From f985680aff03b49a3aa6525166cd8113afb1d86f Mon Sep 17 00:00:00 2001 From: x4nth055 Date: Thu, 23 Mar 2023 14:46:17 +0100 Subject: [PATCH 001/147] add restful api flask tutorial --- README.md | 1 + web-programming/restful-api-flask/README.md | 1 + web-programming/restful-api-flask/app.py | 22 +++++++++++++ web-programming/restful-api-flask/config.py | 1 + web-programming/restful-api-flask/models.py | 11 +++++++ .../restful-api-flask/requirements.txt | 3 ++ .../restful-api-flask/resources.py | 29 ++++++++++++++++++ web-programming/restful-api-flask/tasks.db | Bin 0 -> 8192 bytes 8 files changed, 68 insertions(+) create mode 100644 web-programming/restful-api-flask/README.md create mode 100644 web-programming/restful-api-flask/app.py create mode 100644 web-programming/restful-api-flask/config.py create mode 100644 web-programming/restful-api-flask/models.py create mode 100644 web-programming/restful-api-flask/requirements.txt create mode 100644 web-programming/restful-api-flask/resources.py create mode 100644 web-programming/restful-api-flask/tasks.db diff --git a/README.md b/README.md index a03bf091..24716adb 100644 --- a/README.md +++ b/README.md @@ -245,6 +245,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy - [How to Build a Web Assistant Using Django and OpenAI GPT-3.5 API in Python](https://www.thepythoncode.com/article/web-assistant-django-with-gpt3-api-python). ([code](web-programming/webassistant)) - [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)) - ### [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)) diff --git a/web-programming/restful-api-flask/README.md b/web-programming/restful-api-flask/README.md new file mode 100644 index 00000000..33cae8c8 --- /dev/null +++ b/web-programming/restful-api-flask/README.md @@ -0,0 +1 @@ +# [How to Create a RESTful API with Flask in Python](https://www.thepythoncode.com/article/create-a-restful-api-with-flask-in-python) \ No newline at end of file diff --git a/web-programming/restful-api-flask/app.py b/web-programming/restful-api-flask/app.py new file mode 100644 index 00000000..32e7fcf8 --- /dev/null +++ b/web-programming/restful-api-flask/app.py @@ -0,0 +1,22 @@ +from flask import Flask +from flask_restful import Api +from models import db +import config +from resources import TaskList + +# Create the Flask application and the Flask-RESTful API manager. +app = Flask(__name__) +app.config.from_object(config) +# Initialize the Flask-SQLAlchemy object. +db.init_app(app) +# Create the Flask-RESTful API manager. +api = Api(app) +# Create the endpoints. +api.add_resource(TaskList, '/tasks') + +if __name__ == '__main__': + # Create the database tables. + with app.app_context(): + db.create_all() + # Start the Flask development web server. + app.run(debug=True) diff --git a/web-programming/restful-api-flask/config.py b/web-programming/restful-api-flask/config.py new file mode 100644 index 00000000..3974b455 --- /dev/null +++ b/web-programming/restful-api-flask/config.py @@ -0,0 +1 @@ +SQLALCHEMY_DATABASE_URI = 'sqlite:///tasks.db' \ No newline at end of file diff --git a/web-programming/restful-api-flask/models.py b/web-programming/restful-api-flask/models.py new file mode 100644 index 00000000..3d792130 --- /dev/null +++ b/web-programming/restful-api-flask/models.py @@ -0,0 +1,11 @@ +from flask_sqlalchemy import SQLAlchemy + +db = SQLAlchemy() + +class Task(db.Model): + id = db.Column(db.Integer, primary_key=True) + description = db.Column(db.String(200), nullable=False) # nullable=False means that the column cannot be empty + + def __repr__(self): + # This method is used to print the object. + return f'Task {self.id}: {self.description}' diff --git a/web-programming/restful-api-flask/requirements.txt b/web-programming/restful-api-flask/requirements.txt new file mode 100644 index 00000000..d3d142f8 --- /dev/null +++ b/web-programming/restful-api-flask/requirements.txt @@ -0,0 +1,3 @@ +Flask +Flask-RESTful +Flask-SQLAlchemy \ No newline at end of file diff --git a/web-programming/restful-api-flask/resources.py b/web-programming/restful-api-flask/resources.py new file mode 100644 index 00000000..ea917f7c --- /dev/null +++ b/web-programming/restful-api-flask/resources.py @@ -0,0 +1,29 @@ +from flask_restful import Resource +from flask import request +from models import Task, db + +class TaskList(Resource): + def get(self): + # Get all the tasks from the database. + tasks = Task.query.all() + # Convert the tasks to JSON and return a response. + task_list = [{'id': task.id, 'description': task.description} for task in tasks] + return {'tasks': task_list} + + def post(self): + # Get the JSON data from the request. + task_data = request.get_json() + # Check if the data is valid. + if not task_data: + return {'message': 'No input data provided'}, 400 + description = task_data.get('description') + if not description: + return {'message': 'Description is required'}, 400 + # Add the task to the database. + new_task = Task(description=description) + db.session.add(new_task) + # Commit the task to the database. + db.session.commit() + # Return a message to the user. + return {'message': 'Task added', 'task': {'id': new_task.id, 'description': new_task.description}} + diff --git a/web-programming/restful-api-flask/tasks.db b/web-programming/restful-api-flask/tasks.db new file mode 100644 index 0000000000000000000000000000000000000000..6273f7df04327b2064f580d97222ef0d99bc3a0c GIT binary patch literal 8192 zcmeI%u};G<5C-6Lf`l3gr3@A8Z6p#x6}$kZvY-$GxB=ABC9Y(JQj~aLW#J`x2HpW@ zs1Oq~;y?Lp-|41rvpl;koVUDOw=3iMn1&<-oiGzo95M>I2@x(^nmi`HGM`Ctz@ORlBb2@ zzQb8Ea5YcMN}Vaq<#om7T~QoxGN^6$*t!?**3Wz|_2gXY>}WjBe+=E|>6O%TzEpG0 zT%9L*|2B`(4@y77hJXMBAOHafKmY;|fB*y_009U<;I9b`qnP$>_|;#u)-+4g`CSoH KzA}ydZhZlecsBX~ literal 0 HcmV?d00001 From 0b610c438edf4c3ab209f0b77060fbabaab8b640 Mon Sep 17 00:00:00 2001 From: Yagniksojitra Date: Fri, 7 Apr 2023 03:53:48 +0530 Subject: [PATCH 002/147] Update pdf_cracker.py --- ethical-hacking/pdf-cracker/pdf_cracker.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ethical-hacking/pdf-cracker/pdf_cracker.py b/ethical-hacking/pdf-cracker/pdf_cracker.py index ea1d0728..141aea02 100644 --- a/ethical-hacking/pdf-cracker/pdf_cracker.py +++ b/ethical-hacking/pdf-cracker/pdf_cracker.py @@ -12,6 +12,6 @@ # Password decrypted successfully, break out of the loop print("[+] Password found:", password) break - except pikepdf._qpdf.PasswordError as e: + except pikepdf._core.PasswordError as e: # wrong password, just continue in the loop continue From f8eb70027ac845d970002785f1f0a523289cda98 Mon Sep 17 00:00:00 2001 From: x4nth055 Date: Fri, 7 Apr 2023 17:49:59 +0100 Subject: [PATCH 003/147] add stable diffusion tutorial --- README.md | 1 + ...t_StableDiffusion_PythonCodeTutorial.ipynb | 6326 +++++++++++++++++ .../stable-diffusion-models/README.md | 1 + ...nerate_images_from_text_stablediffusion.py | 372 + .../stable-diffusion-models/requirements.txt | 4 + 5 files changed, 6704 insertions(+) create mode 100644 machine-learning/stable-diffusion-models/GenerateImagesFromText_StableDiffusion_PythonCodeTutorial.ipynb create mode 100644 machine-learning/stable-diffusion-models/README.md create mode 100644 machine-learning/stable-diffusion-models/generate_images_from_text_stablediffusion.py create mode 100644 machine-learning/stable-diffusion-models/requirements.txt diff --git a/README.md b/README.md index 24716adb..c875a72c 100644 --- a/README.md +++ b/README.md @@ -80,6 +80,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy - [Satellite Image Classification using TensorFlow in Python](https://www.thepythoncode.com/article/satellite-image-classification-using-tensorflow-python). ([code](machine-learning/satellite-image-classification)) - [How to Perform Image Segmentation using Transformers in Python](https://www.thepythoncode.com/article/image-segmentation-using-huggingface-transformers-python). ([code](machine-learning/image-segmentation-transformers)) - [How to Fine Tune ViT for Image Classification using Huggingface Transformers in Python](https://www.thepythoncode.com/article/finetune-vit-for-image-classification-using-transformers-in-python). ([code](machine-learning/finetuning-vit-image-classification)) + - [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)) - [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). diff --git a/machine-learning/stable-diffusion-models/GenerateImagesFromText_StableDiffusion_PythonCodeTutorial.ipynb b/machine-learning/stable-diffusion-models/GenerateImagesFromText_StableDiffusion_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..aee6b7dc --- /dev/null +++ b/machine-learning/stable-diffusion-models/GenerateImagesFromText_StableDiffusion_PythonCodeTutorial.ipynb @@ -0,0 +1,6326 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZgIU4Ga56Tiq", + "outputId": "764ce650-379a-4bed-d5fb-b5052af024c9" + }, + "outputs": [], + "source": [ + "%pip install --quiet --upgrade diffusers transformers accelerate" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "S919oAK46Z8x", + "outputId": "74fe51b4-157d-48a0-9067-6947e2a71bb8" + }, + "outputs": [], + "source": [ + "# The xformers package is mandatory to be able to create several 768x768 images.