{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-C875CYSCygt", "outputId": "dd991ed9-d57f-4e5b-bee3-bcb6882369d9" }, "outputs": [], "source": [ "!pip install -qU diffusers transformers accelerate scipy safetensors" ] }, { "cell_type": "markdown", "metadata": { "id": "mAHWEPSfUlmg" }, "source": [ "# Hugging Face Implementation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jor1D7LvDA9l" }, "outputs": [], "source": [ "import requests\n", "from PIL import Image\n", "from io import BytesIO\n", "from diffusers import StableDiffusionUpscalePipeline\n", "import torch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 465, "referenced_widgets": [ "bc72d593b56c42899ff4779e92bcf818", "cabab037d12a455bbd283477e481d4ca", "7cdde9f1b0f1403ababc0e3e120617c4", "28c075d27bf8437c99b4fa8091d4d2c0", "7c5bcff9ea9344dca52dbaa4a6545b50", "2b68707a9749479cab4b4ecd124d87cb", "e7274e83d2c44080bbbb2fc5ec79dfee", "238d055bf5ff4891b2e9c6aa28a89fad", "66a8f48777064cab94ebdfb44044fec7", "c883ad6804cd4f628494b1eb7f432e51", "6f1c7458863047598f98861765f325dd", "6d0c44b7df564b87a2a688d770188184", "502d0b0ef4c549b485c049a2dae9b4f1", "67e1ee8ac39743d2a50db01595591f22", "419cb861b6e84999a3708fd19b0295c6", "fcc7b1147ad94a01ae49b4268e85d310", "f931614414ba46adaab4c0522e65ebf8", "e926a97d3f6149318dd6f01d17e90dd5", "58bccc7add0a45d8b8640c42d89a864c", "b4209eed23cb45038a5c614bfe8bf3b6", "2e922ee442bc49f8b0d7d4bd79d92683", "4d9d00362c1c4f48b5cddc1783956732", "8d91958b962649479d9d85927ea0e3e0", "2f1c260b17204c08870bac68408944c4", "9f17b8692b824c0cb9a98cea381699a9", "9a8c5af4272b4da0ba4aafa4da89817b", "e04b95f508b7431c9f4ee9159e3b5b13", "5896923dddc0466a9023c09c738f404a", "f874ed5ecd6f4eed976ed24b0be4ae51", "cdcf280e7a3043c2a1d59e0eea379d96", "2e945d3f81204a538874a0a832102d15", "417a14ed83ba4535bf47a39b9298aa4f", "c797a3a49d1d44598e8810817eac04f6", "659bd00aed7f4a41a843432966a1ccbf", "274e736175d34926a0c614192288e510", "a266a0d0bdde4a3aa09fec6a0a00abed", "f78f911cd0ac4cdc8fdcbcb29f1a201b", "9e0fde0538f84469bd62e9ea9f589b4f", "bce74d8ba4dd4a35bd276ba2dbf0ffd1", "f24b18d71065474a9f169b4fd66898f6", "bc60bbefdc2a490588dd8f58327fd264", "3119598ffcab4d6f9d1642ee595736ee", "90c7965cfde540158d9e7cb049c2d7d3", "b991b21d18ec471fb18b291a0c10aa6e", "76fd2fe6247a48b7b3a6cc6eef8c9fa7", "29da49469545454997e755839f8c156d", "58aa60b3b1af45d2bdd8870dedd3cb67", "f931d10a75ab4aafb1d6b92a0ba622b3", "b67319578dc84245ad16cb8d25d3173c", "d62c5143fa1544ba9e2d22555dc6c3f9", "ba72519befe047eabb826d1f7ec0e39c", "e76b29381c114f808b3454fb122e8d10", "a1d133bc8fe344e2aed80fe5155177c3", "5d3f04417ce64f80a2843e74bf6208f7", "341fa26d73394e6cb71498a9c95b458c", "6030f4bc6af8461abdc66e4599ef3f1f", "818b00fb8ff04eadb9ccd1bdfe192807", "44480a036c3c4442baa95d6288e83546", "50949912a3ee403e8ddb328e62b61c0d", "8dd862f24b164fc68664c30b3d241e62", "aea53b42c35947c58d2c113bd2043078", "02ce6e12de0f4ff1ab8029cf16e5fb99", "520e09d44ecf488b93694eebe4a02147", "5959385589b344b2bd8fc108067db8a3", "b5a46aec8d594d02b618b9d59e4f4518", "9ee85e252d32428ea44a91c71cd62a36", "bfc87d61a154451294fa790fc30075ea", "c8cabf1d8ffe421296725d8494ba0d3b", "28fe7cfc4f4048eca1120a7228dcd629", "9ecd3535823549bbbcbf3f131704d3eb", "03b9fb0d0fa44ac8bf1a6aa678ff82c8", "59b6eeeaf9464da891bf214ef1bbb7c2", "7d3baaa3ab784895a59534261f43ce7d", "8f487d06f9834f1b8dd0a63ffec62436", "ee5e249b357649ab