Repository: Perp-Neg/Perp-Neg-stablediffusion Branch: main Commit: 347c203e71e3 Files: 144 Total size: 7.9 MB Directory structure: gitextract_bvrh37j4/ ├── .gitattributes ├── LICENSE ├── README.md ├── notebooks/ │ └── demo.ipynb ├── perpneg_diffusion/ │ ├── __init__.py │ └── perpneg_stable_diffusion/ │ ├── __init__.py │ ├── pipeline_composable_stable_diffusion.py │ ├── pipeline_composable_stable_diffusion_3d_rotation.py │ ├── pipeline_composable_stable_diffusion_rotation.py │ ├── pipeline_perpneg_stable_diffusion.py │ ├── pipeline_perpneg_stable_diffusion_rotation.py │ └── safety_checker.py ├── scripts/ │ └── image_sample_stable_diffusion.py ├── setup.py └── stable-dreamfusion/ ├── .gitignore ├── LICENSE ├── activation.py ├── assets/ │ ├── advanced.md │ └── update_logs.md ├── config/ │ ├── anya.csv │ ├── car.csv │ └── corgi.csv ├── docker/ │ ├── Dockerfile │ └── README.md ├── dpt.py ├── encoding.py ├── evaluation/ │ ├── Prompt.py │ ├── mesh_to_video.py │ ├── r_precision.py │ └── readme.md ├── freqencoder/ │ ├── __init__.py │ ├── backend.py │ ├── freq.py │ ├── setup.py │ └── src/ │ ├── bindings.cpp │ ├── freqencoder.cu │ └── freqencoder.h ├── gridencoder/ │ ├── __init__.py │ ├── backend.py │ ├── grid.py │ ├── setup.py │ └── src/ │ ├── bindings.cpp │ ├── gridencoder.cu │ └── gridencoder.h ├── guidance/ │ ├── clip_utils.py │ ├── if_utils.py │ ├── perpneg_utils.py │ ├── sd_utils.py │ └── zero123_utils.py ├── ldm/ │ ├── extras.py │ ├── guidance.py │ ├── lr_scheduler.py │ ├── models/ │ │ ├── autoencoder.py │ │ └── diffusion/ │ │ ├── __init__.py │ │ ├── classifier.py │ │ ├── ddim.py │ │ ├── ddpm.py │ │ ├── plms.py │ │ └── sampling_util.py │ ├── modules/ │ │ ├── attention.py │ │ ├── diffusionmodules/ │ │ │ ├── __init__.py │ │ │ ├── model.py │ │ │ ├── openaimodel.py │ │ │ └── util.py │ │ ├── distributions/ │ │ │ ├── __init__.py │ │ │ └── distributions.py │ │ ├── ema.py │ │ ├── encoders/ │ │ │ ├── __init__.py │ │ │ └── modules.py │ │ ├── evaluate/ │ │ │ ├── adm_evaluator.py │ │ │ ├── evaluate_perceptualsim.py │ │ │ ├── frechet_video_distance.py │ │ │ ├── ssim.py │ │ │ └── torch_frechet_video_distance.py │ │ ├── image_degradation/ │ │ │ ├── __init__.py │ │ │ ├── bsrgan.py │ │ │ ├── bsrgan_light.py │ │ │ └── utils_image.py │ │ ├── losses/ │ │ │ ├── __init__.py │ │ │ ├── contperceptual.py │ │ │ └── vqperceptual.py │ │ └── x_transformer.py │ ├── thirdp/ │ │ └── psp/ │ │ ├── helpers.py │ │ ├── id_loss.py │ │ └── model_irse.py │ └── util.py ├── main.py ├── meshutils.py ├── nerf/ │ ├── gui.py │ ├── network.py │ ├── network_grid.py │ ├── network_grid_taichi.py │ ├── network_grid_tcnn.py │ ├── provider.py │ ├── renderer.py │ └── utils.py ├── optimizer.py ├── preprocess_image.py ├── pretrained/ │ └── zero123/ │ └── sd-objaverse-finetune-c_concat-256.yaml ├── raymarching/ │ ├── __init__.py │ ├── backend.py │ ├── raymarching.py │ ├── setup.py │ └── src/ │ ├── bindings.cpp │ ├── raymarching.cu │ └── raymarching.h ├── readme.md ├── requirements.txt ├── scripts/ │ ├── install_ext.sh │ ├── res64.args │ ├── run.sh │ ├── run2.sh │ ├── run3.sh │ ├── run4.sh │ ├── run5.sh │ ├── run6.sh │ ├── run_if.sh │ ├── run_if2.sh │ ├── run_if2_perpneg.sh │ ├── run_image.sh │ ├── run_image_anya.sh │ ├── run_image_hard_examples.sh │ ├── run_image_procedure.sh │ ├── run_image_text.sh │ └── run_images.sh ├── shencoder/ │ ├── __init__.py │ ├── backend.py │ ├── setup.py │ ├── sphere_harmonics.py │ └── src/ │ ├── bindings.cpp │ ├── shencoder.cu │ └── shencoder.h ├── taichi_modules/ │ ├── __init__.py │ ├── hash_encoder.py │ ├── intersection.py │ ├── ray_march.py │ ├── utils.py │ ├── volume_render_test.py │ └── volume_train.py └── tets/ ├── 128_tets.npz ├── 32_tets.npz ├── 64_tets.npz ├── README.md └── generate_tets.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitattributes ================================================ # Auto detect text files and perform LF normalization * text=auto ================================================ FILE: LICENSE ================================================ MIT License Copyright (c) 2023 Perp-Neg Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.md ================================================ ### [Project Page](https://perp-neg.github.io/) | [Paper](https://arxiv.org/abs/2304.04968) | [Huggingface][huggingface-demo] [![][huggingface]][huggingface-demo] [huggingface]: [huggingface-demo]: ![Video Demo](assets/output.gif) # Perp-Neg Stable Diffusion This is the repository for using Perp-Neg sampling with Stable Diffusion model, as presented in [Re-imagine the Negative Prompt Algorithm: Transform 2D Diffusion into 3D, alleviate Janus problem and Beyond.](https://Perp-Neg.github.io). ## Running code **Run compositional ebm with diffusion model** ``` python scripts/image_sample_stable_diffusion.py --prompt="a boy wearing sunglasses|sunglasses with black frame" --weights="1|-1.5" --pipeline='cebm' --num_images=1 ``` **Run PerpNeg** ``` python scripts/image_sample_stable_diffusion.py --prompt="a boy wearing sunglasses|sunglasses with black frame" --weights="1|-1.5" --pipeline='perpneg' --num_images=1 ``` **Run PerpNeg-rotation** ``` python scripts/image_sample_stable_diffusion.py --prompt="a photo of lion, front view | a photo of lion, side view" --weights="1|-1.5|-1.5" --pipeline='rotation_perpneg' --num_images=1 ``` **PerpNeg-Stable DreamFusion** please use the scripts in stable-dreamfusion/scripts/run_if2_perpneg.sh to run the Stable DreamFusion + PerpNeg to avoid the Janus problem. This code is manily based on [Stable-DreamFusion](https://github.com/ashawkey/stable-dreamfusion) If you find it useful for your work, please cite: ``` @article{armandpour2023re, title={Re-imagine the Negative Prompt Algorithm: Transform 2D Diffusion into 3D, alleviate Janus problem and Beyond}, author={Armandpour, Mohammadreza and Zheng, Huangjie and Sadeghian, Ali and Sadeghian, Amir and Zhou, Mingyuan}, journal={arXiv preprint arXiv:2304.04968}, year={2023} } ``` ================================================ FILE: notebooks/demo.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 301, "referenced_widgets": [ "ffaa1a5508fd423ba90aa435e1e900b6", "6256a498f2524ac2aad4716164da874a", "be79104a922c4b918cea7528aa213fbb", "6146c7ee93ca4a61bb978eea839adba7", "64cc7cdcffee4cd5889b0c464dd68acb", "315aab75bc724665bc2e79b8af1114e8", "a0a3f02145b84953a040c50d72b07b63", "1a68aa400e8c4217ad0847d84921f862", "3154df922df642f7bdf454e4e8d15d79", "d2be532bb1e44a14a94538eafa1fc556", "38fb08268ca34f46b6646f963fdffc5b", "4360519a8e7c4bcc8e6a81037cc50561", "df7e6f661c7742e1a692938e9c946704", "02e1e306949d4a18b2aa3d0973e8f2fa" ] }, "id": "ZtIStDEBnoz9", "outputId": "f86bd33b-649e-4241-a8fb-18dc88bfb4ca", "pycharm": { "name": "#%%\n" }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Token is valid.\n", "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n", "You might have to re-authenticate when pushing to the Hugging Face Hub.\n", "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n", "\n", "git config --global credential.helper store\n", "\n", "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n", "Token has not been saved to git credential helper.\n", "Your token has been saved to /home/hjzheng/.cache/huggingface/token\n", "Login successful\n" ] } ], "source": [ "from huggingface_hub import notebook_login\n", "notebook_login()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Obtaining file:///data/hjzheng/perpneg-diffusion\n", " Preparing metadata (setup.py) ... \u001b[?25ldone\n", "\u001b[?25hRequirement already satisfied: Pillow in /home/hjzheng/anaconda3/lib/python3.9/site-packages (from perpneg-diffusion==2.0) (9.2.0)\n", "Requirement already satisfied: attrs in /home/hjzheng/anaconda3/lib/python3.9/site-packages (from perpneg-diffusion==2.0) (21.4.0)\n", "Requirement already satisfied: torch in /home/hjzheng/.local/lib/python3.9/site-packages (from perpneg-diffusion==2.0) (2.0.0)\n", "Requirement already satisfied: filelock in /home/hjzheng/anaconda3/lib/python3.9/site-packages (from perpneg-diffusion==2.0) (3.6.0)\n", 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packaging>=20.0 in /home/hjzheng/anaconda3/lib/python3.9/site-packages (from transformers->perpneg-diffusion==2.0) (21.3)\n", "Requirement already satisfied: pyyaml>=5.1 in /home/hjzheng/anaconda3/lib/python3.9/site-packages (from transformers->perpneg-diffusion==2.0) (6.0)\n", "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /home/hjzheng/anaconda3/lib/python3.9/site-packages (from packaging>=20.0->transformers->perpneg-diffusion==2.0) (3.0.9)\n", "Requirement already satisfied: MarkupSafe>=0.23 in /home/hjzheng/anaconda3/lib/python3.9/site-packages (from jinja2->torch->perpneg-diffusion==2.0) (2.0.1)\n", "Requirement already satisfied: mpmath>=0.19 in /home/hjzheng/anaconda3/lib/python3.9/site-packages (from sympy->torch->perpneg-diffusion==2.0) (1.2.1)\n", "Installing collected packages: perpneg-diffusion\n", " Attempting uninstall: perpneg-diffusion\n", " Found existing installation: perpneg-diffusion 2.0\n", " Uninstalling perpneg-diffusion-2.0:\n", " Successfully uninstalled perpneg-diffusion-2.0\n", " Running setup.py develop for perpneg-diffusion\n", "Successfully installed perpneg-diffusion-2.0\n" ] } ], "source": [ "!pip install git+https://github.com/Perp-Neg/Perp-Neg-stablediffusion.git\n", "!pip install diffusers" ] }, { "cell_type": "markdown", "metadata": { "id": "vGySZS39FcY9", "pycharm": { "name": "#%% md\n" } }, "source": [ "# **Perp-Neg using pretrained Stable-Diffusion 1v-4**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 579, "referenced_widgets": [ "7a15fac547de426682b10aab3ee8ab28", "f0386c3b5c3b4bbc91baa3a300241bdc", "0b29c9cf11a14cfc91b3ed25834b3db8", "f1f2d0ab1787476aab96fbb78176f4b0", "ea2170f36d9a4fabb8d3570d190f3406", "d374aac4cc4141088044e2c663baa146", "3b43d183fa31402cb33ed6733dac0f8e", "622b9e3567984a57811d1fb68dca3c8c", "8fc8e833967e4948a3dec4714b1e432a", "c529baa978e94cbfaee08055fc5d5012", "808b869f427b4628bb1790165c00c64a", 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"d2483a9369a3434d9ce003c5e9a41ef4", "9bf892af07694f4494f2a080acbea909", "9c22d4e42f264aa89559c48987c3f908", "5b9aa13e72e34e659dd8044c0d148aa8", "b3a5fd88df1049bdb5db14cfcf1b30d5", "7a10cfda8ebe483b8d1e17dc1dc79e80", "755cae0a3665483b931d5de85214cf9c", "fd75f9deec6b4cf1961251b6817036cd", "0dd302fed0164b1fa1edbd59b56d6dd3", "484f734a4cc248ffbe74859872974f9d", "2e5788b50f3046f693a5138241335d93", "ce375a1a01b64a5585d4c1f03a8e0dd3", "62219a1fb7b7454ba179ceeacd71fdce", "1c737663b397455f93a61aee29b38e36", "f4c275484d57412aa7bfd58a3102f05d", "3fbd4fc1ec424d559b950487a541fdc0", "3af0e0a5f3634842972c62c1fee1d7c2", "62806d8bd0d84777b3d85e10fae882a4", "52a5694271084eb3b95d1af2719ca4c0", "84963e80fcdb4ca18e726397d8795e84", "912b82d360df42b78b32696dd37e3d5e", "e5a6ad2492f74626b23b328da9bba0bb", "8c41f01c30af4278a8d70d30a6fcfd0e", "6d1830c28f694585b801920f4ec0289e", "ee018e8cdc014996ae7a7e29aec51aee", "6f22e9410daa4dfe882bd53ccecd094e", "35ac8bc3a4e74377be312b87c65ec0e3", "11222bf1b1ac43c0ad8a6a1dd3e10b90", "6b2ba9b98e3a4a82a5f76710abc14c57", "07be7854de894d6499a5adc33bc8a333", "260085a5d23c4d5cad21d446aa1bb0a5", "3521caaa92ba43748c280a3113ad8cb6", "0f0d59bd022f429898cc6ec4f2d065b5", "444c6dfda1e441f88ec0bd3a10cfce74", "ed1ea149b4f643a3af6468e6f7275455", "d7495cb2e92d4415963bd9de02dd0fbb", "c627b6dd164643f884f2171b8d436379", "74dd629bc88249e69b6dde2dc7853297", "ddd9defb7fb64d0a98f574c511c51b7a", "9c35fd30ddc44935ab42a92fabff5434", "7225f778b0af42b1a31df41a823fae1e", "29c502ee32c54a64ab946286a7e774fa", "18e22e8d6420498ab759fe5c01189a53", "ab07702d6b454d40a328e231f2a9e375", "7f724391b4ef4ee088bc379ab8829682", "877d26f3563e4d6289033606f616bf8e", "adaab63e9c04450f8f2bf24ec224a9b0", "bb7cded3863e430ea91af9d8e4c5cc74", "d05a851310ba40f9af88854d780592d7", "a9bfc166ead14363b615fc462bdf75a2", "dedea924f0fb427585780a7f39f0dbcb", "c1e1d7073e6d41e8b451e2b6480ca899", "9e242fbc45cd40a2bfb5131a6310a75e", "babe5c904c47478fbdfc1b2dfa100e69", "e7c244e63c144b00812a87b9ed1ec0f8", "73840385d3544d9c9f4bddebe63e143c", "ec7311e77dd145eaacb05fc4a9132900", "908c09b9c3834ad9980557a8665708d8", "0a737abc8dbc4fd692d038bc15ae04f7", "6d0eae6bcfa545408011ec1c2e3256d2", "a20493f67fdd4d509cb4a205695a0b64", "8d0c37c3a4004293b7f402d02156bdf3", "8f4421503d80478482de92fc345abb05", "2c1ac055fbf341d68bbb075d6dede5c6", "a4e0981f8cf741ceb58652d5e43a323a", "66f2c5b960d14c5daafc0d5240ec1215", "cff3083a76e34a2db0cb78de06bd2243", "aa80a5d8c85e4209b38c0d7b20bd4a19" ] }, "id": "8fNLZCu5Fic_", "outputId": "a2049c35-8cc7-4314-a337-cd2afb00bfe2", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cuda\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "32a75109602c441abe015ba38cf202aa", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Fetching 20 files: 0%| | 0/20 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "composing ['an armchair in the shape of an avocado', 'cushion in the armchair']...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f2e2e70bd0b4422eb3cd6d8a6dff4ddc", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/51 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "composing ['an armchair in the shape of an avocado', 'cushion in the armchair']...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "31231bebba524111aced3b9504668ef3", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/51 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Sampling parameters\n", "#@markdown Here is an example of composing sentences using Negation operator (NOT). \\\n", "#@markdown Negative weights should be assigned to the textes you wish to negate. \\\n", "#@markdown `prompt`: when composing multiple sentences, using `|` as the delimiter.\\\n", "#@markdown `weight`: weight indicates the weight importance of sentence when composing, also using `|` as the delimiter.\n", "prompt = \"an armchair in the shape of an avocado | cushion in the armchair\" #@param{type: 'string'}\n", "weights = \"1 | -0.3\" #@param{type: 'string'}\n", "scale = 7.5 #@param{type: 'number'}\n", "steps = 50 #@param{type: 'number'}\n", "seed = 145 #@param{type: 'number'}\n", "\n", "# positive prompt only\n", "with autocast('cpu' if not has_cuda else 'cuda'):\n", " generator = th.Generator('cuda').manual_seed(seed)\n", " image = pipe(\"an armchair in the shape of an avocado\", guidance_scale=scale, generator=generator,\n", " num_inference_steps=steps, weights=\"1\")[\"images\"][0]\n", " display(image)\n", "\n", "# composing prompts with CEBM\n", "with autocast('cpu' if not has_cuda else 'cuda'):\n", " generator = th.Generator('cuda').manual_seed(seed)\n", " image = pipe(prompt, guidance_scale=scale, generator=generator,\n", " num_inference_steps=steps, weights=weights)[\"images\"][0]\n", " display(image)\n", " \n", "# fusing prompts with perpneg\n", "with autocast('cpu' if not has_cuda else 'cuda'):\n", " generator = th.Generator('cuda').manual_seed(seed)\n", " image_perpneg = pipe_perpneg(prompt, guidance_scale=scale, generator=generator,\n", " num_inference_steps=steps, weights=weights)[\"images\"][0]\n", " display(image_perpneg)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4324d67021194c94917a9bb831f50f3e", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/51 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "composing ['A boy wearing sunglasses', 'a pair of sunglasses with white frame']...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "307a8d1b35354dfa852a782fb1919846", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/51 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "composing ['A boy wearing sunglasses', 'a pair of sunglasses with white frame']...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c875171e64ed498b98d6a2f0b02f94aa", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/51 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Sampling parameters\n", "#@markdown Here is an example of composing sentences using Negation operator (NOT). \\\n", "#@markdown Negative weights should be assigned to the textes you wish to negate. \\\n", "#@markdown `prompt`: when composing multiple sentences, using `|` as the delimiter.\\\n", "#@markdown `weight`: weight indicates the weight importance of sentence when composing, also using `|` as the delimiter.\n", "prompt = \"A boy wearing sunglasses | a pair of sunglasses with white frame\" #@param{type: 'string'}\n", "weights = \"1 | -0.35\" #@param{type: 'string'}\n", "scale = 11 #@param{type: 'number'}\n", "steps = 50 #@param{type: 'number'}\n", "seed = 200 #@param{type: 'number'}\n", "\n", "# positive prompt only\n", "with autocast('cpu' if not has_cuda else 'cuda'):\n", " generator = th.Generator('cuda').manual_seed(seed)\n", " image = pipe(\"A boy wearing sunglasses\", guidance_scale=scale, generator=generator,\n", " num_inference_steps=steps, weights=\"1\")[\"images\"][0]\n", " display(image)\n", "\n", "# composing prompts with CEBM\n", "with autocast('cpu' if not has_cuda else 'cuda'):\n", " generator = th.Generator('cuda').manual_seed(seed)\n", " image = pipe(prompt, guidance_scale=scale, generator=generator,\n", " num_inference_steps=steps, weights=weights)[\"images\"][0]\n", " display(image)\n", " \n", "# fusing prompts with perpneg\n", "with autocast('cpu' if not has_cuda else 'cuda'):\n", " generator = th.Generator('cuda').manual_seed(seed)\n", " image_perpneg = pipe_perpneg(prompt, guidance_scale=scale, generator=generator,\n", " num_inference_steps=steps, weights=weights)[\"images\"][0]\n", " display(image_perpneg)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "45a37250e89948ce8e6db806df7b2667", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/51 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "composing ['A painting of a cute corgi wearing a crown', 'a crown with red ruby decoration']...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "98d14fed51ce4cb29eac55104371d487", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/51 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "composing ['A painting of a cute corgi wearing a crown', 'a crown with red ruby decoration']...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d3c31f37ff6344bd92731d057cc2d3b2", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/51 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Sampling parameters\n", "#@markdown Here is an example of composing sentences using Negation operator (NOT). \\\n", "#@markdown Negative weights should be assigned to the textes you wish to negate. \\\n", "#@markdown `prompt`: when composing multiple sentences, using `|` as the delimiter.\\\n", "#@markdown `weight`: weight indicates the weight importance of sentence when composing, also using `|` as the delimiter.\n", "prompt = \"A painting of a cute corgi wearing a crown | a crown with red ruby decoration\" #| green cushion in the chair\" #@param{type: 'string'}\n", "weights = \"1 | -0.8\" #@param{type: 'string'}\n", "scale = 3.5 #@param{type: 'number'}\n", "steps = 50 #@param{type: 'number'}\n", "seed = 200\n", "\n", "# positive prompt only\n", "with autocast('cpu' if not has_cuda else 'cuda'):\n", " generator = th.Generator('cuda').manual_seed(seed)\n", " image = pipe(\"A painting of a cute corgi wearing a crown\", guidance_scale=scale, generator=generator,\n", " num_inference_steps=steps, weights=\"1\")[\"images\"][0]\n", " display(image)\n", "\n", "# composing prompts with CEBM\n", "with autocast('cpu' if not has_cuda else 'cuda'):\n", " generator = th.Generator('cuda').manual_seed(seed)\n", " image = pipe(prompt, guidance_scale=scale, generator=generator,\n", " num_inference_steps=steps, weights=weights)[\"images\"][0]\n", " display(image)\n", " \n", "# fusing prompts with perpneg\n", "with autocast('cpu' if not has_cuda else 'cuda'):\n", " generator = th.Generator('cuda').manual_seed(seed)\n", " image_perpneg = pipe_perpneg(prompt, guidance_scale=scale, generator=generator,\n", " num_inference_steps=steps, weights=weights)[\"images\"][0]\n", " display(image_perpneg)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "36ba7447317b47e9b2be3ec3586deed4", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/51 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "composing ['a photo of an astronaut riding a horse', 'a jumping horse', 'a white horse']...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "52995e9c61bd429c9b25a878b2adf587", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/51 [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "composing ['a photo of an astronaut riding a horse', 'a jumping horse', 'a white horse']...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8d89fad91594458eb8280f73dc3eda74", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/51 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Sampling parameters\n", "#@markdown Here is an example of composing sentences using Negation operator (NOT). \\\n", "#@markdown Negative weights should be assigned to the textes you wish to negate. \\\n", "#@markdown `prompt`: when composing multiple sentences, using `|` as the delimiter.\\\n", "#@markdown `weight`: weight indicates the weight importance of sentence when composing, also using `|` as the delimiter.\n", "prompt = \"a photo of an astronaut riding a horse | a jumping horse | a white horse\" #| green cushion in the chair\" #@param{type: 'string'}\n", "weights = \"1 | -0.17 | -0.1\" #@param{type: 'string'}\n", "scale = 10 #@param{type: 'number'}\n", "steps = 50 #@param{type: 'number'}\n", "seed = 1988\n", "generator = th.Generator('cuda').manual_seed(seed)\n", "\n", "# positive prompt only\n", "with autocast('cpu' if not has_cuda else 'cuda'):\n", " generator = th.Generator('cuda').manual_seed(seed)\n", " image = pipe(\"a photo of an astronaut riding a horse\", guidance_scale=scale, generator=generator,\n", " num_inference_steps=steps, weights=\"1\")[\"images\"][0]\n", " display(image)\n", "\n", "# composing prompts with CEBM\n", "with autocast('cpu' if not has_cuda else 'cuda'):\n", " generator = th.Generator('cuda').manual_seed(seed)\n", " image = pipe(prompt, guidance_scale=scale, generator=generator,\n", " num_inference_steps=steps, weights=weights)[\"images\"][0]\n", " display(image)\n", " \n", "# fusing prompts with perpneg\n", "with autocast('cpu' if not has_cuda else 'cuda'):\n", " generator = th.Generator('cuda').manual_seed(seed)\n", " image_perpneg = pipe_perpneg(prompt, guidance_scale=scale, generator=generator,\n", " num_inference_steps=steps, weights=weights)[\"images\"][0]\n", " display(image_perpneg)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "provenance": [] }, "interpreter": { "hash": "e7d6e62d90e7e85f9a0faa7f0b1d576302d7ae6108e9fe361594f8e1c8b05781" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "0013f4a6fd59428abe6bfae6f3b772cc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", 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Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
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""" ================================================ FILE: perpneg_diffusion/perpneg_stable_diffusion/__init__.py ================================================ """ A codebase for performing model inference with a text-conditional diffusion model. """ from dataclasses import dataclass from typing import List, Union import numpy as np import PIL from diffusers.utils import BaseOutput @dataclass class StableDiffusionPipelineOutput(BaseOutput): """ Output class for Stable Diffusion pipelines. Args: images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. nsfw_content_detected (`List[bool]`) List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content. """ images: Union[List[PIL.Image.Image], np.ndarray] nsfw_content_detected: List[bool] ================================================ FILE: perpneg_diffusion/perpneg_stable_diffusion/pipeline_composable_stable_diffusion.py ================================================ """ modified based on diffusion library from Huggingface: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py """ import inspect import warnings from typing import List, Optional, Union import torch from tqdm.auto import tqdm from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer from diffusers.models import AutoencoderKL, UNet2DConditionModel from diffusers.pipeline_utils import DiffusionPipeline from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler from .safety_checker import StableDiffusionSafetyChecker from . import StableDiffusionPipelineOutput class ComposableStableDiffusionPipeline(DiffusionPipeline): r""" Pipeline for text-to-image generation using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offsensive or harmful. Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details. feature_extractor ([`CLIPFeatureExtractor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPFeatureExtractor, ): super().__init__() #scheduler = scheduler.set_format("pt") self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"): r""" Enable sliced attention computation. When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease. Args: slice_size (`str` or `int`, *optional*, defaults to `"auto"`): When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`. """ if slice_size == "auto": # half the attention head size is usually a good trade-off between # speed and memory slice_size = self.unet.config.attention_head_dim // 2 self.unet.set_attention_slice(slice_size) def disable_attention_slicing(self): r""" Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go back to computing attention in one step. """ # set slice_size = `None` to disable `attention slicing` self.enable_attention_slicing(None) @torch.no_grad() def __call__( self, prompt: Union[str, List[str]], height: Optional[int] = 512, width: Optional[int] = 512, num_inference_steps: Optional[int] = 50, guidance_scale: Optional[float] = 7.5, eta: Optional[float] = 0.0, generator: Optional[torch.Generator] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, weights: Optional[str] = "", **kwargs, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. height (`int`, *optional*, defaults to 512): The height in pixels of the generated image. width (`int`, *optional*, defaults to 512): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ if "torch_device" in kwargs: device = kwargs.pop("torch_device") warnings.warn( "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0." " Consider using `pipe.to(torch_device)` instead." ) # Set device as before (to be removed in 0.3.0) if device is None: device = "cuda" if torch.cuda.is_available() else "cpu" self.to(device) if isinstance(prompt, str): batch_size = 1 elif isinstance(prompt, list): batch_size = len(prompt) else: raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if '|' in prompt: prompt = [x.strip() for x in prompt.split('|')] print(f"composing {prompt}...") # get prompt text embeddings text_input = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0] if not weights: # specify weights for prompts (excluding the unconditional score) print('using equal weights for all prompts...') pos_weights = torch.tensor([1 / (text_embeddings.shape[0] - 1)] * (text_embeddings.shape[0] - 1), device=self.device).reshape(-1, 1, 1, 1) neg_weights = torch.tensor([1.], device=self.device).reshape(-1, 1, 1, 1) mask = torch.tensor([False] + [True] * pos_weights.shape[0], dtype=torch.bool) else: # set prompt weight for each num_prompts = len(prompt) if isinstance(prompt, list) else 1 weights = [float(w.strip()) for w in weights.split("|")] if len(weights) < num_prompts: weights.append(1.) weights = torch.tensor(weights, device=self.device) assert len(weights) == text_embeddings.shape[0], "weights specified are not equal to the number of prompts" pos_weights = [] neg_weights = [] mask = [] # first one is unconditional score for w in weights: if w > 0: pos_weights.append(w) mask.append(True) else: neg_weights.append(abs(w)) mask.append(False) # normalize the weights pos_weights = torch.tensor(pos_weights, device=self.device).reshape(-1, 1, 1, 1) pos_weights = pos_weights / pos_weights.sum() neg_weights = torch.tensor(neg_weights, device=self.device).reshape(-1, 1, 1, 1) neg_weights = neg_weights / neg_weights.sum() mask = torch.tensor(mask, device=self.device, dtype=torch.bool) # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance: max_length = text_input.input_ids.shape[-1] if torch.all(mask): # no negative prompts, so we use empty string as the negative prompt uncond_input = self.tokenizer( [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt" ) uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0] # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) # update negative weights neg_weights = torch.tensor([1.], device=self.device) mask = torch.tensor([False] + mask.detach().tolist(), device=self.device, dtype=torch.bool) # get the initial random noise unless the user supplied it # Unlike in other pipelines, latents need to be generated in the target device # for 1-to-1 results reproducibility with the CompVis implementation. # However this currently doesn't work in `mps`. latents_device = "cpu" if self.device.type == "mps" else self.device latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8) if latents is None: latents = torch.randn( latents_shape, generator=generator, device=latents_device, ) else: if latents.shape != latents_shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") latents = latents.to(self.device) # set timesteps accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys()) extra_set_kwargs = {} if accepts_offset: extra_set_kwargs["offset"] = 1 self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs) # if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas if isinstance(self.scheduler, LMSDiscreteScheduler): latents = latents * self.scheduler.sigmas[0] # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * text_embeddings.shape[0]) if do_classifier_free_guidance else latents if isinstance(self.scheduler, LMSDiscreteScheduler): sigma = self.scheduler.sigmas[i] # the model input needs to be scaled to match the continuous ODE formulation in K-LMS latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5) # reduce memory by predicting each score sequentially noise_preds = [] # predict the noise residual for latent_in, text_embedding_in in zip( torch.chunk(latent_model_input, chunks=latent_model_input.shape[0], dim=0), torch.chunk(text_embeddings, chunks=text_embeddings.shape[0], dim=0)): noise_preds.append(self.unet(latent_in, t, encoder_hidden_states=text_embedding_in).sample) noise_preds = torch.cat(noise_preds, dim=0) # perform guidance if do_classifier_free_guidance: noise_pred_uncond = (noise_preds[~mask] * neg_weights).sum(dim=0, keepdims=True) noise_pred_text = (noise_preds[mask] * pos_weights).sum(dim=0, keepdims=True) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 if isinstance(self.scheduler, LMSDiscreteScheduler): latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample else: latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # scale and decode the image latents with vae latents = 1 / 0.18215 * latents image = self.vae.decode(latents).sample image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).numpy() # run safety checker #safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device) #image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values) has_nsfw_concept = [False for i in range(image.shape[0])] print(has_nsfw_concept) if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: perpneg_diffusion/perpneg_stable_diffusion/pipeline_composable_stable_diffusion_3d_rotation.py ================================================ """ modified based on diffusion library from Huggingface: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py """ import inspect from itertools import accumulate import warnings from typing import List, Optional, Union import torch from tqdm.auto import tqdm from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer from diffusers.models import AutoencoderKL, UNet2DConditionModel from diffusers.pipeline_utils import DiffusionPipeline from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler from .safety_checker import StableDiffusionSafetyChecker from . import StableDiffusionPipelineOutput def get_prependicualr_component(x, y): assert x.shape == y.shape # print(((torch.mul(x, y).sum())/(torch.norm(y)**2)).shape) return x - ((torch.mul(x, y).sum())/(torch.norm(y)**2)) * y def weighted_prependicualr_aggricator(delta_noise_pred_pos, w_pos, delta_noise_pred_neg, w_neg): main_positive = delta_noise_pred_pos[0].unsqueeze(0) accumulated_output = 0 for i, complementory_positive in enumerate(delta_noise_pred_pos[1:]): accumulated_output += w_pos[i] * get_prependicualr_component(complementory_positive.unsqueeze(0), main_positive) for i, w_n in enumerate(w_neg): accumulated_output -= w_n * get_prependicualr_component(delta_noise_pred_neg[i].unsqueeze(0), main_positive) return accumulated_output + main_positive class ComposableStableDiffusionPipeline_perpneg(DiffusionPipeline): r""" Pipeline for text-to-image generation using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offsensive or harmful. Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details. feature_extractor ([`CLIPFeatureExtractor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPFeatureExtractor, ): super().__init__() #scheduler = scheduler.set_format("pt") self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"): r""" Enable sliced attention computation. When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease. Args: slice_size (`str` or `int`, *optional*, defaults to `"auto"`): When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`. """ if slice_size == "auto": # half the attention head size is usually a good trade-off between # speed and memory slice_size = self.unet.config.attention_head_dim // 2 self.unet.set_attention_slice(slice_size) def disable_attention_slicing(self): r""" Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go back to computing attention in one step. """ # set slice_size = `None` to disable `attention slicing` self.enable_attention_slicing(None) @torch.no_grad() def __call__( self, prompt: Union[str, List[str]], combined_w: Union[float, List[float]] = 0.5, height: Optional[int] = 512, width: Optional[int] = 512, num_inference_steps: Optional[int] = 50, guidance_scale: Optional[float] = 7.5, eta: Optional[float] = 0.0, generator: Optional[torch.Generator] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, weights: Optional[str] = "", **kwargs, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. height (`int`, *optional*, defaults to 512): The height in pixels of the generated image. width (`int`, *optional*, defaults to 512): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ if "torch_device" in kwargs: device = kwargs.pop("torch_device") warnings.warn( "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0." " Consider using `pipe.to(torch_device)` instead." ) # Set device as before (to be removed in 0.3.0) if device is None: device = "cuda" if torch.cuda.is_available() else "cpu" self.to(device) if isinstance(prompt, str): batch_size = 1 elif isinstance(prompt, list): batch_size = len(prompt) else: raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if '|' in prompt: prompt = [x.strip() for x in prompt.split('|')] print(f"composing {prompt}...") # get prompt text embeddings text_input = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0] print(text_embeddings.shape) text_embeddings = combined_w * text_embeddings[0].unsqueeze(0) + (1 - combined_w) * text_embeddings[1].unsqueeze(0) print(text_embeddings.shape) # if not weights: # # specify weights for prompts (excluding the unconditional score) # print('using equal weights for all prompts...') # pos_weights = torch.tensor([1 / (text_embeddings.shape[0] - 1)] * (text_embeddings.shape[0] - 1), # device=self.device).reshape(-1, 1, 1, 1) # neg_weights = torch.tensor([1.], device=self.device).reshape(-1, 1, 1, 1) # mask = torch.tensor([False] + [True] * pos_weights.shape[0], dtype=torch.bool) # else: weights = [float(w.strip()) for w in weights.split("|")] weights = torch.tensor(weights, device=self.device) assert len(weights) == text_embeddings.shape[0], "weights specified are not equal to the number of prompts" pos_weights = [] neg_weights = [] mask = [] # first one is unconditional score for w in weights: if w > 0: pos_weights.append(w) mask.append(True) else: neg_weights.append(abs(w)) mask.append(False) # normalize the weights pos_weights = torch.tensor(pos_weights, device=self.device).reshape(-1, 1, 1, 1) neg_weights = torch.tensor(neg_weights, device=self.device).reshape(-1, 1, 1, 1) mask = torch.tensor(mask, device=self.device, dtype=torch.bool) # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # get unconditional embeddings for classifier free guidance # if do_classifier_free_guidance: max_length = text_input.input_ids.shape[-1] uncond_input = self.tokenizer( [""] * 1, padding="max_length", max_length=max_length, return_tensors="pt" ) uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0] # if torch.all(mask): # # no negative prompts, so we use empty string as the negative prompt # uncond_input = self.tokenizer( # [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt" # ) # uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0] # # For classifier free guidance, we need to do two forward passes. # # Here we concatenate the unconditional and text embeddings into a single batch # # to avoid doing two forward passes # text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) # # update negative weights # neg_weights = torch.tensor([1.], device=self.device) # mask = torch.tensor([False] + mask.detach().tolist(), device=self.device, dtype=torch.bool) # get the initial random noise unless the user supplied it # Unlike in other pipelines, latents need to be generated in the target device # for 1-to-1 results reproducibility with the CompVis implementation. # However this currently doesn't work in `mps`. latents_device = "cpu" if self.device.type == "mps" else self.device latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8) if latents is None: latents = torch.randn( latents_shape, generator=generator, device=latents_device, ) else: if latents.shape != latents_shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") latents = latents.to(self.device) # set timesteps accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys()) extra_set_kwargs = {} if accepts_offset: extra_set_kwargs["offset"] = 1 self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs) # if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas if isinstance(self.scheduler, LMSDiscreteScheduler): latents = latents * self.scheduler.sigmas[0] # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * text_embeddings.shape[0]) if do_classifier_free_guidance else latents if isinstance(self.scheduler, LMSDiscreteScheduler): sigma = self.scheduler.sigmas[i] # the model input needs to be scaled to match the continuous ODE formulation in K-LMS latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5) # reduce memory by predicting each score sequentially noise_preds = [] # predict the noise residual for latent_in, text_embedding_in in zip( torch.chunk(latent_model_input, chunks=latent_model_input.shape[0], dim=0), torch.chunk(text_embeddings, chunks=text_embeddings.shape[0], dim=0)): noise_preds.append(self.unet(latent_in, t, encoder_hidden_states=text_embedding_in).sample) noise_preds = torch.cat(noise_preds, dim=0) noise_pred_uncond = self.unet(latents, t, encoder_hidden_states=uncond_embeddings).sample # perform guidance if do_classifier_free_guidance: # noise_pred_uncond = (noise_preds[~mask] * neg_weights).sum(dim=0, keepdims=True) # noise_pred_text = (noise_preds[mask] * pos_weights).sum(dim=0, keepdims=True) # noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # print("we are here") delta_noise_pred_neg = noise_preds[~mask] - noise_pred_uncond delta_noise_pred_pos = noise_preds[mask] - noise_pred_uncond noise_pred = noise_pred_uncond + guidance_scale * weighted_prependicualr_aggricator(delta_noise_pred_pos, pos_weights, delta_noise_pred_neg, neg_weights) # print("we are here11") # diff_v = noise_pred_text - noise_pred_uncond # diff_v = diff_v - (torch.mul(diff_v, noise_pred_text).sum()/torch.norm(noise_pred_text)**2) * noise_pred_text # noise_pred = noise_pred_text + guidance_scale * diff_v ## another try # noise_pred_uncond_prependicular = noise_pred_uncond - (torch.mul(noise_pred_uncond, noise_pred_text).sum()/torch.norm(noise_pred_text)**2) * noise_pred_text # noise_pred = (guidance_scale) * noise_pred_text - (guidance_scale-1) * noise_pred_uncond_prependicular # print("we are here") # compute the previous noisy sample x_t -> x_t-1 if isinstance(self.scheduler, LMSDiscreteScheduler): latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample else: latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # scale and decode the image latents with vae latents = 1 / 0.18215 * latents image = self.vae.decode(latents).sample image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).numpy() # # run safety checker # safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device) # image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values) has_nsfw_concept = [False for i in range(image.shape[0])] if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: perpneg_diffusion/perpneg_stable_diffusion/pipeline_composable_stable_diffusion_rotation.py ================================================ """ modified based on diffusion library from Huggingface: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py """ import inspect from itertools import accumulate import warnings from typing import List, Optional, Union import torch from tqdm.auto import tqdm from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer from diffusers.models import AutoencoderKL, UNet2DConditionModel from diffusers.pipeline_utils import DiffusionPipeline from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler from .safety_checker import StableDiffusionSafetyChecker from . import StableDiffusionPipelineOutput def get_prependicualr_component(x, y): assert x.shape == y.shape # print(((torch.mul(x, y).sum())/(torch.norm(y)**2)).shape) return x - ((torch.mul(x, y).sum())/(torch.norm(y)**2)) * y def weighted_prependicualr_aggricator(delta_noise_pred_pos, w_pos, delta_noise_pred_neg, w_neg): main_positive = delta_noise_pred_pos[0].unsqueeze(0) accumulated_output = 0 for i, complementory_positive in enumerate(delta_noise_pred_pos[1:]): accumulated_output += w_pos[i] * get_prependicualr_component(complementory_positive.unsqueeze(0), main_positive) for i, w_n in enumerate(w_neg): accumulated_output -= w_n * get_prependicualr_component(delta_noise_pred_neg[i].unsqueeze(0), main_positive) return accumulated_output + main_positive class ComposableStableDiffusionPipeline_perpneg(DiffusionPipeline): r""" Pipeline for text-to-image generation using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offsensive or harmful. Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details. feature_extractor ([`CLIPFeatureExtractor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPFeatureExtractor, ): super().__init__() #scheduler = scheduler.set_format("pt") self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"): r""" Enable sliced attention computation. When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease. Args: slice_size (`str` or `int`, *optional*, defaults to `"auto"`): When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`. """ if slice_size == "auto": # half the attention head size is usually a good trade-off between # speed and memory slice_size = self.unet.config.attention_head_dim // 2 self.unet.set_attention_slice(slice_size) def disable_attention_slicing(self): r""" Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go back to computing attention in one step. """ # set slice_size = `None` to disable `attention slicing` self.enable_attention_slicing(None) @torch.no_grad() def __call__( self, prompt: Union[str, List[str]], combined_w: Union[float, List[float]] = 0.5, height: Optional[int] = 512, width: Optional[int] = 512, num_inference_steps: Optional[int] = 50, guidance_scale: Optional[float] = 7.5, eta: Optional[float] = 0.0, generator: Optional[torch.Generator] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, weights: Optional[str] = "", **kwargs, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. height (`int`, *optional*, defaults to 512): The height in pixels of the generated image. width (`int`, *optional*, defaults to 512): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ if "torch_device" in kwargs: device = kwargs.pop("torch_device") warnings.warn( "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0." " Consider using `pipe.to(torch_device)` instead." ) # Set device as before (to be removed in 0.3.0) if device is None: device = "cuda" if torch.cuda.is_available() else "cpu" self.to(device) if isinstance(prompt, str): batch_size = 1 elif isinstance(prompt, list): batch_size = len(prompt) else: raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if '|' in prompt: prompt = [x.strip() for x in prompt.split('|')] print(f"composing {prompt}...") # get prompt text embeddings text_input = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0] print(text_embeddings.shape) text_embeddings = combined_w * text_embeddings[0].unsqueeze(0) + (1 - combined_w) * text_embeddings[1].unsqueeze(0) print(text_embeddings.shape) # if not weights: # # specify weights for prompts (excluding the unconditional score) # print('using equal weights for all prompts...') # pos_weights = torch.tensor([1 / (text_embeddings.shape[0] - 1)] * (text_embeddings.shape[0] - 1), # device=self.device).reshape(-1, 1, 1, 1) # neg_weights = torch.tensor([1.], device=self.device).reshape(-1, 1, 1, 1) # mask = torch.tensor([False] + [True] * pos_weights.shape[0], dtype=torch.bool) # else: weights = [float(w.strip()) for w in weights.split("|")] weights = torch.tensor(weights, device=self.device) assert len(weights) == text_embeddings.shape[0], "weights specified are not equal to the number of prompts" pos_weights = [] neg_weights = [] mask = [] # first one is unconditional score for w in weights: if w > 0: pos_weights.append(w) mask.append(True) else: neg_weights.append(abs(w)) mask.append(False) # normalize the weights pos_weights = torch.tensor(pos_weights, device=self.device).reshape(-1, 1, 1, 1) neg_weights = torch.tensor(neg_weights, device=self.device).reshape(-1, 1, 1, 1) mask = torch.tensor(mask, device=self.device, dtype=torch.bool) # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # get unconditional embeddings for classifier free guidance # if do_classifier_free_guidance: max_length = text_input.input_ids.shape[-1] uncond_input = self.tokenizer( [""] * 1, padding="max_length", max_length=max_length, return_tensors="pt" ) uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0] # if torch.all(mask): # # no negative prompts, so we use empty string as the negative prompt # uncond_input = self.tokenizer( # [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt" # ) # uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0] # # For classifier free guidance, we need to do two forward passes. # # Here we concatenate the unconditional and text embeddings into a single batch # # to avoid doing two forward passes # text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) # # update negative weights # neg_weights = torch.tensor([1.], device=self.device) # mask = torch.tensor([False] + mask.detach().tolist(), device=self.device, dtype=torch.bool) # get the initial random noise unless the user supplied it # Unlike in other pipelines, latents need to be generated in the target device # for 1-to-1 results reproducibility with the CompVis implementation. # However this currently doesn't work in `mps`. latents_device = "cpu" if self.device.type == "mps" else self.device latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8) if latents is None: latents = torch.randn( latents_shape, generator=generator, device=latents_device, ) else: if latents.shape != latents_shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") latents = latents.to(self.device) # set timesteps accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys()) extra_set_kwargs = {} if accepts_offset: extra_set_kwargs["offset"] = 1 self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs) # if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas if isinstance(self.scheduler, LMSDiscreteScheduler): latents = latents * self.scheduler.sigmas[0] # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * text_embeddings.shape[0]) if do_classifier_free_guidance else latents if isinstance(self.scheduler, LMSDiscreteScheduler): sigma = self.scheduler.sigmas[i] # the model input needs to be scaled to match the continuous ODE formulation in K-LMS latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5) # reduce memory by predicting each score sequentially noise_preds = [] # predict the noise residual for latent_in, text_embedding_in in zip( torch.chunk(latent_model_input, chunks=latent_model_input.shape[0], dim=0), torch.chunk(text_embeddings, chunks=text_embeddings.shape[0], dim=0)): noise_preds.append(self.unet(latent_in, t, encoder_hidden_states=text_embedding_in).sample) noise_preds = torch.cat(noise_preds, dim=0) noise_pred_uncond = self.unet(latents, t, encoder_hidden_states=uncond_embeddings).sample # perform guidance if do_classifier_free_guidance: # noise_pred_uncond = (noise_preds[~mask] * neg_weights).sum(dim=0, keepdims=True) # noise_pred_text = (noise_preds[mask] * pos_weights).sum(dim=0, keepdims=True) # noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # print("we are here") delta_noise_pred_neg = noise_preds[~mask] - noise_pred_uncond delta_noise_pred_pos = noise_preds[mask] - noise_pred_uncond noise_pred = noise_pred_uncond + guidance_scale * weighted_prependicualr_aggricator(delta_noise_pred_pos, pos_weights, delta_noise_pred_neg, neg_weights) # print("we are here11") # diff_v = noise_pred_text - noise_pred_uncond # diff_v = diff_v - (torch.mul(diff_v, noise_pred_text).sum()/torch.norm(noise_pred_text)**2) * noise_pred_text # noise_pred = noise_pred_text + guidance_scale * diff_v ## another try # noise_pred_uncond_prependicular = noise_pred_uncond - (torch.mul(noise_pred_uncond, noise_pred_text).sum()/torch.norm(noise_pred_text)**2) * noise_pred_text # noise_pred = (guidance_scale) * noise_pred_text - (guidance_scale-1) * noise_pred_uncond_prependicular # print("we are here") # compute the previous noisy sample x_t -> x_t-1 if isinstance(self.scheduler, LMSDiscreteScheduler): latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample else: latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # scale and decode the image latents with vae latents = 1 / 0.18215 * latents image = self.vae.decode(latents).sample image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).numpy() # # run safety checker # safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device) # image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values) has_nsfw_concept = [False for i in range(image.shape[0])] if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: perpneg_diffusion/perpneg_stable_diffusion/pipeline_perpneg_stable_diffusion.py ================================================ """ modified based on diffusion library from Huggingface: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py """ import inspect from itertools import accumulate import warnings from typing import List, Optional, Union import torch from tqdm.auto import tqdm from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer from diffusers.models import AutoencoderKL, UNet2DConditionModel from diffusers.pipeline_utils import DiffusionPipeline from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler from .safety_checker import StableDiffusionSafetyChecker from . import StableDiffusionPipelineOutput def get_prependicualr_component(x, y): assert x.shape == y.shape return x - ((torch.mul(x, y).sum())/(torch.norm(y)**2)) * y def weighted_prependicualr_aggricator(delta_noise_pred_pos, w_pos, delta_noise_pred_neg, w_neg): main_positive = delta_noise_pred_pos[0].unsqueeze(0) accumulated_output = 0 for i, complementory_positive in enumerate(delta_noise_pred_pos[1:]): accumulated_output += w_pos[i] * get_prependicualr_component(complementory_positive.unsqueeze(0), main_positive) for i, w_n in enumerate(w_neg): accumulated_output -= w_n * get_prependicualr_component(delta_noise_pred_neg[i].unsqueeze(0), main_positive) return accumulated_output + main_positive class PerpStableDiffusionPipeline(DiffusionPipeline): r""" Pipeline for text-to-image generation using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offsensive or harmful. Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details. feature_extractor ([`CLIPFeatureExtractor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPFeatureExtractor, ): super().__init__() #scheduler = scheduler.set_format("pt") self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"): r""" Enable sliced attention computation. When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease. Args: slice_size (`str` or `int`, *optional*, defaults to `"auto"`): When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`. """ if slice_size == "auto": # half the attention head size is usually a good trade-off between # speed and memory slice_size = self.unet.config.attention_head_dim // 2 self.unet.set_attention_slice(slice_size) def disable_attention_slicing(self): r""" Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go back to computing attention in one step. """ # set slice_size = `None` to disable `attention slicing` self.enable_attention_slicing(None) @torch.no_grad() def __call__( self, prompt: Union[str, List[str]], height: Optional[int] = 512, width: Optional[int] = 512, num_inference_steps: Optional[int] = 50, guidance_scale: Optional[float] = 7.5, eta: Optional[float] = 0.0, generator: Optional[torch.Generator] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, weights: Optional[str] = "", **kwargs, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. height (`int`, *optional*, defaults to 512): The height in pixels of the generated image. width (`int`, *optional*, defaults to 512): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ if "torch_device" in kwargs: device = kwargs.pop("torch_device") warnings.warn( "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0." " Consider using `pipe.to(torch_device)` instead." ) # Set device as before (to be removed in 0.3.0) if device is None: device = "cuda" if torch.cuda.is_available() else "cpu" self.to(device) if isinstance(prompt, str): batch_size = 1 elif isinstance(prompt, list): batch_size = len(prompt) else: raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if '|' in prompt: prompt = [x.strip() for x in prompt.split('|')] print(f"composing {prompt}...") # get prompt text embeddings text_input = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0] # if not weights: # # specify weights for prompts (excluding the unconditional score) # print('using equal weights for all prompts...') # pos_weights = torch.tensor([1 / (text_embeddings.shape[0] - 1)] * (text_embeddings.shape[0] - 1), # device=self.device).reshape(-1, 1, 1, 1) # neg_weights = torch.tensor([1.], device=self.device).reshape(-1, 1, 1, 1) # mask = torch.tensor([False] + [True] * pos_weights.shape[0], dtype=torch.bool) # else: weights = [float(w.strip()) for w in weights.split("|")] weights = torch.tensor(weights, device=self.device) assert len(weights) == text_embeddings.shape[0], "weights specified are not equal to the number of prompts" pos_weights = [] neg_weights = [] mask = [] # first one is unconditional score for w in weights: if w > 0: pos_weights.append(w) mask.append(True) else: neg_weights.append(abs(w)) mask.append(False) # normalize the weights pos_weights = torch.tensor(pos_weights, device=self.device).reshape(-1, 1, 1, 1) neg_weights = torch.tensor(neg_weights, device=self.device).reshape(-1, 1, 1, 1) mask = torch.tensor(mask, device=self.device, dtype=torch.bool) # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # get unconditional embeddings for classifier free guidance # if do_classifier_free_guidance: max_length = text_input.input_ids.shape[-1] uncond_input = self.tokenizer( [""] * 1, padding="max_length", max_length=max_length, return_tensors="pt" ) uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0] # get the initial random noise unless the user supplied it # Unlike in other pipelines, latents need to be generated in the target device # for 1-to-1 results reproducibility with the CompVis implementation. # However this currently doesn't work in `mps`. latents_device = "cpu" if self.device.type == "mps" else self.device latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8) if latents is None: latents = torch.randn( latents_shape, generator=generator, device=latents_device, ) else: if latents.shape != latents_shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") latents = latents.to(self.device) # set timesteps accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys()) extra_set_kwargs = {} if accepts_offset: extra_set_kwargs["offset"] = 1 self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs) # if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas if isinstance(self.scheduler, LMSDiscreteScheduler): latents = latents * self.scheduler.sigmas[0] # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * text_embeddings.shape[0]) if do_classifier_free_guidance else latents if isinstance(self.scheduler, LMSDiscreteScheduler): sigma = self.scheduler.sigmas[i] # the model input needs to be scaled to match the continuous ODE formulation in K-LMS latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5) # reduce memory by predicting each score sequentially noise_preds = [] # predict the noise residual for latent_in, text_embedding_in in zip( torch.chunk(latent_model_input, chunks=latent_model_input.shape[0], dim=0), torch.chunk(text_embeddings, chunks=text_embeddings.shape[0], dim=0)): noise_preds.append(self.unet(latent_in, t, encoder_hidden_states=text_embedding_in).sample) noise_preds = torch.cat(noise_preds, dim=0) noise_pred_uncond = self.unet(latents, t, encoder_hidden_states=uncond_embeddings).sample # perform guidance if do_classifier_free_guidance: delta_noise_pred_neg = noise_preds[~mask] - noise_pred_uncond delta_noise_pred_pos = noise_preds[mask] - noise_pred_uncond noise_pred = noise_pred_uncond + guidance_scale * weighted_prependicualr_aggricator(delta_noise_pred_pos, pos_weights, delta_noise_pred_neg, neg_weights) # compute the previous noisy sample x_t -> x_t-1 if isinstance(self.scheduler, LMSDiscreteScheduler): latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample else: latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # scale and decode the image latents with vae latents = 1 / 0.18215 * latents image = self.vae.decode(latents).sample image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).numpy() # run safety checker safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device) image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values) has_nsfw_concept = [False for i in range(image.shape[0])] if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: perpneg_diffusion/perpneg_stable_diffusion/pipeline_perpneg_stable_diffusion_rotation.py ================================================ """ modified based on diffusion library from Huggingface: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py """ import inspect from itertools import accumulate import warnings from typing import List, Optional, Union import torch from tqdm.auto import tqdm from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer from diffusers.models import AutoencoderKL, UNet2DConditionModel from diffusers.pipeline_utils import DiffusionPipeline from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler from .safety_checker import StableDiffusionSafetyChecker from . import StableDiffusionPipelineOutput def get_prependicualr_component(x, y): assert x.shape == y.shape return x - ((torch.mul(x, y).sum())/(torch.norm(y)**2)) * y def weighted_prependicualr_aggricator(delta_noise_pred_pos, w_pos, delta_noise_pred_neg, w_neg): main_positive = delta_noise_pred_pos[0].unsqueeze(0) accumulated_output = 0 for i, complementory_positive in enumerate(delta_noise_pred_pos[1:]): accumulated_output += w_pos[i] * get_prependicualr_component(complementory_positive.unsqueeze(0), main_positive) for i, w_n in enumerate(w_neg): accumulated_output -= w_n * get_prependicualr_component(delta_noise_pred_neg[i].unsqueeze(0), main_positive) return accumulated_output + main_positive class PerpStableDiffusionPipeline(DiffusionPipeline): r""" Pipeline for text-to-image generation using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offsensive or harmful. Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details. feature_extractor ([`CLIPFeatureExtractor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPFeatureExtractor, ): super().__init__() #scheduler = scheduler.set_format("pt") self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, ) def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"): r""" Enable sliced attention computation. When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease. Args: slice_size (`str` or `int`, *optional*, defaults to `"auto"`): When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`. """ if slice_size == "auto": # half the attention head size is usually a good trade-off between # speed and memory slice_size = self.unet.config.attention_head_dim // 2 self.unet.set_attention_slice(slice_size) def disable_attention_slicing(self): r""" Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go back to computing attention in one step. """ # set slice_size = `None` to disable `attention slicing` self.enable_attention_slicing(None) @torch.no_grad() def __call__( self, prompt: Union[str, List[str]], combined_w: Union[float, List[float]] = 0.5, height: Optional[int] = 512, width: Optional[int] = 512, num_inference_steps: Optional[int] = 50, guidance_scale: Optional[float] = 7.5, eta: Optional[float] = 0.0, generator: Optional[torch.Generator] = None, latents: Optional[torch.FloatTensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, weights: Optional[str] = "", **kwargs, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or prompts to guide the image generation. height (`int`, *optional*, defaults to 512): The height in pixels of the generated image. width (`int`, *optional*, defaults to 512): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 7.5): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator`, *optional*): A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the `safety_checker`. """ if "torch_device" in kwargs: device = kwargs.pop("torch_device") warnings.warn( "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0." " Consider using `pipe.to(torch_device)` instead." ) # Set device as before (to be removed in 0.3.0) if device is None: device = "cuda" if torch.cuda.is_available() else "cpu" self.to(device) if isinstance(prompt, str): batch_size = 1 elif isinstance(prompt, list): batch_size = len(prompt) else: raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if '|' in prompt: prompt = [x.strip() for x in prompt.split('|')] print(f"composing {prompt}...") # get prompt text embeddings text_input = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0] main_text = combined_w * text_embeddings[0] + (1 - combined_w) * text_embeddings[1] text_embeddings = torch.cat([main_text.unsqueeze(0), text_embeddings], dim=0) # if not weights: # # specify weights for prompts (excluding the unconditional score) # print('using equal weights for all prompts...') # pos_weights = torch.tensor([1 / (text_embeddings.shape[0] - 1)] * (text_embeddings.shape[0] - 1), # device=self.device).reshape(-1, 1, 1, 1) # neg_weights = torch.tensor([1.], device=self.device).reshape(-1, 1, 1, 1) # mask = torch.tensor([False] + [True] * pos_weights.shape[0], dtype=torch.bool) # else: weights = [float(w.strip()) for w in weights.split("|")] weights = torch.tensor(weights, device=self.device) assert len(weights) == text_embeddings.shape[0], "weights specified are not equal to the number of prompts" pos_weights = [] neg_weights = [] mask = [] # first one is unconditional score for w in weights: if w > 0: pos_weights.append(w) mask.append(True) else: neg_weights.append(abs(w)) mask.append(False) # normalize the weights pos_weights = torch.tensor(pos_weights, device=self.device).reshape(-1, 1, 1, 1) neg_weights = torch.tensor(neg_weights, device=self.device).reshape(-1, 1, 1, 1) mask = torch.tensor(mask, device=self.device, dtype=torch.bool) # text_info['weights'] = torch.cat([torch.ones(1).to(text_embeddings.device), text_info['weights']], dim=0) # decay_w_front_prompt = torch.exp(-(1-combine_w) * opt.front_decay_factor) * opt.scale_front_side_negate # decay factor: 4, 12 # decay_w_side_prompt = torch.exp(-combine_w * opt.side_decay_factor) * opt.scale_front_side_negate # scale: 1 # # print("before text_info,", text_info['neg_weights'][0]) # text_info['weights'][1] *= decay_w_front_prompt # text_info['weights'][2] *= decay_w_side_prompt # text_info['prompt'] = f' {torch.rad2deg(phis) = :.3f} {combine_w = :.3f} $$ ' + text_info['prompt'] # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # get unconditional embeddings for classifier free guidance # if do_classifier_free_guidance: max_length = text_input.input_ids.shape[-1] uncond_input = self.tokenizer( [""] * 1, padding="max_length", max_length=max_length, return_tensors="pt" ) uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0] # get the initial random noise unless the user supplied it # Unlike in other pipelines, latents need to be generated in the target device # for 1-to-1 results reproducibility with the CompVis implementation. # However this currently doesn't work in `mps`. latents_device = "cpu" if self.device.type == "mps" else self.device latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8) if latents is None: latents = torch.randn( latents_shape, generator=generator, device=latents_device, ) else: if latents.shape != latents_shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") latents = latents.to(self.device) # set timesteps accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys()) extra_set_kwargs = {} if accepts_offset: extra_set_kwargs["offset"] = 1 self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs) # if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas if isinstance(self.scheduler, LMSDiscreteScheduler): latents = latents * self.scheduler.sigmas[0] # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * text_embeddings.shape[0]) if do_classifier_free_guidance else latents if isinstance(self.scheduler, LMSDiscreteScheduler): sigma = self.scheduler.sigmas[i] # the model input needs to be scaled to match the continuous ODE formulation in K-LMS latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5) # reduce memory by predicting each score sequentially noise_preds = [] # predict the noise residual for latent_in, text_embedding_in in zip( torch.chunk(latent_model_input, chunks=latent_model_input.shape[0], dim=0), torch.chunk(text_embeddings, chunks=text_embeddings.shape[0], dim=0)): noise_preds.append(self.unet(latent_in, t, encoder_hidden_states=text_embedding_in).sample) noise_preds = torch.cat(noise_preds, dim=0) noise_pred_uncond = self.unet(latents, t, encoder_hidden_states=uncond_embeddings).sample # perform guidance if do_classifier_free_guidance: delta_noise_pred_neg = noise_preds[~mask] - noise_pred_uncond delta_noise_pred_pos = noise_preds[mask] - noise_pred_uncond noise_pred = noise_pred_uncond + guidance_scale * weighted_prependicualr_aggricator(delta_noise_pred_pos, pos_weights, delta_noise_pred_neg, neg_weights) # compute the previous noisy sample x_t -> x_t-1 if isinstance(self.scheduler, LMSDiscreteScheduler): latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample else: latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample # scale and decode the image latents with vae latents = 1 / 0.18215 * latents image = self.vae.decode(latents).sample image = (image / 2 + 0.5).clamp(0, 1) image = image.cpu().permute(0, 2, 3, 1).numpy() # run safety checker safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device) image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values) has_nsfw_concept = [False for i in range(image.shape[0])] if output_type == "pil": image = self.numpy_to_pil(image) if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: perpneg_diffusion/perpneg_stable_diffusion/safety_checker.py ================================================ """ file copy from diffusion library from Huggingface: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py """ import numpy as np import torch import torch.nn as nn from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel from diffusers.utils import logging logger = logging.get_logger(__name__) def cosine_distance(image_embeds, text_embeds): normalized_image_embeds = nn.functional.normalize(image_embeds) normalized_text_embeds = nn.functional.normalize(text_embeds) return torch.mm(normalized_image_embeds, normalized_text_embeds.t()) class StableDiffusionSafetyChecker(PreTrainedModel): config_class = CLIPConfig def __init__(self, config: CLIPConfig): super().