\n", + "%pip install -q xformers==0.0.16rc425" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Dn2_-E5Sa9Rn" + }, + "source": [ + "# Using Dreamlike Photoreal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WGIvJ0hE6Z_B" + }, + "outputs": [], + "source": [ + "from diffusers import StableDiffusionPipeline\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 433, + "referenced_widgets": [ + "d02fc695003f435e9ec25e5ab7eec2bc", + "e16097842da34cd0bc24cfaeab3de404", + "0d2fba8088804ecab806472f5396f1bc", + 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"ede27ca6747240eeb41a9dbde664e8a7", + "45ed824c2cd746c09e61dc8a2bdd3384", + "3f94af984ba84b838643b03caf8d2d4e", + "2e9985de92164304b5b4c8b723d24cf1", + "99777597ceee470ca8c8a34f4846435a", + "ca063f5b64d74bc9be303eebe7092f81", + "7d654e85d8794af197e72f905086b9a7", + "f3804bde7c974eaf90df71e9fdd3539b", + "dba967ac191d45a0b604e93f0dc1fffe", + "f8cc05786ad94dcca69f1fedf6d4aa4a" + ] + }, + "id": "JzcSCwsF6aBT", + "outputId": "1f223f71-54ed-49fe-8cb5-dcfc183a7c3f" + }, + "outputs": [], + "source": [ + "model_id = \"dreamlike-art/dreamlike-photoreal-2.0\"\n", + "pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\n", + "pipe = pipe.to(\"cuda\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Sz6SRmjd6pBb" + }, + "outputs": [], + "source": [ + "prompts = [\"Cute Rabbit, Ultra HD, realistic, futuristic, sharp, octane render, photoshopped, photorealistic, soft, pastel, Aesthetic, Magical background\",\n", + " \"Anime style aesthetic landscape, 90's vintage style, digital art, ultra HD, 8k, photoshopped, sharp focus, surrealism, akira style, detailed line art\",\n", + " \"Beautiful, abstract art of a human mind, 3D, highly detailed, 8K, aesthetic\"]\n", + "\n", + "images = []" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 113, + "referenced_widgets": [ + "eb182b33be95418fad1010ccf7b176ab", + "614b85aff85e47debadea7773583b8ab", + "c6305a1adcd946d2a4c66c05e614bcf1", + "d321be86c24d4a35a251d4ef7a75c24d", + "a99edd082dff4f928c3b75abf84ec0ec", + "3d17c5e7b942485d9ca1db572c06afe5", + "29f58c06e0474f4e8f411aaaceb4d7f7", + "f369cc94c2544de3acdbcd35cac6d393", + "0bec42769e7e4492aedd4afb63a91ff3", + "73f2e3215913478aae13131aefadd0a5", + "64f6ec814be648d3ba69f94eb844a049", + "534cf1c7f20a431cab02aea224148db3", + "1997648c2dc946e1bdb18d2730175ea8", + "2496cd9b6ee640f8a6d398b7a56f93b7", + "7f0e7d1bd12b4d0eb800a3d62901c4a8", + "b75a2474c46f4f3b9d84cb981bfffac7", + "ac3ea0ea867b4c30bd4be87aaa602d42", + "ff0910f0831b49ac9faaba1e70c275dc", + "fc5d5de2233543eba400877e7891977a", + "f9a2bf4d86ab403d9e1c7378e91bf467", + "cbcfc8e9b02348a182722c846cecca2a", + "ece36cbb62cd46a8b452fac32dce3493", + "6f400bad53794c7cbed09e4fd59c211d", + "4cecf6bc5f294968bcee7bf65896a31d", + "5bb94e6390af4e81ac0e6b3a47445996", + "af474aa6c91344da9a968e7e2488b74c", + "5a702086896f4a229e326fa05d616b35", + "28cd65b9d6f946abab83e430ab6d2017", + "a432cb1a0bc7418bb90248973e91c452", + "37af49a1966045ee992e01f45ff5df81", + "8120fb8694244e5dbbc448eb2a6e03dc", + "69faef33b09f4da7b1c11639102b2a4f", + "87d35ca268744638ad484ccf1a7fe2ed" + ] + }, + "id": "ovvyensy6pDl", + "outputId": "2b0269af-4978-4a8b-eea9-96c12401dc62" + }, + "outputs": [], + "source": [ + "for i, prompt in enumerate(prompts):\n", + " image = pipe(prompt).images[0]\n", + " image.save(f'result_{i}.jpg')\n", + " images.append(image)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 785 + }, + "id": "vd532OSA8Md7", + "outputId": "a8ddd5b1-376b-4036-d87d-af9dc71c88e0" + }, + "outputs": [], + "source": [ + "images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 785 + }, + "id": "ZpVbvylE8OEt", + "outputId": "5a577720-b68e-4657-9cbb-4112287afa23" + }, + "outputs": [], + "source": [ + "images[1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 785 + }, + "id": "R1DNPbbz8PU-", + "outputId": "893bb392-96f0-4106-e3d7-f6def830ede1" + }, + "outputs": [], + "source": [ + "images[2]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Jd-5c7bouD-_" + }, + "source": [ + "# Manually working with the different components" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "01bGNP1n6aF4" + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch import autocast\n", + "import numpy as np\n", + "\n", + "from transformers import CLIPTextModel, CLIPTokenizer\n", + "\n", + "from diffusers import AutoencoderKL\n", + "from diffusers import LMSDiscreteScheduler\n", + "from diffusers import UNet2DConditionModel\n", + "from diffusers.schedulers.scheduling_ddim import DDIMScheduler\n", + "\n", + "from tqdm import tqdm\n", + "from PIL import Image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3yBgKeUs8LWU" + }, + "outputs": [], + "source": [ + "class ImageDiffusionModel:\n", + "\n", + " def __init__(self, vae, tokenizer, text_encoder, unet, \n", + " scheduler_LMS, scheduler_DDIM):\n", + " self.vae = vae\n", + " self.tokenizer = tokenizer\n", + " self.text_encoder = text_encoder\n", + " self.unet = unet\n", + " self.scheduler_LMS = scheduler_LMS\n", + " self.scheduler_DDIM = scheduler_DDIM\n", + " self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + " \n", + " \n", + " def get_text_embeds(self, text):\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", + "\n", + " return text_embeds\n", + "\n", + " def get_prompt_embeds(self, 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", + " def get_img_latents(self, \n", + " text_embeds, \n", + " height=512, width=512, \n", + " num_inference_steps=50, \n", + " guidance_scale=7.5, \n", + " img_latents=None):\n", + " # if no image latent is passed, start reverse diffusion with random noise\n", + " if img_latents is None:\n", + " img_latents = torch.randn((text_embeds.shape[0] // 2, self.unet.in_channels,\\\n", + " height // 8, width // 8)).to(self.device)\n", + " # set the number of inference steps for the scheduler\n", + " self.scheduler_LMS.set_timesteps(num_inference_steps)\n", + " # scale the latent embeds\n", + " img_latents = img_latents * self.scheduler_LMS.sigmas[0]\n", + " # use autocast for automatic mixed precision (AMP) inference\n", + " with autocast('cuda'):\n", + " for i, t in tqdm(enumerate(self.scheduler_LMS.timesteps)):\n", + " # do a single forward pass for both the conditional and unconditional latents\n", + " latent_model_input = torch.cat([img_latents] * 2)\n", + " sigma = self.scheduler_LMS.sigmas[i]\n", + " latent_model_input = latent_model_input / ((sigma ** 2 + 1) ** 0.5)\n", + " \n", + " # predict noise residuals\n", + " with torch.no_grad():\n", + " noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeds)['sample']\n", + "\n", + " # separate predictions for unconditional and conditional outputs\n", + " noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)\n", + " # perform guidance\n", + " noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)\n", + "\n", + " # remove the noise from the current sample i.e. go from x_t to x_{t-1}\n", + " img_latents = self.scheduler_LMS.step(noise_pred, t, img_latents)['prev_sample']\n", + "\n", + " return img_latents\n", + "\n", + "\n", + " def decode_img_latents(self, img_latents):\n", + " img_latents = img_latents / 0.18215\n", + " with torch.no_grad():\n", + " imgs = self.vae.decode(img_latents)[\"sample\"]\n", + " # load image in the CPU\n", + " imgs = imgs.detach().cpu()\n", + " return imgs\n", + "\n", + "\n", + "\n", + " def transform_imgs(self, imgs):\n", + " # transform images from the range [-1, 1] to [0, 1]\n", + " imgs = (imgs / 2 + 0.5).clamp(0, 1)\n", + " # permute the channels and convert to numpy arrays\n", + " imgs = imgs.permute(0, 2, 3, 1).numpy()\n", + " # scale images to the range [0, 255] and convert to int\n", + " imgs = (imgs * 255).round().astype('uint8') \n", + " # convert to PIL Image objects\n", + " imgs = [Image.fromarray(img) for img in imgs]\n", + " return imgs\n", + " \n", + " \n", + " \n", + " def prompt_to_img(self, \n", + " prompts, \n", + " height=512, width=512, \n", + " num_inference_steps=50, \n", + " guidance_scale=7.5, \n", + " img_latents=None):\n", + " \n", + " # convert prompt to a list\n", + " if