95d493d940bc3b01", "9af01c4399c74223b5d7f94bd48d6952", "aa44d82861804483b44e0f893b698b72", "b7aa53a4de824d7fbec2a07c1771bc24", "17c87915e01b4d2fa79042759bf1a57d", "bafabfceff6e4f76b900878a4077337e", "5d1c6a49ce6f4596946a2fd91bec3312", "795329002e034ec4aead224cf74ea361", "29fb67ab990343b6a374b35718b8a6c1", "0854db4c71b449a9b15e994a38a1ca13", "cd437bc3e2194f1a8c5e83ec98745b58", "18ba1c9da9714f2494c06570e4dc1d64", "085ee070cea44624806bcc7ada5ae36e", "45148fd42fdb47b3a5424faa3c488c25", "fb2398f1e5ff43f7a2f81bd51bf58e2a", "53fa5e54123446199d57c27c3ee8b091", "07a431a9456648b6bcbfa899ee0014ac", "2f972961c0eb411ca45b8af05cc05e3b", "6525d3514cee42f3bd14bdf898b63ad2", "c8ba37c669974bbea2face17223d2bb4", "c87c0b939bbe4e56b46fca4aa580f75a", "639d6afb1b0440f98ce7e26cb621a111", "7a9ce1cc3d71457797b0b1ca2831d514", "c826a36c99ae462abccdc59266c404d3", "5ef86b91d359434a9aa4787dc727cb1b", "62bd9e1add374b9e90c54275bd2e0449", "0d89ddfa9593441097ce8e42614ef775", "e85ceff770654ef1bd935f54ca32ee07", "d0716d01bfdf43ba88f8bbfd63d3285a", "f6579ff0c08648309711a654ec3de748", "665a3a25e6d543d79e2e321aec4954aa", "d3971f94fa5f433580509a27ce3ad017", "d801dfb1d399475e995ca9106f9b3a4d", "ed75698d0ffe4bcf886c72d276bbee17", "a9ab859e76814985877a4bacdb04e95a", "1df97f1113c34b7eb89c7d1c6a81b3bd", "fc9a8403148c431e8b0200d40c6efcb7", "8b24a6ee676942ddaa27f44902ef2ac5", "5b74c15d312f404e88323771b08b0aeb", "7ad5acf646b44af2a1d8558abbb59199", "d22523983b9a4c71a3057f3a281895ee", "c081010577e7490eae450d69b0458a74", "d011d28093544ae692f90a7380908275", "a25cd36e4deb410f9ee54a92a18c939c", "00cdbd64ce7d4b5a835a550ffc6b3510", "7cc78829c6b744c380f52af44b49802a", "03856828685f4f9791a0c00f9b4f5ed5", "80aad8d6ac1b4b7780cd63deed9ceb11", "0fcb664708324ec1bc56c4daa4a2dd15", "37e24969e35c4099933ab57fac8b2599", "efb86ed04a0d477794e70dc92219399b", "3672f34ff79b417abb390592a3b9ed2b", "5af1c44c0f434510abd7fe9d76f263da", "895dd88b169e4688a51873bb1b3d2cd9", "d3165abbffbc4da3bd4f0cb6f3437140", "5a1be5246f2443858bd6933a89a68389", "10d895cb5166417fa2b4cc0d2feb7b6f", "23b55638314c4e2b967f85ab40042a6c", "9c741e36450b4c66875df9dc3ba07507", "cd9c9d32567f4e5aa9291f66b08490bf", "c4995db7a62844b4a00daaf55847cabd", "72fc113c125648538df5059fb578ae19", "3943882166bb4470a800d9563f9e53a2", "ad07cccdd8d44a9ab5e3458e8d902ac6", "ff88d9a5f7d64ad7bb0f09f6c71543f1", "080c7086e8684bb3bb857d23f426d5dd", "37e74a92120d40ab99b8a3efeb397bd8", "9ba492d205294bc0a8d07985ed271358", "0a49275d970741f9b19f24569b80491a", "4178b1767a614f89aa62f12a4e3a7350", "c909bafccc65402fa93afea87f1b784e", "6c32232cfb734ac3a3204a22c414fc18", "1f37677826544166a0b63d36c9c3edac", "a4d93e9fee48468281afac25f551806c", "91f32130b9fc47ceaae99521c0b70015", "e8bec5477f7c43c1a55c852ef8b7cb95", "7a4e5fdddcd34b6cb658b94db24ba474", "e6942466051e4a6a97c36b56d8d4e0c2", "a8dbb00149f148ceaee2474c4304c902", "f3c0042a67e34e72b1088b60c11ba2d0" ] }, "id": "l3QZf9-UDEb0", "outputId": "d2d9ea4c-1665-431b-c71c-bc5441522721" }, "outputs": [], "source": [ "# load model and scheduler\n", "model_id = \"stabilityai/stable-diffusion-x4-upscaler\"\n", "pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)\n", "pipeline = pipeline.to(\"cuda\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1rZBf5X4VfbQ" }, "outputs": [], "source": [ "def get_low_res_img(url, shape):\n", " response = requests.get(url)\n", " low_res_img = Image.open(BytesIO(response.content)).convert(\"RGB\")\n", " low_res_img = low_res_img.resize(shape)\n", " return low_res_img" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145 }, "id": "VSWlrXyIDGSo", "outputId": "1153aadd-bcc2-4365-9ce8-b02590018e49" }, "outputs": [], "source": [ "url = \"https://cdn.pixabay.com/photo/2017/02/07/16/47/kingfisher-2046453_640.jpg\"\n", "shape = (200, 128)\n", "low_res_img = get_low_res_img(url, shape)\n", "\n", "low_res_img" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 561, "referenced_widgets": [ "c1dc0d80451c4d098f16eb6ec7eed752", "d4c5db5f7ffe42beb2065e14cbdd755d", "accd8a5f56cf41c5af297f8bf93f7058", "824b0b410fed4ea1b5bc7f88236fc3e8", "a6b2ca41ffb24b9193a83fd9a4c24a8c", "bc9783a6d9d0437b881b01cad81c0173", "9e5ef9fe15314ce3bf13e61994851485", "ed9e0cfb4635476f9e31c5b48aeafde8", "396aee75c5954aa9b634d79c18177977", "c5f787d7f16542baa5a5657c3ecb14a0", "be0a3bc217b04b2dbd06a90141c0dd35" ] }, "id": "hPtKNnwSDA_u", "outputId": "60b2259e-02a0-445d-da26-eca1d51b4181" }, "outputs": [], "source": [ "prompt = \"an aesthetic kingfisher\"\n", "upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]\n", "upscaled_image" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 561, "referenced_widgets": [ "9c2ff534109548fc8cab92f3b0aefc71", "e417a487b9ab44d68bf5d4155f4ff339", "ce0bc6a269b841e59b3c1b00796b8605", "b014fb9554fb4f61a8d44135a6ad4954", "c30445a77e81411bbad4f90b8c54bc35", "c2ccf29c76d1461c8e820cdd1091684a", "42248bb1fb38481eaa292dbca2d68e38", "ac71f4fe6e804f19b2529c82e5a42049", "518150c24b25401d92cf483e5ecb0253", "d612163ad6d24d91a6d7ee758d8d6367", "ab1c2c3e457944acb16508cf7a721290" ] }, "id": "I1hCWlwXU5ij", "outputId": "fca3425e-973a-4951-df52-6eebba1b96e3" }, "outputs": [], "source": [ "prompt = \"an aesthetic kingfisher, UHD, 4k, hyper realistic, extremely detailed, professional, vibrant, not grainy, smooth\"\n", "upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]\n", "upscaled_image" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 529 }, "id": "4H0IkHfuDBB5", "outputId": "1fceb2fc-7e6c-492f-fc5b-cbd6d64f3d65" }, "outputs": [], "source": [ "upscaled_interpolation = low_res_img.resize((800, 512))\n", "upscaled_interpolation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145 }, "id": "xxVVHJAeDBEM", "outputId": "f099d0db-89ef-49df-92f1-c01c861634e2" }, "outputs": [], "source": [ "url = \"https://cdn.pixabay.com/photo/2022/06/14/20/57/woman-7262808_1280.jpg\"\n", "shape = (200, 128)\n", "low_res_img = get_low_res_img(url, shape)\n", "\n", "low_res_img" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 561, "referenced_widgets": [ "0c21001820524963b1214a2738c28584", "ea062db0a1ad43af805bf2d86d26d369", "2218df295404427eb6086c25f41946c5", "682dc899e5ee4e24a9c0f1fc928fea6c", "f8c3945c2c554cc9b7ea7435525c4ab4", "b9cf936d26124cad959de16fcf5bea63", "b3ae18d50eb4415b950f98bb38362207", "0df5b95ccc3d4550bb1be7c001f54577", "63a7a29ac462471eb67b275c68faff42", "1ae88e18373a4322bddf0e51e5460a89", "9b2140d07da744348068f013152b1160" ] }, "id": "UKtH894dXWHN", "outputId": "44bfe391-7abe-4b99-bfd3-b19e755bfdaa" }, "outputs": [], "source": [ "prompt = \"an old lady\"\n", "upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]\n", "upscaled_image" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 561, "referenced_widgets": [ "cf11071b7b114118a8b0b659167fa09e", "03bce4ac84fd40d485b023e21fe65c4f", "d0e9965e6aa4483da2dfa546b896e645", "22338ed9cec54338ad33267ed579603a", "622d32a9bbda46fca3ee0733be303765", "ec0c44e82a814774823e60634d678b0d", "e71abb2ba1b546ff9d7acd0c174f60d4", "1237bd63fa814b57bbd9741296d71f46", "5b3ca63a1af5452cb81fde6020fd9c53", "a5971d5b793545a3845fbe1029b557e1", "8384173365364cd5996018a775b167e2" ] }, "id": "L8fnlZsaDBHw", "outputId": "9215669a-61be-4a6e-cd6b-85d212df6517" }, "outputs": [], "source": [ "prompt = \"an iranian old lady with black hair, brown scarf, rock background\"\n", "upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]\n", "upscaled_image" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 529 }, "id": "OTJNWtuyXOnE", "outputId": "fe9eb4f3-f7b9-481f-b17b-e2028737141e" }, "outputs": [], "source": [ "upscaled_interpolation = low_res_img.resize((800, 512))\n", "upscaled_interpolation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145 }, "id": "dXXzMj7vXf5W", "outputId": "1895b5c9-d87e-48e8-c580-97a3b81838ed" }, "outputs": [], "source": [ "url = \"https://cdn.pixabay.com/photo/2017/12/28/07/44/zebra-3044577_1280.jpg\"\n", "shape = (450, 128)\n", "low_res_img = get_low_res_img(url, shape)\n", "\n", "low_res_img" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 453, "referenced_widgets": [ "64373eefa4884b3084975549efcbd7fe", "d8b3f3c7b8394b5580d8541f20c090ae", "634af1f0b6894726bebb7b546c667169", "5b89e69b011a40918b1acc0adf141874", "9c01417376444eed820394ef843c0be3", "db833b8a924f43208063cdc7b74220f7", "d74c7ced9e5841e0a3635bf848912874", "6a72b26cbdf041e7a8331fdc1642dee5", "3c4dca0b51954031905bada22feef684", "1e276839600443fa82ca0ab00409fd99", "639d147ac3674094be21de9f3c11477c" ] }, "id": "xjH0CWRHXf7o", "outputId": "b1ed8851-6243-43b8-d995-93129640b70d" }, "outputs": [], "source": [ "prompt = \"zebras drinking water\"\n", "upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]\n", "upscaled_image" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 419 }, "id": "ydbUyEFvXf_E", "outputId": "3028b021-c4a0-4f19-8a2e-0a3e4b19f348" }, "outputs": [], "source": [ "upscaled_interpolation = low_res_img.resize((1800, 512))\n", "upscaled_interpolation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "MFt4Y1AoYWse" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Ng2oJwHqYWvz" }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "id": "NiM8uOTr9DK3" }, "source": [ "# Custom\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yCuWhxws9D24" }, "outputs": [], "source": [ "from tqdm import tqdm\n", "from torch import autocast" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "T7PrARPl9EN2" }, "outputs": [], "source": [ "class CustomSDUpscalingPipeline:\n", " \"\"\"custom implementation of the Stable Diffusion Upscaling Pipeline\"\"\"\n", "\n", " def __init__(self,\n", " vae,\n", " tokenizer,\n", " text_encoder,\n", " unet,\n", " low_res_scheduler,\n", " scheduler,\n", " image_processor):\n", "\n", " self.vae = vae\n", " self.tokenizer = tokenizer\n", " self.text_encoder = text_encoder\n", " self.unet = unet\n", " self.low_res_scheduler = low_res_scheduler\n", " self.scheduler = scheduler\n", " self.image_processor = image_processor\n", " self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", "\n", "\n", "\n", " def get_text_embeds(self, text):\n", " \"\"\"returns