__init__(config) self.vision_model = CLIPVisionModel(config.vision_config) self.visual_projection = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=False) self.concept_embeds = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=False) self.special_care_embeds = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=False) self.register_buffer("concept_embeds_weights", torch.ones(17)) self.register_buffer("special_care_embeds_weights", torch.ones(3)) @torch.no_grad() def forward(self, clip_input, images): pooled_output = self.vision_model(clip_input)[1] # pooled_output image_embeds = self.visual_projection(pooled_output) special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().numpy() cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().numpy() result = [] batch_size = image_embeds.shape[0] for i in range(batch_size): result_img = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []} # increase this value to create a stronger `nfsw` filter # at the cost of increasing the possibility of filtering benign images adjustment = 0.0 for concet_idx in range(len(special_cos_dist[0])): concept_cos = special_cos_dist[i][concet_idx] concept_threshold = self.special_care_embeds_weights[concet_idx].item() result_img["special_scores"][concet_idx] = round(concept_cos - concept_threshold + adjustment, 3) if result_img["special_scores"][concet_idx] > 0: result_img["special_care"].append({concet_idx, result_img["special_scores"][concet_idx]}) adjustment = 0.01 for concet_idx in range(len(cos_dist[0])): concept_cos = cos_dist[i][concet_idx] concept_threshold = self.concept_embeds_weights[concet_idx].item() result_img["concept_scores"][concet_idx] = round(concept_cos - concept_threshold + adjustment, 3) if result_img["concept_scores"][concet_idx] > 0: result_img["bad_concepts"].append(concet_idx) result.append(result_img) has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result] # for idx, has_nsfw_concept in enumerate(has_nsfw_concepts): # if has_nsfw_concept: # images[idx] = np.zeros(images[idx].shape) # black image # if any(has_nsfw_concepts): # logger.warning( # "Potential NSFW content was detected in one or more images. A black image will be returned instead." # " Try again with a different prompt and/or seed." # ) return images, has_nsfw_concepts @torch.inference_mode() def forward_onnx(self, clip_input: torch.FloatTensor, images: torch.FloatTensor): pooled_output = self.vision_model(clip_input)[1] # pooled_output image_embeds = self.visual_projection(pooled_output) special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds) cos_dist = cosine_distance(image_embeds, self.concept_embeds) # increase this value to create a stronger `nsfw` filter # at the cost of increasing the possibility of filtering benign images adjustment = 0.0 special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment # special_scores = special_scores.round(decimals=3) special_care = torch.any(special_scores > 0, dim=1) special_adjustment = special_care * 0.01 special_adjustment = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1]) concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment # concept_scores = concept_scores.round(decimals=3) has_nsfw_concepts = torch.any(concept_scores > 0, dim=1) #images[has_nsfw_concepts] = 0.0 # black image return images, has_nsfw_concepts ================================================ FILE: scripts/image_sample_stable_diffusion.py ================================================ import torch as th import numpy as np import torchvision.utils as tvu import os import argparse parser = argparse.ArgumentParser() parser.add_argument("--prompt", type=str, default="a forest | a camel", help="use '|' as the delimiter to compose separate sentences.") parser.add_argument("--steps", type=int, default=50) parser.add_argument("--scale", type=float, default=7.5) parser.add_argument("--weights", type=str, default="") parser.add_argument("--combined_weights", type=float, default=0) parser.add_argument("--pipeline", type=str, default="") parser.add_argument("--seed", type=int, default=0) parser.add_argument("--num_images", type=int, default=4) args = parser.parse_args() has_cuda = th.cuda.is_available() device = th.device('cpu' if not has_cuda else 'cuda') prompt = args.prompt scale = args.scale steps = args.steps if args.pipeline == "cebm": from perpneg_diffusion.perpneg_stable_diffusion.pipeline_composable_stable_diffusion import ComposableStableDiffusionPipeline pipe = ComposableStableDiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", use_auth_token=True ).to(device) rotation_flag=False elif args.pipeline == "perpneg": from perpneg_diffusion.perpneg_stable_diffusion.pipeline_perpneg_stable_diffusion import PerpStableDiffusionPipeline pipe = PerpStableDiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", use_auth_token=True ).to(device) rotation_flag=False elif args.pipeline == "rotation_perpneg": from perpneg_diffusion.perpneg_stable_diffusion.pipeline_perpneg_stable_diffusion_rotation import PerpStableDiffusionPipeline pipe = PerpStableDiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", use_auth_token=True ).to(device) rotation_flag=True def dummy(images, **kwargs): return images, False pipe.safety_checker = dummy os.makedirs(args.pipeline, exist_ok=True) images = [] generator = th.Generator("cuda").manual_seed(args.seed) for i in range(args.num_images): if rotation_flag: image = pipe(prompt, combined_w=args.combined_weights, guidance_scale=scale, num_inference_steps=steps, weights=args.weights, generator=generator)["images"][0] else: image = pipe(prompt, guidance_scale=scale, num_inference_steps=steps, weights=args.weights, generator=generator)["images"][0] images.append(th.from_numpy(np.array(image)).permute(2, 0, 1) / 255.) grid = tvu.make_grid(th.stack(images, dim=0), nrow=4, padding=0) tvu.save_image(grid, os.path.join(args.pipeline, f'{prompt}_{args.weights}.png')) ================================================ FILE: setup.py ================================================ from setuptools import setup setup( name="perpneg-diffusion", packages=[ "perpneg_diffusion", "perpneg_diffusion.perpneg_stable_diffusion", ], install_requires=[ "Pillow", "attrs", "torch", "filelock", "requests", "tqdm", "ftfy", "regex", "numpy", "blobfile", "torchvision", "diffuser", "transformers" ], author="huangjie and reza", version='2.0', ) ================================================ FILE: stable-dreamfusion/.gitignore ================================================ __pycache__/ build/ *.egg-info/ *.so venv_*/ tmp* # data/ ldm/data/ data2 scripts2 trial*/ .vs/ TOKEN *.ckpt densegridencoder tets/256_tets.npz .vscode/launch.json data2 data/car* data/chair* data/warrior* data/wd* data/space* data/corgi* data/turtle* # Only keep the original image, not the automatically-generated depth, normals, rgba data/baby_phoenix_on_ice_* data/bollywood_actress_* data/beach_house_1_* data/beach_house_2_* data/mona_lisa_* data/futuristic_car_* data/church_ruins_* ================================================ FILE: stable-dreamfusion/LICENSE ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: stable-dreamfusion/activation.py ================================================ import torch from torch.autograd import Function from torch.cuda.amp import custom_bwd, custom_fwd class _trunc_exp(Function): @staticmethod @custom_fwd(cast_inputs=torch.float) def forward(ctx, x): ctx.save_for_backward(x) return torch.exp(x) @staticmethod @custom_bwd def backward(ctx, g): x = ctx.saved_tensors[0] return g * torch.exp(x.clamp(max=15)) trunc_exp = _trunc_exp.apply def biased_softplus(x, bias=0): return torch.nn.functional.softplus(x - bias) ================================================ FILE: stable-dreamfusion/assets/advanced.md ================================================ # Code organization & Advanced tips This is a simple description of the most important implementation details. If you are interested in improving this repo, this might be a starting point. Any contribution would be greatly appreciated! * The SDS loss is located at `./guidance/sd_utils.py > StableDiffusion > train_step`: ```python ## 1. we need to interpolate the NeRF rendering to 512x512, to feed it to SD's VAE. pred_rgb_512 = F.interpolate(pred_rgb, (512, 512), mode='bilinear', align_corners=False) ## 2. image (512x512) --- VAE --> latents (64x64), this is SD's difference from Imagen. latents = self.encode_imgs(pred_rgb_512) ... # timestep sampling, noise adding and UNet noise predicting ## 3. the SDS loss w = (1 - self.alphas[t]) grad = w * (noise_pred - noise) # since UNet part is ignored and cannot simply audodiff, we have two ways to set the grad: # 3.1. call backward and set the grad now (need to retain graph since we will call a second backward for the other losses later) latents.backward(gradient=grad, retain_graph=True) return 0 # dummy loss # 3.2. use a custom function to set a hook in backward, so we only call backward once (credits to @elliottzheng) class SpecifyGradient(torch.autograd.Function): @staticmethod @custom_fwd def forward(ctx, input_tensor, gt_grad): ctx.save_for_backward(gt_grad) # we return a dummy value 1, which will be scaled by amp's scaler so we get the scale in backward. return torch.ones([1], device=input_tensor.device, dtype=input_tensor.dtype) @staticmethod @custom_bwd def backward(ctx, grad_scale): gt_grad, = ctx.saved_tensors gt_grad = gt_grad * grad_scale return gt_grad, None loss = SpecifyGradient.apply(latents, grad) return loss # functional loss ``` * Other regularizations are in `./nerf/utils.py > Trainer > train_step`. * The generation seems quite sensitive to regularizations on weights_sum (alphas for each ray). The original opacity loss tends to make NeRF disappear (zero density everywhere), so we use an entropy loss to replace it for now (encourages alpha to be either 0 or 1). * NeRF Rendering core function: `./nerf/renderer.py > NeRFRenderer > run & run_cuda`. * Shading & normal evaluation: `./nerf/network*.py > NeRFNetwork > forward`. * light direction: current implementation use a plane light source, instead of a point light source. * View-dependent prompting: `./nerf/provider.py > get_view_direction`. * use `--angle_overhead, --angle_front` to set the border. * Network backbone (`./nerf/network*.py`) can be chosen by the `--backbone` option. * Spatial density bias (density blob): `./nerf/network*.py > NeRFNetwork > density_blob`. # Debugging `debugpy-run` is a convenient way to remotely debug this project. Simply replace a command like this one: ```bash python main.py --text "a hamburger" --workspace trial -O --vram_O ``` ... with: ```bash debugpy-run main.py -- --text "a hamburger" --workspace trial -O --vram_O ``` For more details: https://github.com/bulletmark/debugpy-run # Axes and directions of polar, azimuth, etc. in NeRF and Zero123 NeRF_Zero123 This code refers to theta for polar, phi for azimuth. ================================================ FILE: stable-dreamfusion/assets/update_logs.md ================================================ ### 2023.4.19 * Fix depth supervision, migrate depth estimation model to omnidata. * Add normal supervision (also by omnidata). https://user-images.githubusercontent.com/25863658/232403294-b77409bf-ddc7-4bb8-af32-ee0cc123825a.mp4 ### 2023.4.7 Improvement on mesh quality & DMTet finetuning support. https://user-images.githubusercontent.com/25863658/230535363-298c960e-bf9c-4906-8b96-cd60edcb24dd.mp4 ### 2023.3.30 * adopt ideas from [Fantasia3D](https://fantasia3d.github.io/) to concatenate normal and mask as the latent code in a warm up stage, which shows faster convergence of shape. https://user-images.githubusercontent.com/25863658/230535373-6ee28f16-bb21-4ec4-bc86-d46597361a04.mp4 ### 2023.1.30 * Use an MLP to predict the surface normals as in Magic3D to avoid finite difference / second order gradient, generation quality is greatly improved. * More efficient two-pass raymarching in training inspired by nerfacc. https://user-images.githubusercontent.com/25863658/215996308-9fd959f5-b5c7-4a8e-a241-0fe63ec86a4a.mp4 ### 2022.12.3 * Support Stable-diffusion 2.0 base. ### 2022.11.15 * Add the vanilla backbone that is pure-pytorch. ### 2022.10.9 * The shading (partially) starts to work, at least it won't make scene empty. For some prompts, it shows better results (less severe Janus problem). The textureless rendering mode is still disabled. * Enable shading by default (--latent_iter_ratio 1000). ### 2022.10.5 * Basic reproduction finished. * Non --cuda_ray, --tcnn are not working, need to fix. * Shading is not working, disabled in utils.py for now. Surface normals are bad. * Use an entropy loss to regularize weights_sum (alpha), the original L2 reg always leads to degenerated geometry... https://user-images.githubusercontent.com/25863658/194241493-f3e68f78-aefe-479e-a4a8-001424a61b37.mp4 ================================================ FILE: stable-dreamfusion/config/anya.csv ================================================ zero123_weight, radius, polar, azimuth, image 1, 3, 90, 0, data/anya_front_rgba.png 1, 3, 90, 180, data/anya_back_rgba.png ================================================ FILE: stable-dreamfusion/config/car.csv ================================================ zero123_weight, radius, polar, azimuth, image 4, 3.2, 90, 0, data/car_left_rgba.png 1, 3, 90, 90, data/car_front_rgba.png 4, 3.2, 90, 180, data/car_right_rgba.png 1, 3, 90, -90, data/car_back_rgba.png ================================================ FILE: stable-dreamfusion/config/corgi.csv ================================================ zero123_weight, radius, polar, azimuth, image 1, 3.2, 90, 0, data/corgi_puppy_sitting_looking_up_rgba.png ================================================ FILE: stable-dreamfusion/docker/Dockerfile ================================================ FROM nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04 # Remove any third-party apt sources to avoid issues with expiring keys. RUN rm -f /etc/apt/sources.list.d/*.list RUN apt-get update RUN DEBIAN_FRONTEND=noninteractive TZ=Europe/MADRID apt-get install -y tzdata # Install some basic utilities RUN apt-get install -y \ curl \ ca-certificates \ sudo \ git \ bzip2 \ libx11-6 \ python3 \ python3-pip \ libglfw3-dev \ libgles2-mesa-dev \ libglib2.0-0 \ && rm -rf /var/lib/apt/lists/* # Create a working directory RUN mkdir /app WORKDIR /app RUN cd /app RUN git clone https://github.com/ashawkey/stable-dreamfusion.git RUN pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116 WORKDIR /app/stable-dreamfusion RUN pip3 install -r requirements.txt RUN pip3 install git+https://github.com/NVlabs/nvdiffrast/ # Needs nvidia runtime, if you have "No CUDA runtime is found" error: https://stackoverflow.com/questions/59691207/docker-build-with-nvidia-runtime, first answer RUN pip3 install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch RUN pip3 install git+https://github.com/openai/CLIP.git RUN bash scripts/install_ext.sh # Set the default command to python3 #CMD ["python3"] ================================================ FILE: stable-dreamfusion/docker/README.md ================================================ ### Docker installation ## Build image To build the docker image on your own machine, which may take 15-30 mins: ``` docker build -t stable-dreamfusion:latest . ``` If you have the error **No CUDA runtime is found** when building the wheels for tiny-cuda-nn you need to setup the nvidia-runtime for docker. ``` sudo apt-get install nvidia-container-runtime ``` Then edit `/etc/docker/daemon.json` and add the default-runtime: ``` { "runtimes": { "nvidia": { "path": "nvidia-container-runtime", "runtimeArgs": [] } }, "default-runtime": "nvidia" } ``` And restart docker: ``` sudo systemctl restart docker ``` Now you can build tiny-cuda-nn inside docker. ## Download image To download the image (~6GB) instead: ``` docker pull supercabb/stable-dreamfusion:3080_0.0.1 docker tag supercabb/stable-dreamfusion:3080_0.0.1 stable-dreamfusion ``` ## Use image You can launch an interactive shell inside the container: ``` docker run --gpus all -it --rm -v $(cd ~ && pwd):/mnt stable-dreamfusion /bin/bash ``` From this shell, all the code in the repo should work. To run any single command `` inside the docker container: ``` docker run --gpus all -it --rm -v $(cd ~ && pwd):/mnt stable-dreamfusion /bin/bash -c "" ``` To train: ``` export TOKEN="#HUGGING FACE ACCESS TOKEN#" docker run --gpus all -it --rm -v $(cd ~ && pwd):/mnt stable-dreamfusion /bin/bash -c "echo ${TOKEN} > TOKEN \ && python3 main.py --text \"a hamburger\" --workspace trial -O" ``` Run test without gui: ``` export PATH_TO_WORKSPACE="#PATH_TO_WORKSPACE#" docker run --gpus all -it --rm -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix:ro -v $(cd ~ && pwd):/mnt \ -v $(cd ${PATH_TO_WORKSPACE} && pwd):/app/stable-dreamfusion/trial stable-dreamfusion /bin/bash -c "python3 \ main.py --workspace trial -O --test" ``` Run test with gui: ``` export PATH_TO_WORKSPACE="#PATH_TO_WORKSPACE#" xhost + docker run --gpus all -it --rm -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix:ro -v $(cd ~ && pwd):/mnt \ -v $(cd ${PATH_TO_WORKSPACE} && pwd):/app/stable-dreamfusion/trial stable-dreamfusion /bin/bash -c "python3 \ main.py --workspace trial -O --test --gui" xhost - ``` ================================================ FILE: stable-dreamfusion/dpt.py ================================================ import math import types import torch import torch.nn as nn import torch.nn.functional as F import timm class BaseModel(torch.nn.Module): def load(self, path): """Load model from file. Args: path (str): file path """ parameters = torch.load(path, map_location=torch.device('cpu')) if "optimizer" in parameters: parameters = parameters["model"] self.load_state_dict(parameters) def unflatten_with_named_tensor(input, dim, sizes): """Workaround for unflattening with named tensor.""" # tracer acts up with unflatten. See https://github.com/pytorch/pytorch/issues/49538 new_shape = list(input.shape)[:dim] + list(sizes) + list(input.shape)[dim+1:] return input.view(*new_shape) class Slice(nn.Module): def __init__(self, start_index=1): super(Slice, self).__init__() self.start_index = start_index def forward(self, x): return x[:, self.start_index :] class AddReadout(nn.Module): def __init__(self, start_index=1): super(AddReadout, self).__init__() self.start_index = start_index def forward(self, x): if self.start_index == 2: readout = (x[:, 0] + x[:, 1]) / 2 else: readout = x[:, 0] return x[:, self.start_index :] + readout.unsqueeze(1) class ProjectReadout(nn.Module): def __init__(self, in_features, start_index=1): super(ProjectReadout, self).__init__() self.start_index = start_index self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU()) def forward(self, x): readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :]) features = torch.cat((x[:, self.start_index :], readout), -1) return self.project(features) class Transpose(nn.Module): def __init__(self, dim0, dim1): super(Transpose, self).__init__() self.dim0 = dim0 self.dim1 = dim1 def forward(self, x): x = x.transpose(self.dim0, self.dim1) return x def forward_vit(pretrained, x): b, c, h, w = x.shape glob = pretrained.model.forward_flex(x) layer_1 = pretrained.activations["1"] layer_2 = pretrained.activations["2"] layer_3 = pretrained.activations["3"] layer_4 = pretrained.activations["4"] layer_1 = pretrained.act_postprocess1[0:2](layer_1) layer_2 = pretrained.act_postprocess2[0:2](layer_2) layer_3 = pretrained.act_postprocess3[0:2](layer_3) layer_4 = pretrained.act_postprocess4[0:2](layer_4) unflattened_dim = 2 unflattened_size = ( int(torch.div(h, pretrained.model.patch_size[1], rounding_mode='floor')), int(torch.div(w, pretrained.model.patch_size[0], rounding_mode='floor')), ) unflatten = nn.Sequential(nn.Unflatten(unflattened_dim, unflattened_size)) if layer_1.ndim == 3: layer_1 = unflatten(layer_1) if layer_2.ndim == 3: layer_2 = unflatten(layer_2) if layer_3.ndim == 3: layer_3 = unflatten_with_named_tensor(layer_3, unflattened_dim, unflattened_size) if layer_4.ndim == 3: layer_4 = unflatten_with_named_tensor(layer_4, unflattened_dim, unflattened_size) layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1) layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2) layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3) layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4) return layer_1, layer_2, layer_3, layer_4 def _resize_pos_embed(self, posemb, gs_h, gs_w): posemb_tok, posemb_grid = ( posemb[:, : self.start_index], posemb[0, self.start_index :], ) gs_old = int(math.sqrt(posemb_grid.shape[0])) posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear") posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1) posemb = torch.cat([posemb_tok, posemb_grid], dim=1) return posemb def forward_flex(self, x): b, c, h, w = x.shape pos_embed = self._resize_pos_embed( self.pos_embed, torch.div(h, self.patch_size[1], rounding_mode='floor'), torch.div(w, self.patch_size[0], rounding_mode='floor') ) B = x.shape[0] if hasattr(self.patch_embed, "backbone"): x = self.patch_embed.backbone(x) if isinstance(x, (list, tuple)): x = x[-1] # last feature if backbone outputs list/tuple of features x = self.patch_embed.proj(x).flatten(2).transpose(1, 2) if getattr(self, "dist_token", None) is not None: cls_tokens = self.cls_token.expand( B, -1, -1 ) # stole cls_tokens impl from Phil Wang, thanks dist_token = self.dist_token.expand(B, -1, -1) x = torch.cat((cls_tokens, dist_token, x), dim=1) else: cls_tokens = self.cls_token.expand( B, -1, -1 ) # stole cls_tokens impl from Phil Wang, thanks x = torch.cat((cls_tokens, x), dim=1) x = x + pos_embed x = self.pos_drop(x) for blk in self.blocks: x = blk(x) x = self.norm(x) return x activations = {} def get_activation(name): def hook(model, input, output): activations[name] = output return hook def get_readout_oper(vit_features, features, use_readout, start_index=1): if use_readout == "ignore": readout_oper = [Slice(start_index)] * len(features) elif use_readout == "add": readout_oper = [AddReadout(start_index)] * len(features) elif use_readout == "project": readout_oper = [ ProjectReadout(vit_features, start_index) for out_feat in features ] else: assert ( False ), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'" return readout_oper def _make_vit_b16_backbone( model, features=[96, 192, 384, 768], size=[384, 384], hooks=[2, 5, 8, 11], vit_features=768, use_readout="ignore", start_index=1, ): pretrained = nn.Module() pretrained.model = model pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1")) pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2")) pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3")) pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4")) pretrained.activations = activations readout_oper = get_readout_oper(vit_features, features, use_readout, start_index) # 32, 48, 136, 384 pretrained.act_postprocess1 = nn.Sequential( readout_oper[0], Transpose(1, 2), nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), nn.Conv2d( in_channels=vit_features, out_channels=features[0], kernel_size=1, stride=1, padding=0, ), nn.ConvTranspose2d( in_channels=features[0], out_channels=features[0], kernel_size=4, stride=4, padding=0, bias=True, dilation=1, groups=1, ), ) pretrained.act_postprocess2 = nn.Sequential( readout_oper[1], Transpose(1, 2), nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), nn.Conv2d( in_channels=vit_features, out_channels=features[1], kernel_size=1, stride=1, padding=0, ), nn.ConvTranspose2d( in_channels=features[1], out_channels=features[1], kernel_size=2, stride=2, padding=0, bias=True, dilation=1, groups=1, ), ) pretrained.act_postprocess3 = nn.Sequential( readout_oper[2], Transpose(1, 2), nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), nn.Conv2d( in_channels=vit_features, out_channels=features[2], kernel_size=1, stride=1, padding=0, ), ) pretrained.act_postprocess4 = nn.Sequential( readout_oper[3], Transpose(1, 2), nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), nn.Conv2d( in_channels=vit_features, out_channels=features[3], kernel_size=1, stride=1, padding=0, ), nn.Conv2d( in_channels=features[3], out_channels=features[3], kernel_size=3, stride=2, padding=1, ), ) pretrained.model.start_index = start_index pretrained.model.patch_size = [16, 16] # We inject this function into the VisionTransformer instances so that # we can use it with interpolated position embeddings without modifying the library source. pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model) pretrained.model._resize_pos_embed = types.MethodType( _resize_pos_embed, pretrained.model ) return pretrained def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None): model = timm.create_model("vit_large_patch16_384", pretrained=pretrained) hooks = [5, 11, 17, 23] if hooks == None else hooks return _make_vit_b16_backbone( model, features=[256, 512, 1024, 1024], hooks=hooks, vit_features=1024, use_readout=use_readout, ) def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None): model = timm.create_model("vit_base_patch16_384", pretrained=pretrained) hooks = [2, 5, 8, 11] if hooks == None else hooks return _make_vit_b16_backbone( model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout ) def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None): model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained) hooks = [2, 5, 8, 11] if hooks == None else hooks return _make_vit_b16_backbone( model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout ) def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None): model = timm.create_model( "vit_deit_base_distilled_patch16_384", pretrained=pretrained ) hooks = [2, 5, 8, 11] if hooks == None else hooks return _make_vit_b16_backbone( model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout, start_index=2, ) def _make_vit_b_rn50_backbone( model, features=[256, 512, 768, 768], size=[384, 384], hooks=[0, 1, 8, 11], vit_features=768, use_vit_only=False, use_readout="ignore", start_index=1, ): pretrained = nn.Module() pretrained.model = model if use_vit_only == True: pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1")) pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2")) else: pretrained.model.patch_embed.backbone.stages[0].register_forward_hook( get_activation("1") ) pretrained.model.patch_embed.backbone.stages[1].register_forward_hook( get_activation("2") ) pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3")) pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4")) pretrained.activations = activations readout_oper = get_readout_oper(vit_features, features, use_readout, start_index) if use_vit_only == True: pretrained.act_postprocess1 = nn.Sequential( readout_oper[0], Transpose(1, 2), nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), nn.Conv2d( in_channels=vit_features, out_channels=features[0], kernel_size=1, stride=1, padding=0, ), nn.ConvTranspose2d( in_channels=features[0], out_channels=features[0], kernel_size=4, stride=4, padding=0, bias=True, dilation=1, groups=1, ), ) pretrained.act_postprocess2 = nn.Sequential( readout_oper[1], Transpose(1, 2), nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), nn.Conv2d( in_channels=vit_features, out_channels=features[1], kernel_size=1, stride=1, padding=0, ), nn.ConvTranspose2d( in_channels=features[1], out_channels=features[1], kernel_size=2, stride=2, padding=0, bias=True, dilation=1, groups=1, ), ) else: pretrained.act_postprocess1 = nn.Sequential( nn.Identity(), nn.Identity(), nn.Identity() ) pretrained.act_postprocess2 = nn.Sequential( nn.Identity(), nn.Identity(), nn.Identity() ) pretrained.act_postprocess3 = nn.Sequential( readout_oper[2], Transpose(1, 2), nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), nn.Conv2d( in_channels=vit_features, out_channels=features[2], kernel_size=1, stride=1, padding=0, ), ) pretrained.act_postprocess4 = nn.Sequential( readout_oper[3], Transpose(1, 2), nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), nn.Conv2d( in_channels=vit_features, out_channels=features[3], kernel_size=1, stride=1, padding=0, ), nn.Conv2d( in_channels=features[3], out_channels=features[3], kernel_size=3, stride=2, padding=1, ), ) pretrained.model.start_index = start_index pretrained.model.patch_size = [16, 16] # We inject this function into the VisionTransformer instances so that # we can use it with interpolated position embeddings without modifying the library source. pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model) # We inject this function into the VisionTransformer instances so that # we can use it with interpolated position embeddings without modifying the library source. pretrained.model._resize_pos_embed = types.MethodType( _resize_pos_embed, pretrained.model ) return pretrained def _make_pretrained_vitb_rn50_384( pretrained, use_readout="ignore", hooks=None, use_vit_only=False ): model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained) hooks = [0, 1, 8, 11] if hooks == None else hooks return _make_vit_b_rn50_backbone( model, features=[256, 512, 768, 768], size=[384, 384], hooks=hooks, use_vit_only=use_vit_only, use_readout=use_readout, ) def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",): if backbone == "vitl16_384": pretrained = _make_pretrained_vitl16_384( use_pretrained, hooks=hooks, use_readout=use_readout ) scratch = _make_scratch( [256, 512, 1024, 1024], features, groups=groups, expand=expand ) # ViT-L/16 - 85.0% Top1 (backbone) elif backbone == "vitb_rn50_384": pretrained = _make_pretrained_vitb_rn50_384( use_pretrained, hooks=hooks, use_vit_only=use_vit_only, use_readout=use_readout, ) scratch = _make_scratch( [256, 512, 768, 768], features, groups=groups, expand=expand ) # ViT-H/16 - 85.0% Top1 (backbone) elif backbone == "vitb16_384": pretrained = _make_pretrained_vitb16_384( use_pretrained, hooks=hooks, use_readout=use_readout ) scratch = _make_scratch( [96, 192, 384, 768], features, groups=groups, expand=expand ) # ViT-B/16 - 84.6% Top1 (backbone) elif backbone == "resnext101_wsl": pretrained = _make_pretrained_resnext101_wsl(use_pretrained) scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand) # efficientnet_lite3 elif backbone == "efficientnet_lite3": pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable) scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand) # efficientnet_lite3 else: print(f"Backbone '{backbone}' not implemented") assert False return pretrained, scratch def _make_scratch(in_shape, out_shape, groups=1, expand=False): scratch = nn.Module() out_shape1 = out_shape out_shape2 = out_shape out_shape3 = out_shape out_shape4 = out_shape if expand==True: out_shape1 = out_shape out_shape2 = out_shape*2 out_shape3 = out_shape*4 out_shape4 = out_shape*8 scratch.layer1_rn = nn.Conv2d( in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups ) scratch.layer2_rn = nn.Conv2d( in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups ) scratch.layer3_rn = nn.Conv2d( in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups ) scratch.layer4_rn = nn.Conv2d( in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups ) return scratch def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False): efficientnet = torch.hub.load( "rwightman/gen-efficientnet-pytorch", "tf_efficientnet_lite3", pretrained=use_pretrained, exportable=exportable ) return _make_efficientnet_backbone(efficientnet) def _make_efficientnet_backbone(effnet): pretrained = nn.Module() pretrained.layer1 = nn.Sequential( effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2] ) pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3]) pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5]) pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9]) return pretrained def _make_resnet_backbone(resnet): pretrained = nn.Module() pretrained.layer1 = nn.Sequential( resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1 ) pretrained.layer2 = resnet.layer2 pretrained.layer3 = resnet.layer3 pretrained.layer4 = resnet.layer4 return pretrained def _make_pretrained_resnext101_wsl(use_pretrained): resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl") return _make_resnet_backbone(resnet) class Interpolate(nn.Module): """Interpolation module. """ def __init__(self, scale_factor, mode, align_corners=False): """Init. Args: scale_factor (float): scaling mode (str): interpolation mode """ super(Interpolate, self).__init__() self.interp = nn.functional.interpolate self.scale_factor = scale_factor self.mode = mode self.align_corners = align_corners def forward(self, x): """Forward pass. Args: x (tensor): input Returns: tensor: interpolated data """ x = self.interp( x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners ) return x class ResidualConvUnit(nn.Module): """Residual convolution module. """ def __init__(self, features): """Init. Args: features (int): number of features """ super().__init__() self.conv1 = nn.Conv2d( features, features, kernel_size=3, stride=1, padding=1, bias=True ) self.conv2 = nn.Conv2d( features, features, kernel_size=3, stride=1, padding=1, bias=True ) self.relu = nn.ReLU(inplace=True) def forward(self, x): """Forward pass. Args: x (tensor): input Returns: tensor: output """ out = self.relu(x) out = self.conv1(out) out = self.relu(out) out = self.conv2(out) return out + x class FeatureFusionBlock(nn.Module): """Feature fusion block. """ def __init__(self, features): """Init. Args: features (int): number of features """ super(FeatureFusionBlock, self).__init__() self.resConfUnit1 = ResidualConvUnit(features) self.resConfUnit2 = ResidualConvUnit(features) def forward(self, *xs): """Forward pass. Returns: tensor: output """ output = xs[0] if len(xs) == 2: output += self.resConfUnit1(xs[1]) output = self.resConfUnit2(output) output = nn.functional.interpolate( output, scale_factor=2, mode="bilinear", align_corners=True ) return output class ResidualConvUnit_custom(nn.Module): """Residual convolution module. """ def __init__(self, features, activation, bn): """Init. Args: features (int): number of features """ super().__init__() self.bn = bn self.groups=1 self.conv1 = nn.Conv2d( features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups ) self.conv2 = nn.Conv2d( features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups ) if self.bn==True: self.bn1 = nn.BatchNorm2d(features) self.bn2 = nn.BatchNorm2d(features) self.activation = activation self.skip_add = nn.quantized.FloatFunctional() def forward(self, x): """Forward pass. Args: x (tensor): input Returns: tensor: output """ out = self.activation(x) out = self.conv1(out) if self.bn==True: out = self.bn1(out) out = self.activation(out) out = self.conv2(out) if self.bn==True: out = self.bn2(out) if self.groups > 1: out = self.conv_merge(out) return self.skip_add.add(out, x) # return out + x class FeatureFusionBlock_custom(nn.Module): """Feature fusion block. """ def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True): """Init. Args: features (int): number of features """ super(FeatureFusionBlock_custom, self).__init__() self.deconv = deconv self.align_corners = align_corners self.groups=1 self.expand = expand out_features = features if self.expand==True: out_features = features//2 self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1) self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn) self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn) self.skip_add = nn.quantized.FloatFunctional() def forward(self, *xs): """Forward pass. Returns: tensor: output """ output = xs[0] if len(xs) == 2: res = self.resConfUnit1(xs[1]) output = self.skip_add.add(output, res) # output += res output = self.resConfUnit2(output) output = nn.functional.interpolate( output, scale_factor=2, mode="bilinear", align_corners=self.align_corners ) output = self.out_conv(output) return output def _make_fusion_block(features, use_bn): return FeatureFusionBlock_custom( features, nn.ReLU(False), deconv=False, bn=use_bn, expand=False, align_corners=True, ) class DPT(BaseModel): def __init__( self, head, features=256, backbone="vitb_rn50_384", readout="project", channels_last=False, use_bn=False, ): super(DPT, self).__init__() self.channels_last = channels_last hooks = { "vitb_rn50_384": [0, 1, 8, 11], "vitb16_384": [2, 5, 8, 11], "vitl16_384": [5, 11, 17, 23], } # Instantiate backbone and reassemble blocks self.pretrained, self.scratch = _make_encoder( backbone, features, True, # Set to true of you want to train from scratch, uses ImageNet weights groups=1, expand=False, exportable=False, hooks=hooks[backbone], use_readout=readout, ) self.scratch.refinenet1 = _make_fusion_block(features, use_bn) self.scratch.refinenet2 = _make_fusion_block(features, use_bn) self.scratch.refinenet3 = _make_fusion_block(features, use_bn) self.scratch.refinenet4 = _make_fusion_block(features, use_bn) self.scratch.output_conv = head def forward(self, x): if self.channels_last == True: x.contiguous(memory_format=torch.channels_last) layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x) layer_1_rn = self.scratch.layer1_rn(layer_1) layer_2_rn = self.scratch.layer2_rn(layer_2) layer_3_rn = self.scratch.layer3_rn(layer_3) layer_4_rn = self.scratch.layer4_rn(layer_4) path_4 = self.scratch.refinenet4(layer_4_rn) path_3 = self.scratch.refinenet3(path_4, layer_3_rn) path_2 = self.scratch.refinenet2(path_3, layer_2_rn) path_1 = self.scratch.refinenet1(path_2, layer_1_rn) out = self.scratch.output_conv(path_1) return out class DPTDepthModel(DPT): def __init__(self, path=None, non_negative=True, num_channels=1, **kwargs): features = kwargs["features"] if "features" in kwargs else 256 head = nn.Sequential( nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1), Interpolate(scale_factor=2, mode="bilinear", align_corners=True), nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1), nn.ReLU(True), nn.Conv2d(32, num_channels, kernel_size=1, stride=1, padding=0), nn.ReLU(True) if non_negative else nn.Identity(), nn.Identity(), ) super().__init__(head, **kwargs) if path is not None: self.load(path) def forward(self, x): return super().forward(x).squeeze(dim=1) ================================================ FILE: stable-dreamfusion/encoding.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F class FreqEncoder_torch(nn.Module): def __init__(self, input_dim, max_freq_log2, N_freqs, log_sampling=True, include_input=True, periodic_fns=(torch.sin, torch.cos)): super().__init__() self.input_dim = input_dim self.include_input = include_input self.periodic_fns = periodic_fns self.N_freqs = N_freqs self.output_dim = 0 if self.include_input: self.output_dim += self.input_dim self.output_dim += self.input_dim * N_freqs * len(self.periodic_fns) if log_sampling: self.freq_bands = 2 ** torch.linspace(0, max_freq_log2, N_freqs) else: self.freq_bands = torch.linspace(2 ** 0, 2 ** max_freq_log2, N_freqs) self.freq_bands = self.freq_bands.numpy().tolist() def forward(self, input, max_level=None, **kwargs): if max_level is None: max_level = self.N_freqs else: max_level = int(max_level * self.N_freqs) out = [] if self.include_input: out.append(input) for i in range(max_level): freq = self.freq_bands[i] for p_fn in self.periodic_fns: out.append(p_fn(input * freq)) # append 0 if self.N_freqs - max_level > 0: out.append(torch.zeros(*input.shape[:-1], (self.N_freqs - max_level) * 2 * input.shape[-1], device=input.device, dtype=input.dtype)) out = torch.cat(out, dim=-1) return out def get_encoder(encoding, input_dim=3, multires=6, degree=4, num_levels=16, level_dim=2, base_resolution=16, log2_hashmap_size=19, desired_resolution=2048, align_corners=False, interpolation='linear', **kwargs): if encoding == 'None': return lambda x, **kwargs: x, input_dim elif encoding == 'frequency_torch': encoder = FreqEncoder_torch(input_dim=input_dim, max_freq_log2=multires-1, N_freqs=multires, log_sampling=True) elif encoding == 'frequency': # CUDA implementation, faster than torch. from freqencoder import FreqEncoder encoder = FreqEncoder(input_dim=input_dim, degree=multires) elif encoding == 'sphere_harmonics': from shencoder import SHEncoder encoder = SHEncoder(input_dim=input_dim, degree=degree) elif encoding == 'hashgrid': from gridencoder import GridEncoder encoder = GridEncoder(input_dim=input_dim, num_levels=num_levels, level_dim=level_dim, base_resolution=base_resolution, log2_hashmap_size=log2_hashmap_size, desired_resolution=desired_resolution, gridtype='hash', align_corners=align_corners, interpolation=interpolation) elif encoding == 'tiledgrid': from gridencoder import GridEncoder encoder = GridEncoder(input_dim=input_dim, num_levels=num_levels, level_dim=level_dim, base_resolution=base_resolution, log2_hashmap_size=log2_hashmap_size, desired_resolution=desired_resolution, gridtype='tiled', align_corners=align_corners, interpolation=interpolation) elif encoding == 'hashgrid_taichi': from taichi_modules.hash_encoder import HashEncoderTaichi encoder = HashEncoderTaichi(batch_size=4096) #TODO: hard encoded batch size else: raise NotImplementedError('Unknown encoding mode, choose from [None, frequency, sphere_harmonics, hashgrid, tiledgrid]') return encoder, encoder.output_dim ================================================ FILE: stable-dreamfusion/evaluation/Prompt.py ================================================ import textwrap from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForTokenClassification from transformers import pipeline import argparse import sys import warnings warnings.filterwarnings("ignore", category=UserWarning) #python Prompt.py --text "a dog is in front of a rabbit" --model vlt5 if __name__ == '__main__': # Mimic the calling part of the main, using parser = argparse.ArgumentParser() parser.add_argument('--text', default="", type=str, help="text prompt") #parser.add_argument('--workspace', default="trial", type=str, help="workspace") parser.add_argument('--model', default='vlt5', type=str, help="model choices - vlt5, bert, XLNet") opt = parser.parse_args() if opt.model == "vlt5": tokenizer = AutoTokenizer.from_pretrained("Voicelab/vlt5-base-keywords") model = AutoModelForSeq2SeqLM.from_pretrained("Voicelab/vlt5-base-keywords") task_prefix = "Keywords: " inputs = [ opt.text ] for sample in inputs: input_sequences = [task_prefix + sample] input_ids = tokenizer( input_sequences, return_tensors="pt", truncation=True ).input_ids output = model.generate(input_ids, no_repeat_ngram_size=3, num_beams=4) output_text = tokenizer.decode(output[0], skip_special_tokens=True) #print(sample, "\n --->", output_text) elif opt.model == "bert": tokenizer = AutoTokenizer.from_pretrained("yanekyuk/bert-uncased-keyword-extractor") model = AutoModelForTokenClassification.from_pretrained("yanekyuk/bert-uncased-keyword-extractor") text = opt.text input_ids = tokenizer.encode(text, add_special_tokens=True, return_tensors="pt") # Classify tokens outputs = model(input_ids) predictions = outputs.logits.detach().numpy()[0] labels = predictions.argmax(axis=1) labels = labels[1:-1] print(labels) tokens = tokenizer.convert_ids_to_tokens(input_ids[0]) tokens = tokens[1:-1] output_tokens = [tokens[i] for i in range(len(tokens)) if labels[i] != 0] output_text = tokenizer.convert_tokens_to_string(output_tokens) #print(output_text) elif opt.model == "XLNet": tokenizer = AutoTokenizer.from_pretrained("jasminejwebb/KeywordIdentifier") model = AutoModelForTokenClassification.from_pretrained("jasminejwebb/KeywordIdentifier") text = opt.text input_ids = tokenizer.encode(text, add_special_tokens=True, return_tensors="pt") # Classify tokens outputs = model(input_ids) predictions = outputs.logits.detach().numpy()[0] labels = predictions.argmax(axis=1) labels = labels[1:-1] print(labels) tokens = tokenizer.convert_ids_to_tokens(input_ids[0]) tokens = tokens[1:-1] output_tokens = [tokens[i] for i in range(len(tokens)) if labels[i] != 0] output_text = tokenizer.convert_tokens_to_string(output_tokens) #print(output_text) wrapped_text = textwrap.fill(output_text, width=50) print('+' + '-'*52 + '+') for line in wrapped_text.split('\n'): print('| {} |'.format(line.ljust(50))) print('+' + '-'*52 + '+') #print(result) ================================================ FILE: stable-dreamfusion/evaluation/mesh_to_video.py ================================================ import os import numpy as np import trimesh import argparse from pathlib import Path from tqdm import tqdm import pyvista as pv def render_video(anim_mesh): center = anim_mesh.center_mass plotter = pv.Plotter(off_screen=True) plotter.add_mesh(anim_mesh) radius = 10 n_frames = 360 angle_step = 2 * np.pi / n_frames for i in tqdm(range(n_frames)): camera_pos = [center[0] + radius * np.cos(i*angle_step),center[1] + radius *np.sin(i*angle_step),center[2]] plotter.camera_position = (camera_pos, center, (0, 0, 1)) plotter.show(screenshot=f'frame_{i}.png', auto_close=False) plotter.close() os.system('ffmpeg -r 30 -f image2 -s 1920x1080 -i "result/frame_%d.png" -vcodec libx264 -crf 25 -pix_fmt yuv420p result/output.mp4') def generate_mesh(obj1,obj2,transform_vector): # Read 2 objects filename1 = obj1 # Central Object filename2 = obj2 # Surrounding Object mesh1 = trimesh.load_mesh(filename1) mesh2 = trimesh.load_mesh(filename2) extents1 = mesh1.extents extents2 = mesh1.extents radius1 = sum(extents1) / 3.0 radius2 = sum(extents2) / 3.0 center1 = mesh1.center_mass center2 = mesh2.center_mass # Move T1 = -center1 new =[] for i in transform_vector: try: new.append(float(i))*radius1 except: pass transform_vector = new print(T1, transform_vector, radius1) T2 = -center2 + transform_vector # Transform mesh1.apply_translation(T1) mesh2.apply_translation(T2) # merge mesh merged_mesh = trimesh.util.concatenate((mesh1, mesh2)) # save mesh merged_mesh.export('merged_mesh.obj') print("----> merge mesh done") if __name__ == '__main__': parser = argparse.ArgumentParser(description='Generate rotating mesh animation.') parser.add_argument('--center_obj', type=str, help='Input OBJ1 file.') parser.add_argument('--surround_obj', type=str, help='Input OBJ2 file.') parser.add_argument('--transform_vector', help='Transform_vector.') parser.add_argument('--output_file', type=str, default="result/Demo.mp4", help='Output MP4 file.') parser.add_argument('--num_frames', type=int, default=100, help='Number of frames to render.') args = parser.parse_args() #mesh = obj.Obj("wr.obj") generate_mesh(args.center_obj,args.surround_obj,args.transform_vector) input_file = Path("merged_mesh.obj") output_file = Path(args.output_file) out_dir = output_file.parent.joinpath('frames') out_dir.mkdir(parents=True, exist_ok=True) anim_mesh = trimesh.load_mesh(str(input_file)) render_video(anim_mesh) ================================================ FILE: stable-dreamfusion/evaluation/r_precision.py ================================================ from sentence_transformers import SentenceTransformer, util from PIL import Image import argparse import sys if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--text', default="", type=str, help="text prompt") parser.add_argument('--workspace', default="trial", type=str, help="text prompt") parser.add_argument('--latest', default='ep0001', type=str, help="which epoch result you want to use for image path") parser.add_argument('--mode', default='rgb', type=str, help="mode of result, color(rgb) or textureless()") parser.add_argument('--clip', default="clip-ViT-B-32", type=str, help="CLIP model to encode the img and prompt") opt = parser.parse_args() #Load CLIP model model = SentenceTransformer(f'{opt.clip}') #Encode an image: img_emb = model.encode(Image.open(f'../results/{opt.workspace}/validation/df_{opt.latest}_0005_{opt.mode}.png')) #Encode text descriptions text_emb = model.encode([f'{opt.text}']) #Compute cosine similarities cos_scores = util.cos_sim(img_emb, text_emb) print("The final CLIP R-Precision is:", cos_scores[0][0].cpu().numpy()) ================================================ FILE: stable-dreamfusion/evaluation/readme.md ================================================ ### Improvement: - Usage - r_precision.py
For prompt seperation
--text is for the prompt following the author of stable dream fusion
--workspace is the workspace folder which will be created for every prompt fed into stable dreamfusion
--latest is which ckpt is used. Stable dream fusion record every epoch data. Normally is ep0100 unless the training is not finished or we further extend the training
--mode has choices of rgb and depth which is correspondent to color and texture result as original paper Figure 5: Qualitative comparison with baselines.
--clip has choices of clip-ViT-B-32, CLIP B/16, CLIP L/14, same as original paper
```bash python Prompt.py --text "matte painting of a castle made of cheesecake surrounded by a moat made of ice cream" --workspace ../castle --latest ep0100 --mode rgb --clip clip-ViT-B-32 ``` - Prompt.py (model name case sensitive)
For prompt seperation

--text is for the prompt following the author of stable dream fusion
--model is for choose the pretrain models
```bash python Prompt.py --text "a dog is in front of a rabbit" --model vlt5 python Prompt.py --text "a dog is in front of a rabbit" --model bert python Prompt.py --text "a dog is in front of a rabbit" --model XLNet ``` - mesh_to_video.py
--center_obj IS THE CENTER OBJECT
--surround_obj IS THE SURROUNDING OBJECT SUBJECT TO CHANGE
--transform_vector THE X Y Z 3d vector for transform
```bash python mesh_to_video.py --center_obj 'mesh_whiterabbit/mesh.obj' --surround_obj 'mesh_snake/mesh.obj' --transform_vector [1,0,0] ``` ================================================ FILE: stable-dreamfusion/freqencoder/__init__.py ================================================ from .freq import FreqEncoder ================================================ FILE: stable-dreamfusion/freqencoder/backend.py ================================================ import os from torch.utils.cpp_extension import load _src_path = os.path.dirname(os.path.abspath(__file__)) nvcc_flags = [ '-O3', '-std=c++14', '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__', '-use_fast_math' ] if os.name == "posix": c_flags = ['-O3', '-std=c++14'] elif os.name == "nt": c_flags = ['/O2', '/std:c++17'] # find cl.exe def find_cl_path(): import glob for program_files in [r"C:\\Program Files (x86)", r"C:\\Program Files"]: for edition in ["Enterprise", "Professional", "BuildTools", "Community"]: paths = sorted(glob.glob(r"%s\\Microsoft Visual Studio\\*\\%s\\VC\\Tools\\MSVC\\*\\bin\\Hostx64\\x64" % (program_files, edition)), reverse=True) if paths: return paths[0] # If cl.exe is not on path, try to find it. if os.system("where cl.exe >nul 2>nul") != 0: cl_path = find_cl_path() if cl_path is None: raise RuntimeError("Could not locate a supported Microsoft Visual C++ installation") os.environ["PATH"] += ";" + cl_path _backend = load(name='_freqencoder', extra_cflags=c_flags, extra_cuda_cflags=nvcc_flags, sources=[os.path.join(_src_path, 'src', f) for f in [ 'freqencoder.cu', 'bindings.cpp', ]], ) __all__ = ['_backend'] ================================================ FILE: stable-dreamfusion/freqencoder/freq.py ================================================ import numpy as np import torch import torch.nn as nn from torch.autograd import Function from torch.autograd.function import once_differentiable from torch.cuda.amp import custom_bwd, custom_fwd try: import _freqencoder as _backend except ImportError: from .backend import _backend class _freq_encoder(Function): @staticmethod @custom_fwd(cast_inputs=torch.float32) # force float32 for better precision def forward(ctx, inputs, degree, output_dim): # inputs: [B, input_dim], float # RETURN: [B, F], float if not inputs.is_cuda: inputs = inputs.cuda() inputs = inputs.contiguous() B, input_dim = inputs.shape # batch size, coord dim outputs = torch.empty(B, output_dim, dtype=inputs.dtype, device=inputs.device) _backend.freq_encode_forward(inputs, B, input_dim, degree, output_dim, outputs) ctx.save_for_backward(inputs, outputs) ctx.dims = [B, input_dim, degree, output_dim] return outputs @staticmethod #@once_differentiable @custom_bwd def backward(ctx, grad): # grad: [B, C * C] grad = grad.contiguous() inputs, outputs = ctx.saved_tensors B, input_dim, degree, output_dim = ctx.dims grad_inputs = torch.zeros_like(inputs) _backend.freq_encode_backward(grad, outputs, B, input_dim, degree, output_dim, grad_inputs) return grad_inputs, None, None freq_encode = _freq_encoder.apply class FreqEncoder(nn.Module): def __init__(self, input_dim=3, degree=4): super().__init__() self.input_dim = input_dim self.degree = degree self.output_dim = input_dim + input_dim * 2 * degree def __repr__(self): return f"FreqEncoder: input_dim={self.input_dim} degree={self.degree} output_dim={self.output_dim}" def forward(self, inputs, **kwargs): # inputs: [..., input_dim] # return: [..., ] prefix_shape = list(inputs.shape[:-1]) inputs = inputs.reshape(-1, self.input_dim) outputs = freq_encode(inputs, self.degree, self.output_dim) outputs = outputs.reshape(prefix_shape + [self.output_dim]) return outputs ================================================ FILE: stable-dreamfusion/freqencoder/setup.py ================================================ import os from setuptools import setup from torch.utils.cpp_extension import BuildExtension, CUDAExtension _src_path = os.path.dirname(os.path.abspath(__file__)) nvcc_flags = [ '-O3', '-std=c++14', '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__', '-use_fast_math' ] if os.name == "posix": c_flags = ['-O3', '-std=c++14'] elif os.name == "nt": c_flags = ['/O2', '/std:c++17'] # find cl.exe def find_cl_path(): import glob for program_files in [r"C:\\Program Files (x86)", r"C:\\Program Files"]: for edition in ["Enterprise", "Professional", "BuildTools", "Community"]: paths = sorted(glob.glob(r"%s\\Microsoft Visual Studio\\*\\%s\\VC\\Tools\\MSVC\\*\\bin\\Hostx64\\x64" % (program_files, edition)), reverse=True) if paths: return paths[0] # If cl.exe is not on path, try to find it. if os.system("where cl.exe >nul 2>nul") != 0: cl_path = find_cl_path() if cl_path is None: raise RuntimeError("Could not locate a supported Microsoft Visual C++ installation") os.environ["PATH"] += ";" + cl_path setup( name='freqencoder', # package name, import this to use python API ext_modules=[ CUDAExtension( name='_freqencoder', # extension name, import this to use CUDA API sources=[os.path.join(_src_path, 'src', f) for f in [ 'freqencoder.cu', 'bindings.cpp', ]], extra_compile_args={ 'cxx': c_flags, 'nvcc': nvcc_flags, } ), ], cmdclass={ 'build_ext': BuildExtension, } ) ================================================ FILE: stable-dreamfusion/freqencoder/src/bindings.cpp ================================================ #include #include "freqencoder.h" PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("freq_encode_forward", &freq_encode_forward, "freq encode forward (CUDA)"); m.def("freq_encode_backward", &freq_encode_backward, "freq encode backward (CUDA)"); } ================================================ FILE: stable-dreamfusion/freqencoder/src/freqencoder.cu ================================================ #include #include #include #include #include #include #include #include #include #define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") #define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be a contiguous tensor") #define CHECK_IS_INT(x) TORCH_CHECK(x.scalar_type() == at::ScalarType::Int, #x " must be an int tensor") #define CHECK_IS_FLOATING(x) TORCH_CHECK(x.scalar_type() == at::ScalarType::Float || x.scalar_type() == at::ScalarType::Half || x.scalar_type() == at::ScalarType::Double, #x " must be a floating tensor") inline constexpr __device__ float PI() { return 3.141592653589793f; } template __host__ __device__ T div_round_up(T val, T divisor) { return (val + divisor - 1) / divisor; } // inputs: [B, D] // outputs: [B, C], C = D + D * deg * 2 __global__ void kernel_freq( const float * __restrict__ inputs, uint32_t B, uint32_t D, uint32_t deg, uint32_t C, float * outputs ) { // parallel on per-element const uint32_t t = threadIdx.x + blockIdx.x * blockDim.x; if (t >= B * C) return; // get index const uint32_t b = t / C; const uint32_t c = t - b * C; // t % C; // locate inputs += b * D; outputs += t; // write self if (c < D) { outputs[0] = inputs[c]; // write freq } else { const uint32_t col = c / D - 1; const uint32_t d = c % D; const uint32_t freq = col / 2; const float phase_shift = (col % 2) * (PI() / 2); outputs[0] = __sinf(scalbnf(inputs[d], freq) + phase_shift); } } // grad: [B, C], C = D + D * deg * 2 // outputs: [B, C] // grad_inputs: [B, D] __global__ void kernel_freq_backward( const float * __restrict__ grad, const float * __restrict__ outputs, uint32_t B, uint32_t D, uint32_t deg, uint32_t C, float * grad_inputs ) { // parallel on per-element const uint32_t t = threadIdx.x + blockIdx.x * blockDim.x; if (t >= B * D) return; const uint32_t b = t / D; const uint32_t d = t - b * D; // t % D; // locate grad += b * C; outputs += b * C; grad_inputs += t; // register float result = grad[d]; grad += D; outputs += D; for (uint32_t f = 0; f < deg; f++) { result += scalbnf(1.0f, f) * (grad[d] * outputs[D + d] - grad[D + d] * outputs[d]); grad += 2 * D; outputs += 2 * D; } // write grad_inputs[0] = result; } void freq_encode_forward(at::Tensor inputs, const uint32_t B, const uint32_t D, const uint32_t deg, const uint32_t C, at::Tensor outputs) { CHECK_CUDA(inputs); CHECK_CUDA(outputs); CHECK_CONTIGUOUS(inputs); CHECK_CONTIGUOUS(outputs); CHECK_IS_FLOATING(inputs); CHECK_IS_FLOATING(outputs); static constexpr uint32_t N_THREADS = 128; kernel_freq<<>>(inputs.data_ptr(), B, D, deg, C, outputs.data_ptr()); } void freq_encode_backward(at::Tensor grad, at::Tensor outputs, const uint32_t B, const uint32_t D, const uint32_t deg, const uint32_t C, at::Tensor grad_inputs) { CHECK_CUDA(grad); CHECK_CUDA(outputs); CHECK_CUDA(grad_inputs); CHECK_CONTIGUOUS(grad); CHECK_CONTIGUOUS(outputs); CHECK_CONTIGUOUS(grad_inputs); CHECK_IS_FLOATING(grad); CHECK_IS_FLOATING(outputs); CHECK_IS_FLOATING(grad_inputs); static constexpr uint32_t N_THREADS = 128; kernel_freq_backward<<>>(grad.data_ptr(), outputs.data_ptr(), B, D, deg, C, grad_inputs.data_ptr()); } ================================================ FILE: stable-dreamfusion/freqencoder/src/freqencoder.h ================================================ # pragma once #include #include // _backend.freq_encode_forward(inputs, B, input_dim, degree, output_dim, outputs) void freq_encode_forward(at::Tensor inputs, const uint32_t B, const uint32_t D, const uint32_t deg, const uint32_t C, at::Tensor outputs); // _backend.freq_encode_backward(grad, outputs, B, input_dim, degree, output_dim, grad_inputs) void freq_encode_backward(at::Tensor grad, at::Tensor outputs, const uint32_t B, const uint32_t D, const uint32_t deg, const uint32_t C, at::Tensor grad_inputs); ================================================ FILE: stable-dreamfusion/gridencoder/__init__.py ================================================ from .grid import GridEncoder ================================================ FILE: stable-dreamfusion/gridencoder/backend.py ================================================ import os from torch.utils.cpp_extension import load _src_path = os.path.dirname(os.path.abspath(__file__)) nvcc_flags = [ '-O3', '-std=c++14', '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__', ] if os.name == "posix": c_flags = ['-O3', '-std=c++14'] elif os.name == "nt": c_flags = ['/O2', '/std:c++17'] # find cl.exe def find_cl_path(): import glob for program_files in [r"C:\\Program Files (x86)", r"C:\\Program Files"]: for edition in ["Enterprise", "Professional", "BuildTools", "Community"]: paths = sorted(glob.glob(r"%s\\Microsoft Visual Studio\\*\\%s\\VC\\Tools\\MSVC\\*\\bin\\Hostx64\\x64" % (program_files, edition)), reverse=True) if paths: return paths[0] # If cl.exe is not on path, try to find it. if os.system("where cl.exe >nul 2>nul") != 0: cl_path = find_cl_path() if cl_path is None: raise RuntimeError("Could not locate a supported Microsoft Visual C++ installation") os.environ["PATH"] += ";" + cl_path _backend = load(name='_grid_encoder', extra_cflags=c_flags, extra_cuda_cflags=nvcc_flags, sources=[os.path.join(_src_path, 'src', f) for f in [ 'gridencoder.cu', 'bindings.cpp', ]], ) __all__ = ['_backend'] ================================================ FILE: stable-dreamfusion/gridencoder/grid.py ================================================ import math import numpy as np import torch import torch.nn as nn from torch.autograd import Function from torch.autograd.function import once_differentiable from torch.cuda.amp import custom_bwd, custom_fwd try: import _gridencoder as _backend except ImportError: from .backend import _backend _gridtype_to_id = { 'hash': 0, 'tiled': 1, } _interp_to_id = { 'linear': 0, 'smoothstep': 1, } class _grid_encode(Function): @staticmethod @custom_fwd def forward(ctx, inputs, embeddings, offsets, per_level_scale, base_resolution, calc_grad_inputs=False, gridtype=0, align_corners=False, interpolation=0, max_level=None): # inputs: [B, D], float in [0, 1] # embeddings: [sO, C], float # offsets: [L + 1], int # RETURN: [B, F], float inputs = inputs.contiguous() B, D = inputs.shape # batch size, coord dim L = offsets.shape[0] - 1 # level C = embeddings.shape[1] # embedding dim for each level S = np.log2(per_level_scale) # resolution multiplier at each level, apply log2 for later CUDA exp2f H = base_resolution # base resolution max_level = L if max_level is None else max(min(int(math.ceil(max_level * L)), L), 1) # manually handle autocast (only use half precision embeddings, inputs must be float for enough precision) # if C % 2 != 0, force float, since half for atomicAdd is very slow. if torch.is_autocast_enabled() and C % 2 == 0: embeddings = embeddings.to(torch.half) # L first, optimize cache for cuda kernel, but needs an extra permute later outputs = torch.empty(L, B, C, device=inputs.device, dtype=embeddings.dtype) # zero init if we only calculate partial levels if max_level < L: outputs.zero_() if calc_grad_inputs: dy_dx = torch.empty(B, L * D * C, device=inputs.device, dtype=embeddings.dtype) if max_level < L: dy_dx.zero_() else: dy_dx = None _backend.grid_encode_forward(inputs, embeddings, offsets, outputs, B, D, C, L, max_level, S, H, dy_dx, gridtype, align_corners, interpolation) # permute back to [B, L * C] outputs = outputs.permute(1, 0, 2).reshape(B, L * C) ctx.save_for_backward(inputs, embeddings, offsets, dy_dx) ctx.dims = [B, D, C, L, S, H, gridtype, interpolation, max_level] ctx.align_corners = align_corners return outputs @staticmethod #@once_differentiable @custom_bwd def backward(ctx, grad): inputs, embeddings, offsets, dy_dx = ctx.saved_tensors B, D, C, L, S, H, gridtype, interpolation, max_level = ctx.dims align_corners = ctx.align_corners # grad: [B, L * C] --> [L, B, C] grad = grad.view(B, L, C).permute(1, 0, 2).contiguous() grad_embeddings = torch.zeros_like(embeddings) if dy_dx is not None: grad_inputs = torch.zeros_like(inputs, dtype=embeddings.dtype) else: grad_inputs = None _backend.grid_encode_backward(grad, inputs, embeddings, offsets, grad_embeddings, B, D, C, L, max_level, S, H, dy_dx, grad_inputs, gridtype, align_corners, interpolation) if dy_dx is not None: grad_inputs = grad_inputs.to(inputs.dtype) return grad_inputs, grad_embeddings, None, None, None, None, None, None, None, None grid_encode = _grid_encode.apply class GridEncoder(nn.Module): def __init__(self, input_dim=3, num_levels=16, level_dim=2, per_level_scale=2, base_resolution=16, log2_hashmap_size=19, desired_resolution=None, gridtype='hash', align_corners=False, interpolation='linear'): super().__init__() # the finest resolution desired at the last level, if provided, overridee per_level_scale if desired_resolution is not None: per_level_scale = np.exp2(np.log2(desired_resolution / base_resolution) / (num_levels - 1)) self.input_dim = input_dim # coord dims, 2 or 3 self.num_levels = num_levels # num levels, each level multiply resolution by 2 self.level_dim = level_dim # encode channels per level self.per_level_scale = per_level_scale # multiply resolution by this scale at each level. self.log2_hashmap_size = log2_hashmap_size self.base_resolution = base_resolution self.output_dim = num_levels * level_dim self.gridtype = gridtype self.gridtype_id = _gridtype_to_id[gridtype] # "tiled" or "hash" self.interpolation = interpolation self.interp_id = _interp_to_id[interpolation] # "linear" or "smoothstep" self.align_corners = align_corners # allocate parameters offsets = [] offset = 0 self.max_params = 2 ** log2_hashmap_size for i in range(num_levels): resolution = int(np.ceil(base_resolution * per_level_scale ** i)) params_in_level = min(self.max_params, (resolution) ** input_dim) # limit max number params_in_level = int(np.ceil(params_in_level / 8) * 8) # make divisible offsets.append(offset) offset += params_in_level offsets.append(offset) offsets = torch.from_numpy(np.array(offsets, dtype=np.int32)) self.register_buffer('offsets', offsets) self.n_params = offsets[-1] * level_dim # parameters self.embeddings = nn.Parameter(torch.empty(offset, level_dim)) self.reset_parameters() def reset_parameters(self): std = 1e-4 self.embeddings.data.uniform_(-std, std) def __repr__(self): return f"GridEncoder: input_dim={self.input_dim} num_levels={self.num_levels} level_dim={self.level_dim} resolution={self.base_resolution} -> {int(round(self.base_resolution * self.per_level_scale ** (self.num_levels - 1)))} per_level_scale={self.per_level_scale:.4f} params={tuple(self.embeddings.shape)} gridtype={self.gridtype} align_corners={self.align_corners} interpolation={self.interpolation}" def forward(self, inputs, bound=1, max_level=None): # inputs: [..., input_dim], normalized real world positions in [-bound, bound] # max_level: only calculate first max_level levels (None will use all levels) # return: [..., num_levels * level_dim] inputs = (inputs + bound) / (2 * bound) # map to [0, 1] #print('inputs', inputs.shape, inputs.dtype, inputs.min().item(), inputs.max().item()) prefix_shape = list(inputs.shape[:-1]) inputs = inputs.view(-1, self.input_dim) outputs = grid_encode(inputs, self.embeddings, self.offsets, self.per_level_scale, self.base_resolution, inputs.requires_grad, self.gridtype_id, self.align_corners, self.interp_id, max_level) outputs = outputs.view(prefix_shape + [self.output_dim]) #print('outputs', outputs.shape, outputs.dtype, outputs.min().item(), outputs.max().item()) return outputs # always run in float precision! @torch.cuda.amp.autocast(enabled=False) def grad_total_variation(self, weight=1e-7, inputs=None, bound=1, B=1000000): # inputs: [..., input_dim], float in [-b, b], location to calculate TV loss. D = self.input_dim C = self.embeddings.shape[1] # embedding dim for each level L = self.offsets.shape[0] - 1 # level S = np.log2(self.per_level_scale) # resolution multiplier at each level, apply log2 for later CUDA exp2f H = self.base_resolution # base resolution if inputs is None: # randomized in [0, 1] inputs = torch.rand(B, self.input_dim, device=self.embeddings.device) else: inputs = (inputs + bound) / (2 * bound) # map to [0, 1] inputs = inputs.view(-1, self.input_dim) B = inputs.shape[0] if self.embeddings.grad is None: raise ValueError('grad is None, should be called after loss.backward() and before optimizer.step()!') _backend.grad_total_variation(inputs, self.embeddings, self.embeddings.grad, self.offsets, weight, B, D, C, L, S, H, self.gridtype_id, self.align_corners) @torch.cuda.amp.autocast(enabled=False) def grad_weight_decay(self, weight=0.1): # level-wise meaned weight decay (ref: zip-nerf) B = self.embeddings.shape[0] # size of embedding C = self.embeddings.shape[1] # embedding dim for each level L = self.offsets.shape[0] - 1 # level if self.embeddings.grad is None: raise ValueError('grad is None, should be called after loss.backward() and before optimizer.step()!') _backend.grad_weight_decay(self.embeddings, self.embeddings.grad, self.offsets, weight, B, C, L) ================================================ FILE: stable-dreamfusion/gridencoder/setup.py ================================================ import os from setuptools import setup from torch.utils.cpp_extension import BuildExtension, CUDAExtension _src_path = os.path.dirname(os.path.abspath(__file__)) nvcc_flags = [ '-O3', '-std=c++14', '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__', ] if os.name == "posix": c_flags = ['-O3', '-std=c++14'] elif os.name == "nt": c_flags = ['/O2', '/std:c++17'] # find cl.exe def find_cl_path(): import glob for program_files in [r"C:\\Program Files (x86)", r"C:\\Program Files"]: for edition in ["Enterprise", "Professional", "BuildTools", "Community"]: paths = sorted(glob.glob(r"%s\\Microsoft Visual Studio\\*\\%s\\VC\\Tools\\MSVC\\*\\bin\\Hostx64\\x64" % (program_files, edition)), reverse=True) if paths: return paths[0] # If cl.exe is not on path, try to find it. if os.system("where cl.exe >nul 2>nul") != 0: cl_path = find_cl_path() if cl_path is None: raise RuntimeError("Could not locate a supported Microsoft Visual C++ installation") os.environ["PATH"] += ";" + cl_path setup( name='gridencoder', # package name, import this to use python API ext_modules=[ CUDAExtension( name='_gridencoder', # extension name, import this to use CUDA API sources=[os.path.join(_src_path, 'src', f) for f in [ 'gridencoder.cu', 'bindings.cpp', ]], extra_compile_args={ 'cxx': c_flags, 'nvcc': nvcc_flags, } ), ], cmdclass={ 'build_ext': BuildExtension, } ) ================================================ FILE: stable-dreamfusion/gridencoder/src/bindings.cpp ================================================ #include #include "gridencoder.h" PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("grid_encode_forward", &grid_encode_forward, "grid_encode_forward (CUDA)"); m.def("grid_encode_backward", &grid_encode_backward, "grid_encode_backward (CUDA)"); m.def("grad_total_variation", &grad_total_variation, "grad_total_variation (CUDA)"); m.def("grad_weight_decay", &grad_weight_decay, "grad_weight_decay (CUDA)"); } ================================================ FILE: stable-dreamfusion/gridencoder/src/gridencoder.cu ================================================ #include #include #include #include #include #include #include #include #include #define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") #define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be a contiguous tensor") #define CHECK_IS_INT(x) TORCH_CHECK(x.scalar_type() == at::ScalarType::Int, #x " must be an int tensor") #define CHECK_IS_FLOATING(x) TORCH_CHECK(x.scalar_type() == at::ScalarType::Float || x.scalar_type() == at::ScalarType::Half || x.scalar_type() == at::ScalarType::Double, #x " must be a floating tensor") // just for compatability of half precision in AT_DISPATCH_FLOATING_TYPES_AND_HALF... program will never reach here! __device__ inline at::Half atomicAdd(at::Half *address, at::Half val) { // requires CUDA >= 10 and ARCH >= 70 // this is very slow compared to float or __half2, never use it. //return atomicAdd(reinterpret_cast<__half*>(address), val); } template __host__ __device__ inline T div_round_up(T val, T divisor) { return (val + divisor - 1) / divisor; } template __device__ inline T smoothstep(T val) { return val*val*(3.0f - 2.0f * val); } template __device__ inline T smoothstep_derivative(T val) { return 6*val*(1.0f - val); } template __device__ uint32_t fast_hash(const uint32_t pos_grid[D]) { // coherent type of hashing constexpr uint32_t primes[7] = { 1u, 2654435761u, 805459861u, 3674653429u, 2097192037u, 1434869437u, 2165219737u }; uint32_t result = 0; #pragma unroll for (uint32_t i = 0; i < D; ++i) { result ^= pos_grid[i] * primes[i]; } return result; } template __device__ uint32_t get_grid_index(const uint32_t gridtype, const uint32_t ch, const uint32_t hashmap_size, const uint32_t resolution, const uint32_t pos_grid[D]) { uint32_t stride = 1; uint32_t index = 0; #pragma unroll for (uint32_t d = 0; d < D && stride <= hashmap_size; d++) { index += pos_grid[d] * stride; stride *= resolution; } // NOTE: for NeRF, the hash is in fact not necessary. Check https://github.com/NVlabs/instant-ngp/issues/97. // gridtype: 0 == hash, 1 == tiled if (gridtype == 0 && stride > hashmap_size) { index = fast_hash(pos_grid); } return (index % hashmap_size) * C + ch; } template __global__ void kernel_grid( const float * __restrict__ inputs, const scalar_t * __restrict__ grid, const int * __restrict__ offsets, scalar_t * __restrict__ outputs, const uint32_t B, const uint32_t L, const float S, const uint32_t H, scalar_t * __restrict__ dy_dx, const uint32_t gridtype, const bool align_corners, const uint32_t interp ) { const uint32_t b = blockIdx.x * blockDim.x + threadIdx.x; if (b >= B) return; const uint32_t level = blockIdx.y; // locate grid += (uint32_t)offsets[level] * C; inputs += b * D; outputs += level * B * C + b * C; // check input range (should be in [0, 1]) bool flag_oob = false; #pragma unroll for (uint32_t d = 0; d < D; d++) { if (inputs[d] < 0 || inputs[d] > 1) { flag_oob = true; } } // if input out of bound, just set output to 0 if (flag_oob) { #pragma unroll for (uint32_t ch = 0; ch < C; ch++) { outputs[ch] = 0; } if (dy_dx) { dy_dx += b * D * L * C + level * D * C; // B L D C #pragma unroll for (uint32_t d = 0; d < D; d++) { #pragma unroll for (uint32_t ch = 0; ch < C; ch++) { dy_dx[d * C + ch] = 0; } } } return; } const uint32_t hashmap_size = offsets[level + 1] - offsets[level]; const uint32_t resolution = (uint32_t)ceil(exp2f(level * S) * H); // calculate coordinate (always use float for precision!) float pos[D]; float pos_deriv[D]; uint32_t pos_grid[D]; #pragma unroll for (uint32_t d = 0; d < D; d++) { // align_corners if (align_corners) { pos[d] = inputs[d] * (float)(resolution - 1); // [0, resolution - 1] pos_grid[d] = min((uint32_t)floorf(pos[d]), resolution - 2); // left-top corner, [0, resolution - 2] } else { pos[d] = fminf(fmaxf(inputs[d] * (float)resolution - 0.5f, 0.0f), (float)(resolution - 1)); // [-0.5, resolution-0.5] --> [0, resolution - 1] pos_grid[d] = (uint32_t)floorf(pos[d]); // left-top corner, [0, resolution - 1] } pos[d] -= (float)pos_grid[d]; // smoothstep instead of linear if (interp == 1) { pos_deriv[d] = smoothstep_derivative(pos[d]); pos[d] = smoothstep(pos[d]); } else { pos_deriv[d] = 1.0f; } } // verification of alignment // if (level == L - 1 && b < 4) { // printf("[b=%d, l=%d] pos=(%f, %f)+(%d, %d)\n", b, level, pos[0], pos[1], pos_grid[0], pos_grid[1]); // } // interpolate scalar_t results[C] = {0}; // temp results in register #pragma unroll for (uint32_t idx = 0; idx < (1 << D); idx++) { float w = 1; uint32_t pos_grid_local[D]; #pragma unroll for (uint32_t d = 0; d < D; d++) { if ((idx & (1 << d)) == 0) { w *= 1 - pos[d]; pos_grid_local[d] = pos_grid[d]; } else { w *= pos[d]; pos_grid_local[d] = min(pos_grid[d] + 1, resolution - 1); } } uint32_t index = get_grid_index(gridtype, 0, hashmap_size, resolution, pos_grid_local); // writing to register (fast) #pragma unroll for (uint32_t ch = 0; ch < C; ch++) { results[ch] += w * grid[index + ch]; } //printf("[b=%d, l=%d] int %d, idx %d, w %f, val %f\n", b, level, idx, index, w, grid[index]); } // writing to global memory (slow) #pragma unroll for (uint32_t ch = 0; ch < C; ch++) { outputs[ch] = results[ch]; } // prepare dy_dx // differentiable (soft) indexing: https://discuss.pytorch.org/t/differentiable-indexing/17647/9 if (dy_dx) { dy_dx += b * D * L * C + level * D * C; // B L D C #pragma unroll for (uint32_t gd = 0; gd < D; gd++) { scalar_t results_grad[C] = {0}; #pragma unroll for (uint32_t idx = 0; idx < (1 << (D - 1)); idx++) { float w = (float)(align_corners ? resolution - 1 : resolution); uint32_t pos_grid_local[D]; #pragma unroll for (uint32_t nd = 0; nd < D - 1; nd++) { const uint32_t d = (nd >= gd) ? (nd + 1) : nd; if ((idx & (1 << nd)) == 0) { w *= 1 - pos[d]; pos_grid_local[d] = pos_grid[d]; } else { w *= pos[d]; pos_grid_local[d] = min(pos_grid[d] + 1, resolution - 1); } } pos_grid_local[gd] = pos_grid[gd]; uint32_t index_left = get_grid_index(gridtype, 0, hashmap_size, resolution, pos_grid_local); pos_grid_local[gd] = min(pos_grid[gd] + 1, resolution - 1); uint32_t index_right = get_grid_index(gridtype, 0, hashmap_size, resolution, pos_grid_local); #pragma unroll for (uint32_t ch = 0; ch < C; ch++) { results_grad[ch] += w * (grid[index_right + ch] - grid[index_left + ch]) * pos_deriv[gd]; } } #pragma unroll for (uint32_t ch = 0; ch < C; ch++) { dy_dx[gd * C + ch] = results_grad[ch]; } } } } template __global__ void kernel_grid_backward( const scalar_t * __restrict__ grad, const float * __restrict__ inputs, const scalar_t * __restrict__ grid, const int * __restrict__ offsets, scalar_t * __restrict__ grad_grid, const uint32_t B, const uint32_t L, const float S, const uint32_t H, const uint32_t gridtype, const bool align_corners, const uint32_t interp ) { const uint32_t b = (blockIdx.x * blockDim.x + threadIdx.x) * N_C / C; if (b >= B) return; const uint32_t level = blockIdx.y; const uint32_t ch = (blockIdx.x * blockDim.x + threadIdx.x) * N_C - b * C; // locate grad_grid += offsets[level] * C; inputs += b * D; grad += level * B * C + b * C + ch; // L, B, C const uint32_t hashmap_size = offsets[level + 1] - offsets[level]; const uint32_t resolution = (uint32_t)ceil(exp2f(level * S) * H); // check input range (should be in [0, 1]) #pragma unroll for (uint32_t d = 0; d < D; d++) { if (inputs[d] < 0 || inputs[d] > 1) { return; // grad is init as 0, so we simply return. } } // calculate coordinate float pos[D]; uint32_t pos_grid[D]; #pragma unroll for (uint32_t d = 0; d < D; d++) { // align_corners if (align_corners) { pos[d] = inputs[d] * (float)(resolution - 1); // [0, resolution - 1] pos_grid[d] = min((uint32_t)floorf(pos[d]), resolution - 2); // left-top corner, [0, resolution - 2] } else { pos[d] = fminf(fmaxf(inputs[d] * (float)resolution - 0.5f, 0.0f), (float)(resolution - 1)); // [-0.5, resolution-0.5] --> [0, resolution - 1] pos_grid[d] = (uint32_t)floorf(pos[d]); // left-top corner, [0, resolution - 1] } pos[d] -= (float)pos_grid[d]; // smoothstep instead of linear if (interp == 1) { pos[d] = smoothstep(pos[d]); } } scalar_t grad_cur[N_C] = {0}; // fetch to register #pragma unroll for (uint32_t c = 0; c < N_C; c++) { grad_cur[c] = grad[c]; } // interpolate #pragma unroll for (uint32_t idx = 0; idx < (1 << D); idx++) { float w = 1; uint32_t pos_grid_local[D]; #pragma unroll for (uint32_t d = 0; d < D; d++) { if ((idx & (1 << d)) == 0) { w *= 1 - pos[d]; pos_grid_local[d] = pos_grid[d]; } else { w *= pos[d]; pos_grid_local[d] = min(pos_grid[d] + 1, resolution - 1); } } uint32_t index = get_grid_index(gridtype, ch, hashmap_size, resolution, pos_grid_local); // atomicAdd for __half is slow (especially for large values), so we use __half2 if N_C % 2 == 0 // TODO: use float which is better than __half, if N_C % 2 != 0 if (std::is_same::value && N_C % 2 == 0) { #pragma unroll for (uint32_t c = 0; c < N_C; c += 2) { // process two __half at once (by interpreting as a __half2) __half2 v = {(__half)(w * grad_cur[c]), (__half)(w * grad_cur[c + 1])}; atomicAdd((__half2*)&grad_grid[index + c], v); } // float, or __half when N_C % 2 != 0 (which means C == 1) } else { #pragma unroll for (uint32_t c = 0; c < N_C; c++) { atomicAdd(&grad_grid[index + c], w * grad_cur[c]); } } } } template __global__ void kernel_input_backward( const scalar_t * __restrict__ grad, const scalar_t * __restrict__ dy_dx, scalar_t * __restrict__ grad_inputs, uint32_t B, uint32_t L ) { const uint32_t t = threadIdx.x + blockIdx.x * blockDim.x; if (t >= B * D) return; const uint32_t b = t / D; const uint32_t d = t - b * D; dy_dx += b * L * D * C; scalar_t result = 0; # pragma unroll for (int l = 0; l < L; l++) { # pragma unroll for (int ch = 0; ch < C; ch++) { result += grad[l * B * C + b * C + ch] * dy_dx[l * D * C + d * C + ch]; } } grad_inputs[t] = result; } template void kernel_grid_wrapper(const float *inputs, const scalar_t *embeddings, const int *offsets, scalar_t *outputs, const uint32_t B, const uint32_t C, const uint32_t L, const uint32_t max_level, const float S, const uint32_t H, scalar_t *dy_dx, const uint32_t gridtype, const bool align_corners, const uint32_t interp) { static constexpr uint32_t N_THREAD = 512; const dim3 blocks_hashgrid = { div_round_up(B, N_THREAD), max_level, 1 }; switch (C) { case 1: kernel_grid<<>>(inputs, embeddings, offsets, outputs, B, L, S, H, dy_dx, gridtype, align_corners, interp); break; case 2: kernel_grid<<>>(inputs, embeddings, offsets, outputs, B, L, S, H, dy_dx, gridtype, align_corners, interp); break; case 4: kernel_grid<<>>(inputs, embeddings, offsets, outputs, B, L, S, H, dy_dx, gridtype, align_corners, interp); break; case 8: kernel_grid<<>>(inputs, embeddings, offsets, outputs, B, L, S, H, dy_dx, gridtype, align_corners, interp); break; case 16: kernel_grid<<>>(inputs, embeddings, offsets, outputs, B, L, S, H, dy_dx, gridtype, align_corners, interp); break; case 32: kernel_grid<<>>(inputs, embeddings, offsets, outputs, B, L, S, H, dy_dx, gridtype, align_corners, interp); break; default: throw std::runtime_error{"GridEncoding: C must be 1, 2, 4, 8, 16 or 32."}; } } // inputs: [B, D], float, in [0, 1] // embeddings: [sO, C], float // offsets: [L + 1], uint32_t // outputs: [L, B, C], float (L first, so only one level of hashmap needs to fit into cache at a time.) // H: base resolution // dy_dx: [B, L * D * C] template void grid_encode_forward_cuda(const float *inputs, const scalar_t *embeddings, const int *offsets, scalar_t *outputs, const uint32_t B, const uint32_t D, const uint32_t C, const uint32_t L, const uint32_t max_level, const float S, const uint32_t H, scalar_t *dy_dx, const uint32_t gridtype, const bool align_corners, const uint32_t interp) { switch (D) { case 2: kernel_grid_wrapper(inputs, embeddings, offsets, outputs, B, C, L, max_level, S, H, dy_dx, gridtype, align_corners, interp); break; case 3: kernel_grid_wrapper(inputs, embeddings, offsets, outputs, B, C, L, max_level, S, H, dy_dx, gridtype, align_corners, interp); break; case 4: kernel_grid_wrapper(inputs, embeddings, offsets, outputs, B, C, L, max_level, S, H, dy_dx, gridtype, align_corners, interp); break; case 5: kernel_grid_wrapper(inputs, embeddings, offsets, outputs, B, C, L, max_level, S, H, dy_dx, gridtype, align_corners, interp); break; default: throw std::runtime_error{"GridEncoding: D must be 2, 3, 4 or 5."}; } } template void kernel_grid_backward_wrapper(const scalar_t *grad, const float *inputs, const scalar_t *embeddings, const int *offsets, scalar_t *grad_embeddings, const uint32_t B, const uint32_t C, const uint32_t L, const uint32_t max_level, const float S, const uint32_t H, scalar_t *dy_dx, scalar_t *grad_inputs, const uint32_t gridtype, const bool align_corners, const uint32_t interp) { static constexpr uint32_t N_THREAD = 256; const uint32_t N_C = std::min(2u, C); // n_features_per_thread const dim3 blocks_hashgrid = { div_round_up(B * C / N_C, N_THREAD), max_level, 1 }; switch (C) { case 1: kernel_grid_backward<<>>(grad, inputs, embeddings, offsets, grad_embeddings, B, L, S, H, gridtype, align_corners, interp); if (dy_dx) kernel_input_backward<<>>(grad, dy_dx, grad_inputs, B, L); break; case 2: kernel_grid_backward<<>>(grad, inputs, embeddings, offsets, grad_embeddings, B, L, S, H, gridtype, align_corners, interp); if (dy_dx) kernel_input_backward<<>>(grad, dy_dx, grad_inputs, B, L); break; case 4: kernel_grid_backward<<>>(grad, inputs, embeddings, offsets, grad_embeddings, B, L, S, H, gridtype, align_corners, interp); if (dy_dx) kernel_input_backward<<>>(grad, dy_dx, grad_inputs, B, L); break; case 8: kernel_grid_backward<<>>(grad, inputs, embeddings, offsets, grad_embeddings, B, L, S, H, gridtype, align_corners, interp); if (dy_dx) kernel_input_backward<<>>(grad, dy_dx, grad_inputs, B, L); break; case 16: kernel_grid_backward<<>>(grad, inputs, embeddings, offsets, grad_embeddings, B, L, S, H, gridtype, align_corners, interp); if (dy_dx) kernel_input_backward<<>>(grad, dy_dx, grad_inputs, B, L); break; case 32: kernel_grid_backward<<>>(grad, inputs, embeddings, offsets, grad_embeddings, B, L, S, H, gridtype, align_corners, interp); if (dy_dx) kernel_input_backward<<>>(grad, dy_dx, grad_inputs, B, L); break; default: throw std::runtime_error{"GridEncoding: C must be 1, 2, 4, 8, 16 or 32."}; } } // grad: [L, B, C], float // inputs: [B, D], float, in [0, 1] // embeddings: [sO, C], float // offsets: [L + 1], uint32_t // grad_embeddings: [sO, C] // H: base resolution template void grid_encode_backward_cuda(const scalar_t *grad, const float *inputs, const scalar_t *embeddings, const int *offsets, scalar_t *grad_embeddings, const uint32_t B, const uint32_t D, const uint32_t C, const uint32_t L, const uint32_t max_level, const float S, const uint32_t H, scalar_t *dy_dx, scalar_t *grad_inputs, const uint32_t gridtype, const bool align_corners, const uint32_t interp) { switch (D) { case 2: kernel_grid_backward_wrapper(grad, inputs, embeddings, offsets, grad_embeddings, B, C, L, max_level, S, H, dy_dx, grad_inputs, gridtype, align_corners, interp); break; case 3: kernel_grid_backward_wrapper(grad, inputs, embeddings, offsets, grad_embeddings, B, C, L, max_level, S, H, dy_dx, grad_inputs, gridtype, align_corners, interp); break; case 4: kernel_grid_backward_wrapper(grad, inputs, embeddings, offsets, grad_embeddings, B, C, L, max_level, S, H, dy_dx, grad_inputs, gridtype, align_corners, interp); break; case 5: kernel_grid_backward_wrapper(grad, inputs, embeddings, offsets, grad_embeddings, B, C, L, max_level, S, H, dy_dx, grad_inputs, gridtype, align_corners, interp); break; default: throw std::runtime_error{"GridEncoding: D must be 2, 3, 4 or 5."