n", + "images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1.6).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 689, + "referenced_widgets": [ + "6d22121bbb6f4bcaa1b5f355d0f2e8a1", + "ed4320b4d8284ead91830c0b11b9bc56", + "1fe983e43b7f4bca905a65b07eef5491", + "6a234c3c85734a2cae1348a3329bb659", + "d3415f46386c49ce9593585d421657fa", + "f25cbe3e352d44c9849191bbb8e9d4d9", + "3bffddd95fdf4e3c896e1554a239273a", + "e92d28da4f314f73b4c4cccde114f1f4", + "f7728f9c5b0f4f4ca084119f74e9dcaf", + "b77a65348afa4b6aae8540dd5d9e4d2c", + "960b8d20472f40078fc9566abc065cc1" + ] + }, + "id": "LCMg5TPOjP30", + "outputId": "e2405a06-116a-411b-ad80-0b8afa5f5bef" + }, + "outputs": [], + "source": [ + "prompt = \"convert the lady into a highly detailed marble statue\"\n", + "images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=2).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 689, + "referenced_widgets": [ + "16063a5dd86649f2b7f37184c7ac0593", + "fb156e0150e24ec9a367a1e26c4750ec", + "925594a07eba4bdc92e59ef1df5ea63e", + "d5228dfbe50b4d078996b74d1b71ba28", + "6daa2b5b26e4459aa5bdebd0062cf021", + "740417603bd94bf698fdba5b374d7c3a", + "2ddf4013353e48b9a62c70e2eb68502e", + "e25cb8e08bae4c56a84874cf31cd84c4", + "92d4de954f0442778144c26caae5ee83", + "6b04b7bfcca645368b1287a3467bc3a1", + "4e8ec740f2ba45d1817c46b449e34423" + ] + }, + "id": "-nR6LrUMjP7U", + "outputId": "f6c19181-afaa-47a0-a950-a6b0a740ca81" + }, + "outputs": [], + "source": [ + "prompt = \"convert the lady into a highly detailed marble statue\"\n", + "images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 689, + "referenced_widgets": [ + "1b448e7eb7c842c2ad3598e401c5a101", + "b25958353ffe4063bf8d09ed149c2550", + "e25496021c9f4046bb83f585601baaf4", + "69484df819aa44b3858b76432cc84beb", + "f6ecc0d4fe1545d8bd0a16de5960285b", + "b5c2efd1f5044b68bfb75d5fe16d7829", + "48faccb381254cd99a649c239a419649", + "48b23ac8e7ce46239504712135b41444", + "6f520ae078104d70945b5d42ded1a75a", + "a726d364296442e196e973e575578c30", + "94b5651c5cd14901ac59ce22bb80fcfa" + ] + }, + "id": "LUXIW3bkjW3R", + "outputId": "aab0ef83-9119-461c-81e5-4c65d2285595" + }, + "outputs": [], + "source": [ + "prompt = \"convert the lady into a highly detailed marble statue\"\n", + "images = pipe(prompt, image=image, num_inference_steps=30, image_guidance_scale=1).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 689, + "referenced_widgets": [ + "05328f40cf5642359294af84f26355c3", + "792bda382a844c8a9bd9c9de789b44ac", + "db498aee51494067b6ad507e47f37bd4", + "2b7aaec231e244b1899f99e893b053df", + "3c3af60f4f07476c8700910b4259a066", + "1bbd2bbca0c04df982278b7c24c8c320", + "ee190ee62fb24dacb96d9b57e562c3b9", + "ce20d1f9a55a42518c521eb5a1c8b414", + "f019b7a1faf1469c87b048319a9b71ef", + "4afa3f9ee15546c1979113f0e618efcf", + "fa37cd2e2ac94455ab624a75eb0a105d" + ] + }, + "id": "fux7MnDwjW5h", + "outputId": "25cadb9d-9e09-4af1-c49c-7405517bc380" + }, + "outputs": [], + "source": [ + "prompt = \"convert the lady into a highly detailed marble statue\"\n", + "images = pipe(prompt, image=image, num_inference_steps=50, image_guidance_scale=1).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 689, + "referenced_widgets": [ + "0711e6b0af7e4905a8136cf407e2ae8b", + "901f71e702474e77be59006bba599918", + "1d10557816514c4bb08d957fdde13904", + "3bfe8491fdc84726a9a2ea2a9eb33884", + "c4f63d6416174604a065f2dc2b8775a7", + "e82bcca350364e229895710e7d145fbf", + "be553a7df92e48b687fc177f7ba3c5fd", + "9dc480c24e9844599de9f1e5e3638f33", + "dffb565d8879412ba6c9989b02cbe661", + "7be8f4522cdc41f08c69b4a36520eb5a", + "ebfab18cc7484b71bb252d4e242e3b40" + ] + }, + "id": "8_cahN3NjW8y", + "outputId": "dea98f5f-1d7c-45e7-bf5c-cae01347e182" + }, + "outputs": [], + "source": [ + "prompt = \"convert the lady into a highly detailed marble statue\"\n", + "images = pipe(prompt, image=image, num_inference_steps=30, image_guidance_scale=1.6).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 689, + "referenced_widgets": [ + "f001e880d8f3453a9395eb78329dbbc7", + "8b41d5c904a942a0bcee642499015089", + "84df3286d6044ab5aa6e8cb6c8d1b813", + "e0448b4988f643dd9f12e4fca280dbee", + "f259a8f8222c41eea77498acd6d4e4aa", + "7cfb6d760505428e9ffc07fe0fbca962", + "a66a0a813bc24011a91f56778e88244e", + "39a24d7b46f2428597c345b1d0ac8680", + "f48ce6b6925b4b058f8679d73c039ac8", + "75327980774b4419a9ec50f97a0c1d75", + "7adf45fac6804cbcbbe5d7c6cbdfd878" + ] + }, + "id": "A0vo9G6QjjMu", + "outputId": "16526be0-4602-4ede-9b02-a63b322f3866" + }, + "outputs": [], + "source": [ + "prompt = \"convert the lady into a highly detailed marble statue\"\n", + "images = pipe(prompt, image=image, num_inference_steps=50, image_guidance_scale=1.6).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 689, + "referenced_widgets": [ + "12300224bc654d47b7dd02e04835203a", + "dafd2b93bc6245a88e6631f306fc1e9b", + "59514b4f9b8f4930a1b07aad4ab19d25", + "c25bc0fc44214d9f8a5f4abba43eaf09", + "f76d97ab2a6e46d5891af7fc0c3f511b", + "f38555fda53a4edf92271a97db3f7dd5", + "8f135d6a55484caf92a325c375e16005", + "932741fa7e674b6595866eecaf746bc9", + "eb2855fb57d64857ab32fafd3a77b457", + "f40c98061f2946b1885193ee817c52a3", + "9a91880b909e4b05bf668c410f978ca2" + ] + }, + "id": "NhlQaACLjjO4", + "outputId": "939a5324-7595-434f-a4bc-2b53af54a0a4" + }, + "outputs": [], + "source": [ + "prompt = \"convert the lady into a highly detailed marble statue\"\n", + "images = pipe(prompt, image=image, num_inference_steps=100, image_guidance_scale=1.6).images\n", + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 689, + "referenced_widgets": [ + 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b/machine-learning/edit-images-instruct-pix2pix/README.md new file mode 100644 index 00000000..8800db36 --- /dev/null +++ b/machine-learning/edit-images-instruct-pix2pix/README.md @@ -0,0 +1 @@ +# [How to Edit Images using InstructPix2Pix in Python](https://www.thepythoncode.com/article/edit-images-using-instruct-pix2pix-with-huggingface) \ No newline at end of file diff --git a/machine-learning/edit-images-instruct-pix2pix/instruct_pix2pix_pythoncodetutorial.py b/machine-learning/edit-images-instruct-pix2pix/instruct_pix2pix_pythoncodetutorial.py new file mode 100644 index 00000000..97874151 --- /dev/null +++ b/machine-learning/edit-images-instruct-pix2pix/instruct_pix2pix_pythoncodetutorial.py @@ -0,0 +1,389 @@ +# %% +!pip install -qU diffusers accelerate safetensors transformers + +# %% [markdown] +# # Hugging Face + +# %% +import PIL +import requests +import torch +from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler + + +# %% +def download_image(url): + image = PIL.Image.open(requests.get(url, stream=True).raw) + image = PIL.ImageOps.exif_transpose(image) + image = image.convert("RGB") + return image + +# %% +model_id = "timbrooks/instruct-pix2pix" +pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None) +pipe.to("cuda") +pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) + + +# %% +url = "https://cdn.pixabay.com/photo/2013/01/05/21/02/art-74050_640.jpg" +image = download_image(url) +image + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1.5).images +images[0] + +# %% +url = "https://cdn.pixabay.com/photo/2017/02/07/16/47/kingfisher-2046453_640.jpg" +image = download_image(url) +image + +# %% +prompt = "turn the bird to red" +images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images +images[0] + +# %% +url = "https://cdn.pixabay.com/photo/2018/05/08/06/52/vacation-3382400_640.jpg" +image = download_image(url) +image + +# %% +prompt = "turn the suitcase yellow" +images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.7).images +images[0] + +# %% + + +# %% + + +# %% [markdown] +# # Custom implementation + +# %% +from tqdm import tqdm +from torch import autocast + +# %% +class InstructPix2PixPipelineCustom: + """custom implementation of the InstructPix2Pix Pipeline""" + + def __init__(self, + vae, + tokenizer, + text_encoder, + unet, + scheduler, + image_processor): + + self.vae = vae + self.tokenizer = tokenizer + self.text_encoder = text_encoder + self.unet = unet + self.scheduler = scheduler + self.image_processor = image_processor + self.device = 'cuda' if torch.cuda.is_available() else 'cpu' + + + + def get_text_embeds(self, text): + """returns embeddings for the given `text`""" + + # tokenize the text + text_input = self.tokenizer(text, + padding='max_length', + max_length=tokenizer.model_max_length, + truncation=True, + return_tensors='pt') + # embed the text + with torch.no_grad(): + text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0] + return text_embeds + + + def get_prompt_embeds(self, prompt, prompt_negative=None): + """returns prompt embeddings based on classifier free guidance""" + + if isinstance(prompt, str): + prompt = [prompt] + + if prompt_negative is None: + prompt_negative = [''] + elif isinstance(prompt_negative, str): + prompt_negative = [prompt_negative] + + # get conditional prompt embeddings + cond_embeds = self.get_text_embeds(prompt) + # get unconditional prompt embeddings + uncond_embeds = self.get_text_embeds(prompt_negative) + + # instructpix2pix takes conditional embeds first, followed by unconditional embeds twice + # this is different from other diffusion pipelines + prompt_embeds = torch.cat([cond_embeds, uncond_embeds, uncond_embeds]) + return prompt_embeds + + + def transform_image(self, image): + """transform image from pytorch tensor to PIL format""" + image = self.image_processor.postprocess(image, output_type='pil') + return image + + + + def get_image_latents(self, image): + """get image latents to be used with classifier free guidance""" + + # get conditional image embeds + image = image.to(self.device) + image_latents_cond = self.vae.encode(image).latent_dist.mode() + + # get unconditional image embeds + image_latents_uncond = torch.zeros_like(image_latents_cond) + image_latents = torch.cat([image_latents_cond, image_latents_cond, image_latents_uncond]) + + return image_latents + + + + def get_initial_latents(self, height, width, num_channels_latents, batch_size): + """returns noise latent tensor of relevant shape scaled by the scheduler""" + + image_latents = torch.randn((batch_size, num_channels_latents, height, width)) + image_latents = image_latents.to(self.device) + + # scale the initial noise by the standard deviation required by the scheduler + image_latents = image_latents * self.scheduler.init_noise_sigma + return image_latents + + + + def denoise_latents(self, + prompt_embeds, + image_latents, + timesteps, + latents, + guidance_scale, + image_guidance_scale): + """denoises latents from noisy latent to a meaningful latent as conditioned by image_latents""" + + # use autocast for automatic mixed precision (AMP) inference + with autocast('cuda'): + for i, t in tqdm(enumerate(timesteps)): + # duplicate image latents *thrice* to do classifier free guidance + latent_model_input = torch.cat([latents] * 3) + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + latent_model_input = torch.cat([latent_model_input, image_latents], dim=1) + + + # predict noise residuals + with torch.no_grad(): + noise_pred = self.unet(latent_model_input, t, + encoder_hidden_states=prompt_embeds)['sample'] + + # separate predictions into conditional (on text), conditional (on image) and unconditional outputs + noise_pred_text, noise_pred_image, noise_pred_uncond = noise_pred.chunk(3) + # perform guidance + noise_pred = ( + noise_pred_uncond + + guidance_scale * (noise_pred_text - noise_pred_image) + + image_guidance_scale * (noise_pred_image - noise_pred_uncond) + ) + + # remove the noise from the current sample i.e. go from x_t to x_{t-1} + latents = self.scheduler.step(noise_pred, t, latents)['prev_sample'] + + return latents + + + + def __call__(self, + prompt, + image, + prompt_negative=None, + num_inference_steps=20, + guidance_scale=7.5, + image_guidance_scale=1.5): + """generates new image based on the `prompt` and the `image`""" + + # encode input prompt + prompt_embeds = self.get_prompt_embeds(prompt, prompt_negative) + + # preprocess image + image = self.image_processor.preprocess(image) + + # prepare image latents + image = image.half() + image_latents = self.get_image_latents(image) + + # prepare timesteps + self.scheduler.set_timesteps(num_inference_steps) + timesteps = self.scheduler.timesteps + + height_latents, width_latents = image_latents.shape[-2:] + + # prepare the initial image in the latent space (noise on which we will do reverse diffusion) + num_channels_latents = self.vae.config.latent_channels + batch_size = prompt_embeds.shape[0] // 2 + latents = self.get_initial_latents(height_latents, width_latents, num_channels_latents, batch_size) + + # denoise latents + latents = self.denoise_latents(prompt_embeds, + image_latents, + timesteps, + latents, + guidance_scale, + image_guidance_scale) + + # decode latents to get the image into pixel space + latents = latents.to(torch.float16) # change dtype of latents since + image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] + + # convert to PIL Image format + image = image.detach() # detach to remove any computed gradients + image = self.transform_image(image) + + return image + +# %% +# We can get all the components from the InstructPix2Pix Pipeline +vae = pipe.vae +tokenizer = pipe.tokenizer +text_encoder = pipe.text_encoder +unet = pipe.unet +scheduler = pipe.scheduler +image_processor = pipe.image_processor + +# %% +custom_pipe = InstructPix2PixPipelineCustom(vae, tokenizer, text_encoder, unet, scheduler, image_processor) + +# %% +url = "https://cdn.pixabay.com/photo/2013/01/05/21/02/art-74050_640.jpg" +image = download_image(url) +image + +# %% +# sample image 1 +prompt = "convert the lady into a highly detailed marble statue" +images_custom = custom_pipe(prompt, image, num_inference_steps=20) +images_custom[0] + +# %% +url = "https://cdn.pixabay.com/photo/2023/03/22/01/41/little-girl-7868485_640.jpg" +image = download_image(url) +image + +# %% +# sample image 2 +prompt = "turn into 8k anime" +images_custom = custom_pipe(prompt, image, num_inference_steps=20) +images_custom[0] + +# %% [markdown] +# # Limitations + +# %% +prompt = "turn entire pic into anime frame" +images_custom = custom_pipe(prompt, image, num_inference_steps=20) +images_custom[0] + +# %% + + +# %% [markdown] +# # Rough +# + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1.6).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=2).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=30, image_guidance_scale=1).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=50, image_guidance_scale=1).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=30, image_guidance_scale=1.6).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=50, image_guidance_scale=1.6).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=100, image_guidance_scale=1.6).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=100, image_guidance_scale=1.2).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=100, image_guidance_scale=1.3).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=0.8).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=0.6).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.5).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.5, guidance_scale=10).images +images[0] + +# %% +prompt = "convert the lady into a highly detailed marble statue" +images = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.5, guidance_scale=15).images +images[0] + +# %% + + +# %% +prompt = "turn the red wooden stick to brown" +images2 = pipe(prompt, image=images[0], num_inference_steps=10, image_guidance_scale=1).images +images2[0] + + diff --git a/machine-learning/edit-images-instruct-pix2pix/requirements.txt b/machine-learning/edit-images-instruct-pix2pix/requirements.txt new file mode 100644 index 00000000..8780d0fc --- /dev/null +++ b/machine-learning/edit-images-instruct-pix2pix/requirements.txt @@ -0,0 +1,6 @@ +diffusers +accelerate +transformers +torch +Pillow +safetensors \ No newline at end of file diff --git a/machine-learning/nlp/rouge-score/README.md b/machine-learning/nlp/rouge-score/README.md new file mode 100644 index 00000000..21d86a14 --- /dev/null +++ b/machine-learning/nlp/rouge-score/README.md @@ -0,0 +1 @@ +# [How to Calculate ROUGE Score in Python](https://www.thepythoncode.com/article/calculate-rouge-score-in-python) \ No newline at end of file diff --git a/machine-learning/nlp/rouge-score/requirements.txt b/machine-learning/nlp/rouge-score/requirements.txt new file mode 100644 index 00000000..7f26c102 --- /dev/null +++ b/machine-learning/nlp/rouge-score/requirements.txt @@ -0,0 +1 @@ +rouge-score \ No newline at end of file diff --git