isinstance(prompts, str):\n", + " prompts = [prompts]\n", + " \n", + " # get prompt embeddings\n", + " text_embeds = self.get_prompt_embeds(prompts)\n", + "\n", + " # get image embeddings\n", + " img_latents = self.get_img_latents(text_embeds,\n", + " height, width,\n", + " num_inference_steps,\n", + " guidance_scale, \n", + " img_latents)\n", + " # decode the image embeddings\n", + " imgs = self.decode_img_latents(img_latents)\n", + " # convert decoded image to suitable PIL Image format\n", + " imgs = self.transform_imgs(imgs)\n", + "\n", + " return imgs\n", + "\n", + "\n", + "\n", + " def encode_img_latents(self, imgs):\n", + " if not isinstance(imgs, list):\n", + " imgs = [imgs]\n", + " \n", + " imgs = np.stack([np.array(img) for img in imgs], axis=0)\n", + " # scale images to the range [-1, 1]\n", + " imgs = 2 * ((imgs / 255.0) - 0.5)\n", + " imgs = torch.from_numpy(imgs).float().permute(0, 3, 1, 2)\n", + "\n", + " # encode images\n", + " img_latents_dist = self.vae.encode(imgs.to(self.device))\n", + " # img_latents = img_latents_dist.sample()\n", + " img_latents = img_latents_dist[\"latent_dist\"].mean.clone()\n", + " # scale images\n", + " img_latents *= 0.18215\n", + "\n", + " return img_latents\n", + "\n", + "\n", + " def get_img_latents_similar(self,\n", + " img_latents,\n", + " text_embeds, \n", + " height=512, width=512, \n", + " num_inference_steps=50, \n", + " guidance_scale=7.5,\n", + " start_step=10): \n", + " \n", + " # set the number of inference steps for the scheduler\n", + " self.scheduler_DDIM.set_timesteps(num_inference_steps)\n", + "\n", + " if start_step > 0:\n", + " start_timestep = self.scheduler_DDIM.timesteps[start_step]\n", + " start_timesteps = start_timestep.repeat(img_latents.shape[0]).long()\n", + "\n", + " noise = torch.randn_like(img_latents)\n", + " img_latents = scheduler_DDIM.add_noise(img_latents, noise, start_timesteps)\n", + " \n", + " # use autocast for automatic mixed precision (AMP) inference\n", + " with autocast('cuda'):\n", + " for i, t in tqdm(enumerate(self.scheduler_DDIM.timesteps[start_step:])):\n", + " # do a single forward pass for both the conditional and unconditional latents\n", + " latent_model_input = torch.cat([img_latents] * 2)\n", + " \n", + " # predict noise residuals\n", + " with torch.no_grad():\n", + " noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeds)['sample']\n", + "\n", + " # separate predictions for unconditional and conditional outputs\n", + " noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)\n", + " # perform guidance\n", + " noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)\n", + "\n", + " # remove the noise from the current sample i.e. go from x_t to x_{t-1}\n", + " img_latents = self.scheduler_DDIM.step(noise_pred, t, img_latents)['prev_sample']\n", + "\n", + " return img_latents\n", + "\n", + " \n", + " def similar_imgs(self, \n", + " img, \n", + " prompt, \n", + " height=512, width=512,\n", + " num_inference_steps=50, \n", + " guidance_scale=7.5,\n", + " start_step=10):\n", + " \n", + " # get image latents\n", + " img_latents = self.encode_img_latents(img)\n", + "\n", + " if isinstance(prompt, str):\n", + " prompt = [prompt]\n", + "\n", + " text_embeds = self.get_prompt_embeds(prompt)\n", + " \n", + " img_latents = self.get_img_latents_similar(img_latents=img_latents,\n", + " text_embeds=text_embeds,\n", + " height=height, width=width,\n", + " num_inference_steps=num_inference_steps,\n", + " guidance_scale=guidance_scale,\n", + " start_step=start_step) \n", + "\n", + " imgs = self.decode_img_latents(img_latents)\n", + " imgs = self.transform_imgs(imgs)\n", + " # Clear the CUDA cache\n", + " torch.cuda.empty_cache()\n", + "\n", + " return imgs\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kd6TwWqEs4Me" + }, + "outputs": [], + "source": [ + "device = 'cuda'\n", + "\n", + "# model_name = \"dreamlike-art/dreamlike-photoreal-2.0\"\n", + "model_name = \"CompVis/stable-diffusion-v1-4\"\n", + "# Load autoencoder\n", + "vae = AutoencoderKL.from_pretrained(model_name, \n", + " subfolder='vae').to(device)\n", + "\n", + "# Load tokenizer and the text encoder\n", + "tokenizer = CLIPTokenizer.from_pretrained(model_name, subfolder=\"tokenizer\")\n", + "text_encoder = CLIPTextModel.from_pretrained(model_name, subfolder=\"text_encoder\").to(device)\n", + "\n", + "# Load UNet model\n", + "unet = UNet2DConditionModel.from_pretrained(model_name, subfolder='unet').to(device)\n", + "\n", + "# Load scheduler\n", + "scheduler_LMS = LMSDiscreteScheduler(beta_start=0.00085, \n", + " beta_end=0.012, \n", + " beta_schedule='scaled_linear', \n", + " num_train_timesteps=1000)\n", + "\n", + "scheduler_DDIM = DDIMScheduler(beta_start=0.00085, \n", + " beta_end=0.012, \n", + " beta_schedule='scaled_linear', \n", + " num_train_timesteps=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SigUHp47f14I", + "outputId": "bad874ae-1e68-45fe-ef31-9fe887780582" + }, + "outputs": [], + "source": [ + "model = ImageDiffusionModel(vae, tokenizer, text_encoder, unet, scheduler_LMS, scheduler_DDIM)\n", + "\n", + "prompts = [\"A really giant cute pink barbie doll on the top of Burj Khalifa\", \n", + " \"A green, scary aesthetic dragon breathing fire near a group of heroic firefighters\"]\n", + "\n", + "imgs = model.prompt_to_img(prompts)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 529 + }, + "id": "8UpQ8gIWf17j", + "outputId": "165f5a5d-fe20-4303-c46f-b247efd05181" + }, + "outputs": [], + "source": [ + "imgs[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 529 + }, + "id": "NAS1yD8vZym_", + "outputId": "ef57db7c-a6c9-437f-d27e-94b2bab06ea9" + }, + "outputs": [], + "source": [ + "imgs[1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 603 + }, + "id": "nj8pcEOupRES", + "outputId": "0ced4046-ed46-4bd0-8b77-1c23ca73dab6" + }, + "outputs": [], + "source": [ + "prompt = [\"Aesthetic star wars spaceship with an aethethic background, Ultra HD, futuristic, sharp, octane render, neon\"]\n", + "\n", + "imgs = model.prompt_to_img(prompt)\n", + "\n", + "imgs[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GmXyduZ1npqg" + }, + "outputs": [], + "source": [ + "# saving the image\n", + "imgs[0].save(\"spaceship1.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 529 + }, + "id": "RuAHYae4r3MC", + "outputId": "c4be8be3-cacb-48f6-b70c-15ec69afe5b0" + }, + "outputs": [], + "source": [ + "# loading the image again\n", + "original_img = Image.open(\"spaceship1.png\")\n", + "original_img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "qMcpCt20RyKi" + }, + "outputs": [], + "source": [ + "import torch\n", + "import gc\n", + "\n", + "### If you get OOM errors, execute this cell\n", + "# del model\n", + "# Clear the CUDA cache \n", + "torch.cuda.empty_cache()\n", + "gc.collect()\n", + "torch.cuda.empty_cache()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1TQNiEE86Y6E", + "outputId": "2b87847d-6a63-4ec7-9cc1-7ac6a3396a48" + }, + "outputs": [], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 547 + }, + "id": "1vIVmpL4rPmK", + "outputId": "4bbc1c35-6850-41f0-a430-39d764a59f2a" + }, + "outputs": [], + "source": [ + "model = ImageDiffusionModel(vae, tokenizer, text_encoder, unet, scheduler_LMS, scheduler_DDIM)\n", + "\n", + "prompt = \"Aesthetic star wars spaceship with an aethethic background, Ultra HD, futuristic, sharp, octane render, neon\"\n", + "\n", + "imgs = model.similar_imgs(original_img, prompt, num_inference_steps=50, start_step=30)\n", + "imgs[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 547 + }, + "id": "zOL-Y7BFai7d", + "outputId": "666384a3-667d-4715-cbe1-07566afa242d" + }, + "outputs": [], + "source": [ + "# model = ImageDiffusionModel(vae, tokenizer, text_encoder, unet, scheduler_LMS, scheduler_DDIM)\n", + "\n", + "prompt = \"Aesthetic dark star wars spaceship, Ultra HD, futuristic, sharp, octane render, neon\"\n", + "\n", + "imgs = model.similar_imgs(original_img, prompt,\n", + " num_inference_steps=50,\n", + " start_step=40)\n", + "imgs[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "thiXQYcG8Ekv" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Xwtu2l3-8EnJ" + }, + "outputs": [], + "source": [] 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b/machine-learning/stable-diffusion-models/generate_images_from_text_stablediffusion.py @@ -0,0 +1,372 @@ +# %% +%pip install --quiet --upgrade diffusers transformers accelerate + +# %% +# The xformers package is mandatory to be able to create several 768x768 images. +%pip install -q xformers==0.0.16rc425 + +# %% [markdown] +# # Using Dreamlike Photoreal + +# %% +from diffusers import StableDiffusionPipeline +import torch + +# %% +model_id = "dreamlike-art/dreamlike-photoreal-2.0" +pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) +pipe = pipe.to("cuda") + +# %% +prompts = ["Cute Rabbit, Ultra HD, realistic, futuristic, sharp, octane render, photoshopped, photorealistic, soft, pastel, Aesthetic, Magical background", + "Anime style aesthetic landscape, 90's vintage style, digital art, ultra HD, 8k, photoshopped, sharp focus, surrealism, akira style, detailed line art", + "Beautiful, abstract art of a human mind, 3D, highly detailed, 8K, aesthetic"] + +images = [] + +# %% +for i, prompt in enumerate(prompts): + image = pipe(prompt).images[0] + image.save(f'result_{i}.jpg') + images.append(image) + +# %% +images[0] + +# %% +images[1] + +# %% +images[2] + +# %% [markdown] +# # Manually working with the different components + +# %% +import torch +from torch import autocast +import numpy as np + +from transformers import CLIPTextModel, CLIPTokenizer + +from diffusers import AutoencoderKL +from diffusers import LMSDiscreteScheduler +from diffusers import UNet2DConditionModel +from diffusers.schedulers.scheduling_ddim import DDIMScheduler + +from tqdm import tqdm +from PIL import Image + +# %% +class ImageDiffusionModel: + + def __init__(self, vae, tokenizer, text_encoder, unet, + scheduler_LMS, scheduler_DDIM): + self.vae = vae + self.tokenizer = tokenizer + self.text_encoder = text_encoder + self.unet = unet + self.scheduler_LMS = scheduler_LMS + self.scheduler_DDIM = scheduler_DDIM + self.device = 'cuda' if torch.cuda.is_available() else 'cpu' + + + def get_text_embeds(self, 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): + # 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 get_img_latents(self, + text_embeds, + height=512, width=512, + num_inference_steps=50, + guidance_scale=7.5, + img_latents=None): + # if no image latent is passed, start reverse diffusion with random noise + if img_latents is None: + img_latents = torch.randn((text_embeds.shape[0] // 2, self.unet.in_channels,\ + height // 8, width // 8)).to(self.device) + # set the number of inference steps for the scheduler + self.scheduler_LMS.set_timesteps(num_inference_steps) + # scale the latent embeds + img_latents = img_latents * self.scheduler_LMS.sigmas[0] + # use autocast for automatic mixed precision (AMP) inference + with autocast('cuda'): + for i, t in tqdm(enumerate(self.scheduler_LMS.timesteps)): + # do a single forward pass for both the conditional and unconditional latents + latent_model_input = torch.cat([img_latents] * 2) + sigma = self.scheduler_LMS.sigmas[i] + latent_model_input = latent_model_input / ((sigma ** 2 + 1) ** 0.5) + + # predict noise residuals + with torch.no_grad(): + noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeds)['sample'] + + # separate predictions for unconditional and conditional outputs + noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2) + # perform guidance + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond) + + # remove the noise from the current sample i.e. go from x_t to x_{t-1} + img_latents = self.scheduler_LMS.step(noise_pred, t, img_latents)['prev_sample'] + + return img_latents + + + def decode_img_latents(self, img_latents): + img_latents = img_latents / 0.18215 + with torch.no_grad(): + imgs = self.vae.decode(img_latents)["sample"] + # load image in the CPU + imgs = imgs.detach().cpu() + return imgs + + + + def transform_imgs(self, imgs): + # transform images from the range [-1, 1] to [0, 1] + imgs = (imgs / 2 + 0.5).clamp(0, 1) + # permute the channels and convert to numpy arrays + imgs = imgs.permute(0, 2, 3, 1).numpy() + # scale images to the range [0, 255] and convert to int + imgs = (imgs * 255).round().astype('uint8') + # convert to PIL Image objects + imgs = [Image.fromarray(img) for img in imgs] + return imgs + + + + def prompt_to_img(self, + prompts, + height=512, width=512, + num_inference_steps=50, + guidance_scale=7.5, + img_latents=None): + + # convert prompt to a list + if isinstance(prompts, str): + prompts = [prompts] + + # get prompt embeddings + text_embeds = self.get_prompt_embeds(prompts) + + # get image embeddings + img_latents = self.get_img_latents(text_embeds, + height, width, + num_inference_steps, + guidance_scale, + img_latents) + # decode the image embeddings + imgs = self.decode_img_latents(img_latents) + # convert decoded image to suitable PIL Image format + imgs = self.transform_imgs(imgs) + + return imgs + + + + def encode_img_latents(self, imgs): + if not isinstance(imgs, list): + imgs = [imgs] + + imgs = np.stack([np.array(img) for img in imgs], axis=0) + # scale images to the range [-1, 1] + imgs = 2 * ((imgs / 255.0) - 0.5) + imgs = torch.from_numpy(imgs).float().permute(0, 3, 1, 2) + + # encode images + img_latents_dist = self.vae.encode(imgs.to(self.device)) + # img_latents = img_latents_dist.sample() + img_latents = img_latents_dist["latent_dist"].mean.clone() + # scale images + img_latents *= 0.18215 + + return img_latents + + + def get_img_latents_similar(self, + img_latents, + text_embeds, + height=512, width=512, + num_inference_steps=50, + guidance_scale=7.5, + start_step=10): + + # set the number of inference steps for the scheduler + self.scheduler_DDIM.set_timesteps(num_inference_steps) + + if start_step > 0: + start_timestep = self.scheduler_DDIM.timesteps[start_step] + start_timesteps = start_timestep.repeat(img_latents.shape[0]).long() + + noise = torch.randn_like(img_latents) + img_latents = scheduler_DDIM.add_noise(img_latents, noise, start_timesteps) + + # use autocast for automatic mixed precision (AMP) inference + with autocast('cuda'): + for i, t in tqdm(enumerate(self.scheduler_DDIM.timesteps[start_step:])): + # do a single forward pass for both the conditional and unconditional latents + latent_model_input = torch.cat([img_latents] * 2) + + # predict noise residuals + with torch.no_grad(): + noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeds)['sample'] + + # separate predictions for unconditional and conditional outputs + noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2) + # perform guidance + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond) + + # remove the noise from the current sample i.e. go from x_t to x_{t-1} + img_latents = self.scheduler_DDIM.step(noise_pred, t, img_latents)['prev_sample'] + + return img_latents + + + def similar_imgs(self, + img, + prompt, + height=512, width=512, + num_inference_steps=50, + guidance_scale=7.5, + start_step=10): + + # get image latents + img_latents = self.encode_img_latents(img) + + if isinstance(prompt, str): + prompt = [prompt] + + text_embeds = self.get_prompt_embeds(prompt) + + img_latents = self.get_img_latents_similar(img_latents=img_latents, + text_embeds=text_embeds, + height=height, width=width, + num_inference_steps=num_inference_steps, + guidance_scale=guidance_scale, + start_step=start_step) + + imgs = self.decode_img_latents(img_latents) + imgs = self.transform_imgs(imgs) + # Clear the CUDA cache + torch.cuda.empty_cache() + + return imgs + + +# %% +device = 'cuda' + +# model_name = "dreamlike-art/dreamlike-photoreal-2.0" +model_name = "CompVis/stable-diffusion-v1-4" +# Load autoencoder +vae = AutoencoderKL.from_pretrained(model_name, + subfolder='vae').to(device) + +# Load tokenizer and the text encoder +tokenizer = CLIPTokenizer.from_pretrained(model_name, subfolder="tokenizer") +text_encoder = CLIPTextModel.from_pretrained(model_name, subfolder="text_encoder").to(device) + +# Load UNet model +unet = UNet2DConditionModel.from_pretrained(model_name, subfolder='unet').to(device) + +# Load scheduler +scheduler_LMS = LMSDiscreteScheduler(beta_start=0.00085, + beta_end=0.012, + beta_schedule='scaled_linear', + num_train_timesteps=1000) + +scheduler_DDIM = DDIMScheduler(beta_start=0.00085, + beta_end=0.012, + beta_schedule='scaled_linear', + num_train_timesteps=1000) + +# %% +model = ImageDiffusionModel(vae, tokenizer, text_encoder, unet, scheduler_LMS, scheduler_DDIM) + +prompts = ["A really giant cute pink barbie doll on the top of Burj Khalifa", + "A green, scary aesthetic dragon breathing fire near a group of heroic firefighters"] + +imgs = model.prompt_to_img(prompts) + +# %% +imgs[0] + +# %% +imgs[1] + +# %% +prompt = ["Aesthetic star wars spaceship with an aethethic background, Ultra HD, futuristic, sharp, octane render, neon"] + +imgs = model.prompt_to_img(prompt) + +imgs[0] + +# %% +# saving the image +imgs[0].save("spaceship1.png") + +# %% +# loading the image again +original_img = Image.open("spaceship1.png") +original_img + +# %% +import torch +import gc + +### If you get OOM errors, execute this cell +# del model +# Clear