}; } } void grid_encode_forward(const at::Tensor inputs, const at::Tensor embeddings, const at::Tensor offsets, at::Tensor outputs, const uint32_t B, const uint32_t D, const uint32_t C, const uint32_t L, const uint32_t max_level, const float S, const uint32_t H, at::optional dy_dx, const uint32_t gridtype, const bool align_corners, const uint32_t interp) { CHECK_CUDA(inputs); CHECK_CUDA(embeddings); CHECK_CUDA(offsets); CHECK_CUDA(outputs); // CHECK_CUDA(dy_dx); CHECK_CONTIGUOUS(inputs); CHECK_CONTIGUOUS(embeddings); CHECK_CONTIGUOUS(offsets); CHECK_CONTIGUOUS(outputs); // CHECK_CONTIGUOUS(dy_dx); CHECK_IS_FLOATING(inputs); CHECK_IS_FLOATING(embeddings); CHECK_IS_INT(offsets); CHECK_IS_FLOATING(outputs); // CHECK_IS_FLOATING(dy_dx); AT_DISPATCH_FLOATING_TYPES_AND_HALF( embeddings.scalar_type(), "grid_encode_forward", ([&] { grid_encode_forward_cuda(inputs.data_ptr(), embeddings.data_ptr(), offsets.data_ptr(), outputs.data_ptr(), B, D, C, L, max_level, S, H, dy_dx.has_value() ? dy_dx.value().data_ptr() : nullptr, gridtype, align_corners, interp); })); } void grid_encode_backward(const at::Tensor grad, const at::Tensor inputs, const at::Tensor embeddings, const at::Tensor offsets, at::Tensor grad_embeddings, const uint32_t B, const uint32_t D, const uint32_t C, const uint32_t L, const uint32_t max_level, const float S, const uint32_t H, const at::optional dy_dx, at::optional grad_inputs, const uint32_t gridtype, const bool align_corners, const uint32_t interp) { CHECK_CUDA(grad); CHECK_CUDA(inputs); CHECK_CUDA(embeddings); CHECK_CUDA(offsets); CHECK_CUDA(grad_embeddings); // CHECK_CUDA(dy_dx); // CHECK_CUDA(grad_inputs); CHECK_CONTIGUOUS(grad); CHECK_CONTIGUOUS(inputs); CHECK_CONTIGUOUS(embeddings); CHECK_CONTIGUOUS(offsets); CHECK_CONTIGUOUS(grad_embeddings); // CHECK_CONTIGUOUS(dy_dx); // CHECK_CONTIGUOUS(grad_inputs); CHECK_IS_FLOATING(grad); CHECK_IS_FLOATING(inputs); CHECK_IS_FLOATING(embeddings); CHECK_IS_INT(offsets); CHECK_IS_FLOATING(grad_embeddings); // CHECK_IS_FLOATING(dy_dx); // CHECK_IS_FLOATING(grad_inputs); AT_DISPATCH_FLOATING_TYPES_AND_HALF( grad.scalar_type(), "grid_encode_backward", ([&] { grid_encode_backward_cuda(grad.data_ptr(), inputs.data_ptr(), embeddings.data_ptr(), offsets.data_ptr(), grad_embeddings.data_ptr(), B, D, C, L, max_level, S, H, dy_dx.has_value() ? dy_dx.value().data_ptr() : nullptr, grad_inputs.has_value() ? grad_inputs.value().data_ptr() : nullptr, gridtype, align_corners, interp); })); } template __global__ void kernel_grad_tv( const scalar_t * __restrict__ inputs, const scalar_t * __restrict__ grid, scalar_t * __restrict__ grad, const int * __restrict__ offsets, const float weight, const uint32_t B, const uint32_t L, const float S, const uint32_t H, const uint32_t gridtype, const bool align_corners ) { const uint32_t b = blockIdx.x * blockDim.x + threadIdx.x; if (b >= B) return; const uint32_t level = blockIdx.y; // locate inputs += b * D; grid += (uint32_t)offsets[level] * C; grad += (uint32_t)offsets[level] * C; // check input range (should be in [0, 1]) bool flag_oob = false; #pragma unroll for (uint32_t d = 0; d < D; d++) { if (inputs[d] < 0 || inputs[d] > 1) { flag_oob = true; } } // if input out of bound, do nothing if (flag_oob) return; const uint32_t hashmap_size = offsets[level + 1] - offsets[level]; const uint32_t resolution = (uint32_t)ceil(exp2f(level * S) * H); // calculate coordinate float pos[D]; uint32_t pos_grid[D]; // [0, resolution] #pragma unroll for (uint32_t d = 0; d < D; d++) { // align_corners if (align_corners) { pos[d] = inputs[d] * (float)(resolution - 1); // [0, resolution - 1] pos_grid[d] = min((uint32_t)floorf(pos[d]), resolution - 2); // left-top corner, [0, resolution - 2] } else { pos[d] = fminf(fmaxf(inputs[d] * (float)resolution - 0.5f, 0.0f), (float)(resolution - 1)); // [-0.5, resolution-0.5] --> [0, resolution - 1] pos_grid[d] = (uint32_t)floorf(pos[d]); // left-top corner, [0, resolution - 1] } } //printf("[b=%d, l=%d] pos=(%f, %f)+(%d, %d)\n", b, level, pos[0], pos[1], pos_grid[0], pos_grid[1]); // total variation on pos_grid scalar_t results[C] = {0}; // temp results in register scalar_t idelta[C] = {0}; uint32_t index = get_grid_index(gridtype, 0, hashmap_size, resolution, pos_grid); scalar_t w = weight / (2 * D); #pragma unroll for (uint32_t d = 0; d < D; d++) { uint32_t cur_d = pos_grid[d]; scalar_t grad_val; // right side if (cur_d < resolution) { pos_grid[d] = cur_d + 1; uint32_t index_right = get_grid_index(gridtype, 0, hashmap_size, resolution, pos_grid); #pragma unroll for (uint32_t ch = 0; ch < C; ch++) { grad_val = (grid[index + ch] - grid[index_right + ch]); results[ch] += grad_val; idelta[ch] += grad_val * grad_val; } } // left side if (cur_d > 0) { pos_grid[d] = cur_d - 1; uint32_t index_left = get_grid_index(gridtype, 0, hashmap_size, resolution, pos_grid); #pragma unroll for (uint32_t ch = 0; ch < C; ch++) { grad_val = (grid[index + ch] - grid[index_left + ch]); results[ch] += grad_val; idelta[ch] += grad_val * grad_val; } } // reset pos_grid[d] = cur_d; } // writing to global memory (slow) #pragma unroll for (uint32_t ch = 0; ch < C; ch++) { // index may collide, so use atomic! atomicAdd(&grad[index + ch], w * results[ch] * rsqrtf(idelta[ch] + 1e-9f)); } } template void kernel_grad_tv_wrapper(const scalar_t *inputs, const scalar_t *embeddings, scalar_t *grad, const int *offsets, const float weight, const uint32_t B, const uint32_t C, const uint32_t L, const float S, const uint32_t H, const uint32_t gridtype, const bool align_corners) { static constexpr uint32_t N_THREAD = 512; const dim3 blocks_hashgrid = { div_round_up(B, N_THREAD), L, 1 }; switch (C) { case 1: kernel_grad_tv<<>>(inputs, embeddings, grad, offsets, weight, B, L, S, H, gridtype, align_corners); break; case 2: kernel_grad_tv<<>>(inputs, embeddings, grad, offsets, weight, B, L, S, H, gridtype, align_corners); break; case 4: kernel_grad_tv<<>>(inputs, embeddings, grad, offsets, weight, B, L, S, H, gridtype, align_corners); break; case 8: kernel_grad_tv<<>>(inputs, embeddings, grad, offsets, weight, B, L, S, H, gridtype, align_corners); break; case 16: kernel_grad_tv<<>>(inputs, embeddings, grad, offsets, weight, B, L, S, H, gridtype, align_corners); break; case 32: kernel_grad_tv<<>>(inputs, embeddings, grad, offsets, weight, B, L, S, H, gridtype, align_corners); break; default: throw std::runtime_error{"GridEncoding: C must be 1, 2, 4, 8, 16 or 32."}; } } template void grad_total_variation_cuda(const scalar_t *inputs, const scalar_t *embeddings, scalar_t *grad, const int *offsets, const float weight, const uint32_t B, const uint32_t D, const uint32_t C, const uint32_t L, const float S, const uint32_t H, const uint32_t gridtype, const bool align_corners) { switch (D) { case 2: kernel_grad_tv_wrapper(inputs, embeddings, grad, offsets, weight, B, C, L, S, H, gridtype, align_corners); break; case 3: kernel_grad_tv_wrapper(inputs, embeddings, grad, offsets, weight, B, C, L, S, H, gridtype, align_corners); break; case 4: kernel_grad_tv_wrapper(inputs, embeddings, grad, offsets, weight, B, C, L, S, H, gridtype, align_corners); break; case 5: kernel_grad_tv_wrapper(inputs, embeddings, grad, offsets, weight, B, C, L, S, H, gridtype, align_corners); break; default: throw std::runtime_error{"GridEncoding: D must be 2, 3, 4, or 5."}; } } void grad_total_variation(const at::Tensor inputs, const at::Tensor embeddings, at::Tensor grad, const at::Tensor offsets, const float weight, const uint32_t B, const uint32_t D, const uint32_t C, const uint32_t L, const float S, const uint32_t H, const uint32_t gridtype, const bool align_corners) { AT_DISPATCH_FLOATING_TYPES_AND_HALF( embeddings.scalar_type(), "grad_total_variation", ([&] { grad_total_variation_cuda(inputs.data_ptr(), embeddings.data_ptr(), grad.data_ptr(), offsets.data_ptr(), weight, B, D, C, L, S, H, gridtype, align_corners); })); } template __global__ void kernel_grad_wd( const scalar_t * __restrict__ grid, scalar_t * __restrict__ grad, const int * __restrict__ offsets, const float weight, const uint32_t B, const uint32_t L, const uint32_t C ) { const uint32_t b = blockIdx.x * blockDim.x + threadIdx.x; if (b >= B * C) return; // locate grid += b; grad += b; // decide in which level is this thread... uint32_t level = 0; const uint32_t n = b / C; // binary search b in offsets uint32_t l = 0, r = L; while (l < r) { uint32_t m = (l + r) / 2; if (offsets[m] <= n) { level = m; l = m + 1; } else { r = m; } } const uint32_t hashmap_size = offsets[level + 1] - offsets[level]; grad[0] += 2 * weight * grid[0] / hashmap_size; } void grad_weight_decay(const at::Tensor embeddings, at::Tensor grad, const at::Tensor offsets, const float weight, const uint32_t B, const uint32_t C, const uint32_t L) { AT_DISPATCH_FLOATING_TYPES_AND_HALF( embeddings.scalar_type(), "grad_weight_decay", ([&] { static constexpr uint32_t N_THREAD = 1024; const dim3 blocks_hashgrid = { div_round_up(B * C, N_THREAD), 1, 1 }; kernel_grad_wd<<>>(embeddings.data_ptr(), grad.data_ptr(), offsets.data_ptr(), weight, B, L, C); })); } ================================================ FILE: stable-dreamfusion/gridencoder/src/gridencoder.h ================================================ #ifndef _HASH_ENCODE_H #define _HASH_ENCODE_H #include #include // inputs: [B, D], float, in [0, 1] // embeddings: [sO, C], float // offsets: [L + 1], uint32_t // outputs: [B, L * C], float // H: base resolution void grid_encode_forward(const at::Tensor inputs, const at::Tensor embeddings, const at::Tensor offsets, at::Tensor outputs, const uint32_t B, const uint32_t D, const uint32_t C, const uint32_t L, const uint32_t max_level, const float S, const uint32_t H, at::optional dy_dx, const uint32_t gridtype, const bool align_corners, const uint32_t interp); void grid_encode_backward(const at::Tensor grad, const at::Tensor inputs, const at::Tensor embeddings, const at::Tensor offsets, at::Tensor grad_embeddings, const uint32_t B, const uint32_t D, const uint32_t C, const uint32_t L, const uint32_t max_level, const float S, const uint32_t H, const at::optional dy_dx, at::optional grad_inputs, const uint32_t gridtype, const bool align_corners, const uint32_t interp); void grad_total_variation(const at::Tensor inputs, const at::Tensor embeddings, at::Tensor grad, const at::Tensor offsets, const float weight, const uint32_t B, const uint32_t D, const uint32_t C, const uint32_t L, const float S, const uint32_t H, const uint32_t gridtype, const bool align_corners); void grad_weight_decay(const at::Tensor embeddings, at::Tensor grad, const at::Tensor offsets, const float weight, const uint32_t B, const uint32_t C, const uint32_t L); #endif ================================================ FILE: stable-dreamfusion/guidance/clip_utils.py ================================================ import torch import torch.nn as nn import torchvision.transforms as T import torchvision.transforms.functional as TF import clip class CLIP(nn.Module): def __init__(self, device, **kwargs): super().__init__() self.device = device self.clip_model, self.clip_preprocess = clip.load("ViT-B/16", device=self.device, jit=False) self.aug = T.Compose([ T.Resize((224, 224)), T.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), ]) def get_text_embeds(self, prompt, **kwargs): text = clip.tokenize(prompt).to(self.device) text_z = self.clip_model.encode_text(text) text_z = text_z / text_z.norm(dim=-1, keepdim=True) return text_z def get_img_embeds(self, image, **kwargs): image_z = self.clip_model.encode_image(self.aug(image)) image_z = image_z / image_z.norm(dim=-1, keepdim=True) return image_z def train_step(self, clip_z, pred_rgb, grad_scale=10, **kwargs): """ Args: grad_scale: scalar or 1-tensor of size [B], i.e. 1 grad_scale per batch item. """ # TODO: resize the image from NeRF-rendered resolution (e.g. 128x128) to what CLIP expects (512x512), to prevent Pytorch warning about `antialias=None`. image_z = self.clip_model.encode_image(self.aug(pred_rgb)) image_z = image_z / image_z.norm(dim=-1, keepdim=True) # normalize features loss = 0 if 'image' in clip_z: loss -= ((image_z * clip_z['image']).sum(-1) * grad_scale).mean() if 'text' in clip_z: loss -= ((image_z * clip_z['text']).sum(-1) * grad_scale).mean() return loss ================================================ FILE: stable-dreamfusion/guidance/if_utils.py ================================================ from transformers import logging from diffusers import IFPipeline, DDPMScheduler # suppress partial model loading warning logging.set_verbosity_error() import torch import torch.nn as nn import torch.nn.functional as F from torch.cuda.amp import custom_bwd, custom_fwd from .perpneg_utils import weighted_perpendicular_aggregator class SpecifyGradient(torch.autograd.Function): @staticmethod @custom_fwd def forward(ctx, input_tensor, gt_grad): ctx.save_for_backward(gt_grad) # we return a dummy value 1, which will be scaled by amp's scaler so we get the scale in backward. return torch.ones([1], device=input_tensor.device, dtype=input_tensor.dtype) @staticmethod @custom_bwd def backward(ctx, grad_scale): gt_grad, = ctx.saved_tensors gt_grad = gt_grad * grad_scale return gt_grad, None def seed_everything(seed): torch.manual_seed(seed) torch.cuda.manual_seed(seed) #torch.backends.cudnn.deterministic = True #torch.backends.cudnn.benchmark = True class IF(nn.Module): def __init__(self, device, vram_O, t_range=[0.02, 0.98]): super().__init__() self.device = device print(f'[INFO] loading DeepFloyd IF-I-XL...') model_key = "DeepFloyd/IF-I-XL-v1.0" is_torch2 = torch.__version__[0] == '2' # Create model pipe = IFPipeline.from_pretrained(model_key, variant="fp16", torch_dtype=torch.float16) if not is_torch2: pipe.enable_xformers_memory_efficient_attention() if vram_O: pipe.unet.to(memory_format=torch.channels_last) pipe.enable_attention_slicing(1) pipe.enable_model_cpu_offload() else: pipe.to(device) self.unet = pipe.unet self.tokenizer = pipe.tokenizer self.text_encoder = pipe.text_encoder self.unet = pipe.unet self.scheduler = pipe.scheduler self.pipe = pipe self.num_train_timesteps = self.scheduler.config.num_train_timesteps self.min_step = int(self.num_train_timesteps * t_range[0]) self.max_step = int(self.num_train_timesteps * t_range[1]) self.alphas = self.scheduler.alphas_cumprod.to(self.device) # for convenience print(f'[INFO] loaded DeepFloyd IF-I-XL!') @torch.no_grad() def get_text_embeds(self, prompt): # prompt: [str] # TODO: should I add the preprocessing at https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py#LL486C10-L486C28 prompt = self.pipe._text_preprocessing(prompt, clean_caption=False) inputs = self.tokenizer(prompt, padding='max_length', max_length=77, truncation=True, add_special_tokens=True, return_tensors='pt') embeddings = self.text_encoder(inputs.input_ids.to(self.device))[0] return embeddings def train_step(self, text_embeddings, pred_rgb, guidance_scale=100, grad_scale=1): # [0, 1] to [-1, 1] and make sure shape is [64, 64] images = F.interpolate(pred_rgb, (64, 64), mode='bilinear', align_corners=False) * 2 - 1 # timestep ~ U(0.02, 0.98) to avoid very high/low noise level t = torch.randint(self.min_step, self.max_step + 1, (images.shape[0],), dtype=torch.long, device=self.device) # predict the noise residual with unet, NO grad! with torch.no_grad(): # add noise noise = torch.randn_like(images) images_noisy = self.scheduler.add_noise(images, noise, t) # pred noise model_input = torch.cat([images_noisy] * 2) model_input = self.scheduler.scale_model_input(model_input, t) tt = torch.cat([t] * 2) noise_pred = self.unet(model_input, tt, encoder_hidden_states=text_embeddings).sample noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1], dim=1) noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1], dim=1) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # TODO: how to use the variance here? # noise_pred = torch.cat([noise_pred, predicted_variance], dim=1) # w(t), sigma_t^2 w = (1 - self.alphas[t]) grad = grad_scale * w[:, None, None, None] * (noise_pred - noise) grad = torch.nan_to_num(grad) # since we omitted an item in grad, we need to use the custom function to specify the gradient loss = SpecifyGradient.apply(images, grad) return loss def train_step_perpneg(self, text_embeddings, weights, pred_rgb, guidance_scale=100, grad_scale=1): B = pred_rgb.shape[0] K = (text_embeddings.shape[0] // B) - 1 # maximum number of prompts # [0, 1] to [-1, 1] and make sure shape is [64, 64] images = F.interpolate(pred_rgb, (64, 64), mode='bilinear', align_corners=False) * 2 - 1 # timestep ~ U(0.02, 0.98) to avoid very high/low noise level t = torch.randint(self.min_step, self.max_step + 1, (images.shape[0],), dtype=torch.long, device=self.device) # predict the noise residual with unet, NO grad! with torch.no_grad(): # add noise noise = torch.randn_like(images) images_noisy = self.scheduler.add_noise(images, noise, t) # pred noise model_input = torch.cat([images_noisy] * (1 + K)) model_input = self.scheduler.scale_model_input(model_input, t) tt = torch.cat([t] * (1 + K)) unet_output = self.unet(model_input, tt, encoder_hidden_states=text_embeddings).sample noise_pred_uncond, noise_pred_text = unet_output[:B], unet_output[B:] noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1], dim=1) noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1], dim=1) # noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) delta_noise_preds = noise_pred_text - noise_pred_uncond.repeat(K, 1, 1, 1) noise_pred = noise_pred_uncond + guidance_scale * weighted_perpendicular_aggregator(delta_noise_preds, weights, B) # w(t), sigma_t^2 w = (1 - self.alphas[t]) grad = grad_scale * w[:, None, None, None] * (noise_pred - noise) grad = torch.nan_to_num(grad) # since we omitted an item in grad, we need to use the custom function to specify the gradient loss = SpecifyGradient.apply(images, grad) return loss @torch.no_grad() def produce_imgs(self, text_embeddings, height=64, width=64, num_inference_steps=50, guidance_scale=7.5): images = torch.randn((1, 3, height, width), device=text_embeddings.device, dtype=text_embeddings.dtype) images = images * self.scheduler.init_noise_sigma self.scheduler.set_timesteps(num_inference_steps) for i, t in enumerate(self.scheduler.timesteps): # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes. model_input = torch.cat([images] * 2) model_input = self.scheduler.scale_model_input(model_input, t) # predict the noise residual noise_pred = self.unet(model_input, t, encoder_hidden_states=text_embeddings).sample # perform guidance noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1], dim=1) noise_pred_text, predicted_variance = noise_pred_text.split(model_input.shape[1], dim=1) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) noise_pred = torch.cat([noise_pred, predicted_variance], dim=1) # compute the previous noisy sample x_t -> x_t-1 images = self.scheduler.step(noise_pred, t, images).prev_sample images = (images + 1) / 2 return images def prompt_to_img(self, prompts, negative_prompts='', height=512, width=512, num_inference_steps=50, guidance_scale=7.5, latents=None): if isinstance(prompts, str): prompts = [prompts] if isinstance(negative_prompts, str): negative_prompts = [negative_prompts] # Prompts -> text embeds pos_embeds = self.get_text_embeds(prompts) # [1, 77, 768] neg_embeds = self.get_text_embeds(negative_prompts) text_embeds = torch.cat([neg_embeds, pos_embeds], dim=0) # [2, 77, 768] # Text embeds -> img imgs = self.produce_imgs(text_embeds, height=height, width=width, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale) # [1, 4, 64, 64] # Img to Numpy imgs = imgs.detach().cpu().permute(0, 2, 3, 1).numpy() imgs = (imgs * 255).round().astype('uint8') return imgs if __name__ == '__main__': import argparse import matplotlib.pyplot as plt parser = argparse.ArgumentParser() parser.add_argument('prompt', type=str) parser.add_argument('--negative', default='', type=str) parser.add_argument('--vram_O', action='store_true', help="optimization for low VRAM usage") parser.add_argument('-H', type=int, default=64) parser.add_argument('-W', type=int, default=64) parser.add_argument('--seed', type=int, default=0) parser.add_argument('--steps', type=int, default=50) opt = parser.parse_args() seed_everything(opt.seed) device = torch.device('cuda') sd = IF(device, opt.vram_O) imgs = sd.prompt_to_img(opt.prompt, opt.negative, opt.H, opt.W, opt.steps) # visualize image plt.imshow(imgs[0]) plt.show() ================================================ FILE: stable-dreamfusion/guidance/perpneg_utils.py ================================================ import torch # Please refer to the https://perp-neg.github.io/ for details about the paper and algorithm def get_perpendicular_component(x, y): assert x.shape == y.shape return x - ((torch.mul(x, y).sum())/max(torch.norm(y)**2, 1e-6)) * y def batch_get_perpendicular_component(x, y): assert x.shape == y.shape result = [] for i in range(x.shape[0]): result.append(get_perpendicular_component(x[i], y[i])) return torch.stack(result) def weighted_perpendicular_aggregator(delta_noise_preds, weights, batch_size): """ Notes: - weights: an array with the weights for combining the noise predictions - delta_noise_preds: [B x K, 4, 64, 64], K = max_prompts_per_dir """ delta_noise_preds = delta_noise_preds.split(batch_size, dim=0) # K x [B, 4, 64, 64] weights = weights.split(batch_size, dim=0) # K x [B] # print(f"{weights[0].shape = } {weights = }") assert torch.all(weights[0] == 1.0) main_positive = delta_noise_preds[0] # [B, 4, 64, 64] accumulated_output = torch.zeros_like(main_positive) for i, complementary_noise_pred in enumerate(delta_noise_preds[1:], start=1): # print(f"\n{i = }, {weights[i] = }, {weights[i].shape = }\n") idx_non_zero = torch.abs(weights[i]) > 1e-4 # print(f"{idx_non_zero.shape = }, {idx_non_zero = }") # print(f"{weights[i][idx_non_zero].shape = }, {weights[i][idx_non_zero] = }") # print(f"{complementary_noise_pred.shape = }, {complementary_noise_pred[idx_non_zero].shape = }") # print(f"{main_positive.shape = }, {main_positive[idx_non_zero].shape = }") if sum(idx_non_zero) == 0: continue accumulated_output[idx_non_zero] += weights[i][idx_non_zero].reshape(-1, 1, 1, 1) * batch_get_perpendicular_component(complementary_noise_pred[idx_non_zero], main_positive[idx_non_zero]) assert accumulated_output.shape == main_positive.shape, f"{accumulated_output.shape = }, {main_positive.shape = }" return accumulated_output + main_positive ================================================ FILE: stable-dreamfusion/guidance/sd_utils.py ================================================ from transformers import CLIPTextModel, CLIPTokenizer, logging from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler, DDIMScheduler, StableDiffusionPipeline from diffusers.utils.import_utils import is_xformers_available from os.path import isfile from pathlib import Path # suppress partial model loading warning logging.set_verbosity_error() import torch import torch.nn as nn import torch.nn.functional as F from torchvision.utils import save_image from torch.cuda.amp import custom_bwd, custom_fwd from .perpneg_utils import weighted_perpendicular_aggregator class SpecifyGradient(torch.autograd.Function): @staticmethod @custom_fwd def forward(ctx, input_tensor, gt_grad): ctx.save_for_backward(gt_grad) # we return a dummy value 1, which will be scaled by amp's scaler so we get the scale in backward. return torch.ones([1], device=input_tensor.device, dtype=input_tensor.dtype) @staticmethod @custom_bwd def backward(ctx, grad_scale): gt_grad, = ctx.saved_tensors gt_grad = gt_grad * grad_scale return gt_grad, None def seed_everything(seed): torch.manual_seed(seed) torch.cuda.manual_seed(seed) #torch.backends.cudnn.deterministic = True #torch.backends.cudnn.benchmark = True class StableDiffusion(nn.Module): def __init__(self, device, fp16, vram_O, sd_version='2.1', hf_key=None, t_range=[0.02, 0.98]): super().__init__() self.device = device self.sd_version = sd_version print(f'[INFO] loading stable diffusion...') if hf_key is not None: print(f'[INFO] using hugging face custom model key: {hf_key}') model_key = hf_key elif self.sd_version == '2.1': model_key = "stabilityai/stable-diffusion-2-1-base" elif self.sd_version == '2.0': model_key = "stabilityai/stable-diffusion-2-base" elif self.sd_version == '1.5': model_key = "runwayml/stable-diffusion-v1-5" else: raise ValueError(f'Stable-diffusion version {self.sd_version} not supported.') self.precision_t = torch.float16 if fp16 else torch.float32 # Create model pipe = StableDiffusionPipeline.from_pretrained(model_key, torch_dtype=self.precision_t) if vram_O: pipe.enable_sequential_cpu_offload() pipe.enable_vae_slicing() pipe.unet.to(memory_format=torch.channels_last) pipe.enable_attention_slicing(1) # pipe.enable_model_cpu_offload() else: pipe.to(device) self.vae = pipe.vae self.tokenizer = pipe.tokenizer self.text_encoder = pipe.text_encoder self.unet = pipe.unet self.scheduler = DDIMScheduler.from_pretrained(model_key, subfolder="scheduler", torch_dtype=self.precision_t) del pipe self.num_train_timesteps = self.scheduler.config.num_train_timesteps self.min_step = int(self.num_train_timesteps * t_range[0]) self.max_step = int(self.num_train_timesteps * t_range[1]) self.alphas = self.scheduler.alphas_cumprod.to(self.device) # for convenience print(f'[INFO] loaded stable diffusion!') @torch.no_grad() def get_text_embeds(self, prompt): # prompt: [str] inputs = self.tokenizer(prompt, padding='max_length', max_length=self.tokenizer.model_max_length, return_tensors='pt') embeddings = self.text_encoder(inputs.input_ids.to(self.device))[0] return embeddings def train_step(self, text_embeddings, pred_rgb, guidance_scale=100, as_latent=False, grad_scale=1, save_guidance_path:Path=None): if as_latent: latents = F.interpolate(pred_rgb, (64, 64), mode='bilinear', align_corners=False) * 2 - 1 else: # interp to 512x512 to be fed into vae. pred_rgb_512 = F.interpolate(pred_rgb, (512, 512), mode='bilinear', align_corners=False) # encode image into latents with vae, requires grad! latents = self.encode_imgs(pred_rgb_512) # timestep ~ U(0.02, 0.98) to avoid very high/low noise level t = torch.randint(self.min_step, self.max_step + 1, (latents.shape[0],), dtype=torch.long, device=self.device) # predict the noise residual with unet, NO grad! with torch.no_grad(): # add noise noise = torch.randn_like(latents) latents_noisy = self.scheduler.add_noise(latents, noise, t) # pred noise latent_model_input = torch.cat([latents_noisy] * 2) tt = torch.cat([t] * 2) noise_pred = self.unet(latent_model_input, tt, encoder_hidden_states=text_embeddings).sample # perform guidance (high scale from paper!) noise_pred_uncond, noise_pred_pos = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_pos - noise_pred_uncond) # import kiui # latents_tmp = torch.randn((1, 4, 64, 64), device=self.device) # latents_tmp = latents_tmp.detach() # kiui.lo(latents_tmp) # self.scheduler.set_timesteps(30) # for i, t in enumerate(self.scheduler.timesteps): # latent_model_input = torch.cat([latents_tmp] * 3) # noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)['sample'] # noise_pred_uncond, noise_pred_pos = noise_pred.chunk(2) # noise_pred = noise_pred_uncond + 10 * (noise_pred_pos - noise_pred_uncond) # latents_tmp = self.scheduler.step(noise_pred, t, latents_tmp)['prev_sample'] # imgs = self.decode_latents(latents_tmp) # kiui.vis.plot_image(imgs) # w(t), sigma_t^2 w = (1 - self.alphas[t]) grad = grad_scale * w[:, None, None, None] * (noise_pred - noise) grad = torch.nan_to_num(grad) if save_guidance_path: with torch.no_grad(): if as_latent: pred_rgb_512 = self.decode_latents(latents) # visualize predicted denoised image # The following block of code is equivalent to `predict_start_from_noise`... # see zero123_utils.py's version for a simpler implementation. alphas = self.scheduler.alphas.to(latents) total_timesteps = self.max_step - self.min_step + 1 index = total_timesteps - t.to(latents.device) - 1 b = len(noise_pred) a_t = alphas[index].reshape(b,1,1,1).to(self.device) sqrt_one_minus_alphas = torch.sqrt(1 - alphas) sqrt_one_minus_at = sqrt_one_minus_alphas[index].reshape((b,1,1,1)).to(self.device) pred_x0 = (latents_noisy - sqrt_one_minus_at * noise_pred) / a_t.sqrt() # current prediction for x_0 result_hopefully_less_noisy_image = self.decode_latents(pred_x0.to(latents.type(self.precision_t))) # visualize noisier image result_noisier_image = self.decode_latents(latents_noisy.to(pred_x0).type(self.precision_t)) # TODO: also denoise all-the-way # all 3 input images are [1, 3, H, W], e.g. [1, 3, 512, 512] viz_images = torch.cat([pred_rgb_512, result_noisier_image, result_hopefully_less_noisy_image],dim=0) save_image(viz_images, save_guidance_path) # since we omitted an item in grad, we need to use the custom function to specify the gradient loss = SpecifyGradient.apply(latents, grad) return loss def train_step_perpneg(self, text_embeddings, weights, pred_rgb, guidance_scale=100, as_latent=False, grad_scale=1, save_guidance_path:Path=None): B = pred_rgb.shape[0] K = (text_embeddings.shape[0] // B) - 1 # maximum number of prompts if as_latent: latents = F.interpolate(pred_rgb, (64, 64), mode='bilinear', align_corners=False) * 2 - 1 else: # interp to 512x512 to be fed into vae. pred_rgb_512 = F.interpolate(pred_rgb, (512, 512), mode='bilinear', align_corners=False) # encode image into latents with vae, requires grad! latents = self.encode_imgs(pred_rgb_512) # timestep ~ U(0.02, 0.98) to avoid very high/low noise level t = torch.randint(self.min_step, self.max_step + 1, (latents.shape[0],), dtype=torch.long, device=self.device) # predict the noise residual with unet, NO grad! with torch.no_grad(): # add noise noise = torch.randn_like(latents) latents_noisy = self.scheduler.add_noise(latents, noise, t) # pred noise latent_model_input = torch.cat([latents_noisy] * (1 + K)) tt = torch.cat([t] * (1 + K)) unet_output = self.unet(latent_model_input, tt, encoder_hidden_states=text_embeddings).sample # perform guidance (high scale from paper!) noise_pred_uncond, noise_pred_text = unet_output[:B], unet_output[B:] delta_noise_preds = noise_pred_text - noise_pred_uncond.repeat(K, 1, 1, 1) noise_pred = noise_pred_uncond + guidance_scale * weighted_perpendicular_aggregator(delta_noise_preds, weights, B) # import kiui # latents_tmp = torch.randn((1, 4, 64, 64), device=self.device) # latents_tmp = latents_tmp.detach() # kiui.lo(latents_tmp) # self.scheduler.set_timesteps(30) # for i, t in enumerate(self.scheduler.timesteps): # latent_model_input = torch.cat([latents_tmp] * 3) # noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)['sample'] # noise_pred_uncond, noise_pred_pos = noise_pred.chunk(2) # noise_pred = noise_pred_uncond + 10 * (noise_pred_pos - noise_pred_uncond) # latents_tmp = self.scheduler.step(noise_pred, t, latents_tmp)['prev_sample'] # imgs = self.decode_latents(latents_tmp) # kiui.vis.plot_image(imgs) # w(t), sigma_t^2 w = (1 - self.alphas[t]) grad = grad_scale * w[:, None, None, None] * (noise_pred - noise) grad = torch.nan_to_num(grad) if save_guidance_path: with torch.no_grad(): if as_latent: pred_rgb_512 = self.decode_latents(latents) # visualize predicted denoised image # The following block of code is equivalent to `predict_start_from_noise`... # see zero123_utils.py's version for a simpler implementation. alphas = self.scheduler.alphas.to(latents) total_timesteps = self.max_step - self.min_step + 1 index = total_timesteps - t.to(latents.device) - 1 b = len(noise_pred) a_t = alphas[index].reshape(b,1,1,1).to(self.device) sqrt_one_minus_alphas = torch.sqrt(1 - alphas) sqrt_one_minus_at = sqrt_one_minus_alphas[index].reshape((b,1,1,1)).to(self.device) pred_x0 = (latents_noisy - sqrt_one_minus_at * noise_pred) / a_t.sqrt() # current prediction for x_0 result_hopefully_less_noisy_image = self.decode_latents(pred_x0.to(latents.type(self.precision_t))) # visualize noisier image result_noisier_image = self.decode_latents(latents_noisy.to(pred_x0).type(self.precision_t)) # all 3 input images are [1, 3, H, W], e.g. [1, 3, 512, 512] viz_images = torch.cat([pred_rgb_512, result_noisier_image, result_hopefully_less_noisy_image],dim=0) save_image(viz_images, save_guidance_path) # since we omitted an item in grad, we need to use the custom function to specify the gradient loss = SpecifyGradient.apply(latents, grad) # print("we did it") return loss @torch.no_grad() def produce_latents(self, text_embeddings, height=512, width=512, num_inference_steps=50, guidance_scale=7.5, latents=None): if latents is None: latents = torch.randn((text_embeddings.shape[0] // 2, self.unet.in_channels, height // 8, width // 8), device=self.device) self.scheduler.set_timesteps(num_inference_steps) for i, t in enumerate(self.scheduler.timesteps): # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes. latent_model_input = torch.cat([latents] * 2) # predict the noise residual noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)['sample'] # perform guidance noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents)['prev_sample'] return latents def decode_latents(self, latents): latents = 1 / self.vae.config.scaling_factor * latents imgs = self.vae.decode(latents).sample imgs = (imgs / 2 + 0.5).clamp(0, 1) return imgs def encode_imgs(self, imgs): # imgs: [B, 3, H, W] imgs = 2 * imgs - 1 posterior = self.vae.encode(imgs).latent_dist latents = posterior.sample() * self.vae.config.scaling_factor return latents def prompt_to_img(self, prompts, negative_prompts='', height=512, width=512, num_inference_steps=50, guidance_scale=7.5, latents=None): if isinstance(prompts, str): prompts = [prompts] if isinstance(negative_prompts, str): negative_prompts = [negative_prompts] # Prompts -> text embeds pos_embeds = self.get_text_embeds(prompts) # [1, 77, 768] neg_embeds = self.get_text_embeds(negative_prompts) text_embeds = torch.cat([neg_embeds, pos_embeds], dim=0) # [2, 77, 768] # Text embeds -> img latents latents = self.produce_latents(text_embeds, height=height, width=width, latents=latents, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale) # [1, 4, 64, 64] # Img latents -> imgs imgs = self.decode_latents(latents) # [1, 3, 512, 512] # Img to Numpy imgs = imgs.detach().cpu().permute(0, 2, 3, 1).numpy() imgs = (imgs * 255).round().astype('uint8') return imgs if __name__ == '__main__': import argparse import matplotlib.pyplot as plt parser = argparse.ArgumentParser() parser.add_argument('prompt', type=str) parser.add_argument('--negative', default='', type=str) parser.add_argument('--sd_version', type=str, default='2.1', choices=['1.5', '2.0', '2.1'], help="stable diffusion version") parser.add_argument('--hf_key', type=str, default=None, help="hugging face Stable diffusion model key") parser.add_argument('--fp16', action='store_true', help="use float16 for training") parser.add_argument('--vram_O', action='store_true', help="optimization for low VRAM usage") parser.add_argument('-H', type=int, default=512) parser.add_argument('-W', type=int, default=512) parser.add_argument('--seed', type=int, default=0) parser.add_argument('--steps', type=int, default=50) opt = parser.parse_args() seed_everything(opt.seed) device = torch.device('cuda') sd = StableDiffusion(device, opt.fp16, opt.vram_O, opt.sd_version, opt.hf_key) imgs = sd.prompt_to_img(opt.prompt, opt.negative, opt.H, opt.W, opt.steps) # visualize image plt.imshow(imgs[0]) plt.show() ================================================ FILE: stable-dreamfusion/guidance/zero123_utils.py ================================================ import math import numpy as np from omegaconf import OmegaConf from pathlib import Path import torch import torch.nn as nn import torch.nn.functional as F from torch.cuda.amp import custom_bwd, custom_fwd from torchvision.utils import save_image from diffusers import DDIMScheduler import sys from os import path sys.path.append(path.dirname(path.dirname(path.abspath(__file__)))) from ldm.util import instantiate_from_config class SpecifyGradient(torch.autograd.Function): @staticmethod @custom_fwd def forward(ctx, input_tensor, gt_grad): ctx.save_for_backward(gt_grad) # we return a dummy value 1, which will be scaled by amp's scaler so we get the scale in backward. return torch.ones([1], device=input_tensor.device, dtype=input_tensor.dtype) @staticmethod @custom_bwd def backward(ctx, grad_scale): gt_grad, = ctx.saved_tensors gt_grad = gt_grad * grad_scale return gt_grad, None # load model def load_model_from_config(config, ckpt, device, vram_O=False, verbose=False): pl_sd = torch.load(ckpt, map_location='cpu') if 'global_step' in pl_sd and verbose: print(f'[INFO] Global Step: {pl_sd["global_step"]}') sd = pl_sd['state_dict'] model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) if len(m) > 0 and verbose: print('[INFO] missing keys: \n', m) if len(u) > 0 and verbose: print('[INFO] unexpected keys: \n', u) # manually load ema and delete it to save GPU memory if model.use_ema: if verbose: print('[INFO] loading EMA...') model.model_ema.copy_to(model.model) del model.model_ema if vram_O: # we don't need decoder del model.first_stage_model.decoder torch.cuda.empty_cache() model.eval().to(device) return model class Zero123(nn.Module): def __init__(self, device, fp16, config='./pretrained/zero123/sd-objaverse-finetune-c_concat-256.yaml', ckpt='./pretrained/zero123/105000.ckpt', vram_O=False, t_range=[0.02, 0.98], opt=None): super().__init__() self.device = device self.fp16 = fp16 self.vram_O = vram_O self.t_range = t_range self.opt = opt self.config = OmegaConf.load(config) # TODO: seems it cannot load into fp16... self.model = load_model_from_config(self.config, ckpt, device=self.device, vram_O=vram_O) # timesteps: use diffuser for convenience... hope it's alright. self.num_train_timesteps = self.config.model.params.timesteps self.scheduler = DDIMScheduler( self.num_train_timesteps, self.config.model.params.linear_start, self.config.model.params.linear_end, beta_schedule='scaled_linear', clip_sample=False, set_alpha_to_one=False, steps_offset=1, ) self.min_step = int(self.num_train_timesteps * t_range[0]) self.max_step = int(self.num_train_timesteps * t_range[1]) self.alphas = self.scheduler.alphas_cumprod.to(self.device) # for convenience @torch.no_grad() def get_img_embeds(self, x): # x: image tensor [B, 3, 256, 256] in [0, 1] x = x * 2 - 1 c = [self.model.get_learned_conditioning(xx.unsqueeze(0)) for xx in x] #.tile(n_samples, 1, 1) v = [self.model.encode_first_stage(xx.unsqueeze(0)).mode() for xx in x] return c, v def angle_between(self, sph_v1, sph_v2): def sph2cart(sv): r, theta, phi = sv[0], sv[1], sv[2] return torch.tensor([r * torch.sin(theta) * torch.cos(phi), r * torch.sin(theta) * torch.sin(phi), r * torch.cos(theta)]) def unit_vector(v): return v / torch.linalg.norm(v) def angle_between_2_sph(sv1, sv2): v1, v2 = sph2cart(sv1), sph2cart(sv2) v1_u, v2_u = unit_vector(v1), unit_vector(v2) return torch.arccos(torch.clip(torch.dot(v1_u, v2_u), -1.0, 1.0)) angles = torch.empty(len(sph_v1), len(sph_v2)) for i, sv1 in enumerate(sph_v1): for j, sv2 in enumerate(sph_v2): angles[i][j] = angle_between_2_sph(sv1, sv2) return angles def train_step(self, embeddings, pred_rgb, polar, azimuth, radius, guidance_scale=3, as_latent=False, grad_scale=1, save_guidance_path:Path=None): # pred_rgb: tensor [1, 3, H, W] in [0, 1] # adjust SDS scale based on how far the novel view is from the known view ref_radii = embeddings['ref_radii'] ref_polars = embeddings['ref_polars'] ref_azimuths = embeddings['ref_azimuths'] v1 = torch.stack([radius + ref_radii[0], torch.deg2rad(polar + ref_polars[0]), torch.deg2rad(azimuth + ref_azimuths[0])], dim=-1) # polar,azimuth,radius are all actually delta wrt default v2 = torch.stack([torch.tensor(ref_radii), torch.deg2rad(torch.tensor(ref_polars)), torch.deg2rad(torch.tensor(ref_azimuths))], dim=-1) angles = torch.rad2deg(self.angle_between(v1, v2)).to(self.device) if self.opt.zero123_grad_scale == 'angle': grad_scale = (angles.min(dim=1)[0] / (180/len(ref_azimuths))) * grad_scale # rethink 180/len(ref_azimuths) # claforte: try inverting grad_scale or just fixing it to 1.0 elif self.opt.zero123_grad_scale == 'None': grad_scale = 1.0 # claforte: I think this might converge faster...? else: assert False, f'Unrecognized `zero123_grad_scale`: {self.opt.zero123_grad_scale}' if as_latent: latents = F.interpolate(pred_rgb, (32, 32), mode='bilinear', align_corners=False) * 2 - 1 else: pred_rgb_256 = F.interpolate(pred_rgb, (256, 256), mode='bilinear', align_corners=False) latents = self.encode_imgs(pred_rgb_256) t = torch.randint(self.min_step, self.max_step + 1, (latents.shape[0],), dtype=torch.long, device=self.device) # Set weights acc to closeness in angle if len(ref_azimuths) > 1: inv_angles = 1/angles inv_angles[inv_angles > 100] = 100 inv_angles /= inv_angles.max(dim=-1, keepdim=True)[0] inv_angles[inv_angles < 0.1] = 0 else: inv_angles = torch.tensor([1.]).to(self.device) # Multiply closeness-weight by user-given weights zero123_ws = torch.tensor(embeddings['zero123_ws'])[None, :].to(self.device) * inv_angles zero123_ws /= zero123_ws.max(dim=-1, keepdim=True)[0] zero123_ws[zero123_ws < 0.1] = 0 with torch.no_grad(): noise = torch.randn_like(latents) latents_noisy = self.scheduler.add_noise(latents, noise, t) x_in = torch.cat([latents_noisy] * 2) t_in = torch.cat([t] * 2) noise_preds = [] # Loop through each ref image for (zero123_w, c_crossattn, c_concat, ref_polar, ref_azimuth, ref_radius) in zip(zero123_ws.T, embeddings['c_crossattn'], embeddings['c_concat'], ref_polars, ref_azimuths, ref_radii): # polar,azimuth,radius are all actually delta wrt default p = polar + ref_polars[0] - ref_polar a = azimuth + ref_azimuths[0] - ref_azimuth a[a > 180] -= 360 # range in [-180, 180] r = radius + ref_radii[0] - ref_radius # T = torch.tensor([math.radians(p), math.sin(math.radians(-a)), math.cos(math.radians(a)), r]) # T = T[None, None, :].to(self.device) T = torch.stack([torch.deg2rad(p), torch.sin(torch.deg2rad(-a)), torch.cos(torch.deg2rad(a)), r], dim=-1)[:, None, :] cond = {} clip_emb = self.model.cc_projection(torch.cat([c_crossattn.repeat(len(T), 1, 1), T], dim=-1)) cond['c_crossattn'] = [torch.cat([torch.zeros_like(clip_emb).to(self.device), clip_emb], dim=0)] cond['c_concat'] = [torch.cat([torch.zeros_like(c_concat).repeat(len(T), 1, 1, 1).to(self.device), c_concat.repeat(len(T), 1, 1, 1)], dim=0)] noise_pred = self.model.apply_model(x_in, t_in, cond) noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond) noise_preds.append(zero123_w[:, None, None, None] * noise_pred) noise_pred = torch.stack(noise_preds).sum(dim=0) / zero123_ws.sum(dim=-1)[:, None, None, None] w = (1 - self.alphas[t]) grad = (grad_scale * w)[:, None, None, None] * (noise_pred - noise) grad = torch.nan_to_num(grad) # import kiui # if not as_latent: # kiui.vis.plot_image(pred_rgb_256) # kiui.vis.plot_matrix(latents) # kiui.vis.plot_matrix(grad) # import kiui # latents = torch.randn((1, 4, 32, 32), device=self.device) # kiui.lo(latents) # self.scheduler.set_timesteps(30) # with torch.no_grad(): # for i, t in enumerate(self.scheduler.timesteps): # x_in = torch.cat([latents] * 2) # t_in = torch.cat([t.view(1)] * 2).to(self.device) # noise_pred = self.model.apply_model(x_in, t_in, cond) # noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2) # noise_pred = noise_pred_uncond + 3 * (noise_pred_cond - noise_pred_uncond) # latents = self.scheduler.step(noise_pred, t, latents)['prev_sample'] # imgs = self.decode_latents(latents) # print(polar, azimuth, radius) # kiui.vis.plot_image(pred_rgb_256, imgs) if save_guidance_path: with torch.no_grad(): if as_latent: pred_rgb_256 = self.decode_latents(latents) # claforte: test! # visualize predicted denoised image result_hopefully_less_noisy_image = self.decode_latents(self.model.predict_start_from_noise(latents_noisy, t, noise_pred)) # visualize noisier image result_noisier_image = self.decode_latents(latents_noisy) # TODO: also denoise all-the-way # all 3 input images are [1, 3, H, W], e.g. [1, 3, 512, 512] viz_images = torch.cat([pred_rgb_256, result_noisier_image, result_hopefully_less_noisy_image],dim=-1) save_image(viz_images, save_guidance_path) # since we omitted an item in grad, we need to use the custom function to specify the gradient loss = SpecifyGradient.apply(latents, grad) return loss # verification @torch.no_grad() def __call__(self, image, # image tensor [1, 3, H, W] in [0, 1] polar=0, azimuth=0, radius=0, # new view params scale=3, ddim_steps=50, ddim_eta=1, h=256, w=256, # diffusion params c_crossattn=None, c_concat=None, post_process=True, ): if c_crossattn is None: embeddings = self.get_img_embeds(image) T = torch.tensor([math.radians(polar), math.sin(math.radians(azimuth)), math.cos(math.radians(azimuth)), radius]) T = T[None, None, :].to(self.device) cond = {} clip_emb = self.model.cc_projection(torch.cat([embeddings['c_crossattn'] if c_crossattn is None else c_crossattn, T], dim=-1)) cond['c_crossattn'] = [torch.cat([torch.zeros_like(clip_emb).to(self.device), clip_emb], dim=0)] cond['c_concat'] = [torch.cat([torch.zeros_like(embeddings['c_concat']).to(self.device), embeddings['c_concat']], dim=0)] if c_concat is None else [torch.cat([torch.zeros_like(c_concat).to(self.device), c_concat], dim=0)] # produce latents loop latents = torch.randn((1, 4, h // 8, w // 8), device=self.device) self.scheduler.set_timesteps(ddim_steps) for i, t in enumerate(self.scheduler.timesteps): x_in = torch.cat([latents] * 2) t_in = torch.cat([t.view(1)] * 2).to(self.device) noise_pred = self.model.apply_model(x_in, t_in, cond) noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2) noise_pred = noise_pred_uncond + scale * (noise_pred_cond - noise_pred_uncond) latents = self.scheduler.step(noise_pred, t, latents, eta=ddim_eta)['prev_sample'] imgs = self.decode_latents(latents) imgs = imgs.cpu().numpy().transpose(0, 2, 3, 1) if post_process else imgs return imgs def decode_latents(self, latents): # zs: [B, 4, 32, 32] Latent space image # with self.model.ema_scope(): imgs = self.model.decode_first_stage(latents) imgs = (imgs / 2 + 0.5).clamp(0, 1) return imgs # [B, 3, 256, 256] RGB space image def encode_imgs(self, imgs): # imgs: [B, 3, 256, 256] RGB space image # with self.model.ema_scope(): imgs = imgs * 2 - 1 latents = torch.cat([self.model.get_first_stage_encoding(self.model.encode_first_stage(img.unsqueeze(0))) for img in imgs], dim=0) return latents # [B, 4, 32, 32] Latent space image if __name__ == '__main__': import cv2 import argparse import numpy as np import matplotlib.pyplot as plt parser = argparse.ArgumentParser() parser.add_argument('input', type=str) parser.add_argument('--fp16', action='store_true', help="use float16 for training") # no use now, can only run in fp32 parser.add_argument('--polar', type=float, default=0, help='delta polar angle in [-90, 90]') parser.add_argument('--azimuth', type=float, default=0, help='delta azimuth angle in [-180, 180]') parser.add_argument('--radius', type=float, default=0, help='delta camera radius multiplier in [-0.5, 0.5]') opt = parser.parse_args() device = torch.device('cuda') print(f'[INFO] loading image from {opt.input} ...') image = cv2.imread(opt.input, cv2.IMREAD_UNCHANGED) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image = cv2.resize(image, (256, 256), interpolation=cv2.INTER_AREA) image = image.astype(np.float32) / 255.0 image = torch.from_numpy(image).permute(2, 0, 1).unsqueeze(0).contiguous().to(device) print(f'[INFO] loading model ...') zero123 = Zero123(device, opt.fp16, opt=opt) print(f'[INFO] running model ...') outputs = zero123(image, polar=opt.polar, azimuth=opt.azimuth, radius=opt.radius) plt.imshow(outputs[0]) plt.show() ================================================ FILE: stable-dreamfusion/ldm/extras.py ================================================ from pathlib import Path from omegaconf import OmegaConf import torch from ldm.util import instantiate_from_config import logging from contextlib import contextmanager from contextlib import contextmanager import logging @contextmanager def all_logging_disabled(highest_level=logging.CRITICAL): """ A context manager that will prevent any logging messages triggered during the body from being processed. :param highest_level: the maximum logging level in use. This would only need to be changed if a custom level greater than CRITICAL is defined. https://gist.github.com/simon-weber/7853144 """ # two kind-of hacks here: # * can't get the highest logging level in effect => delegate to the user # * can't get the current module-level override => use an undocumented # (but non-private!) interface previous_level = logging.root.manager.disable logging.disable(highest_level) try: yield finally: logging.disable(previous_level) def load_training_dir(train_dir, device, epoch="last"): """Load a checkpoint and config from training directory""" train_dir = Path(train_dir) ckpt = list(train_dir.rglob(f"*{epoch}.ckpt")) assert len(ckpt) == 1, f"found {len(ckpt)} matching ckpt files" config = list(train_dir.rglob(f"*-project.yaml")) assert len(ckpt) > 0, f"didn't find any config in {train_dir}" if len(config) > 1: print(f"found {len(config)} matching config files") config = sorted(config)[-1] print(f"selecting {config}") else: config = config[0] config = OmegaConf.load(config) return load_model_from_config(config, ckpt[0], device) def load_model_from_config(config, ckpt, device="cpu", verbose=False): """Loads a model from config and a ckpt if config is a path will use omegaconf to load """ if isinstance(config, (str, Path)): config = OmegaConf.load(config) with all_logging_disabled(): print(f"Loading model from {ckpt}") pl_sd = torch.load(ckpt, map_location="cpu") global_step = pl_sd["global_step"] sd = pl_sd["state_dict"] model = instantiate_from_config(config.model) m, u = model.load_state_dict(sd, strict=False) if len(m) > 0 and verbose: print("missing keys:") print(m) if len(u) > 0 and verbose: print("unexpected keys:") model.to(device) model.eval() model.cond_stage_model.device = device return model ================================================ FILE: stable-dreamfusion/ldm/guidance.py ================================================ from typing import List, Tuple from scipy import interpolate import numpy as np import torch import matplotlib.pyplot as plt from IPython.display import clear_output import abc class GuideModel(torch.nn.Module, abc.ABC): def __init__(self) -> None: super().__init__() @abc.abstractmethod def preprocess(self, x_img): pass @abc.abstractmethod def compute_loss(self, inp): pass class Guider(torch.nn.Module): def __init__(self, sampler, guide_model, scale=1.0, verbose=False): """Apply classifier guidance Specify a guidance scale as either a scalar Or a schedule as a list of tuples t = 0->1 and scale, e.g. [(0, 10), (0.5, 20), (1, 50)] """ super().__init__() self.sampler = sampler self.index = 0 self.show = verbose self.guide_model = guide_model self.history = [] if isinstance(scale, (Tuple, List)): times = np.array([x[0] for x in scale]) values = np.array([x[1] for x in scale]) self.scale_schedule = {"times": times, "values": values} else: self.scale_schedule = float(scale) self.ddim_timesteps = sampler.ddim_timesteps self.ddpm_num_timesteps = sampler.ddpm_num_timesteps def get_scales(self): if isinstance(self.scale_schedule, float): return len(self.ddim_timesteps)*[self.scale_schedule] interpolater = interpolate.interp1d(self.scale_schedule["times"], self.scale_schedule["values"]) fractional_steps = np.array(self.ddim_timesteps)/self.ddpm_num_timesteps return interpolater(fractional_steps) def modify_score(self, model, e_t, x, t, c): # TODO look up index by t scale = self.get_scales()[self.index] if (scale == 0): return e_t sqrt_1ma = self.sampler.ddim_sqrt_one_minus_alphas[self.index].to(x.device) with torch.enable_grad(): x_in = x.detach().requires_grad_(True) pred_x0 = model.predict_start_from_noise(x_in, t=t, noise=e_t) x_img = model.first_stage_model.decode((1/0.18215)*pred_x0) inp = self.guide_model.preprocess(x_img) loss = self.guide_model.compute_loss(inp) grads = torch.autograd.grad(loss.sum(), x_in)[0] correction = grads * scale if self.show: clear_output(wait=True) print(loss.item(), scale, correction.abs().max().item(), e_t.abs().max().item()) self.history.append([loss.item(), scale, correction.min().item(), correction.max().item()]) plt.imshow((inp[0].detach().permute(1,2,0).clamp(-1,1).cpu()+1)/2) plt.axis('off') plt.show() plt.imshow(correction[0][0].detach().cpu()) plt.axis('off') plt.show() e_t_mod = e_t - sqrt_1ma*correction if self.show: fig, axs = plt.subplots(1, 3) axs[0].imshow(e_t[0][0].detach().cpu(), vmin=-2, vmax=+2) axs[1].imshow(e_t_mod[0][0].detach().cpu(), vmin=-2, vmax=+2) axs[2].imshow(correction[0][0].detach().cpu(), vmin=-2, vmax=+2) plt.show() self.index += 1 return e_t_mod ================================================ FILE: stable-dreamfusion/ldm/lr_scheduler.py ================================================ import numpy as np class LambdaWarmUpCosineScheduler: """ note: use with a base_lr of 1.0 """ def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0): self.lr_warm_up_steps = warm_up_steps self.lr_start = lr_start self.lr_min = lr_min self.lr_max = lr_max self.lr_max_decay_steps = max_decay_steps self.last_lr = 0. self.verbosity_interval = verbosity_interval def schedule(self, n, **kwargs): if self.verbosity_interval > 0: if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}") if n < self.lr_warm_up_steps: lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start self.last_lr = lr return lr else: t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps) t = min(t, 1.0) lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * ( 1 + np.cos(t * np.pi)) self.last_lr = lr return lr def __call__(self, n, **kwargs): return self.schedule(n,**kwargs) class LambdaWarmUpCosineScheduler2: """ supports repeated iterations, configurable via lists note: use with a base_lr of 1.0. """ def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0): assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths) self.lr_warm_up_steps = warm_up_steps self.f_start = f_start self.f_min = f_min self.f_max = f_max self.cycle_lengths = cycle_lengths self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths)) self.last_f = 0. self.verbosity_interval = verbosity_interval def find_in_interval(self, n): interval = 0 for cl in self.cum_cycles[1:]: if n <= cl: return interval interval += 1 def schedule(self, n, **kwargs): cycle = self.find_in_interval(n) n = n - self.cum_cycles[cycle] if self.verbosity_interval > 0: if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, " f"current cycle {cycle}") if n < self.lr_warm_up_steps[cycle]: f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle] self.last_f = f return f else: t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]) t = min(t, 1.0) f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * ( 1 + np.cos(t * np.pi)) self.last_f = f return f def __call__(self, n, **kwargs): return self.schedule(n, **kwargs) class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2): def schedule(self, n, **kwargs): cycle = self.find_in_interval(n) n = n - self.cum_cycles[cycle] if self.verbosity_interval > 0: if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, " f"current cycle {cycle}") if n < self.lr_warm_up_steps[cycle]: f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle] self.last_f = f return f else: f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle]) self.last_f = f return f ================================================ FILE: stable-dreamfusion/ldm/models/autoencoder.py ================================================ import torch import pytorch_lightning as pl import torch.nn.functional as F from contextlib import contextmanager from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer from ldm.modules.diffusionmodules.model import Encoder, Decoder from ldm.modules.distributions.distributions import DiagonalGaussianDistribution from ldm.util import instantiate_from_config class VQModel(pl.LightningModule): def __init__(self, ddconfig, lossconfig, n_embed, embed_dim, ckpt_path=None, ignore_keys=[], image_key="image", colorize_nlabels=None, monitor=None, batch_resize_range=None, scheduler_config=None, lr_g_factor=1.0, remap=None, sane_index_shape=False, # tell vector quantizer to return indices as bhw use_ema=False ): super().__init__() self.embed_dim = embed_dim self.n_embed = n_embed self.image_key = image_key self.encoder = Encoder(**ddconfig) self.decoder = Decoder(**ddconfig) self.loss = instantiate_from_config(lossconfig) self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25, remap=remap, sane_index_shape=sane_index_shape) self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1) self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) if colorize_nlabels is not None: assert type(colorize_nlabels)==int self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) if monitor is not None: self.monitor = monitor self.batch_resize_range = batch_resize_range if self.batch_resize_range is not None: print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.") self.use_ema = use_ema if self.use_ema: self.model_ema = LitEma(self) print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) self.scheduler_config = scheduler_config self.lr_g_factor = lr_g_factor @contextmanager def ema_scope(self, context=None): if self.use_ema: self.model_ema.store(self.parameters()) self.model_ema.copy_to(self) if context is not None: print(f"{context}: Switched to EMA weights") try: yield None finally: if self.use_ema: self.model_ema.restore(self.parameters()) if context is not None: print(f"{context}: Restored training weights") def init_from_ckpt(self, path, ignore_keys=list()): sd = torch.load(path, map_location="cpu")["state_dict"] keys = list(sd.keys()) for k in keys: for ik in ignore_keys: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] missing, unexpected = self.load_state_dict(sd, strict=False) print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") if len(missing) > 0: print(f"Missing Keys: {missing}") print(f"Unexpected Keys: {unexpected}") def on_train_batch_end(self, *args, **kwargs): if self.use_ema: self.model_ema(self) def encode(self, x): h = self.encoder(x) h = self.quant_conv(h) quant, emb_loss, info = self.quantize(h) return quant, emb_loss, info def encode_to_prequant(self, x): h = self.encoder(x) h = self.quant_conv(h) return h def decode(self, quant): quant = self.post_quant_conv(quant) dec = self.decoder(quant) return dec def decode_code(self, code_b): quant_b = self.quantize.embed_code(code_b) dec = self.decode(quant_b) return dec def forward(self, input, return_pred_indices=False): quant, diff, (_,_,ind) = self.encode(input) dec = self.decode(quant) if return_pred_indices: return dec, diff, ind return dec, diff def get_input(self, batch, k): x = batch[k] if len(x.shape) == 3: x = x[..., None] x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() if self.batch_resize_range is not None: lower_size = self.batch_resize_range[0] upper_size = self.batch_resize_range[1] if self.global_step <= 4: # do the first few batches with max size to avoid later oom new_resize = upper_size else: new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16)) if new_resize != x.shape[2]: x = F.interpolate(x, size=new_resize, mode="bicubic") x = x.detach() return x def training_step(self, batch, batch_idx, optimizer_idx): # https://github.com/pytorch/pytorch/issues/37142 # try not to fool the heuristics x = self.get_input(batch, self.image_key) xrec, qloss, ind = self(x, return_pred_indices=True) if optimizer_idx == 0: # autoencode aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train", predicted_indices=ind) self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) return aeloss if optimizer_idx == 1: # discriminator discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train") self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True) return discloss def validation_step(self, batch, batch_idx): log_dict = self._validation_step(batch, batch_idx) with self.ema_scope(): log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema") return log_dict def _validation_step(self, batch, batch_idx, suffix=""): x = self.get_input(batch, self.image_key) xrec, qloss, ind = self(x, return_pred_indices=True) aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, self.global_step, last_layer=self.get_last_layer(), split="val"+suffix, predicted_indices=ind ) discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, self.global_step, last_layer=self.get_last_layer(), split="val"+suffix, predicted_indices=ind ) rec_loss = log_dict_ae[f"val{suffix}/rec_loss"] self.log(f"val{suffix}/rec_loss", rec_loss, prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) self.log(f"val{suffix}/aeloss", aeloss, prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) if version.parse(pl.__version__) >= version.parse('1.4.0'): del log_dict_ae[f"val{suffix}/rec_loss"] self.log_dict(log_dict_ae) self.log_dict(log_dict_disc) return self.log_dict def configure_optimizers(self): lr_d = self.learning_rate lr_g = self.lr_g_factor*self.learning_rate print("lr_d", lr_d) print("lr_g", lr_g) opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ list(self.decoder.parameters())+ list(self.quantize.parameters())+ list(self.quant_conv.parameters())+ list(self.post_quant_conv.parameters()), lr=lr_g, betas=(0.5, 0.9)) opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), lr=lr_d, betas=(0.5, 0.9)) if self.scheduler_config is not None: scheduler = instantiate_from_config(self.scheduler_config) print("Setting up LambdaLR scheduler...") scheduler = [ { 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1 }, { 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1 }, ] return [opt_ae, opt_disc], scheduler return [opt_ae, opt_disc], [] def get_last_layer(self): return self.decoder.conv_out.weight def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs): log = dict() x = self.get_input(batch, self.image_key) x = x.to(self.device) if only_inputs: log["inputs"] = x return log xrec, _ = self(x) if x.shape[1] > 3: # colorize with random projection assert xrec.shape[1] > 3 x = self.to_rgb(x) xrec = self.to_rgb(xrec) log["inputs"] = x log["reconstructions"] = xrec if plot_ema: with self.ema_scope(): xrec_ema, _ = self(x) if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema) log["reconstructions_ema"] = xrec_ema return log def to_rgb(self, x): assert self.image_key == "segmentation" if not hasattr(self, "colorize"): self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) x = F.conv2d(x, weight=self.colorize) x = 2.*(x-x.min())/(x.max()-x.min()) - 1. return x class VQModelInterface(VQModel): def __init__(self, embed_dim, *args, **kwargs): super().__init__(embed_dim=embed_dim, *args, **kwargs) self.embed_dim = embed_dim def encode(self, x): h = self.encoder(x) h = self.quant_conv(h) return h def decode(self, h, force_not_quantize=False): # also go through quantization layer if not force_not_quantize: quant, emb_loss, info = self.quantize(h) else: quant = h quant = self.post_quant_conv(quant) dec = self.decoder(quant) return dec class AutoencoderKL(pl.LightningModule): def __init__(self, ddconfig, lossconfig, embed_dim, ckpt_path=None, ignore_keys=[], image_key="image", colorize_nlabels=None, monitor=None, ): super().__init__() self.image_key = image_key self.encoder = Encoder(**ddconfig) self.decoder = Decoder(**ddconfig) self.loss = instantiate_from_config(lossconfig) assert ddconfig["double_z"] self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) self.embed_dim = embed_dim if colorize_nlabels is not None: assert type(colorize_nlabels)==int self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) if monitor is not None: self.monitor = monitor if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) def init_from_ckpt(self, path, ignore_keys=list()): sd = torch.load(path, map_location="cpu")["state_dict"] keys = list(sd.keys()) for k in keys: for ik in ignore_keys: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] self.load_state_dict(sd, strict=False) print(f"Restored from {path}") def encode(self, x): h = self.encoder(x) moments = self.quant_conv(h) posterior = DiagonalGaussianDistribution(moments) return posterior def decode(self, z): z = self.post_quant_conv(z) dec = self.decoder(z) return dec def forward(self, input, sample_posterior=True): posterior = self.encode(input) if sample_posterior: z = posterior.sample() else: z = posterior.mode() dec = self.decode(z) return dec, posterior def get_input(self, batch, k): x = batch[k] if len(x.shape) == 3: x = x[..., None] x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() return x def training_step(self, batch, batch_idx, optimizer_idx): inputs = self.get_input(batch, self.image_key) reconstructions, posterior = self(inputs) if optimizer_idx == 0: # train encoder+decoder+logvar aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train") self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) return aeloss if optimizer_idx == 1: # train the discriminator discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train") self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) return discloss def validation_step(self, batch, batch_idx): inputs = self.get_input(batch, self.image_key) reconstructions, posterior = self(inputs) aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step, last_layer=self.get_last_layer(), split="val") discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step, last_layer=self.get_last_layer(), split="val") self.log("val/rec_loss", log_dict_ae["val/rec_loss"]) self.log_dict(log_dict_ae) self.log_dict(log_dict_disc) return self.log_dict def configure_optimizers(self): lr = self.learning_rate opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ list(self.decoder.parameters())+ list(self.quant_conv.parameters())+ list(self.post_quant_conv.parameters()), lr=lr, betas=(0.5, 0.9)) opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9)) return [opt_ae, opt_disc], [] def get_last_layer(self): return self.decoder.conv_out.weight @torch.no_grad() def log_images(self, batch, only_inputs=False, **kwargs): log = dict() x = self.get_input(batch, self.image_key) x = x.to(self.device) if not only_inputs: xrec, posterior = self(x) if x.shape[1] > 3: # colorize with random projection assert xrec.shape[1] > 3 x = self.to_rgb(x) xrec = self.to_rgb(xrec) log["samples"] = self.decode(torch.randn_like(posterior.sample())) log["reconstructions"] = xrec log["inputs"] = x return log def to_rgb(self, x): assert self.image_key == "segmentation" if not hasattr(self, "colorize"): self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) x = F.conv2d(x, weight=self.colorize) x = 2.*(x-x.min())/(x.max()-x.min()) - 1. return x class IdentityFirstStage(torch.nn.Module): def __init__(self, *args, vq_interface=False, **kwargs): self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff super().__init__() def encode(self, x, *args, **kwargs): return x def decode(self, x, *args, **kwargs): return x def quantize(self, x, *args, **kwargs): if self.vq_interface: return x, None, [None, None, None] return x def forward(self, x, *args, **kwargs): return x ================================================ FILE: stable-dreamfusion/ldm/models/diffusion/__init__.py ================================================ ================================================ FILE: stable-dreamfusion/ldm/models/diffusion/classifier.py ================================================ import os import torch import pytorch_lightning as pl from omegaconf import OmegaConf from torch.nn import functional as F from torch.optim import AdamW from torch.optim.lr_scheduler import LambdaLR from copy import deepcopy from einops import rearrange from glob import glob from natsort import natsorted from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config __models__ = { 'class_label': EncoderUNetModel, 'segmentation': UNetModel } def disabled_train(self, mode=True): """Overwrite model.train with this function to make sure train/eval mode does not change anymore.""" return self class NoisyLatentImageClassifier(pl.LightningModule): def __init__(self, diffusion_path, num_classes, ckpt_path=None, pool='attention', label_key=None, diffusion_ckpt_path=None, scheduler_config=None, weight_decay=1.e-2, log_steps=10, monitor='val/loss', *args, **kwargs): super().__init__(*args, **kwargs) self.num_classes = num_classes # get latest config of diffusion model diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1] self.diffusion_config = OmegaConf.load(diffusion_config).model self.diffusion_config.params.ckpt_path = diffusion_ckpt_path self.load_diffusion() self.monitor = monitor self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1 self.log_time_interval = self.diffusion_model.num_timesteps // log_steps self.log_steps = log_steps self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \ else self.diffusion_model.cond_stage_key assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params' if self.label_key not in __models__: raise NotImplementedError() self.load_classifier(ckpt_path, pool) self.scheduler_config = scheduler_config self.use_scheduler = self.scheduler_config is not None self.weight_decay = weight_decay def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): sd = torch.load(path, map_location="cpu") if "state_dict" in list(sd.keys()): sd = sd["state_dict"] keys = list(sd.keys()) for k in keys: for ik in ignore_keys: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( sd, strict=False) print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") if len(missing) > 0: print(f"Missing Keys: {missing}") if len(unexpected) > 0: print(f"Unexpected Keys: {unexpected}") def load_diffusion(self): model = instantiate_from_config(self.diffusion_config) self.diffusion_model = model.eval() self.diffusion_model.train = disabled_train for param in self.diffusion_model.parameters(): param.requires_grad = False def load_classifier(self, ckpt_path, pool): model_config = deepcopy(self.diffusion_config.params.unet_config.params) model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels model_config.out_channels = self.num_classes if self.label_key == 'class_label': model_config.pool = pool self.model = __models__[self.label_key](**model_config) if ckpt_path is not None: print('#####################################################################') print(f'load from ckpt "{ckpt_path}"') print('#####################################################################') self.init_from_ckpt(ckpt_path) @torch.no_grad() def get_x_noisy(self, x, t, noise=None): noise = default(noise, lambda: torch.randn_like(x)) continuous_sqrt_alpha_cumprod = None if self.diffusion_model.use_continuous_noise: continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1) # todo: make sure t+1 is correct here return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise, continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod) def forward(self, x_noisy, t, *args, **kwargs): return self.model(x_noisy, t) @torch.no_grad() def get_input(self, batch, k): x = batch[k] if len(x.shape) == 3: x = x[..., None] x = rearrange(x, 'b h w c -> b c h w') x = x.to(memory_format=torch.contiguous_format).float() return x @torch.no_grad() def get_conditioning(self, batch, k=None): if k is None: k = self.label_key assert k is not None, 'Needs to provide label key' targets = batch[k].to(self.device) if self.label_key == 'segmentation': targets = rearrange(targets, 'b h w c -> b c h w') for down in range(self.numd): h, w = targets.shape[-2:] targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest') # targets = rearrange(targets,'b c h w -> b h w c') return targets def compute_top_k(self, logits, labels, k, reduction="mean"): _, top_ks = torch.topk(logits, k, dim=1) if reduction == "mean": return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item() elif reduction == "none": return (top_ks == labels[:, None]).float().sum(dim=-1) def on_train_epoch_start(self): # save some memory self.diffusion_model.model.to('cpu') @torch.no_grad() def write_logs(self, loss, logits, targets): log_prefix = 'train' if self.training else 'val' log = {} log[f"{log_prefix}/loss"] = loss.mean() log[f"{log_prefix}/acc@1"] = self.compute_top_k( logits, targets, k=1, reduction="mean" ) log[f"{log_prefix}/acc@5"] = self.compute_top_k( logits, targets, k=5, reduction="mean" ) self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True) self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False) self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True) lr = self.optimizers().param_groups[0]['lr'] self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True) def shared_step(self, batch, t=None): x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key) targets = self.get_conditioning(batch) if targets.dim() == 4: targets = targets.argmax(dim=1) if t is None: t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long() else: t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long() x_noisy = self.get_x_noisy(x, t) logits = self(x_noisy, t) loss = F.cross_entropy(logits, targets, reduction='none') self.write_logs(loss.detach(), logits.detach(), targets.detach()) loss = loss.mean() return loss, logits, x_noisy, targets def training_step(self, batch, batch_idx): loss, *_ = self.shared_step(batch) return loss def reset_noise_accs(self): self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)} def on_validation_start(self): self.reset_noise_accs() @torch.no_grad() def validation_step(self, batch, batch_idx): loss, *_ = self.shared_step(batch) for t in self.noisy_acc: _, logits, _, targets = self.shared_step(batch, t) self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean')) self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean')) return loss def configure_optimizers(self): optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay) if self.use_scheduler: scheduler = instantiate_from_config(self.scheduler_config) print("Setting up LambdaLR scheduler...") scheduler = [ { 'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1 }] return [optimizer], scheduler return optimizer @torch.no_grad() def log_images(self, batch, N=8, *args, **kwargs): log = dict() x = self.get_input(batch, self.diffusion_model.first_stage_key) log['inputs'] = x y = self.get_conditioning(batch) if self.label_key == 'class_label': y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) log['labels'] = y if ismap(y): log['labels'] = self.diffusion_model.to_rgb(y) for step in range(self.log_steps): current_time = step * self.log_time_interval _, logits, x_noisy, _ = self.shared_step(batch, t=current_time) log[f'inputs@t{current_time}'] = x_noisy pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes) pred = rearrange(pred, 'b h w c -> b c h w') log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred) for key in log: log[key] = log[key][:N] return log ================================================ FILE: stable-dreamfusion/ldm/models/diffusion/ddim.py ================================================ """SAMPLING ONLY.""" import torch import numpy as np from tqdm import tqdm from functools import partial from einops import rearrange from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor from ldm.models.diffusion.sampling_util import renorm_thresholding, norm_thresholding, spatial_norm_thresholding class DDIMSampler(object): def __init__(self, model, schedule="linear", **kwargs): super().__init__() self.model = model self.ddpm_num_timesteps = model.num_timesteps self.schedule = schedule def to(self, device): """Same as to in torch module Don't really underestand why this isn't a module in the first place""" for k, v in self.