embeddings for the given `text`\"\"\"\n", "\n", " # tokenize the text\n", " text_input = self.tokenizer(text,\n", " padding='max_length',\n", " max_length=tokenizer.model_max_length,\n", " truncation=True,\n", " return_tensors='pt')\n", " # embed the text\n", " with torch.no_grad():\n", " text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0]\n", " return text_embeds\n", "\n", "\n", "\n", " def get_prompt_embeds(self, prompt):\n", " \"\"\"returns prompt embeddings based on classifier free guidance\"\"\"\n", "\n", " if isinstance(prompt, str):\n", " prompt = [prompt]\n", " # get conditional prompt embeddings\n", " cond_embeds = self.get_text_embeds(prompt)\n", " # get unconditional prompt embeddings\n", " uncond_embeds = self.get_text_embeds([''] * len(prompt))\n", " # concatenate the above 2 embeds for classfier free guidance\n", " prompt_embeds = torch.cat([uncond_embeds, cond_embeds])\n", " return prompt_embeds\n", "\n", "\n", " def transform_image(self, image):\n", " \"\"\"convert image from pytorch tensor to PIL format\"\"\"\n", "\n", " image = self.image_processor.postprocess(image, output_type='pil')\n", " return image\n", "\n", "\n", "\n", " def get_initial_latents(self, height, width, num_channels_latents, batch_size):\n", " \"\"\"returns noise latent tensor of relevant shape scaled by the scheduler\"\"\"\n", "\n", " image_latents = torch.randn((batch_size, num_channels_latents, height, width)).to(self.device)\n", " # scale the initial noise by the standard deviation required by the scheduler\n", " image_latents = image_latents * self.scheduler.init_noise_sigma\n", " return image_latents\n", "\n", "\n", "\n", " def denoise_latents(self,\n", " prompt_embeds,\n", " image,\n", " timesteps,\n", " latents,\n", " noise_level,\n", " guidance_scale):\n", " \"\"\"denoises latents from noisy latent to a meaningful latents\"\"\"\n", "\n", " # use autocast for automatic mixed precision (AMP) inference\n", " with autocast('cuda'):\n", " for i, t in tqdm(enumerate(timesteps)):\n", " # duplicate image latents to do classifier free guidance\n", " latent_model_input = torch.cat([latents] * 2)\n", " latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n", " latent_model_input = torch.cat([latent_model_input, image], dim=1)\n", "\n", " # predict noise residuals\n", " with torch.no_grad():\n", " noise_pred = self.unet(\n", " latent_model_input,\n", " t,\n", " encoder_hidden_states=prompt_embeds,\n", " class_labels=noise_level\n", " )['sample']\n", "\n", " # separate predictions for unconditional and conditional outputs\n", " noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n", "\n", " # perform guidance\n", " noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n", "\n", " # remove the noise from the current sample i.e. go from x_t to x_{t-1}\n", " latents = self.scheduler.step(noise_pred, t, latents)['prev_sample']\n", "\n", " return latents\n", "\n", "\n", "\n", " def __call__(self,\n", " prompt,\n", " image,\n", " num_inference_steps=20,\n", " guidance_scale=9.0,\n", " noise_level=20):\n", " \"\"\"generates new image based on the `prompt` and the `image`\"\"\"\n", "\n", " # encode input prompt\n", " prompt_embeds = self.get_prompt_embeds(prompt)\n", "\n", " # preprocess image\n", " image = self.image_processor.preprocess(image).to(self.device)\n", "\n", " # prepare