the CUDA cache +torch.cuda.empty_cache() +gc.collect() +torch.cuda.empty_cache() + +# %% +!nvidia-smi + +# %% +model = ImageDiffusionModel(vae, tokenizer, text_encoder, unet, scheduler_LMS, scheduler_DDIM) + +prompt = "Aesthetic star wars spaceship with an aethethic background, Ultra HD, futuristic, sharp, octane render, neon" + +imgs = model.similar_imgs(original_img, prompt, num_inference_steps=50, start_step=30) +imgs[0] + +# %% +# model = ImageDiffusionModel(vae, tokenizer, text_encoder, unet, scheduler_LMS, scheduler_DDIM) + +prompt = "Aesthetic dark star wars spaceship, Ultra HD, futuristic, sharp, octane render, neon" + +imgs = model.similar_imgs(original_img, prompt, + num_inference_steps=50, + start_step=40) +imgs[0] + +# %% + + +# %% + + +# %% + + + diff --git a/machine-learning/stable-diffusion-models/requirements.txt b/machine-learning/stable-diffusion-models/requirements.txt new file mode 100644 index 00000000..9033779d --- /dev/null +++ b/machine-learning/stable-diffusion-models/requirements.txt @@ -0,0 +1,4 @@ +diffusers +transformers +accelerate +xformers==0.0.16rc425 \ No newline at end of file From 82a1489d2b90a7a26ffcd49408054947cb533388 Mon Sep 17 00:00:00 2001 From: x4nth055 Date: Sat, 8 Apr 2023 08:59:46 +0100 Subject: [PATCH 004/147] add tictactoe game tutorial --- README.md | 1 + gui-programming/tictactoe-game/README.md | 1 + .../tictactoe-game/requirements.txt | 1 + gui-programming/tictactoe-game/tictactoe.py | 206 ++++++++++++++++++ 4 files changed, 209 insertions(+) create mode 100644 gui-programming/tictactoe-game/README.md create mode 100644 gui-programming/tictactoe-game/requirements.txt create mode 100644 gui-programming/tictactoe-game/tictactoe.py diff --git a/README.md b/README.md index c875a72c..7b6d7557 100644 --- a/README.md +++ b/README.md @@ -271,5 +271,6 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy - [How to Make a Real-Time GUI Spelling Checker in Python](https://www.thepythoncode.com/article/make-a-realtime-spelling-checker-gui-python). ([code](gui-programming/realtime-spelling-checker)) - [How to Create a GUI Hangman Game using PyGame in Python](https://www.thepythoncode.com/article/hangman-gui-game-with-pygame-in-python). ([code](gui-programming/hangman-game-gui)) - [How to Make a Tetris Game using PyGame in Python](https://www.thepythoncode.com/article/create-a-tetris-game-with-pygame-in-python). ([code](gui-programming/tetris-game)) + - [How to Build a Tic Tac Toe Game in Python](https://www.thepythoncode.com/article/make-a-tic-tac-toe-game-pygame-in-python). ([code](gui-programmming/tictactoe-game)) For any feedback, please consider pulling requests. diff --git a/gui-programming/tictactoe-game/README.md b/gui-programming/tictactoe-game/README.md new file mode 100644 index 00000000..8ae7c363 --- /dev/null +++ b/gui-programming/tictactoe-game/README.md @@ -0,0 +1 @@ +# [How to Build a Tic Tac Toe Game in Python](https://www.thepythoncode.com/article/make-a-tic-tac-toe-game-pygame-in-python) \ No newline at end of file diff --git a/gui-programming/tictactoe-game/requirements.txt b/gui-programming/tictactoe-game/requirements.txt new file mode 100644 index 00000000..231dd178 --- /dev/null +++ b/gui-programming/tictactoe-game/requirements.txt @@ -0,0 +1 @@ +pygame \ No newline at end of file diff --git a/gui-programming/tictactoe-game/tictactoe.py b/gui-programming/tictactoe-game/tictactoe.py new file mode 100644 index 00000000..5c72a43b --- /dev/null +++ b/gui-programming/tictactoe-game/tictactoe.py @@ -0,0 +1,206 @@ +import pygame +from pygame.locals import * + +pygame.init() +pygame.font.init() + +window_size = (450, 500) + +screen = pygame.display.set_mode(window_size) +pygame.display.set_caption("Tic Tac Toe") + + +class TicTacToe(): + + def __init__(self, table_size): + self.table_size = table_size + self.cell_size = table_size // 3 + self.table_space = 20 + + self.player = "X" + self.winner = None + self.taking_move = True + self.running = True + self.table = [] + for col in range(3): + self.table.append([]) + for row in range(3): + self.table[col].append("-") + + self.background_color = (255, 174, 66) + self.table_color = (50, 50, 50) + self.line_color = (190, 0, 10) + self.instructions_color = (17, 53, 165) + self.game_over_bg_color = (47, 98, 162) + self.game_over_color = (255, 179, 1) + self.font = pygame.font.SysFont("Courier New", 35) + self.FPS = pygame.time.Clock() + + + # draws table representation + def _draw_table(self): + tb_space_point = (self.table_space, self.table_size - self.table_space) + cell_space_point = (self.cell_size, self.cell_size * 2) + r1 = pygame.draw.line(screen, self.table_color, [tb_space_point[0], cell_space_point[0]], [tb_space_point[1], cell_space_point[0]], 8) + c1 = pygame.draw.line(screen, self.table_color, [cell_space_point[0], tb_space_point[0]], [cell_space_point[0], tb_space_point[1]], 8) + r2 = pygame.draw.line(screen, self.table_color, [tb_space_point[0], cell_space_point[1]], [tb_space_point[1], cell_space_point[1]], 8) + c2 = pygame.draw.line(screen, self.table_color, [cell_space_point[1], tb_space_point[0]], [cell_space_point[1], tb_space_point[1]], 8) + + + def _change_player(self): + self.player = "O" if self.player == "X" else "X" + + + # processing clicks to move + def _move(self, pos): + try: + x, y = pos[0] // self.cell_size, pos[1] // self.cell_size + if self.table[x][y] == "-": + self.table[x][y] = self.player + self._draw_char(x,y,self.player) + self._game_check() + self._change_player() + except: + print("Click inside the table only") + + + # draws character of the recent player to the selected table cell + def _draw_char(self, x, y, player): + if self.player == "O": + img = pygame.image.load("images/Tc-O.png") + elif self.player == "X": + img = pygame.image.load("images/Tc-X.png") + img = pygame.transform.scale(img, (self.cell_size, self.cell_size)) + screen.blit(img, (x * self.cell_size, y * self.cell_size, self.cell_size, self.cell_size)) + + + # instructions and game-state messages + def _message(self): + if self.winner is not None: + screen.fill(self.game_over_bg_color, (130, 445, 193, 35)) + msg = self.font.render(f'{self.winner} WINS!!', True, self.game_over_color) + screen.blit(msg,(144,445)) + elif not self.taking_move: + screen.fill(self.game_over_bg_color, (130, 445, 193, 35)) + instructions = self.font.render('DRAW!!', True, self.game_over_color) + screen.blit(instructions,(165,445)) + else: + screen.fill(self.background_color, (135, 445, 188, 35)) + instructions = self.font.render(f'{self.player} to move', True, self.instructions_color) + screen.blit(instructions,(135,445)) + + + def _game_check(self): + # vertical check + for x_index, col in enumerate(self.table): + win = True + pattern_list = [] + for y_index, content in enumerate(col): + if content != self.player: + win = False + break + else: + pattern_list.append((x_index, y_index)) + if win == True: + self._pattern_strike(pattern_list[0],pattern_list[-1],"ver") + self.winner = self.player + self.taking_move = False + break + + # horizontal check + for row in range(len(self.table)): + win = True + pattern_list = [] + for col in range(len(self.table)): + if self.table[col][row] != self.player: + win = False + break + else: + pattern_list.append((col, row)) + if win == True: + self._pattern_strike(pattern_list[0],pattern_list[-1],"hor") + self.winner = self.player + self.taking_move = False + break + + # left diagonal check + for index, row in enumerate(self.table): + win = True + if row[index] != self.player: + win = False + break + if win == True: + self._pattern_strike((0,0),(2,2),"left-diag") + self.winner = self.player + self.taking_move = False + + # right diagonal check + for index, row in enumerate(self.table[::-1]): + win = True + if row[index] != self.player: + win = False + break + if win == True: + self._pattern_strike((2,0),(0,2),"right-diag") + self.winner = self.player + self.taking_move = False + + # blank table cells check + blank_cells = 0 + for row in self.table: + for cell in row: + if cell == "-": + blank_cells += 1 + if blank_cells == 0: + self.taking_move = False + + + # strikes a line to winning patterns if already has + def _pattern_strike(self, start_point, end_point, line_type): + # gets the middle value of the cell + mid_val = self.cell_size // 2 + + # for the vertical winning pattern + if line_type == "ver": + start_x, start_y = start_point[0] * self.cell_size + mid_val, self.table_space + end_x, end_y = end_point[0] * self.cell_size + mid_val, self.table_size - self.table_space + + # for the horizontal winning pattern + elif line_type == "hor": + start_x, start_y = self.table_space, start_point[-1] * self.cell_size + mid_val + end_x, end_y = self.table_size - self.table_space, end_point[-1] * self.cell_size + mid_val + + # for the diagonal winning pattern from top-left to bottom right + elif line_type == "left-diag": + start_x, start_y = self.table_space, self.table_space + end_x, end_y = self.table_size - self.table_space, self.table_size - self.table_space + + # for the diagonal winning pattern from top-right to bottom-left + elif line_type == "right-diag": + start_x, start_y = self.table_size - self.table_space, self.table_space + end_x, end_y = self.table_space, self.table_size - self.table_space + + # draws the line strike + line_strike = pygame.draw.line(screen, self.line_color, [start_x, start_y], [end_x, end_y], 8) + + + def main(self): + screen.fill(self.background_color) + self._draw_table() + while self.running: + self._message() + for self.event in pygame.event.get(): + if self.event.type == pygame.QUIT: + self.running = False + + if self.event.type == pygame.MOUSEBUTTONDOWN: + if self.taking_move: + self._move(self.event.pos) + + pygame.display.flip() + self.FPS.tick(60) + + +if __name__ == "__main__": + g = TicTacToe(window_size[0]) + g.main() \ No newline at end of file From b0c9a4691ba267243a4377b5b929e8af3c12ce9a Mon Sep 17 00:00:00 2001 From: x4nth055 Date: Sat, 8 Apr 2023 09:00:26 +0100 Subject: [PATCH 005/147] fix error in README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7b6d7557..a2022e4e 100644 --- a/README.md +++ b/README.md @@ -271,6 +271,6 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy - [How to Make a Real-Time GUI Spelling Checker in Python](https://www.thepythoncode.com/article/make-a-realtime-spelling-checker-gui-python). ([code](gui-programming/realtime-spelling-checker)) - [How to Create a GUI Hangman Game using PyGame in Python](https://www.thepythoncode.com/article/hangman-gui-game-with-pygame-in-python). ([code](gui-programming/hangman-game-gui)) - [How to Make a Tetris Game using PyGame in Python](https://www.thepythoncode.com/article/create-a-tetris-game-with-pygame-in-python). ([code](gui-programming/tetris-game)) - - [How to Build a Tic Tac Toe Game in Python](https://www.thepythoncode.com/article/make-a-tic-tac-toe-game-pygame-in-python). ([code](gui-programmming/tictactoe-game)) + - [How to Build a Tic Tac Toe Game in Python](https://www.thepythoncode.com/article/make-a-tic-tac-toe-game-pygame-in-python). ([code](gui-programming/tictactoe-game)) For any feedback, please consider pulling requests. From 51ac5aa5f79eeb251a537bdd23285c205c9db61f Mon Sep 17 00:00:00 2001 From: x4nth055 Date: Thu, 13 Apr 2023 08:49:48 +0100 Subject: [PATCH 006/147] add depth2img similar image generation tutorial --- README.md | 1 + ...ffusion_Depth2img_PythonCodeTutorial.ipynb | 4802 +++++++++++++++++ .../depth2image-stable-diffusion/README.md | 1 + .../depth2image-stable-diffusion/image11.png | Bin 0 -> 968011 bytes .../depth2image-stable-diffusion/image12.png | Bin 0 -> 1060656 bytes .../depth2image-stable-diffusion/image15.png | Bin 0 -> 984414 bytes .../depth2image-stable-diffusion/image16.png | Bin 0 -> 448475 bytes .../depth2image-stable-diffusion/image3.png | Bin 0 -> 948154 bytes .../requirements.txt | 5 + 9 files changed, 4809 insertions(+) create mode 100644 machine-learning/depth2image-stable-diffusion/Img2imgGenerationStableDiffusion_Depth2img_PythonCodeTutorial.ipynb create mode 100644 machine-learning/depth2image-stable-diffusion/README.md create mode 100644 machine-learning/depth2image-stable-diffusion/image11.png create mode 100644 machine-learning/depth2image-stable-diffusion/image12.png create mode 100644 machine-learning/depth2image-stable-diffusion/image15.png create mode 100644 machine-learning/depth2image-stable-diffusion/image16.png create mode 100644 machine-learning/depth2image-stable-diffusion/image3.png create mode 100644 machine-learning/depth2image-stable-diffusion/requirements.txt diff --git a/README.md b/README.md index a2022e4e..6b67906b 100644 --- a/README.md +++ b/README.md @@ -81,6 +81,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy - [How to Perform Image Segmentation using Transformers in Python](https://www.thepythoncode.com/article/image-segmentation-using-huggingface-transformers-python). ([code](machine-learning/image-segmentation-transformers)) - [How to Fine Tune ViT for Image Classification using Huggingface Transformers in Python](https://www.thepythoncode.com/article/finetune-vit-for-image-classification-using-transformers-in-python). ([code](machine-learning/finetuning-vit-image-classification)) - [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)) - [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). diff --git a/machine-learning/depth2image-stable-diffusion/Img2imgGenerationStableDiffusion_Depth2img_PythonCodeTutorial.ipynb b/machine-learning/depth2image-stable-diffusion/Img2imgGenerationStableDiffusion_Depth2img_PythonCodeTutorial.ipynb new file mode 100644 index 00000000..cbff7872 --- /dev/null +++ b/machine-learning/depth2image-stable-diffusion/Img2imgGenerationStableDiffusion_Depth2img_PythonCodeTutorial.ipynb @@ -0,0 +1,4802 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "VHvuI4dpcxYM" + }, + "source": [ + "# Part 1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OHttLFAGRCX0" + }, + "source": [ + "Note: Before running the code, make sure you upload the images you want to edit to Colab." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tEWYSTAiPUgA" + }, + "outputs": [], + "source": [ + "%pip install --quiet --upgrade diffusers transformers scipy ftfy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "krtAPL7tKA-J" + }, + "outputs": [], + "source": [ + "%pip install --quiet --upgrade accelerate" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "U7eXBBRZPqel" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from tqdm import tqdm\n", + "from PIL import Image\n", + "\n", + "import torch\n", + "from torch import autocast\n", + "\n", + "from transformers import CLIPTextModel, CLIPTokenizer\n", + "from transformers import DPTForDepthEstimation, DPTFeatureExtractor\n", + "\n", + "from diffusers import AutoencoderKL, UNet2DConditionModel\n", + "from diffusers.schedulers.scheduling_pndm import PNDMScheduler" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0B1NCql5jkmo" + }, + "source": [ + "## Model definition" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fK0Cu4xnPe4E" + }, + "outputs": [], + "source": [ + "class DiffusionPipeline:\n", + "\n", + " def __init__(self, \n", + " vae, \n", + " tokenizer, \n", + " text_encoder, \n", + " unet, \n", + " scheduler):\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.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + " \n", + " \n", + " def get_text_embeds(self, text):\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):\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", + "\n", + " def decode_img_latents(self, img_latents):\n", + " img_latents = 1 / self.vae.config.scaling_factor * img_latents\n", + " with torch.no_grad():\n", + " img = self.vae.decode(img_latents).sample\n", + " \n", + " img = (img / 2 + 0.5).clamp(0, 1)\n", + " img = img.cpu().permute(0, 2, 3, 1).float().numpy()\n", + " return img\n", + "\n", + "\n", + "\n", + " def transform_img(self, img):\n", + " # scale images to the range [0, 255] and convert to int\n", + " img = (img * 255).round().astype('uint8')\n", + " # convert to PIL Image objects\n", + " img = [Image.fromarray(i) for i in img]\n", + " return img\n", + "\n", + "\n", + " def encode_img_latents(self, img, latent_timestep):\n", + " if not isinstance(img, list):\n", + " img = [img]\n", + " \n", + " img = np.stack([np.array(i) for i in img], axis=0)\n", + " # scale images to the range [-1, 1]\n", + " img = 2 * ((img / 255.0) - 0.5)\n", + " img = torch.from_numpy(img).float().permute(0, 3, 1, 2)\n", + " img = img.to(self.device)\n", + "\n", + " # encode images\n", + " img_latents_dist = self.vae.encode(img)\n", + " img_latents = img_latents_dist.latent_dist.sample()\n", + " \n", + " # scale images\n", + " img_latents = self.vae.config.scaling_factor * img_latents\n", + " \n", + " # add noise to the latents\n", + " noise = torch.randn(img_latents.shape).to(self.device)\n", + " img_latents = self.scheduler.add_noise(img_latents, noise, latent_timestep)\n", + "\n", + " return img_latents\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2uME1Fs7Vq8y" + }, + "outputs": [], + "source": [ + "class Depth2ImgPipeline(DiffusionPipeline):\n", + " def __init__(self, \n", + " vae, \n", + " tokenizer, \n", + " text_encoder, \n", + " unet, \n", + " scheduler, \n", + " depth_feature_extractor, \n", + " depth_estimator):\n", + " \n", + " super().