__dict__.items(): if isinstance(v, torch.Tensor): new_v = getattr(self, k).to(device) setattr(self, k, new_v) def register_buffer(self, name, attr): if type(attr) == torch.Tensor: if attr.device != torch.device("cuda"): attr = attr.to(torch.device("cuda")) setattr(self, name, attr) def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) alphas_cumprod = self.model.alphas_cumprod assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) self.register_buffer('betas', to_torch(self.model.betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) # calculations for diffusion q(x_t | x_{t-1}) and others self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) # ddim sampling parameters ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), ddim_timesteps=self.ddim_timesteps, eta=ddim_eta,verbose=verbose) self.register_buffer('ddim_sigmas', ddim_sigmas) self.register_buffer('ddim_alphas', ddim_alphas) self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) @torch.no_grad() def sample(self, S, batch_size, shape, conditioning=None, callback=None, normals_sequence=None, img_callback=None, quantize_x0=False, eta=0., mask=None, x0=None, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, verbose=True, x_T=None, log_every_t=100, unconditional_guidance_scale=1., unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... dynamic_threshold=None, **kwargs ): if conditioning is not None: if isinstance(conditioning, dict): ctmp = conditioning[list(conditioning.keys())[0]] while isinstance(ctmp, list): ctmp = ctmp[0] cbs = ctmp.shape[0] if cbs != batch_size: print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") else: if conditioning.shape[0] != batch_size: print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) # sampling C, H, W = shape size = (batch_size, C, H, W) # print(f'Data shape for DDIM sampling is {size}, eta {eta}') samples, intermediates = self.ddim_sampling(conditioning, size, callback=callback, img_callback=img_callback, quantize_denoised=quantize_x0, mask=mask, x0=x0, ddim_use_original_steps=False, noise_dropout=noise_dropout, temperature=temperature, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, x_T=x_T, log_every_t=log_every_t, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, dynamic_threshold=dynamic_threshold, ) return samples, intermediates @torch.no_grad() def ddim_sampling(self, cond, shape, x_T=None, ddim_use_original_steps=False, callback=None, timesteps=None, quantize_denoised=False, mask=None, x0=None, img_callback=None, log_every_t=100, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None, t_start=-1): device = self.model.betas.device b = shape[0] if x_T is None: img = torch.randn(shape, device=device) else: img = x_T if timesteps is None: timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps elif timesteps is not None and not ddim_use_original_steps: subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 timesteps = self.ddim_timesteps[:subset_end] timesteps = timesteps[:t_start] intermediates = {'x_inter': [img], 'pred_x0': [img]} time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps) total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] # print(f"Running DDIM Sampling with {total_steps} timesteps") iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) for i, step in enumerate(iterator): index = total_steps - i - 1 ts = torch.full((b,), step, device=device, dtype=torch.long) if mask is not None: assert x0 is not None img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? img = img_orig * mask + (1. - mask) * img outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, quantize_denoised=quantize_denoised, temperature=temperature, noise_dropout=noise_dropout, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, dynamic_threshold=dynamic_threshold) img, pred_x0 = outs if callback: img = callback(i, img, pred_x0) if img_callback: img_callback(pred_x0, i) if index % log_every_t == 0 or index == total_steps - 1: intermediates['x_inter'].append(img) intermediates['pred_x0'].append(pred_x0) return img, intermediates @torch.no_grad() def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None): b, *_, device = *x.shape, x.device if unconditional_conditioning is None or unconditional_guidance_scale == 1.: e_t = self.model.apply_model(x, t, c) else: x_in = torch.cat([x] * 2) t_in = torch.cat([t] * 2) if isinstance(c, dict): assert isinstance(unconditional_conditioning, dict) c_in = dict() for k in c: if isinstance(c[k], list): c_in[k] = [torch.cat([ unconditional_conditioning[k][i], c[k][i]]) for i in range(len(c[k]))] else: c_in[k] = torch.cat([ unconditional_conditioning[k], c[k]]) else: c_in = torch.cat([unconditional_conditioning, c]) e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) if score_corrector is not None: assert self.model.parameterization == "eps" e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas # select parameters corresponding to the currently considered timestep a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) # current prediction for x_0 pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() print(t, sqrt_one_minus_at, a_t) if quantize_denoised: pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) if dynamic_threshold is not None: pred_x0 = norm_thresholding(pred_x0, dynamic_threshold) # direction pointing to x_t dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature if noise_dropout > 0.: noise = torch.nn.functional.dropout(noise, p=noise_dropout) x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise return x_prev, pred_x0 @torch.no_grad() def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None, unconditional_guidance_scale=1.0, unconditional_conditioning=None): num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0] assert t_enc <= num_reference_steps num_steps = t_enc if use_original_steps: alphas_next = self.alphas_cumprod[:num_steps] alphas = self.alphas_cumprod_prev[:num_steps] else: alphas_next = self.ddim_alphas[:num_steps] alphas = torch.tensor(self.ddim_alphas_prev[:num_steps]) x_next = x0 intermediates = [] inter_steps = [] for i in tqdm(range(num_steps), desc='Encoding Image'): t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long) if unconditional_guidance_scale == 1.: noise_pred = self.model.apply_model(x_next, t, c) else: assert unconditional_conditioning is not None e_t_uncond, noise_pred = torch.chunk( self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)), torch.cat((unconditional_conditioning, c))), 2) noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond) xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next weighted_noise_pred = alphas_next[i].sqrt() * ( (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred x_next = xt_weighted + weighted_noise_pred if return_intermediates and i % ( num_steps // return_intermediates) == 0 and i < num_steps - 1: intermediates.append(x_next) inter_steps.append(i) elif return_intermediates and i >= num_steps - 2: intermediates.append(x_next) inter_steps.append(i) out = {'x_encoded': x_next, 'intermediate_steps': inter_steps} if return_intermediates: out.update({'intermediates': intermediates}) return x_next, out @torch.no_grad() def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): # fast, but does not allow for exact reconstruction # t serves as an index to gather the correct alphas if use_original_steps: sqrt_alphas_cumprod = self.sqrt_alphas_cumprod sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod else: sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas if noise is None: noise = torch.randn_like(x0) return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise) @torch.no_grad() def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, use_original_steps=False): timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps timesteps = timesteps[:t_start] time_range = np.flip(timesteps) total_steps = timesteps.shape[0] # print(f"Running DDIM Sampling with {total_steps} timesteps") iterator = tqdm(time_range, desc='Decoding image', total=total_steps) x_dec = x_latent for i, step in enumerate(iterator): index = total_steps - i - 1 ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long) x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning) return x_dec ================================================ FILE: stable-dreamfusion/ldm/models/diffusion/ddpm.py ================================================ """ wild mixture of https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py https://github.com/CompVis/taming-transformers -- merci """ import torch import torch.nn as nn import numpy as np import pytorch_lightning as pl from torch.optim.lr_scheduler import LambdaLR from einops import rearrange, repeat from contextlib import contextmanager, nullcontext from functools import partial import itertools from tqdm import tqdm from torchvision.utils import make_grid from pytorch_lightning.utilities.rank_zero import rank_zero_only from omegaconf import ListConfig from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config from ldm.modules.ema import LitEma from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like from ldm.models.diffusion.ddim import DDIMSampler from ldm.modules.attention import CrossAttention __conditioning_keys__ = {'concat': 'c_concat', 'crossattn': 'c_crossattn', 'adm': 'y'} def disabled_train(self, mode=True): """Overwrite model.train with this function to make sure train/eval mode does not change anymore.""" return self def uniform_on_device(r1, r2, shape, device): return (r1 - r2) * torch.rand(*shape, device=device) + r2 class DDPM(pl.LightningModule): # classic DDPM with Gaussian diffusion, in image space def __init__(self, unet_config, timesteps=1000, beta_schedule="linear", loss_type="l2", ckpt_path=None, ignore_keys=[], load_only_unet=False, monitor="val/loss", use_ema=True, first_stage_key="image", image_size=256, channels=3, log_every_t=100, clip_denoised=True, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3, given_betas=None, original_elbo_weight=0., v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta l_simple_weight=1., conditioning_key=None, parameterization="eps", # all assuming fixed variance schedules scheduler_config=None, use_positional_encodings=False, learn_logvar=False, logvar_init=0., make_it_fit=False, ucg_training=None, ): super().__init__() assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"' self.parameterization = parameterization print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode") self.cond_stage_model = None self.clip_denoised = clip_denoised self.log_every_t = log_every_t self.first_stage_key = first_stage_key self.image_size = image_size # try conv? self.channels = channels self.use_positional_encodings = use_positional_encodings self.model = DiffusionWrapper(unet_config, conditioning_key) count_params(self.model, verbose=True) self.use_ema = use_ema if self.use_ema: self.model_ema = LitEma(self.model) print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") self.use_scheduler = scheduler_config is not None if self.use_scheduler: self.scheduler_config = scheduler_config self.v_posterior = v_posterior self.original_elbo_weight = original_elbo_weight self.l_simple_weight = l_simple_weight if monitor is not None: self.monitor = monitor self.make_it_fit = make_it_fit if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet) self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) self.loss_type = loss_type self.learn_logvar = learn_logvar self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,)) if self.learn_logvar: self.logvar = nn.Parameter(self.logvar, requires_grad=True) self.ucg_training = ucg_training or dict() if self.ucg_training: self.ucg_prng = np.random.RandomState() def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): if exists(given_betas): betas = given_betas else: betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) alphas = 1. - betas alphas_cumprod = np.cumprod(alphas, axis=0) alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.linear_start = linear_start self.linear_end = linear_end assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' to_torch = partial(torch.tensor, dtype=torch.float32) self.register_buffer('betas', to_torch(betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) # calculations for diffusion q(x_t | x_{t-1}) and others self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) # calculations for posterior q(x_{t-1} | x_t, x_0) posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / ( 1. - alphas_cumprod) + self.v_posterior * betas # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) self.register_buffer('posterior_variance', to_torch(posterior_variance)) # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) self.register_buffer('posterior_mean_coef1', to_torch( betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) self.register_buffer('posterior_mean_coef2', to_torch( (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) if self.parameterization == "eps": lvlb_weights = self.betas ** 2 / ( 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)) elif self.parameterization == "x0": lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod)) else: raise NotImplementedError("mu not supported") # TODO how to choose this term lvlb_weights[0] = lvlb_weights[1] self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) assert not torch.isnan(self.lvlb_weights).all() @contextmanager def ema_scope(self, context=None): if self.use_ema: self.model_ema.store(self.model.parameters()) self.model_ema.copy_to(self.model) if context is not None: print(f"{context}: Switched to EMA weights") try: yield None finally: if self.use_ema: self.model_ema.restore(self.model.parameters()) if context is not None: print(f"{context}: Restored training weights") @torch.no_grad() def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): sd = torch.load(path, map_location="cpu") if "state_dict" in list(sd.keys()): sd = sd["state_dict"] keys = list(sd.keys()) if self.make_it_fit: n_params = len([name for name, _ in itertools.chain(self.named_parameters(), self.named_buffers())]) for name, param in tqdm( itertools.chain(self.named_parameters(), self.named_buffers()), desc="Fitting old weights to new weights", total=n_params ): if not name in sd: continue old_shape = sd[name].shape new_shape = param.shape assert len(old_shape)==len(new_shape) if len(new_shape) > 2: # we only modify first two axes assert new_shape[2:] == old_shape[2:] # assumes first axis corresponds to output dim if not new_shape == old_shape: new_param = param.clone() old_param = sd[name] if len(new_shape) == 1: for i in range(new_param.shape[0]): new_param[i] = old_param[i % old_shape[0]] elif len(new_shape) >= 2: for i in range(new_param.shape[0]): for j in range(new_param.shape[1]): new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]] n_used_old = torch.ones(old_shape[1]) for j in range(new_param.shape[1]): n_used_old[j % old_shape[1]] += 1 n_used_new = torch.zeros(new_shape[1]) for j in range(new_param.shape[1]): n_used_new[j] = n_used_old[j % old_shape[1]] n_used_new = n_used_new[None, :] while len(n_used_new.shape) < len(new_shape): n_used_new = n_used_new.unsqueeze(-1) new_param /= n_used_new sd[name] = new_param missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( sd, strict=False) print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") if len(missing) > 0: print(f"Missing Keys: {missing}") if len(unexpected) > 0: print(f"Unexpected Keys: {unexpected}") def q_mean_variance(self, x_start, t): """ Get the distribution q(x_t | x_0). :param x_start: the [N x C x ...] tensor of noiseless inputs. :param t: the number of diffusion steps (minus 1). Here, 0 means one step. :return: A tuple (mean, variance, log_variance), all of x_start's shape. """ mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start) variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape) return mean, variance, log_variance def predict_start_from_noise(self, x_t, t, noise): return ( extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise ) def q_posterior(self, x_start, x_t, t): posterior_mean = ( extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t ) posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape) posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape) return posterior_mean, posterior_variance, posterior_log_variance_clipped def p_mean_variance(self, x, t, clip_denoised: bool): model_out = self.model(x, t) if self.parameterization == "eps": x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) elif self.parameterization == "x0": x_recon = model_out if clip_denoised: x_recon.clamp_(-1., 1.) model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) return model_mean, posterior_variance, posterior_log_variance @torch.no_grad() def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): b, *_, device = *x.shape, x.device model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised) noise = noise_like(x.shape, device, repeat_noise) # no noise when t == 0 nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise @torch.no_grad() def p_sample_loop(self, shape, return_intermediates=False): device = self.betas.device b = shape[0] img = torch.randn(shape, device=device) intermediates = [img] for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps): img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long), clip_denoised=self.clip_denoised) if i % self.log_every_t == 0 or i == self.num_timesteps - 1: intermediates.append(img) if return_intermediates: return img, intermediates return img @torch.no_grad() def sample(self, batch_size=16, return_intermediates=False): image_size = self.image_size channels = self.channels return self.p_sample_loop((batch_size, channels, image_size, image_size), return_intermediates=return_intermediates) def q_sample(self, x_start, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) def get_loss(self, pred, target, mean=True): if self.loss_type == 'l1': loss = (target - pred).abs() if mean: loss = loss.mean() elif self.loss_type == 'l2': if mean: loss = torch.nn.functional.mse_loss(target, pred) else: loss = torch.nn.functional.mse_loss(target, pred, reduction='none') else: raise NotImplementedError("unknown loss type '{loss_type}'") return loss def p_losses(self, x_start, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) model_out = self.model(x_noisy, t) loss_dict = {} if self.parameterization == "eps": target = noise elif self.parameterization == "x0": target = x_start else: raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported") loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3]) log_prefix = 'train' if self.training else 'val' loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()}) loss_simple = loss.mean() * self.l_simple_weight loss_vlb = (self.lvlb_weights[t] * loss).mean() loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb}) loss = loss_simple + self.original_elbo_weight * loss_vlb loss_dict.update({f'{log_prefix}/loss': loss}) return loss, loss_dict def forward(self, x, *args, **kwargs): # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size # assert h == img_size and w == img_size, f'height and width of image must be {img_size}' t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() return self.p_losses(x, t, *args, **kwargs) def get_input(self, batch, k): x = batch[k] if len(x.shape) == 3: x = x[..., None] x = rearrange(x, 'b h w c -> b c h w') x = x.to(memory_format=torch.contiguous_format).float() return x def shared_step(self, batch): x = self.get_input(batch, self.first_stage_key) loss, loss_dict = self(x) return loss, loss_dict def training_step(self, batch, batch_idx): for k in self.ucg_training: p = self.ucg_training[k]["p"] val = self.ucg_training[k]["val"] if val is None: val = "" for i in range(len(batch[k])): if self.ucg_prng.choice(2, p=[1-p, p]): batch[k][i] = val loss, loss_dict = self.shared_step(batch) self.log_dict(loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True) self.log("global_step", self.global_step, prog_bar=True, logger=True, on_step=True, on_epoch=False) if self.use_scheduler: lr = self.optimizers().param_groups[0]['lr'] self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False) return loss @torch.no_grad() def validation_step(self, batch, batch_idx): _, loss_dict_no_ema = self.shared_step(batch) with self.ema_scope(): _, loss_dict_ema = self.shared_step(batch) loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema} self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) def on_train_batch_end(self, *args, **kwargs): if self.use_ema: self.model_ema(self.model) def _get_rows_from_list(self, samples): n_imgs_per_row = len(samples) denoise_grid = rearrange(samples, 'n b c h w -> b n c h w') denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) return denoise_grid @torch.no_grad() def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): log = dict() x = self.get_input(batch, self.first_stage_key) N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) x = x.to(self.device)[:N] log["inputs"] = x # get diffusion row diffusion_row = list() x_start = x[:n_row] for t in range(self.num_timesteps): if t % self.log_every_t == 0 or t == self.num_timesteps - 1: t = repeat(torch.tensor([t]), '1 -> b', b=n_row) t = t.to(self.device).long() noise = torch.randn_like(x_start) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) diffusion_row.append(x_noisy) log["diffusion_row"] = self._get_rows_from_list(diffusion_row) if sample: # get denoise row with self.ema_scope("Plotting"): samples, denoise_row = self.sample(batch_size=N, return_intermediates=True) log["samples"] = samples log["denoise_row"] = self._get_rows_from_list(denoise_row) if return_keys: if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: return log else: return {key: log[key] for key in return_keys} return log def configure_optimizers(self): lr = self.learning_rate params = list(self.model.parameters()) if self.learn_logvar: params = params + [self.logvar] opt = torch.optim.AdamW(params, lr=lr) return opt class LatentDiffusion(DDPM): """main class""" def __init__(self, first_stage_config, cond_stage_config, num_timesteps_cond=None, cond_stage_key="image", cond_stage_trainable=False, concat_mode=True, cond_stage_forward=None, conditioning_key=None, scale_factor=1.0, scale_by_std=False, unet_trainable=True, *args, **kwargs): self.num_timesteps_cond = default(num_timesteps_cond, 1) self.scale_by_std = scale_by_std assert self.num_timesteps_cond <= kwargs['timesteps'] # for backwards compatibility after implementation of DiffusionWrapper if conditioning_key is None: conditioning_key = 'concat' if concat_mode else 'crossattn' if cond_stage_config == '__is_unconditional__': conditioning_key = None ckpt_path = kwargs.pop("ckpt_path", None) ignore_keys = kwargs.pop("ignore_keys", []) super().__init__(conditioning_key=conditioning_key, *args, **kwargs) self.concat_mode = concat_mode self.cond_stage_trainable = cond_stage_trainable self.unet_trainable = unet_trainable self.cond_stage_key = cond_stage_key try: self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 except: self.num_downs = 0 if not scale_by_std: self.scale_factor = scale_factor else: self.register_buffer('scale_factor', torch.tensor(scale_factor)) self.instantiate_first_stage(first_stage_config) self.instantiate_cond_stage(cond_stage_config) self.cond_stage_forward = cond_stage_forward # construct linear projection layer for concatenating image CLIP embedding and RT self.cc_projection = nn.Linear(772, 768) nn.init.eye_(list(self.cc_projection.parameters())[0][:768, :768]) nn.init.zeros_(list(self.cc_projection.parameters())[1]) self.cc_projection.requires_grad_(True) self.clip_denoised = False self.bbox_tokenizer = None self.restarted_from_ckpt = False if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys) self.restarted_from_ckpt = True def make_cond_schedule(self, ): self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() self.cond_ids[:self.num_timesteps_cond] = ids @rank_zero_only @torch.no_grad() def on_train_batch_start(self, batch, batch_idx, dataloader_idx): # only for very first batch if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt: assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' # set rescale weight to 1./std of encodings print("### USING STD-RESCALING ###") x = super().get_input(batch, self.first_stage_key) x = x.to(self.device) encoder_posterior = self.encode_first_stage(x) z = self.get_first_stage_encoding(encoder_posterior).detach() del self.scale_factor self.register_buffer('scale_factor', 1. / z.flatten().std()) print(f"setting self.scale_factor to {self.scale_factor}") print("### USING STD-RESCALING ###") def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) self.shorten_cond_schedule = self.num_timesteps_cond > 1 if self.shorten_cond_schedule: self.make_cond_schedule() def instantiate_first_stage(self, config): model = instantiate_from_config(config) self.first_stage_model = model.eval() self.first_stage_model.train = disabled_train for param in self.first_stage_model.parameters(): param.requires_grad = False def instantiate_cond_stage(self, config): if not self.cond_stage_trainable: if config == "__is_first_stage__": print("Using first stage also as cond stage.") self.cond_stage_model = self.first_stage_model elif config == "__is_unconditional__": print(f"Training {self.__class__.__name__} as an unconditional model.") self.cond_stage_model = None # self.be_unconditional = True else: model = instantiate_from_config(config) self.cond_stage_model = model.eval() self.cond_stage_model.train = disabled_train for param in self.cond_stage_model.parameters(): param.requires_grad = False else: assert config != '__is_first_stage__' assert config != '__is_unconditional__' model = instantiate_from_config(config) self.cond_stage_model = model def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): denoise_row = [] for zd in tqdm(samples, desc=desc): denoise_row.append(self.decode_first_stage(zd.to(self.device), force_not_quantize=force_no_decoder_quantization)) n_imgs_per_row = len(denoise_row) denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) return denoise_grid def get_first_stage_encoding(self, encoder_posterior): if isinstance(encoder_posterior, DiagonalGaussianDistribution): z = encoder_posterior.sample() elif isinstance(encoder_posterior, torch.Tensor): z = encoder_posterior else: raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") return self.scale_factor * z def get_learned_conditioning(self, c): if self.cond_stage_forward is None: if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): c = self.cond_stage_model.encode(c) if isinstance(c, DiagonalGaussianDistribution): c = c.mode() else: c = self.cond_stage_model(c) else: assert hasattr(self.cond_stage_model, self.cond_stage_forward) c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) return c def meshgrid(self, h, w): y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) arr = torch.cat([y, x], dim=-1) return arr def delta_border(self, h, w): """ :param h: height :param w: width :return: normalized distance to image border, wtith min distance = 0 at border and max dist = 0.5 at image center """ lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) arr = self.meshgrid(h, w) / lower_right_corner dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0] return edge_dist def get_weighting(self, h, w, Ly, Lx, device): weighting = self.delta_border(h, w) weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"], self.split_input_params["clip_max_weight"], ) weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) if self.split_input_params["tie_braker"]: L_weighting = self.delta_border(Ly, Lx) L_weighting = torch.clip(L_weighting, self.split_input_params["clip_min_tie_weight"], self.split_input_params["clip_max_tie_weight"]) L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) weighting = weighting * L_weighting return weighting def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code """ :param x: img of size (bs, c, h, w) :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) """ bs, nc, h, w = x.shape # number of crops in image Ly = (h - kernel_size[0]) // stride[0] + 1 Lx = (w - kernel_size[1]) // stride[1] + 1 if uf == 1 and df == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) elif uf > 1 and df == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), dilation=1, padding=0, stride=(stride[0] * uf, stride[1] * uf)) fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2) weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)) elif df > 1 and uf == 1: fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) unfold = torch.nn.Unfold(**fold_params) fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df), dilation=1, padding=0, stride=(stride[0] // df, stride[1] // df)) fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2) weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype) normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)) else: raise NotImplementedError return fold, unfold, normalization, weighting @torch.no_grad() def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False, cond_key=None, return_original_cond=False, bs=None, uncond=0.05): x = super().get_input(batch, k) T = batch['T'].to(memory_format=torch.contiguous_format).float() if bs is not None: x = x[:bs] T = T[:bs].to(self.device) x = x.to(self.device) encoder_posterior = self.encode_first_stage(x) z = self.get_first_stage_encoding(encoder_posterior).detach() cond_key = cond_key or self.cond_stage_key xc = super().get_input(batch, cond_key).to(self.device) if bs is not None: xc = xc[:bs] cond = {} # To support classifier-free guidance, randomly drop out only text conditioning 5%, only image conditioning 5%, and both 5%. random = torch.rand(x.size(0), device=x.device) prompt_mask = rearrange(random < 2 * uncond, "n -> n 1 1") input_mask = 1 - rearrange((random >= uncond).float() * (random < 3 * uncond).float(), "n -> n 1 1 1") null_prompt = self.get_learned_conditioning([""]) # z.shape: [8, 4, 64, 64]; c.shape: [8, 1, 768] # print('=========== xc shape ===========', xc.shape) with torch.enable_grad(): clip_emb = self.get_learned_conditioning(xc).detach() null_prompt = self.get_learned_conditioning([""]).detach() cond["c_crossattn"] = [self.cc_projection(torch.cat([torch.where(prompt_mask, null_prompt, clip_emb), T[:, None, :]], dim=-1))] cond["c_concat"] = [input_mask * self.encode_first_stage((xc.to(self.device))).mode().detach()] out = [z, cond] if return_first_stage_outputs: xrec = self.decode_first_stage(z) out.extend([x, xrec]) if return_original_cond: out.append(xc) return out # @torch.no_grad() def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): if predict_cids: if z.dim() == 4: z = torch.argmax(z.exp(), dim=1).long() z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) z = rearrange(z, 'b h w c -> b c h w').contiguous() z = 1. / self.scale_factor * z if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) uf = self.split_input_params["vqf"] bs, nc, h, w = z.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) z = unfold(z) # (bn, nc * prod(**ks), L) # 1. Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) # 2. apply model loop over last dim if isinstance(self.first_stage_model, VQModelInterface): output_list = [self.first_stage_model.decode(z[:, :, :, :, i], force_not_quantize=predict_cids or force_not_quantize) for i in range(z.shape[-1])] else: output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) o = o * weighting # Reverse 1. reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization # norm is shape (1, 1, h, w) return decoded else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) else: if isinstance(self.first_stage_model, VQModelInterface): return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) else: return self.first_stage_model.decode(z) # @torch.no_grad() # wasted two hours to find this bug... why no grad here! def encode_first_stage(self, x): if hasattr(self, "split_input_params"): if self.split_input_params["patch_distributed_vq"]: ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) df = self.split_input_params["vqf"] self.split_input_params['original_image_size'] = x.shape[-2:] bs, nc, h, w = x.shape if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) print("reducing Kernel") if stride[0] > h or stride[1] > w: stride = (min(stride[0], h), min(stride[1], w)) print("reducing stride") fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df) z = unfold(x) # (bn, nc * prod(**ks), L) # Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) output_list = [self.first_stage_model.encode(z[:, :, :, :, i]) for i in range(z.shape[-1])] o = torch.stack(output_list, axis=-1) o = o * weighting # Reverse reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together decoded = fold(o) decoded = decoded / normalization return decoded else: return self.first_stage_model.encode(x) else: return self.first_stage_model.encode(x) def shared_step(self, batch, **kwargs): x, c = self.get_input(batch, self.first_stage_key) loss = self(x, c) return loss def forward(self, x, c, *args, **kwargs): t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() if self.model.conditioning_key is not None: assert c is not None # if self.cond_stage_trainable: # c = self.get_learned_conditioning(c) if self.shorten_cond_schedule: # TODO: drop this option tc = self.cond_ids[t].to(self.device) c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) return self.p_losses(x, c, t, *args, **kwargs) def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset def rescale_bbox(bbox): x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2]) y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3]) w = min(bbox[2] / crop_coordinates[2], 1 - x0) h = min(bbox[3] / crop_coordinates[3], 1 - y0) return x0, y0, w, h return [rescale_bbox(b) for b in bboxes] def apply_model(self, x_noisy, t, cond, return_ids=False): if isinstance(cond, dict): # hybrid case, cond is exptected to be a dict pass else: if not isinstance(cond, list): cond = [cond] key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' cond = {key: cond} if hasattr(self, "split_input_params"): assert len(cond) == 1 # todo can only deal with one conditioning atm assert not return_ids ks = self.split_input_params["ks"] # eg. (128, 128) stride = self.split_input_params["stride"] # eg. (64, 64) h, w = x_noisy.shape[-2:] fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride) z = unfold(x_noisy) # (bn, nc * prod(**ks), L) # Reshape to img shape z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])] if self.cond_stage_key in ["image", "LR_image", "segmentation", 'bbox_img'] and self.model.conditioning_key: # todo check for completeness c_key = next(iter(cond.keys())) # get key c = next(iter(cond.values())) # get value assert (len(c) == 1) # todo extend to list with more than one elem c = c[0] # get element c = unfold(c) c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L ) cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])] elif self.cond_stage_key == 'coordinates_bbox': assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size' # assuming padding of unfold is always 0 and its dilation is always 1 n_patches_per_row = int((w - ks[0]) / stride[0] + 1) full_img_h, full_img_w = self.split_input_params['original_image_size'] # as we are operating on latents, we need the factor from the original image size to the # spatial latent size to properly rescale the crops for regenerating the bbox annotations num_downs = self.first_stage_model.encoder.num_resolutions - 1 rescale_latent = 2 ** (num_downs) # get top left postions of patches as conforming for the bbbox tokenizer, therefore we # need to rescale the tl patch coordinates to be in between (0,1) tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w, rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h) for patch_nr in range(z.shape[-1])] # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w) patch_limits = [(x_tl, y_tl, rescale_latent * ks[0] / full_img_w, rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates] # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates] # tokenize crop coordinates for the bounding boxes of the respective patches patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device) for bbox in patch_limits] # list of length l with tensors of shape (1, 2) # cut tknzd crop position from conditioning assert isinstance(cond, dict), 'cond must be dict to be fed into model' cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device) adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]) adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n') adapted_cond = self.get_learned_conditioning(adapted_cond) adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1]) cond_list = [{'c_crossattn': [e]} for e in adapted_cond] else: cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient # apply model by loop over crops output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])] assert not isinstance(output_list[0], tuple) # todo cant deal with multiple model outputs check this never happens o = torch.stack(output_list, axis=-1) o = o * weighting # Reverse reshape to img shape o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) # stitch crops together x_recon = fold(o) / normalization else: x_recon = self.model(x_noisy, t, **cond) if isinstance(x_recon, tuple) and not return_ids: return x_recon[0] else: return x_recon def _predict_eps_from_xstart(self, x_t, t, pred_xstart): return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) def _prior_bpd(self, x_start): """ Get the prior KL term for the variational lower-bound, measured in bits-per-dim. This term can't be optimized, as it only depends on the encoder. :param x_start: the [N x C x ...] tensor of inputs. :return: a batch of [N] KL values (in bits), one per batch element. """ batch_size = x_start.shape[0] t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) return mean_flat(kl_prior) / np.log(2.0) def p_losses(self, x_start, cond, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) model_output = self.apply_model(x_noisy, t, cond) loss_dict = {} prefix = 'train' if self.training else 'val' if self.parameterization == "x0": target = x_start elif self.parameterization == "eps": target = noise else: raise NotImplementedError() loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) logvar_t = self.logvar[t].to(self.device) loss = loss_simple / torch.exp(logvar_t) + logvar_t # loss = loss_simple / torch.exp(self.logvar) + self.logvar if self.learn_logvar: loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) loss_dict.update({'logvar': self.logvar.data.mean()}) loss = self.l_simple_weight * loss.mean() loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) loss += (self.original_elbo_weight * loss_vlb) loss_dict.update({f'{prefix}/loss': loss}) return loss, loss_dict def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False, return_x0=False, score_corrector=None, corrector_kwargs=None): t_in = t model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) if score_corrector is not None: assert self.parameterization == "eps" model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) if return_codebook_ids: model_out, logits = model_out if self.parameterization == "eps": x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) elif self.parameterization == "x0": x_recon = model_out else: raise NotImplementedError() if clip_denoised: x_recon.clamp_(-1., 1.) if quantize_denoised: x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) if return_codebook_ids: return model_mean, posterior_variance, posterior_log_variance, logits elif return_x0: return model_mean, posterior_variance, posterior_log_variance, x_recon else: return model_mean, posterior_variance, posterior_log_variance @torch.no_grad() def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, return_codebook_ids=False, quantize_denoised=False, return_x0=False, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): b, *_, device = *x.shape, x.device outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, return_codebook_ids=return_codebook_ids, quantize_denoised=quantize_denoised, return_x0=return_x0, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) if return_codebook_ids: raise DeprecationWarning("Support dropped.") model_mean, _, model_log_variance, logits = outputs elif return_x0: model_mean, _, model_log_variance, x0 = outputs else: model_mean, _, model_log_variance = outputs noise = noise_like(x.shape, device, repeat_noise) * temperature if noise_dropout > 0.: noise = torch.nn.functional.dropout(noise, p=noise_dropout) # no noise when t == 0 nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) if return_codebook_ids: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1) if return_x0: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 else: return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise @torch.no_grad() def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False, img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None, log_every_t=None): if not log_every_t: log_every_t = self.log_every_t timesteps = self.num_timesteps if batch_size is not None: b = batch_size if batch_size is not None else shape[0] shape = [batch_size] + list(shape) else: b = batch_size = shape[0] if x_T is None: img = torch.randn(shape, device=self.device) else: img = x_T intermediates = [] if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else list(map(lambda x: x[:batch_size], cond[key])) for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] if start_T is not None: timesteps = min(timesteps, start_T) iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', total=timesteps) if verbose else reversed( range(0, timesteps)) if type(temperature) == float: temperature = [temperature] * timesteps for i in iterator: ts = torch.full((b,), i, device=self.device, dtype=torch.long) if self.shorten_cond_schedule: assert self.model.conditioning_key != 'hybrid' tc = self.cond_ids[ts].to(cond.device) cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) img, x0_partial = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, quantize_denoised=quantize_denoised, return_x0=True, temperature=temperature[i], noise_dropout=noise_dropout, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) if mask is not None: assert x0 is not None img_orig = self.q_sample(x0, ts) img = img_orig * mask + (1. - mask) * img if i % log_every_t == 0 or i == timesteps - 1: intermediates.append(x0_partial) if callback: callback(i) if img_callback: img_callback(img, i) return img, intermediates @torch.no_grad() def p_sample_loop(self, cond, shape, return_intermediates=False, x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False, mask=None, x0=None, img_callback=None, start_T=None, log_every_t=None): if not log_every_t: log_every_t = self.log_every_t device = self.betas.device b = shape[0] if x_T is None: img = torch.randn(shape, device=device) else: img = x_T intermediates = [img] if timesteps is None: timesteps = self.num_timesteps if start_T is not None: timesteps = min(timesteps, start_T) iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed( range(0, timesteps)) if mask is not None: assert x0 is not None assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match for i in iterator: ts = torch.full((b,), i, device=device, dtype=torch.long) if self.shorten_cond_schedule: assert self.model.conditioning_key != 'hybrid' tc = self.cond_ids[ts].to(cond.device) cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) img = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, quantize_denoised=quantize_denoised) if mask is not None: img_orig = self.q_sample(x0, ts) img = img_orig * mask + (1. - mask) * img if i % log_every_t == 0 or i == timesteps - 1: intermediates.append(img) if callback: callback(i) if img_callback: img_callback(img, i) if return_intermediates: return img, intermediates return img @torch.no_grad() def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, verbose=True, timesteps=None, quantize_denoised=False, mask=None, x0=None, shape=None,**kwargs): if shape is None: shape = (batch_size, self.channels, self.image_size, self.image_size) if cond is not None: if isinstance(cond, dict): cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else list(map(lambda x: x[:batch_size], cond[key])) for key in cond} else: cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] return self.p_sample_loop(cond, shape, return_intermediates=return_intermediates, x_T=x_T, verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised, mask=mask, x0=x0) @torch.no_grad() def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs): if ddim: ddim_sampler = DDIMSampler(self) shape = (self.channels, self.image_size, self.image_size) samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size, shape, cond, verbose=False, **kwargs) else: samples, intermediates = self.sample(cond=cond, batch_size=batch_size, return_intermediates=True, **kwargs) return samples, intermediates @torch.no_grad() def get_unconditional_conditioning(self, batch_size, null_label=None, image_size=512): if null_label is not None: xc = null_label if isinstance(xc, ListConfig): xc = list(xc) if isinstance(xc, dict) or isinstance(xc, list): c = self.get_learned_conditioning(xc) else: if hasattr(xc, "to"): xc = xc.to(self.device) c = self.get_learned_conditioning(xc) else: # todo: get null label from cond_stage_model raise NotImplementedError() c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device) cond = {} cond["c_crossattn"] = [c] cond["c_concat"] = [torch.zeros([batch_size, 4, image_size // 8, image_size // 8]).to(self.device)] return cond @torch.no_grad() def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True, **kwargs): ema_scope = self.ema_scope if use_ema_scope else nullcontext use_ddim = ddim_steps is not None log = dict() z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, return_first_stage_outputs=True, force_c_encode=True, return_original_cond=True, bs=N) N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) log["inputs"] = x log["reconstruction"] = xrec if self.model.conditioning_key is not None: if hasattr(self.cond_stage_model, "decode"): xc = self.cond_stage_model.decode(c) log["conditioning"] = xc elif self.cond_stage_key in ["caption", "txt"]: xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2]//25) log["conditioning"] = xc elif self.cond_stage_key == 'class_label': xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2]//25) log['conditioning'] = xc elif isimage(xc): log["conditioning"] = xc if ismap(xc): log["original_conditioning"] = self.to_rgb(xc) if plot_diffusion_rows: # get diffusion row diffusion_row = list() z_start = z[:n_row] for t in range(self.num_timesteps): if t % self.log_every_t == 0 or t == self.num_timesteps - 1: t = repeat(torch.tensor([t]), '1 -> b', b=n_row) t = t.to(self.device).long() noise = torch.randn_like(z_start) z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) diffusion_row.append(self.decode_first_stage(z_noisy)) diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) log["diffusion_row"] = diffusion_grid if sample: # get denoise row with ema_scope("Sampling"): samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, ddim_steps=ddim_steps,eta=ddim_eta) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) x_samples = self.decode_first_stage(samples) log["samples"] = x_samples if plot_denoise_rows: denoise_grid = self._get_denoise_row_from_list(z_denoise_row) log["denoise_row"] = denoise_grid if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance( self.first_stage_model, IdentityFirstStage): # also display when quantizing x0 while sampling with ema_scope("Plotting Quantized Denoised"): samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, ddim_steps=ddim_steps,eta=ddim_eta, quantize_denoised=True) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, # quantize_denoised=True) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_x0_quantized"] = x_samples if unconditional_guidance_scale > 1.0: uc = self.get_unconditional_conditioning(N, unconditional_guidance_label, image_size=x.shape[-1]) # uc = torch.zeros_like(c) with ema_scope("Sampling with classifier-free guidance"): samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, ddim_steps=ddim_steps, eta=ddim_eta, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=uc, ) x_samples_cfg = self.decode_first_stage(samples_cfg) log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg if inpaint: # make a simple center square b, h, w = z.shape[0], z.shape[2], z.shape[3] mask = torch.ones(N, h, w).to(self.device) # zeros will be filled in mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0. mask = mask[:, None, ...] with ema_scope("Plotting Inpaint"): samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta, ddim_steps=ddim_steps, x0=z[:N], mask=mask) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_inpainting"] = x_samples log["mask"] = mask # outpaint mask = 1. - mask with ema_scope("Plotting Outpaint"): samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta, ddim_steps=ddim_steps, x0=z[:N], mask=mask) x_samples = self.decode_first_stage(samples.to(self.device)) log["samples_outpainting"] = x_samples if plot_progressive_rows: with ema_scope("Plotting Progressives"): img, progressives = self.progressive_denoising(c, shape=(self.channels, self.image_size, self.image_size), batch_size=N) prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") log["progressive_row"] = prog_row if return_keys: if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: return log else: return {key: log[key] for key in return_keys} return log def configure_optimizers(self): lr = self.learning_rate params = [] if self.unet_trainable == "attn": print("Training only unet attention layers") for n, m in self.model.named_modules(): if isinstance(m, CrossAttention) and n.endswith('attn2'): params.extend(m.parameters()) if self.unet_trainable == "conv_in": print("Training only unet input conv layers") params = list(self.model.diffusion_model.input_blocks[0][0].parameters()) elif self.unet_trainable is True or self.unet_trainable == "all": print("Training the full unet") params = list(self.model.parameters()) else: raise ValueError(f"Unrecognised setting for unet_trainable: {self.unet_trainable}") if self.cond_stage_trainable: print(f"{self.__class__.__name__}: Also optimizing conditioner params!") params = params + list(self.cond_stage_model.parameters()) if self.learn_logvar: print('Diffusion model optimizing logvar') params.append(self.logvar) if self.cc_projection is not None: params = params + list(self.cc_projection.parameters()) print('========== optimizing for cc projection weight ==========') opt = torch.optim.AdamW([{"params": self.model.parameters(), "lr": lr}, {"params": self.cc_projection.parameters(), "lr": 10. * lr}], lr=lr) if self.use_scheduler: assert 'target' in self.scheduler_config scheduler = instantiate_from_config(self.scheduler_config) print("Setting up LambdaLR scheduler...") scheduler = [ { 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), 'interval': 'step', 'frequency': 1 }] return [opt], scheduler return opt @torch.no_grad() def to_rgb(self, x): x = x.float() if not hasattr(self, "colorize"): self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) x = nn.functional.conv2d(x, weight=self.colorize) x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. return x class DiffusionWrapper(pl.LightningModule): def __init__(self, diff_model_config, conditioning_key): super().__init__() self.diffusion_model = instantiate_from_config(diff_model_config) self.conditioning_key = conditioning_key assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm'] def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=None): if self.conditioning_key is None: out = self.diffusion_model(x, t) elif self.conditioning_key == 'concat': xc = torch.cat([x] + c_concat, dim=1) out = self.diffusion_model(xc, t) elif self.conditioning_key == 'crossattn': # c_crossattn dimension: torch.Size([8, 1, 768]) 1 # cc dimension: torch.Size([8, 1, 768] cc = torch.cat(c_crossattn, 1) out = self.diffusion_model(x, t, context=cc) elif self.conditioning_key == 'hybrid': xc = torch.cat([x] + c_concat, dim=1) cc = torch.cat(c_crossattn, 1) out = self.diffusion_model(xc, t, context=cc) elif self.conditioning_key == 'hybrid-adm': assert c_adm is not None xc = torch.cat([x] + c_concat, dim=1) cc = torch.cat(c_crossattn, 1) out = self.diffusion_model(xc, t, context=cc, y=c_adm) elif self.conditioning_key == 'adm': cc = c_crossattn[0] out = self.diffusion_model(x, t, y=cc) else: raise NotImplementedError() return out class LatentUpscaleDiffusion(LatentDiffusion): def __init__(self, *args, low_scale_config, low_scale_key="LR", **kwargs): super().__init__(*args, **kwargs) # assumes that neither the cond_stage nor the low_scale_model contain trainable params assert not self.cond_stage_trainable self.instantiate_low_stage(low_scale_config) self.low_scale_key = low_scale_key def instantiate_low_stage(self, config): model = instantiate_from_config(config) self.low_scale_model = model.eval() self.low_scale_model.train = disabled_train for param in self.low_scale_model.parameters(): param.requires_grad = False @torch.no_grad() def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False): if not log_mode: z, c = super().get_input(batch, k, force_c_encode=True, bs=bs) else: z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, force_c_encode=True, return_original_cond=True, bs=bs) x_low = batch[self.low_scale_key][:bs] x_low = rearrange(x_low, 'b h w c -> b c h w') x_low = x_low.to(memory_format=torch.contiguous_format).float() zx, noise_level = self.low_scale_model(x_low) all_conds = {"c_concat": [zx], "c_crossattn": [c], "c_adm": noise_level} #import pudb; pu.db if log_mode: # TODO: maybe disable if too expensive interpretability = False if interpretability: zx = zx[:, :, ::2, ::2] x_low_rec = self.low_scale_model.decode(zx) return z, all_conds, x, xrec, xc, x_low, x_low_rec, noise_level return z, all_conds @torch.no_grad() def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True, **kwargs): ema_scope = self.ema_scope if use_ema_scope else nullcontext use_ddim = ddim_steps is not None log = dict() z, c, x, xrec, xc, x_low, x_low_rec, noise_level = self.get_input(batch, self.first_stage_key, bs=N, log_mode=True) N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) log["inputs"] = x log["reconstruction"] = xrec log["x_lr"] = x_low log[f"x_lr_rec_@noise_levels{'-'.join(map(lambda x: str(x), list(noise_level.cpu().numpy())))}"] = x_low_rec if self.model.conditioning_key is not None: if hasattr(self.cond_stage_model, "decode"): xc = self.cond_stage_model.decode(c) log["conditioning"] = xc elif self.cond_stage_key in ["caption", "txt"]: xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2]//25) log["conditioning"] = xc elif self.cond_stage_key == 'class_label': xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2]//25) log['conditioning'] = xc elif isimage(xc): log["conditioning"] = xc if ismap(xc): log["original_conditioning"] = self.to_rgb(xc) if plot_diffusion_rows: # get diffusion row diffusion_row = list() z_start = z[:n_row] for t in range(self.num_timesteps): if t % self.log_every_t == 0 or t == self.num_timesteps - 1: t = repeat(torch.tensor([t]), '1 -> b', b=n_row) t = t.to(self.device).long() noise = torch.randn_like(z_start) z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) diffusion_row.append(self.decode_first_stage(z_noisy)) diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) log["diffusion_row"] = diffusion_grid if sample: # get denoise row with ema_scope("Sampling"): samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, ddim_steps=ddim_steps, eta=ddim_eta) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) x_samples = self.decode_first_stage(samples) log["samples"] = x_samples if plot_denoise_rows: denoise_grid = self._get_denoise_row_from_list(z_denoise_row) log["denoise_row"] = denoise_grid if unconditional_guidance_scale > 1.0: uc_tmp = self.get_unconditional_conditioning(N, unconditional_guidance_label) # TODO explore better "unconditional" choices for the other keys # maybe guide away from empty text label and highest noise level and maximally degraded zx? uc = dict() for k in c: if k == "c_crossattn": assert isinstance(c[k], list) and len(c[k]) == 1 uc[k] = [uc_tmp] elif k == "c_adm": # todo: only run with text-based guidance? assert isinstance(c[k], torch.Tensor) uc[k] = torch.ones_like(c[k]) * self.low_scale_model.max_noise_level elif isinstance(c[k], list): uc[k] = [c[k][i] for i in range(len(c[k]))] else: uc[k] = c[k] with ema_scope("Sampling with classifier-free guidance"): samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, ddim_steps=ddim_steps, eta=ddim_eta, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=uc, ) x_samples_cfg = self.decode_first_stage(samples_cfg) log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg if plot_progressive_rows: with ema_scope("Plotting Progressives"): img, progressives = self.progressive_denoising(c, shape=(self.channels, self.image_size, self.image_size), batch_size=N) prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") log["progressive_row"] = prog_row return log class LatentInpaintDiffusion(LatentDiffusion): """ can either run as pure inpainting model (only concat mode) or with mixed conditionings, e.g. mask as concat and text via cross-attn. To disable finetuning mode, set finetune_keys to None """ def __init__(self, finetune_keys=("model.diffusion_model.input_blocks.0.0.weight", "model_ema.diffusion_modelinput_blocks00weight" ), concat_keys=("mask", "masked_image"), masked_image_key="masked_image", keep_finetune_dims=4, # if model was trained without concat mode before and we would like to keep these channels c_concat_log_start=None, # to log reconstruction of c_concat codes c_concat_log_end=None, *args, **kwargs ): ckpt_path = kwargs.pop("ckpt_path", None) ignore_keys = kwargs.pop("ignore_keys", list()) super().__init__(*args, **kwargs) self.masked_image_key = masked_image_key assert self.masked_image_key in concat_keys self.finetune_keys = finetune_keys self.concat_keys = concat_keys self.keep_dims = keep_finetune_dims self.c_concat_log_start = c_concat_log_start self.c_concat_log_end = c_concat_log_end if exists(self.finetune_keys): assert exists(ckpt_path), 'can only finetune from a given checkpoint' if exists(ckpt_path): self.init_from_ckpt(ckpt_path, ignore_keys) def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): sd = torch.load(path, map_location="cpu") if "state_dict" in list(sd.keys()): sd = sd["state_dict"] keys = list(sd.keys()) for k in keys: for ik in ignore_keys: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] # make it explicit, finetune by including extra input channels if exists(self.finetune_keys) and k in self.finetune_keys: new_entry = None for name, param in self.named_parameters(): if name in self.finetune_keys: print(f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only") new_entry = torch.zeros_like(param) # zero init assert exists(new_entry), 'did not find matching parameter to modify' new_entry[:, :self.keep_dims, ...] = sd[k] sd[k] = new_entry missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(sd, strict=False) print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") if len(missing) > 0: print(f"Missing Keys: {missing}") if len(unexpected) > 0: print(f"Unexpected Keys: {unexpected}") @torch.no_grad() def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False): # note: restricted to non-trainable encoders currently assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpainting' z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, force_c_encode=True, return_original_cond=True, bs=bs) assert exists(self.concat_keys) c_cat = list() for ck in self.concat_keys: cc = rearrange(batch[ck], 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float() if bs is not None: cc = cc[:bs] cc = cc.to(self.device) bchw = z.shape if ck != self.masked_image_key: cc = torch.nn.functional.interpolate(cc, size=bchw[-2:]) else: cc = self.get_first_stage_encoding(self.encode_first_stage(cc)) c_cat.append(cc) c_cat = torch.cat(c_cat, dim=1) all_conds = {"c_concat": [c_cat], "c_crossattn": [c]} if return_first_stage_outputs: return z, all_conds, x, xrec, xc return z, all_conds @torch.no_grad() def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True, **kwargs): ema_scope = self.ema_scope if use_ema_scope else nullcontext use_ddim = ddim_steps is not None log = dict() z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True) c_cat, c = c["c_concat"][0], c["c_crossattn"][0] N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) log["inputs"] = x log["reconstruction"] = xrec if self.model.conditioning_key is not None: if hasattr(self.cond_stage_model, "decode"): xc = self.cond_stage_model.decode(c) log["conditioning"] = xc elif self.cond_stage_key in ["caption", "txt"]: xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25) log["conditioning"] = xc elif self.cond_stage_key == 'class_label': xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25) log['conditioning'] = xc elif isimage(xc): log["conditioning"] = xc if ismap(xc): log["original_conditioning"] = self.to_rgb(xc) if not (self.c_concat_log_start is None and self.c_concat_log_end is None): log["c_concat_decoded"] = self.decode_first_stage(c_cat[:,self.c_concat_log_start:self.c_concat_log_end]) if plot_diffusion_rows: # get diffusion row diffusion_row = list() z_start = z[:n_row] for t in range(self.num_timesteps): if t % self.log_every_t == 0 or t == self.num_timesteps - 1: t = repeat(torch.tensor([t]), '1 -> b', b=n_row) t = t.to(self.device).long() noise = torch.randn_like(z_start) z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) diffusion_row.append(self.decode_first_stage(z_noisy)) diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) log["diffusion_row"] = diffusion_grid if sample: # get denoise row with ema_scope("Sampling"): samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]}, batch_size=N, ddim=use_ddim, ddim_steps=ddim_steps, eta=ddim_eta) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) x_samples = self.decode_first_stage(samples) log["samples"] = x_samples if plot_denoise_rows: denoise_grid = self._get_denoise_row_from_list(z_denoise_row) log["denoise_row"] = denoise_grid if unconditional_guidance_scale > 1.0: uc_cross = self.get_unconditional_conditioning(N, unconditional_guidance_label) uc_cat = c_cat uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]} with ema_scope("Sampling with classifier-free guidance"): samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]}, batch_size=N, ddim=use_ddim, ddim_steps=ddim_steps, eta=ddim_eta, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=uc_full, ) x_samples_cfg = self.decode_first_stage(samples_cfg) log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg log["masked_image"] = rearrange(batch["masked_image"], 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float() return log class Layout2ImgDiffusion(LatentDiffusion): # TODO: move all layout-specific hacks to this class def __init__(self, cond_stage_key, *args, **kwargs): assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"' super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs) def log_images(self, batch, N=8, *args, **kwargs): logs = super().log_images(batch=batch, N=N, *args, **kwargs) key = 'train' if self.training else 'validation' dset = self.trainer.datamodule.datasets[key] mapper = dset.conditional_builders[self.cond_stage_key] bbox_imgs = [] map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno)) for tknzd_bbox in batch[self.cond_stage_key][:N]: bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256)) bbox_imgs.append(bboximg) cond_img = torch.stack(bbox_imgs, dim=0) logs['bbox_image'] = cond_img return logs class SimpleUpscaleDiffusion(LatentDiffusion): def __init__(self, *args, low_scale_key="LR", **kwargs): super().__init__(*args, **kwargs) # assumes that neither the cond_stage nor the low_scale_model contain trainable params assert not self.cond_stage_trainable self.low_scale_key = low_scale_key @torch.no_grad() def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False): if not log_mode: z, c = super().get_input(batch, k, force_c_encode=True, bs=bs) else: z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, force_c_encode=True, return_original_cond=True, bs=bs) x_low = batch[self.low_scale_key][:bs] x_low = rearrange(x_low, 'b h w c -> b c h w') x_low = x_low.to(memory_format=torch.contiguous_format).float() encoder_posterior = self.encode_first_stage(x_low) zx = self.get_first_stage_encoding(encoder_posterior).detach() all_conds = {"c_concat": [zx], "c_crossattn": [c]} if log_mode: # TODO: maybe disable if too expensive interpretability = False if interpretability: zx = zx[:, :, ::2, ::2] return z, all_conds, x, xrec, xc, x_low return z, all_conds @torch.no_grad() def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True, **kwargs): ema_scope = self.ema_scope if use_ema_scope else nullcontext use_ddim = ddim_steps is not None log = dict() z, c, x, xrec, xc, x_low = self.get_input(batch, self.first_stage_key, bs=N, log_mode=True) N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) log["inputs"] = x log["reconstruction"] = xrec log["x_lr"] = x_low if self.model.conditioning_key is not None: if hasattr(self.cond_stage_model, "decode"): xc = self.cond_stage_model.decode(c) log["conditioning"] = xc elif self.cond_stage_key in ["caption", "txt"]: xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2]//25) log["conditioning"] = xc elif self.cond_stage_key == 'class_label': xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2]//25) log['conditioning'] = xc elif isimage(xc): log["conditioning"] = xc if ismap(xc): log["original_conditioning"] = self.to_rgb(xc) if sample: # get denoise row with ema_scope("Sampling"): samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, ddim_steps=ddim_steps, eta=ddim_eta) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) x_samples = self.decode_first_stage(samples) log["samples"] = x_samples if unconditional_guidance_scale > 1.0: uc_tmp = self.get_unconditional_conditioning(N, unconditional_guidance_label) uc = dict() for k in c: if k == "c_crossattn": assert isinstance(c[k], list) and len(c[k]) == 1 uc[k] = [uc_tmp] elif isinstance(c[k], list): uc[k] = [c[k][i] for i in range(len(c[k]))] else: uc[k] = c[k] with ema_scope("Sampling with classifier-free guidance"): samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, ddim_steps=ddim_steps, eta=ddim_eta, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=uc, ) x_samples_cfg = self.decode_first_stage(samples_cfg) log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg return log class MultiCatFrameDiffusion(LatentDiffusion): def __init__(self, *args, low_scale_key="LR", **kwargs): super().__init__(*args, **kwargs) # assumes that neither the cond_stage nor the low_scale_model contain trainable params assert not self.cond_stage_trainable self.low_scale_key = low_scale_key @torch.no_grad() def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False): n = 2 if not log_mode: z, c = super().get_input(batch, k, force_c_encode=True, bs=bs) else: z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True, force_c_encode=True, return_original_cond=True, bs=bs) cat_conds = batch[self.low_scale_key][:bs] cats = [] for i in range(n): x_low = cat_conds[:,:,:,3*i:3*(i+1)] x_low = rearrange(x_low, 'b h w c -> b c h w') x_low = x_low.to(memory_format=torch.contiguous_format).float() encoder_posterior = self.encode_first_stage(x_low) zx = self.get_first_stage_encoding(encoder_posterior).detach() cats.append(zx) all_conds = {"c_concat": [torch.cat(cats, dim=1)], "c_crossattn": [c]} if log_mode: # TODO: maybe disable if too expensive interpretability = False if interpretability: zx = zx[:, :, ::2, ::2] return z, all_conds, x, xrec, xc, x_low return z, all_conds @torch.no_grad() def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True, **kwargs): ema_scope = self.ema_scope if use_ema_scope else nullcontext use_ddim = ddim_steps is not None log = dict() z, c, x, xrec, xc, x_low = self.get_input(batch, self.first_stage_key, bs=N, log_mode=True) N = min(x.shape[0], N) n_row = min(x.shape[0], n_row) log["inputs"] = x log["reconstruction"] = xrec log["x_lr"] = x_low if self.model.conditioning_key is not None: if hasattr(self.cond_stage_model, "decode"): xc = self.cond_stage_model.decode(c) log["conditioning"] = xc elif self.cond_stage_key in ["caption", "txt"]: xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2]//25) log["conditioning"] = xc elif self.cond_stage_key == 'class_label': xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2]//25) log['conditioning'] = xc elif isimage(xc): log["conditioning"] = xc if ismap(xc): log["original_conditioning"] = self.to_rgb(xc) if sample: # get denoise row with ema_scope("Sampling"): samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, ddim_steps=ddim_steps, eta=ddim_eta) # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) x_samples = self.decode_first_stage(samples) log["samples"] = x_samples if unconditional_guidance_scale > 1.0: uc_tmp = self.get_unconditional_conditioning(N, unconditional_guidance_label) uc = dict() for k in c: if k == "c_crossattn": assert isinstance(c[k], list) and len(c[k]) == 1 uc[k] = [uc_tmp] elif isinstance(c[k], list): uc[k] = [c[k][i] for i in range(len(c[k]))] else: uc[k] = c[k] with ema_scope("Sampling with classifier-free guidance"): samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, ddim_steps=ddim_steps, eta=ddim_eta, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=uc, ) x_samples_cfg = self.decode_first_stage(samples_cfg) log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg return log ================================================ FILE: stable-dreamfusion/ldm/models/diffusion/plms.py ================================================ """SAMPLING ONLY.""" import torch import numpy as np from tqdm import tqdm from functools import partial from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like from ldm.models.diffusion.sampling_util import norm_thresholding class PLMSSampler(object): def __init__(self, model, schedule="linear", **kwargs): super().__init__() self.model = model self.ddpm_num_timesteps = model.num_timesteps self.schedule = schedule def register_buffer(self, name, attr): if type(attr) == torch.Tensor: if attr.device != torch.device("cuda"): attr = attr.to(torch.device("cuda")) setattr(self, name, attr) def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): if ddim_eta != 0: raise ValueError('ddim_eta must be 0 for PLMS') self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) alphas_cumprod = self.model.alphas_cumprod assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) self.register_buffer('betas', to_torch(self.model.betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) # calculations for diffusion q(x_t | x_{t-1}) and others self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) # ddim sampling parameters ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), ddim_timesteps=self.ddim_timesteps, eta=ddim_eta,verbose=verbose) self.register_buffer('ddim_sigmas', ddim_sigmas) self.register_buffer('ddim_alphas', ddim_alphas) self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) @torch.no_grad() def sample(self, S, batch_size, shape, conditioning=None, callback=None, normals_sequence=None, img_callback=None, quantize_x0=False, eta=0., mask=None, x0=None, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, verbose=True, x_T=None, log_every_t=100, unconditional_guidance_scale=1., unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... dynamic_threshold=None, **kwargs ): if conditioning is not None: if isinstance(conditioning, dict): ctmp = conditioning[list(conditioning.keys())[0]] while isinstance(ctmp, list): ctmp = ctmp[0] cbs = ctmp.shape[0] if cbs != batch_size: print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") else: if conditioning.shape[0] != batch_size: print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) # sampling C, H, W = shape size = (batch_size, C, H, W) print(f'Data shape for PLMS sampling is {size}') samples, intermediates = self.plms_sampling(conditioning, size, callback=callback, img_callback=img_callback, quantize_denoised=quantize_x0, mask=mask, x0=x0, ddim_use_original_steps=False, noise_dropout=noise_dropout, temperature=temperature, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, x_T=x_T, log_every_t=log_every_t, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, dynamic_threshold=dynamic_threshold, ) return samples, intermediates @torch.no_grad() def plms_sampling(self, cond, shape, x_T=None, ddim_use_original_steps=False, callback=None, timesteps=None, quantize_denoised=False, mask=None, x0=None, img_callback=None, log_every_t=100, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None): device = self.model.betas.device b = shape[0] if x_T is None: img = torch.randn(shape, device=device) else: img = x_T if timesteps is None: timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps elif timesteps is not None and not ddim_use_original_steps: subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 timesteps = self.ddim_timesteps[:subset_end] intermediates = {'x_inter': [img], 'pred_x0': [img]} time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps) total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] print(f"Running PLMS Sampling with {total_steps} timesteps") iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps) old_eps = [] for i, step in enumerate(iterator): index = total_steps - i - 1 ts = torch.full((b,), step, device=device, dtype=torch.long) ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long) if mask is not None: assert x0 is not None img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? img = img_orig * mask + (1. - mask) * img outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, quantize_denoised=quantize_denoised, temperature=temperature, noise_dropout=noise_dropout, score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, unconditional_guidance_scale=unconditional_guidance_scale, unconditional_conditioning=unconditional_conditioning, old_eps=old_eps, t_next=ts_next, dynamic_threshold=dynamic_threshold) img, pred_x0, e_t = outs old_eps.append(e_t) if len(old_eps) >= 4: old_eps.pop(0) if callback: callback(i) if img_callback: img_callback(pred_x0, i) if index % log_every_t == 0 or index == total_steps - 1: intermediates['x_inter'].append(img) intermediates['pred_x0'].append(pred_x0) return img, intermediates @torch.no_grad() def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, dynamic_threshold=None): b, *_, device = *x.shape, x.device def get_model_output(x, t): if unconditional_conditioning is None or unconditional_guidance_scale == 1.: e_t = self.model.apply_model(x, t, c) else: x_in = torch.cat([x] * 2) t_in = torch.cat([t] * 2) if isinstance(c, dict): assert isinstance(unconditional_conditioning, dict) c_in = dict() for k in c: if isinstance(c[k], list): c_in[k] = [torch.cat([ unconditional_conditioning[k][i], c[k][i]]) for i in range(len(c[k]))] else: c_in[k] = torch.cat([ unconditional_conditioning[k], c[k]]) else: c_in = torch.cat([unconditional_conditioning, c]) e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) if score_corrector is not None: assert self.model.parameterization == "eps" e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) return e_t alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas def get_x_prev_and_pred_x0(e_t, index): # select parameters corresponding to the currently considered timestep a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) # current prediction for x_0 pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() if quantize_denoised: pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) if dynamic_threshold is not None: pred_x0 = norm_thresholding(pred_x0, dynamic_threshold) # direction pointing to x_t dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature if noise_dropout > 0.: noise = torch.nn.functional.dropout(noise, p=noise_dropout) x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise return x_prev, pred_x0 e_t = get_model_output(x, t) if len(old_eps) == 0: # Pseudo Improved Euler (2nd order) x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index) e_t_next = get_model_output(x_prev, t_next) e_t_prime = (e_t + e_t_next) / 2 elif len(old_eps) == 1: # 2nd order Pseudo Linear Multistep (Adams-Bashforth) e_t_prime = (3 * e_t - old_eps[-1]) / 2 elif len(old_eps) == 2: # 3nd order Pseudo Linear Multistep (Adams-Bashforth) e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12 elif len(old_eps) >= 3: # 4nd order Pseudo Linear Multistep (Adams-Bashforth) e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24 x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index) return x_prev, pred_x0, e_t ================================================ FILE: stable-dreamfusion/ldm/models/diffusion/sampling_util.py ================================================ import torch import numpy as np def append_dims(x, target_dims): """Appends dimensions to the end of a tensor until it has target_dims dimensions. From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py""" dims_to_append = target_dims - x.ndim if dims_to_append < 0: raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') return x[(...,) + (None,) * dims_to_append] def renorm_thresholding(x0, value): # renorm pred_max = x0.max() pred_min = x0.min() pred_x0 = (x0 - pred_min) / (pred_max - pred_min) # 0 ... 1 pred_x0 = 2 * pred_x0 - 1. # -1 ... 1 s = torch.quantile( rearrange(pred_x0, 'b ... -> b (...)').abs(), value, dim=-1 ) s.clamp_(min=1.0) s = s.view(-1, *((1,) * (pred_x0.ndim - 1))) # clip by threshold # pred_x0 = pred_x0.clamp(-s, s) / s # needs newer pytorch # TODO bring back to pure-gpu with min/max # temporary hack: numpy on cpu pred_x0 = np.clip(pred_x0.cpu().numpy(), -s.cpu().numpy(), s.cpu().numpy()) / s.cpu().numpy() pred_x0 = torch.tensor(pred_x0).to(self.model.device) # re.renorm pred_x0 = (pred_x0 + 1.) / 2. # 0 ... 1 pred_x0 = (pred_max - pred_min) * pred_x0 + pred_min # orig range return pred_x0 def norm_thresholding(x0, value): s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim) return x0 * (value / s) def spatial_norm_thresholding(x0, value): # b c h w s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value) return x0 * (value / s) ================================================ FILE: stable-dreamfusion/ldm/modules/attention.py ================================================ from inspect import isfunction import math import torch import torch.nn.functional as F from torch import nn, einsum from einops import rearrange, repeat from ldm.modules.diffusionmodules.util import checkpoint def exists(val): return val is not None def uniq(arr): return{el: True for el in arr}.keys() def default(val, d): if exists(val): return val return d() if isfunction(d) else d def max_neg_value(t): return -torch.finfo(t.dtype).max def init_(tensor): dim = tensor.shape[-1] std = 1 / math.sqrt(dim) tensor.uniform_(-std, std) return tensor # feedforward class GEGLU(nn.Module): def __init__(self, dim_in, dim_out): super().__init__() self.proj = nn.Linear(dim_in, dim_out * 2) def forward(self, x): x, gate = self.proj(x).chunk(2, dim=-1) return x * F.gelu(gate) class FeedForward(nn.Module): def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): super().__init__() inner_dim = int(dim * mult) dim_out = default(dim_out, dim) project_in = nn.Sequential( nn.Linear(dim, inner_dim), nn.GELU() ) if not glu else GEGLU(dim, inner_dim) self.net = nn.Sequential( project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out) ) def forward(self, x): return self.net(x) def zero_module(module): """ Zero out the parameters of a module and return it. """ for p in module.parameters(): p.detach().zero_() return module def Normalize(in_channels): return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) class LinearAttention(nn.Module): def __init__(self, dim, heads=4, dim_head=32): super().__init__() self.heads = heads hidden_dim = dim_head * heads self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False) self.to_out = nn.Conv2d(hidden_dim, dim, 1) def forward(self, x): b, c, h, w = x.shape qkv = self.to_qkv(x) q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3) k = k.softmax(dim=-1) context = torch.einsum('bhdn,bhen->bhde', k, v) out = torch.einsum('bhde,bhdn->bhen', context, q) out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) return self.to_out(out) class SpatialSelfAttention(nn.Module): def __init__(self, in_channels): super().__init__() self.in_channels = in_channels self.norm = Normalize(in_channels) self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) def forward(self, x): h_ = x h_ = self.norm(h_) q = self.q(h_) k = self.k(h_) v = self.v(h_) # compute attention b,c,h,w = q.shape q = rearrange(q, 'b c h w -> b (h w) c') k = rearrange(k, 'b c h w -> b c (h w)') w_ = torch.einsum('bij,bjk->bik', q, k) w_ = w_ * (int(c)**(-0.5)) w_ = torch.nn.functional.softmax(w_, dim=2) # attend to values v = rearrange(v, 'b c h w -> b c (h w)') w_ = rearrange(w_, 'b i j -> b j i') h_ = torch.einsum('bij,bjk->bik', v, w_) h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h) h_ = self.proj_out(h_) return x+h_ class CrossAttention(nn.Module): def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.): super().__init__() inner_dim = dim_head * heads context_dim = default(context_dim, query_dim) self.scale = dim_head ** -0.5 self.heads = heads self.to_q = nn.Linear(query_dim, inner_dim, bias=False) self.to_k = nn.Linear(context_dim, inner_dim, bias=False) self.to_v = nn.Linear(context_dim, inner_dim, bias=False) self.to_out = nn.Sequential( nn.Linear(inner_dim, query_dim), nn.Dropout(dropout) ) def forward(self, x, context=None, mask=None): h = self.heads q = self.to_q(x) context = default(context, x) k = self.to_k(context) v = self.to_v(context) q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) sim = einsum('b i d, b j d -> b i j', q, k) * self.scale if exists(mask): mask = rearrange(mask, 'b ... -> b (...)') max_neg_value = -torch.finfo(sim.dtype).max mask = repeat(mask, 'b j -> (b h) () j', h=h) sim.masked_fill_(~mask, max_neg_value) # attention, what we cannot get enough of attn = sim.softmax(dim=-1) out = einsum('b i j, b j d -> b i d', attn, v) out = rearrange(out, '(b h) n d -> b n (h d)', h=h) return self.to_out(out) class BasicTransformerBlock(nn.Module): def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, disable_self_attn=False): super().