timesteps\n", " self.scheduler.set_timesteps(num_inference_steps, device=self.device)\n", " timesteps = self.scheduler.timesteps\n", "\n", " # add noise to image\n", " noise_level = torch.tensor([noise_level], device=self.device)\n", " noise = torch.randn(image.shape, device=self.device)\n", " image = self.low_res_scheduler.add_noise(image, noise, noise_level)\n", "\n", " # duplicate image for classifier free guidance\n", " image = torch.cat([image] * 2)\n", " noise_level = torch.cat([noise_level] * image.shape[0])\n", "\n", " # prepare the initial image in the latent space (noise on which we will do reverse diffusion)\n", " num_channels_latents = self.vae.config.latent_channels\n", " batch_size = prompt_embeds.shape[0] // 2\n", " height, width = image.shape[2:]\n", " latents = self.get_initial_latents(height, width, num_channels_latents, batch_size)\n", "\n", " # denoise latents\n", " latents = self.denoise_latents(prompt_embeds,\n", " image,\n", " timesteps,\n", " latents,\n", " noise_level,\n", " guidance_scale)\n", "\n", " # decode latents to get the image into pixel space\n", " latents = latents.to(torch.float16)\n", " image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n", "\n", " # convert to PIL Image format\n", " image = self.transform_image(image.detach()) # detach to remove any computed gradients\n", "\n", " return image" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "iPMCQB179EQN" }, "outputs": [], "source": [ "# get all the components from the SD Upscaler pipeline\n", "vae = pipeline.vae\n", "tokenizer = pipeline.tokenizer\n", "text_encoder = pipeline.text_encoder\n", "unet = pipeline.unet\n", "low_res_scheduler = pipeline.low_res_scheduler\n", "scheduler = pipeline.scheduler\n", "image_processor = pipeline.image_processor\n", "\n", "custom_pipe = CustomSDUpscalingPipeline(vae, tokenizer, text_encoder, unet, low_res_scheduler, scheduler, image_processor)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HUxdvfo7eLcq" }, "outputs": [], "source": [ "url = \"https://cdn.pixabay.com/photo/2017/02/07/16/47/kingfisher-2046453_640.jpg\"\n", "shape = (200, 128)\n", "low_res_img = get_low_res_img(url, shape)\n", "\n", "low_res_img" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 546 }, "id": "SgbP2oQl9EUk", "outputId": "b1b3d70c-58ef-497a-d87b-2c15073e4d2a" }, "outputs": [], "source": [ "prompt = \"an aesthetic kingfisher\"\n", "upscaled_image = custom_pipe(prompt=prompt, image=low_res_img)[0]\n", "upscaled_image" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145 }, "id": "Wf8MTwFCeRrR", "outputId": "17827131-0f99-408e-b61d-ff802509baa9" }, "outputs": [], "source": [ "url = \"https://cdn.pixabay.com/photo/2018/07/31/22/08/lion-3576045_1280.jpg\"\n", "shape = (200, 128)\n", "low_res_img = get_low_res_img(url, shape)\n", "\n", "low_res_img" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 546 }, "id": "QzkJk4Jo9Eca", "outputId": "a5ddbb9a-7526-48f5-f449-22e54445fae2" }, "outputs": [], "source": [ "prompt = \"a professional photograph of a lion's face\"\n", "upscaled_image = custom_pipe(prompt=prompt, image=low_res_img)[0]\n", "upscaled_image" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 529 }, "id": "tT3jd43tdbeg", "outputId": "d7a8e0a7-1ed1-4c18-8b6c-b5dcbf4c4fb5" }, "outputs": [], "source": [ 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