__init__(vae, tokenizer, text_encoder, unet, scheduler)\n", + "\n", + " self.depth_feature_extractor = depth_feature_extractor\n", + " self.depth_estimator = depth_estimator\n", + "\n", + "\n", + " def get_depth_mask(self, img):\n", + " if not isinstance(img, list):\n", + " img = [img]\n", + "\n", + " width, height = img[0].size\n", + " \n", + " # pre-process the input image and get its pixel values\n", + " pixel_values = self.depth_feature_extractor(img, return_tensors=\"pt\").pixel_values\n", + "\n", + " # use autocast for automatic mixed precision (AMP) inference\n", + " with autocast('cuda'):\n", + " depth_mask = self.depth_estimator(pixel_values).predicted_depth\n", + " \n", + " # get the depth mask\n", + " depth_mask = torch.nn.functional.interpolate(depth_mask.unsqueeze(1),\n", + " size=(height//8, width//8),\n", + " mode='bicubic',\n", + " align_corners=False)\n", + " \n", + " # scale the mask to range [-1, 1]\n", + " depth_min = torch.amin(depth_mask, dim=[1, 2, 3], keepdim=True)\n", + " depth_max = torch.amax(depth_mask, dim=[1, 2, 3], keepdim=True)\n", + " depth_mask = 2.0 * (depth_mask - depth_min) / (depth_max - depth_min) - 1.0\n", + " depth_mask = depth_mask.to(self.device)\n", + "\n", + " # replicate the mask for classifier free guidance \n", + " depth_mask = torch.cat([depth_mask] * 2)\n", + " return depth_mask\n", + "\n", + "\n", + "\n", + " \n", + " def denoise_latents(self, \n", + " img,\n", + " prompt_embeds,\n", + " depth_mask,\n", + " strength,\n", + " num_inference_steps=50,\n", + " guidance_scale=7.5,\n", + " height=512, width=512):\n", + " \n", + " # clip the value of strength to ensure strength lies in [0, 1]\n", + " strength = max(min(strength, 1), 0)\n", + "\n", + " # compute timesteps\n", + " self.scheduler.set_timesteps(num_inference_steps)\n", + "\n", + " init_timestep = int(num_inference_steps * strength)\n", + " t_start = num_inference_steps - init_timestep\n", + " \n", + " timesteps = self.scheduler.timesteps[t_start: ]\n", + " num_inference_steps = num_inference_steps - t_start\n", + "\n", + " latent_timestep = timesteps[:1].repeat(1)\n", + "\n", + " latents = self.encode_img_latents(img, latent_timestep)\n", + "\n", + " # use autocast for automatic mixed precision (AMP) inference\n", + " with autocast('cuda'):\n", + " for i, t in tqdm(enumerate(timesteps)):\n", + " latent_model_input = torch.cat([latents] * 2)\n", + " latent_model_input = torch.cat([latent_model_input, depth_mask], dim=1)\n", + " \n", + " # predict noise residuals\n", + " with torch.no_grad():\n", + " noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds)['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", + " def __call__(self, \n", + " prompt, \n", + " img, \n", + " strength=0.8,\n", + " num_inference_steps=50,\n", + " guidance_scale=7.5,\n", + " height=512, width=512):\n", + "\n", + "\n", + " prompt_embeds = self.get_prompt_embeds(prompt)\n", + "\n", + " depth_mask = self.get_depth_mask(img)\n", + "\n", + " latents = self.denoise_latents(img,\n", + " prompt_embeds,\n", + " depth_mask,\n", + " strength,\n", + " num_inference_steps,\n", + " guidance_scale,\n", + " height, width)\n", + "\n", + " img = self.decode_img_latents(latents)\n", + "\n", + " img = self.transform_img(img)\n", + " \n", + " return img\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IQ0YObQCjbUU" + }, + "source": [ + "## Create instance of the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SjVpjMXOPe6h" + }, + "outputs": [], + "source": [ + "device = 'cuda'\n", + "\n", + "# Load autoencoder\n", + "vae = AutoencoderKL.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='vae').to(device)\n", + "\n", + "# Load tokenizer and the text encoder\n", + "tokenizer = CLIPTokenizer.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='tokenizer')\n", + "text_encoder = CLIPTextModel.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='text_encoder').to(device)\n", + "\n", + "# Load UNet model\n", + "unet = UNet2DConditionModel.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='unet').to(device)\n", + "\n", + "# Load scheduler\n", + "scheduler = PNDMScheduler(beta_start=0.00085, \n", + " beta_end=0.012, \n", + " beta_schedule='scaled_linear', \n", + " num_train_timesteps=1000)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "H5FipQQOKbiS", + "outputId": "ff6719d7-cc17-4e3b-ff60-9342fe911af1" + }, + "outputs": [], + "source": [ + "# Load DPT Depth Estimator\n", + "depth_estimator = DPTForDepthEstimation.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='depth_estimator')\n", + "\n", + "# Load DPT Feature Extractor\n", + "depth_feature_extractor = DPTFeatureExtractor.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='feature_extractor')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IgWXvP7TPe8p" + }, + "outputs": [], + "source": [ + "depth2img = Depth2ImgPipeline(vae, \n", + " tokenizer, \n", + " text_encoder, \n", + " unet, \n", + " scheduler,\n", + " depth_feature_extractor,\n", + " depth_estimator)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "u3End173jf5D" + }, + "source": [ + "## Examples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 497 + }, + "id": "PvdI6VVnPfAE", + "outputId": "6ddb97f9-ef83-4430-eecb-3f156fe0c3dd" + }, + "outputs": [], + "source": [ + "import urllib.parse as parse\n", + "import os\n", + "import requests\n", + "\n", + "# a function to determine whether a string is a URL or not\n", + "def is_url(string):\n", + " try:\n", + " result = parse.urlparse(string)\n", + " return all([result.scheme, result.netloc, result.path])\n", + " except:\n", + " return False\n", + "\n", + "\n", + "# a function to load an image\n", + "def load_image(image_path):\n", + " if is_url(image_path):\n", + " return Image.open(requests.get(image_path, stream=True).raw)\n", + " elif os.path.exists(image_path):\n", + " return Image.open(image_path)\n", + "\n", + "\n", + "url = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n", + "img = load_image(url)\n", + "img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 514 + }, + "id": "B1FRHDHtFZ2x", + "outputId": "b10b39ee-4ba5-4769-920f-1ea71c5de282" + }, + "outputs": [], + "source": [ + "depth2img(\"two tigers\", img)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 529 + }, + "id": "WMLYkwDoGKUk", + "outputId": "8e5d015f-187c-49b1-f863-4fba0abd2f45" + }, + "outputs": [], + "source": [ + "img = load_image(\"image16.png\")\n", + "img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + }, + "id": "bjBvAVZVGKWr", + "outputId": "ad13f40d-de3a-480c-843c-b4e481a8b7a2" + }, + "outputs": [], + "source": [ + "prompt = \"A boulder with gemstones falling down a hill\"\n", + "depth2img(prompt, img)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 529 + }, + "id": "6eTFNAewGKZJ", + "outputId": "4f906958-c76a-4cfe-dc2a-ca60272d1d08" + }, + "outputs": [], + "source": [ + "img = load_image(\"image11.png\").resize((512, 512))\n", + "img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DlJv2-1eN0da", + "outputId": "a879b4b6-9c03-47be-8834-494365cb58b3" + }, + "outputs": [], + "source": [ + "import gc\n", + "import torch\n", + "\n", + "# Run this cell if you get OOM - Out of Memory - errors\n", + "torch.cuda.empty_cache()\n", + "gc.collect()\n", + "torch.cuda.empty_cache()\n", + "gc.collect()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AhvSACo4N6g2", + "outputId": "6292f90c-aaf7-4c73-9384-d5e59bf59b5b" + }, + "outputs": [], + "source": [ + "# just to check GPU memory\n", + "!nvidia-smi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + }, + "id": "Uy0ziM_xGKbP", + "outputId": "aa03b031-d5f7-48fa-ad48-22e7edcee10a" + }, + "outputs": [], + "source": [ + "prompt = \"A futuristic city on the edge of space, a robotic bionic singularity portal, sci fi, utopian, tim hildebrandt, wayne barlowe, bruce pennington, donato giancola, larry elmore\"\n", + "depth2img(prompt, img)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RryrZSIucvee" + }, + "source": [ + "# Part 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "37BGMrgkbgOX" + }, + "outputs": [], + "source": [ + "import torch\n", + "import requests\n", + "from PIL import Image\n", + "from diffusers import StableDiffusionDepth2ImgPipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1jqWIRGBbgR8" + }, + "outputs": [], + "source": [ + "pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(\n", + " \"stabilityai/stable-diffusion-2-depth\",\n", + " torch_dtype=torch.float16,\n", + ").to(\"cuda\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a11H5bbvfjs1" + }, + "source": [ + "## Impact of negative prompt example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 382 + }, + "id": "7_HoqLyqrlta", + "outputId": "26b03e01-d07d-4a6f-e9f5-642ddbb299f2" + }, + "outputs": [], + "source": [ + "img = load_image(\"https://images.pexels.com/photos/406152/pexels-photo-406152.jpeg?auto=compress&cs=tinysrgb&w=600\")\n", + "img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 409, + "referenced_widgets": [ + "b168918d31b34653897402751e9bbecc", + "ed9bd4313a1a477a820abec0b6f911f6", + "889529d86c064f73844e8074765f2cf0", + "95ebc05ee7f640af86a54aadcd40ddbe", + "a654808c82cc4672a19728ac22f26e5e", + "f228ea9baedf457985c032deab6b96df", + "665e483725974558b1712ef1fa235a7d", + "2540be75baeb49769f279ad95778a857", + "b2a6584f8d6f4c2ab221f8f90a1a560a", + "ed5435b53cb646d080918c6720e222bd", + "cd0e13f6e6dc444c80f7464747846e21" + ] + }, + "id": "mBngqY_Q7zRb", + "outputId": "8c7a6992-514c-4bbf-dcb3-4b6f5774d311" + }, + "outputs": [], + "source": [ + "prompt = \"A salad with tomatoes and guanas chips mixed with ketchup and mustard and bay leaf and guacamole and onions and ketchup and luscious patty with sesame seeds and cashews and onions and ketchup, ethereal,\"\n", + "pipe(prompt=prompt, image=img, negative_prompt=None, strength=0.7).images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 409, + "referenced_widgets": [ + "71f962cddf284a1fbc5f882dc61d673a", + "2ad534b30374491ca6b99f035efa1004", + "db69c9d0fd214da8b42b854d71b87f54", + "6041b74d03bd48f0adea3662f0b9bc27", + "8ec8c55bace044fd885ed6ada6c3cd23", + "b3c65e0ad46a4fdcb639e3c98983f1b7", + "7d12e7d3738f4094a2c1d7a56a03c928", + "5e4d7e7e89cf4b8e9cab8d34d88d662e", + "5066ac8781ec4f00b27ec6be27eb8506", + "a3a361916b864a3cadb650a563842b16", + "279e8805d1a8468496bf091b2854683e" + ] + }, + "id": "aj197V-_RYWl", + "outputId": "038b263c-01d7-4e7d-f49b-5b26d3661152" + }, + "outputs": [], + "source": [ + "prompt = \"A salad with tomatoes and guanas chips mixed with ketchup and mustard and bay leaf and guacamole and onions and ketchup and luscious patty with sesame seeds and cashews and onions and ketchup, ethereal,\"\n", + "n_prompt = \"ugly, deformed, not detailed, bad architectures, blurred, too much blurred, motion blur\"\n", + "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.7).images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 785 + }, + "id": "qnPTkMvOQeM0", + "outputId": "ce7d32c7-fac1-4df7-b9e2-ea9cedd12b5a" + }, + "outputs": [], + "source": [ + "img = load_image(\"image15.png\")\n", + "img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "3f2e54c7185e4251962269369e30a9ec", + "f037d2a0f68e4647ab8d1e3c278aac8f", + "77bdcf219f2e4aa8ba1725329011f2bc", + "bf5ffbefc35449659cc9aae956e9a292", + "6186ca097ee64f289f1becdd180cf1aa", + "f50555e1470b4a8e95ea430871af77cf", + "9f6a32585a514034975b71b2954b639f", + "41c7ad82fa3f4a24a13a09f384e8fd51", + "3cd041ccf57c46ff91b714b7c572f581", + "7d60b538a6e248369d2df053e9548a14", + "a2bc87dd43e14ee0abba026d57cb349f" + ] + }, + "id": "8thR1RQ08HPI", + "outputId": "5b9c661a-e291-4d68-8fde-5bb6f081d8f9" + }, + "outputs": [], + "source": [ + "prompt = \"Last remaining old man on earth\"\n", + "n_prompt = \"bad anatomy, ugly, wrinkles\"\n", + "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.7).images[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_HH2z2bXfaYV" + }, + "source": [ + "## Changing strength - Futuristic city example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cNIgur9_91-8" + }, + "outputs": [], + "source": [ + "img = load_image(\"image11.png\")\n", + "img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "95b5dce9d5e140e78849c5aae374f27c", + "606165cb16e74f20a46035ca4ba95e5c", + "a83bca5872324f19a6a024c536141795", + "0f79744697af4a53835dee31a1b4f748", + "7dd5be64da7a41f8875765186bdd0b10", + "8dccd6692afc41caa28072f35667bfac", + "9e67bf17f1d64768954b47e3e1001b41", + "70b8ac7c48d14a08b0d0eb840d37d410", + "650ea058ba304da9b8febd6a72e10260", + "f3d5fc14999740b2bb7e4c21d1413b37", + "b274cfa1678e49938e163e2d17d7f179" + ] + }, + "id": "aQBJTWJG92Mr", + "outputId": "d612403b-593c-4796-af26-bbd61e1c1ee6" + }, + "outputs": [], + "source": [ + "prompt = \"A futuristic city\"\n", + "pipe(prompt=prompt, image=img, negative_prompt=None, strength=0.7).images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "c33aa07ac5a741adbef140e57b9702e2", + "3b4ca1a9965f4aeabff645cc5d6aa92e", + "71d6d4c2b61d4739ae78f77f4bababc3", + "80e10875f83e4889ad1840255ef9c49d", + "e7da8d3d63f740968550b2852880ccc1", + "6f02e4b70ac145a1a9b0d9a97f8a0d6b", + "61e7f8494b86497fbe3d96f37206d930", + "fe00c60d6a684ade8f1faf85cd0a825e", + "89174c17b503496ca0652319664f6084", + "8d89224fff864932a91dc1d1545e1ea6", + "07d4c54ed84242068b64353eec6593fa" + ] + }, + "id": "bbgJ1lQw92PO", + "outputId": "c35afeb3-e07f-406e-9088-2b032292e405" + }, + "outputs": [], + "source": [ + "prompt = \"Futuristic city, modern, highly detailed, aesthetic, octane render, 8K, UHD, photoshopped\"\n", + "n_prompt = \"ugly, deformed, not detailed, bad architectures, blurred, too much blurred, motion blur\"\n", + "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.7).images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "cdcc3550ed8340ebb2e8f0f903500c9c", + "42b12e19632c48e8809ccc1dfde61417", + "acc1e1e48c2b4c0a8c765978c5211c4a", + "b86219c801bb4d2e8395ce17518fa432", + "3ff6eda7d146478880b0d26670caa23c", + "7a282eb45d6f4d5da0c6f5241b023b32", + "af74c20beba94d84b5cf97818b6bd191", + "fff5255694d74756a08b556c51affbd9", + "890360c4bd374d74943cb359ba838ab7", + "96aed170045943a28f42d6b825c66cf3", + "2cc5cba3a49842fab50519d9968a4e53" + ] + }, + "id": "kjubi4RD92Sn", + "outputId": "3717a51a-63b1-45a9-df8a-f096aa533ded" + }, + "outputs": [], + "source": [ + "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.1).images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "5d7dd248760841d9b95a356cdcdd6797", + "e9454ab1e9034e908b84be5c6bf6f069", + "970fd0cc56d748ccb2ee2287b33ec5fa", + "52ee43cf76e948e4944d0539462e41f4", + "9d8254d8917c4874b980ae8e4c72b324", + "7f58a5010d77407ca459b1d7209e9280", + "af2c00007cd34322be1faf9aa4925e2d", + "75193c4a20844ca2988c9f9a3ffc66c5", + "c9ca13d641734c4e956555fcbfe2156a", + "d97e3dc3965247c3b5f19925e1a481f2", + "6c4f2c3e26614dce84bc1bf63b1e263f" + ] + }, + "id": "fHqLC7NGALi9", + "outputId": "96b0fda0-40a3-4d7a-f702-19c48230c2c9" + }, + "outputs": [], + "source": [ + "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.5).images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "11fb8904566f4751beffc60b2785b97e", + "e9797440d4904681b6d32ba13e38cf69", + "a2d89cde3ec1423798f3591a2aadbaef", + "e9de6d1bfc2541fbaf1c5315be405904", + "07a62b7083044ede9ec79902d595e4b9", + "23e3dc5b455d445a957cf44e6c913b30", + "fd80da5cf56c463ca277dafa9792c922", + "62dbb831c6af4a369214dbe8c8a41c77", + "bb79f73fb99f4d5885400f7f69c8edcc", + "b6001677e22a4bf1b3565b55bebf0be1", + "b3dba2bfc0c9401a908c7a25a8fbe952" + ] + }, + "id": "dqMjMcJMARNr", + "outputId": "b07157f3-ece3-428b-b595-53f2a4304898" + }, + "outputs": [], + "source": [ + "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.9).images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "7174c6d2b8844dde9ab72574b22b9767", + "3f267dc1656b4c2e9a0496da73081b15", + "fd926a30faed41618593cdb01204d0cc", + "708184f7d3b240a38f17656f8bbfd8ae", + "a9651ab87eae4180a9729b7e28f11376", + "061de01186f440bea0491134ff347c86", + "15b2d929ce0a4c70a156302db442e1a9", + "4c55892c8dd3450a9904bce9e9b80057", + "02dae045c1e7433cbdd872206d35e2f4", + "d75aad33f36b432c8175e076ce4cc99e", + "a3073940c26743b79c7477cbc92cb8e0" + ] + }, + "id": "HRkexFAEALls", + "outputId": "0905b122-6f11-41c2-8982-6167973e70fd" + }, + "outputs": [], + "source": [ + "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=1).images[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IXvOug64fM6H" + }, + "source": [ + "## Article beginning examples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 785 + }, + "id": "HhA7oF_OA6SY", + "outputId": "66afe568-50ff-4bc1-fe07-ca1807fec2bc" + }, + "outputs": [], + "source": [ + "img = load_image(\"image12.png\")\n", + "img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817, + "referenced_widgets": [ + "f7214d3edc9c4b38af73090d49d89ddf", + "0e19b13015304e75abe29a63bd8a9052", + "c2bf3e4561894b4f98047c14c8c6fcaf", + "3758340ddeb543a9854df0e5502725eb", + "eb34aef46c21431bbca887976b630a88", + "253c99a742054b8aa6f2e8e20093cfce", + "13263d08c8324e46a5661cca111e6bb4", + "91cb9127553d41719924473661d25b51", + "040ee5bc127a48a3b3a8727aefac4ae8", + "92ef2a1653944c10b5515f618811f749", + 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