__init__() self.disable_self_attn = disable_self_attn self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, context_dim=context_dim if self.disable_self_attn else None) # is a self-attention if not self.disable_self_attn self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none self.norm1 = nn.LayerNorm(dim) self.norm2 = nn.LayerNorm(dim) self.norm3 = nn.LayerNorm(dim) self.checkpoint = checkpoint def forward(self, x, context=None): return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint) def _forward(self, x, context=None): x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x x = self.attn2(self.norm2(x), context=context) + x x = self.ff(self.norm3(x)) + x return x class SpatialTransformer(nn.Module): """ Transformer block for image-like data. First, project the input (aka embedding) and reshape to b, t, d. Then apply standard transformer action. Finally, reshape to image """ def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, disable_self_attn=False): super().__init__() self.in_channels = in_channels inner_dim = n_heads * d_head self.norm = Normalize(in_channels) self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) self.transformer_blocks = nn.ModuleList( [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim, disable_self_attn=disable_self_attn) for d in range(depth)] ) self.proj_out = zero_module(nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)) def forward(self, x, context=None): # note: if no context is given, cross-attention defaults to self-attention b, c, h, w = x.shape x_in = x x = self.norm(x) x = self.proj_in(x) x = rearrange(x, 'b c h w -> b (h w) c').contiguous() for block in self.transformer_blocks: x = block(x, context=context) x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous() x = self.proj_out(x) return x + x_in ================================================ FILE: stable-dreamfusion/ldm/modules/diffusionmodules/__init__.py ================================================ ================================================ FILE: stable-dreamfusion/ldm/modules/diffusionmodules/model.py ================================================ # pytorch_diffusion + derived encoder decoder import math import torch import torch.nn as nn import numpy as np from einops import rearrange from ldm.util import instantiate_from_config from ldm.modules.attention import LinearAttention def get_timestep_embedding(timesteps, embedding_dim): """ This matches the implementation in Denoising Diffusion Probabilistic Models: From Fairseq. Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of "Attention Is All You Need". """ assert len(timesteps.shape) == 1 half_dim = embedding_dim // 2 emb = math.log(10000) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) emb = emb.to(device=timesteps.device) emb = timesteps.float()[:, None] * emb[None, :] emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) if embedding_dim % 2 == 1: # zero pad emb = torch.nn.functional.pad(emb, (0,1,0,0)) return emb def nonlinearity(x): # swish return x*torch.sigmoid(x) def Normalize(in_channels, num_groups=32): return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) class Upsample(nn.Module): def __init__(self, in_channels, with_conv): super().__init__() self.with_conv = with_conv if self.with_conv: self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") if self.with_conv: x = self.conv(x) return x class Downsample(nn.Module): def __init__(self, in_channels, with_conv): super().__init__() self.with_conv = with_conv if self.with_conv: # no asymmetric padding in torch conv, must do it ourselves self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) def forward(self, x): if self.with_conv: pad = (0,1,0,1) x = torch.nn.functional.pad(x, pad, mode="constant", value=0) x = self.conv(x) else: x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) return x class ResnetBlock(nn.Module): def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512): super().__init__() self.in_channels = in_channels out_channels = in_channels if out_channels is None else out_channels self.out_channels = out_channels self.use_conv_shortcut = conv_shortcut self.norm1 = Normalize(in_channels) self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) if temb_channels > 0: self.temb_proj = torch.nn.Linear(temb_channels, out_channels) self.norm2 = Normalize(out_channels) self.dropout = torch.nn.Dropout(dropout) self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) if self.in_channels != self.out_channels: if self.use_conv_shortcut: self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) else: self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) def forward(self, x, temb): h = x h = self.norm1(h) h = nonlinearity(h) h = self.conv1(h) if temb is not None: h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None] h = self.norm2(h) h = nonlinearity(h) h = self.dropout(h) h = self.conv2(h) if self.in_channels != self.out_channels: if self.use_conv_shortcut: x = self.conv_shortcut(x) else: x = self.nin_shortcut(x) return x+h class LinAttnBlock(LinearAttention): """to match AttnBlock usage""" def __init__(self, in_channels): super().__init__(dim=in_channels, heads=1, dim_head=in_channels) class AttnBlock(nn.Module): def __init__(self, in_channels): super().__init__() self.in_channels = in_channels self.norm = Normalize(in_channels) self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) def forward(self, x): h_ = x h_ = self.norm(h_) q = self.q(h_) k = self.k(h_) v = self.v(h_) # compute attention b,c,h,w = q.shape q = q.reshape(b,c,h*w) q = q.permute(0,2,1) # b,hw,c k = k.reshape(b,c,h*w) # b,c,hw w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] w_ = w_ * (int(c)**(-0.5)) w_ = torch.nn.functional.softmax(w_, dim=2) # attend to values v = v.reshape(b,c,h*w) w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q) h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] h_ = h_.reshape(b,c,h,w) h_ = self.proj_out(h_) return x+h_ def make_attn(in_channels, attn_type="vanilla"): assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown' print(f"making attention of type '{attn_type}' with {in_channels} in_channels") if attn_type == "vanilla": return AttnBlock(in_channels) elif attn_type == "none": return nn.Identity(in_channels) else: return LinAttnBlock(in_channels) class Model(nn.Module): def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"): super().__init__() if use_linear_attn: attn_type = "linear" self.ch = ch self.temb_ch = self.ch*4 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels self.use_timestep = use_timestep if self.use_timestep: # timestep embedding self.temb = nn.Module() self.temb.dense = nn.ModuleList([ torch.nn.Linear(self.ch, self.temb_ch), torch.nn.Linear(self.temb_ch, self.temb_ch), ]) # downsampling self.conv_in = torch.nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) curr_res = resolution in_ch_mult = (1,)+tuple(ch_mult) self.down = nn.ModuleList() for i_level in range(self.num_resolutions): block = nn.ModuleList() attn = nn.ModuleList() block_in = ch*in_ch_mult[i_level] block_out = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks): block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=attn_type)) down = nn.Module() down.block = block down.attn = attn if i_level != self.num_resolutions-1: down.downsample = Downsample(block_in, resamp_with_conv) curr_res = curr_res // 2 self.down.append(down) # middle self.mid = nn.Module() self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) # upsampling self.up = nn.ModuleList() for i_level in reversed(range(self.num_resolutions)): block = nn.ModuleList() attn = nn.ModuleList() block_out = ch*ch_mult[i_level] skip_in = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks+1): if i_block == self.num_res_blocks: skip_in = ch*in_ch_mult[i_level] block.append(ResnetBlock(in_channels=block_in+skip_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=attn_type)) up = nn.Module() up.block = block up.attn = attn if i_level != 0: up.upsample = Upsample(block_in, resamp_with_conv) curr_res = curr_res * 2 self.up.insert(0, up) # prepend to get consistent order # end self.norm_out = Normalize(block_in) self.conv_out = torch.nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) def forward(self, x, t=None, context=None): #assert x.shape[2] == x.shape[3] == self.resolution if context is not None: # assume aligned context, cat along channel axis x = torch.cat((x, context), dim=1) if self.use_timestep: # timestep embedding assert t is not None temb = get_timestep_embedding(t, self.ch) temb = self.temb.dense[0](temb) temb = nonlinearity(temb) temb = self.temb.dense[1](temb) else: temb = None # downsampling hs = [self.conv_in(x)] for i_level in range(self.num_resolutions): for i_block in range(self.num_res_blocks): h = self.down[i_level].block[i_block](hs[-1], temb) if len(self.down[i_level].attn) > 0: h = self.down[i_level].attn[i_block](h) hs.append(h) if i_level != self.num_resolutions-1: hs.append(self.down[i_level].downsample(hs[-1])) # middle h = hs[-1] h = self.mid.block_1(h, temb) h = self.mid.attn_1(h) h = self.mid.block_2(h, temb) # upsampling for i_level in reversed(range(self.num_resolutions)): for i_block in range(self.num_res_blocks+1): h = self.up[i_level].block[i_block]( torch.cat([h, hs.pop()], dim=1), temb) if len(self.up[i_level].attn) > 0: h = self.up[i_level].attn[i_block](h) if i_level != 0: h = self.up[i_level].upsample(h) # end h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) return h def get_last_layer(self): return self.conv_out.weight class Encoder(nn.Module): def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", **ignore_kwargs): super().__init__() if use_linear_attn: attn_type = "linear" self.ch = ch self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels # downsampling self.conv_in = torch.nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) curr_res = resolution in_ch_mult = (1,)+tuple(ch_mult) self.in_ch_mult = in_ch_mult self.down = nn.ModuleList() for i_level in range(self.num_resolutions): block = nn.ModuleList() attn = nn.ModuleList() block_in = ch*in_ch_mult[i_level] block_out = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks): block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=attn_type)) down = nn.Module() down.block = block down.attn = attn if i_level != self.num_resolutions-1: down.downsample = Downsample(block_in, resamp_with_conv) curr_res = curr_res // 2 self.down.append(down) # middle self.mid = nn.Module() self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) # end self.norm_out = Normalize(block_in) self.conv_out = torch.nn.Conv2d(block_in, 2*z_channels if double_z else z_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): # timestep embedding temb = None # downsampling hs = [self.conv_in(x)] for i_level in range(self.num_resolutions): for i_block in range(self.num_res_blocks): h = self.down[i_level].block[i_block](hs[-1], temb) if len(self.down[i_level].attn) > 0: h = self.down[i_level].attn[i_block](h) hs.append(h) if i_level != self.num_resolutions-1: hs.append(self.down[i_level].downsample(hs[-1])) # middle h = hs[-1] h = self.mid.block_1(h, temb) h = self.mid.attn_1(h) h = self.mid.block_2(h, temb) # end h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) return h class Decoder(nn.Module): def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, attn_type="vanilla", **ignorekwargs): super().__init__() if use_linear_attn: attn_type = "linear" self.ch = ch self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks self.resolution = resolution self.in_channels = in_channels self.give_pre_end = give_pre_end self.tanh_out = tanh_out # compute in_ch_mult, block_in and curr_res at lowest res in_ch_mult = (1,)+tuple(ch_mult) block_in = ch*ch_mult[self.num_resolutions-1] curr_res = resolution // 2**(self.num_resolutions-1) self.z_shape = (1,z_channels,curr_res,curr_res) print("Working with z of shape {} = {} dimensions.".format( self.z_shape, np.prod(self.z_shape))) # z to block_in self.conv_in = torch.nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1) # middle self.mid = nn.Module() self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout) # upsampling self.up = nn.ModuleList() for i_level in reversed(range(self.num_resolutions)): block = nn.ModuleList() attn = nn.ModuleList() block_out = ch*ch_mult[i_level] for i_block in range(self.num_res_blocks+1): block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out if curr_res in attn_resolutions: attn.append(make_attn(block_in, attn_type=attn_type)) up = nn.Module() up.block = block up.attn = attn if i_level != 0: up.upsample = Upsample(block_in, resamp_with_conv) curr_res = curr_res * 2 self.up.insert(0, up) # prepend to get consistent order # end self.norm_out = Normalize(block_in) self.conv_out = torch.nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) def forward(self, z): #assert z.shape[1:] == self.z_shape[1:] self.last_z_shape = z.shape # timestep embedding temb = None # z to block_in h = self.conv_in(z) # middle h = self.mid.block_1(h, temb) h = self.mid.attn_1(h) h = self.mid.block_2(h, temb) # upsampling for i_level in reversed(range(self.num_resolutions)): for i_block in range(self.num_res_blocks+1): h = self.up[i_level].block[i_block](h, temb) if len(self.up[i_level].attn) > 0: h = self.up[i_level].attn[i_block](h) if i_level != 0: h = self.up[i_level].upsample(h) # end if self.give_pre_end: return h h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) if self.tanh_out: h = torch.tanh(h) return h class SimpleDecoder(nn.Module): def __init__(self, in_channels, out_channels, *args, **kwargs): super().__init__() self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1), ResnetBlock(in_channels=in_channels, out_channels=2 * in_channels, temb_channels=0, dropout=0.0), ResnetBlock(in_channels=2 * in_channels, out_channels=4 * in_channels, temb_channels=0, dropout=0.0), ResnetBlock(in_channels=4 * in_channels, out_channels=2 * in_channels, temb_channels=0, dropout=0.0), nn.Conv2d(2*in_channels, in_channels, 1), Upsample(in_channels, with_conv=True)]) # end self.norm_out = Normalize(in_channels) self.conv_out = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): for i, layer in enumerate(self.model): if i in [1,2,3]: x = layer(x, None) else: x = layer(x) h = self.norm_out(x) h = nonlinearity(h) x = self.conv_out(h) return x class UpsampleDecoder(nn.Module): def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution, ch_mult=(2,2), dropout=0.0): super().__init__() # upsampling self.temb_ch = 0 self.num_resolutions = len(ch_mult) self.num_res_blocks = num_res_blocks block_in = in_channels curr_res = resolution // 2 ** (self.num_resolutions - 1) self.res_blocks = nn.ModuleList() self.upsample_blocks = nn.ModuleList() for i_level in range(self.num_resolutions): res_block = [] block_out = ch * ch_mult[i_level] for i_block in range(self.num_res_blocks + 1): res_block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout)) block_in = block_out self.res_blocks.append(nn.ModuleList(res_block)) if i_level != self.num_resolutions - 1: self.upsample_blocks.append(Upsample(block_in, True)) curr_res = curr_res * 2 # end self.norm_out = Normalize(block_in) self.conv_out = torch.nn.Conv2d(block_in, out_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): # upsampling h = x for k, i_level in enumerate(range(self.num_resolutions)): for i_block in range(self.num_res_blocks + 1): h = self.res_blocks[i_level][i_block](h, None) if i_level != self.num_resolutions - 1: h = self.upsample_blocks[k](h) h = self.norm_out(h) h = nonlinearity(h) h = self.conv_out(h) return h class LatentRescaler(nn.Module): def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2): super().__init__() # residual block, interpolate, residual block self.factor = factor self.conv_in = nn.Conv2d(in_channels, mid_channels, kernel_size=3, stride=1, padding=1) self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, out_channels=mid_channels, temb_channels=0, dropout=0.0) for _ in range(depth)]) self.attn = AttnBlock(mid_channels) self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, out_channels=mid_channels, temb_channels=0, dropout=0.0) for _ in range(depth)]) self.conv_out = nn.Conv2d(mid_channels, out_channels, kernel_size=1, ) def forward(self, x): x = self.conv_in(x) for block in self.res_block1: x = block(x, None) x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor)))) x = self.attn(x) for block in self.res_block2: x = block(x, None) x = self.conv_out(x) return x class MergedRescaleEncoder(nn.Module): def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks, attn_resolutions, dropout=0.0, resamp_with_conv=True, ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1): super().__init__() intermediate_chn = ch * ch_mult[-1] self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult, z_channels=intermediate_chn, double_z=False, resolution=resolution, attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, out_ch=None) self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn, mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth) def forward(self, x): x = self.encoder(x) x = self.rescaler(x) return x class MergedRescaleDecoder(nn.Module): def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8), dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1): super().__init__() tmp_chn = z_channels*ch_mult[-1] self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks, ch_mult=ch_mult, resolution=resolution, ch=ch) self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn, out_channels=tmp_chn, depth=rescale_module_depth) def forward(self, x): x = self.rescaler(x) x = self.decoder(x) return x class Upsampler(nn.Module): def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2): super().__init__() assert out_size >= in_size num_blocks = int(np.log2(out_size//in_size))+1 factor_up = 1.+ (out_size % in_size) print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}") self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels, out_channels=in_channels) self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2, attn_resolutions=[], in_channels=None, ch=in_channels, ch_mult=[ch_mult for _ in range(num_blocks)]) def forward(self, x): x = self.rescaler(x) x = self.decoder(x) return x class Resize(nn.Module): def __init__(self, in_channels=None, learned=False, mode="bilinear"): super().__init__() self.with_conv = learned self.mode = mode if self.with_conv: print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode") raise NotImplementedError() assert in_channels is not None # no asymmetric padding in torch conv, must do it ourselves self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=4, stride=2, padding=1) def forward(self, x, scale_factor=1.0): if scale_factor==1.0: return x else: x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor) return x class FirstStagePostProcessor(nn.Module): def __init__(self, ch_mult:list, in_channels, pretrained_model:nn.Module=None, reshape=False, n_channels=None, dropout=0., pretrained_config=None): super().__init__() if pretrained_config is None: assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' self.pretrained_model = pretrained_model else: assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' self.instantiate_pretrained(pretrained_config) self.do_reshape = reshape if n_channels is None: n_channels = self.pretrained_model.encoder.ch self.proj_norm = Normalize(in_channels,num_groups=in_channels//2) self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3, stride=1,padding=1) blocks = [] downs = [] ch_in = n_channels for m in ch_mult: blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout)) ch_in = m * n_channels downs.append(Downsample(ch_in, with_conv=False)) self.model = nn.ModuleList(blocks) self.downsampler = nn.ModuleList(downs) def instantiate_pretrained(self, config): model = instantiate_from_config(config) self.pretrained_model = model.eval() # self.pretrained_model.train = False for param in self.pretrained_model.parameters(): param.requires_grad = False @torch.no_grad() def encode_with_pretrained(self,x): c = self.pretrained_model.encode(x) if isinstance(c, DiagonalGaussianDistribution): c = c.mode() return c def forward(self,x): z_fs = self.encode_with_pretrained(x) z = self.proj_norm(z_fs) z = self.proj(z) z = nonlinearity(z) for submodel, downmodel in zip(self.model,self.downsampler): z = submodel(z,temb=None) z = downmodel(z) if self.do_reshape: z = rearrange(z,'b c h w -> b (h w) c') return z ================================================ FILE: stable-dreamfusion/ldm/modules/diffusionmodules/openaimodel.py ================================================ from abc import abstractmethod from functools import partial import math from typing import Iterable import numpy as np import torch as th import torch.nn as nn import torch.nn.functional as F from ldm.modules.diffusionmodules.util import ( checkpoint, conv_nd, linear, avg_pool_nd, zero_module, normalization, timestep_embedding, ) from ldm.modules.attention import SpatialTransformer from ldm.util import exists # dummy replace def convert_module_to_f16(x): pass def convert_module_to_f32(x): pass ## go class AttentionPool2d(nn.Module): """ Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py """ def __init__( self, spacial_dim: int, embed_dim: int, num_heads_channels: int, output_dim: int = None, ): super().__init__() self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5) self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1) self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1) self.num_heads = embed_dim // num_heads_channels self.attention = QKVAttention(self.num_heads) def forward(self, x): b, c, *_spatial = x.shape x = x.reshape(b, c, -1) # NC(HW) x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1) x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1) x = self.qkv_proj(x) x = self.attention(x) x = self.c_proj(x) return x[:, :, 0] class TimestepBlock(nn.Module): """ Any module where forward() takes timestep embeddings as a second argument. """ @abstractmethod def forward(self, x, emb): """ Apply the module to `x` given `emb` timestep embeddings. """ class TimestepEmbedSequential(nn.Sequential, TimestepBlock): """ A sequential module that passes timestep embeddings to the children that support it as an extra input. """ def forward(self, x, emb, context=None): for layer in self: if isinstance(layer, TimestepBlock): x = layer(x, emb) elif isinstance(layer, SpatialTransformer): x = layer(x, context) else: x = layer(x) return x class Upsample(nn.Module): """ An upsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then upsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims if use_conv: self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding) def forward(self, x): assert x.shape[1] == self.channels if self.dims == 3: x = F.interpolate( x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" ) else: x = F.interpolate(x, scale_factor=2, mode="nearest") if self.use_conv: x = self.conv(x) return x class TransposedUpsample(nn.Module): 'Learned 2x upsampling without padding' def __init__(self, channels, out_channels=None, ks=5): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2) def forward(self,x): return self.up(x) class Downsample(nn.Module): """ A downsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then downsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims stride = 2 if dims != 3 else (1, 2, 2) if use_conv: self.op = conv_nd( dims, self.channels, self.out_channels, 3, stride=stride, padding=padding ) else: assert self.channels == self.out_channels self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) def forward(self, x): assert x.shape[1] == self.channels return self.op(x) class ResBlock(TimestepBlock): """ A residual block that can optionally change the number of channels. :param channels: the number of input channels. :param emb_channels: the number of timestep embedding channels. :param dropout: the rate of dropout. :param out_channels: if specified, the number of out channels. :param use_conv: if True and out_channels is specified, use a spatial convolution instead of a smaller 1x1 convolution to change the channels in the skip connection. :param dims: determines if the signal is 1D, 2D, or 3D. :param use_checkpoint: if True, use gradient checkpointing on this module. :param up: if True, use this block for upsampling. :param down: if True, use this block for downsampling. """ def __init__( self, channels, emb_channels, dropout, out_channels=None, use_conv=False, use_scale_shift_norm=False, dims=2, use_checkpoint=False, up=False, down=False, ): super().__init__() self.channels = channels self.emb_channels = emb_channels self.dropout = dropout self.out_channels = out_channels or channels self.use_conv = use_conv self.use_checkpoint = use_checkpoint self.use_scale_shift_norm = use_scale_shift_norm self.in_layers = nn.Sequential( normalization(channels), nn.SiLU(), conv_nd(dims, channels, self.out_channels, 3, padding=1), ) self.updown = up or down if up: self.h_upd = Upsample(channels, False, dims) self.x_upd = Upsample(channels, False, dims) elif down: self.h_upd = Downsample(channels, False, dims) self.x_upd = Downsample(channels, False, dims) else: self.h_upd = self.x_upd = nn.Identity() self.emb_layers = nn.Sequential( nn.SiLU(), linear( emb_channels, 2 * self.out_channels if use_scale_shift_norm else self.out_channels, ), ) self.out_layers = nn.Sequential( normalization(self.out_channels), nn.SiLU(), nn.Dropout(p=dropout), zero_module( conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1) ), ) if self.out_channels == channels: self.skip_connection = nn.Identity() elif use_conv: self.skip_connection = conv_nd( dims, channels, self.out_channels, 3, padding=1 ) else: self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) def forward(self, x, emb): """ Apply the block to a Tensor, conditioned on a timestep embedding. :param x: an [N x C x ...] Tensor of features. :param emb: an [N x emb_channels] Tensor of timestep embeddings. :return: an [N x C x ...] Tensor of outputs. """ return checkpoint( self._forward, (x, emb), self.parameters(), self.use_checkpoint ) def _forward(self, x, emb): if self.updown: in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] h = in_rest(x) h = self.h_upd(h) x = self.x_upd(x) h = in_conv(h) else: h = self.in_layers(x) emb_out = self.emb_layers(emb).type(h.dtype) while len(emb_out.shape) < len(h.shape): emb_out = emb_out[..., None] if self.use_scale_shift_norm: out_norm, out_rest = self.out_layers[0], self.out_layers[1:] scale, shift = th.chunk(emb_out, 2, dim=1) h = out_norm(h) * (1 + scale) + shift h = out_rest(h) else: h = h + emb_out h = self.out_layers(h) return self.skip_connection(x) + h class AttentionBlock(nn.Module): """ An attention block that allows spatial positions to attend to each other. Originally ported from here, but adapted to the N-d case. https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. """ def __init__( self, channels, num_heads=1, num_head_channels=-1, use_checkpoint=False, use_new_attention_order=False, ): super().__init__() self.channels = channels if num_head_channels == -1: self.num_heads = num_heads else: assert ( channels % num_head_channels == 0 ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" self.num_heads = channels // num_head_channels self.use_checkpoint = use_checkpoint self.norm = normalization(channels) self.qkv = conv_nd(1, channels, channels * 3, 1) if use_new_attention_order: # split qkv before split heads self.attention = QKVAttention(self.num_heads) else: # split heads before split qkv self.attention = QKVAttentionLegacy(self.num_heads) self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) def forward(self, x): return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!! #return pt_checkpoint(self._forward, x) # pytorch def _forward(self, x): b, c, *spatial = x.shape x = x.reshape(b, c, -1) qkv = self.qkv(self.norm(x)) h = self.attention(qkv) h = self.proj_out(h) return (x + h).reshape(b, c, *spatial) def count_flops_attn(model, _x, y): """ A counter for the `thop` package to count the operations in an attention operation. Meant to be used like: macs, params = thop.profile( model, inputs=(inputs, timestamps), custom_ops={QKVAttention: QKVAttention.count_flops}, ) """ b, c, *spatial = y[0].shape num_spatial = int(np.prod(spatial)) # We perform two matmuls with the same number of ops. # The first computes the weight matrix, the second computes # the combination of the value vectors. matmul_ops = 2 * b * (num_spatial ** 2) * c model.total_ops += th.DoubleTensor([matmul_ops]) class QKVAttentionLegacy(nn.Module): """ A module which performs QKV attention. Matches legacy QKVAttention + input/output heads shaping """ def __init__(self, n_heads): super().__init__() self.n_heads = n_heads def forward(self, qkv): """ Apply QKV attention. :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x T] tensor after attention. """ bs, width, length = qkv.shape assert width % (3 * self.n_heads) == 0 ch = width // (3 * self.n_heads) q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) scale = 1 / math.sqrt(math.sqrt(ch)) weight = th.einsum( "bct,bcs->bts", q * scale, k * scale ) # More stable with f16 than dividing afterwards weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) a = th.einsum("bts,bcs->bct", weight, v) return a.reshape(bs, -1, length) @staticmethod def count_flops(model, _x, y): return count_flops_attn(model, _x, y) class QKVAttention(nn.Module): """ A module which performs QKV attention and splits in a different order. """ def __init__(self, n_heads): super().__init__() self.n_heads = n_heads def forward(self, qkv): """ Apply QKV attention. :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x T] tensor after attention. """ bs, width, length = qkv.shape assert width % (3 * self.n_heads) == 0 ch = width // (3 * self.n_heads) q, k, v = qkv.chunk(3, dim=1) scale = 1 / math.sqrt(math.sqrt(ch)) weight = th.einsum( "bct,bcs->bts", (q * scale).view(bs * self.n_heads, ch, length), (k * scale).view(bs * self.n_heads, ch, length), ) # More stable with f16 than dividing afterwards weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) return a.reshape(bs, -1, length) @staticmethod def count_flops(model, _x, y): return count_flops_attn(model, _x, y) class UNetModel(nn.Module): """ The full UNet model with attention and timestep embedding. :param in_channels: channels in the input Tensor. :param model_channels: base channel count for the model. :param out_channels: channels in the output Tensor. :param num_res_blocks: number of residual blocks per downsample. :param attention_resolutions: a collection of downsample rates at which attention will take place. May be a set, list, or tuple. For example, if this contains 4, then at 4x downsampling, attention will be used. :param dropout: the dropout probability. :param channel_mult: channel multiplier for each level of the UNet. :param conv_resample: if True, use learned convolutions for upsampling and downsampling. :param dims: determines if the signal is 1D, 2D, or 3D. :param num_classes: if specified (as an int), then this model will be class-conditional with `num_classes` classes. :param use_checkpoint: use gradient checkpointing to reduce memory usage. :param num_heads: the number of attention heads in each attention layer. :param num_heads_channels: if specified, ignore num_heads and instead use a fixed channel width per attention head. :param num_heads_upsample: works with num_heads to set a different number of heads for upsampling. Deprecated. :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. :param resblock_updown: use residual blocks for up/downsampling. :param use_new_attention_order: use a different attention pattern for potentially increased efficiency. """ def __init__( self, image_size, in_channels, model_channels, out_channels, num_res_blocks, attention_resolutions, dropout=0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, num_classes=None, use_checkpoint=False, use_fp16=False, num_heads=-1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, resblock_updown=False, use_new_attention_order=False, use_spatial_transformer=False, # custom transformer support transformer_depth=1, # custom transformer support context_dim=None, # custom transformer support n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model legacy=True, disable_self_attentions=None, num_attention_blocks=None ): super().__init__() if use_spatial_transformer: assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' if context_dim is not None: assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' from omegaconf.listconfig import ListConfig if type(context_dim) == ListConfig: context_dim = list(context_dim) if num_heads_upsample == -1: num_heads_upsample = num_heads if num_heads == -1: assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' if num_head_channels == -1: assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' self.image_size = image_size self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels if isinstance(num_res_blocks, int): self.num_res_blocks = len(channel_mult) * [num_res_blocks] else: if len(num_res_blocks) != len(channel_mult): raise ValueError("provide num_res_blocks either as an int (globally constant) or " "as a list/tuple (per-level) with the same length as channel_mult") self.num_res_blocks = num_res_blocks #self.num_res_blocks = num_res_blocks if disable_self_attentions is not None: # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not assert len(disable_self_attentions) == len(channel_mult) if num_attention_blocks is not None: assert len(num_attention_blocks) == len(self.num_res_blocks) assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks)))) print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. " f"This option has LESS priority than attention_resolutions {attention_resolutions}, " f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, " f"attention will still not be set.") # todo: convert to warning self.attention_resolutions = attention_resolutions self.dropout = dropout self.channel_mult = channel_mult self.conv_resample = conv_resample self.num_classes = num_classes self.use_checkpoint = use_checkpoint self.dtype = th.float16 if use_fp16 else th.float32 self.num_heads = num_heads self.num_head_channels = num_head_channels self.num_heads_upsample = num_heads_upsample self.predict_codebook_ids = n_embed is not None time_embed_dim = model_channels * 4 self.time_embed = nn.Sequential( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) if self.num_classes is not None: self.label_emb = nn.Embedding(num_classes, time_embed_dim) self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( conv_nd(dims, in_channels, model_channels, 3, padding=1) ) ] ) self._feature_size = model_channels input_block_chans = [model_channels] ch = model_channels ds = 1 for level, mult in enumerate(channel_mult): for nr in range(self.num_res_blocks[level]): layers = [ ResBlock( ch, time_embed_dim, dropout, out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ) ] ch = mult * model_channels if ds in attention_resolutions: if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels if exists(disable_self_attentions): disabled_sa = disable_self_attentions[level] else: disabled_sa = False if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: layers.append( AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads, num_head_channels=dim_head, use_new_attention_order=use_new_attention_order, ) if not use_spatial_transformer else SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, disable_self_attn=disabled_sa ) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch input_block_chans.append(ch) if level != len(channel_mult) - 1: out_ch = ch self.input_blocks.append( TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, down=True, ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch ) ) ) ch = out_ch input_block_chans.append(ch) ds *= 2 self._feature_size += ch if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels self.middle_block = TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ), AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads, num_head_channels=dim_head, use_new_attention_order=use_new_attention_order, ) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim ), ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ), ) self._feature_size += ch self.output_blocks = nn.ModuleList([]) for level, mult in list(enumerate(channel_mult))[::-1]: for i in range(self.num_res_blocks[level] + 1): ich = input_block_chans.pop() layers = [ ResBlock( ch + ich, time_embed_dim, dropout, out_channels=model_channels * mult, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ) ] ch = model_channels * mult if ds in attention_resolutions: if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if legacy: #num_heads = 1 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels if exists(disable_self_attentions): disabled_sa = disable_self_attentions[level] else: disabled_sa = False if not exists(num_attention_blocks) or i < num_attention_blocks[level]: layers.append( AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads_upsample, num_head_channels=dim_head, use_new_attention_order=use_new_attention_order, ) if not use_spatial_transformer else SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, disable_self_attn=disabled_sa ) ) if level and i == self.num_res_blocks[level]: out_ch = ch layers.append( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, up=True, ) if resblock_updown else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) ) ds //= 2 self.output_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch self.out = nn.Sequential( normalization(ch), nn.SiLU(), zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), ) if self.predict_codebook_ids: self.id_predictor = nn.Sequential( normalization(ch), conv_nd(dims, model_channels, n_embed, 1), #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits ) def convert_to_fp16(self): """ Convert the torso of the model to float16. """ self.input_blocks.apply(convert_module_to_f16) self.middle_block.apply(convert_module_to_f16) self.output_blocks.apply(convert_module_to_f16) def convert_to_fp32(self): """ Convert the torso of the model to float32. """ self.input_blocks.apply(convert_module_to_f32) self.middle_block.apply(convert_module_to_f32) self.output_blocks.apply(convert_module_to_f32) def forward(self, x, timesteps=None, context=None, y=None,**kwargs): """ Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps. :param context: conditioning plugged in via crossattn :param y: an [N] Tensor of labels, if class-conditional. :return: an [N x C x ...] Tensor of outputs. """ assert (y is not None) == ( self.num_classes is not None ), "must specify y if and only if the model is class-conditional" hs = [] t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) emb = self.time_embed(t_emb) if self.num_classes is not None: assert y.shape == (x.shape[0],) emb = emb + self.label_emb(y) h = x.type(self.dtype) for module in self.input_blocks: h = module(h, emb, context) hs.append(h) h = self.middle_block(h, emb, context) for module in self.output_blocks: h = th.cat([h, hs.pop()], dim=1) h = module(h, emb, context) h = h.type(x.dtype) if self.predict_codebook_ids: return self.id_predictor(h) else: return self.out(h) class EncoderUNetModel(nn.Module): """ The half UNet model with attention and timestep embedding. For usage, see UNet. """ def __init__( self, image_size, in_channels, model_channels, out_channels, num_res_blocks, attention_resolutions, dropout=0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, use_checkpoint=False, use_fp16=False, num_heads=1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, resblock_updown=False, use_new_attention_order=False, pool="adaptive", *args, **kwargs ): super().__init__() if num_heads_upsample == -1: num_heads_upsample = num_heads self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels self.num_res_blocks = num_res_blocks self.attention_resolutions = attention_resolutions self.dropout = dropout self.channel_mult = channel_mult self.conv_resample = conv_resample self.use_checkpoint = use_checkpoint self.dtype = th.float16 if use_fp16 else th.float32 self.num_heads = num_heads self.num_head_channels = num_head_channels self.num_heads_upsample = num_heads_upsample time_embed_dim = model_channels * 4 self.time_embed = nn.Sequential( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( conv_nd(dims, in_channels, model_channels, 3, padding=1) ) ] ) self._feature_size = model_channels input_block_chans = [model_channels] ch = model_channels ds = 1 for level, mult in enumerate(channel_mult): for _ in range(num_res_blocks): layers = [ ResBlock( ch, time_embed_dim, dropout, out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ) ] ch = mult * model_channels if ds in attention_resolutions: layers.append( AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads, num_head_channels=num_head_channels, use_new_attention_order=use_new_attention_order, ) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch input_block_chans.append(ch) if level != len(channel_mult) - 1: out_ch = ch self.input_blocks.append( TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, down=True, ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch ) ) ) ch = out_ch input_block_chans.append(ch) ds *= 2 self._feature_size += ch self.middle_block = TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ), AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads, num_head_channels=num_head_channels, use_new_attention_order=use_new_attention_order, ), ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ), ) self._feature_size += ch self.pool = pool if pool == "adaptive": self.out = nn.Sequential( normalization(ch), nn.SiLU(), nn.AdaptiveAvgPool2d((1, 1)), zero_module(conv_nd(dims, ch, out_channels, 1)), nn.Flatten(), ) elif pool == "attention": assert num_head_channels != -1 self.out = nn.Sequential( normalization(ch), nn.SiLU(), AttentionPool2d( (image_size // ds), ch, num_head_channels, out_channels ), ) elif pool == "spatial": self.out = nn.Sequential( nn.Linear(self._feature_size, 2048), nn.ReLU(), nn.Linear(2048, self.out_channels), ) elif pool == "spatial_v2": self.out = nn.Sequential( nn.Linear(self._feature_size, 2048), normalization(2048), nn.SiLU(), nn.Linear(2048, self.out_channels), ) else: raise NotImplementedError(f"Unexpected {pool} pooling") def convert_to_fp16(self): """ Convert the torso of the model to float16. """ self.input_blocks.apply(convert_module_to_f16) self.middle_block.apply(convert_module_to_f16) def convert_to_fp32(self): """ Convert the torso of the model to float32. """ self.input_blocks.apply(convert_module_to_f32) self.middle_block.apply(convert_module_to_f32) def forward(self, x, timesteps): """ Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps. :return: an [N x K] Tensor of outputs. """ emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) results = [] h = x.type(self.dtype) for module in self.input_blocks: h = module(h, emb) if self.pool.startswith("spatial"): results.append(h.type(x.dtype).mean(dim=(2, 3))) h = self.middle_block(h, emb) if self.pool.startswith("spatial"): results.append(h.type(x.dtype).mean(dim=(2, 3))) h = th.cat(results, axis=-1) return self.out(h) else: h = h.type(x.dtype) return self.out(h) ================================================ FILE: stable-dreamfusion/ldm/modules/diffusionmodules/util.py ================================================ # adopted from # https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py # and # https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py # and # https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py # # thanks! import os import math import torch import torch.nn as nn import numpy as np from einops import repeat from ldm.util import instantiate_from_config def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): if schedule == "linear": betas = ( torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 ) elif schedule == "cosine": timesteps = ( torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s ) alphas = timesteps / (1 + cosine_s) * np.pi / 2 alphas = torch.cos(alphas).pow(2) alphas = alphas / alphas[0] betas = 1 - alphas[1:] / alphas[:-1] betas = np.clip(betas, a_min=0, a_max=0.999) elif schedule == "sqrt_linear": betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) elif schedule == "sqrt": betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 else: raise ValueError(f"schedule '{schedule}' unknown.") return betas.numpy() def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): if ddim_discr_method == 'uniform': c = num_ddpm_timesteps // num_ddim_timesteps ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) elif ddim_discr_method == 'quad': ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) else: raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') # assert ddim_timesteps.shape[0] == num_ddim_timesteps # add one to get the final alpha values right (the ones from first scale to data during sampling) steps_out = ddim_timesteps + 1 if verbose: print(f'Selected timesteps for ddim sampler: {steps_out}') return steps_out def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): # select alphas for computing the variance schedule alphas = alphacums[ddim_timesteps] alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) # according the the formula provided in https://arxiv.org/abs/2010.02502 sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) if verbose: print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') print(f'For the chosen value of eta, which is {eta}, ' f'this results in the following sigma_t schedule for ddim sampler {sigmas}') return sigmas, alphas, alphas_prev def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): """ Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. :param num_diffusion_timesteps: the number of betas to produce. :param alpha_bar: a lambda that takes an argument t from 0 to 1 and produces the cumulative product of (1-beta) up to that part of the diffusion process. :param max_beta: the maximum beta to use; use values lower than 1 to prevent singularities. """ betas = [] for i in range(num_diffusion_timesteps): t1 = i / num_diffusion_timesteps t2 = (i + 1) / num_diffusion_timesteps betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) return np.array(betas) def extract_into_tensor(a, t, x_shape): b, *_ = t.shape out = a.gather(-1, t) return out.reshape(b, *((1,) * (len(x_shape) - 1))) def checkpoint(func, inputs, params, flag): """ Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pass. :param func: the function to evaluate. :param inputs: the argument sequence to pass to `func`. :param params: a sequence of parameters `func` depends on but does not explicitly take as arguments. :param flag: if False, disable gradient checkpointing. """ if flag: args = tuple(inputs) + tuple(params) return CheckpointFunction.apply(func, len(inputs), *args) else: return func(*inputs) class CheckpointFunction(torch.autograd.Function): @staticmethod def forward(ctx, run_function, length, *args): ctx.run_function = run_function ctx.input_tensors = list(args[:length]) ctx.input_params = list(args[length:]) with torch.no_grad(): output_tensors = ctx.run_function(*ctx.input_tensors) return output_tensors @staticmethod def backward(ctx, *output_grads): ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] with torch.enable_grad(): # Fixes a bug where the first op in run_function modifies the # Tensor storage in place, which is not allowed for detach()'d # Tensors. shallow_copies = [x.view_as(x) for x in ctx.input_tensors] output_tensors = ctx.run_function(*shallow_copies) input_grads = torch.autograd.grad( output_tensors, ctx.input_tensors + ctx.input_params, output_grads, allow_unused=True, ) del ctx.input_tensors del ctx.input_params del output_tensors return (None, None) + input_grads def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): """ Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be fractional. :param dim: the dimension of the output. :param max_period: controls the minimum frequency of the embeddings. :return: an [N x dim] Tensor of positional embeddings. """ if not repeat_only: half = dim // 2 freqs = torch.exp( -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half ).to(device=timesteps.device) args = timesteps[:, None].float() * freqs[None] embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) if dim % 2: embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) else: embedding = repeat(timesteps, 'b -> b d', d=dim) return embedding def zero_module(module): """ Zero out the parameters of a module and return it. """ for p in module.parameters(): p.detach().zero_() return module def scale_module(module, scale): """ Scale the parameters of a module and return it. """ for p in module.parameters(): p.detach().mul_(scale) return module def mean_flat(tensor): """ Take the mean over all non-batch dimensions. """ return tensor.mean(dim=list(range(1, len(tensor.shape)))) def normalization(channels): """ Make a standard normalization layer. :param channels: number of input channels. :return: an nn.Module for normalization. """ return GroupNorm32(32, channels) # PyTorch 1.7 has SiLU, but we support PyTorch 1.5. class SiLU(nn.Module): def forward(self, x): return x * torch.sigmoid(x) class GroupNorm32(nn.GroupNorm): def forward(self, x): return super().forward(x.float()).type(x.dtype) def conv_nd(dims, *args, **kwargs): """ Create a 1D, 2D, or 3D convolution module. """ if dims == 1: return nn.Conv1d(*args, **kwargs) elif dims == 2: return nn.Conv2d(*args, **kwargs) elif dims == 3: return nn.Conv3d(*args, **kwargs) raise ValueError(f"unsupported dimensions: {dims}") def linear(*args, **kwargs): """ Create a linear module. """ return nn.Linear(*args, **kwargs) def avg_pool_nd(dims, *args, **kwargs): """ Create a 1D, 2D, or 3D average pooling module. """ if dims == 1: return nn.AvgPool1d(*args, **kwargs) elif dims == 2: return nn.AvgPool2d(*args, **kwargs) elif dims == 3: return nn.AvgPool3d(*args, **kwargs) raise ValueError(f"unsupported dimensions: {dims}") class HybridConditioner(nn.Module): def __init__(self, c_concat_config, c_crossattn_config): super().__init__() self.concat_conditioner = instantiate_from_config(c_concat_config) self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) def forward(self, c_concat, c_crossattn): c_concat = self.concat_conditioner(c_concat) c_crossattn = self.crossattn_conditioner(c_crossattn) return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} def noise_like(shape, device, repeat=False): repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) noise = lambda: torch.randn(shape, device=device) return repeat_noise() if repeat else noise() ================================================ FILE: stable-dreamfusion/ldm/modules/distributions/__init__.py ================================================ ================================================ FILE: stable-dreamfusion/ldm/modules/distributions/distributions.py ================================================ import torch import numpy as np class AbstractDistribution: def sample(self): raise NotImplementedError() def mode(self): raise NotImplementedError() class DiracDistribution(AbstractDistribution): def __init__(self, value): self.value = value def sample(self): return self.value def mode(self): return self.value class DiagonalGaussianDistribution(object): def __init__(self, parameters, deterministic=False): self.parameters = parameters self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) self.logvar = torch.clamp(self.logvar, -30.0, 20.0) self.deterministic = deterministic self.std = torch.exp(0.5 * self.logvar) self.var = torch.exp(self.logvar) if self.deterministic: self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) def sample(self): x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) return x def kl(self, other=None): if self.deterministic: return torch.Tensor([0.]) else: if other is None: return 0.5 * torch.sum(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=[1, 2, 3]) else: return 0.5 * torch.sum( torch.pow(self.mean - other.mean, 2) / other.var + self.var / other.var - 1.0 - self.logvar + other.logvar, dim=[1, 2, 3]) def nll(self, sample, dims=[1,2,3]): if self.deterministic: return torch.Tensor([0.]) logtwopi = np.log(2.0 * np.pi) return 0.5 * torch.sum( logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims) def mode(self): return self.mean def normal_kl(mean1, logvar1, mean2, logvar2): """ source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 Compute the KL divergence between two gaussians. Shapes are automatically broadcasted, so batches can be compared to scalars, among other use cases. """ tensor = None for obj in (mean1, logvar1, mean2, logvar2): if isinstance(obj, torch.Tensor): tensor = obj break assert tensor is not None, "at least one argument must be a Tensor" # Force variances to be Tensors. Broadcasting helps convert scalars to # Tensors, but it does not work for torch.exp(). logvar1, logvar2 = [ x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) for x in (logvar1, logvar2) ] return 0.5 * ( -1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) ) ================================================ FILE: stable-dreamfusion/ldm/modules/ema.py ================================================ import torch from torch import nn class LitEma(nn.Module): def __init__(self, model, decay=0.9999, use_num_upates=True): super().__init__() if decay < 0.0 or decay > 1.0: raise ValueError('Decay must be between 0 and 1') self.m_name2s_name = {} self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates else torch.tensor(-1,dtype=torch.int)) for name, p in model.named_parameters(): if p.requires_grad: #remove as '.'-character is not allowed in buffers s_name = name.replace('.','') self.m_name2s_name.update({name:s_name}) self.register_buffer(s_name,p.clone().detach().data) self.collected_params = [] def forward(self,model): decay = self.decay if self.num_updates >= 0: self.num_updates += 1 decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates)) one_minus_decay = 1.0 - decay with torch.no_grad(): m_param = dict(model.named_parameters()) shadow_params = dict(self.named_buffers()) for key in m_param: if m_param[key].requires_grad: sname = self.m_name2s_name[key] shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) else: assert not key in self.m_name2s_name def copy_to(self, model): m_param = dict(model.named_parameters()) shadow_params = dict(self.named_buffers()) for key in m_param: if m_param[key].requires_grad: m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) else: assert not key in self.m_name2s_name def store(self, parameters): """ Save the current parameters for restoring later. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be temporarily stored. """ self.collected_params = [param.clone() for param in parameters] def restore(self, parameters): """ Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without affecting the original optimization process. Store the parameters before the `copy_to` method. After validation (or model saving), use this to restore the former parameters. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be updated with the stored parameters. """ for c_param, param in zip(self.collected_params, parameters): param.data.copy_(c_param.data) ================================================ FILE: stable-dreamfusion/ldm/modules/encoders/__init__.py ================================================ ================================================ FILE: stable-dreamfusion/ldm/modules/encoders/modules.py ================================================ import torch import torch.nn as nn import numpy as np from functools import partial import kornia from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test from ldm.util import default import clip class AbstractEncoder(nn.Module): def __init__(self): super().__init__() def encode(self, *args, **kwargs): raise NotImplementedError class IdentityEncoder(AbstractEncoder): def encode(self, x): return x class FaceClipEncoder(AbstractEncoder): def __init__(self, augment=True, retreival_key=None): super().__init__() self.encoder = FrozenCLIPImageEmbedder() self.augment = augment self.retreival_key = retreival_key def forward(self, img): encodings = [] with torch.no_grad(): x_offset = 125 if self.retreival_key: # Assumes retrieved image are packed into the second half of channels face = img[:,3:,190:440,x_offset:(512-x_offset)] other = img[:,:3,...].clone() else: face = img[:,:,190:440,x_offset:(512-x_offset)] other = img.clone() if self.augment: face = K.RandomHorizontalFlip()(face) other[:,:,190:440,x_offset:(512-x_offset)] *= 0 encodings = [ self.encoder.encode(face), self.encoder.encode(other), ] return torch.cat(encodings, dim=1) def encode(self, img): if isinstance(img, list): # Uncondition return torch.zeros((1, 2, 768), device=self.encoder.model.visual.conv1.weight.device) return self(img) class FaceIdClipEncoder(AbstractEncoder): def __init__(self): super().__init__() self.encoder = FrozenCLIPImageEmbedder() for p in self.encoder.parameters(): p.requires_grad = False self.id = FrozenFaceEncoder("/home/jpinkney/code/stable-diffusion/model_ir_se50.pth", augment=True) def forward(self, img): encodings = [] with torch.no_grad(): face = kornia.geometry.resize(img, (256, 256), interpolation='bilinear', align_corners=True) other = img.clone() other[:,:,184:452,122:396] *= 0 encodings = [ self.id.encode(face), self.encoder.encode(other), ] return torch.cat(encodings, dim=1) def encode(self, img): if isinstance(img, list): # Uncondition return torch.zeros((1, 2, 768), device=self.encoder.model.visual.conv1.weight.device) return self(img) class ClassEmbedder(nn.Module): def __init__(self, embed_dim, n_classes=1000, key='class'): super().__init__() self.key = key self.embedding = nn.Embedding(n_classes, embed_dim) def forward(self, batch, key=None): if key is None: key = self.key # this is for use in crossattn c = batch[key][:, None] c = self.embedding(c) return c class TransformerEmbedder(AbstractEncoder): """Some transformer encoder layers""" def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"): super().__init__() self.device = device self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, attn_layers=Encoder(dim=n_embed, depth=n_layer)) def forward(self, tokens): tokens = tokens.to(self.device) # meh z = self.transformer(tokens, return_embeddings=True) return z def encode(self, x): return self(x) class BERTTokenizer(AbstractEncoder): """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)""" def __init__(self, device="cuda", vq_interface=True, max_length=77): super().__init__() from transformers import BertTokenizerFast # TODO: add to reuquirements self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") self.device = device self.vq_interface = vq_interface self.max_length = max_length def forward(self, text): batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt") tokens = batch_encoding["input_ids"].to(self.device) return tokens @torch.no_grad() def encode(self, text): tokens = self(text) if not self.vq_interface: return tokens return None, None, [None, None, tokens] def decode(self, text): return text class BERTEmbedder(AbstractEncoder): """Uses the BERT tokenizr model and add some transformer encoder layers""" def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77, device="cuda",use_tokenizer=True, embedding_dropout=0.0): super().__init__() self.use_tknz_fn = use_tokenizer if self.use_tknz_fn: self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len) self.device = device self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, attn_layers=Encoder(dim=n_embed, depth=n_layer), emb_dropout=embedding_dropout) def forward(self, text): if self.use_tknz_fn: tokens = self.tknz_fn(text)#.to(self.device) else: tokens = text z = self.transformer(tokens, return_embeddings=True) return z def encode(self, text): # output of length 77 return self(text) from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel def disabled_train(self, mode=True): """Overwrite model.train with this function to make sure train/eval mode does not change anymore.""" return self class FrozenT5Embedder(AbstractEncoder): """Uses the T5 transformer encoder for text""" def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl super().__init__() self.tokenizer = T5Tokenizer.from_pretrained(version) self.transformer = T5EncoderModel.from_pretrained(version) self.device = device self.max_length = max_length # TODO: typical value? self.freeze() def freeze(self): self.transformer = self.transformer.eval() #self.train = disabled_train for param in self.parameters(): param.requires_grad = False def forward(self, text): batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt") tokens = batch_encoding["input_ids"].to(self.device) outputs = self.transformer(input_ids=tokens) z = outputs.last_hidden_state return z def encode(self, text): return self(text) from ldm.thirdp.psp.id_loss import IDFeatures import kornia.augmentation as K class FrozenFaceEncoder(AbstractEncoder): def __init__(self, model_path, augment=False): super().__init__() self.loss_fn = IDFeatures(model_path) # face encoder is frozen for p in self.loss_fn.parameters(): p.requires_grad = False # Mapper is trainable self.mapper = torch.nn.Linear(512, 768) p = 0.25 if augment: self.augment = K.AugmentationSequential( K.RandomHorizontalFlip(p=0.5), K.RandomEqualize(p=p), # K.RandomPlanckianJitter(p=p), # K.RandomPlasmaBrightness(p=p), # K.RandomPlasmaContrast(p=p), # K.ColorJiggle(0.02, 0.2, 0.2, p=p), ) else: self.augment = False def forward(self, img): if isinstance(img, list): # Uncondition return torch.zeros((1, 1, 768), device=self.mapper.weight.device) if self.augment is not None: # Transforms require 0-1 img = self.augment((img + 1)/2) img = 2*img - 1 feat = self.loss_fn(img, crop=True) feat = self.mapper(feat.unsqueeze(1)) return feat def encode(self, img): return self(img) class FrozenCLIPEmbedder(AbstractEncoder): """Uses the CLIP transformer encoder for text (from huggingface)""" def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77): # clip-vit-base-patch32 super().__init__() self.tokenizer = CLIPTokenizer.from_pretrained(version) self.transformer = CLIPTextModel.from_pretrained(version) self.device = device self.max_length = max_length # TODO: typical value? self.freeze() def freeze(self): self.transformer = self.transformer.eval() #self.train = disabled_train for param in self.parameters(): param.requires_grad = False def forward(self, text): batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt") tokens = batch_encoding["input_ids"].to(self.device) outputs = self.transformer(input_ids=tokens) z = outputs.last_hidden_state return z def encode(self, text): return self(text) import torch.nn.functional as F from transformers import CLIPVisionModel class ClipImageProjector(AbstractEncoder): """ Uses the CLIP image encoder. """ def __init__(self, version="openai/clip-vit-large-patch14", max_length=77): # clip-vit-base-patch32 super().__init__() self.model = CLIPVisionModel.from_pretrained(version) self.model.train() self.max_length = max_length # TODO: typical value? self.antialias = True self.mapper = torch.nn.Linear(1024, 768) self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False) self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False) null_cond = self.get_null_cond(version, max_length) self.register_buffer('null_cond', null_cond) @torch.no_grad() def get_null_cond(self, version, max_length): device = self.mean.device embedder = FrozenCLIPEmbedder(version=version, device=device, max_length=max_length) null_cond = embedder([""]) return null_cond def preprocess(self, x): # Expects inputs in the range -1, 1 x = kornia.geometry.resize(x, (224, 224), interpolation='bicubic',align_corners=True, antialias=self.antialias) x = (x + 1.) / 2. # renormalize according to clip x = kornia.enhance.normalize(x, self.mean, self.std) return x def forward(self, x): if isinstance(x, list): return self.null_cond # x is assumed to be in range [-1,1] x = self.preprocess(x) outputs = self.model(pixel_values=x) last_hidden_state = outputs.last_hidden_state last_hidden_state = self.mapper(last_hidden_state) return F.pad(last_hidden_state, [0,0, 0,self.max_length-last_hidden_state.shape[1], 0,0]) def encode(self, im): return self(im) class ProjectedFrozenCLIPEmbedder(AbstractEncoder): def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77): # clip-vit-base-patch32 super().__init__() self.embedder = FrozenCLIPEmbedder(version=version, device=device, max_length=max_length) self.projection = torch.nn.Linear(768, 768) def forward(self, text): z = self.embedder(text) return self.projection(z) def encode(self, text): return self(text) class FrozenCLIPImageEmbedder(AbstractEncoder): """ Uses the CLIP image encoder. Not actually frozen... If you want that set cond_stage_trainable=False in cfg """ def __init__( self, model='ViT-L/14', jit=False, device='cpu', antialias=False, ): super().__init__() self.model, _ = clip.load(name=model, device=device, jit=jit) # We don't use the text part so delete it del self.model.transformer self.antialias = antialias self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False) self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False) def preprocess(self, x): # Expects inputs in the range -1, 1 x = kornia.geometry.resize(x, (224, 224), interpolation='bicubic',align_corners=True, antialias=self.antialias) x = (x + 1.) / 2. # renormalize according to clip x = kornia.enhance.normalize(x, self.mean, self.std) return x def forward(self, x): # x is assumed to be in range [-1,1] if isinstance(x, list): # [""] denotes condition dropout for ucg device = self.model.visual.conv1.weight.device return torch.zeros(1, 768, device=device) return self.model.encode_image(self.preprocess(x)).float() def encode(self, im): return self(im).unsqueeze(1) from torchvision import transforms import random class FrozenCLIPImageMutliEmbedder(AbstractEncoder): """ Uses the CLIP image encoder. Not actually frozen... If you want that set cond_stage_trainable=False in cfg """ def __init__( self, model='ViT-L/14', jit=False, device='cpu', antialias=True, max_crops=5, ): super().__init__() self.model, _ = clip.load(name=model, device=device, jit=jit) # We don't use the text part so delete it del self.model.transformer self.antialias = antialias self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False) self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False) self.max_crops = max_crops def preprocess(self, x): # Expects inputs in the range -1, 1 randcrop = transforms.RandomResizedCrop(224, scale=(0.085, 1.0), ratio=(1,1)) max_crops = self.max_crops patches = [] crops = [randcrop(x) for _ in range(max_crops)] patches.extend(crops) x = torch.cat(patches, dim=0) x = (x + 1.) / 2. # renormalize according to clip x = kornia.enhance.normalize(x, self.mean, self.std) return x def forward(self, x): # x is assumed to be in range [-1,1] if isinstance(x, list): # [""] denotes condition dropout for ucg device = self.model.visual.conv1.weight.device return torch.zeros(1, self.max_crops, 768, device=device) batch_tokens = [] for im in x: patches = self.preprocess(im.unsqueeze(0)) tokens = self.model.encode_image(patches).float() for t in tokens: if random.random() < 0.1: t *= 0 batch_tokens.append(tokens.unsqueeze(0)) return torch.cat(batch_tokens, dim=0) def encode(self, im): return self(im) class SpatialRescaler(nn.Module): def __init__(self, n_stages=1, method='bilinear', multiplier=0.5, in_channels=3, out_channels=None, bias=False): super().__init__() self.n_stages = n_stages assert self.n_stages >= 0 assert method in ['nearest','linear','bilinear','trilinear','bicubic','area'] self.multiplier = multiplier self.interpolator = partial(torch.nn.functional.interpolate, mode=method) self.remap_output = out_channels is not None if self.remap_output: print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.') self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias) def forward(self,x): for stage in range(self.n_stages): x = self.interpolator(x, scale_factor=self.multiplier) if self.remap_output: x = self.channel_mapper(x) return x def encode(self, x): return self(x) from ldm.util import instantiate_from_config from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like class LowScaleEncoder(nn.Module): def __init__(self, model_config, linear_start, linear_end, timesteps=1000, max_noise_level=250, output_size=64, scale_factor=1.0): super().