{ "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", "52403e4bc442416fac24e0880f2a3faf", "38eb1d06f0094645944b5d305b575927", "4e264753749d48a8aab85b9f8fe30aba", "014e701444c54eee97d0b36ea38344ab", "b57cb9d64aa44f748fc6cffc6f95f5a3", "335f1c1d85ec4723b8796b79d518c7be", "e9ef39e82cf645e396daea04a1e178f1", "97570891602f4963bdfaacd309438ca7", "036af165dae748a19a3691dbe74e7a1a", "f9f461994c7e41aba3124dd5ee0b1525", "3e7b34f2b1764dbb82caaf245771abc0", "02f92b3266a54cc1885140f1a378d784", "d504edae2a2f4beb851928ca770b2701", "93d2d9e917d84072ae401952c12904bf", "636973b7927b44bbaac28737227e7599", "9a3d9f15c9b8495498d8910dc60d9fc0", "aec13914c37649feb868b8f531a07a83", "4b4b8aee32724ff5b0a40f9af956fe72", "b3967f91f621495cb1715427fcc57011", "d5b65c5e85704b02be5a0fa2ab7569fa", "fd3e1648c2b84f5f81cd7ee3d2fbc4c0", "25d1091281ad407c833a36c7e421e1e2", "15c1713762124e828b20f54684e496d0", "e16487cbe8d74b5baad0d41dbdeeedbb", "d4b746bc99074570a82175ef438cfb10", 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"2b5e8dcb60ee4f65af5505ffa7ea3ae2", "9b27785afb0243f5b2298cb272c510e2", "6ba872bd45c34e6c8202cc3722dc9285", "4d73b627ecc74bf7ad18f5effa245097", "35bd707e26044bd1a361c2fc2c11ea29", "ea8e9b50f98c43b58d7faccbfd6821cb", "940a28141ae14329831b67e91ff1091a", "f1f865efa3a6448ea9c930a1f08c50bf", "ede9d9c5d44146849e44c3b1476299eb", "d8424b9018444e088fe5908db8a9db12", "f00bbbb986ed49e79560422ba4c379c8", "108208309c4b47aa8eee3a1321fc81ed", "d0c00b0f105741f9a155e941dd5d704f", "16e7c873157c444bb0ca1b92e85b4701", "4f4bfc7bd24148e6ac524b57cc6d8020", "2a8de4ddaf4747ceb0a153851cd765ca", "c3e33bee752849c692d00ceabd561feb", "97dcb5d933594743aea62d09aa27d30d", "f17e8c0187db4242af4a803aed44fde9", "483e47a822fe43d6ae4c114bfbb8be8a", "c905c8d69bcb404aa39ca499c96549e3", "bd5c5827932b4dabbac38adb33544e48", "dddba90edf87434aa404eea38eaa63f2", "a32f66c9b59e4ae2bca41c1c582119e7", "e8cbb2d5adae4b439639631ea833be72", "06f53eb5d18941d8a947bcb21676e9c7", "455bc344fe60421da1ac931657cbd162", "88491458b25e4ae5a66afeaae7f76c12", "32bb55abea4f4ea2a7ea8a28a45eeb16", "90d2a57096964b7a9e2aef14ec54e2a7", "a526959014a644cba47969ebba62fa7c", "ceb41aa2ac6c4c1982598a5085f7a977", "8dd1b3d2ce124d2a92d69bb1897a078e", "c7eafdcd51dc419c99b3248093fbfbff", "5eca9f78607349a9893b0c88f3c53181", "719559db7a7943fbb823795a1697a10c", "445925a5c9da455c90fd9225ab521c1f", "2898c9af6aea4c79a4f9e89e24275083", "c34ca1ea918344188c3241400aa3e775", "dd9945400a2c413589af32550427f92d", "8328f2c010f4426fbfef22482701f516", "edaf046faeb8459bbe937cae180bfc4e", "8cb0f3ae820947a5984dd422b33776dd", "ea6ed2a4fd9c49579f25b2f0e9af3f3c", "41b442152042436ea0703d3c1181824d", "f3af911e563d47eca89517bdc434c911", "b5462a2b462147899d82f6b4617cf781", "d84093973fe84b06bb05f125ab7a11bb", "3cdb72138ffe4261a6cfcd9bcf4776c4", "3bdc5d0f6e324afaae6d5a4aabff98d2", "18a72653fc284d29be02e4d2a477ecdd", "ff1597bd843b4770b93ffa71f0b05da4", "b3bd2d853e02483080ff7dfbd5b448e0", "55f31f27ce954a0c8c0d1e63938c7bc0", "40ef1d0b44164265bea4eea32ce1b907", "3da34455151b42319fc32e56afeb7959", "1bf842268bf64cadbf2bda6da0d4a690", "452c129fabca48efa832b81481703bab", "6e327baf7a9144fcaff7c6317d018862", "72d25c37112c403eaf801833a03a47b2", "8c4e2621ec244a2db21262a8ed0e4ab7", "965fb38eae6a45678d87fc3e24eb5241", "0da3c6cd22da482c88af347dcbdf0d8e", "f7dd7600e3374757bdb1dad3d2e29d4e", "dffc9bcfd178487e93b34f91eae3b8e8", "7016696c087c4b3d85d5928cca064add", "f4accfef05c24411bbd0fae1f7fbf7cb", "941873b1d19b4c2da615ed461fc6cbc7", "6ea8901001af4f2aa0039b18f5e758a4", "ecc46cbc78fe4d9f8af50970839d3891", "7301b1053b6446da9756caeb59d5be8b", "94fba57a53d04861ab5a889193d27f83", "aafa0b9b142a44f79368f411710ea55c", "2aeb8135603c46c1aa80e2c3580b897f", "a2630966b93643e99721ea631339d088", "6ca84bcd6bea4a92a80d50351aa1be4f", "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, "referenced_widgets": [ "6846be1897c64311a10d421483131bf9", "147cd6ad4d4c4fca82a7a531684ffd2a", "6f88f6e516064ed1a82e1d69742d324c", "9998dc803aec438ab2bbdcb1edb8ab4e", "67f164f2226d430eb0c2cd3c63d14368", "4fbfe981703e4365a6e944cb9f3913d5", "ce5e2b3caef7421da3383cbdfc5b271f", "f1a22eae63cb4eb982a02ccd11422014", "aac46407f5d74ebbb6a58b10de9b86f4", "713f6c4e65a5418481d59fb756bd043c", "365b7759e93949dd891bacc424e797f3", "9e9bd7cc85b04308b20943578ad51fa7", "743a58e2430947808aac30953973ec71", "600acc1527c4482fb7307f2d423a0a01", "84f1832335f44d73b6a1a588f0e79a11", "03264d2988b849369f26e62a0a010d51", "a56166c024494356bc1b9a9e9a434c86", "e404d2f75cdc4a9397c82bca53ca3fad", "f16aa25f9d2b49cc87cde26cf7dc316f", "a263feafcec44c2eab4150af170d670a", "e90005c8a5664c6f80e37268666f1a9f", "783dca59f6a54589b6fb848cb13c5f3f", "72fb9eafc22a4533a62e3189f3b65b04", "febabb96db10490f835e4c5f6512ea61", "5fc991cde70546d3a665982956cca6b5", "f5e6af9d73ed4f3d9726bffba48540ee", "7027773c6c484afa9ee32448a320f948", "834aa09c8e984c819849676acdf25dae", "6224c6c1d4b145ed86564e7d62fbe4c2", "2dc4775bcef749b7bbbb3f71d714211c", "838cb3152f3c48ec814de02def9b75f2", "0a6ed397228f4a1fafe9dfecc4c39ef6", "e2eaf1b78df545feb426d75ec556f8a3", "db3e2ef136864292b6cc51ede3713a4d", "554525ce609044b68804e97c6b45c510", "97dfae674a3b415d9015760abea61968", "e4394ac60eef460e8f37fac24327ab77", "ffd1b0965ac748a3857a947c94e192dc", "0d45fef5bbf447938e7621e77207e1ae", "681be606b86c42fd9cad68e63e2782de", "a0f1a81a8d8f4e05a26d7a0a61565c53", "7f36f3521fa14a37b0fad0702bf9a4e3", "51b27a7dc0d34dd09b1ea17aba1fb697", "2a8c5504fb034f608567f8d706389d79", "ebe0534f9b4c4f92ae488a8d3e5867d6", "e7a84d718566443bba6c79729de0518a", "8cec2e55fc3542789938c24adb050ca9", "5248a6e5d8044c53803426afaf2439af", "10ae3f8453744a7e9737a9a0914dfeab", "81b870c0f0b6482a9f1dbd3fac0d935c", "ff464c023f0546209b15c44a39b4b75e", "ec7bdbec2fc342f38039b8420a162a25", "dab4d54863e24f169f18999da88be46f", "d50a15bbc94f49c39941bacd8c7b99ba", "a8dcc8f3435a4870a76be1729b003b6f", "e19591097be54f3d954a75bf14968b46", "f92c9532d4644693831a67344d499ff5", "a283d06ba73a4fc98e18e7080e794edf", "5837463f803c4c93b91a21d9854ae574", "ca5bf86a004a45a6ae2067fdc6eef091", "c389029d6e9445febf2f623921140abe", "9eda2eb5008b4f2d8269523ee5ce2ffe", "44bc01157b4945b7b81401f978dce8ca", "e39e7bf535fb4492ae9ac75591e79287", "cb157284f72d428fb786527ce87d11a2", "24030dedd2f24e2cbfa3833249d09810", "b3d9135383894e049a5f6f7fc77d7b5a", "a97373cd0b29464dac7d2b3bfe3aa276", "10bfbe36a3e24b30a6e300e852ff7811", "af944c373890491698424b1ca1c6969b", "3b250841cf664fcd8a57fb25d23b29ee", "745c74b4ac1942c497d0bee51b2e7b17", "89feac2a7b7d47388548f946fff4c862", "6e7d3e445440469baa5aaab1cbbb9291", "a3ca3b9ce6b24d43bdf8361379b6d502", "b82b0f73fa4d4fc395d106930015c7a0", "bd3c80a835d242d19fe187d65e737d6c", "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": 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