a/machine-learning/nlp/rouge-score/rouge.py b/machine-learning/nlp/rouge-score/rouge.py new file mode 100644 index 00000000..4b00c4c7 --- /dev/null +++ b/machine-learning/nlp/rouge-score/rouge.py @@ -0,0 +1,22 @@ +from rouge_score import rouge_scorer + +scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True) + +# Single reference +candidate_summary = "the cat was found under the bed" +reference_summary = "the cat was under the bed" +scores = scorer.score(reference_summary, candidate_summary) +for key in scores: + print(f'{key}: {scores[key]}') + +# Multiple references +candidate_summary = "the cat was found under the bed" +reference_summaries = ["the cat was under the bed", "found a cat under the bed"] +scores = {key: [] for key in ['rouge1', 'rouge2', 'rougeL']} +for ref in reference_summaries: + temp_scores = scorer.score(ref, candidate_summary) + for key in temp_scores: + scores[key].append(temp_scores[key]) + +for key in scores: + print(f'{key}:\n{scores[key]}') \ No newline at end of file diff --git a/machine-learning/stable-diffusion-upscaler/README.md b/machine-learning/stable-diffusion-upscaler/README.md new file mode 100644 index 00000000..3ae8e02d --- /dev/null +++ b/machine-learning/stable-diffusion-upscaler/README.md @@ -0,0 +1 @@ +# [How to Upscale Images using Stable Diffusion in Python](https://www.thepythoncode.com/article/upscale-images-using-stable-diffusion-x4-upscaler-huggingface) \ No newline at end of file diff --git a/machine-learning/stable-diffusion-upscaler/SDUpscaler_PythonCodeTutorial.ipynb b/machine-learning/stable-diffusion-upscaler/SDUpscaler_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..3fdee1e8 --- /dev/null +++ b/machine-learning/stable-diffusion-upscaler/SDUpscaler_PythonCodeTutorial.ipynb @@ -0,0 +1,7341 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-C875CYSCygt", + "outputId": "dd991ed9-d57f-4e5b-bee3-bcb6882369d9" + }, + "outputs": [], + "source": [ + "!pip install -qU diffusers transformers accelerate scipy safetensors" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mAHWEPSfUlmg" + }, + "source": [ + "# Hugging Face Implementation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jor1D7LvDA9l" + }, + "outputs": [], + "source": [ + "import requests\n", + "from PIL import Image\n", + "from io import BytesIO\n", + "from diffusers import StableDiffusionUpscalePipeline\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + 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"5a1be5246f2443858bd6933a89a68389", + "10d895cb5166417fa2b4cc0d2feb7b6f", + "23b55638314c4e2b967f85ab40042a6c", + "9c741e36450b4c66875df9dc3ba07507", + "cd9c9d32567f4e5aa9291f66b08490bf", + "c4995db7a62844b4a00daaf55847cabd", + "72fc113c125648538df5059fb578ae19", + "3943882166bb4470a800d9563f9e53a2", + "ad07cccdd8d44a9ab5e3458e8d902ac6", + "ff88d9a5f7d64ad7bb0f09f6c71543f1", + "080c7086e8684bb3bb857d23f426d5dd", + "37e74a92120d40ab99b8a3efeb397bd8", + "9ba492d205294bc0a8d07985ed271358", + "0a49275d970741f9b19f24569b80491a", + "4178b1767a614f89aa62f12a4e3a7350", + "c909bafccc65402fa93afea87f1b784e", + "6c32232cfb734ac3a3204a22c414fc18", + "1f37677826544166a0b63d36c9c3edac", + "a4d93e9fee48468281afac25f551806c", + "91f32130b9fc47ceaae99521c0b70015", + "e8bec5477f7c43c1a55c852ef8b7cb95", + "7a4e5fdddcd34b6cb658b94db24ba474", + "e6942466051e4a6a97c36b56d8d4e0c2", + "a8dbb00149f148ceaee2474c4304c902", + "f3c0042a67e34e72b1088b60c11ba2d0" + ] + }, + "id": "l3QZf9-UDEb0", + "outputId": "d2d9ea4c-1665-431b-c71c-bc5441522721" + }, + "outputs": [], + "source": [ + "# load model and scheduler\n", + "model_id = \"stabilityai/stable-diffusion-x4-upscaler\"\n", + "pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)\n", + "pipeline = pipeline.to(\"cuda\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1rZBf5X4VfbQ" + }, + "outputs": [], + "source": [ + "def get_low_res_img(url, shape):\n", + " response = requests.get(url)\n", + " low_res_img = Image.open(BytesIO(response.content)).convert(\"RGB\")\n", + " low_res_img = low_res_img.resize(shape)\n", + " return low_res_img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145 + }, + "id": "VSWlrXyIDGSo", + "outputId": "1153aadd-bcc2-4365-9ce8-b02590018e49" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2017/02/07/16/47/kingfisher-2046453_640.jpg\"\n", + "shape = (200, 128)\n", + "low_res_img = get_low_res_img(url, shape)\n", + "\n", + "low_res_img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 561, + "referenced_widgets": [ + "c1dc0d80451c4d098f16eb6ec7eed752", + "d4c5db5f7ffe42beb2065e14cbdd755d", + "accd8a5f56cf41c5af297f8bf93f7058", + "824b0b410fed4ea1b5bc7f88236fc3e8", + "a6b2ca41ffb24b9193a83fd9a4c24a8c", + "bc9783a6d9d0437b881b01cad81c0173", + "9e5ef9fe15314ce3bf13e61994851485", + "ed9e0cfb4635476f9e31c5b48aeafde8", + "396aee75c5954aa9b634d79c18177977", + "c5f787d7f16542baa5a5657c3ecb14a0", + "be0a3bc217b04b2dbd06a90141c0dd35" + ] + }, + "id": "hPtKNnwSDA_u", + "outputId": "60b2259e-02a0-445d-da26-eca1d51b4181" + }, + "outputs": [], + "source": [ + "prompt = \"an aesthetic kingfisher\"\n", + "upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]\n", + "upscaled_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 561, + "referenced_widgets": [ + "9c2ff534109548fc8cab92f3b0aefc71", + "e417a487b9ab44d68bf5d4155f4ff339", + "ce0bc6a269b841e59b3c1b00796b8605", + "b014fb9554fb4f61a8d44135a6ad4954", + "c30445a77e81411bbad4f90b8c54bc35", + "c2ccf29c76d1461c8e820cdd1091684a", + "42248bb1fb38481eaa292dbca2d68e38", + "ac71f4fe6e804f19b2529c82e5a42049", + "518150c24b25401d92cf483e5ecb0253", + "d612163ad6d24d91a6d7ee758d8d6367", + "ab1c2c3e457944acb16508cf7a721290" + ] + }, + "id": "I1hCWlwXU5ij", + "outputId": "fca3425e-973a-4951-df52-6eebba1b96e3" + }, + "outputs": [], + "source": [ + "prompt = \"an aesthetic kingfisher, UHD, 4k, hyper realistic, extremely detailed, professional, vibrant, not grainy, smooth\"\n", + "upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]\n", + "upscaled_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 529 + }, + "id": "4H0IkHfuDBB5", + "outputId": "1fceb2fc-7e6c-492f-fc5b-cbd6d64f3d65" + }, + "outputs": [], + "source": [ + "upscaled_interpolation = low_res_img.resize((800, 512))\n", + "upscaled_interpolation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145 + }, + "id": "xxVVHJAeDBEM", + "outputId": "f099d0db-89ef-49df-92f1-c01c861634e2" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2022/06/14/20/57/woman-7262808_1280.jpg\"\n", + "shape = (200, 128)\n", + "low_res_img = get_low_res_img(url, shape)\n", + "\n", + "low_res_img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 561, + "referenced_widgets": [ + "0c21001820524963b1214a2738c28584", + "ea062db0a1ad43af805bf2d86d26d369", + "2218df295404427eb6086c25f41946c5", + "682dc899e5ee4e24a9c0f1fc928fea6c", + "f8c3945c2c554cc9b7ea7435525c4ab4", + "b9cf936d26124cad959de16fcf5bea63", + "b3ae18d50eb4415b950f98bb38362207", + "0df5b95ccc3d4550bb1be7c001f54577", + "63a7a29ac462471eb67b275c68faff42", + "1ae88e18373a4322bddf0e51e5460a89", + "9b2140d07da744348068f013152b1160" + ] + }, + "id": "UKtH894dXWHN", + "outputId": "44bfe391-7abe-4b99-bfd3-b19e755bfdaa" + }, + "outputs": [], + "source": [ + "prompt = \"an old lady\"\n", + "upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]\n", + "upscaled_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 561, + "referenced_widgets": [ + "cf11071b7b114118a8b0b659167fa09e", + "03bce4ac84fd40d485b023e21fe65c4f", + "d0e9965e6aa4483da2dfa546b896e645", + "22338ed9cec54338ad33267ed579603a", + "622d32a9bbda46fca3ee0733be303765", + "ec0c44e82a814774823e60634d678b0d", + "e71abb2ba1b546ff9d7acd0c174f60d4", + "1237bd63fa814b57bbd9741296d71f46", + "5b3ca63a1af5452cb81fde6020fd9c53", + "a5971d5b793545a3845fbe1029b557e1", + "8384173365364cd5996018a775b167e2" + ] + }, + "id": "L8fnlZsaDBHw", + "outputId": "9215669a-61be-4a6e-cd6b-85d212df6517" + }, + "outputs": [], + "source": [ + "prompt = \"an iranian old lady with black hair, brown scarf, rock background\"\n", + "upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]\n", + "upscaled_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 529 + }, + "id": "OTJNWtuyXOnE", + "outputId": "fe9eb4f3-f7b9-481f-b17b-e2028737141e" + }, + "outputs": [], + "source": [ + "upscaled_interpolation = low_res_img.resize((800, 512))\n", + "upscaled_interpolation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145 + }, + "id": "dXXzMj7vXf5W", + "outputId": "1895b5c9-d87e-48e8-c580-97a3b81838ed" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2017/12/28/07/44/zebra-3044577_1280.jpg\"\n", + "shape = (450, 128)\n", + "low_res_img = get_low_res_img(url, shape)\n", + "\n", + "low_res_img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 453, + "referenced_widgets": [ + "64373eefa4884b3084975549efcbd7fe", + "d8b3f3c7b8394b5580d8541f20c090ae", + "634af1f0b6894726bebb7b546c667169", + "5b89e69b011a40918b1acc0adf141874", + "9c01417376444eed820394ef843c0be3", + "db833b8a924f43208063cdc7b74220f7", + "d74c7ced9e5841e0a3635bf848912874", + "6a72b26cbdf041e7a8331fdc1642dee5", + "3c4dca0b51954031905bada22feef684", + "1e276839600443fa82ca0ab00409fd99", + "639d147ac3674094be21de9f3c11477c" + ] + }, + "id": "xjH0CWRHXf7o", + "outputId": "b1ed8851-6243-43b8-d995-93129640b70d" + }, + "outputs": [], + "source": [ + "prompt = \"zebras drinking water\"\n", + "upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]\n", + "upscaled_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "id": "ydbUyEFvXf_E", + "outputId": "3028b021-c4a0-4f19-8a2e-0a3e4b19f348" + }, + "outputs": [], + "source": [ + "upscaled_interpolation = low_res_img.resize((1800, 512))\n", + "upscaled_interpolation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MFt4Y1AoYWse" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Ng2oJwHqYWvz" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NiM8uOTr9DK3" + }, + "source": [ + "# Custom\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yCuWhxws9D24" + }, + "outputs": [], + "source": [ + "from tqdm import tqdm\n", + "from torch import autocast" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "T7PrARPl9EN2" + }, + "outputs": [], + "source": [ + "class CustomSDUpscalingPipeline:\n", + " \"\"\"custom implementation of the Stable Diffusion Upscaling Pipeline\"\"\"\n", + "\n", + " def __init__(self,\n", + " vae,\n", + " tokenizer,\n", + " text_encoder,\n", + " unet,\n", + " low_res_scheduler,\n", + " scheduler,\n", + " image_processor):\n", + "\n", + " self.vae = vae\n", + " self.tokenizer = tokenizer\n", + " self.text_encoder = text_encoder\n", + " self.unet = unet\n", + " self.low_res_scheduler = low_res_scheduler\n", + " self.scheduler = scheduler\n", + " self.image_processor = image_processor\n", + " self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + "\n", + "\n", + "\n", + " def get_text_embeds(self, text):\n", + " \"\"\"returns embeddings for the given `text`\"\"\"\n", + "\n", + " # tokenize the text\n", + " text_input = self.tokenizer(text,\n", + " padding='max_length',\n", + " max_length=tokenizer.model_max_length,\n", + " truncation=True,\n", + " return_tensors='pt')\n", + " # embed