__init__() self.max_noise_level = max_noise_level self.model = instantiate_from_config(model_config) self.augmentation_schedule = self.register_schedule(timesteps=timesteps, linear_start=linear_start, linear_end=linear_end) self.out_size = output_size self.scale_factor = scale_factor def register_schedule(self, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) alphas = 1. - betas alphas_cumprod = np.cumprod(alphas, axis=0) alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) timesteps, = betas.shape self.num_timesteps = int(timesteps) self.linear_start = linear_start self.linear_end = linear_end assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' to_torch = partial(torch.tensor, dtype=torch.float32) self.register_buffer('betas', to_torch(betas)) self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) # calculations for diffusion q(x_t | x_{t-1}) and others self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) def q_sample(self, x_start, t, noise=None): noise = default(noise, lambda: torch.randn_like(x_start)) return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) def forward(self, x): z = self.model.encode(x).sample() z = z * self.scale_factor noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() z = self.q_sample(z, noise_level) if self.out_size is not None: z = torch.nn.functional.interpolate(z, size=self.out_size, mode="nearest") # TODO: experiment with mode # z = z.repeat_interleave(2, -2).repeat_interleave(2, -1) return z, noise_level def decode(self, z): z = z / self.scale_factor return self.model.decode(z) if __name__ == "__main__": from ldm.util import count_params sentences = ["a hedgehog drinking a whiskey", "der mond ist aufgegangen", "Ein Satz mit vielen Sonderzeichen: äöü ß ?! : 'xx-y/@s'"] model = FrozenT5Embedder(version="google/t5-v1_1-xl").cuda() count_params(model, True) z = model(sentences) print(z.shape) model = FrozenCLIPEmbedder().cuda() count_params(model, True) z = model(sentences) print(z.shape) print("done.") ================================================ FILE: stable-dreamfusion/ldm/modules/evaluate/adm_evaluator.py ================================================ import argparse import io import os import random import warnings import zipfile from abc import ABC, abstractmethod from contextlib import contextmanager from functools import partial from multiprocessing import cpu_count from multiprocessing.pool import ThreadPool from typing import Iterable, Optional, Tuple import yaml import numpy as np import requests import tensorflow.compat.v1 as tf from scipy import linalg from tqdm.auto import tqdm INCEPTION_V3_URL = "https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/classify_image_graph_def.pb" INCEPTION_V3_PATH = "classify_image_graph_def.pb" FID_POOL_NAME = "pool_3:0" FID_SPATIAL_NAME = "mixed_6/conv:0" REQUIREMENTS = f"This script has the following requirements: \n" \ 'tensorflow-gpu>=2.0' + "\n" + 'scipy' + "\n" + "requests" + "\n" + "tqdm" def main(): parser = argparse.ArgumentParser() parser.add_argument("--ref_batch", help="path to reference batch npz file") parser.add_argument("--sample_batch", help="path to sample batch npz file") args = parser.parse_args() config = tf.ConfigProto( allow_soft_placement=True # allows DecodeJpeg to run on CPU in Inception graph ) config.gpu_options.allow_growth = True evaluator = Evaluator(tf.Session(config=config)) print("warming up TensorFlow...") # This will cause TF to print a bunch of verbose stuff now rather # than after the next print(), to help prevent confusion. evaluator.warmup() print("computing reference batch activations...") ref_acts = evaluator.read_activations(args.ref_batch) print("computing/reading reference batch statistics...") ref_stats, ref_stats_spatial = evaluator.read_statistics(args.ref_batch, ref_acts) print("computing sample batch activations...") sample_acts = evaluator.read_activations(args.sample_batch) print("computing/reading sample batch statistics...") sample_stats, sample_stats_spatial = evaluator.read_statistics(args.sample_batch, sample_acts) print("Computing evaluations...") is_ = evaluator.compute_inception_score(sample_acts[0]) print("Inception Score:", is_) fid = sample_stats.frechet_distance(ref_stats) print("FID:", fid) sfid = sample_stats_spatial.frechet_distance(ref_stats_spatial) print("sFID:", sfid) prec, recall = evaluator.compute_prec_recall(ref_acts[0], sample_acts[0]) print("Precision:", prec) print("Recall:", recall) savepath = '/'.join(args.sample_batch.split('/')[:-1]) results_file = os.path.join(savepath,'evaluation_metrics.yaml') print(f'Saving evaluation results to "{results_file}"') results = { 'IS': is_, 'FID': fid, 'sFID': sfid, 'Precision:':prec, 'Recall': recall } with open(results_file, 'w') as f: yaml.dump(results, f, default_flow_style=False) class InvalidFIDException(Exception): pass class FIDStatistics: def __init__(self, mu: np.ndarray, sigma: np.ndarray): self.mu = mu self.sigma = sigma def frechet_distance(self, other, eps=1e-6): """ Compute the Frechet distance between two sets of statistics. """ # https://github.com/bioinf-jku/TTUR/blob/73ab375cdf952a12686d9aa7978567771084da42/fid.py#L132 mu1, sigma1 = self.mu, self.sigma mu2, sigma2 = other.mu, other.sigma mu1 = np.atleast_1d(mu1) mu2 = np.atleast_1d(mu2) sigma1 = np.atleast_2d(sigma1) sigma2 = np.atleast_2d(sigma2) assert ( mu1.shape == mu2.shape ), f"Training and test mean vectors have different lengths: {mu1.shape}, {mu2.shape}" assert ( sigma1.shape == sigma2.shape ), f"Training and test covariances have different dimensions: {sigma1.shape}, {sigma2.shape}" diff = mu1 - mu2 # product might be almost singular covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) if not np.isfinite(covmean).all(): msg = ( "fid calculation produces singular product; adding %s to diagonal of cov estimates" % eps ) warnings.warn(msg) offset = np.eye(sigma1.shape[0]) * eps covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset)) # numerical error might give slight imaginary component if np.iscomplexobj(covmean): if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): m = np.max(np.abs(covmean.imag)) raise ValueError("Imaginary component {}".format(m)) covmean = covmean.real tr_covmean = np.trace(covmean) return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean class Evaluator: def __init__( self, session, batch_size=64, softmax_batch_size=512, ): self.sess = session self.batch_size = batch_size self.softmax_batch_size = softmax_batch_size self.manifold_estimator = ManifoldEstimator(session) with self.sess.graph.as_default(): self.image_input = tf.placeholder(tf.float32, shape=[None, None, None, 3]) self.softmax_input = tf.placeholder(tf.float32, shape=[None, 2048]) self.pool_features, self.spatial_features = _create_feature_graph(self.image_input) self.softmax = _create_softmax_graph(self.softmax_input) def warmup(self): self.compute_activations(np.zeros([1, 8, 64, 64, 3])) def read_activations(self, npz_path: str) -> Tuple[np.ndarray, np.ndarray]: with open_npz_array(npz_path, "arr_0") as reader: return self.compute_activations(reader.read_batches(self.batch_size)) def compute_activations(self, batches: Iterable[np.ndarray],silent=False) -> Tuple[np.ndarray, np.ndarray]: """ Compute image features for downstream evals. :param batches: a iterator over NHWC numpy arrays in [0, 255]. :return: a tuple of numpy arrays of shape [N x X], where X is a feature dimension. The tuple is (pool_3, spatial). """ preds = [] spatial_preds = [] it = batches if silent else tqdm(batches) for batch in it: batch = batch.astype(np.float32) pred, spatial_pred = self.sess.run( [self.pool_features, self.spatial_features], {self.image_input: batch} ) preds.append(pred.reshape([pred.shape[0], -1])) spatial_preds.append(spatial_pred.reshape([spatial_pred.shape[0], -1])) return ( np.concatenate(preds, axis=0), np.concatenate(spatial_preds, axis=0), ) def read_statistics( self, npz_path: str, activations: Tuple[np.ndarray, np.ndarray] ) -> Tuple[FIDStatistics, FIDStatistics]: obj = np.load(npz_path) if "mu" in list(obj.keys()): return FIDStatistics(obj["mu"], obj["sigma"]), FIDStatistics( obj["mu_s"], obj["sigma_s"] ) return tuple(self.compute_statistics(x) for x in activations) def compute_statistics(self, activations: np.ndarray) -> FIDStatistics: mu = np.mean(activations, axis=0) sigma = np.cov(activations, rowvar=False) return FIDStatistics(mu, sigma) def compute_inception_score(self, activations: np.ndarray, split_size: int = 5000) -> float: softmax_out = [] for i in range(0, len(activations), self.softmax_batch_size): acts = activations[i : i + self.softmax_batch_size] softmax_out.append(self.sess.run(self.softmax, feed_dict={self.softmax_input: acts})) preds = np.concatenate(softmax_out, axis=0) # https://github.com/openai/improved-gan/blob/4f5d1ec5c16a7eceb206f42bfc652693601e1d5c/inception_score/model.py#L46 scores = [] for i in range(0, len(preds), split_size): part = preds[i : i + split_size] kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0))) kl = np.mean(np.sum(kl, 1)) scores.append(np.exp(kl)) return float(np.mean(scores)) def compute_prec_recall( self, activations_ref: np.ndarray, activations_sample: np.ndarray ) -> Tuple[float, float]: radii_1 = self.manifold_estimator.manifold_radii(activations_ref) radii_2 = self.manifold_estimator.manifold_radii(activations_sample) pr = self.manifold_estimator.evaluate_pr( activations_ref, radii_1, activations_sample, radii_2 ) return (float(pr[0][0]), float(pr[1][0])) class ManifoldEstimator: """ A helper for comparing manifolds of feature vectors. Adapted from https://github.com/kynkaat/improved-precision-and-recall-metric/blob/f60f25e5ad933a79135c783fcda53de30f42c9b9/precision_recall.py#L57 """ def __init__( self, session, row_batch_size=10000, col_batch_size=10000, nhood_sizes=(3,), clamp_to_percentile=None, eps=1e-5, ): """ Estimate the manifold of given feature vectors. :param session: the TensorFlow session. :param row_batch_size: row batch size to compute pairwise distances (parameter to trade-off between memory usage and performance). :param col_batch_size: column batch size to compute pairwise distances. :param nhood_sizes: number of neighbors used to estimate the manifold. :param clamp_to_percentile: prune hyperspheres that have radius larger than the given percentile. :param eps: small number for numerical stability. """ self.distance_block = DistanceBlock(session) self.row_batch_size = row_batch_size self.col_batch_size = col_batch_size self.nhood_sizes = nhood_sizes self.num_nhoods = len(nhood_sizes) self.clamp_to_percentile = clamp_to_percentile self.eps = eps def warmup(self): feats, radii = ( np.zeros([1, 2048], dtype=np.float32), np.zeros([1, 1], dtype=np.float32), ) self.evaluate_pr(feats, radii, feats, radii) def manifold_radii(self, features: np.ndarray) -> np.ndarray: num_images = len(features) # Estimate manifold of features by calculating distances to k-NN of each sample. radii = np.zeros([num_images, self.num_nhoods], dtype=np.float32) distance_batch = np.zeros([self.row_batch_size, num_images], dtype=np.float32) seq = np.arange(max(self.nhood_sizes) + 1, dtype=np.int32) for begin1 in range(0, num_images, self.row_batch_size): end1 = min(begin1 + self.row_batch_size, num_images) row_batch = features[begin1:end1] for begin2 in range(0, num_images, self.col_batch_size): end2 = min(begin2 + self.col_batch_size, num_images) col_batch = features[begin2:end2] # Compute distances between batches. distance_batch[ 0 : end1 - begin1, begin2:end2 ] = self.distance_block.pairwise_distances(row_batch, col_batch) # Find the k-nearest neighbor from the current batch. radii[begin1:end1, :] = np.concatenate( [ x[:, self.nhood_sizes] for x in _numpy_partition(distance_batch[0 : end1 - begin1, :], seq, axis=1) ], axis=0, ) if self.clamp_to_percentile is not None: max_distances = np.percentile(radii, self.clamp_to_percentile, axis=0) radii[radii > max_distances] = 0 return radii def evaluate(self, features: np.ndarray, radii: np.ndarray, eval_features: np.ndarray): """ Evaluate if new feature vectors are at the manifold. """ num_eval_images = eval_features.shape[0] num_ref_images = radii.shape[0] distance_batch = np.zeros([self.row_batch_size, num_ref_images], dtype=np.float32) batch_predictions = np.zeros([num_eval_images, self.num_nhoods], dtype=np.int32) max_realism_score = np.zeros([num_eval_images], dtype=np.float32) nearest_indices = np.zeros([num_eval_images], dtype=np.int32) for begin1 in range(0, num_eval_images, self.row_batch_size): end1 = min(begin1 + self.row_batch_size, num_eval_images) feature_batch = eval_features[begin1:end1] for begin2 in range(0, num_ref_images, self.col_batch_size): end2 = min(begin2 + self.col_batch_size, num_ref_images) ref_batch = features[begin2:end2] distance_batch[ 0 : end1 - begin1, begin2:end2 ] = self.distance_block.pairwise_distances(feature_batch, ref_batch) # From the minibatch of new feature vectors, determine if they are in the estimated manifold. # If a feature vector is inside a hypersphere of some reference sample, then # the new sample lies at the estimated manifold. # The radii of the hyperspheres are determined from distances of neighborhood size k. samples_in_manifold = distance_batch[0 : end1 - begin1, :, None] <= radii batch_predictions[begin1:end1] = np.any(samples_in_manifold, axis=1).astype(np.int32) max_realism_score[begin1:end1] = np.max( radii[:, 0] / (distance_batch[0 : end1 - begin1, :] + self.eps), axis=1 ) nearest_indices[begin1:end1] = np.argmin(distance_batch[0 : end1 - begin1, :], axis=1) return { "fraction": float(np.mean(batch_predictions)), "batch_predictions": batch_predictions, "max_realisim_score": max_realism_score, "nearest_indices": nearest_indices, } def evaluate_pr( self, features_1: np.ndarray, radii_1: np.ndarray, features_2: np.ndarray, radii_2: np.ndarray, ) -> Tuple[np.ndarray, np.ndarray]: """ Evaluate precision and recall efficiently. :param features_1: [N1 x D] feature vectors for reference batch. :param radii_1: [N1 x K1] radii for reference vectors. :param features_2: [N2 x D] feature vectors for the other batch. :param radii_2: [N x K2] radii for other vectors. :return: a tuple of arrays for (precision, recall): - precision: an np.ndarray of length K1 - recall: an np.ndarray of length K2 """ features_1_status = np.zeros([len(features_1), radii_2.shape[1]], dtype=np.bool) features_2_status = np.zeros([len(features_2), radii_1.shape[1]], dtype=np.bool) for begin_1 in range(0, len(features_1), self.row_batch_size): end_1 = begin_1 + self.row_batch_size batch_1 = features_1[begin_1:end_1] for begin_2 in range(0, len(features_2), self.col_batch_size): end_2 = begin_2 + self.col_batch_size batch_2 = features_2[begin_2:end_2] batch_1_in, batch_2_in = self.distance_block.less_thans( batch_1, radii_1[begin_1:end_1], batch_2, radii_2[begin_2:end_2] ) features_1_status[begin_1:end_1] |= batch_1_in features_2_status[begin_2:end_2] |= batch_2_in return ( np.mean(features_2_status.astype(np.float64), axis=0), np.mean(features_1_status.astype(np.float64), axis=0), ) class DistanceBlock: """ Calculate pairwise distances between vectors. Adapted from https://github.com/kynkaat/improved-precision-and-recall-metric/blob/f60f25e5ad933a79135c783fcda53de30f42c9b9/precision_recall.py#L34 """ def __init__(self, session): self.session = session # Initialize TF graph to calculate pairwise distances. with session.graph.as_default(): self._features_batch1 = tf.placeholder(tf.float32, shape=[None, None]) self._features_batch2 = tf.placeholder(tf.float32, shape=[None, None]) distance_block_16 = _batch_pairwise_distances( tf.cast(self._features_batch1, tf.float16), tf.cast(self._features_batch2, tf.float16), ) self.distance_block = tf.cond( tf.reduce_all(tf.math.is_finite(distance_block_16)), lambda: tf.cast(distance_block_16, tf.float32), lambda: _batch_pairwise_distances(self._features_batch1, self._features_batch2), ) # Extra logic for less thans. self._radii1 = tf.placeholder(tf.float32, shape=[None, None]) self._radii2 = tf.placeholder(tf.float32, shape=[None, None]) dist32 = tf.cast(self.distance_block, tf.float32)[..., None] self._batch_1_in = tf.math.reduce_any(dist32 <= self._radii2, axis=1) self._batch_2_in = tf.math.reduce_any(dist32 <= self._radii1[:, None], axis=0) def pairwise_distances(self, U, V): """ Evaluate pairwise distances between two batches of feature vectors. """ return self.session.run( self.distance_block, feed_dict={self._features_batch1: U, self._features_batch2: V}, ) def less_thans(self, batch_1, radii_1, batch_2, radii_2): return self.session.run( [self._batch_1_in, self._batch_2_in], feed_dict={ self._features_batch1: batch_1, self._features_batch2: batch_2, self._radii1: radii_1, self._radii2: radii_2, }, ) def _batch_pairwise_distances(U, V): """ Compute pairwise distances between two batches of feature vectors. """ with tf.variable_scope("pairwise_dist_block"): # Squared norms of each row in U and V. norm_u = tf.reduce_sum(tf.square(U), 1) norm_v = tf.reduce_sum(tf.square(V), 1) # norm_u as a column and norm_v as a row vectors. norm_u = tf.reshape(norm_u, [-1, 1]) norm_v = tf.reshape(norm_v, [1, -1]) # Pairwise squared Euclidean distances. D = tf.maximum(norm_u - 2 * tf.matmul(U, V, False, True) + norm_v, 0.0) return D class NpzArrayReader(ABC): @abstractmethod def read_batch(self, batch_size: int) -> Optional[np.ndarray]: pass @abstractmethod def remaining(self) -> int: pass def read_batches(self, batch_size: int) -> Iterable[np.ndarray]: def gen_fn(): while True: batch = self.read_batch(batch_size) if batch is None: break yield batch rem = self.remaining() num_batches = rem // batch_size + int(rem % batch_size != 0) return BatchIterator(gen_fn, num_batches) class BatchIterator: def __init__(self, gen_fn, length): self.gen_fn = gen_fn self.length = length def __len__(self): return self.length def __iter__(self): return self.gen_fn() class StreamingNpzArrayReader(NpzArrayReader): def __init__(self, arr_f, shape, dtype): self.arr_f = arr_f self.shape = shape self.dtype = dtype self.idx = 0 def read_batch(self, batch_size: int) -> Optional[np.ndarray]: if self.idx >= self.shape[0]: return None bs = min(batch_size, self.shape[0] - self.idx) self.idx += bs if self.dtype.itemsize == 0: return np.ndarray([bs, *self.shape[1:]], dtype=self.dtype) read_count = bs * np.prod(self.shape[1:]) read_size = int(read_count * self.dtype.itemsize) data = _read_bytes(self.arr_f, read_size, "array data") return np.frombuffer(data, dtype=self.dtype).reshape([bs, *self.shape[1:]]) def remaining(self) -> int: return max(0, self.shape[0] - self.idx) class MemoryNpzArrayReader(NpzArrayReader): def __init__(self, arr): self.arr = arr self.idx = 0 @classmethod def load(cls, path: str, arr_name: str): with open(path, "rb") as f: arr = np.load(f)[arr_name] return cls(arr) def read_batch(self, batch_size: int) -> Optional[np.ndarray]: if self.idx >= self.arr.shape[0]: return None res = self.arr[self.idx : self.idx + batch_size] self.idx += batch_size return res def remaining(self) -> int: return max(0, self.arr.shape[0] - self.idx) @contextmanager def open_npz_array(path: str, arr_name: str) -> NpzArrayReader: with _open_npy_file(path, arr_name) as arr_f: version = np.lib.format.read_magic(arr_f) if version == (1, 0): header = np.lib.format.read_array_header_1_0(arr_f) elif version == (2, 0): header = np.lib.format.read_array_header_2_0(arr_f) else: yield MemoryNpzArrayReader.load(path, arr_name) return shape, fortran, dtype = header if fortran or dtype.hasobject: yield MemoryNpzArrayReader.load(path, arr_name) else: yield StreamingNpzArrayReader(arr_f, shape, dtype) def _read_bytes(fp, size, error_template="ran out of data"): """ Copied from: https://github.com/numpy/numpy/blob/fb215c76967739268de71aa4bda55dd1b062bc2e/numpy/lib/format.py#L788-L886 Read from file-like object until size bytes are read. Raises ValueError if not EOF is encountered before size bytes are read. Non-blocking objects only supported if they derive from io objects. Required as e.g. ZipExtFile in python 2.6 can return less data than requested. """ data = bytes() while True: # io files (default in python3) return None or raise on # would-block, python2 file will truncate, probably nothing can be # done about that. note that regular files can't be non-blocking try: r = fp.read(size - len(data)) data += r if len(r) == 0 or len(data) == size: break except io.BlockingIOError: pass if len(data) != size: msg = "EOF: reading %s, expected %d bytes got %d" raise ValueError(msg % (error_template, size, len(data))) else: return data @contextmanager def _open_npy_file(path: str, arr_name: str): with open(path, "rb") as f: with zipfile.ZipFile(f, "r") as zip_f: if f"{arr_name}.npy" not in zip_f.namelist(): raise ValueError(f"missing {arr_name} in npz file") with zip_f.open(f"{arr_name}.npy", "r") as arr_f: yield arr_f def _download_inception_model(): if os.path.exists(INCEPTION_V3_PATH): return print("downloading InceptionV3 model...") with requests.get(INCEPTION_V3_URL, stream=True) as r: r.raise_for_status() tmp_path = INCEPTION_V3_PATH + ".tmp" with open(tmp_path, "wb") as f: for chunk in tqdm(r.iter_content(chunk_size=8192)): f.write(chunk) os.rename(tmp_path, INCEPTION_V3_PATH) def _create_feature_graph(input_batch): _download_inception_model() prefix = f"{random.randrange(2**32)}_{random.randrange(2**32)}" with open(INCEPTION_V3_PATH, "rb") as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) pool3, spatial = tf.import_graph_def( graph_def, input_map={f"ExpandDims:0": input_batch}, return_elements=[FID_POOL_NAME, FID_SPATIAL_NAME], name=prefix, ) _update_shapes(pool3) spatial = spatial[..., :7] return pool3, spatial def _create_softmax_graph(input_batch): _download_inception_model() prefix = f"{random.randrange(2**32)}_{random.randrange(2**32)}" with open(INCEPTION_V3_PATH, "rb") as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) (matmul,) = tf.import_graph_def( graph_def, return_elements=[f"softmax/logits/MatMul"], name=prefix ) w = matmul.inputs[1] logits = tf.matmul(input_batch, w) return tf.nn.softmax(logits) def _update_shapes(pool3): # https://github.com/bioinf-jku/TTUR/blob/73ab375cdf952a12686d9aa7978567771084da42/fid.py#L50-L63 ops = pool3.graph.get_operations() for op in ops: for o in op.outputs: shape = o.get_shape() if shape._dims is not None: # pylint: disable=protected-access # shape = [s.value for s in shape] TF 1.x shape = [s for s in shape] # TF 2.x new_shape = [] for j, s in enumerate(shape): if s == 1 and j == 0: new_shape.append(None) else: new_shape.append(s) o.__dict__["_shape_val"] = tf.TensorShape(new_shape) return pool3 def _numpy_partition(arr, kth, **kwargs): num_workers = min(cpu_count(), len(arr)) chunk_size = len(arr) // num_workers extra = len(arr) % num_workers start_idx = 0 batches = [] for i in range(num_workers): size = chunk_size + (1 if i < extra else 0) batches.append(arr[start_idx : start_idx + size]) start_idx += size with ThreadPool(num_workers) as pool: return list(pool.map(partial(np.partition, kth=kth, **kwargs), batches)) if __name__ == "__main__": print(REQUIREMENTS) main() ================================================ FILE: stable-dreamfusion/ldm/modules/evaluate/evaluate_perceptualsim.py ================================================ import argparse import glob import os from tqdm import tqdm from collections import namedtuple import numpy as np import torch import torchvision.transforms as transforms from torchvision import models from PIL import Image from ldm.modules.evaluate.ssim import ssim transform = transforms.Compose([transforms.ToTensor()]) def normalize_tensor(in_feat, eps=1e-10): norm_factor = torch.sqrt(torch.sum(in_feat ** 2, dim=1)).view( in_feat.size()[0], 1, in_feat.size()[2], in_feat.size()[3] ) return in_feat / (norm_factor.expand_as(in_feat) + eps) def cos_sim(in0, in1): in0_norm = normalize_tensor(in0) in1_norm = normalize_tensor(in1) N = in0.size()[0] X = in0.size()[2] Y = in0.size()[3] return torch.mean( torch.mean( torch.sum(in0_norm * in1_norm, dim=1).view(N, 1, X, Y), dim=2 ).view(N, 1, 1, Y), dim=3, ).view(N) class squeezenet(torch.nn.Module): def __init__(self, requires_grad=False, pretrained=True): super(squeezenet, self).__init__() pretrained_features = models.squeezenet1_1( pretrained=pretrained ).features self.slice1 = torch.nn.Sequential() self.slice2 = torch.nn.Sequential() self.slice3 = torch.nn.Sequential() self.slice4 = torch.nn.Sequential() self.slice5 = torch.nn.Sequential() self.slice6 = torch.nn.Sequential() self.slice7 = torch.nn.Sequential() self.N_slices = 7 for x in range(2): self.slice1.add_module(str(x), pretrained_features[x]) for x in range(2, 5): self.slice2.add_module(str(x), pretrained_features[x]) for x in range(5, 8): self.slice3.add_module(str(x), pretrained_features[x]) for x in range(8, 10): self.slice4.add_module(str(x), pretrained_features[x]) for x in range(10, 11): self.slice5.add_module(str(x), pretrained_features[x]) for x in range(11, 12): self.slice6.add_module(str(x), pretrained_features[x]) for x in range(12, 13): self.slice7.add_module(str(x), pretrained_features[x]) if not requires_grad: for param in self.parameters(): param.requires_grad = False def forward(self, X): h = self.slice1(X) h_relu1 = h h = self.slice2(h) h_relu2 = h h = self.slice3(h) h_relu3 = h h = self.slice4(h) h_relu4 = h h = self.slice5(h) h_relu5 = h h = self.slice6(h) h_relu6 = h h = self.slice7(h) h_relu7 = h vgg_outputs = namedtuple( "SqueezeOutputs", ["relu1", "relu2", "relu3", "relu4", "relu5", "relu6", "relu7"], ) out = vgg_outputs( h_relu1, h_relu2, h_relu3, h_relu4, h_relu5, h_relu6, h_relu7 ) return out class alexnet(torch.nn.Module): def __init__(self, requires_grad=False, pretrained=True): super(alexnet, self).__init__() alexnet_pretrained_features = models.alexnet( pretrained=pretrained ).features self.slice1 = torch.nn.Sequential() self.slice2 = torch.nn.Sequential() self.slice3 = torch.nn.Sequential() self.slice4 = torch.nn.Sequential() self.slice5 = torch.nn.Sequential() self.N_slices = 5 for x in range(2): self.slice1.add_module(str(x), alexnet_pretrained_features[x]) for x in range(2, 5): self.slice2.add_module(str(x), alexnet_pretrained_features[x]) for x in range(5, 8): self.slice3.add_module(str(x), alexnet_pretrained_features[x]) for x in range(8, 10): self.slice4.add_module(str(x), alexnet_pretrained_features[x]) for x in range(10, 12): self.slice5.add_module(str(x), alexnet_pretrained_features[x]) if not requires_grad: for param in self.parameters(): param.requires_grad = False def forward(self, X): h = self.slice1(X) h_relu1 = h h = self.slice2(h) h_relu2 = h h = self.slice3(h) h_relu3 = h h = self.slice4(h) h_relu4 = h h = self.slice5(h) h_relu5 = h alexnet_outputs = namedtuple( "AlexnetOutputs", ["relu1", "relu2", "relu3", "relu4", "relu5"] ) out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5) return out class vgg16(torch.nn.Module): def __init__(self, requires_grad=False, pretrained=True): super(vgg16, self).__init__() vgg_pretrained_features = models.vgg16(pretrained=pretrained).features self.slice1 = torch.nn.Sequential() self.slice2 = torch.nn.Sequential() self.slice3 = torch.nn.Sequential() self.slice4 = torch.nn.Sequential() self.slice5 = torch.nn.Sequential() self.N_slices = 5 for x in range(4): self.slice1.add_module(str(x), vgg_pretrained_features[x]) for x in range(4, 9): self.slice2.add_module(str(x), vgg_pretrained_features[x]) for x in range(9, 16): self.slice3.add_module(str(x), vgg_pretrained_features[x]) for x in range(16, 23): self.slice4.add_module(str(x), vgg_pretrained_features[x]) for x in range(23, 30): self.slice5.add_module(str(x), vgg_pretrained_features[x]) if not requires_grad: for param in self.parameters(): param.requires_grad = False def forward(self, X): h = self.slice1(X) h_relu1_2 = h h = self.slice2(h) h_relu2_2 = h h = self.slice3(h) h_relu3_3 = h h = self.slice4(h) h_relu4_3 = h h = self.slice5(h) h_relu5_3 = h vgg_outputs = namedtuple( "VggOutputs", ["relu1_2", "relu2_2", "relu3_3", "relu4_3", "relu5_3"], ) out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3) return out class resnet(torch.nn.Module): def __init__(self, requires_grad=False, pretrained=True, num=18): super(resnet, self).__init__() if num == 18: self.net = models.resnet18(pretrained=pretrained) elif num == 34: self.net = models.resnet34(pretrained=pretrained) elif num == 50: self.net = models.resnet50(pretrained=pretrained) elif num == 101: self.net = models.resnet101(pretrained=pretrained) elif num == 152: self.net = models.resnet152(pretrained=pretrained) self.N_slices = 5 self.conv1 = self.net.conv1 self.bn1 = self.net.bn1 self.relu = self.net.relu self.maxpool = self.net.maxpool self.layer1 = self.net.layer1 self.layer2 = self.net.layer2 self.layer3 = self.net.layer3 self.layer4 = self.net.layer4 def forward(self, X): h = self.conv1(X) h = self.bn1(h) h = self.relu(h) h_relu1 = h h = self.maxpool(h) h = self.layer1(h) h_conv2 = h h = self.layer2(h) h_conv3 = h h = self.layer3(h) h_conv4 = h h = self.layer4(h) h_conv5 = h outputs = namedtuple( "Outputs", ["relu1", "conv2", "conv3", "conv4", "conv5"] ) out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5) return out # Off-the-shelf deep network class PNet(torch.nn.Module): """Pre-trained network with all channels equally weighted by default""" def __init__(self, pnet_type="vgg", pnet_rand=False, use_gpu=True): super(PNet, self).__init__() self.use_gpu = use_gpu self.pnet_type = pnet_type self.pnet_rand = pnet_rand self.shift = torch.Tensor([-0.030, -0.088, -0.188]).view(1, 3, 1, 1) self.scale = torch.Tensor([0.458, 0.448, 0.450]).view(1, 3, 1, 1) if self.pnet_type in ["vgg", "vgg16"]: self.net = vgg16(pretrained=not self.pnet_rand, requires_grad=False) elif self.pnet_type == "alex": self.net = alexnet( pretrained=not self.pnet_rand, requires_grad=False ) elif self.pnet_type[:-2] == "resnet": self.net = resnet( pretrained=not self.pnet_rand, requires_grad=False, num=int(self.pnet_type[-2:]), ) elif self.pnet_type == "squeeze": self.net = squeezenet( pretrained=not self.pnet_rand, requires_grad=False ) self.L = self.net.N_slices if use_gpu: self.net.cuda() self.shift = self.shift.cuda() self.scale = self.scale.cuda() def forward(self, in0, in1, retPerLayer=False): in0_sc = (in0 - self.shift.expand_as(in0)) / self.scale.expand_as(in0) in1_sc = (in1 - self.shift.expand_as(in0)) / self.scale.expand_as(in0) outs0 = self.net.forward(in0_sc) outs1 = self.net.forward(in1_sc) if retPerLayer: all_scores = [] for (kk, out0) in enumerate(outs0): cur_score = 1.0 - cos_sim(outs0[kk], outs1[kk]) if kk == 0: val = 1.0 * cur_score else: val = val + cur_score if retPerLayer: all_scores += [cur_score] if retPerLayer: return (val, all_scores) else: return val # The SSIM metric def ssim_metric(img1, img2, mask=None): return ssim(img1, img2, mask=mask, size_average=False) # The PSNR metric def psnr(img1, img2, mask=None,reshape=False): b = img1.size(0) if not (mask is None): b = img1.size(0) mse_err = (img1 - img2).pow(2) * mask if reshape: mse_err = mse_err.reshape(b, -1).sum(dim=1) / ( 3 * mask.reshape(b, -1).sum(dim=1).clamp(min=1) ) else: mse_err = mse_err.view(b, -1).sum(dim=1) / ( 3 * mask.view(b, -1).sum(dim=1).clamp(min=1) ) else: if reshape: mse_err = (img1 - img2).pow(2).reshape(b, -1).mean(dim=1) else: mse_err = (img1 - img2).pow(2).view(b, -1).mean(dim=1) psnr = 10 * (1 / mse_err).log10() return psnr # The perceptual similarity metric def perceptual_sim(img1, img2, vgg16): # First extract features dist = vgg16(img1 * 2 - 1, img2 * 2 - 1) return dist def load_img(img_name, size=None): try: img = Image.open(img_name) if type(size) == int: img = img.resize((size, size)) elif size is not None: img = img.resize((size[1], size[0])) img = transform(img).cuda() img = img.unsqueeze(0) except Exception as e: print("Failed at loading %s " % img_name) print(e) img = torch.zeros(1, 3, 256, 256).cuda() raise return img def compute_perceptual_similarity(folder, pred_img, tgt_img, take_every_other): # Load VGG16 for feature similarity vgg16 = PNet().to("cuda") vgg16.eval() vgg16.cuda() values_percsim = [] values_ssim = [] values_psnr = [] folders = os.listdir(folder) for i, f in tqdm(enumerate(sorted(folders))): pred_imgs = glob.glob(folder + f + "/" + pred_img) tgt_imgs = glob.glob(folder + f + "/" + tgt_img) assert len(tgt_imgs) == 1 perc_sim = 10000 ssim_sim = -10 psnr_sim = -10 for p_img in pred_imgs: t_img = load_img(tgt_imgs[0]) p_img = load_img(p_img, size=t_img.shape[2:]) t_perc_sim = perceptual_sim(p_img, t_img, vgg16).item() perc_sim = min(perc_sim, t_perc_sim) ssim_sim = max(ssim_sim, ssim_metric(p_img, t_img).item()) psnr_sim = max(psnr_sim, psnr(p_img, t_img).item()) values_percsim += [perc_sim] values_ssim += [ssim_sim] values_psnr += [psnr_sim] if take_every_other: n_valuespercsim = [] n_valuesssim = [] n_valuespsnr = [] for i in range(0, len(values_percsim) // 2): n_valuespercsim += [ min(values_percsim[2 * i], values_percsim[2 * i + 1]) ] n_valuespsnr += [max(values_psnr[2 * i], values_psnr[2 * i + 1])] n_valuesssim += [max(values_ssim[2 * i], values_ssim[2 * i + 1])] values_percsim = n_valuespercsim values_ssim = n_valuesssim values_psnr = n_valuespsnr avg_percsim = np.mean(np.array(values_percsim)) std_percsim = np.std(np.array(values_percsim)) avg_psnr = np.mean(np.array(values_psnr)) std_psnr = np.std(np.array(values_psnr)) avg_ssim = np.mean(np.array(values_ssim)) std_ssim = np.std(np.array(values_ssim)) return { "Perceptual similarity": (avg_percsim, std_percsim), "PSNR": (avg_psnr, std_psnr), "SSIM": (avg_ssim, std_ssim), } def compute_perceptual_similarity_from_list(pred_imgs_list, tgt_imgs_list, take_every_other, simple_format=True): # Load VGG16 for feature similarity vgg16 = PNet().to("cuda") vgg16.eval() vgg16.cuda() values_percsim = [] values_ssim = [] values_psnr = [] equal_count = 0 ambig_count = 0 for i, tgt_img in enumerate(tqdm(tgt_imgs_list)): pred_imgs = pred_imgs_list[i] tgt_imgs = [tgt_img] assert len(tgt_imgs) == 1 if type(pred_imgs) != list: pred_imgs = [pred_imgs] perc_sim = 10000 ssim_sim = -10 psnr_sim = -10 assert len(pred_imgs)>0 for p_img in pred_imgs: t_img = load_img(tgt_imgs[0]) p_img = load_img(p_img, size=t_img.shape[2:]) t_perc_sim = perceptual_sim(p_img, t_img, vgg16).item() perc_sim = min(perc_sim, t_perc_sim) ssim_sim = max(ssim_sim, ssim_metric(p_img, t_img).item()) psnr_sim = max(psnr_sim, psnr(p_img, t_img).item()) values_percsim += [perc_sim] values_ssim += [ssim_sim] if psnr_sim != np.float("inf"): values_psnr += [psnr_sim] else: if torch.allclose(p_img, t_img): equal_count += 1 print("{} equal src and wrp images.".format(equal_count)) else: ambig_count += 1 print("{} ambiguous src and wrp images.".format(ambig_count)) if take_every_other: n_valuespercsim = [] n_valuesssim = [] n_valuespsnr = [] for i in range(0, len(values_percsim) // 2): n_valuespercsim += [ min(values_percsim[2 * i], values_percsim[2 * i + 1]) ] n_valuespsnr += [max(values_psnr[2 * i], values_psnr[2 * i + 1])] n_valuesssim += [max(values_ssim[2 * i], values_ssim[2 * i + 1])] values_percsim = n_valuespercsim values_ssim = n_valuesssim values_psnr = n_valuespsnr avg_percsim = np.mean(np.array(values_percsim)) std_percsim = np.std(np.array(values_percsim)) avg_psnr = np.mean(np.array(values_psnr)) std_psnr = np.std(np.array(values_psnr)) avg_ssim = np.mean(np.array(values_ssim)) std_ssim = np.std(np.array(values_ssim)) if simple_format: # just to make yaml formatting readable return { "Perceptual similarity": [float(avg_percsim), float(std_percsim)], "PSNR": [float(avg_psnr), float(std_psnr)], "SSIM": [float(avg_ssim), float(std_ssim)], } else: return { "Perceptual similarity": (avg_percsim, std_percsim), "PSNR": (avg_psnr, std_psnr), "SSIM": (avg_ssim, std_ssim), } def compute_perceptual_similarity_from_list_topk(pred_imgs_list, tgt_imgs_list, take_every_other, resize=False): # Load VGG16 for feature similarity vgg16 = PNet().to("cuda") vgg16.eval() vgg16.cuda() values_percsim = [] values_ssim = [] values_psnr = [] individual_percsim = [] individual_ssim = [] individual_psnr = [] for i, tgt_img in enumerate(tqdm(tgt_imgs_list)): pred_imgs = pred_imgs_list[i] tgt_imgs = [tgt_img] assert len(tgt_imgs) == 1 if type(pred_imgs) != list: assert False pred_imgs = [pred_imgs] perc_sim = 10000 ssim_sim = -10 psnr_sim = -10 sample_percsim = list() sample_ssim = list() sample_psnr = list() for p_img in pred_imgs: if resize: t_img = load_img(tgt_imgs[0], size=(256,256)) else: t_img = load_img(tgt_imgs[0]) p_img = load_img(p_img, size=t_img.shape[2:]) t_perc_sim = perceptual_sim(p_img, t_img, vgg16).item() sample_percsim.append(t_perc_sim) perc_sim = min(perc_sim, t_perc_sim) t_ssim = ssim_metric(p_img, t_img).item() sample_ssim.append(t_ssim) ssim_sim = max(ssim_sim, t_ssim) t_psnr = psnr(p_img, t_img).item() sample_psnr.append(t_psnr) psnr_sim = max(psnr_sim, t_psnr) values_percsim += [perc_sim] values_ssim += [ssim_sim] values_psnr += [psnr_sim] individual_percsim.append(sample_percsim) individual_ssim.append(sample_ssim) individual_psnr.append(sample_psnr) if take_every_other: assert False, "Do this later, after specifying topk to get proper results" n_valuespercsim = [] n_valuesssim = [] n_valuespsnr = [] for i in range(0, len(values_percsim) // 2): n_valuespercsim += [ min(values_percsim[2 * i], values_percsim[2 * i + 1]) ] n_valuespsnr += [max(values_psnr[2 * i], values_psnr[2 * i + 1])] n_valuesssim += [max(values_ssim[2 * i], values_ssim[2 * i + 1])] values_percsim = n_valuespercsim values_ssim = n_valuesssim values_psnr = n_valuespsnr avg_percsim = np.mean(np.array(values_percsim)) std_percsim = np.std(np.array(values_percsim)) avg_psnr = np.mean(np.array(values_psnr)) std_psnr = np.std(np.array(values_psnr)) avg_ssim = np.mean(np.array(values_ssim)) std_ssim = np.std(np.array(values_ssim)) individual_percsim = np.array(individual_percsim) individual_psnr = np.array(individual_psnr) individual_ssim = np.array(individual_ssim) return { "avg_of_best": { "Perceptual similarity": [float(avg_percsim), float(std_percsim)], "PSNR": [float(avg_psnr), float(std_psnr)], "SSIM": [float(avg_ssim), float(std_ssim)], }, "individual": { "PSIM": individual_percsim, "PSNR": individual_psnr, "SSIM": individual_ssim, } } if __name__ == "__main__": args = argparse.ArgumentParser() args.add_argument("--folder", type=str, default="") args.add_argument("--pred_image", type=str, default="") args.add_argument("--target_image", type=str, default="") args.add_argument("--take_every_other", action="store_true", default=False) args.add_argument("--output_file", type=str, default="") opts = args.parse_args() folder = opts.folder pred_img = opts.pred_image tgt_img = opts.target_image results = compute_perceptual_similarity( folder, pred_img, tgt_img, opts.take_every_other ) f = open(opts.output_file, 'w') for key in results: print("%s for %s: \n" % (key, opts.folder)) print( "\t {:0.4f} | {:0.4f} \n".format(results[key][0], results[key][1]) ) f.write("%s for %s: \n" % (key, opts.folder)) f.write( "\t {:0.4f} | {:0.4f} \n".format(results[key][0], results[key][1]) ) f.close() ================================================ FILE: stable-dreamfusion/ldm/modules/evaluate/frechet_video_distance.py ================================================ # coding=utf-8 # Copyright 2022 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python2, python3 """Minimal Reference implementation for the Frechet Video Distance (FVD). FVD is a metric for the quality of video generation models. It is inspired by the FID (Frechet Inception Distance) used for images, but uses a different embedding to be better suitable for videos. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import six import tensorflow.compat.v1 as tf import tensorflow_gan as tfgan import tensorflow_hub as hub def preprocess(videos, target_resolution): """Runs some preprocessing on the videos for I3D model. Args: videos: [batch_size, num_frames, height, width, depth] The videos to be preprocessed. We don't care about the specific dtype of the videos, it can be anything that tf.image.resize_bilinear accepts. Values are expected to be in the range 0-255. target_resolution: (width, height): target video resolution Returns: videos: [batch_size, num_frames, height, width, depth] """ videos_shape = list(videos.shape) all_frames = tf.reshape(videos, [-1] + videos_shape[-3:]) resized_videos = tf.image.resize_bilinear(all_frames, size=target_resolution) target_shape = [videos_shape[0], -1] + list(target_resolution) + [3] output_videos = tf.reshape(resized_videos, target_shape) scaled_videos = 2. * tf.cast(output_videos, tf.float32) / 255. - 1 return scaled_videos def _is_in_graph(tensor_name): """Checks whether a given tensor does exists in the graph.""" try: tf.get_default_graph().get_tensor_by_name(tensor_name) except KeyError: return False return True def create_id3_embedding(videos,warmup=False,batch_size=16): """Embeds the given videos using the Inflated 3D Convolution ne twork. Downloads the graph of the I3D from tf.hub and adds it to the graph on the first call. Args: videos: [batch_size, num_frames, height=224, width=224, depth=3]. Expected range is [-1, 1]. Returns: embedding: [batch_size, embedding_size]. embedding_size depends on the model used. Raises: ValueError: when a provided embedding_layer is not supported. """ # batch_size = 16 module_spec = "https://tfhub.dev/deepmind/i3d-kinetics-400/1" # Making sure that we import the graph separately for # each different input video tensor. module_name = "fvd_kinetics-400_id3_module_" + six.ensure_str( videos.name).replace(":", "_") assert_ops = [ tf.Assert( tf.reduce_max(videos) <= 1.001, ["max value in frame is > 1", videos]), tf.Assert( tf.reduce_min(videos) >= -1.001, ["min value in frame is < -1", videos]), tf.assert_equal( tf.shape(videos)[0], batch_size, ["invalid frame batch size: ", tf.shape(videos)], summarize=6), ] with tf.control_dependencies(assert_ops): videos = tf.identity(videos) module_scope = "%s_apply_default/" % module_name # To check whether the module has already been loaded into the graph, we look # for a given tensor name. If this tensor name exists, we assume the function # has been called before and the graph was imported. Otherwise we import it. # Note: in theory, the tensor could exist, but have wrong shapes. # This will happen if create_id3_embedding is called with a frames_placehoder # of wrong size/batch size, because even though that will throw a tf.Assert # on graph-execution time, it will insert the tensor (with wrong shape) into # the graph. This is why we need the following assert. if warmup: video_batch_size = int(videos.shape[0]) assert video_batch_size in [batch_size, -1, None], f"Invalid batch size {video_batch_size}" tensor_name = module_scope + "RGB/inception_i3d/Mean:0" if not _is_in_graph(tensor_name): i3d_model = hub.Module(module_spec, name=module_name) i3d_model(videos) # gets the kinetics-i3d-400-logits layer tensor_name = module_scope + "RGB/inception_i3d/Mean:0" tensor = tf.get_default_graph().get_tensor_by_name(tensor_name) return tensor def calculate_fvd(real_activations, generated_activations): """Returns a list of ops that compute metrics as funcs of activations. Args: real_activations: [num_samples, embedding_size] generated_activations: [num_samples, embedding_size] Returns: A scalar that contains the requested FVD. """ return tfgan.eval.frechet_classifier_distance_from_activations( real_activations, generated_activations) ================================================ FILE: stable-dreamfusion/ldm/modules/evaluate/ssim.py ================================================ # MIT Licence # Methods to predict the SSIM, taken from # https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py from math import exp import torch import torch.nn.functional as F from torch.autograd import Variable def gaussian(window_size, sigma): gauss = torch.Tensor( [ exp(-((x - window_size // 2) ** 2) / float(2 * sigma ** 2)) for x in range(window_size) ] ) return gauss / gauss.sum() def create_window(window_size, channel): _1D_window = gaussian(window_size, 1.5).unsqueeze(1) _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) window = Variable( _2D_window.expand(channel, 1, window_size, window_size).contiguous() ) return window def _ssim( img1, img2, window, window_size, channel, mask=None, size_average=True ): mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel) mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel) mu1_sq = mu1.pow(2) mu2_sq = mu2.pow(2) mu1_mu2 = mu1 * mu2 sigma1_sq = ( F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq ) sigma2_sq = ( F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq ) sigma12 = ( F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2 ) C1 = (0.01) ** 2 C2 = (0.03) ** 2 ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ( (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2) ) if not (mask is None): b = mask.size(0) ssim_map = ssim_map.mean(dim=1, keepdim=True) * mask ssim_map = ssim_map.view(b, -1).sum(dim=1) / mask.view(b, -1).sum( dim=1 ).clamp(min=1) return ssim_map import pdb pdb.set_trace if size_average: return ssim_map.mean() else: return ssim_map.mean(1).mean(1).mean(1) class SSIM(torch.nn.Module): def __init__(self, window_size=11, size_average=True): super(SSIM, self).__init__() self.window_size = window_size self.size_average = size_average self.channel = 1 self.window = create_window(window_size, self.channel) def forward(self, img1, img2, mask=None): (_, channel, _, _) = img1.size() if ( channel == self.channel and self.window.data.type() == img1.data.type() ): window = self.window else: window = create_window(self.window_size, channel) if img1.is_cuda: window = window.cuda(img1.get_device()) window = window.type_as(img1) self.window = window self.channel = channel return _ssim( img1, img2, window, self.window_size, channel, mask, self.size_average, ) def ssim(img1, img2, window_size=11, mask=None, size_average=True): (_, channel, _, _) = img1.size() window = create_window(window_size, channel) if img1.is_cuda: window = window.cuda(img1.get_device()) window = window.type_as(img1) return _ssim(img1, img2, window, window_size, channel, mask, size_average) ================================================ FILE: stable-dreamfusion/ldm/modules/evaluate/torch_frechet_video_distance.py ================================================ # based on https://github.com/universome/fvd-comparison/blob/master/compare_models.py; huge thanks! import os import numpy as np import io import re import requests import html import hashlib import urllib import urllib.request import scipy.linalg import multiprocessing as mp import glob from tqdm import tqdm from typing import Any, List, Tuple, Union, Dict, Callable from torchvision.io import read_video import torch; torch.set_grad_enabled(False) from einops import rearrange from nitro.util import isvideo def compute_frechet_distance(mu_sample,sigma_sample,mu_ref,sigma_ref) -> float: print('Calculate frechet distance...') m = np.square(mu_sample - mu_ref).sum() s, _ = scipy.linalg.sqrtm(np.dot(sigma_sample, sigma_ref), disp=False) # pylint: disable=no-member fid = np.real(m + np.trace(sigma_sample + sigma_ref - s * 2)) return float(fid) def compute_stats(feats: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: mu = feats.mean(axis=0) # [d] sigma = np.cov(feats, rowvar=False) # [d, d] return mu, sigma def open_url(url: str, num_attempts: int = 10, verbose: bool = True, return_filename: bool = False) -> Any: """Download the given URL and return a binary-mode file object to access the data.""" assert num_attempts >= 1 # Doesn't look like an URL scheme so interpret it as a local filename. if not re.match('^[a-z]+://', url): return url if return_filename else open(url, "rb") # Handle file URLs. This code handles unusual file:// patterns that # arise on Windows: # # file:///c:/foo.txt # # which would translate to a local '/c:/foo.txt' filename that's # invalid. Drop the forward slash for such pathnames. # # If you touch this code path, you should test it on both Linux and # Windows. # # Some internet resources suggest using urllib.request.url2pathname() but # but that converts forward slashes to backslashes and this causes # its own set of problems. if url.startswith('file://'): filename = urllib.parse.urlparse(url).path if re.match(r'^/[a-zA-Z]:', filename): filename = filename[1:] return filename if return_filename else open(filename, "rb") url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest() # Download. url_name = None url_data = None with requests.Session() as session: if verbose: print("Downloading %s ..." % url, end="", flush=True) for attempts_left in reversed(range(num_attempts)): try: with session.get(url) as res: res.raise_for_status() if len(res.content) == 0: raise IOError("No data received") if len(res.content) < 8192: content_str = res.content.decode("utf-8") if "download_warning" in res.headers.get("Set-Cookie", ""): links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link] if len(links) == 1: url = requests.compat.urljoin(url, links[0]) raise IOError("Google Drive virus checker nag") if "Google Drive - Quota exceeded" in content_str: raise IOError("Google Drive download quota exceeded -- please try again later") match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", "")) url_name = match[1] if match else url url_data = res.content if verbose: print(" done") break except KeyboardInterrupt: raise except: if not attempts_left: if verbose: print(" failed") raise if verbose: print(".", end="", flush=True) # Return data as file object. assert not return_filename return io.BytesIO(url_data) def load_video(ip): vid, *_ = read_video(ip) vid = rearrange(vid, 't h w c -> t c h w').to(torch.uint8) return vid def get_data_from_str(input_str,nprc = None): assert os.path.isdir(input_str), f'Specified input folder "{input_str}" is not a directory' vid_filelist = glob.glob(os.path.join(input_str,'*.mp4')) print(f'Found {len(vid_filelist)} videos in dir {input_str}') if nprc is None: try: nprc = mp.cpu_count() except NotImplementedError: print('WARNING: cpu_count() not avlailable, using only 1 cpu for video loading') nprc = 1 pool = mp.Pool(processes=nprc) vids = [] for v in tqdm(pool.imap_unordered(load_video,vid_filelist),total=len(vid_filelist),desc='Loading videos...'): vids.append(v) vids = torch.stack(vids,dim=0).float() return vids def get_stats(stats): assert os.path.isfile(stats) and stats.endswith('.npz'), f'no stats found under {stats}' print(f'Using precomputed statistics under {stats}') stats = np.load(stats) stats = {key: stats[key] for key in stats.files} return stats @torch.no_grad() def compute_fvd(ref_input, sample_input, bs=32, ref_stats=None, sample_stats=None, nprc_load=None): calc_stats = ref_stats is None or sample_stats is None if calc_stats: only_ref = sample_stats is not None only_sample = ref_stats is not None if isinstance(ref_input,str) and not only_sample: ref_input = get_data_from_str(ref_input,nprc_load) if isinstance(sample_input, str) and not only_ref: sample_input = get_data_from_str(sample_input, nprc_load) stats = compute_statistics(sample_input,ref_input, device='cuda' if torch.cuda.is_available() else 'cpu', bs=bs, only_ref=only_ref, only_sample=only_sample) if only_ref: stats.update(get_stats(sample_stats)) elif only_sample: stats.update(get_stats(ref_stats)) else: stats = get_stats(sample_stats) stats.update(get_stats(ref_stats)) fvd = compute_frechet_distance(**stats) return {'FVD' : fvd,} @torch.no_grad() def compute_statistics(videos_fake, videos_real, device: str='cuda', bs=32, only_ref=False,only_sample=False) -> Dict: detector_url = 'https://www.dropbox.com/s/ge9e5ujwgetktms/i3d_torchscript.pt?dl=1' detector_kwargs = dict(rescale=True, resize=True, return_features=True) # Return raw features before the softmax layer. with open_url(detector_url, verbose=False) as f: detector = torch.jit.load(f).eval().to(device) assert not (only_sample and only_ref), 'only_ref and only_sample arguments are mutually exclusive' ref_embed, sample_embed = [], [] info = f'Computing I3D activations for FVD score with batch size {bs}' if only_ref: if not isvideo(videos_real): # if not is video we assume to have numpy arrays pf shape (n_vids, t, h, w, c) in range [0,255] videos_real = torch.from_numpy(videos_real).permute(0, 4, 1, 2, 3).float() print(videos_real.shape) if videos_real.shape[0] % bs == 0: n_secs = videos_real.shape[0] // bs else: n_secs = videos_real.shape[0] // bs + 1 videos_real = torch.tensor_split(videos_real, n_secs, dim=0) for ref_v in tqdm(videos_real, total=len(videos_real),desc=info): feats_ref = detector(ref_v.to(device).contiguous(), **detector_kwargs).cpu().numpy() ref_embed.append(feats_ref) elif only_sample: if not isvideo(videos_fake): # if not is video we assume to have numpy arrays pf shape (n_vids, t, h, w, c) in range [0,255] videos_fake = torch.from_numpy(videos_fake).permute(0, 4, 1, 2, 3).float() print(videos_fake.shape) if videos_fake.shape[0] % bs == 0: n_secs = videos_fake.shape[0] // bs else: n_secs = videos_fake.shape[0] // bs + 1 videos_real = torch.tensor_split(videos_real, n_secs, dim=0) for sample_v in tqdm(videos_fake, total=len(videos_real),desc=info): feats_sample = detector(sample_v.to(device).contiguous(), **detector_kwargs).cpu().numpy() sample_embed.append(feats_sample) else: if not isvideo(videos_real): # if not is video we assume to have numpy arrays pf shape (n_vids, t, h, w, c) in range [0,255] videos_real = torch.from_numpy(videos_real).permute(0, 4, 1, 2, 3).float() if not isvideo(videos_fake): videos_fake = torch.from_numpy(videos_fake).permute(0, 4, 1, 2, 3).float() if videos_fake.shape[0] % bs == 0: n_secs = videos_fake.shape[0] // bs else: n_secs = videos_fake.shape[0] // bs + 1 videos_real = torch.tensor_split(videos_real, n_secs, dim=0) videos_fake = torch.tensor_split(videos_fake, n_secs, dim=0) for ref_v, sample_v in tqdm(zip(videos_real,videos_fake),total=len(videos_fake),desc=info): # print(ref_v.shape) # ref_v = torch.nn.functional.interpolate(ref_v, size=(sample_v.shape[2], 256, 256), mode='trilinear', align_corners=False) # sample_v = torch.nn.functional.interpolate(sample_v, size=(sample_v.shape[2], 256, 256), mode='trilinear', align_corners=False) feats_sample = detector(sample_v.to(device).contiguous(), **detector_kwargs).cpu().numpy() feats_ref = detector(ref_v.to(device).contiguous(), **detector_kwargs).cpu().numpy() sample_embed.append(feats_sample) ref_embed.append(feats_ref) out = dict() if len(sample_embed) > 0: sample_embed = np.concatenate(sample_embed,axis=0) mu_sample, sigma_sample = compute_stats(sample_embed) out.update({'mu_sample': mu_sample, 'sigma_sample': sigma_sample}) if len(ref_embed) > 0: ref_embed = np.concatenate(ref_embed,axis=0) mu_ref, sigma_ref = compute_stats(ref_embed) out.update({'mu_ref': mu_ref, 'sigma_ref': sigma_ref}) return out ================================================ FILE: stable-dreamfusion/ldm/modules/image_degradation/__init__.py ================================================ from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light ================================================ FILE: stable-dreamfusion/ldm/modules/image_degradation/bsrgan.py ================================================ # -*- coding: utf-8 -*- """ # -------------------------------------------- # Super-Resolution # -------------------------------------------- # # Kai Zhang (cskaizhang@gmail.com) # https://github.com/cszn # From 2019/03--2021/08 # -------------------------------------------- """ import numpy as np import cv2 import torch from functools import partial import random from scipy import ndimage import scipy import scipy.stats as ss from scipy.interpolate import interp2d from scipy.linalg import orth import albumentations import ldm.modules.image_degradation.utils_image as util def modcrop_np(img, sf): ''' Args: img: numpy image, WxH or WxHxC sf: scale factor Return: cropped image ''' w, h = img.shape[:2] im = np.copy(img) return im[:w - w % sf, :h - h % sf, ...] """ # -------------------------------------------- # anisotropic Gaussian kernels # -------------------------------------------- """ def analytic_kernel(k): """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" k_size = k.shape[0] # Calculate the big kernels size big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) # Loop over the small kernel to fill the big one for r in range(k_size): for c in range(k_size): big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k # Crop the edges of the big kernel to ignore very small values and increase run time of SR crop = k_size // 2 cropped_big_k = big_k[crop:-crop, crop:-crop] # Normalize to 1 return cropped_big_k / cropped_big_k.sum() def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): """ generate an anisotropic Gaussian kernel Args: ksize : e.g., 15, kernel size theta : [0, pi], rotation angle range l1 : [0.1,50], scaling of eigenvalues l2 : [0.1,l1], scaling of eigenvalues If l1 = l2, will get an isotropic Gaussian kernel. Returns: k : kernel """ v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.])) V = np.array([[v[0], v[1]], [v[1], -v[0]]]) D = np.array([[l1, 0], [0, l2]]) Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) return k def gm_blur_kernel(mean, cov, size=15): center = size / 2.0 + 0.5 k = np.zeros([size, size]) for y in range(size): for x in range(size): cy = y - center + 1 cx = x - center + 1 k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) k = k / np.sum(k) return k def shift_pixel(x, sf, upper_left=True): """shift pixel for super-resolution with different scale factors Args: x: WxHxC or WxH sf: scale factor upper_left: shift direction """ h, w = x.shape[:2] shift = (sf - 1) * 0.5 xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) if upper_left: x1 = xv + shift y1 = yv + shift else: x1 = xv - shift y1 = yv - shift x1 = np.clip(x1, 0, w - 1) y1 = np.clip(y1, 0, h - 1) if x.ndim == 2: x = interp2d(xv, yv, x)(x1, y1) if x.ndim == 3: for i in range(x.shape[-1]): x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) return x def blur(x, k): ''' x: image, NxcxHxW k: kernel, Nx1xhxw ''' n, c = x.shape[:2] p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate') k = k.repeat(1, c, 1, 1) k = k.view(-1, 1, k.shape[2], k.shape[3]) x = x.view(1, -1, x.shape[2], x.shape[3]) x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) x = x.view(n, c, x.shape[2], x.shape[3]) return x def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0): """" # modified version of https://github.com/assafshocher/BlindSR_dataset_generator # Kai Zhang # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var # max_var = 2.5 * sf """ # Set random eigen-vals (lambdas) and angle (theta) for COV matrix lambda_1 = min_var + np.random.rand() * (max_var - min_var) lambda_2 = min_var + np.random.rand() * (max_var - min_var) theta = np.random.rand() * np.pi # random theta noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 # Set COV matrix using Lambdas and Theta LAMBDA = np.diag([lambda_1, lambda_2]) Q = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) SIGMA = Q @ LAMBDA @ Q.T INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] # Set expectation position (shifting kernel for aligned image) MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) MU = MU[None, None, :, None] # Create meshgrid for Gaussian [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) Z = np.stack([X, Y], 2)[:, :, :, None] # Calcualte Gaussian for every pixel of the kernel ZZ = Z - MU ZZ_t = ZZ.transpose(0, 1, 3, 2) raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) # shift the kernel so it will be centered # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) # Normalize the kernel and return # kernel = raw_kernel_centered / np.sum(raw_kernel_centered) kernel = raw_kernel / np.sum(raw_kernel) return kernel def fspecial_gaussian(hsize, sigma): hsize = [hsize, hsize] siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] std = sigma [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) arg = -(x * x + y * y) / (2 * std * std) h = np.exp(arg) h[h < scipy.finfo(float).eps * h.max()] = 0 sumh = h.sum() if sumh != 0: h = h / sumh return h def fspecial_laplacian(alpha): alpha = max([0, min([alpha, 1])]) h1 = alpha / (alpha + 1) h2 = (1 - alpha) / (alpha + 1) h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] h = np.array(h) return h def fspecial(filter_type, *args, **kwargs): ''' python code from: https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py ''' if filter_type == 'gaussian': return fspecial_gaussian(*args, **kwargs) if filter_type == 'laplacian': return fspecial_laplacian(*args, **kwargs) """ # -------------------------------------------- # degradation models # -------------------------------------------- """ def bicubic_degradation(x, sf=3): ''' Args: x: HxWxC image, [0, 1] sf: down-scale factor Return: bicubicly downsampled LR image ''' x = util.imresize_np(x, scale=1 / sf) return x def srmd_degradation(x, k, sf=3): ''' blur + bicubic downsampling Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2018learning, title={Learning a single convolutional super-resolution network for multiple degradations}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={3262--3271}, year={2018} } ''' x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror' x = bicubic_degradation(x, sf=sf) return x def dpsr_degradation(x, k, sf=3): ''' bicubic downsampling + blur Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2019deep, title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={1671--1681}, year={2019} } ''' x = bicubic_degradation(x, sf=sf) x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') return x def classical_degradation(x, k, sf=3): ''' blur + downsampling Args: x: HxWxC image, [0, 1]/[0, 255] k: hxw, double sf: down-scale factor Return: downsampled LR image ''' x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) st = 0 return x[st::sf, st::sf, ...] def add_sharpening(img, weight=0.5, radius=50, threshold=10): """USM sharpening. borrowed from real-ESRGAN Input image: I; Blurry image: B. 1. K = I + weight * (I - B) 2. Mask = 1 if abs(I - B) > threshold, else: 0 3. Blur mask: 4. Out = Mask * K + (1 - Mask) * I Args: img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. weight (float): Sharp weight. Default: 1. radius (float): Kernel size of Gaussian blur. Default: 50. threshold (int): """ if radius % 2 == 0: radius += 1 blur = cv2.GaussianBlur(img, (radius, radius), 0) residual = img - blur mask = np.abs(residual) * 255 > threshold mask = mask.astype('float32') soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) K = img + weight * residual K = np.clip(K, 0, 1) return soft_mask * K + (1 - soft_mask) * img def add_blur(img, sf=4): wd2 = 4.0 + sf wd = 2.0 + 0.2 * sf if random.random() < 0.5: l1 = wd2 * random.random() l2 = wd2 * random.random() k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2) else: k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random()) img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror') return img def add_resize(img, sf=4): rnum = np.random.rand() if rnum > 0.8: # up sf1 = random.uniform(1, 2) elif rnum < 0.7: # down sf1 = random.uniform(0.5 / sf, 1) else: sf1 = 1.0 img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) img = np.clip(img, 0.0, 1.0) return img # def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): # noise_level = random.randint(noise_level1, noise_level2) # rnum = np.random.rand() # if rnum > 0.6: # add color Gaussian noise # img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) # elif rnum < 0.4: # add grayscale Gaussian noise # img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) # else: # add noise # L = noise_level2 / 255. # D = np.diag(np.random.rand(3)) # U = orth(np.random.rand(3, 3)) # conv = np.dot(np.dot(np.transpose(U), D), U) # img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) # img = np.clip(img, 0.0, 1.0) # return img def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): noise_level = random.randint(noise_level1, noise_level2) rnum = np.random.rand() if rnum > 0.6: # add color Gaussian noise img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) elif rnum < 0.4: # add grayscale Gaussian noise img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) else: # add noise L = noise_level2 / 255. D = np.diag(np.random.rand(3)) U = orth(np.random.rand(3, 3)) conv = np.dot(np.dot(np.transpose(U), D), U) img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) img = np.clip(img, 0.0, 1.0) return img def add_speckle_noise(img, noise_level1=2, noise_level2=25): noise_level = random.randint(noise_level1, noise_level2) img = np.clip(img, 0.0, 1.0) rnum = random.random() if rnum > 0.6: img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) elif rnum < 0.4: img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) else: L = noise_level2 / 255. D = np.diag(np.random.rand(3)) U = orth(np.random.rand(3, 3)) conv = np.dot(np.dot(np.transpose(U), D), U) img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) img = np.clip(img, 0.0, 1.0) return img def add_Poisson_noise(img): img = np.clip((img * 255.0).round(), 0, 255) / 255. vals = 10 ** (2 * random.random() + 2.0) # [2, 4] if random.random() < 0.5: img = np.random.poisson(img * vals).astype(np.float32) / vals else: img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255. noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray img += noise_gray[:, :, np.newaxis] img = np.clip(img, 0.0, 1.0) return img def add_JPEG_noise(img): quality_factor = random.randint(30, 95) img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]) img = cv2.imdecode(encimg, 1) img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) return img def random_crop(lq, hq, sf=4, lq_patchsize=64): h, w = lq.shape[:2] rnd_h = random.randint(0, h - lq_patchsize) rnd_w = random.randint(0, w - lq_patchsize) lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :] rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :] return lq, hq def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): """ This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] """ isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 sf_ori = sf h1, w1 = img.shape[:2] img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop h, w = img.shape[:2] if h < lq_patchsize * sf or w < lq_patchsize * sf: raise ValueError(f'img size ({h1}X{w1}) is too small!') hq = img.copy() if sf == 4 and random.random() < scale2_prob: # downsample1 if np.random.rand() < 0.5: img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), interpolation=random.choice([1, 2, 3])) else: img = util.imresize_np(img, 1 / 2, True) img = np.clip(img, 0.0, 1.0) sf = 2 shuffle_order = random.sample(range(7), 7) idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) if idx1 > idx2: # keep downsample3 last shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] for i in shuffle_order: if i == 0: img = add_blur(img, sf=sf) elif i == 1: img = add_blur(img, sf=sf) elif i == 2: a, b = img.shape[1], img.shape[0] # downsample2 if random.random() < 0.75: sf1 = random.uniform(1, 2 * sf) img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) else: k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) k_shifted = shift_pixel(k, sf) k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror') img = img[0::sf, 0::sf, ...] # nearest downsampling img = np.clip(img, 0.0, 1.0) elif i == 3: # downsample3 img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) img = np.clip(img, 0.0, 1.0) elif i == 4: # add Gaussian noise img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) elif i == 5: # add JPEG noise if random.random() < jpeg_prob: img = add_JPEG_noise(img) elif i == 6: # add processed camera sensor noise if random.random() < isp_prob and isp_model is not None: with torch.no_grad(): img, hq = isp_model.forward(img.copy(), hq) # add final JPEG compression noise img = add_JPEG_noise(img) # random crop img, hq = random_crop(img, hq, sf_ori, lq_patchsize) return img, hq # todo no isp_model? def degradation_bsrgan_variant(image, sf=4, isp_model=None): """ This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] """ image = util.uint2single(image) isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 sf_ori = sf h1, w1 = image.shape[:2] image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop h, w = image.shape[:2] hq = image.copy() if sf == 4 and random.random() < scale2_prob: # downsample1 if np.random.rand() < 0.5: image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), interpolation=random.choice([1, 2, 3])) else: image = util.imresize_np(image, 1 / 2, True) image = np.clip(image, 0.0, 1.0) sf = 2 shuffle_order = random.sample(range(7), 7) idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) if idx1 > idx2: # keep downsample3 last shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] for i in shuffle_order: if i == 0: image = add_blur(image, sf=sf) elif i == 1: image = add_blur(image, sf=sf) elif i == 2: a, b = image.shape[1], image.shape[0] # downsample2 if random.random() < 0.75: sf1 = random.uniform(1, 2 * sf) image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), interpolation=random.choice([1, 2, 3])) else: k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) k_shifted = shift_pixel(k, sf) k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror') image = image[0::sf, 0::sf, ...] # nearest downsampling image = np.clip(image, 0.0, 1.0) elif i == 3: # downsample3 image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) image = np.clip(image, 0.0, 1.0) elif i == 4: # add Gaussian noise image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25) elif i == 5: # add JPEG noise if random.random() < jpeg_prob: image = add_JPEG_noise(image) # elif i == 6: # # add processed camera sensor noise # if random.random() < isp_prob and isp_model is not None: # with torch.no_grad(): # img, hq = isp_model.forward(img.copy(), hq) # add final JPEG compression noise image = add_JPEG_noise(image) image = util.single2uint(image) example = {"image":image} return example # TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc... def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None): """ This is an extended degradation model by combining the degradation models of BSRGAN and Real-ESRGAN ---------- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) sf: scale factor use_shuffle: the degradation shuffle use_sharp: sharpening the img Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] """ h1, w1 = img.shape[:2] img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop h, w = img.shape[:2] if h < lq_patchsize * sf or w < lq_patchsize * sf: raise ValueError(f'img size ({h1}X{w1}) is too small!') if use_sharp: img = add_sharpening(img) hq = img.copy() if random.random() < shuffle_prob: shuffle_order = random.sample(range(13), 13) else: shuffle_order = list(range(13)) # local shuffle for noise, JPEG is always the last one shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6))) shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13))) poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1 for i in shuffle_order: if i == 0: img = add_blur(img, sf=sf) elif i == 1: img = add_resize(img, sf=sf) elif i == 2: img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) elif i == 3: if random.random() < poisson_prob: img = add_Poisson_noise(img) elif i == 4: if random.random() < speckle_prob: img = add_speckle_noise(img) elif i == 5: if random.random() < isp_prob and isp_model is not None: with torch.no_grad(): img, hq = isp_model.forward(img.copy(), hq) elif i == 6: img = add_JPEG_noise(img) elif i == 7: img = add_blur(img, sf=sf) elif i == 8: img = add_resize(img, sf=sf) elif i == 9: img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25) elif i == 10: if random.random() < poisson_prob: img = add_Poisson_noise(img) elif i == 11: if random.random() < speckle_prob: img = add_speckle_noise(img) elif i == 12: if random.random() < isp_prob and isp_model is not None: with torch.no_grad(): img, hq = isp_model.forward(img.copy(), hq) else: print('check the shuffle!') # resize to desired size img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])), interpolation=random.choice([1, 2, 3])) # add final JPEG compression noise img = add_JPEG_noise(img) # random crop img, hq = random_crop(img, hq, sf, lq_patchsize) return img, hq if __name__ == '__main__': print("hey") img = util.imread_uint('utils/test.png', 3) print(img) img = util.uint2single(img) print(img) img = img[:448, :448] h = img.shape[0] // 4 print("resizing to", h) sf = 4 deg_fn = partial(degradation_bsrgan_variant, sf=sf) for i in range(20): print(i) img_lq = deg_fn(img) print(img_lq) img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"] print(img_lq.shape) print("bicubic", img_lq_bicubic.shape) print(img_hq.shape) lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), interpolation=0) lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), interpolation=0) img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1) util.imsave(img_concat, str(i) + '.png') ================================================ FILE: stable-dreamfusion/ldm/modules/image_degradation/bsrgan_light.py ================================================ # -*- coding: utf-8 -*- import numpy as np import cv2 import torch from functools import partial import random from scipy import ndimage import scipy import scipy.stats as ss from scipy.interpolate import interp2d from scipy.linalg import orth import albumentations import ldm.modules.image_degradation.utils_image as util """ # -------------------------------------------- # Super-Resolution # -------------------------------------------- # # Kai Zhang (cskaizhang@gmail.com) # https://github.com/cszn # From 2019/03--2021/08 # -------------------------------------------- """ def modcrop_np(img, sf): ''' Args: img: numpy image, WxH or WxHxC sf: scale factor Return: cropped image ''' w, h = img.shape[:2] im = np.copy(img) return im[:w - w % sf, :h - h % sf, ...] """ # -------------------------------------------- # anisotropic Gaussian kernels # -------------------------------------------- """ def analytic_kernel(k): """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)""" k_size = k.shape[0] # Calculate the big kernels size big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2)) # Loop over the small kernel to fill the big one for r in range(k_size): for c in range(k_size): big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k # Crop the edges of the big kernel to ignore very small values and increase run time of SR crop = k_size // 2 cropped_big_k = big_k[crop:-crop, crop:-crop] # Normalize to 1 return cropped_big_k / cropped_big_k.sum() def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): """ generate an anisotropic Gaussian kernel Args: ksize : e.g., 15, kernel size theta : [0, pi], rotation angle range l1 : [0.1,50], scaling of eigenvalues l2 : [0.1,l1], scaling of eigenvalues If l1 = l2, will get an isotropic Gaussian kernel. Returns: k : kernel """ v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.])) V = np.array([[v[0], v[1]], [v[1], -v[0]]]) D = np.array([[l1, 0], [0, l2]]) Sigma = np.dot(np.dot(V, D), np.linalg.inv(V)) k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize) return k def gm_blur_kernel(mean, cov, size=15): center = size / 2.0 + 0.5 k = np.zeros([size, size]) for y in range(size): for x in range(size): cy = y - center + 1 cx = x - center + 1 k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov) k = k / np.sum(k) return k def shift_pixel(x, sf, upper_left=True): """shift pixel for super-resolution with different scale factors Args: x: WxHxC or WxH sf: scale factor upper_left: shift direction """ h, w = x.shape[:2] shift = (sf - 1) * 0.5 xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0) if upper_left: x1 = xv + shift y1 = yv + shift else: x1 = xv - shift y1 = yv - shift x1 = np.clip(x1, 0, w - 1) y1 = np.clip(y1, 0, h - 1) if x.ndim == 2: x = interp2d(xv, yv, x)(x1, y1) if x.ndim == 3: for i in range(x.shape[-1]): x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1) return x def blur(x, k): ''' x: image, NxcxHxW k: kernel, Nx1xhxw ''' n, c = x.shape[:2] p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2 x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate') k = k.repeat(1, c, 1, 1) k = k.view(-1, 1, k.shape[2], k.shape[3]) x = x.view(1, -1, x.shape[2], x.shape[3]) x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c) x = x.view(n, c, x.shape[2], x.shape[3]) return x def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0): """" # modified version of https://github.com/assafshocher/BlindSR_dataset_generator # Kai Zhang # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var # max_var = 2.5 * sf """ # Set random eigen-vals (lambdas) and angle (theta) for COV matrix lambda_1 = min_var + np.random.rand() * (max_var - min_var) lambda_2 = min_var + np.random.rand() * (max_var - min_var) theta = np.random.rand() * np.pi # random theta noise = -noise_level + np.random.rand(*k_size) * noise_level * 2 # Set COV matrix using Lambdas and Theta LAMBDA = np.diag([lambda_1, lambda_2]) Q = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) SIGMA = Q @ LAMBDA @ Q.T INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :] # Set expectation position (shifting kernel for aligned image) MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2) MU = MU[None, None, :, None] # Create meshgrid for Gaussian [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1])) Z = np.stack([X, Y], 2)[:, :, :, None] # Calcualte Gaussian for every pixel of the kernel ZZ = Z - MU ZZ_t = ZZ.transpose(0, 1, 3, 2) raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise) # shift the kernel so it will be centered # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor) # Normalize the kernel and return # kernel = raw_kernel_centered / np.sum(raw_kernel_centered) kernel = raw_kernel / np.sum(raw_kernel) return kernel def fspecial_gaussian(hsize, sigma): hsize = [hsize, hsize] siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0] std = sigma [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)) arg = -(x * x + y * y) / (2 * std * std) h = np.exp(arg) h[h < scipy.finfo(float).eps * h.max()] = 0 sumh = h.sum() if sumh != 0: h = h / sumh return h def fspecial_laplacian(alpha): alpha = max([0, min([alpha, 1])]) h1 = alpha / (alpha + 1) h2 = (1 - alpha) / (alpha + 1) h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]] h = np.array(h) return h def fspecial(filter_type, *args, **kwargs): ''' python code from: https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py ''' if filter_type == 'gaussian': return fspecial_gaussian(*args, **kwargs) if filter_type == 'laplacian': return fspecial_laplacian(*args, **kwargs) """ # -------------------------------------------- # degradation models # -------------------------------------------- """ def bicubic_degradation(x, sf=3): ''' Args: x: HxWxC image, [0, 1] sf: down-scale factor Return: bicubicly downsampled LR image ''' x = util.imresize_np(x, scale=1 / sf) return x def srmd_degradation(x, k, sf=3): ''' blur + bicubic downsampling Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2018learning, title={Learning a single convolutional super-resolution network for multiple degradations}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={3262--3271}, year={2018} } ''' x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror' x = bicubic_degradation(x, sf=sf) return x def dpsr_degradation(x, k, sf=3): ''' bicubic downsampling + blur Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2019deep, title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={1671--1681}, year={2019} } ''' x = bicubic_degradation(x, sf=sf) x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap') return x def classical_degradation(x, k, sf=3): ''' blur + downsampling Args: x: HxWxC image, [0, 1]/[0, 255] k: hxw, double sf: down-scale factor Return: downsampled LR image ''' x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2)) st = 0 return x[st::sf, st::sf, ...] def add_sharpening(img, weight=0.5, radius=50, threshold=10): """USM sharpening. borrowed from real-ESRGAN Input image: I; Blurry image: B. 1. K = I + weight * (I - B) 2. Mask = 1 if abs(I - B) > threshold, else: 0 3. Blur mask: 4. Out = Mask * K + (1 - Mask) * I Args: img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. weight (float): Sharp weight. Default: 1. radius (float): Kernel size of Gaussian blur. Default: 50. threshold (int): """ if radius % 2 == 0: radius += 1 blur = cv2.GaussianBlur(img, (radius, radius), 0) residual = img - blur mask = np.abs(residual) * 255 > threshold mask = mask.astype('float32') soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) K = img + weight * residual K = np.clip(K, 0, 1) return soft_mask * K + (1 - soft_mask) * img def add_blur(img, sf=4): wd2 = 4.0 + sf wd = 2.0 + 0.2 * sf wd2 = wd2/4 wd = wd/4 if random.random() < 0.5: l1 = wd2 * random.random() l2 = wd2 * random.random() k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2) else: k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random()) img = ndimage.convolve(img, np.expand_dims(k, axis=2), mode='mirror') return img def add_resize(img, sf=4): rnum = np.random.rand() if rnum > 0.8: # up sf1 = random.uniform(1, 2) elif rnum < 0.7: # down sf1 = random.uniform(0.5 / sf, 1) else: sf1 = 1.0 img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) img = np.clip(img, 0.0, 1.0) return img # def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): # noise_level = random.randint(noise_level1, noise_level2) # rnum = np.random.rand() # if rnum > 0.6: # add color Gaussian noise # img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) # elif rnum < 0.4: # add grayscale Gaussian noise # img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) # else: # add noise # L = noise_level2 / 255. # D = np.diag(np.random.rand(3)) # U = orth(np.random.rand(3, 3)) # conv = np.dot(np.dot(np.transpose(U), D), U) # img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) # img = np.clip(img, 0.0, 1.0) # return img def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): noise_level = random.randint(noise_level1, noise_level2) rnum = np.random.rand() if rnum > 0.6: # add color Gaussian noise img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) elif rnum < 0.4: # add grayscale Gaussian noise img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) else: # add noise L = noise_level2 / 255. D = np.diag(np.random.rand(3)) U = orth(np.random.rand(3, 3)) conv = np.dot(np.dot(np.transpose(U), D), U) img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) img = np.clip(img, 0.0, 1.0) return img def add_speckle_noise(img, noise_level1=2, noise_level2=25): noise_level = random.randint(noise_level1, noise_level2) img = np.clip(img, 0.0, 1.0) rnum = random.random() if rnum > 0.6: img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32) elif rnum < 0.4: img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32) else: L = noise_level2 / 255. D = np.diag(np.random.rand(3)) U = orth(np.random.rand(3, 3)) conv = np.dot(np.dot(np.transpose(U), D), U) img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) img = np.clip(img, 0.0, 1.0) return img def add_Poisson_noise(img): img = np.clip((img * 255.0).round(), 0, 255) / 255. vals = 10 ** (2 * random.random() + 2.0) # [2, 4] if random.random() < 0.5: img = np.random.poisson(img * vals).astype(np.float32) / vals else: img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114]) img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255. noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray img += noise_gray[:, :, np.newaxis] img = np.clip(img, 0.0, 1.0) return img def add_JPEG_noise(img): quality_factor = random.randint(80, 95) img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR) result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]) img = cv2.imdecode(encimg, 1) img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB) return img def random_crop(lq, hq, sf=4, lq_patchsize=64): h, w = lq.shape[:2] rnd_h = random.randint(0, h - lq_patchsize) rnd_w = random.randint(0, w - lq_patchsize) lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :] rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf) hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :] return lq, hq def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): """ This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] """ isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 sf_ori = sf h1, w1 = img.shape[:2] img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop h, w = img.shape[:2] if h < lq_patchsize * sf or w < lq_patchsize * sf: raise ValueError(f'img size ({h1}X{w1}) is too small!') hq = img.copy() if sf == 4 and random.random() < scale2_prob: # downsample1 if np.random.rand() < 0.5: img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])), interpolation=random.choice([1, 2, 3])) else: img = util.imresize_np(img, 1 / 2, True) img = np.clip(img, 0.0, 1.0) sf = 2 shuffle_order = random.sample(range(7), 7) idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) if idx1 > idx2: # keep downsample3 last shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] for i in shuffle_order: if i == 0: img = add_blur(img, sf=sf) elif i == 1: img = add_blur(img, sf=sf) elif i == 2: a, b = img.shape[1], img.shape[0] # downsample2 if random.random() < 0.75: sf1 = random.uniform(1, 2 * sf) img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3])) else: k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) k_shifted = shift_pixel(k, sf) k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel img = ndimage.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror') img = img[0::sf, 0::sf, ...] # nearest downsampling img = np.clip(img, 0.0, 1.0) elif i == 3: # downsample3 img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) img = np.clip(img, 0.0, 1.0) elif i == 4: # add Gaussian noise img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8) elif i == 5: # add JPEG noise if random.random() < jpeg_prob: img = add_JPEG_noise(img) elif i == 6: # add processed camera sensor noise if random.random() < isp_prob and isp_model is not None: with torch.no_grad(): img, hq = isp_model.forward(img.copy(), hq) # add final JPEG compression noise img = add_JPEG_noise(img) # random crop img, hq = random_crop(img, hq, sf_ori, lq_patchsize) return img, hq # todo no isp_model? def degradation_bsrgan_variant(image, sf=4, isp_model=None): """ This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] """ image = util.uint2single(image) isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25 sf_ori = sf h1, w1 = image.shape[:2] image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop h, w = image.shape[:2] hq = image.copy() if sf == 4 and random.random() < scale2_prob: # downsample1 if np.random.rand() < 0.5: image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])), interpolation=random.choice([1, 2, 3])) else: image = util.imresize_np(image, 1 / 2, True) image = np.clip(image, 0.0, 1.0) sf = 2 shuffle_order = random.sample(range(7), 7) idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3) if idx1 > idx2: # keep downsample3 last shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1] for i in shuffle_order: if i == 0: image = add_blur(image, sf=sf) # elif i == 1: # image = add_blur(image, sf=sf) if i == 0: pass elif i == 2: a, b = image.shape[1], image.shape[0] # downsample2 if random.random() < 0.8: sf1 = random.uniform(1, 2 * sf) image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])), interpolation=random.choice([1, 2, 3])) else: k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf)) k_shifted = shift_pixel(k, sf) k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel image = ndimage.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror') image = image[0::sf, 0::sf, ...] # nearest downsampling image = np.clip(image, 0.0, 1.0) elif i == 3: # downsample3 image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3])) image = np.clip(image, 0.0, 1.0) elif i == 4: # add Gaussian noise image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2) elif i == 5: # add JPEG noise if random.random() < jpeg_prob: image = add_JPEG_noise(image) # # elif i == 6: # # add processed camera sensor noise # if random.random() < isp_prob and isp_model is not None: # with torch.no_grad(): # img, hq = isp_model.forward(img.copy(), hq) # add final JPEG compression noise image = add_JPEG_noise(image) image = util.single2uint(image) example = {"image": image} return example if __name__ == '__main__': print("hey") img = util.imread_uint('utils/test.png', 3) img = img[:448, :448] h = img.shape[0] // 4 print("resizing to", h) sf = 4 deg_fn = partial(degradation_bsrgan_variant, sf=sf) for i in range(20): print(i) img_hq = img img_lq = deg_fn(img)["image"] img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq) print(img_lq) img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"] print(img_lq.shape) print("bicubic", img_lq_bicubic.shape) print(img_hq.shape) lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), interpolation=0) lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])), interpolation=0) img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1) util.imsave(img_concat, str(i) + '.png') ================================================ FILE: stable-dreamfusion/ldm/modules/image_degradation/utils_image.py ================================================ import os import math import random import numpy as np import torch import cv2 from torchvision.utils import make_grid from datetime import datetime #import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" ''' # -------------------------------------------- # Kai Zhang (github: https://github.com/cszn) # 03/Mar/2019 # -------------------------------------------- # https://github.com/twhui/SRGAN-pyTorch # https://github.com/xinntao/BasicSR # -------------------------------------------- ''' IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif'] def is_image_file(filename): return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) def get_timestamp(): return datetime.now().strftime('%y%m%d-%H%M%S') def imshow(x, title=None, cbar=False, figsize=None): plt.figure(figsize=figsize) plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray') if title: plt.title(title) if cbar: plt.colorbar() plt.show() def surf(Z, cmap='rainbow', figsize=None): plt.figure(figsize=figsize) ax3 = plt.axes(projection='3d') w, h = Z.shape[:2] xx = np.arange(0,w,1) yy = np.arange(0,h,1) X, Y = np.meshgrid(xx, yy) ax3.plot_surface(X,Y,Z,cmap=cmap) #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap) plt.show() ''' # -------------------------------------------- # get image pathes # -------------------------------------------- ''' def get_image_paths(dataroot): paths = None # return None if dataroot is None if dataroot is not None: paths = sorted(_get_paths_from_images(dataroot)) return paths def _get_paths_from_images(path): assert os.path.isdir(path), '{:s} is not a valid directory'.format(path) images = [] for dirpath, _, fnames in sorted(os.walk(path)): for fname in sorted(fnames): if is_image_file(fname): img_path = os.path.join(dirpath, fname) images.append(img_path) assert images, '{:s} has no valid image file'.format(path) return images ''' # -------------------------------------------- # split large images into small images # -------------------------------------------- ''' def patches_from_image(img, p_size=512, p_overlap=64, p_max=800): w, h = img.shape[:2] patches = [] if w > p_max and h > p_max: w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int)) h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int)) w1.append(w-p_size) h1.append(h-p_size) # print(w1) # print(h1) for i in w1: for j in h1: patches.append(img[i:i+p_size, j:j+p_size,:]) else: patches.append(img) return patches def imssave(imgs, img_path): """ imgs: list, N images of size WxHxC """ img_name, ext = os.path.splitext(os.path.basename(img_path)) for i, img in enumerate(imgs): if img.ndim == 3: img = img[:, :, [2, 1, 0]] new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png') cv2.imwrite(new_path, img) def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000): """ split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size), and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max) will be splitted. Args: original_dataroot: taget_dataroot: p_size: size of small images p_overlap: patch size in training is a good choice p_max: images with smaller size than (p_max)x(p_max) keep unchanged. """ paths = get_image_paths(original_dataroot) for img_path in paths: # img_name, ext = os.path.splitext(os.path.basename(img_path)) img = imread_uint(img_path, n_channels=n_channels) patches = patches_from_image(img, p_size, p_overlap, p_max) imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path))) #if original_dataroot == taget_dataroot: #del img_path ''' # -------------------------------------------- # makedir # -------------------------------------------- ''' def mkdir(path): if not os.path.exists(path): os.makedirs(path) def mkdirs(paths): if isinstance(paths, str): mkdir(paths) else: for path in paths: mkdir(path) def mkdir_and_rename(path): if os.path.exists(path): new_name = path + '_archived_' + get_timestamp() print('Path already exists. Rename it to [{:s}]'.format(new_name)) os.rename(path, new_name) os.makedirs(path) ''' # -------------------------------------------- # read image from path # opencv is fast, but read BGR numpy image # -------------------------------------------- ''' # -------------------------------------------- # get uint8 image of size HxWxn_channles (RGB) # -------------------------------------------- def imread_uint(path, n_channels=3): # input: path # output: HxWx3(RGB or GGG), or HxWx1 (G) if n_channels == 1: img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE img = np.expand_dims(img, axis=2) # HxWx1 elif n_channels == 3: img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G if img.ndim == 2: img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG else: img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB return img # -------------------------------------------- # matlab's imwrite # -------------------------------------------- def imsave(img, img_path): img = np.squeeze(img) if img.ndim == 3: img = img[:, :, [2, 1, 0]] cv2.imwrite(img_path, img) def imwrite(img, img_path): img = np.squeeze(img) if img.ndim == 3: img = img[:, :, [2, 1, 0]] cv2.imwrite(img_path, img) # -------------------------------------------- # get single image of size HxWxn_channles (BGR) # -------------------------------------------- def read_img(path): # read image by cv2 # return: Numpy float32, HWC, BGR, [0,1] img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE img = img.astype(np.float32) / 255. if img.ndim == 2: img = np.expand_dims(img, axis=2) # some images have 4 channels if img.shape[2] > 3: img = img[:, :, :3] return img ''' # -------------------------------------------- # image format conversion # -------------------------------------------- # numpy(single) <---> numpy(unit) # numpy(single) <---> tensor # numpy(unit) <---> tensor # -------------------------------------------- ''' # -------------------------------------------- # numpy(single) [0, 1] <---> numpy(unit) # -------------------------------------------- def uint2single(img): return np.float32(img/255.) def single2uint(img): return np.uint8((img.clip(0, 1)*255.).round()) def uint162single(img): return np.float32(img/65535.) def single2uint16(img): return np.uint16((img.clip(0, 1)*65535.).round()) # -------------------------------------------- # numpy(unit) (HxWxC or HxW) <---> tensor # -------------------------------------------- # convert uint to 4-dimensional torch tensor def uint2tensor4(img): if img.ndim == 2: img = np.expand_dims(img, axis=2) return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0) # convert uint to 3-dimensional torch tensor def uint2tensor3(img): if img.ndim == 2: img = np.expand_dims(img, axis=2) return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.) # convert 2/3/4-dimensional torch tensor to uint def tensor2uint(img): img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy() if img.ndim == 3: img = np.transpose(img, (1, 2, 0)) return np.uint8((img*255.0).round()) # -------------------------------------------- # numpy(single) (HxWxC) <---> tensor # -------------------------------------------- # convert single (HxWxC) to 3-dimensional torch tensor def single2tensor3(img): return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float() # convert single (HxWxC) to 4-dimensional torch tensor def single2tensor4(img): return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0) # convert torch tensor to single def tensor2single(img): img = img.data.squeeze().float().cpu().numpy() if img.ndim == 3: img = np.transpose(img, (1, 2, 0)) return img # convert torch tensor to single def tensor2single3(img): img = img.data.squeeze().float().cpu().numpy() if img.ndim == 3: img = np.transpose(img, (1, 2, 0)) elif img.ndim == 2: img = np.expand_dims(img, axis=2) return img def single2tensor5(img): return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0) def single32tensor5(img): return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0) def single42tensor4(img): return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float() # from skimage.io import imread, imsave def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)): ''' Converts a torch Tensor into an image Numpy array of BGR channel order Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default) ''' tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1] n_dim = tensor.dim() if n_dim == 4: n_img = len(tensor) img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy() img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR elif n_dim == 3: img_np = tensor.numpy() img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR elif n_dim == 2: img_np = tensor.numpy() else: raise TypeError( 'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim)) if out_type == np.uint8: img_np = (img_np * 255.0).round() # Important. Unlike matlab, numpy.unit8() WILL NOT round by default. return img_np.astype(out_type) ''' # -------------------------------------------- # Augmentation, flipe and/or rotate # -------------------------------------------- # The following two are enough. # (1) augmet_img: numpy image of WxHxC or WxH # (2) augment_img_tensor4: tensor image 1xCxWxH # -------------------------------------------- ''' def augment_img(img, mode=0): '''Kai Zhang (github: https://github.com/cszn) ''' if mode == 0: return img elif mode == 1: return np.flipud(np.rot90(img)) elif mode == 2: return np.flipud(img) elif mode == 3: return np.rot90(img, k=3) elif mode == 4: return np.flipud(np.rot90(img, k=2)) elif mode == 5: return np.rot90(img) elif mode == 6: return np.rot90(img, k=2) elif mode == 7: return np.flipud(np.rot90(img, k=3)) def augment_img_tensor4(img, mode=0): '''Kai Zhang (github: https://github.com/cszn) ''' if mode == 0: return img elif mode == 1: return img.rot90(1, [2, 3]).flip([2]) elif mode == 2: return img.flip([2]) elif mode == 3: return img.rot90(3, [2, 3]) elif mode == 4: return img.rot90(2, [2, 3]).flip([2]) elif mode == 5: return img.rot90(1, [2, 3]) elif mode == 6: return img.rot90(2, [2, 3]) elif mode == 7: return img.rot90(3, [2, 3]).flip([2]) def augment_img_tensor(img, mode=0): '''Kai Zhang (github: https://github.com/cszn) ''' img_size = img.size() img_np = img.data.cpu().numpy() if len(img_size) == 3: img_np = np.transpose(img_np, (1, 2, 0)) elif len(img_size) == 4: img_np = np.transpose(img_np, (2, 3, 1, 0)) img_np = augment_img(img_np, mode=mode) img_tensor = torch.from_numpy(np.ascontiguousarray(img_np)) if len(img_size) == 3: img_tensor = img_tensor.permute(2, 0, 1) elif len(img_size) == 4: img_tensor = img_tensor.permute(3, 2, 0, 1) return img_tensor.type_as(img) def augment_img_np3(img, mode=0): if mode == 0: return img elif mode == 1: return img.transpose(1, 0, 2) elif mode == 2: return img[::-1, :, :] elif mode == 3: img = img[::-1, :, :] img = img.transpose(1, 0, 2) return img elif mode == 4: return img[:, ::-1, :] elif mode == 5: img = img[:, ::-1, :] img = img.transpose(1, 0, 2) return img elif mode == 6: img = img[:, ::-1, :] img = img[::-1, :, :] return img elif mode == 7: img = img[:, ::-1, :] img = img[::-1, :, :] img = img.transpose(1, 0, 2) return img def augment_imgs(img_list, hflip=True, rot=True): # horizontal flip OR rotate hflip = hflip and random.random() < 0.5 vflip = rot and random.random() < 0.5 rot90 = rot and random.random() < 0.5 def _augment(img): if hflip: img = img[:, ::-1, :] if vflip: img = img[::-1, :, :] if rot90: img = img.transpose(1, 0, 2) return img return [_augment(img) for img in img_list] ''' # -------------------------------------------- # modcrop and shave # -------------------------------------------- ''' def modcrop(img_in, scale): # img_in: Numpy, HWC or HW img = np.copy(img_in) if img.ndim == 2: H, W = img.shape H_r, W_r = H % scale, W % scale img = img[:H - H_r, :W - W_r] elif img.ndim == 3: H, W, C = img.shape H_r, W_r = H % scale, W % scale img = img[:H - H_r, :W - W_r, :] else: raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim)) return img def shave(img_in, border=0): # img_in: Numpy, HWC or HW img = np.copy(img_in) h, w = img.shape[:2] img = img[border:h-border, border:w-border] return img ''' # -------------------------------------------- # image processing process on numpy image # channel_convert(in_c, tar_type, img_list): # rgb2ycbcr(img, only_y=True): # bgr2ycbcr(img, only_y=True): # ycbcr2rgb(img): # -------------------------------------------- ''' def rgb2ycbcr(img, only_y=True): '''same as matlab rgb2ycbcr only_y: only return Y channel Input: uint8, [0, 255] float, [0, 1] ''' in_img_type = img.dtype img.astype(np.float32) if in_img_type != np.uint8: img *= 255. # convert if only_y: rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0 else: rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128] if in_img_type == np.uint8: rlt = rlt.round() else: rlt /= 255. return rlt.astype(in_img_type) def ycbcr2rgb(img): '''same as matlab ycbcr2rgb Input: uint8, [0, 255] float, [0, 1] ''' in_img_type = img.dtype img.astype(np.float32) if in_img_type != np.uint8: img *= 255. # convert rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071], [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836] if in_img_type == np.uint8: rlt = rlt.round() else: rlt /= 255. return rlt.astype(in_img_type) def bgr2ycbcr(img, only_y=True): '''bgr version of rgb2ycbcr only_y: only return Y channel Input: uint8, [0, 255] float, [0, 1] ''' in_img_type = img.dtype img.astype(np.float32) if in_img_type != np.uint8: img *= 255. # convert if only_y: rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0 else: rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128] if in_img_type == np.uint8: rlt = rlt.round() else: rlt /= 255. return rlt.astype(in_img_type) def channel_convert(in_c, tar_type, img_list): # conversion among BGR, gray and y if in_c == 3 and tar_type == 'gray': # BGR to gray gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list] return [np.expand_dims(img, axis=2) for img in gray_list] elif in_c == 3 and tar_type == 'y': # BGR to y y_list = [bgr2ycbcr(img, only_y=True) for img in img_list] return [np.expand_dims(img, axis=2) for img in y_list] elif in_c == 1 and tar_type == 'RGB': # gray/y to BGR return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list] else: return img_list ''' # -------------------------------------------- # metric, PSNR and SSIM # -------------------------------------------- ''' # -------------------------------------------- # PSNR # -------------------------------------------- def calculate_psnr(img1, img2, border=0): # img1 and img2 have range [0, 255] #img1 = img1.squeeze() #img2 = img2.squeeze() if not img1.shape == img2.shape: raise ValueError('Input images must have the same dimensions.') h, w = img1.shape[:2] img1 = img1[border:h-border, border:w-border] img2 = img2[border:h-border, border:w-border] img1 = img1.astype(np.float64) img2 = img2.astype(np.float64) mse = np.mean((img1 - img2)**2) if mse == 0: return float('inf') return 20 * math.log10(255.0 / math.sqrt(mse)) # -------------------------------------------- # SSIM # -------------------------------------------- def calculate_ssim(img1, img2, border=0): '''calculate SSIM the same outputs as MATLAB's img1, img2: [0, 255] ''' #img1 = img1.squeeze() #img2 = img2.squeeze() if not img1.shape == img2.shape: raise ValueError('Input images must have the same dimensions.') h, w = img1.shape[:2] img1 = img1[border:h-border, border:w-border] img2 = img2[border:h-border, border:w-border] if img1.ndim == 2: return ssim(img1, img2) elif img1.ndim == 3: if img1.shape[2] == 3: ssims = [] for i in range(3): ssims.append(ssim(img1[:,:,i], img2[:,:,i])) return np.array(ssims).mean() elif img1.shape[2] == 1: return ssim(np.squeeze(img1), np.squeeze(img2)) else: raise ValueError('Wrong input image dimensions.') def ssim(img1, img2): C1 = (0.01 * 255)**2 C2 = (0.03 * 255)**2 img1 = img1.astype(np.float64) img2 = img2.astype(np.float64) kernel = cv2.getGaussianKernel(11, 1.5) window = np.outer(kernel, kernel.transpose()) mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5] mu1_sq = mu1**2 mu2_sq = mu2**2 mu1_mu2 = mu1 * mu2 sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2 ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)) return ssim_map.mean() ''' # -------------------------------------------- # matlab's bicubic imresize (numpy and torch) [0, 1] # -------------------------------------------- ''' # matlab 'imresize' function, now only support 'bicubic' def cubic(x): absx = torch.abs(x) absx2 = absx**2 absx3 = absx**3 return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \ (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx)) def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing): if (scale < 1) and (antialiasing): # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width kernel_width = kernel_width / scale # Output-space coordinates x = torch.linspace(1, out_length, out_length) # Input-space coordinates. Calculate the inverse mapping such that 0.5 # in output space maps to 0.5 in input space, and 0.5+scale in output # space maps to 1.5 in input space. u = x / scale + 0.5 * (1 - 1 / scale) # What is the left-most pixel that can be involved in the computation? left = torch.floor(u - kernel_width / 2) # What is the maximum number of pixels that can be involved in the # computation? Note: it's OK to use an extra pixel here; if the # corresponding weights are all zero, it will be eliminated at the end # of this function. P = math.ceil(kernel_width) + 2 # The indices of the input pixels involved in computing the k-th output # pixel are in row k of the indices matrix. indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view( 1, P).expand(out_length, P) # The weights used to compute the k-th output pixel are in row k of the # weights matrix. distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices # apply cubic kernel if (scale < 1) and (antialiasing): weights = scale * cubic(distance_to_center * scale) else: weights = cubic(distance_to_center) # Normalize the weights matrix so that each row sums to 1. weights_sum = torch.sum(weights, 1).view(out_length, 1) weights = weights / weights_sum.expand(out_length, P) # If a column in weights is all zero, get rid of it. only consider the first and last column. weights_zero_tmp = torch.sum((weights == 0), 0) if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6): indices = indices.narrow(1, 1, P - 2) weights = weights.narrow(1, 1, P - 2) if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6): indices = indices.narrow(1, 0, P - 2) weights = weights.narrow(1, 0, P - 2) weights = weights.contiguous() indices = indices.contiguous() sym_len_s = -indices.min() + 1 sym_len_e = indices.max() - in_length indices = indices + sym_len_s - 1 return weights, indices, int(sym_len_s), int(sym_len_e) # -------------------------------------------- # imresize for tensor image [0, 1] # -------------------------------------------- def imresize(img, scale, antialiasing=True): # Now the scale should be the same for H and W # input: img: pytorch tensor, CHW or HW [0,1] # output: CHW or HW [0,1] w/o round need_squeeze = True if img.dim() == 2 else False if need_squeeze: img.unsqueeze_(0) in_C, in_H, in_W = img.size() out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) kernel_width = 4 kernel = 'cubic' # Return the desired dimension order for performing the resize. The # strategy is to perform the resize first along the dimension with the # smallest scale factor. # Now we do not support this. # get weights and indices weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( in_H, out_H, scale, kernel, kernel_width, antialiasing) weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( in_W, out_W, scale, kernel, kernel_width, antialiasing) # process H dimension # symmetric copying img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W) img_aug.narrow(1, sym_len_Hs, in_H).copy_(img) sym_patch = img[:, :sym_len_Hs, :] inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(1, inv_idx) img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv) sym_patch = img[:, -sym_len_He:, :] inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(1, inv_idx) img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) out_1 = torch.FloatTensor(in_C, out_H, in_W) kernel_width = weights_H.size(1) for i in range(out_H): idx = int(indices_H[i][0]) for j in range(out_C): out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i]) # process W dimension # symmetric copying out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We) out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1) sym_patch = out_1[:, :, :sym_len_Ws] inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(2, inv_idx) out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv) sym_patch = out_1[:, :, -sym_len_We:] inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(2, inv_idx) out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) out_2 = torch.FloatTensor(in_C, out_H, out_W) kernel_width = weights_W.size(1) for i in range(out_W): idx = int(indices_W[i][0]) for j in range(out_C): out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i]) if need_squeeze: out_2.squeeze_() return out_2 # -------------------------------------------- # imresize for numpy image [0, 1] # -------------------------------------------- def imresize_np(img, scale, antialiasing=True): # Now the scale should be the same for H and W # input: img: Numpy, HWC or HW [0,1] # output: HWC or HW [0,1] w/o round img = torch.from_numpy(img) need_squeeze = True if img.dim() == 2 else False if need_squeeze: img.unsqueeze_(2) in_H, in_W, in_C = img.size() out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale) kernel_width = 4 kernel = 'cubic' # Return the desired dimension order for performing the resize. The # strategy is to perform the resize first along the dimension with the # smallest scale factor. # Now we do not support this. # get weights and indices weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices( in_H, out_H, scale, kernel, kernel_width, antialiasing) weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices( in_W, out_W, scale, kernel, kernel_width, antialiasing) # process H dimension # symmetric copying img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C) img_aug.narrow(0, sym_len_Hs, in_H).copy_(img) sym_patch = img[:sym_len_Hs, :, :] inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(0, inv_idx) img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv) sym_patch = img[-sym_len_He:, :, :] inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(0, inv_idx) img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv) out_1 = torch.FloatTensor(out_H, in_W, in_C) kernel_width = weights_H.size(1) for i in range(out_H): idx = int(indices_H[i][0]) for j in range(out_C): out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i]) # process W dimension # symmetric copying out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C) out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1) sym_patch = out_1[:, :sym_len_Ws, :] inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(1, inv_idx) out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv) sym_patch = out_1[:, -sym_len_We:, :] inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() sym_patch_inv = sym_patch.index_select(1, inv_idx) out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv) out_2 = torch.FloatTensor(out_H, out_W, in_C) kernel_width = weights_W.size(1) for i in range(out_W): idx = int(indices_W[i][0]) for j in range(out_C): out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i]) if need_squeeze: out_2.squeeze_() return out_2.numpy() if __name__ == '__main__': print('---') # img = imread_uint('test.bmp', 3) # img = uint2single(img) # img_bicubic = imresize_np(img, 1/4) ================================================ FILE: stable-dreamfusion/ldm/modules/losses/__init__.py ================================================ from ldm.modules.losses.contperceptual import LPIPSWithDiscriminator ================================================ FILE: stable-dreamfusion/ldm/modules/losses/contperceptual.py ================================================ import torch import torch.nn as nn from taming.modules.losses.vqperceptual import * # TODO: taming dependency yes/no? class LPIPSWithDiscriminator(nn.Module): def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0, disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, disc_loss="hinge"): super().__init__() assert disc_loss in ["hinge", "vanilla"] self.kl_weight = kl_weight self.pixel_weight = pixelloss_weight self.perceptual_loss = LPIPS().eval() self.perceptual_weight = perceptual_weight # output log variance self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init) self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm ).apply(weights_init) self.discriminator_iter_start = disc_start self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss self.disc_factor = disc_factor self.discriminator_weight = disc_weight self.disc_conditional = disc_conditional def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): if last_layer is not None: nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] else: nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0] g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0] d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() d_weight = d_weight * self.discriminator_weight return d_weight def forward(self, inputs, reconstructions, posteriors, optimizer_idx, global_step, last_layer=None, cond=None, split="train", weights=None): rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) if self.perceptual_weight > 0: p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous()) rec_loss = rec_loss + self.perceptual_weight * p_loss nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar weighted_nll_loss = nll_loss if weights is not None: weighted_nll_loss = weights*nll_loss weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0] nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] kl_loss = posteriors.kl() kl_loss = torch.sum(kl_loss) / kl_loss.shape[0] # now the GAN part if optimizer_idx == 0: # generator update if cond is None: assert not self.disc_conditional logits_fake = self.discriminator(reconstructions.contiguous()) else: assert self.disc_conditional logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1)) g_loss = -torch.mean(logits_fake) if self.disc_factor > 0.0: try: d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer) except RuntimeError: assert not self.training d_weight = torch.tensor(0.0) else: d_weight = torch.tensor(0.0) disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/logvar".format(split): self.logvar.detach(), "{}/kl_loss".format(split): kl_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(), "{}/rec_loss".format(split): rec_loss.detach().mean(), "{}/d_weight".format(split): d_weight.detach(), "{}/disc_factor".format(split): torch.tensor(disc_factor), "{}/g_loss".format(split): g_loss.detach().mean(), } return loss, log if optimizer_idx == 1: # second pass for discriminator update if cond is None: logits_real = self.discriminator(inputs.contiguous().detach()) logits_fake = self.discriminator(reconstructions.contiguous().detach()) else: logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1)) logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1)) disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(), "{}/logits_real".format(split): logits_real.detach().mean(), "{}/logits_fake".format(split): logits_fake.detach().mean() } return d_loss, log ================================================ FILE: stable-dreamfusion/ldm/modules/losses/vqperceptual.py ================================================ import torch from torch import nn import torch.nn.functional as F from einops import repeat from taming.modules.discriminator.model import NLayerDiscriminator, weights_init from taming.modules.losses.lpips import LPIPS from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights): assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0] loss_real = torch.mean(F.relu(1. - logits_real), dim=[1,2,3]) loss_fake = torch.mean(F.relu(1. + logits_fake), dim=[1,2,3]) loss_real = (weights * loss_real).sum() / weights.sum() loss_fake = (weights * loss_fake).sum() / weights.sum() d_loss = 0.5 * (loss_real + loss_fake) return d_loss def adopt_weight(weight, global_step, threshold=0, value=0.): if global_step < threshold: weight = value return weight def measure_perplexity(predicted_indices, n_embed): # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed) avg_probs = encodings.mean(0) perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp() cluster_use = torch.sum(avg_probs > 0) return perplexity, cluster_use def l1(x, y): return torch.abs(x-y) def l2(x, y): return torch.pow((x-y), 2) class VQLPIPSWithDiscriminator(nn.Module): def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, disc_ndf=64, disc_loss="hinge", n_classes=None, perceptual_loss="lpips", pixel_loss="l1"): super().__init__() assert disc_loss in ["hinge", "vanilla"] assert perceptual_loss in ["lpips", "clips", "dists"] assert pixel_loss in ["l1", "l2"] self.codebook_weight = codebook_weight self.pixel_weight = pixelloss_weight if perceptual_loss == "lpips": print(f"{self.__class__.__name__}: Running with LPIPS.") self.perceptual_loss = LPIPS().eval() else: raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<") self.perceptual_weight = perceptual_weight if pixel_loss == "l1": self.pixel_loss = l1 else: self.pixel_loss = l2 self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm, ndf=disc_ndf ).apply(weights_init) self.discriminator_iter_start = disc_start if disc_loss == "hinge": self.disc_loss = hinge_d_loss elif disc_loss == "vanilla": self.disc_loss = vanilla_d_loss else: raise ValueError(f"Unknown GAN loss '{disc_loss}'.") print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.") self.disc_factor = disc_factor self.discriminator_weight = disc_weight self.disc_conditional = disc_conditional self.n_classes = n_classes def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): if last_layer is not None: nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] else: nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0] g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0] d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() d_weight = d_weight * self.discriminator_weight return d_weight def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx, global_step, last_layer=None, cond=None, split="train", predicted_indices=None): if not exists(codebook_loss): codebook_loss = torch.tensor([0.]).to(inputs.device) #rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous()) if self.perceptual_weight > 0: p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous()) rec_loss = rec_loss + self.perceptual_weight * p_loss else: p_loss = torch.tensor([0.0]) nll_loss = rec_loss #nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] nll_loss = torch.mean(nll_loss) # now the GAN part if optimizer_idx == 0: # generator update if cond is None: assert not self.disc_conditional logits_fake = self.discriminator(reconstructions.contiguous()) else: assert self.disc_conditional logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1)) g_loss = -torch.mean(logits_fake) try: d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer) except RuntimeError: assert not self.training d_weight = torch.tensor(0.0) disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean() log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/quant_loss".format(split): codebook_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(), "{}/rec_loss".format(split): rec_loss.detach().mean(), "{}/p_loss".format(split): p_loss.detach().mean(), "{}/d_weight".format(split): d_weight.detach(), "{}/disc_factor".format(split): torch.tensor(disc_factor), "{}/g_loss".format(split): g_loss.detach().mean(), } if predicted_indices is not None: assert self.n_classes is not None with torch.no_grad(): perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes) log[f"{split}/perplexity"] = perplexity log[f"{split}/cluster_usage"] = cluster_usage return loss, log if optimizer_idx == 1: # second pass for discriminator update if cond is None: logits_real = self.discriminator(inputs.contiguous().detach()) logits_fake = self.discriminator(reconstructions.contiguous().detach()) else: logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1)) logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1)) disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(), "{}/logits_real".format(split): logits_real.detach().mean(), "{}/logits_fake".format(split): logits_fake.detach().mean() } return d_loss, log ================================================ FILE: stable-dreamfusion/ldm/modules/x_transformer.py ================================================ """shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers""" import torch from torch import nn, einsum import torch.nn.functional as F from functools import partial from inspect import isfunction from collections import namedtuple from einops import rearrange, repeat, reduce # constants DEFAULT_DIM_HEAD = 64 Intermediates = namedtuple('Intermediates', [ 'pre_softmax_attn', 'post_softmax_attn' ]) LayerIntermediates = namedtuple('Intermediates', [ 'hiddens', 'attn_intermediates' ]) class AbsolutePositionalEmbedding(nn.Module): def __init__(self, dim, max_seq_len): super().__init__() self.emb = nn.Embedding(max_seq_len, dim) self.init_() def init_(self): nn.init.normal_(self.emb.weight, std=0.02) def forward(self, x): n = torch.arange(x.shape[1], device=x.device) return self.emb(n)[None, :, :] class FixedPositionalEmbedding(nn.Module): def __init__(self, dim): super().__init__() inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim)) self.register_buffer('inv_freq', inv_freq) def forward(self, x, seq_dim=1, offset=0): t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq) emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1) return emb[None, :, :] # helpers def exists(val): return val is not None def default(val, d): if exists(val): return val return d() if isfunction(d) else d def always(val): def inner(*args, **kwargs): return val return inner def not_equals(val): def inner(x): return x != val return inner def equals(val): def inner(x): return x == val return inner def max_neg_value(tensor): return -torch.finfo(tensor.dtype).max # keyword argument helpers def pick_and_pop(keys, d): values = list(map(lambda key: d.pop(key), keys)) return dict(zip(keys, values)) def group_dict_by_key(cond, d): return_val = [dict(), dict()] for key in d.keys(): match = bool(cond(key)) ind = int(not match) return_val[ind][key] = d[key] return (*return_val,) def string_begins_with(prefix, str): return str.startswith(prefix) def group_by_key_prefix(prefix, d): return group_dict_by_key(partial(string_begins_with, prefix), d) def groupby_prefix_and_trim(prefix, d): kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d) kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items()))) return kwargs_without_prefix, kwargs # classes class Scale(nn.Module): def __init__(self, value, fn): super().__init__() self.value = value self.fn = fn def forward(self, x, **kwargs): x, *rest = self.fn(x, **kwargs) return (x * self.value, *rest) class Rezero(nn.Module): def __init__(self, fn): super().__init__() self.fn = fn self.g = nn.Parameter(torch.zeros(1)) def forward(self, x, **kwargs): x, *rest = self.fn(x, **kwargs) return (x * self.g, *rest) class ScaleNorm(nn.Module): def __init__(self, dim, eps=1e-5): super().__init__() self.scale = dim ** -0.5 self.eps = eps self.g = nn.Parameter(torch.ones(1)) def forward(self, x): norm = torch.norm(x, dim=-1, keepdim=True) * self.scale return x / norm.clamp(min=self.eps) * self.g class RMSNorm(nn.Module): def __init__(self, dim, eps=1e-8): super().__init__() self.scale = dim ** -0.5 self.eps = eps self.g = nn.Parameter(torch.ones(dim)) def forward(self, x): norm = torch.norm(x, dim=-1, keepdim=True) * self.scale return x / norm.clamp(min=self.eps) * self.g class Residual(nn.Module): def forward(self, x, residual): return x + residual class GRUGating(nn.Module): def __init__(self, dim): super().__init__() self.gru = nn.GRUCell(dim, dim) def forward(self, x, residual): gated_output = self.gru( rearrange(x, 'b n d -> (b n) d'), rearrange(residual, 'b n d -> (b n) d') ) return gated_output.reshape_as(x) # feedforward class GEGLU(nn.Module): def __init__(self, dim_in, dim_out): super().__init__() self.proj = nn.Linear(dim_in, dim_out * 2) def forward(self, x): x, gate = self.proj(x).chunk(2, dim=-1) return x * F.gelu(gate) class FeedForward(nn.Module): def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): super().__init__() inner_dim = int(dim * mult) dim_out = default(dim_out, dim) project_in = nn.Sequential( nn.Linear(dim, inner_dim), nn.GELU() ) if not glu else GEGLU(dim, inner_dim) self.net = nn.Sequential( project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out) ) def forward(self, x): return self.net(x) # attention. class Attention(nn.Module): def __init__( self, dim, dim_head=DEFAULT_DIM_HEAD, heads=8, causal=False, mask=None, talking_heads=False, sparse_topk=None, use_entmax15=False, num_mem_kv=0, dropout=0., on_attn=False ): super().__init__() if use_entmax15: raise NotImplementedError("Check out entmax activation instead of softmax activation!") self.scale = dim_head ** -0.5 self.heads = heads self.causal = causal self.mask = mask inner_dim = dim_head * heads self.to_q = nn.Linear(dim, inner_dim, bias=False) self.to_k = nn.Linear(dim, inner_dim, bias=False) self.to_v = nn.Linear(dim, inner_dim, bias=False) self.dropout = nn.Dropout(dropout) # talking heads self.talking_heads = talking_heads if talking_heads: self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads)) self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads)) # explicit topk sparse attention self.sparse_topk = sparse_topk # entmax #self.attn_fn = entmax15 if use_entmax15 else F.softmax self.attn_fn = F.softmax # add memory key / values self.num_mem_kv = num_mem_kv if num_mem_kv > 0: self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) # attention on attention self.attn_on_attn = on_attn self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim) def forward( self, x, context=None, mask=None, context_mask=None, rel_pos=None, sinusoidal_emb=None, prev_attn=None, mem=None ): b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device kv_input = default(context, x) q_input = x k_input = kv_input v_input = kv_input if exists(mem): k_input = torch.cat((mem, k_input), dim=-2) v_input = torch.cat((mem, v_input), dim=-2) if exists(sinusoidal_emb): # in shortformer, the query would start at a position offset depending on the past cached memory offset = k_input.shape[-2] - q_input.shape[-2] q_input = q_input + sinusoidal_emb(q_input, offset=offset) k_input = k_input + sinusoidal_emb(k_input) q = self.to_q(q_input) k = self.to_k(k_input) v = self.to_v(v_input) q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v)) input_mask = None if any(map(exists, (mask, context_mask))): q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool()) k_mask = q_mask if not exists(context) else context_mask k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool()) q_mask = rearrange(q_mask, 'b i -> b () i ()') k_mask = rearrange(k_mask, 'b j -> b () () j') input_mask = q_mask * k_mask if self.num_mem_kv > 0: mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v)) k = torch.cat((mem_k, k), dim=-2) v = torch.cat((mem_v, v), dim=-2) if exists(input_mask): input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True) dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale mask_value = max_neg_value(dots) if exists(prev_attn): dots = dots + prev_attn pre_softmax_attn = dots if talking_heads: dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous() if exists(rel_pos): dots = rel_pos(dots) if exists(input_mask): dots.masked_fill_(~input_mask, mask_value) del input_mask if self.causal: i, j = dots.shape[-2:] r = torch.arange(i, device=device) mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j') mask = F.pad(mask, (j - i, 0), value=False) dots.masked_fill_(mask, mask_value) del mask if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]: top, _ = dots.topk(self.sparse_topk, dim=-1) vk = top[..., -1].unsqueeze(-1).expand_as(dots) mask = dots < vk dots.masked_fill_(mask, mask_value) del mask attn = self.attn_fn(dots, dim=-1) post_softmax_attn = attn attn = self.dropout(attn) if talking_heads: attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous() out = einsum('b h i j, b h j d -> b h i d', attn, v) out = rearrange(out, 'b h n d -> b n (h d)') intermediates = Intermediates( pre_softmax_attn=pre_softmax_attn, post_softmax_attn=post_softmax_attn ) return self.to_out(out), intermediates class AttentionLayers(nn.Module): def __init__( self, dim, depth, heads=8, causal=False, cross_attend=False, only_cross=False, use_scalenorm=False, use_rmsnorm=False, use_rezero=False, rel_pos_num_buckets=32, rel_pos_max_distance=128, position_infused_attn=False, custom_layers=None, sandwich_coef=None, par_ratio=None, residual_attn=False, cross_residual_attn=False, macaron=False, pre_norm=True, gate_residual=False, **kwargs ): super().__init__() ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs) attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs) dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD) self.dim = dim self.depth = depth self.layers = nn.ModuleList([]) self.has_pos_emb = position_infused_attn self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None self.rotary_pos_emb = always(None) assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance' self.rel_pos = None self.pre_norm = pre_norm self.residual_attn = residual_attn self.cross_residual_attn = cross_residual_attn norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm norm_class = RMSNorm if use_rmsnorm else norm_class norm_fn = partial(norm_class, dim) norm_fn = nn.Identity if use_rezero else norm_fn branch_fn = Rezero if use_rezero else None if cross_attend and not only_cross: default_block = ('a', 'c', 'f') elif cross_attend and only_cross: default_block = ('c', 'f') else: default_block = ('a', 'f') if macaron: default_block = ('f',) + default_block if exists(custom_layers): layer_types = custom_layers elif exists(par_ratio): par_depth = depth * len(default_block) assert 1 < par_ratio <= par_depth, 'par ratio out of range' default_block = tuple(filter(not_equals('f'), default_block)) par_attn = par_depth // par_ratio depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper par_width = (depth_cut + depth_cut // par_attn) // par_attn assert len(default_block) <= par_width, 'default block is too large for par_ratio' par_block = default_block + ('f',) * (par_width - len(default_block)) par_head = par_block * par_attn layer_types = par_head + ('f',) * (par_depth - len(par_head)) elif exists(sandwich_coef): assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth' layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef else: layer_types = default_block * depth self.layer_types = layer_types self.num_attn_layers = len(list(filter(equals('a'), layer_types))) for layer_type in self.layer_types: if layer_type == 'a': layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs) elif layer_type == 'c': layer = Attention(dim, heads=heads, **attn_kwargs) elif layer_type == 'f': layer = FeedForward(dim, **ff_kwargs) layer = layer if not macaron else Scale(0.5, layer) else: raise Exception(f'invalid layer type {layer_type}') if isinstance(layer, Attention) and exists(branch_fn): layer = branch_fn(layer) if gate_residual: residual_fn = GRUGating(dim) else: residual_fn = Residual() self.layers.append(nn.ModuleList([ norm_fn(), layer, residual_fn ])) def forward( self, x, context=None, mask=None, context_mask=None, mems=None, return_hiddens=False ): hiddens = [] intermediates = [] prev_attn = None prev_cross_attn = None mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)): is_last = ind == (len(self.layers) - 1) if layer_type == 'a': hiddens.append(x) layer_mem = mems.pop(0) residual = x if self.pre_norm: x = norm(x) if layer_type == 'a': out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos, prev_attn=prev_attn, mem=layer_mem) elif layer_type == 'c': out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn) elif layer_type == 'f': out = block(x) x = residual_fn(out, residual) if layer_type in ('a', 'c'): intermediates.append(inter) if layer_type == 'a' and self.residual_attn: prev_attn = inter.pre_softmax_attn elif layer_type == 'c' and self.cross_residual_attn: prev_cross_attn = inter.pre_softmax_attn if not self.pre_norm and not is_last: x = norm(x) if return_hiddens: intermediates = LayerIntermediates( hiddens=hiddens, attn_intermediates=intermediates ) return x, intermediates return x class Encoder(AttentionLayers): def __init__(self, **kwargs): assert 'causal' not in kwargs, 'cannot set causality on encoder' super().__init__(causal=False, **kwargs) class TransformerWrapper(nn.Module): def __init__( self, *, num_tokens, max_seq_len, attn_layers, emb_dim=None, max_mem_len=0., emb_dropout=0., num_memory_tokens=None, tie_embedding=False, use_pos_emb=True ): super().__init__() assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder' dim = attn_layers.dim emb_dim = default(emb_dim, dim) self.max_seq_len = max_seq_len self.max_mem_len = max_mem_len self.num_tokens = num_tokens self.token_emb = nn.Embedding(num_tokens, emb_dim) self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if ( use_pos_emb and not attn_layers.has_pos_emb) else always(0) self.emb_dropout = nn.Dropout(emb_dropout) self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity() self.attn_layers = attn_layers self.norm = nn.LayerNorm(dim) self.init_() self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t() # memory tokens (like [cls]) from Memory Transformers paper num_memory_tokens = default(num_memory_tokens, 0) self.num_memory_tokens = num_memory_tokens if num_memory_tokens > 0: self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim)) # let funnel encoder know number of memory tokens, if specified if hasattr(attn_layers, 'num_memory_tokens'): attn_layers.num_memory_tokens = num_memory_tokens def init_(self): nn.init.normal_(self.token_emb.weight, std=0.02) def forward( self, x, return_embeddings=False, mask=None, return_mems=False, return_attn=False, mems=None, **kwargs ): b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens x = self.token_emb(x) x += self.pos_emb(x) x = self.emb_dropout(x) x = self.project_emb(x) if num_mem > 0: mem = repeat(self.memory_tokens, 'n d -> b n d', b=b) x = torch.cat((mem, x), dim=1) # auto-handle masking after appending memory tokens if exists(mask): mask = F.pad(mask, (num_mem, 0), value=True) x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs) x = self.norm(x) mem, x = x[:, :num_mem], x[:, num_mem:] out = self.to_logits(x) if not return_embeddings else x if return_mems: hiddens = intermediates.hiddens new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems)) return out, new_mems if return_attn: attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates)) return out, attn_maps return out ================================================ FILE: stable-dreamfusion/ldm/thirdp/psp/helpers.py ================================================ # https://github.com/eladrich/pixel2style2pixel from collections import namedtuple import torch from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module """ ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) """ class Flatten(Module): def forward(self, input): return input.view(input.size(0), -1) def l2_norm(input, axis=1): norm = torch.norm(input, 2, axis, True) output = torch.div(input, norm) return output class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])): """ A named tuple describing a ResNet block. """ def get_block(in_channel, depth, num_units, stride=2): return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)] def get_blocks(num_layers): if num_layers == 50: blocks = [ get_block(in_channel=64, depth=64, num_units=3), get_block(in_channel=64, depth=128, num_units=4), get_block(in_channel=128, depth=256, num_units=14), get_block(in_channel=256, depth=512, num_units=3) ] elif num_layers == 100: blocks = [ get_block(in_channel=64, depth=64, num_units=3), get_block(in_channel=64, depth=128, num_units=13), get_block(in_channel=128, depth=256, num_units=30), get_block(in_channel=256, depth=512, num_units=3) ] elif num_layers == 152: blocks = [ get_block(in_channel=64, depth=64, num_units=3), get_block(in_channel=64, depth=128, num_units=8), get_block(in_channel=128, depth=256, num_units=36), get_block(in_channel=256, depth=512, num_units=3) ] else: raise ValueError("Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers)) return blocks class SEModule(Module): def __init__(self, channels, reduction): super(SEModule, self).__init__() self.avg_pool = AdaptiveAvgPool2d(1) self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False) self.relu = ReLU(inplace=True) self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False) self.sigmoid = Sigmoid() def forward(self, x): module_input = x x = self.avg_pool(x) x = self.fc1(x) x = self.relu(x) x = self.fc2(x) x = self.sigmoid(x) return module_input * x class bottleneck_IR(Module): def __init__(self, in_channel, depth, stride): super(bottleneck_IR, self).__init__() if in_channel == depth: self.shortcut_layer = MaxPool2d(1, stride) else: self.shortcut_layer = Sequential( Conv2d(in_channel, depth, (1, 1), stride, bias=False), BatchNorm2d(depth) ) self.res_layer = Sequential( BatchNorm2d(in_channel), Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth), Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth) ) def forward(self, x): shortcut = self.shortcut_layer(x) res = self.res_layer(x) return res + shortcut class bottleneck_IR_SE(Module): def __init__(self, in_channel, depth, stride): super(bottleneck_IR_SE, self).__init__() if in_channel == depth: self.shortcut_layer = MaxPool2d(1, stride) else: self.shortcut_layer = Sequential( Conv2d(in_channel, depth, (1, 1), stride, bias=False), BatchNorm2d(depth) ) self.res_layer = Sequential( BatchNorm2d(in_channel), Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth), Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth), SEModule(depth, 16) ) def forward(self, x): shortcut = self.shortcut_layer(x) res = self.res_layer(x) return res + shortcut ================================================ FILE: stable-dreamfusion/ldm/thirdp/psp/id_loss.py ================================================ # https://github.com/eladrich/pixel2style2pixel import torch from torch import nn from ldm.thirdp.psp.model_irse import Backbone class IDFeatures(nn.Module): def __init__(self, model_path): super(IDFeatures, self).__init__() print('Loading ResNet ArcFace') self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se') self.facenet.load_state_dict(torch.load(model_path, map_location="cpu")) self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112)) self.facenet.eval() def forward(self, x, crop=False): # Not sure of the image range here if crop: x = torch.nn.functional.interpolate(x, (256, 256), mode="area") x = x[:, :, 35:223, 32:220] x = self.face_pool(x) x_feats = self.facenet(x) return x_feats ================================================ FILE: stable-dreamfusion/ldm/thirdp/psp/model_irse.py ================================================ # https://github.com/eladrich/pixel2style2pixel from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module from ldm.thirdp.psp.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm """ Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) """ class Backbone(Module): def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True): super(Backbone, self).__init__() assert input_size in [112, 224], "input_size should be 112 or 224" assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152" assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se" blocks = get_blocks(num_layers) if mode == 'ir': unit_module = bottleneck_IR elif mode == 'ir_se': unit_module = bottleneck_IR_SE self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False), BatchNorm2d(64), PReLU(64)) if input_size == 112: self.output_layer = Sequential(BatchNorm2d(512), Dropout(drop_ratio), Flatten(), Linear(512 * 7 * 7, 512), BatchNorm1d(512, affine=affine)) else: self.output_layer = Sequential(BatchNorm2d(512), Dropout(drop_ratio), Flatten(), Linear(512 * 14 * 14, 512), BatchNorm1d(512, affine=affine)) modules = [] for block in blocks: for bottleneck in block: modules.append(unit_module(bottleneck.in_channel, bottleneck.depth, bottleneck.stride)) self.body = Sequential(*modules) def forward(self, x): x = self.input_layer(x) x = self.body(x) x = self.output_layer(x) return l2_norm(x) def IR_50(input_size): """Constructs a ir-50 model.""" model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False) return model def IR_101(input_size): """Constructs a ir-101 model.""" model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False) return model def IR_152(input_size): """Constructs a ir-152 model.""" model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False) return model def IR_SE_50(input_size): """Constructs a ir_se-50 model.""" model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False) return model def IR_SE_101(input_size): """Constructs a ir_se-101 model.""" model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False) return model def IR_SE_152(input_size): """Constructs a ir_se-152 model.""" model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False) return model ================================================ FILE: stable-dreamfusion/ldm/util.py ================================================ import importlib import torchvision import torch from torch import optim import numpy as np from inspect import isfunction from PIL import Image, ImageDraw, ImageFont import os import numpy as np import matplotlib.pyplot as plt from PIL import Image import torch import time import cv2 import PIL def pil_rectangle_crop(im): width, height = im.size # Get dimensions if width <= height: left = 0 right = width top = (height - width)/2 bottom = (height + width)/2 else: top = 0 bottom = height left = (width - height) / 2 bottom = (width + height) / 2 # Crop the center of the image im = im.crop((left, top, right, bottom)) return im def log_txt_as_img(wh, xc, size=10): # wh a tuple of (width, height) # xc a list of captions to plot b = len(xc) txts = list() for bi in range(b): txt = Image.new("RGB", wh, color="white") draw = ImageDraw.Draw(txt) font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) nc = int(40 * (wh[0] / 256)) lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) try: draw.text((0, 0), lines, fill="black", font=font) except UnicodeEncodeError: print("Cant encode string for logging. Skipping.") txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 txts.append(txt) txts = np.stack(txts) txts = torch.tensor(txts) return txts def ismap(x): if not isinstance(x, torch.Tensor): return False return (len(x.shape) == 4) and (x.shape[1] > 3) def isimage(x): if not isinstance(x,torch.Tensor): return False return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) def exists(x): return x is not None def default(val, d): if exists(val): return val return d() if isfunction(d) else d def mean_flat(tensor): """ https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 Take the mean over all non-batch dimensions. """ return tensor.mean(dim=list(range(1, len(tensor.shape)))) def count_params(model, verbose=False): total_params = sum(p.numel() for p in model.parameters()) if verbose: print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") return total_params def instantiate_from_config(config): if not "target" in config: if config == '__is_first_stage__': return None elif config == "__is_unconditional__": return None raise KeyError("Expected key `target` to instantiate.") return get_obj_from_str(config["target"])(**config.get("params", dict())) def get_obj_from_str(string, reload=False): module, cls = string.rsplit(".", 1) if reload: module_imp = importlib.import_module(module) importlib.reload(module_imp) return getattr(importlib.import_module(module, package=None), cls) class AdamWwithEMAandWings(optim.Optimizer): # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298 def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code ema_power=1., param_names=()): """AdamW that saves EMA versions of the parameters.""" if not 0.0 <= lr: raise ValueError("Invalid learning rate: {}".format(lr)) if not 0.0 <= eps: raise ValueError("Invalid epsilon value: {}".format(eps)) if not 0.0 <= betas[0] < 1.0: raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) if not 0.0 <= betas[1] < 1.0: raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) if not 0.0 <= weight_decay: raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) if not 0.0 <= ema_decay <= 1.0: raise ValueError("Invalid ema_decay value: {}".format(ema_decay)) defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay, ema_power=ema_power, param_names=param_names) super().__init__(params, defaults) def __setstate__(self, state): super().__setstate__(state) for group in self.param_groups: group.setdefault('amsgrad', False) @torch.no_grad() def step(self, closure=None): """Performs a single optimization step. Args: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: with torch.enable_grad(): loss = closure() for group in self.param_groups: params_with_grad = [] grads = [] exp_avgs = [] exp_avg_sqs = [] ema_params_with_grad = [] state_sums = [] max_exp_avg_sqs = [] state_steps = [] amsgrad = group['amsgrad'] beta1, beta2 = group['betas'] ema_decay = group['ema_decay'] ema_power = group['ema_power'] for p in group['params']: if p.grad is None: continue params_with_grad.append(p) if p.grad.is_sparse: raise RuntimeError('AdamW does not support sparse gradients') grads.append(p.grad) state = self.state[p] # State initialization if len(state) == 0: state['step'] = 0 # Exponential moving average of gradient values state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) # Exponential moving average of squared gradient values state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) if amsgrad: # Maintains max of all exp. moving avg. of sq. grad. values state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) # Exponential moving average of parameter values state['param_exp_avg'] = p.detach().float().clone() exp_avgs.append(state['exp_avg']) exp_avg_sqs.append(state['exp_avg_sq']) ema_params_with_grad.append(state['param_exp_avg']) if amsgrad: max_exp_avg_sqs.append(state['max_exp_avg_sq']) # update the steps for each param group update state['step'] += 1 # record the step after step update state_steps.append(state['step']) optim._functional.adamw(params_with_grad, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, amsgrad=amsgrad, beta1=beta1, beta2=beta2, lr=group['lr'], weight_decay=group['weight_decay'], eps=group['eps'], maximize=False) cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power) for param, ema_param in zip(params_with_grad, ema_params_with_grad): ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay) return loss ================================================ FILE: stable-dreamfusion/main.py ================================================ import torch import argparse import pandas as pd import sys from nerf.provider import NeRFDataset from nerf.utils import * # torch.autograd.set_detect_anomaly(True) if __name__ == '__main__': # See https://stackoverflow.com/questions/27433316/how-to-get-argparse-to-read-arguments-from-a-file-with-an-option-rather-than-pre class LoadFromFile (argparse.Action): def __call__ (self, parser, namespace, values, option_string = None): with values as f: # parse arguments in the file and store them in the target namespace parser.parse_args(f.read().split(), namespace) parser = argparse.ArgumentParser() parser.add_argument('--file', type=open, action=LoadFromFile, help="specify a file filled with more arguments") parser.add_argument('--text', default=None, help="text prompt") parser.add_argument('--negative', default='', type=str, help="negative text prompt") parser.add_argument('-O', action='store_true', help="equals --fp16 --cuda_ray") parser.add_argument('-O2', action='store_true', help="equals --backbone vanilla") parser.add_argument('--test', action='store_true', help="test mode") parser.add_argument('--six_views', action='store_true', help="six_views mode: save the images of the six views") parser.add_argument('--eval_interval', type=int, default=1, help="evaluate on the valid set every interval epochs") parser.add_argument('--test_interval', type=int, default=100, help="test on the test set every interval epochs") parser.add_argument('--workspace', type=str, default='workspace') parser.add_argument('--seed', default=None) parser.add_argument('--image', default=None, help="image prompt") parser.add_argument('--image_config', default=None, help="image config csv") parser.add_argument('--known_view_interval', type=int, default=4, help="train default view with RGB loss every & iters, only valid if --image is not None.") parser.add_argument('--IF', action='store_true', help="experimental: use DeepFloyd IF as the guidance model for nerf stage") parser.add_argument('--guidance', type=str, nargs='*', default=['SD'], help='guidance model') parser.add_argument('--guidance_scale', type=float, default=100, help="diffusion model classifier-free guidance scale") parser.add_argument('--save_mesh', action='store_true', help="export an obj mesh with texture") parser.add_argument('--mcubes_resolution', type=int, default=256, help="mcubes resolution for extracting mesh") parser.add_argument('--decimate_target', type=int, default=5e4, help="target face number for mesh decimation") parser.add_argument('--dmtet', action='store_true', help="use dmtet finetuning") parser.add_argument('--tet_grid_size', type=int, default=128, help="tet grid size") parser.add_argument('--init_with', type=str, default='', help="ckpt to init dmtet") parser.add_argument('--lock_geo', action='store_true', help="disable dmtet to learn geometry") ## Perp-Neg options parser.add_argument('--perpneg', action='store_true', help="use perp_neg") parser.add_argument('--negative_w', type=float, default=-2, help="scale of the weight of the negative prompt, the larger the better at avoiding janus problem, but may cause flat faces, vary between 0 to -4") parser.add_argument('--front_decay_factor', type=float, default=2, help="decay factor for the front prompt") parser.add_argument('--side_decay_factor', type=float, default=10, help="decay factor for the side prompt") ### training options parser.add_argument('--iters', type=int, default=10000, help="training iters") parser.add_argument('--lr', type=float, default=1e-3, help="max learning rate") parser.add_argument('--ckpt', type=str, default='latest', help="possible options are ['latest', 'scratch', 'best', 'latest_model']") parser.add_argument('--cuda_ray', action='store_true', help="use CUDA raymarching instead of pytorch") parser.add_argument('--taichi_ray', action='store_true', help="use taichi raymarching") parser.add_argument('--max_steps', type=int, default=1024, help="max num steps sampled per ray (only valid when using --cuda_ray)") parser.add_argument('--num_steps', type=int, default=64, help="num steps sampled per ray (only valid when not using --cuda_ray)") parser.add_argument('--upsample_steps', type=int, default=32, help="num steps up-sampled per ray (only valid when not using --cuda_ray)") parser.add_argument('--update_extra_interval', type=int, default=16, help="iter interval to update extra status (only valid when using --cuda_ray)") parser.add_argument('--max_ray_batch', type=int, default=4096, help="batch size of rays at inference to avoid OOM (only valid when not using --cuda_ray)") parser.add_argument('--latent_iter_ratio', type=float, default=0.2, help="training iters that only use albedo shading") parser.add_argument('--albedo_iter_ratio', type=float, default=0, help="training iters that only use albedo shading") parser.add_argument('--min_ambient_ratio', type=float, default=0.1, help="minimum ambient ratio to use in lambertian shading") parser.add_argument('--textureless_ratio', type=float, default=0.2, help="ratio of textureless shading") parser.add_argument('--jitter_pose', action='store_true', help="add jitters to the randomly sampled camera poses") parser.add_argument('--jitter_center', type=float, default=0.2, help="amount of jitter to add to sampled camera pose's center (camera location)") parser.add_argument('--jitter_target', type=float, default=0.2, help="amount of jitter to add to sampled camera pose's target (i.e. 'look-at')") parser.add_argument('--jitter_up', type=float, default=0.02, help="amount of jitter to add to sampled camera pose's up-axis (i.e. 