the text\n", + " with torch.no_grad():\n", + " text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0]\n", + " return text_embeds\n", + "\n", + "\n", + "\n", + " def get_prompt_embeds(self, prompt):\n", + " \"\"\"returns prompt embeddings based on classifier free guidance\"\"\"\n", + "\n", + " if isinstance(prompt, str):\n", + " prompt = [prompt]\n", + " # get conditional prompt embeddings\n", + " cond_embeds = self.get_text_embeds(prompt)\n", + " # get unconditional prompt embeddings\n", + " uncond_embeds = self.get_text_embeds([''] * len(prompt))\n", + " # concatenate the above 2 embeds for classfier free guidance\n", + " prompt_embeds = torch.cat([uncond_embeds, cond_embeds])\n", + " return prompt_embeds\n", + "\n", + "\n", + " def transform_image(self, image):\n", + " \"\"\"convert image from pytorch tensor to PIL format\"\"\"\n", + "\n", + " image = self.image_processor.postprocess(image, output_type='pil')\n", + " return image\n", + "\n", + "\n", + "\n", + " def get_initial_latents(self, height, width, num_channels_latents, batch_size):\n", + " \"\"\"returns noise latent tensor of relevant shape scaled by the scheduler\"\"\"\n", + "\n", + " image_latents = torch.randn((batch_size, num_channels_latents, height, width)).to(self.device)\n", + " # scale the initial noise by the standard deviation required by the scheduler\n", + " image_latents = image_latents * self.scheduler.init_noise_sigma\n", + " return image_latents\n", + "\n", + "\n", + "\n", + " def denoise_latents(self,\n", + " prompt_embeds,\n", + " image,\n", + " timesteps,\n", + " latents,\n", + " noise_level,\n", + " guidance_scale):\n", + " \"\"\"denoises latents from noisy latent to a meaningful latents\"\"\"\n", + "\n", + " # use autocast for automatic mixed precision (AMP) inference\n", + " with autocast('cuda'):\n", + " for i, t in tqdm(enumerate(timesteps)):\n", + " # duplicate image latents to do classifier free guidance\n", + " latent_model_input = torch.cat([latents] * 2)\n", + " latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n", + " latent_model_input = torch.cat([latent_model_input, image], dim=1)\n", + "\n", + " # predict noise residuals\n", + " with torch.no_grad():\n", + " noise_pred = self.unet(\n", + " latent_model_input,\n", + " t,\n", + " encoder_hidden_states=prompt_embeds,\n", + " class_labels=noise_level\n", + " )['sample']\n", + "\n", + " # separate predictions for unconditional and conditional outputs\n", + " noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n", + "\n", + " # perform guidance\n", + " noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n", + "\n", + " # remove the noise from the current sample i.e. go from x_t to x_{t-1}\n", + " latents = self.scheduler.step(noise_pred, t, latents)['prev_sample']\n", + "\n", + " return latents\n", + "\n", + "\n", + "\n", + " def __call__(self,\n", + " prompt,\n", + " image,\n", + " num_inference_steps=20,\n", + " guidance_scale=9.0,\n", + " noise_level=20):\n", + " \"\"\"generates new image based on the `prompt` and the `image`\"\"\"\n", + "\n", + " # encode input prompt\n", + " prompt_embeds = self.get_prompt_embeds(prompt)\n", + "\n", + " # preprocess image\n", + " image = self.image_processor.preprocess(image).to(self.device)\n", + "\n", + " # prepare timesteps\n", + " self.scheduler.set_timesteps(num_inference_steps, device=self.device)\n", + " timesteps = self.scheduler.timesteps\n", + "\n", + " # add noise to image\n", + " noise_level = torch.tensor([noise_level], device=self.device)\n", + " noise = torch.randn(image.shape, device=self.device)\n", + " image = self.low_res_scheduler.add_noise(image, noise, noise_level)\n", + "\n", + " # duplicate image for classifier free guidance\n", + " image = torch.cat([image] * 2)\n", + " noise_level = torch.cat([noise_level] * image.shape[0])\n", + "\n", + " # prepare the initial image in the latent space (noise on which we will do reverse diffusion)\n", + " num_channels_latents = self.vae.config.latent_channels\n", + " batch_size = prompt_embeds.shape[0] // 2\n", + " height, width = image.shape[2:]\n", + " latents = self.get_initial_latents(height, width, num_channels_latents, batch_size)\n", + "\n", + " # denoise latents\n", + " latents = self.denoise_latents(prompt_embeds,\n", + " image,\n", + " timesteps,\n", + " latents,\n", + " noise_level,\n", + " guidance_scale)\n", + "\n", + " # decode latents to get the image into pixel space\n", + " latents = latents.to(torch.float16)\n", + " image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n", + "\n", + " # convert to PIL Image format\n", + " image = self.transform_image(image.detach()) # detach to remove any computed gradients\n", + "\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iPMCQB179EQN" + }, + "outputs": [], + "source": [ + "# get all the components from the SD Upscaler pipeline\n", + "vae = pipeline.vae\n", + "tokenizer = pipeline.tokenizer\n", + "text_encoder = pipeline.text_encoder\n", + "unet = pipeline.unet\n", + "low_res_scheduler = pipeline.low_res_scheduler\n", + "scheduler = pipeline.scheduler\n", + "image_processor = pipeline.image_processor\n", + "\n", + "custom_pipe = CustomSDUpscalingPipeline(vae, tokenizer, text_encoder, unet, low_res_scheduler, scheduler, image_processor)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HUxdvfo7eLcq" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2017/02/07/16/47/kingfisher-2046453_640.jpg\"\n", + "shape = (200, 128)\n", + "low_res_img = get_low_res_img(url, shape)\n", + "\n", + "low_res_img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + }, + "id": "SgbP2oQl9EUk", + "outputId": "b1b3d70c-58ef-497a-d87b-2c15073e4d2a" + }, + "outputs": [], + "source": [ + "prompt = \"an aesthetic kingfisher\"\n", + "upscaled_image = custom_pipe(prompt=prompt, image=low_res_img)[0]\n", + "upscaled_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145 + }, + "id": "Wf8MTwFCeRrR", + "outputId": "17827131-0f99-408e-b61d-ff802509baa9" + }, + "outputs": [], + "source": [ + "url = \"https://cdn.pixabay.com/photo/2018/07/31/22/08/lion-3576045_1280.jpg\"\n", + "shape = (200, 128)\n", + "low_res_img = get_low_res_img(url, shape)\n", + "\n", + "low_res_img" + ] + }, + { + "cell_type": "code", + "execution_count": null, 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mode 100644 index 00000000..06efe53c --- /dev/null +++ b/machine-learning/stable-diffusion-upscaler/stable_diffusion_upscaler.py @@ -0,0 +1,303 @@ +# %% +!pip install -qU diffusers transformers accelerate scipy safetensors + +# %% [markdown] +# # Hugging Face Implementation + +# %% +import requests +from PIL import Image +from io import BytesIO +from diffusers import StableDiffusionUpscalePipeline +import torch + +# %% +# load model and scheduler +model_id = "stabilityai/stable-diffusion-x4-upscaler" +pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16) +pipeline = pipeline.to("cuda") + +# %% +def get_low_res_img(url, shape): + response = requests.get(url) + low_res_img = Image.open(BytesIO(response.content)).convert("RGB") + low_res_img = low_res_img.resize(shape) + return low_res_img + +# %% +url = "https://cdn.pixabay.com/photo/2017/02/07/16/47/kingfisher-2046453_640.jpg" +shape = (200, 128) +low_res_img = get_low_res_img(url, shape) + +low_res_img + +# %% +prompt = "an aesthetic kingfisher" +upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0] +upscaled_image + +# %% +prompt = "an aesthetic kingfisher, UHD, 4k, hyper realistic, extremely detailed, professional, vibrant, not grainy, smooth" +upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0] +upscaled_image + +# %% +upscaled_interpolation = low_res_img.resize((800, 512)) +upscaled_interpolation + +# %% +url = "https://cdn.pixabay.com/photo/2022/06/14/20/57/woman-7262808_1280.jpg" +shape = (200, 128) +low_res_img = get_low_res_img(url, shape) + +low_res_img + +# %% +prompt = "an old lady" +upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0] +upscaled_image + +# %% +prompt = "an iranian old lady with black hair, brown scarf, rock background" +upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0] +upscaled_image + +# %% +upscaled_interpolation = low_res_img.resize((800, 512)) +upscaled_interpolation + +# %% +url = "https://cdn.pixabay.com/photo/2017/12/28/07/44/zebra-3044577_1280.jpg" +shape = (450, 128) +low_res_img = get_low_res_img(url, shape) + +low_res_img + +# %% +prompt = "zebras drinking water" +upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0] +upscaled_image + +# %% +upscaled_interpolation = low_res_img.resize((1800, 512)) +upscaled_interpolation + +# %% + + +# %% + + +# %% [markdown] +# # Custom +# + +# %% +from tqdm import tqdm +from torch import autocast + +# %% +class CustomSDUpscalingPipeline: + """custom implementation of the Stable Diffusion Upscaling Pipeline""" + + def __init__(self, + vae, + tokenizer, + text_encoder, + unet, + low_res_scheduler, + scheduler, + image_processor): + + self.vae = vae + self.tokenizer = tokenizer + self.text_encoder = text_encoder + self.unet = unet + self.low_res_scheduler = low_res_scheduler + self.scheduler = scheduler + self.image_processor = image_processor + self.device = 'cuda' if torch.cuda.is_available() else 'cpu' + + + + def get_text_embeds(self, text): + """returns embeddings for the given `text`""" + + # tokenize the text + text_input = self.tokenizer(text, + padding='max_length', + max_length=tokenizer.model_max_length, + truncation=True, + return_tensors='pt') + # embed the text + with torch.no_grad(): + text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0] + return text_embeds + + + + def get_prompt_embeds(self, prompt): + """returns prompt embeddings based on classifier free guidance""" + + if isinstance(prompt, str): + prompt = [prompt] + # get conditional prompt embeddings + cond_embeds = self.get_text_embeds(prompt) + # get unconditional prompt embeddings + uncond_embeds = self.get_text_embeds([''] * len(prompt)) + # concatenate the above 2 embeds for classfier free guidance + prompt_embeds = torch.cat([uncond_embeds, cond_embeds]) + return prompt_embeds + + + def transform_image(self, image): + """convert image from pytorch tensor to PIL format""" + + image = self.image_processor.postprocess(image, output_type='pil') + return image + + + + def get_initial_latents(self, height, width, num_channels_latents, batch_size): + """returns noise latent tensor of relevant shape scaled by the scheduler""" + + image_latents = torch.randn((batch_size, num_channels_latents, height, width)).to(self.device) + # scale the initial noise by the standard deviation required by the scheduler + image_latents = image_latents * self.scheduler.init_noise_sigma + return image_latents + + + + def denoise_latents(self, + prompt_embeds, + image, + timesteps, + latents, + noise_level, + guidance_scale): + """denoises latents from noisy latent to a meaningful latents""" + + # use autocast for automatic mixed precision (AMP) inference + with autocast('cuda'): + for i, t in tqdm(enumerate(timesteps)): + # duplicate image latents to do classifier free guidance + latent_model_input = torch.cat([latents] * 2) + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + latent_model_input = torch.cat([latent_model_input, image], dim=1) + + # predict noise residuals + with torch.no_grad(): + noise_pred = self.unet( + latent_model_input, + t, + encoder_hidden_states=prompt_embeds, + class_labels=noise_level + )['sample'] + + # separate predictions for unconditional and conditional outputs + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + + # perform guidance + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # remove the noise from the current sample i.e. go from x_t to x_{t-1} + latents = self.scheduler.step(noise_pred, t, latents)['prev_sample'] + + return latents + + + + def __call__(self, + prompt, + image, + num_inference_steps=20, + guidance_scale=9.0, + noise_level=20): + """generates new image based on the `prompt` and the `image`""" + + # encode input prompt + prompt_embeds = self.get_prompt_embeds(prompt) + + # preprocess image + image = self.image_processor.preprocess(image).to(self.device) + + # prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=self.device) + timesteps = self.scheduler.timesteps + + # add noise to image + noise_level = torch.tensor([noise_level], device=self.device) + noise = torch.randn(image.shape, device=self.device) + image = self.low_res_scheduler.add_noise(image, noise, noise_level) + + # duplicate image for classifier free guidance + image = torch.cat([image] * 2) + noise_level = torch.cat([noise_level] * image.shape[0]) + + # prepare the initial image in the latent space (noise on which we will do reverse diffusion) + num_channels_latents = self.vae.config.latent_channels + batch_size = prompt_embeds.shape[0] // 2 + height, width = image.shape[2:] + latents = self.get_initial_latents(height, width, num_channels_latents, batch_size) + + # denoise latents + latents = self.denoise_latents(prompt_embeds, + image, + timesteps, + latents, + noise_level, + guidance_scale) + + # decode latents to get the image into pixel space + latents = latents.to(torch.float16) + image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] + + # convert to PIL Image format + image = self.transform_image(image.detach()) # detach to remove any computed gradients + + return image + +# %% +# get all the components from the SD Upscaler pipeline +vae = pipeline.vae +tokenizer = pipeline.tokenizer +text_encoder = pipeline.text_encoder +unet = pipeline.unet +low_res_scheduler = pipeline.low_res_scheduler +scheduler = pipeline.scheduler +image_processor = pipeline.image_processor + +custom_pipe = CustomSDUpscalingPipeline(vae, tokenizer, text_encoder, unet, low_res_scheduler, scheduler, image_processor) + +# %% +url = "https://cdn.pixabay.com/photo/2017/02/07/16/47/kingfisher-2046453_640.jpg" +shape = (200, 128) +low_res_img = get_low_res_img(url, shape) + +low_res_img + +# %% +prompt = "an aesthetic kingfisher" +upscaled_image = custom_pipe(prompt=prompt, image=low_res_img)[0] +upscaled_image + +# %% +url = "https://cdn.pixabay.com/photo/2018/07/31/22/08/lion-3576045_1280.jpg" +shape = (200, 128) +low_res_img = get_low_res_img(url, shape) + +low_res_img + +# %% +prompt = "a professional photograph of a lion's face" +upscaled_image = custom_pipe(prompt=prompt, image=low_res_img)[0] +upscaled_image + +# %% +upscaled_interpolation = low_res_img.resize((800, 512)) +upscaled_interpolation + +# %% + + + diff --git a/machine-learning/visual-question-answering/000000007226.jpg b/machine-learning/visual-question-answering/000000007226.jpg new file mode 100644 index 00000000..56932377 Binary files /dev/null and b/machine-learning/visual-question-answering/000000007226.jpg differ diff --git a/machine-learning/visual-question-answering/README.md b/machine-learning/visual-question-answering/README.md new file mode 100644 index 00000000..a88ef88c --- /dev/null +++ b/machine-learning/visual-question-answering/README.md @@ -0,0 +1 @@ +# [Visual Question Answering with Transformers](https://www.thepythoncode.com/article/visual-question-answering-with-transformers-in-python) \ No newline at end of file diff --git a/machine-learning/visual-question-answering/Running_BLIP2.ipynb b/machine-learning/visual-question-answering/Running_BLIP2.ipynb new file mode 100644 index 00000000..5b880995 --- /dev/null +++ b/machine-learning/visual-question-answering/Running_BLIP2.ipynb @@ -0,0 +1,912 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "2d87ad23-587a-4b20-8121-1d1748ac301a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting transformers\n", + " Downloading transformers-4.30.2-py3-none-any.whl (7.2 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m50.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", + "\u001b[?25hCollecting 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install transformers accelerate" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "52e4776c-8820-4ee6-9ae4-9db51e2ed365", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "device(type='cuda', index=0)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import requests\n", + "from PIL import Image\n", + "from transformers import Blip2Processor, Blip2ForConditionalGeneration\n", + "import torch\n", + "import os\n", + "\n", + "device = torch.device(\"cuda\", 0)\n", + "device" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e4ad6102-160e-487d-99c0-da50a52a5e4e", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6b01bf8e2d2a4680ba09d412a2a0286d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading (…)rocessor_config.json: 0%| | 0.00/432 [00:00