'camera roll')") parser.add_argument('--uniform_sphere_rate', type=float, default=0, help="likelihood of sampling camera location uniformly on the sphere surface area") parser.add_argument('--grad_clip', type=float, default=-1, help="clip grad of all grad to this limit, negative value disables it") parser.add_argument('--grad_clip_rgb', type=float, default=-1, help="clip grad of rgb space grad to this limit, negative value disables it") # model options parser.add_argument('--bg_radius', type=float, default=1.4, help="if positive, use a background model at sphere(bg_radius)") parser.add_argument('--density_activation', type=str, default='exp', choices=['softplus', 'exp'], help="density activation function") parser.add_argument('--density_thresh', type=float, default=10, help="threshold for density grid to be occupied") parser.add_argument('--blob_density', type=float, default=5, help="max (center) density for the density blob") parser.add_argument('--blob_radius', type=float, default=0.2, help="control the radius for the density blob") # network backbone parser.add_argument('--backbone', type=str, default='grid', choices=['grid_tcnn', 'grid', 'vanilla', 'grid_taichi'], help="nerf backbone") parser.add_argument('--optim', type=str, default='adan', choices=['adan', 'adam'], help="optimizer") parser.add_argument('--sd_version', type=str, default='2.1', choices=['1.5', '2.0', '2.1'], help="stable diffusion version") parser.add_argument('--hf_key', type=str, default=None, help="hugging face Stable diffusion model key") # try this if CUDA OOM parser.add_argument('--fp16', action='store_true', help="use float16 for training") parser.add_argument('--vram_O', action='store_true', help="optimization for low VRAM usage") # rendering resolution in training, increase these for better quality / decrease these if CUDA OOM even if --vram_O enabled. parser.add_argument('--w', type=int, default=64, help="render width for NeRF in training") parser.add_argument('--h', type=int, default=64, help="render height for NeRF in training") parser.add_argument('--known_view_scale', type=float, default=1.5, help="multiply --h/w by this for known view rendering") parser.add_argument('--known_view_noise_scale', type=float, default=2e-3, help="random camera noise added to rays_o and rays_d") parser.add_argument('--dmtet_reso_scale', type=float, default=8, help="multiply --h/w by this for dmtet finetuning") parser.add_argument('--batch_size', type=int, default=1, help="images to render per batch using NeRF") ### dataset options parser.add_argument('--bound', type=float, default=1, help="assume the scene is bounded in box(-bound, bound)") parser.add_argument('--dt_gamma', type=float, default=0, help="dt_gamma (>=0) for adaptive ray marching. set to 0 to disable, >0 to accelerate rendering (but usually with worse quality)") parser.add_argument('--min_near', type=float, default=0.01, help="minimum near distance for camera") parser.add_argument('--radius_range', type=float, nargs='*', default=[3.0, 3.5], help="training camera radius range") parser.add_argument('--theta_range', type=float, nargs='*', default=[45, 105], help="training camera range along the polar angles (i.e. up and down). See advanced.md for details.") parser.add_argument('--phi_range', type=float, nargs='*', default=[-180, 180], help="training camera range along the azimuth angles (i.e. left and right). See advanced.md for details.") parser.add_argument('--fovy_range', type=float, nargs='*', default=[10, 30], help="training camera fovy range") parser.add_argument('--default_radius', type=float, default=3.2, help="radius for the default view") parser.add_argument('--default_polar', type=float, default=90, help="polar for the default view") parser.add_argument('--default_azimuth', type=float, default=0, help="azimuth for the default view") parser.add_argument('--default_fovy', type=float, default=20, help="fovy for the default view") parser.add_argument('--progressive_view', action='store_true', help="progressively expand view sampling range from default to full") parser.add_argument('--progressive_view_init_ratio', type=float, default=0.2, help="initial ratio of final range, used for progressive_view") parser.add_argument('--progressive_level', action='store_true', help="progressively increase gridencoder's max_level") parser.add_argument('--angle_overhead', type=float, default=30, help="[0, angle_overhead] is the overhead region") parser.add_argument('--angle_front', type=float, default=60, help="[0, angle_front] is the front region, [180, 180+angle_front] the back region, otherwise the side region.") parser.add_argument('--t_range', type=float, nargs='*', default=[0.02, 0.98], help="stable diffusion time steps range") parser.add_argument('--dont_override_stuff',action='store_true', help="Don't override t_range, etc.") ### regularizations parser.add_argument('--lambda_entropy', type=float, default=1e-3, help="loss scale for alpha entropy") parser.add_argument('--lambda_opacity', type=float, default=0, help="loss scale for alpha value") parser.add_argument('--lambda_orient', type=float, default=1e-2, help="loss scale for orientation") parser.add_argument('--lambda_tv', type=float, default=0, help="loss scale for total variation") parser.add_argument('--lambda_wd', type=float, default=0, help="loss scale") parser.add_argument('--lambda_mesh_normal', type=float, default=0.5, help="loss scale for mesh normal smoothness") parser.add_argument('--lambda_mesh_laplacian', type=float, default=0.5, help="loss scale for mesh laplacian") parser.add_argument('--lambda_guidance', type=float, default=1, help="loss scale for SDS") parser.add_argument('--lambda_rgb', type=float, default=1000, help="loss scale for RGB") parser.add_argument('--lambda_mask', type=float, default=500, help="loss scale for mask (alpha)") parser.add_argument('--lambda_normal', type=float, default=0, help="loss scale for normal map") parser.add_argument('--lambda_depth', type=float, default=10, help="loss scale for relative depth") parser.add_argument('--lambda_2d_normal_smooth', type=float, default=0, help="loss scale for 2D normal image smoothness") parser.add_argument('--lambda_3d_normal_smooth', type=float, default=0, help="loss scale for 3D normal image smoothness") ### debugging options parser.add_argument('--save_guidance', action='store_true', help="save images of the per-iteration NeRF renders, added noise, denoised (i.e. guidance), fully-denoised. Useful for debugging, but VERY SLOW and takes lots of memory!") parser.add_argument('--save_guidance_interval', type=int, default=10, help="save guidance every X step") ### GUI options parser.add_argument('--gui', action='store_true', help="start a GUI") parser.add_argument('--W', type=int, default=800, help="GUI width") parser.add_argument('--H', type=int, default=800, help="GUI height") parser.add_argument('--radius', type=float, default=5, help="default GUI camera radius from center") parser.add_argument('--fovy', type=float, default=20, help="default GUI camera fovy") parser.add_argument('--light_theta', type=float, default=60, help="default GUI light direction in [0, 180], corresponding to elevation [90, -90]") parser.add_argument('--light_phi', type=float, default=0, help="default GUI light direction in [0, 360), azimuth") parser.add_argument('--max_spp', type=int, default=1, help="GUI rendering max sample per pixel") parser.add_argument('--zero123_config', type=str, default='./pretrained/zero123/sd-objaverse-finetune-c_concat-256.yaml', help="config file for zero123") parser.add_argument('--zero123_ckpt', type=str, default='./pretrained/zero123/105000.ckpt', help="ckpt for zero123") parser.add_argument('--zero123_grad_scale', type=str, default='angle', help="whether to scale the gradients based on 'angle' or 'None'") parser.add_argument('--dataset_size_train', type=int, default=100, help="Length of train dataset i.e. # of iterations per epoch") parser.add_argument('--dataset_size_valid', type=int, default=8, help="# of frames to render in the turntable video in validation") parser.add_argument('--dataset_size_test', type=int, default=100, help="# of frames to render in the turntable video at test time") parser.add_argument('--exp_start_iter', type=int, default=None, help="start iter # for experiment, to calculate progressive_view and progressive_level") parser.add_argument('--exp_end_iter', type=int, default=None, help="end iter # for experiment, to calculate progressive_view and progressive_level") opt = parser.parse_args() if opt.O: opt.fp16 = True opt.cuda_ray = True elif opt.O2: opt.fp16 = True opt.backbone = 'vanilla' opt.progressive_level = True if opt.IF: if 'SD' in opt.guidance: opt.guidance.remove('SD') opt.guidance.append('IF') opt.latent_iter_ratio = 0 # must not do as_latent opt.images, opt.ref_radii, opt.ref_polars, opt.ref_azimuths, opt.zero123_ws = [], [], [], [], [] opt.default_zero123_w = 1 opt.exp_start_iter = opt.exp_start_iter or 0 opt.exp_end_iter = opt.exp_end_iter or opt.iters # parameters for image-conditioned generation if opt.image is not None or opt.image_config is not None: if opt.text is None: # use zero123 guidance model when only providing image opt.guidance = ['zero123'] if not opt.dont_override_stuff: opt.fovy_range = [opt.default_fovy, opt.default_fovy] # fix fov as zero123 doesn't support changing fov opt.guidance_scale = 5 opt.lambda_3d_normal_smooth = 10 else: # use stable-diffusion when providing both text and image opt.guidance = ['SD', 'clip'] if not opt.dont_override_stuff: opt.guidance_scale = 10 opt.t_range = [0.2, 0.6] opt.known_view_interval = 2 opt.lambda_3d_normal_smooth = 20 opt.bg_radius = -1 # smoothness opt.lambda_entropy = 1 opt.lambda_orient = 1 # latent warmup is not needed opt.latent_iter_ratio = 0 if not opt.dont_override_stuff: opt.albedo_iter_ratio = 0 # make shape init more stable opt.progressive_view = True opt.progressive_level = True if opt.image is not None: opt.images += [opt.image] opt.ref_radii += [opt.default_radius] opt.ref_polars += [opt.default_polar] opt.ref_azimuths += [opt.default_azimuth] opt.zero123_ws += [opt.default_zero123_w] if opt.image_config is not None: # for multiview (zero123) conf = pd.read_csv(opt.image_config, skipinitialspace=True) opt.images += list(conf.image) opt.ref_radii += list(conf.radius) opt.ref_polars += list(conf.polar) opt.ref_azimuths += list(conf.azimuth) opt.zero123_ws += list(conf.zero123_weight) if opt.image is None: opt.default_radius = opt.ref_radii[0] opt.default_polar = opt.ref_polars[0] opt.default_azimuth = opt.ref_azimuths[0] opt.default_zero123_w = opt.zero123_ws[0] # reset to None if len(opt.images) == 0: opt.images = None # default parameters for finetuning if opt.dmtet: opt.h = int(opt.h * opt.dmtet_reso_scale) opt.w = int(opt.w * opt.dmtet_reso_scale) opt.known_view_scale = 1 if not opt.dont_override_stuff: opt.t_range = [0.02, 0.50] # ref: magic3D if opt.images is not None: opt.lambda_normal = 0 opt.lambda_depth = 0 if opt.text is not None and not opt.dont_override_stuff: opt.t_range = [0.20, 0.50] # assume finetuning opt.latent_iter_ratio = 0 opt.albedo_iter_ratio = 0 opt.progressive_view = False # opt.progressive_level = False # record full range for progressive view expansion if opt.progressive_view: if not opt.dont_override_stuff: # disable as they disturb progressive view opt.jitter_pose = False opt.uniform_sphere_rate = 0 # back up full range opt.full_radius_range = opt.radius_range opt.full_theta_range = opt.theta_range opt.full_phi_range = opt.phi_range opt.full_fovy_range = opt.fovy_range if opt.backbone == 'vanilla': from nerf.network import NeRFNetwork elif opt.backbone == 'grid': from nerf.network_grid import NeRFNetwork elif opt.backbone == 'grid_tcnn': from nerf.network_grid_tcnn import NeRFNetwork elif opt.backbone == 'grid_taichi': opt.cuda_ray = False opt.taichi_ray = True import taichi as ti from nerf.network_grid_taichi import NeRFNetwork taichi_half2_opt = True taichi_init_args = {"arch": ti.cuda, "device_memory_GB": 4.0} if taichi_half2_opt: taichi_init_args["half2_vectorization"] = True ti.init(**taichi_init_args) else: raise NotImplementedError(f'--backbone {opt.backbone} is not implemented!') print(opt) if opt.seed is not None: seed_everything(int(opt.seed)) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = NeRFNetwork(opt).to(device) if opt.dmtet and opt.init_with != '': if opt.init_with.endswith('.pth'): # load pretrained weights to init dmtet state_dict = torch.load(opt.init_with, map_location=device) model.load_state_dict(state_dict['model'], strict=False) if opt.cuda_ray: model.mean_density = state_dict['mean_density'] model.init_tet() else: # assume a mesh to init dmtet (experimental, not working well now!) import trimesh mesh = trimesh.load(opt.init_with, force='mesh', skip_material=True, process=False) model.init_tet(mesh=mesh) print(model) if opt.six_views: guidance = None # no need to load guidance model at test trainer = Trainer(' '.join(sys.argv), 'df', opt, model, guidance, device=device, workspace=opt.workspace, fp16=opt.fp16, use_checkpoint=opt.ckpt) test_loader = NeRFDataset(opt, device=device, type='six_views', H=opt.H, W=opt.W, size=6).dataloader(batch_size=1) trainer.test(test_loader, write_video=False) if opt.save_mesh: trainer.save_mesh() elif opt.test: guidance = None # no need to load guidance model at test trainer = Trainer(' '.join(sys.argv), 'df', opt, model, guidance, device=device, workspace=opt.workspace, fp16=opt.fp16, use_checkpoint=opt.ckpt) if opt.gui: from nerf.gui import NeRFGUI gui = NeRFGUI(opt, trainer) gui.render() else: test_loader = NeRFDataset(opt, device=device, type='test', H=opt.H, W=opt.W, size=opt.dataset_size_test).dataloader(batch_size=1) trainer.test(test_loader) if opt.save_mesh: trainer.save_mesh() else: train_loader = NeRFDataset(opt, device=device, type='train', H=opt.h, W=opt.w, size=opt.dataset_size_train * opt.batch_size).dataloader() if opt.optim == 'adan': from optimizer import Adan # Adan usually requires a larger LR optimizer = lambda model: Adan(model.get_params(5 * opt.lr), eps=1e-8, weight_decay=2e-5, max_grad_norm=5.0, foreach=False) else: # adam optimizer = lambda model: torch.optim.Adam(model.get_params(opt.lr), betas=(0.9, 0.99), eps=1e-15) if opt.backbone == 'vanilla': scheduler = lambda optimizer: optim.lr_scheduler.LambdaLR(optimizer, lambda iter: 0.1 ** min(iter / opt.iters, 1)) else: scheduler = lambda optimizer: optim.lr_scheduler.LambdaLR(optimizer, lambda iter: 1) # fixed # scheduler = lambda optimizer: optim.lr_scheduler.LambdaLR(optimizer, lambda iter: 0.1 ** min(iter / opt.iters, 1)) guidance = nn.ModuleDict() if 'SD' in opt.guidance: from guidance.sd_utils import StableDiffusion guidance['SD'] = StableDiffusion(device, opt.fp16, opt.vram_O, opt.sd_version, opt.hf_key, opt.t_range) if 'IF' in opt.guidance: from guidance.if_utils import IF guidance['IF'] = IF(device, opt.vram_O, opt.t_range) if 'zero123' in opt.guidance: from guidance.zero123_utils import Zero123 guidance['zero123'] = Zero123(device=device, fp16=opt.fp16, config=opt.zero123_config, ckpt=opt.zero123_ckpt, vram_O=opt.vram_O, t_range=opt.t_range, opt=opt) if 'clip' in opt.guidance: from guidance.clip_utils import CLIP guidance['clip'] = CLIP(device) trainer = Trainer(' '.join(sys.argv), 'df', opt, model, guidance, device=device, workspace=opt.workspace, optimizer=optimizer, ema_decay=0.95, fp16=opt.fp16, lr_scheduler=scheduler, use_checkpoint=opt.ckpt, scheduler_update_every_step=True) trainer.default_view_data = train_loader._data.get_default_view_data() if opt.gui: from nerf.gui import NeRFGUI gui = NeRFGUI(opt, trainer, train_loader) gui.render() else: valid_loader = NeRFDataset(opt, device=device, type='val', H=opt.H, W=opt.W, size=opt.dataset_size_valid).dataloader(batch_size=1) test_loader = NeRFDataset(opt, device=device, type='test', H=opt.H, W=opt.W, size=opt.dataset_size_test).dataloader(batch_size=1) max_epoch = np.ceil(opt.iters / len(train_loader)).astype(np.int32) trainer.train(train_loader, valid_loader, test_loader, max_epoch) if opt.save_mesh: trainer.save_mesh() ================================================ FILE: stable-dreamfusion/meshutils.py ================================================ import numpy as np import pymeshlab as pml def poisson_mesh_reconstruction(points, normals=None): # points/normals: [N, 3] np.ndarray import open3d as o3d pcd = o3d.geometry.PointCloud() pcd.points = o3d.utility.Vector3dVector(points) # outlier removal pcd, ind = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=10) # normals if normals is None: pcd.estimate_normals() else: pcd.normals = o3d.utility.Vector3dVector(normals[ind]) # visualize o3d.visualization.draw_geometries([pcd], point_show_normal=False) mesh, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(pcd, depth=9) vertices_to_remove = densities < np.quantile(densities, 0.1) mesh.remove_vertices_by_mask(vertices_to_remove) # visualize o3d.visualization.draw_geometries([mesh]) vertices = np.asarray(mesh.vertices) triangles = np.asarray(mesh.triangles) print(f'[INFO] poisson mesh reconstruction: {points.shape} --> {vertices.shape} / {triangles.shape}') return vertices, triangles def decimate_mesh(verts, faces, target, backend='pymeshlab', remesh=False, optimalplacement=True): # optimalplacement: default is True, but for flat mesh must turn False to prevent spike artifect. _ori_vert_shape = verts.shape _ori_face_shape = faces.shape if backend == 'pyfqmr': import pyfqmr solver = pyfqmr.Simplify() solver.setMesh(verts, faces) solver.simplify_mesh(target_count=target, preserve_border=False, verbose=False) verts, faces, normals = solver.getMesh() else: m = pml.Mesh(verts, faces) ms = pml.MeshSet() ms.add_mesh(m, 'mesh') # will copy! # filters # ms.meshing_decimation_clustering(threshold=pml.Percentage(1)) ms.meshing_decimation_quadric_edge_collapse(targetfacenum=int(target), optimalplacement=optimalplacement) if remesh: # ms.apply_coord_taubin_smoothing() ms.meshing_isotropic_explicit_remeshing(iterations=3, targetlen=pml.Percentage(1)) # extract mesh m = ms.current_mesh() verts = m.vertex_matrix() faces = m.face_matrix() print(f'[INFO] mesh decimation: {_ori_vert_shape} --> {verts.shape}, {_ori_face_shape} --> {faces.shape}') return verts, faces def clean_mesh(verts, faces, v_pct=1, min_f=8, min_d=5, repair=True, remesh=True, remesh_size=0.01): # verts: [N, 3] # faces: [N, 3] _ori_vert_shape = verts.shape _ori_face_shape = faces.shape m = pml.Mesh(verts, faces) ms = pml.MeshSet() ms.add_mesh(m, 'mesh') # will copy! # filters ms.meshing_remove_unreferenced_vertices() # verts not refed by any faces if v_pct > 0: ms.meshing_merge_close_vertices(threshold=pml.Percentage(v_pct)) # 1/10000 of bounding box diagonal ms.meshing_remove_duplicate_faces() # faces defined by the same verts ms.meshing_remove_null_faces() # faces with area == 0 if min_d > 0: ms.meshing_remove_connected_component_by_diameter(mincomponentdiag=pml.Percentage(min_d)) if min_f > 0: ms.meshing_remove_connected_component_by_face_number(mincomponentsize=min_f) if repair: # ms.meshing_remove_t_vertices(method=0, threshold=40, repeat=True) ms.meshing_repair_non_manifold_edges(method=0) ms.meshing_repair_non_manifold_vertices(vertdispratio=0) if remesh: # ms.apply_coord_taubin_smoothing() ms.meshing_isotropic_explicit_remeshing(iterations=3, targetlen=pml.AbsoluteValue(remesh_size)) # extract mesh m = ms.current_mesh() verts = m.vertex_matrix() faces = m.face_matrix() print(f'[INFO] mesh cleaning: {_ori_vert_shape} --> {verts.shape}, {_ori_face_shape} --> {faces.shape}') return verts, faces ================================================ FILE: stable-dreamfusion/nerf/gui.py ================================================ import math import torch import numpy as np import dearpygui.dearpygui as dpg from scipy.spatial.transform import Rotation as R from nerf.utils import * class OrbitCamera: def __init__(self, W, H, r=2, fovy=60): self.W = W self.H = H self.radius = r # camera distance from center self.fovy = fovy # in degree self.center = np.array([0, 0, 0], dtype=np.float32) # look at this point self.rot = R.from_matrix(np.eye(3)) self.up = np.array([0, 1, 0], dtype=np.float32) # need to be normalized! self.near = 0.001 self.far = 1000 # pose @property def pose(self): # first move camera to radius res = np.eye(4, dtype=np.float32) res[2, 3] = self.radius # rotate rot = np.eye(4, dtype=np.float32) rot[:3, :3] = self.rot.as_matrix() res = rot @ res # translate res[:3, 3] -= self.center return res # intrinsics @property def intrinsics(self): focal = self.H / (2 * np.tan(np.deg2rad(self.fovy) / 2)) return np.array([focal, focal, self.W // 2, self.H // 2]) @property def mvp(self): focal = self.H / (2 * np.tan(np.deg2rad(self.fovy) / 2)) projection = np.array([ [2*focal/self.W, 0, 0, 0], [0, -2*focal/self.H, 0, 0], [0, 0, -(self.far+self.near)/(self.far-self.near), -(2*self.far*self.near)/(self.far-self.near)], [0, 0, -1, 0] ], dtype=np.float32) return projection @ np.linalg.inv(self.pose) # [4, 4] def orbit(self, dx, dy): # rotate along camera up/side axis! side = self.rot.as_matrix()[:3, 0] # why this is side --> ? # already normalized. rotvec_x = self.up * np.deg2rad(-0.1 * dx) rotvec_y = side * np.deg2rad(-0.1 * dy) self.rot = R.from_rotvec(rotvec_x) * R.from_rotvec(rotvec_y) * self.rot def scale(self, delta): self.radius *= 1.1 ** (-delta) def pan(self, dx, dy, dz=0): # pan in camera coordinate system (careful on the sensitivity!) self.center += 0.0005 * self.rot.as_matrix()[:3, :3] @ np.array([dx, -dy, dz]) class NeRFGUI: def __init__(self, opt, trainer, loader=None, debug=True): self.opt = opt # shared with the trainer's opt to support in-place modification of rendering parameters. self.W = opt.W self.H = opt.H self.cam = OrbitCamera(opt.W, opt.H, r=opt.radius, fovy=opt.fovy) self.debug = debug self.bg_color = torch.ones(3, dtype=torch.float32) # default white bg self.training = False self.step = 0 # training step self.trainer = trainer self.loader = loader self.render_buffer = np.zeros((self.W, self.H, 3), dtype=np.float32) self.need_update = True # camera moved, should reset accumulation self.spp = 1 # sample per pixel self.light_dir = np.array([opt.light_theta, opt.light_phi]) self.ambient_ratio = 1.0 self.mode = 'image' # choose from ['image', 'depth'] self.shading = 'albedo' self.dynamic_resolution = True if not self.opt.dmtet else False self.downscale = 1 self.train_steps = 16 dpg.create_context() self.register_dpg() self.test_step() def __del__(self): dpg.destroy_context() def train_step(self): starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True) starter.record() outputs = self.trainer.train_gui(self.loader, step=self.train_steps) ender.record() torch.cuda.synchronize() t = starter.elapsed_time(ender) self.step += self.train_steps self.need_update = True dpg.set_value("_log_train_time", f'{t:.4f}ms ({int(1000/t)} FPS)') dpg.set_value("_log_train_log", f'step = {self.step: 5d} (+{self.train_steps: 2d}), loss = {outputs["loss"]:.4f}, lr = {outputs["lr"]:.5f}') # dynamic train steps # max allowed train time per-frame is 500 ms full_t = t / self.train_steps * 16 train_steps = min(16, max(4, int(16 * 500 / full_t))) if train_steps > self.train_steps * 1.2 or train_steps < self.train_steps * 0.8: self.train_steps = train_steps def prepare_buffer(self, outputs): if self.mode == 'image': return outputs['image'].astype(np.float32) else: depth = outputs['depth'].astype(np.float32) depth = (depth - depth.min()) / (depth.max() - depth.min() + 1e-6) return np.expand_dims(depth, -1).repeat(3, -1) def test_step(self): if self.need_update or self.spp < self.opt.max_spp: starter, ender = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True) starter.record() outputs = self.trainer.test_gui(self.cam.pose, self.cam.intrinsics, self.cam.mvp, self.W, self.H, self.bg_color, self.spp, self.downscale, self.light_dir, self.ambient_ratio, self.shading) ender.record() torch.cuda.synchronize() t = starter.elapsed_time(ender) # update dynamic resolution if self.dynamic_resolution: # max allowed infer time per-frame is 200 ms full_t = t / (self.downscale ** 2) downscale = min(1, max(1/4, math.sqrt(200 / full_t))) if downscale > self.downscale * 1.2 or downscale < self.downscale * 0.8: self.downscale = downscale if self.need_update: self.render_buffer = self.prepare_buffer(outputs) self.spp = 1 self.need_update = False else: self.render_buffer = (self.render_buffer * self.spp + self.prepare_buffer(outputs)) / (self.spp + 1) self.spp += 1 dpg.set_value("_log_infer_time", f'{t:.4f}ms ({int(1000/t)} FPS)') dpg.set_value("_log_resolution", f'{int(self.downscale * self.W)}x{int(self.downscale * self.H)}') dpg.set_value("_log_spp", self.spp) dpg.set_value("_texture", self.render_buffer) def register_dpg(self): ### register texture with dpg.texture_registry(show=False): dpg.add_raw_texture(self.W, self.H, self.render_buffer, format=dpg.mvFormat_Float_rgb, tag="_texture") ### register window # the rendered image, as the primary window with dpg.window(tag="_primary_window", width=self.W, height=self.H): # add the texture dpg.add_image("_texture") dpg.set_primary_window("_primary_window", True) # control window with dpg.window(label="Control", tag="_control_window", width=400, height=300): # text prompt if self.opt.text is not None: dpg.add_text("text: " + self.opt.text, tag="_log_prompt_text") if self.opt.negative != '': dpg.add_text("negative text: " + self.opt.negative, tag="_log_prompt_negative_text") # button theme with dpg.theme() as theme_button: with dpg.theme_component(dpg.mvButton): dpg.add_theme_color(dpg.mvThemeCol_Button, (23, 3, 18)) dpg.add_theme_color(dpg.mvThemeCol_ButtonHovered, (51, 3, 47)) dpg.add_theme_color(dpg.mvThemeCol_ButtonActive, (83, 18, 83)) dpg.add_theme_style(dpg.mvStyleVar_FrameRounding, 5) dpg.add_theme_style(dpg.mvStyleVar_FramePadding, 3, 3) # time if not self.opt.test: with dpg.group(horizontal=True): dpg.add_text("Train time: ") dpg.add_text("no data", tag="_log_train_time") with dpg.group(horizontal=True): dpg.add_text("Infer time: ") dpg.add_text("no data", tag="_log_infer_time") with dpg.group(horizontal=True): dpg.add_text("SPP: ") dpg.add_text("1", tag="_log_spp") # train button if not self.opt.test: with dpg.collapsing_header(label="Train", default_open=True): with dpg.group(horizontal=True): dpg.add_text("Train: ") def callback_train(sender, app_data): if self.training: self.training = False dpg.configure_item("_button_train", label="start") else: self.training = True dpg.configure_item("_button_train", label="stop") dpg.add_button(label="start", tag="_button_train", callback=callback_train) dpg.bind_item_theme("_button_train", theme_button) def callback_reset(sender, app_data): @torch.no_grad() def weight_reset(m: nn.Module): reset_parameters = getattr(m, "reset_parameters", None) if callable(reset_parameters): m.reset_parameters() self.trainer.model.apply(fn=weight_reset) self.trainer.model.reset_extra_state() # for cuda_ray density_grid and step_counter self.need_update = True dpg.add_button(label="reset", tag="_button_reset", callback=callback_reset) dpg.bind_item_theme("_button_reset", theme_button) with dpg.group(horizontal=True): dpg.add_text("Checkpoint: ") def callback_save(sender, app_data): self.trainer.save_checkpoint(full=True, best=False) dpg.set_value("_log_ckpt", "saved " + os.path.basename(self.trainer.stats["checkpoints"][-1])) self.trainer.epoch += 1 # use epoch to indicate different calls. dpg.add_button(label="save", tag="_button_save", callback=callback_save) dpg.bind_item_theme("_button_save", theme_button) dpg.add_text("", tag="_log_ckpt") # save mesh with dpg.group(horizontal=True): dpg.add_text("Marching Cubes: ") def callback_mesh(sender, app_data): self.trainer.save_mesh() dpg.set_value("_log_mesh", "saved " + f'{self.trainer.name}_{self.trainer.epoch}.ply') self.trainer.epoch += 1 # use epoch to indicate different calls. dpg.add_button(label="mesh", tag="_button_mesh", callback=callback_mesh) dpg.bind_item_theme("_button_mesh", theme_button) dpg.add_text("", tag="_log_mesh") with dpg.group(horizontal=True): dpg.add_text("", tag="_log_train_log") # rendering options with dpg.collapsing_header(label="Options", default_open=True): # dynamic rendering resolution with dpg.group(horizontal=True): def callback_set_dynamic_resolution(sender, app_data): if self.dynamic_resolution: self.dynamic_resolution = False self.downscale = 1 else: self.dynamic_resolution = True self.need_update = True dpg.add_checkbox(label="dynamic resolution", default_value=self.dynamic_resolution, callback=callback_set_dynamic_resolution) dpg.add_text(f"{self.W}x{self.H}", tag="_log_resolution") # mode combo def callback_change_mode(sender, app_data): self.mode = app_data self.need_update = True dpg.add_combo(('image', 'depth'), label='mode', default_value=self.mode, callback=callback_change_mode) # bg_color picker def callback_change_bg(sender, app_data): self.bg_color = torch.tensor(app_data[:3], dtype=torch.float32) # only need RGB in [0, 1] self.need_update = True dpg.add_color_edit((255, 255, 255), label="Background Color", width=200, tag="_color_editor", no_alpha=True, callback=callback_change_bg) # fov slider def callback_set_fovy(sender, app_data): self.cam.fovy = app_data self.need_update = True dpg.add_slider_int(label="FoV (vertical)", min_value=1, max_value=120, format="%d deg", default_value=self.cam.fovy, callback=callback_set_fovy) # dt_gamma slider def callback_set_dt_gamma(sender, app_data): self.opt.dt_gamma = app_data self.need_update = True dpg.add_slider_float(label="dt_gamma", min_value=0, max_value=0.1, format="%.5f", default_value=self.opt.dt_gamma, callback=callback_set_dt_gamma) # max_steps slider def callback_set_max_steps(sender, app_data): self.opt.max_steps = app_data self.need_update = True dpg.add_slider_int(label="max steps", min_value=1, max_value=1024, format="%d", default_value=self.opt.max_steps, callback=callback_set_max_steps) # aabb slider def callback_set_aabb(sender, app_data, user_data): # user_data is the dimension for aabb (xmin, ymin, zmin, xmax, ymax, zmax) self.trainer.model.aabb_infer[user_data] = app_data # also change train aabb ? [better not...] #self.trainer.model.aabb_train[user_data] = app_data self.need_update = True dpg.add_separator() dpg.add_text("Axis-aligned bounding box:") with dpg.group(horizontal=True): dpg.add_slider_float(label="x", width=150, min_value=-self.opt.bound, max_value=0, format="%.2f", default_value=-self.opt.bound, callback=callback_set_aabb, user_data=0) dpg.add_slider_float(label="", width=150, min_value=0, max_value=self.opt.bound, format="%.2f", default_value=self.opt.bound, callback=callback_set_aabb, user_data=3) with dpg.group(horizontal=True): dpg.add_slider_float(label="y", width=150, min_value=-self.opt.bound, max_value=0, format="%.2f", default_value=-self.opt.bound, callback=callback_set_aabb, user_data=1) dpg.add_slider_float(label="", width=150, min_value=0, max_value=self.opt.bound, format="%.2f", default_value=self.opt.bound, callback=callback_set_aabb, user_data=4) with dpg.group(horizontal=True): dpg.add_slider_float(label="z", width=150, min_value=-self.opt.bound, max_value=0, format="%.2f", default_value=-self.opt.bound, callback=callback_set_aabb, user_data=2) dpg.add_slider_float(label="", width=150, min_value=0, max_value=self.opt.bound, format="%.2f", default_value=self.opt.bound, callback=callback_set_aabb, user_data=5) # light dir def callback_set_light_dir(sender, app_data, user_data): self.light_dir[user_data] = app_data self.need_update = True dpg.add_separator() dpg.add_text("Plane Light Direction:") with dpg.group(horizontal=True): dpg.add_slider_float(label="theta", min_value=0, max_value=180, format="%.2f", default_value=self.opt.light_theta, callback=callback_set_light_dir, user_data=0) with dpg.group(horizontal=True): dpg.add_slider_float(label="phi", min_value=0, max_value=360, format="%.2f", default_value=self.opt.light_phi, callback=callback_set_light_dir, user_data=1) # ambient ratio def callback_set_abm_ratio(sender, app_data): self.ambient_ratio = app_data self.need_update = True dpg.add_slider_float(label="ambient", min_value=0, max_value=1.0, format="%.5f", default_value=self.ambient_ratio, callback=callback_set_abm_ratio) # shading mode def callback_change_shading(sender, app_data): self.shading = app_data self.need_update = True dpg.add_combo(('albedo', 'lambertian', 'textureless', 'normal'), label='shading', default_value=self.shading, callback=callback_change_shading) # debug info if self.debug: with dpg.collapsing_header(label="Debug"): # pose dpg.add_separator() dpg.add_text("Camera Pose:") dpg.add_text(str(self.cam.pose), tag="_log_pose") ### register camera handler def callback_camera_drag_rotate(sender, app_data): if not dpg.is_item_focused("_primary_window"): return dx = app_data[1] dy = app_data[2] self.cam.orbit(dx, dy) self.need_update = True if self.debug: dpg.set_value("_log_pose", str(self.cam.pose)) def callback_camera_wheel_scale(sender, app_data): if not dpg.is_item_focused("_primary_window"): return delta = app_data self.cam.scale(delta) self.need_update = True if self.debug: dpg.set_value("_log_pose", str(self.cam.pose)) def callback_camera_drag_pan(sender, app_data): if not dpg.is_item_focused("_primary_window"): return dx = app_data[1] dy = app_data[2] self.cam.pan(dx, dy) self.need_update = True if self.debug: dpg.set_value("_log_pose", str(self.cam.pose)) with dpg.handler_registry(): dpg.add_mouse_drag_handler(button=dpg.mvMouseButton_Left, callback=callback_camera_drag_rotate) dpg.add_mouse_wheel_handler(callback=callback_camera_wheel_scale) dpg.add_mouse_drag_handler(button=dpg.mvMouseButton_Right, callback=callback_camera_drag_pan) dpg.create_viewport(title='torch-ngp', width=self.W, height=self.H, resizable=False) # TODO: seems dearpygui doesn't support resizing texture... # def callback_resize(sender, app_data): # self.W = app_data[0] # self.H = app_data[1] # # how to reload texture ??? # dpg.set_viewport_resize_callback(callback_resize) ### global theme with dpg.theme() as theme_no_padding: with dpg.theme_component(dpg.mvAll): # set all padding to 0 to avoid scroll bar dpg.add_theme_style(dpg.mvStyleVar_WindowPadding, 0, 0, category=dpg.mvThemeCat_Core) dpg.add_theme_style(dpg.mvStyleVar_FramePadding, 0, 0, category=dpg.mvThemeCat_Core) dpg.add_theme_style(dpg.mvStyleVar_CellPadding, 0, 0, category=dpg.mvThemeCat_Core) dpg.bind_item_theme("_primary_window", theme_no_padding) dpg.setup_dearpygui() #dpg.show_metrics() dpg.show_viewport() def render(self): while dpg.is_dearpygui_running(): # update texture every frame if self.training: self.train_step() self.test_step() dpg.render_dearpygui_frame() ================================================ FILE: stable-dreamfusion/nerf/network.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from activation import trunc_exp from .renderer import NeRFRenderer import numpy as np from encoding import get_encoder from .utils import safe_normalize # TODO: not sure about the details... class ResBlock(nn.Module): def __init__(self, dim_in, dim_out, bias=True): super().__init__() self.dim_in = dim_in self.dim_out = dim_out self.dense = nn.Linear(self.dim_in, self.dim_out, bias=bias) self.norm = nn.LayerNorm(self.dim_out) self.activation = nn.SiLU(inplace=True) if self.dim_in != self.dim_out: self.skip = nn.Linear(self.dim_in, self.dim_out, bias=False) else: self.skip = None def forward(self, x): # x: [B, C] identity = x out = self.dense(x) out = self.norm(out) if self.skip is not None: identity = self.skip(identity) out += identity out = self.activation(out) return out class BasicBlock(nn.Module): def __init__(self, dim_in, dim_out, bias=True): super().__init__() self.dim_in = dim_in self.dim_out = dim_out self.dense = nn.Linear(self.dim_in, self.dim_out, bias=bias) self.activation = nn.ReLU(inplace=True) def forward(self, x): # x: [B, C] out = self.dense(x) out = self.activation(out) return out class MLP(nn.Module): def __init__(self, dim_in, dim_out, dim_hidden, num_layers, bias=True, block=BasicBlock): super().__init__() self.dim_in = dim_in self.dim_out = dim_out self.dim_hidden = dim_hidden self.num_layers = num_layers net = [] for l in range(num_layers): if l == 0: net.append(BasicBlock(self.dim_in, self.dim_hidden, bias=bias)) elif l != num_layers - 1: net.append(block(self.dim_hidden, self.dim_hidden, bias=bias)) else: net.append(nn.Linear(self.dim_hidden, self.dim_out, bias=bias)) self.net = nn.ModuleList(net) def forward(self, x): for l in range(self.num_layers): x = self.net[l](x) return x class NeRFNetwork(NeRFRenderer): def __init__(self, opt, num_layers=5, # 5 in paper hidden_dim=64, # 128 in paper num_layers_bg=2, # 3 in paper hidden_dim_bg=32, # 64 in paper encoding='frequency_torch', # pure pytorch ): super().__init__(opt) self.num_layers = num_layers self.hidden_dim = hidden_dim self.encoder, self.in_dim = get_encoder(encoding, input_dim=3, multires=12) self.sigma_net = MLP(self.in_dim, 4, hidden_dim, num_layers, bias=True, block=ResBlock) self.density_activation = trunc_exp if self.opt.density_activation == 'exp' else F.softplus # background network if self.opt.bg_radius > 0: self.num_layers_bg = num_layers_bg self.hidden_dim_bg = hidden_dim_bg self.encoder_bg, self.in_dim_bg = get_encoder(encoding, input_dim=3, multires=4) self.bg_net = MLP(self.in_dim_bg, 3, hidden_dim_bg, num_layers_bg, bias=True) else: self.bg_net = None def common_forward(self, x): # x: [N, 3], in [-bound, bound] # sigma enc = self.encoder(x, bound=self.bound, max_level=self.max_level) h = self.sigma_net(enc) sigma = self.density_activation(h[..., 0] + self.density_blob(x)) albedo = torch.sigmoid(h[..., 1:]) return sigma, albedo # ref: https://github.com/zhaofuq/Instant-NSR/blob/main/nerf/network_sdf.py#L192 def finite_difference_normal(self, x, epsilon=1e-2): # x: [N, 3] dx_pos, _ = self.common_forward((x + torch.tensor([[epsilon, 0.00, 0.00]], device=x.device)).clamp(-self.bound, self.bound)) dx_neg, _ = self.common_forward((x + torch.tensor([[-epsilon, 0.00, 0.00]], device=x.device)).clamp(-self.bound, self.bound)) dy_pos, _ = self.common_forward((x + torch.tensor([[0.00, epsilon, 0.00]], device=x.device)).clamp(-self.bound, self.bound)) dy_neg, _ = self.common_forward((x + torch.tensor([[0.00, -epsilon, 0.00]], device=x.device)).clamp(-self.bound, self.bound)) dz_pos, _ = self.common_forward((x + torch.tensor([[0.00, 0.00, epsilon]], device=x.device)).clamp(-self.bound, self.bound)) dz_neg, _ = self.common_forward((x + torch.tensor([[0.00, 0.00, -epsilon]], device=x.device)).clamp(-self.bound, self.bound)) normal = torch.stack([ 0.5 * (dx_pos - dx_neg) / epsilon, 0.5 * (dy_pos - dy_neg) / epsilon, 0.5 * (dz_pos - dz_neg) / epsilon ], dim=-1) return -normal def normal(self, x): with torch.enable_grad(): with torch.cuda.amp.autocast(enabled=False): x.requires_grad_(True) sigma, albedo = self.common_forward(x) # query gradient normal = - torch.autograd.grad(torch.sum(sigma), x, create_graph=True)[0] # [N, 3] # normal = self.finite_difference_normal(x) normal = safe_normalize(normal) normal = torch.nan_to_num(normal) return normal def forward(self, x, d, l=None, ratio=1, shading='albedo'): # x: [N, 3], in [-bound, bound] # d: [N, 3], view direction, nomalized in [-1, 1] # l: [3], plane light direction, nomalized in [-1, 1] # ratio: scalar, ambient ratio, 1 == no shading (albedo only), 0 == only shading (textureless) if shading == 'albedo': # no need to query normal sigma, color = self.common_forward(x) normal = None else: # query normal # sigma, albedo = self.common_forward(x) # normal = self.normal(x) with torch.enable_grad(): with torch.cuda.amp.autocast(enabled=False): x.requires_grad_(True) sigma, albedo = self.common_forward(x) # query gradient normal = - torch.autograd.grad(torch.sum(sigma), x, create_graph=True)[0] # [N, 3] normal = safe_normalize(normal) normal = torch.nan_to_num(normal) # lambertian shading lambertian = ratio + (1 - ratio) * (normal * l).sum(-1).clamp(min=0) # [N,] if shading == 'textureless': color = lambertian.unsqueeze(-1).repeat(1, 3) elif shading == 'normal': color = (normal + 1) / 2 else: # 'lambertian' color = albedo * lambertian.unsqueeze(-1) return sigma, color, normal def density(self, x): # x: [N, 3], in [-bound, bound] sigma, albedo = self.common_forward(x) return { 'sigma': sigma, 'albedo': albedo, } def background(self, d): h = self.encoder_bg(d) # [N, C] h = self.bg_net(h) # sigmoid activation for rgb rgbs = torch.sigmoid(h) return rgbs # optimizer utils def get_params(self, lr): params = [ # {'params': self.encoder.parameters(), 'lr': lr * 10}, {'params': self.sigma_net.parameters(), 'lr': lr}, ] if self.opt.bg_radius > 0: # params.append({'params': self.encoder_bg.parameters(), 'lr': lr * 10}) params.append({'params': self.bg_net.parameters(), 'lr': lr}) if self.opt.dmtet and not self.opt.lock_geo: params.append({'params': self.sdf, 'lr': lr}) params.append({'params': self.deform, 'lr': lr}) return params ================================================ FILE: stable-dreamfusion/nerf/network_grid.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from activation import trunc_exp, biased_softplus from .renderer import NeRFRenderer import numpy as np from encoding import get_encoder from .utils import safe_normalize class MLP(nn.Module): def __init__(self, dim_in, dim_out, dim_hidden, num_layers, bias=True): super().__init__() self.dim_in = dim_in self.dim_out = dim_out self.dim_hidden = dim_hidden self.num_layers = num_layers net = [] for l in range(num_layers): net.append(nn.Linear(self.dim_in if l == 0 else self.dim_hidden, self.dim_out if l == num_layers - 1 else self.dim_hidden, bias=bias)) self.net = nn.ModuleList(net) def forward(self, x): for l in range(self.num_layers): x = self.net[l](x) if l != self.num_layers - 1: x = F.relu(x, inplace=True) return x class NeRFNetwork(NeRFRenderer): def __init__(self, opt, num_layers=3, hidden_dim=64, num_layers_bg=2, hidden_dim_bg=32, ): super().__init__(opt) self.num_layers = num_layers self.hidden_dim = hidden_dim self.encoder, self.in_dim = get_encoder('hashgrid', input_dim=3, log2_hashmap_size=19, desired_resolution=2048 * self.bound, interpolation='smoothstep') self.sigma_net = MLP(self.in_dim, 4, hidden_dim, num_layers, bias=True) # self.normal_net = MLP(self.in_dim, 3, hidden_dim, num_layers, bias=True) self.density_activation = trunc_exp if self.opt.density_activation == 'exp' else biased_softplus # background network if self.opt.bg_radius > 0: self.num_layers_bg = num_layers_bg self.hidden_dim_bg = hidden_dim_bg # use a very simple network to avoid it learning the prompt... self.encoder_bg, self.in_dim_bg = get_encoder('frequency', input_dim=3, multires=6) self.bg_net = MLP(self.in_dim_bg, 3, hidden_dim_bg, num_layers_bg, bias=True) else: self.bg_net = None def common_forward(self, x): # sigma enc = self.encoder(x, bound=self.bound, max_level=self.max_level) h = self.sigma_net(enc) sigma = self.density_activation(h[..., 0] + self.density_blob(x)) albedo = torch.sigmoid(h[..., 1:]) return sigma, albedo # ref: https://github.com/zhaofuq/Instant-NSR/blob/main/nerf/network_sdf.py#L192 def finite_difference_normal(self, x, epsilon=1e-2): # x: [N, 3] dx_pos, _ = self.common_forward((x + torch.tensor([[epsilon, 0.00, 0.00]], device=x.device)).clamp(-self.bound, self.bound)) dx_neg, _ = self.common_forward((x + torch.tensor([[-epsilon, 0.00, 0.00]], device=x.device)).clamp(-self.bound, self.bound)) dy_pos, _ = self.common_forward((x + torch.tensor([[0.00, epsilon, 0.00]], device=x.device)).clamp(-self.bound, self.bound)) dy_neg, _ = self.common_forward((x + torch.tensor([[0.00, -epsilon, 0.00]], device=x.device)).clamp(-self.bound, self.bound)) dz_pos, _ = self.common_forward((x + torch.tensor([[0.00, 0.00, epsilon]], device=x.device)).clamp(-self.bound, self.bound)) dz_neg, _ = self.common_forward((x + torch.tensor([[0.00, 0.00, -epsilon]], device=x.device)).clamp(-self.bound, self.bound)) normal = torch.stack([ 0.5 * (dx_pos - dx_neg) / epsilon, 0.5 * (dy_pos - dy_neg) / epsilon, 0.5 * (dz_pos - dz_neg) / epsilon ], dim=-1) return -normal def normal(self, x): normal = self.finite_difference_normal(x) normal = safe_normalize(normal) normal = torch.nan_to_num(normal) return normal def forward(self, x, d, l=None, ratio=1, shading='albedo'): # x: [N, 3], in [-bound, bound] # d: [N, 3], view direction, nomalized in [-1, 1] # l: [3], plane light direction, nomalized in [-1, 1] # ratio: scalar, ambient ratio, 1 == no shading (albedo only), 0 == only shading (textureless) sigma, albedo = self.common_forward(x) if shading == 'albedo': normal = None color = albedo else: # lambertian shading # normal = self.normal_net(enc) normal = self.normal(x) lambertian = ratio + (1 - ratio) * (normal * l).sum(-1).clamp(min=0) # [N,] if shading == 'textureless': color = lambertian.unsqueeze(-1).repeat(1, 3) elif shading == 'normal': color = (normal + 1) / 2 else: # 'lambertian' color = albedo * lambertian.unsqueeze(-1) return sigma, color, normal def density(self, x): # x: [N, 3], in [-bound, bound] sigma, albedo = self.common_forward(x) return { 'sigma': sigma, 'albedo': albedo, } def background(self, d): h = self.encoder_bg(d) # [N, C] h = self.bg_net(h) # sigmoid activation for rgb rgbs = torch.sigmoid(h) return rgbs # optimizer utils def get_params(self, lr): params = [ {'params': self.encoder.parameters(), 'lr': lr * 10}, {'params': self.sigma_net.parameters(), 'lr': lr}, # {'params': self.normal_net.parameters(), 'lr': lr}, ] if self.opt.bg_radius > 0: # params.append({'params': self.encoder_bg.parameters(), 'lr': lr * 10}) params.append({'params': self.bg_net.parameters(), 'lr': lr}) if self.opt.dmtet and not self.opt.lock_geo: params.append({'params': self.sdf, 'lr': lr}) params.append({'params': self.deform, 'lr': lr}) return params ================================================ FILE: stable-dreamfusion/nerf/network_grid_taichi.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from activation import trunc_exp from .renderer import NeRFRenderer import numpy as np from encoding import get_encoder from .utils import safe_normalize class MLP(nn.Module): def __init__(self, dim_in, dim_out, dim_hidden, num_layers, bias=True): super().__init__() self.dim_in = dim_in self.dim_out = dim_out self.dim_hidden = dim_hidden self.num_layers = num_layers net = [] for l in range(num_layers): net.append(nn.Linear(self.dim_in if l == 0 else self.dim_hidden, self.dim_out if l == num_layers - 1 else self.dim_hidden, bias=bias)) self.net = nn.ModuleList(net) def forward(self, x): for l in range(self.num_layers): x = self.net[l](x) if l != self.num_layers - 1: x = F.relu(x, inplace=True) return x class NeRFNetwork(NeRFRenderer): def __init__(self, opt, num_layers=2, hidden_dim=32, num_layers_bg=2, hidden_dim_bg=16, ): super().__init__(opt) self.num_layers = num_layers self.hidden_dim = hidden_dim self.encoder, self.in_dim = get_encoder('hashgrid_taichi', input_dim=3, log2_hashmap_size=19, desired_resolution=2048 * self.bound, interpolation='smoothstep') self.sigma_net = MLP(self.in_dim, 4, hidden_dim, num_layers, bias=True) # self.normal_net = MLP(self.in_dim, 3, hidden_dim, num_layers, bias=True) self.density_activation = trunc_exp if self.opt.density_activation == 'exp' else F.softplus # background network if self.opt.bg_radius > 0: self.num_layers_bg = num_layers_bg self.hidden_dim_bg = hidden_dim_bg # use a very simple network to avoid it learning the prompt... self.encoder_bg, self.in_dim_bg = get_encoder('frequency_torch', input_dim=3, multires=4) # TODO: freq encoder can be replaced by a Taichi implementation self.bg_net = MLP(self.in_dim_bg, 3, hidden_dim_bg, num_layers_bg, bias=True) else: self.bg_net = None def common_forward(self, x): # sigma enc = self.encoder(x, bound=self.bound) h = self.sigma_net(enc) sigma = self.density_activation(h[..., 0] + self.density_blob(x)) albedo = torch.sigmoid(h[..., 1:]) return sigma, albedo # ref: https://github.com/zhaofuq/Instant-NSR/blob/main/nerf/network_sdf.py#L192 def finite_difference_normal(self, x, epsilon=1e-2): # x: [N, 3] dx_pos, _ = self.common_forward((x + torch.tensor([[epsilon, 0.00, 0.00]], device=x.device)).clamp(-self.bound, self.bound)) dx_neg, _ = self.common_forward((x + torch.tensor([[-epsilon, 0.00, 0.00]], device=x.device)).clamp(-self.bound, self.bound)) dy_pos, _ = self.common_forward((x + torch.tensor([[0.00, epsilon, 0.00]], device=x.device)).clamp(-self.bound, self.bound)) dy_neg, _ = self.common_forward((x + torch.tensor([[0.00, -epsilon, 0.00]], device=x.device)).clamp(-self.bound, self.bound)) dz_pos, _ = self.common_forward((x + torch.tensor([[0.00, 0.00, epsilon]], device=x.device)).clamp(-self.bound, self.bound)) dz_neg, _ = self.common_forward((x + torch.tensor([[0.00, 0.00, -epsilon]], device=x.device)).clamp(-self.bound, self.bound)) normal = torch.stack([ 0.5 * (dx_pos - dx_neg) / epsilon, 0.5 * (dy_pos - dy_neg) / epsilon, 0.5 * (dz_pos - dz_neg) / epsilon ], dim=-1) return -normal def normal(self, x): normal = self.finite_difference_normal(x) normal = safe_normalize(normal) normal = torch.nan_to_num(normal) return normal def forward(self, x, d, l=None, ratio=1, shading='albedo'): # x: [N, 3], in [-bound, bound] # d: [N, 3], view direction, nomalized in [-1, 1] # l: [3], plane light direction, nomalized in [-1, 1] # ratio: scalar, ambient ratio, 1 == no shading (albedo only), 0 == only shading (textureless) sigma, albedo = self.common_forward(x) if shading == 'albedo': normal = None color = albedo else: # lambertian shading # normal = self.normal_net(enc) normal = self.normal(x) lambertian = ratio + (1 - ratio) * (normal * l).sum(-1).clamp(min=0) # [N,] if shading == 'textureless': color = lambertian.unsqueeze(-1).repeat(1, 3) elif shading == 'normal': color = (normal + 1) / 2 else: # 'lambertian' color = albedo * lambertian.unsqueeze(-1) return sigma, color, normal def density(self, x): # x: [N, 3], in [-bound, bound] sigma, albedo = self.common_forward(x) return { 'sigma': sigma, 'albedo': albedo, } def background(self, d): h = self.encoder_bg(d) # [N, C] h = self.bg_net(h) # sigmoid activation for rgb rgbs = torch.sigmoid(h) return rgbs # optimizer utils def get_params(self, lr): params = [ {'params': self.encoder.parameters(), 'lr': lr * 10}, {'params': self.sigma_net.parameters(), 'lr': lr}, # {'params': self.normal_net.parameters(), 'lr': lr}, ] if self.opt.bg_radius > 0: # params.append({'params': self.encoder_bg.parameters(), 'lr': lr * 10}) params.append({'params': self.bg_net.parameters(), 'lr': lr}) if self.opt.dmtet and not self.opt.lock_geo: params.append({'params': self.sdf, 'lr': lr}) params.append({'params': self.deform, 'lr': lr}) return params ================================================ FILE: stable-dreamfusion/nerf/network_grid_tcnn.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from activation import trunc_exp, biased_softplus from .renderer import NeRFRenderer import numpy as np from encoding import get_encoder from .utils import safe_normalize import tinycudann as tcnn class MLP(nn.Module): def __init__(self, dim_in, dim_out, dim_hidden, num_layers, bias=True): super().__init__() self.dim_in = dim_in self.dim_out = dim_out self.dim_hidden = dim_hidden self.num_layers = num_layers net = [] for l in range(num_layers): net.append(nn.Linear(self.dim_in if l == 0 else self.dim_hidden, self.dim_out if l == num_layers - 1 else self.dim_hidden, bias=bias)) self.net = nn.ModuleList(net) def forward(self, x): for l in range(self.num_layers): x = self.net[l](x) if l != self.num_layers - 1: x = F.relu(x, inplace=True) return x class NeRFNetwork(NeRFRenderer): def __init__(self, opt, num_layers=3, hidden_dim=64, num_layers_bg=2, hidden_dim_bg=32, ): super().__init__(opt) self.num_layers = num_layers self.hidden_dim = hidden_dim self.encoder = tcnn.Encoding( n_input_dims=3, encoding_config={ "otype": "HashGrid", "n_levels": 16, "n_features_per_level": 2, "log2_hashmap_size": 19, "base_resolution": 16, "interpolation": "Smoothstep", "per_level_scale": np.exp2(np.log2(2048 * self.bound / 16) / (16 - 1)), }, dtype=torch.float32, # ENHANCE: default float16 seems unstable... ) self.in_dim = self.encoder.n_output_dims # use torch MLP, as tcnn MLP doesn't impl second-order derivative self.sigma_net = MLP(self.in_dim, 4, hidden_dim, num_layers, bias=True) self.density_activation = trunc_exp if self.opt.density_activation == 'exp' else biased_softplus # background network if self.opt.bg_radius > 0: self.num_layers_bg = num_layers_bg self.hidden_dim_bg = hidden_dim_bg # use a very simple network to avoid it learning the prompt... self.encoder_bg, self.in_dim_bg = get_encoder('frequency', input_dim=3, multires=6) self.bg_net = MLP(self.in_dim_bg, 3, hidden_dim_bg, num_layers_bg, bias=True) else: self.bg_net = None def common_forward(self, x): # sigma enc = self.encoder((x + self.bound) / (2 * self.bound)).float() h = self.sigma_net(enc) sigma = self.density_activation(h[..., 0] + self.density_blob(x)) albedo = torch.sigmoid(h[..., 1:]) return sigma, albedo def normal(self, x): with torch.enable_grad(): with torch.cuda.amp.autocast(enabled=False): x.requires_grad_(True) sigma, albedo = self.common_forward(x) # query gradient normal = - torch.autograd.grad(torch.sum(sigma), x, create_graph=True)[0] # [N, 3] # normal = self.finite_difference_normal(x) normal = safe_normalize(normal) normal = torch.nan_to_num(normal) return normal def forward(self, x, d, l=None, ratio=1, shading='albedo'): # x: [N, 3], in [-bound, bound] # d: [N, 3], view direction, nomalized in [-1, 1] # l: [3], plane light direction, nomalized in [-1, 1] # ratio: scalar, ambient ratio, 1 == no shading (albedo only), 0 == only shading (textureless) if shading == 'albedo': sigma, albedo = self.common_forward(x) normal = None color = albedo else: # lambertian shading with torch.enable_grad(): with torch.cuda.amp.autocast(enabled=False): x.requires_grad_(True) sigma, albedo = self.common_forward(x) normal = - torch.autograd.grad(torch.sum(sigma), x, create_graph=True)[0] # [N, 3] normal = safe_normalize(normal) normal = torch.nan_to_num(normal) lambertian = ratio + (1 - ratio) * (normal * l).sum(-1).clamp(min=0) # [N,] if shading == 'textureless': color = lambertian.unsqueeze(-1).repeat(1, 3) elif shading == 'normal': color = (normal + 1) / 2 else: # 'lambertian' color = albedo * lambertian.unsqueeze(-1) return sigma, color, normal def density(self, x): # x: [N, 3], in [-bound, bound] sigma, albedo = self.common_forward(x) return { 'sigma': sigma, 'albedo': albedo, } def background(self, d): h = self.encoder_bg(d) # [N, C] h = self.bg_net(h) # sigmoid activation for rgb rgbs = torch.sigmoid(h) return rgbs # optimizer utils def get_params(self, lr): params = [ {'params': self.encoder.parameters(), 'lr': lr * 10}, {'params': self.sigma_net.parameters(), 'lr': lr}, ] if self.opt.bg_radius > 0: params.append({'params': self.bg_net.parameters(), 'lr': lr}) if self.opt.dmtet and not self.opt.lock_geo: params.append({'params': self.sdf, 'lr': lr}) params.append({'params': self.deform, 'lr': lr}) return params ================================================ FILE: stable-dreamfusion/nerf/provider.py ================================================ import os import cv2 import glob import json import tqdm import random import numpy as np from scipy.spatial.transform import Slerp, Rotation import trimesh import torch import torch.nn.functional as F from torch.utils.data import DataLoader from .utils import get_rays, safe_normalize DIR_COLORS = np.array([ [255, 0, 0, 255], # front [0, 255, 0, 255], # side [0, 0, 255, 255], # back [255, 255, 0, 255], # side [255, 0, 255, 255], # overhead [0, 255, 255, 255], # bottom ], dtype=np.uint8) def visualize_poses(poses, dirs, size=0.1): # poses: [B, 4, 4], dirs: [B] axes = trimesh.creation.axis(axis_length=4) sphere = trimesh.creation.icosphere(radius=1) objects = [axes, sphere] for pose, dir in zip(poses, dirs): # a camera is visualized with 8 line segments. pos = pose[:3, 3] a = pos + size * pose[:3, 0] + size * pose[:3, 1] - size * pose[:3, 2] b = pos - size * pose[:3, 0] + size * pose[:3, 1] - size * pose[:3, 2] c = pos - size * pose[:3, 0] - size * pose[:3, 1] - size * pose[:3, 2] d = pos + size * pose[:3, 0] - size * pose[:3, 1] - size * pose[:3, 2] segs = np.array([[pos, a], [pos, b], [pos, c], [pos, d], [a, b], [b, c], [c, d], [d, a]]) segs = trimesh.load_path(segs) # different color for different dirs segs.colors = DIR_COLORS[[dir]].repeat(len(segs.entities), 0) objects.append(segs) trimesh.Scene(objects).show() def get_view_direction(thetas, phis, overhead, front): # phis: [B,]; thetas: [B,] # front = 0 [-front/2, front/2) # side (cam left) = 1 [front/2, 180-front/2) # back = 2 [180-front/2, 180+front/2) # side (cam right) = 3 [180+front/2, 360-front/2) # top = 4 [0, overhead] # bottom = 5 [180-overhead, 180] res = torch.zeros(thetas.shape[0], dtype=torch.long) # first determine by phis phis = phis % (2 * np.pi) res[(phis < front / 2) | (phis >= 2 * np.pi - front / 2)] = 0 res[(phis >= front / 2) & (phis < np.pi - front / 2)] = 1 res[(phis >= np.pi - front / 2) & (phis < np.pi + front / 2)] = 2 res[(phis >= np.pi + front / 2) & (phis < 2 * np.pi - front / 2)] = 3 # override by thetas res[thetas <= overhead] = 4 res[thetas >= (np.pi - overhead)] = 5 return res def rand_poses(size, device, opt, radius_range=[1, 1.5], theta_range=[0, 120], phi_range=[0, 360], return_dirs=False, angle_overhead=30, angle_front=60, uniform_sphere_rate=0.5): ''' generate random poses from an orbit camera Args: size: batch size of generated poses. device: where to allocate the output. radius: camera radius theta_range: [min, max], should be in [0, pi] phi_range: [min, max], should be in [0, 2 * pi] Return: poses: [size, 4, 4] ''' theta_range = np.array(theta_range) / 180 * np.pi phi_range = np.array(phi_range) / 180 * np.pi angle_overhead = angle_overhead / 180 * np.pi angle_front = angle_front / 180 * np.pi radius = torch.rand(size, device=device) * (radius_range[1] - radius_range[0]) + radius_range[0] if random.random() < uniform_sphere_rate: unit_centers = F.normalize( torch.stack([ torch.randn(size, device=device), torch.abs(torch.randn(size, device=device)), torch.randn(size, device=device), ], dim=-1), p=2, dim=1 ) thetas = torch.acos(unit_centers[:,1]) phis = torch.atan2(unit_centers[:,0], unit_centers[:,2]) phis[phis < 0] += 2 * np.pi centers = unit_centers * radius.unsqueeze(-1) else: thetas = torch.rand(size, device=device) * (theta_range[1] - theta_range[0]) + theta_range[0] phis = torch.rand(size, device=device) * (phi_range[1] - phi_range[0]) + phi_range[0] phis[phis < 0] += 2 * np.pi centers = torch.stack([ radius * torch.sin(thetas) * torch.sin(phis), radius * torch.cos(thetas), radius * torch.sin(thetas) * torch.cos(phis), ], dim=-1) # [B, 3] targets = 0 # jitters if opt.jitter_pose: jit_center = opt.jitter_center # 0.015 # was 0.2 jit_target = opt.jitter_target centers += torch.rand_like(centers) * jit_center - jit_center/2.0 targets += torch.randn_like(centers) * jit_target # lookat forward_vector = safe_normalize(centers - targets) up_vector = torch.FloatTensor([0, 1, 0]).to(device).unsqueeze(0).repeat(size, 1) right_vector = safe_normalize(torch.cross(forward_vector, up_vector, dim=-1)) if opt.jitter_pose: up_noise = torch.randn_like(up_vector) * opt.jitter_up else: up_noise = 0 up_vector = safe_normalize(torch.cross(right_vector, forward_vector, dim=-1) + up_noise) poses = torch.eye(4, dtype=torch.float, device=device).unsqueeze(0).repeat(size, 1, 1) poses[:, :3, :3] = torch.stack((right_vector, up_vector, forward_vector), dim=-1) poses[:, :3, 3] = centers if return_dirs: dirs = get_view_direction(thetas, phis, angle_overhead, angle_front) else: dirs = None # back to degree thetas = thetas / np.pi * 180 phis = phis / np.pi * 180 return poses, dirs, thetas, phis, radius def circle_poses(device, radius=torch.tensor([3.2]), theta=torch.tensor([60]), phi=torch.tensor([0]), return_dirs=False, angle_overhead=30, angle_front=60): theta = theta / 180 * np.pi phi = phi / 180 * np.pi angle_overhead = angle_overhead / 180 * np.pi angle_front = angle_front / 180 * np.pi centers = torch.stack([ radius * torch.sin(theta) * torch.sin(phi), radius * torch.cos(theta), radius * torch.sin(theta) * torch.cos(phi), ], dim=-1) # [B, 3] # lookat forward_vector = safe_normalize(centers) up_vector = torch.FloatTensor([0, 1, 0]).to(device).unsqueeze(0).repeat(len(centers), 1) right_vector = safe_normalize(torch.cross(forward_vector, up_vector, dim=-1)) up_vector = safe_normalize(torch.cross(right_vector, forward_vector, dim=-1)) poses = torch.eye(4, dtype=torch.float, device=device).unsqueeze(0).repeat(len(centers), 1, 1) poses[:, :3, :3] = torch.stack((right_vector, up_vector, forward_vector), dim=-1) poses[:, :3, 3] = centers if return_dirs: dirs = get_view_direction(theta, phi, angle_overhead, angle_front) else: dirs = None return poses, dirs class NeRFDataset: def __init__(self, opt, device, type='train', H=256, W=256, size=100): super().__init__() self.opt = opt self.device = device self.type = type # train, val, test self.H = H self.W = W self.size = size self.training = self.type in ['train', 'all'] self.cx = self.H / 2 self.cy = self.W / 2 self.near = self.opt.min_near self.far = 1000 # infinite # [debug] visualize poses # poses, dirs, _, _, _ = rand_poses(100, self.device, opt, radius_range=self.opt.radius_range, angle_overhead=self.opt.angle_overhead, angle_front=self.opt.angle_front, jitter=self.opt.jitter_pose, uniform_sphere_rate=1) # visualize_poses(poses.detach().cpu().numpy(), dirs.detach().cpu().numpy()) def get_default_view_data(self): H = int(self.opt.known_view_scale * self.H) W = int(self.opt.known_view_scale * self.W) cx = H / 2 cy = W / 2 radii = torch.FloatTensor(self.opt.ref_radii).to(self.device) thetas = torch.FloatTensor(self.opt.ref_polars).to(self.device) phis = torch.FloatTensor(self.opt.ref_azimuths).to(self.device) poses, dirs = circle_poses(self.device, radius=radii, theta=thetas, phi=phis, return_dirs=True, angle_overhead=self.opt.angle_overhead, angle_front=self.opt.angle_front) fov = self.opt.default_fovy focal = H / (2 * np.tan(np.deg2rad(fov) / 2)) intrinsics = np.array([focal, focal, cx, cy]) projection = torch.tensor([ [2*focal/W, 0, 0, 0], [0, -2*focal/H, 0, 0], [0, 0, -(self.far+self.near)/(self.far-self.near), -(2*self.far*self.near)/(self.far-self.near)], [0, 0, -1, 0] ], dtype=torch.float32, device=self.device).unsqueeze(0).repeat(len(radii), 1, 1) mvp = projection @ torch.inverse(poses) # [B, 4, 4] # sample a low-resolution but full image rays = get_rays(poses, intrinsics, H, W, -1) data = { 'H': H, 'W': W, 'rays_o': rays['rays_o'], 'rays_d': rays['rays_d'], 'dir': dirs, 'mvp': mvp, 'polar': self.opt.ref_polars, 'azimuth': self.opt.ref_azimuths, 'radius': self.opt.ref_radii, } return data def collate(self, index): B = len(index) if self.training: # random pose on the fly poses, dirs, thetas, phis, radius = rand_poses(B, self.device, self.opt, radius_range=self.opt.radius_range, theta_range=self.opt.theta_range, phi_range=self.opt.phi_range, return_dirs=True, angle_overhead=self.opt.angle_overhead, angle_front=self.opt.angle_front, uniform_sphere_rate=self.opt.uniform_sphere_rate) # random focal fov = random.random() * (self.opt.fovy_range[1] - self.opt.fovy_range[0]) + self.opt.fovy_range[0] elif self.type == 'six_views': # six views thetas_six = [90, 90, 90, 90, 1e-3, 179.999] phis_six = [ 0, 90, 180, -90, 0, 0] thetas = torch.FloatTensor([thetas_six[index[0]]]).to(self.device) phis = torch.FloatTensor([phis_six[index[0]]]).to(self.device) radius = torch.FloatTensor([self.opt.default_radius]).to(self.device) poses, dirs = circle_poses(self.device, radius=radius, theta=thetas, phi=phis, return_dirs=True, angle_overhead=self.opt.angle_overhead, angle_front=self.opt.angle_front) # fixed focal fov = self.opt.default_fovy else: # circle pose thetas = torch.FloatTensor([self.opt.default_polar]).to(self.device) phis = torch.FloatTensor([(index[0] / self.size) * 360]).to(self.device) radius = torch.FloatTensor([self.opt.default_radius]).to(self.device) poses, dirs = circle_poses(self.device, radius=radius, theta=thetas, phi=phis, return_dirs=True, angle_overhead=self.opt.angle_overhead, angle_front=self.opt.angle_front) # fixed focal fov = self.opt.default_fovy focal = self.H / (2 * np.tan(np.deg2rad(fov) / 2)) intrinsics = np.array([focal, focal, self.cx, self.cy]) projection = torch.tensor([ [2*focal/self.W, 0, 0, 0], [0, -2*focal/self.H, 0, 0], [0, 0, -(self.far+self.near)/(self.far-self.near), -(2*self.far*self.near)/(self.far-self.near)], [0, 0, -1, 0] ], dtype=torch.float32, device=self.device).unsqueeze(0) mvp = projection @ torch.inverse(poses) # [1, 4, 4] # sample a low-resolution but full image rays = get_rays(poses, intrinsics, self.H, self.W, -1) # delta polar/azimuth/radius to default view delta_polar = thetas - self.opt.default_polar delta_azimuth = phis - self.opt.default_azimuth delta_azimuth[delta_azimuth > 180] -= 360 # range in [-180, 180] delta_radius = radius - self.opt.default_radius data = { 'H': self.H, 'W': self.W, 'rays_o': rays['rays_o'], 'rays_d': rays['rays_d'], 'dir': dirs, 'mvp': mvp, 'polar': delta_polar, 'azimuth': delta_azimuth, 'radius': delta_radius, } return data def dataloader(self, batch_size=None): batch_size = batch_size or self.opt.batch_size loader = DataLoader(list(range(self.size)), batch_size=batch_size, collate_fn=self.collate, shuffle=self.training, num_workers=0) loader._data = self return loader ================================================ FILE: stable-dreamfusion/nerf/renderer.py ================================================ import os import math import cv2 import trimesh import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import nvdiffrast.torch as dr import mcubes import raymarching from meshutils import decimate_mesh, clean_mesh, poisson_mesh_reconstruction from .utils import custom_meshgrid, safe_normalize def sample_pdf(bins, weights, n_samples, det=False): # This implementation is from NeRF # bins: [B, T], old_z_vals # weights: [B, T - 1], bin weights. # return: [B, n_samples], new_z_vals # Get pdf weights = weights + 1e-5 # prevent nans pdf = weights / torch.sum(weights, -1, keepdim=True) cdf = torch.cumsum(pdf, -1) cdf = torch.cat([torch.zeros_like(cdf[..., :1]), cdf], -1) # Take uniform samples if det: u = torch.linspace(0. + 0.5 / n_samples, 1. - 0.5 / n_samples, steps=n_samples).to(weights.device) u = u.expand(list(cdf.shape[:-1]) + [n_samples]) else: u = torch.rand(list(cdf.shape[:-1]) + [n_samples]).to(weights.device) # Invert CDF u = u.contiguous() inds = torch.searchsorted(cdf, u, right=True) below = torch.max(torch.zeros_like(inds - 1), inds - 1) above = torch.min((cdf.shape[-1] - 1) * torch.ones_like(inds), inds) inds_g = torch.stack([below, above], -1) # (B, n_samples, 2) matched_shape = [inds_g.shape[0], inds_g.shape[1], cdf.shape[-1]] cdf_g = torch.gather(cdf.unsqueeze(1).expand(matched_shape), 2, inds_g) bins_g = torch.gather(bins.unsqueeze(1).expand(matched_shape), 2, inds_g) denom = (cdf_g[..., 1] - cdf_g[..., 0]) denom = torch.where(denom < 1e-5, torch.ones_like(denom), denom) t = (u - cdf_g[..., 0]) / denom samples = bins_g[..., 0] + t * (bins_g[..., 1] - bins_g[..., 0]) return samples @torch.cuda.amp.autocast(enabled=False) def near_far_from_bound(rays_o, rays_d, bound, type='cube', min_near=0.05): # rays: [B, N, 3], [B, N, 3] # bound: int, radius for ball or half-edge-length for cube # return near [B, N, 1], far [B, N, 1] radius = rays_o.norm(dim=-1, keepdim=True) if type == 'sphere': near = radius - bound # [B, N, 1] far = radius + bound elif type == 'cube': tmin = (-bound - rays_o) / (rays_d + 1e-15) # [B, N, 3] tmax = (bound - rays_o) / (rays_d + 1e-15) near = torch.where(tmin < tmax, tmin, tmax).max(dim=-1, keepdim=True)[0] far = torch.where(tmin > tmax, tmin, tmax).min(dim=-1, keepdim=True)[0] # if far < near, means no intersection, set both near and far to inf (1e9 here) mask = far < near near[mask] = 1e9 far[mask] = 1e9 # restrict near to a minimal value near = torch.clamp(near, min=min_near) return near, far def plot_pointcloud(pc, color=None): # pc: [N, 3] # color: [N, 3/4] print('[visualize points]', pc.shape, pc.dtype, pc.min(0), pc.max(0)) pc = trimesh.PointCloud(pc, color) # axis axes = trimesh.creation.axis(axis_length=4) # sphere sphere = trimesh.creation.icosphere(radius=1) trimesh.Scene([pc, axes, sphere]).show() class DMTet(): def __init__(self, device): self.device = device self.triangle_table = torch.tensor([ [-1, -1, -1, -1, -1, -1], [ 1, 0, 2, -1, -1, -1], [ 4, 0, 3, -1, -1, -1], [ 1, 4, 2, 1, 3, 4], [ 3, 1, 5, -1, -1, -1], [ 2, 3, 0, 2, 5, 3], [ 1, 4, 0, 1, 5, 4], [ 4, 2, 5, -1, -1, -1], [ 4, 5, 2, -1, -1, -1], [ 4, 1, 0, 4, 5, 1], [ 3, 2, 0, 3, 5, 2], [ 1, 3, 5, -1, -1, -1], [ 4, 1, 2, 4, 3, 1], [ 3, 0, 4, -1, -1, -1], [ 2, 0, 1, -1, -1, -1], [-1, -1, -1, -1, -1, -1] ], dtype=torch.long, device=device) self.num_triangles_table = torch.tensor([0,1,1,2,1,2,2,1,1,2,2,1,2,1,1,0], dtype=torch.long, device=device) self.base_tet_edges = torch.tensor([0,1,0,2,0,3,1,2,1,3,2,3], dtype=torch.long, device=device) def sort_edges(self, edges_ex2): with torch.no_grad(): order = (edges_ex2[:,0] > edges_ex2[:,1]).long() order = order.unsqueeze(dim=1) a = torch.gather(input=edges_ex2, index=order, dim=1) b = torch.gather(input=edges_ex2, index=1-order, dim=1) return torch.stack([a, b],-1) def __call__(self, pos_nx3, sdf_n, tet_fx4): # pos_nx3: [N, 3] # sdf_n: [N] # tet_fx4: [F, 4] with torch.no_grad(): occ_n = sdf_n > 0 occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1,4) occ_sum = torch.sum(occ_fx4, -1) # [F,] valid_tets = (occ_sum>0) & (occ_sum<4) occ_sum = occ_sum[valid_tets] # find all vertices all_edges = tet_fx4[valid_tets][:,self.base_tet_edges].reshape(-1,2) all_edges = self.sort_edges(all_edges) unique_edges, idx_map = torch.unique(all_edges,dim=0, return_inverse=True) unique_edges = unique_edges.long() mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1,2).sum(-1) == 1 mapping = torch.ones((unique_edges.shape[0]), dtype=torch.long, device=self.device) * -1 mapping[mask_edges] = torch.arange(mask_edges.sum(), dtype=torch.long,device=self.device) idx_map = mapping[idx_map] # map edges to verts interp_v = unique_edges[mask_edges] edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1,2,3) edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1,2,1) edges_to_interp_sdf[:,-1] *= -1 denominator = edges_to_interp_sdf.sum(1,keepdim = True) edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1])/denominator verts = (edges_to_interp * edges_to_interp_sdf).sum(1) idx_map = idx_map.reshape(-1,6) v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=self.device)) tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1) num_triangles = self.num_triangles_table[tetindex] # Generate triangle indices faces = torch.cat(( torch.gather(input=idx_map[num_triangles == 1], dim=1, index=self.triangle_table[tetindex[num_triangles == 1]][:, :3]).reshape(-1,3), torch.gather(input=idx_map[num_triangles == 2], dim=1, index=self.triangle_table[tetindex[num_triangles == 2]][:, :6]).reshape(-1,3), ), dim=0) return verts, faces def compute_edge_to_face_mapping(attr_idx): with torch.no_grad(): # Get unique edges # Create all edges, packed by triangle all_edges = torch.cat(( torch.stack((attr_idx[:, 0], attr_idx[:, 1]), dim=-1), torch.stack((attr_idx[:, 1], attr_idx[:, 2]), dim=-1), torch.stack((attr_idx[:, 2], attr_idx[:, 0]), dim=-1), ), dim=-1).view(-1, 2) # Swap edge order so min index is always first order = (all_edges[:, 0] > all_edges[:, 1]).long().unsqueeze(dim=1) sorted_edges = torch.cat(( torch.gather(all_edges, 1, order), torch.gather(all_edges, 1, 1 - order) ), dim=-1) # Elliminate duplicates and return inverse mapping unique_edges, idx_map = torch.unique(sorted_edges, dim=0, return_inverse=True) tris = torch.arange(attr_idx.shape[0]).repeat_interleave(3).cuda() tris_per_edge = torch.zeros((unique_edges.shape[0], 2), dtype=torch.int64).cuda() # Compute edge to face table mask0 = order[:,0] == 0 mask1 = order[:,0] == 1 tris_per_edge[idx_map[mask0], 0] = tris[mask0] tris_per_edge[idx_map[mask1], 1] = tris[mask1] return tris_per_edge @torch.cuda.amp.autocast(enabled=False) def normal_consistency(face_normals, t_pos_idx): tris_per_edge = compute_edge_to_face_mapping(t_pos_idx) # Fetch normals for both faces sharind an edge n0 = face_normals[tris_per_edge[:, 0], :] n1 = face_normals[tris_per_edge[:, 1], :] # Compute error metric based on normal difference term = torch.clamp(torch.sum(n0 * n1, -1, keepdim=True), min=-1.0, max=1.0) term = (1.0 - term) return torch.mean(torch.abs(term)) def laplacian_uniform(verts, faces): V = verts.shape[0] F = faces.shape[0] # Neighbor indices ii = faces[:, [1, 2, 0]].flatten() jj = faces[:, [2, 0, 1]].flatten() adj = torch.stack([torch.cat([ii, jj]), torch.cat([jj, ii])], dim=0).unique(dim=1) adj_values = torch.ones(adj.shape[1], device=verts.device, dtype=torch.float) # Diagonal indices diag_idx = adj[0] # Build the sparse matrix idx = torch.cat((adj, torch.stack((diag_idx, diag_idx), dim=0)), dim=1) values = torch.cat((-adj_values, adj_values)) # The coalesce operation sums the duplicate indices, resulting in the # correct diagonal return torch.sparse_coo_tensor(idx, values, (V,V)).coalesce() @torch.cuda.amp.autocast(enabled=False) def laplacian_smooth_loss(verts, faces): with torch.no_grad(): L = laplacian_uniform(verts, faces.long()) loss = L.mm(verts) loss = loss.norm(dim=1) loss = loss.mean() return loss class NeRFRenderer(nn.Module): def __init__(self, opt): super().__init__() self.opt = opt self.bound = opt.bound self.cascade = 1 + math.ceil(math.log2(opt.bound)) self.grid_size = 128 self.max_level = None self.dmtet = opt.dmtet self.cuda_ray = opt.cuda_ray self.taichi_ray = opt.taichi_ray self.min_near = opt.min_near self.density_thresh = opt.density_thresh # prepare aabb with a 6D tensor (xmin, ymin, zmin, xmax, ymax, zmax) # NOTE: aabb (can be rectangular) is only used to generate points, we still rely on bound (always cubic) to calculate density grid and hashing. aabb_train = torch.FloatTensor([-opt.bound, -opt.bound, -opt.bound, opt.bound, opt.bound, opt.bound]) aabb_infer = aabb_train.clone() self.register_buffer('aabb_train', aabb_train) self.register_buffer('aabb_infer', aabb_infer) self.glctx = None # extra state for cuda raymarching if self.cuda_ray: # density grid density_grid = torch.zeros([self.cascade, self.grid_size ** 3]) # [CAS, H * H * H] density_bitfield = torch.zeros(self.cascade * self.grid_size ** 3 // 8, dtype=torch.uint8) # [CAS * H * H * H // 8] self.register_buffer('density_grid', density_grid) self.register_buffer('density_bitfield', density_bitfield) self.mean_density = 0 self.iter_density = 0 if self.opt.dmtet: # load dmtet vertices tets = np.load('tets/{}_tets.npz'.format(self.opt.tet_grid_size)) self.verts = - torch.tensor(tets['vertices'], dtype=torch.float32, device='cuda') * 2 # covers [-1, 1] self.indices = torch.tensor(tets['indices'], dtype=torch.long, device='cuda') self.tet_scale = torch.tensor([1, 1, 1], dtype=torch.float32, device='cuda') self.dmtet = DMTet('cuda') # vert sdf and deform sdf = torch.nn.Parameter(torch.zeros_like(self.verts[..., 0]), requires_grad=True) self.register_parameter('sdf', sdf) deform = torch.nn.Parameter(torch.zeros_like(self.verts), requires_grad=True) self.register_parameter('deform', deform) edges = torch.tensor([0,1, 0,2, 0,3, 1,2, 1,3, 2,3], dtype=torch.long, device="cuda") # six edges for each tetrahedron. all_edges = self.indices[:,edges].reshape(-1,2) # [M * 6, 2] all_edges_sorted = torch.sort(all_edges, dim=1)[0] self.all_edges = torch.unique(all_edges_sorted, dim=0) if self.opt.h <= 2048 and self.opt.w <= 2048: self.glctx = dr.RasterizeCudaContext() else: self.glctx = dr.RasterizeGLContext() if self.taichi_ray: from einops import rearrange from taichi_modules import RayMarcherTaichi from taichi_modules import VolumeRendererTaichi from taichi_modules import RayAABBIntersector as RayAABBIntersectorTaichi from taichi_modules import raymarching_test as raymarching_test_taichi from taichi_modules import composite_test as composite_test_fw from taichi_modules import packbits as packbits_taichi self.rearrange = rearrange self.packbits_taichi = packbits_taichi self.ray_aabb_intersector = RayAABBIntersectorTaichi self.raymarching_test_taichi = raymarching_test_taichi self.composite_test_fw = composite_test_fw self.ray_marching = RayMarcherTaichi(batch_size=4096) # TODO: hard encoded batch size self.volume_render = VolumeRendererTaichi(batch_size=4096) # TODO: hard encoded batch size # density grid density_grid = torch.zeros([self.cascade, self.grid_size ** 3]) # [CAS, H * H * H] density_bitfield = torch.zeros(self.cascade * self.grid_size ** 3 // 8, dtype=torch.uint8) # [CAS * H * H * H // 8] self.register_buffer('density_grid', density_grid) self.register_buffer('density_bitfield', density_bitfield) self.mean_density = 0 self.iter_density = 0 @torch.no_grad() def density_blob(self, x): # x: [B, N, 3] d = (x ** 2).sum(-1) if self.opt.density_activation == 'exp': g = self.opt.blob_density * torch.exp(- d / (2 * self.opt.blob_radius ** 2)) else: g = self.opt.blob_density * (1 - torch.sqrt(d) / self.opt.blob_radius) return g def forward(self, x, d): raise NotImplementedError() def density(self, x): raise NotImplementedError() def reset_extra_state(self): if not (self.cuda_ray or self.taichi_ray): return # density grid self.density_grid.zero_() self.mean_density = 0 self.iter_density = 0 @torch.no_grad() def export_mesh(self, path, resolution=None, decimate_target=-1, S=128): if self.opt.dmtet: sdf = self.sdf deform = torch.tanh(self.deform) / self.opt.tet_grid_size vertices, triangles = self.dmtet(self.verts + deform, sdf, self.indices) vertices = vertices.detach().cpu().numpy() triangles = triangles.detach().cpu().numpy() else: if resolution is None: resolution = self.grid_size if self.cuda_ray: density_thresh = min(self.mean_density, self.density_thresh) \ if np.greater(self.mean_density, 0) else self.density_thresh else: density_thresh = self.density_thresh # TODO: use a larger thresh to extract a surface mesh from the density field, but this value is very empirical... if self.opt.density_activation == 'softplus': density_thresh = density_thresh * 25 sigmas = np.zeros([resolution, resolution, resolution], dtype=np.float32) # query X = torch.linspace(-1, 1, resolution).split(S) Y = torch.linspace(-1, 1, resolution).split(S) Z = torch.linspace(-1, 1, resolution).split(S) for xi, xs in enumerate(X): for yi, ys in enumerate(Y): for zi, zs in enumerate(Z): xx, yy, zz = custom_meshgrid(xs, ys, zs) pts = torch.cat([xx.reshape(-1, 1), yy.reshape(-1, 1), zz.reshape(-1, 1)], dim=-1) # [S, 3] val = self.density(pts.to(self.aabb_train.device)) sigmas[xi * S: xi * S + len(xs), yi * S: yi * S + len(ys), zi * S: zi * S + len(zs)] = val['sigma'].reshape(len(xs), len(ys), len(zs)).detach().cpu().numpy() # [S, 1] --> [x, y, z] print(f'[INFO] marching cubes thresh: {density_thresh} ({sigmas.min()} ~ {sigmas.max()})') vertices, triangles = mcubes.marching_cubes(sigmas, density_thresh) vertices = vertices / (resolution - 1.0) * 2 - 1 # clean vertices = vertices.astype(np.float32) triangles = triangles.astype(np.int32) vertices, triangles = clean_mesh(vertices, triangles, remesh=True, remesh_size=0.01) # decimation if decimate_target > 0 and triangles.shape[0] > decimate_target: vertices, triangles = decimate_mesh(vertices, triangles, decimate_target) v = torch.from_numpy(vertices).contiguous().float().to(self.aabb_train.device) f = torch.from_numpy(triangles).contiguous().int().to(self.aabb_train.device) # mesh = trimesh.Trimesh(vertices, triangles, process=False) # important, process=True leads to seg fault... # mesh.export(os.path.join(path, f'mesh.ply')) def _export(v, f, h0=2048, w0=2048, ssaa=1, name=''): # v, f: torch Tensor device = v.device v_np = v.cpu().numpy() # [N, 3] f_np = f.cpu().numpy() # [M, 3] print(f'[INFO] running xatlas to unwrap UVs for mesh: v={v_np.shape} f={f_np.shape}') # unwrap uvs import xatlas import nvdiffrast.torch as dr from sklearn.neighbors import NearestNeighbors from scipy.ndimage import binary_dilation, binary_erosion atlas = xatlas.Atlas() atlas.add_mesh(v_np, f_np) chart_options = xatlas.ChartOptions() chart_options.max_iterations = 4 # for faster unwrap... atlas.generate(chart_options=chart_options) vmapping, ft_np, vt_np = atlas[0] # [N], [M, 3], [N, 2] # vmapping, ft_np, vt_np = xatlas.parametrize(v_np, f_np) # [N], [M, 3], [N, 2] vt = torch.from_numpy(vt_np.astype(np.float32)).float().to(device) ft = torch.from_numpy(ft_np.astype(np.int64)).int().to(device) # render uv maps uv = vt * 2.0 - 1.0 # uvs to range [-1, 1] uv = torch.cat((uv, torch.zeros_like(uv[..., :1]), torch.ones_like(uv[..., :1])), dim=-1) # [N, 4] if ssaa > 1: h = int(h0 * ssaa) w = int(w0 * ssaa) else: h, w = h0, w0 if self.glctx is None: if h <= 2048 and w <= 2048: self.glctx = dr.RasterizeCudaContext() else: self.glctx = dr.RasterizeGLContext() rast, _ = dr.rasterize(self.glctx, uv.unsqueeze(0), ft, (h, w)) # [1, h, w, 4] xyzs, _ = dr.interpolate(v.unsqueeze(0), rast, f) # [1, h, w, 3] mask, _ = dr.interpolate(torch.ones_like(v[:, :1]).unsqueeze(0), rast, f) # [1, h, w, 1] # masked query xyzs = xyzs.view(-1, 3) mask = (mask > 0).view(-1) feats = torch.zeros(h * w, 3, device=device, dtype=torch.float32) if mask.any(): xyzs = xyzs[mask] # [M, 3] # batched inference to avoid OOM all_feats = [] head = 0 while head < xyzs.shape[0]: tail = min(head + 640000, xyzs.shape[0]) results_ = self.density(xyzs[head:tail]) all_feats.append(results_['albedo'].float()) head += 640000 feats[mask] = torch.cat(all_feats, dim=0) feats = feats.view(h, w, -1) mask = mask.view(h, w) # quantize [0.0, 1.0] to [0, 255] feats = feats.cpu().numpy() feats = (feats * 255).astype(np.uint8) ### NN search as an antialiasing ... mask = mask.cpu().numpy() inpaint_region = binary_dilation(mask, iterations=3) inpaint_region[mask] = 0 search_region = mask.copy() not_search_region = binary_erosion(search_region, iterations=2) search_region[not_search_region] = 0 search_coords = np.stack(np.nonzero(search_region), axis=-1) inpaint_coords = np.stack(np.nonzero(inpaint_region), axis=-1) knn = NearestNeighbors(n_neighbors=1, algorithm='kd_tree').fit(search_coords) _, indices = knn.kneighbors(inpaint_coords) feats[tuple(inpaint_coords.T)] = feats[tuple(search_coords[indices[:, 0]].T)] feats = cv2.cvtColor(feats, cv2.COLOR_RGB2BGR) # do ssaa after the NN search, in numpy if ssaa > 1: feats = cv2.resize(feats, (w0, h0), interpolation=cv2.INTER_LINEAR) cv2.imwrite(os.path.join(path, f'{name}albedo.png'), feats) # save obj (v, vt, f /) obj_file = os.path.join(path, f'{name}mesh.obj') mtl_file = os.path.join(path, f'{name}mesh.mtl') print(f'[INFO] writing obj mesh to {obj_file}') with open(obj_file, "w") as fp: fp.write(f'mtllib {name}mesh.mtl \n') print(f'[INFO] writing vertices {v_np.shape}') for v in v_np: fp.write(f'v {v[0]} {v[1]} {v[2]} \n') print(f'[INFO] writing vertices texture coords {vt_np.shape}') for v in vt_np: fp.write(f'vt {v[0]} {1 - v[1]} \n') print(f'[INFO] writing faces {f_np.shape}') fp.write(f'usemtl mat0 \n') for i in range(len(f_np)): fp.write(f"f {f_np[i, 0] + 1}/{ft_np[i, 0] + 1} {f_np[i, 1] + 1}/{ft_np[i, 1] + 1} {f_np[i, 2] + 1}/{ft_np[i, 2] + 1} \n") with open(mtl_file, "w") as fp: fp.write(f'newmtl mat0 \n') fp.write(f'Ka 1.000000 1.000000 1.000000 \n') fp.write(f'Kd 1.000000 1.000000 1.000000 \n') fp.write(f'Ks 0.000000 0.000000 0.000000 \n') fp.write(f'Tr 1.000000 \n') fp.write(f'illum 1 \n') fp.write(f'Ns 0.000000 \n') fp.write(f'map_Kd {name}albedo.png \n') _export(v, f) def run(self, rays_o, rays_d, light_d=None, ambient_ratio=1.0, shading='albedo', bg_color=None, perturb=False, **kwargs): # rays_o, rays_d: [B, N, 3] # bg_color: [BN, 3] in range [0, 1] # return: image: [B, N, 3], depth: [B, N] prefix = rays_o.shape[:-1] rays_o = rays_o.contiguous().view(-1, 3) rays_d = rays_d.contiguous().view(-1, 3) N = rays_o.shape[0] # N = B * N, in fact device = rays_o.device results = {} # choose aabb aabb = self.aabb_train if self.training else self.aabb_infer # sample steps # nears, fars = raymarching.near_far_from_aabb(rays_o, rays_d, aabb, self.min_near) # nears.unsqueeze_(-1) # fars.unsqueeze_(-1) nears, fars = near_far_from_bound(rays_o, rays_d, self.bound, type='sphere', min_near=self.min_near) # random sample light_d if not provided if light_d is None: # gaussian noise around the ray origin, so the light always face the view dir (avoid dark face) light_d = safe_normalize(rays_o + torch.randn(3, device=rays_o.device)) # [N, 3] #print(f'nears = {nears.min().item()} ~ {nears.max().item()}, fars = {fars.min().item()} ~ {fars.max().item()}') z_vals = torch.linspace(0.0, 1.0, self.opt.num_steps, device=device).unsqueeze(0) # [1, T] z_vals = z_vals.expand((N, self.opt.num_steps)) # [N, T] z_vals = nears + (fars - nears) * z_vals # [N, T], in [nears, fars] # perturb z_vals sample_dist = (fars - nears) / self.opt.num_steps if perturb: z_vals = z_vals + (torch.rand(z_vals.shape, device=device) - 0.5) * sample_dist #z_vals = z_vals.clamp(nears, fars) # avoid out of bounds xyzs. # generate xyzs xyzs = rays_o.unsqueeze(-2) + rays_d.unsqueeze(-2) * z_vals.unsqueeze(-1) # [N, 1, 3] * [N, T, 1] -> [N, T, 3] xyzs = torch.min(torch.max(xyzs, aabb[:3]), aabb[3:]) # a manual clip. #plot_pointcloud(xyzs.reshape(-1, 3).detach().cpu().numpy()) # query SDF and RGB density_outputs = self.density(xyzs.reshape(-1, 3)) #sigmas = density_outputs['sigma'].view(N, self.opt.num_steps) # [N, T] for k, v in density_outputs.items(): density_outputs[k] = v.view(N, self.opt.num_steps, -1) # upsample z_vals (nerf-like) if self.opt.upsample_steps > 0: with torch.no_grad(): deltas = z_vals[..., 1:] - z_vals[..., :-1] # [N, T-1] deltas = torch.cat([deltas, sample_dist * torch.ones_like(deltas[..., :1])], dim=-1) alphas = 1 - torch.exp(-deltas * density_outputs['sigma'].squeeze(-1)) # [N, T] alphas_shifted = torch.cat([torch.ones_like(alphas[..., :1]), 1 - alphas + 1e-15], dim=-1) # [N, T+1] weights = alphas * torch.cumprod(alphas_shifted, dim=-1)[..., :-1] # [N, T] # sample new z_vals z_vals_mid = (z_vals[..., :-1] + 0.5 * deltas[..., :-1]) # [N, T-1] new_z_vals = sample_pdf(z_vals_mid, weights[:, 1:-1], self.opt.upsample_steps, det=not self.training).detach() # [N, t] new_xyzs = rays_o.unsqueeze(-2) + rays_d.unsqueeze(-2) * new_z_vals.unsqueeze(-1) # [N, 1, 3] * [N, t, 1] -> [N, t, 3] new_xyzs = torch.min(torch.max(new_xyzs, aabb[:3]), aabb[3:]) # a manual clip. # only forward new points to save computation new_density_outputs = self.density(new_xyzs.reshape(-1, 3)) #new_sigmas = new_density_outputs['sigma'].view(N, self.opt.upsample_steps) # [N, t] for k, v in new_density_outputs.items(): new_density_outputs[k] = v.view(N, self.opt.upsample_steps, -1) # re-order z_vals = torch.cat([z_vals, new_z_vals], dim=1) # [N, T+t] z_vals, z_index = torch.sort(z_vals, dim=1) xyzs = torch.cat([xyzs, new_xyzs], dim=1) # [N, T+t, 3] xyzs = torch.gather(xyzs, dim=1, index=z_index.unsqueeze(-1).expand_as(xyzs)) for k in density_outputs: tmp_output = torch.cat([density_outputs[k], new_density_outputs[k]], dim=1) density_outputs[k] = torch.gather(tmp_output, dim=1, index=z_index.unsqueeze(-1).expand_as(tmp_output)) deltas = z_vals[..., 1:] - z_vals[..., :-1] # [N, T+t-1] deltas = torch.cat([deltas, sample_dist * torch.ones_like(deltas[..., :1])], dim=-1) alphas = 1 - torch.exp(-deltas * density_outputs['sigma'].squeeze(-1)) # [N, T+t] alphas_shifted = torch.cat([torch.ones_like(alphas[..., :1]), 1 - alphas + 1e-15], dim=-1) # [N, T+t+1] weights = alphas * torch.cumprod(alphas_shifted, dim=-1)[..., :-1] # [N, T+t] dirs = rays_d.view(-1, 1, 3).expand_as(xyzs) light_d = light_d.view(-1, 1, 3).expand_as(xyzs) for k, v in density_outputs.items(): density_outputs[k] = v.view(-1, v.shape[-1]) dirs = safe_normalize(dirs) sigmas, rgbs, normals = self(xyzs.reshape(-1, 3), dirs.reshape(-1, 3), light_d.reshape(-1, 3), ratio=ambient_ratio, shading=shading) rgbs = rgbs.view(N, -1, 3) # [N, T+t, 3] if normals is not None: normals = normals.view(N, -1, 3) # calculate weight_sum (mask) weights_sum = weights.sum(dim=-1) # [N] # calculate depth depth = torch.sum(weights * z_vals, dim=-1) # calculate color image = torch.sum(weights.unsqueeze(-1) * rgbs, dim=-2) # [N, 3], in [0, 1] # mix background color if bg_color is None: if self.opt.bg_radius > 0: # use the bg model to calculate bg_color bg_color = self.background(rays_d) # [N, 3] else: bg_color = 1 image = image + (1 - weights_sum).unsqueeze(-1) * bg_color image = image.view(*prefix, 3) depth = depth.view(*prefix) weights_sum = weights_sum.reshape(*prefix) if self.training: if self.opt.lambda_orient > 0 and normals is not None: # orientation loss loss_orient = weights.detach() * (normals * dirs).sum(-1).clamp(min=0) ** 2 results['loss_orient'] = loss_orient.sum(-1).mean() if self.opt.lambda_3d_normal_smooth > 0 and normals is not None: normals_perturb = self.normal(xyzs + torch.randn_like(xyzs) * 1e-2) results['loss_normal_perturb'] = (normals - normals_perturb).abs().mean() if (self.opt.lambda_2d_normal_smooth > 0 or self.opt.lambda_normal > 0) and normals is not None: normal_image = torch.sum(weights.unsqueeze(-1) * (normals + 1) / 2, dim=-2) # [N, 3], in [0, 1] results['normal_image'] = normal_image results['image'] = image results['depth'] = depth results['weights'] = weights results['weights_sum'] = weights_sum return results def run_cuda(self, rays_o, rays_d, light_d=None, ambient_ratio=1.0, shading='albedo', bg_color=None, perturb=False, T_thresh=1e-4, binarize=False, **kwargs): # rays_o, rays_d: [B, N, 3] # return: image: [B, N, 3], depth: [B, N] prefix = rays_o.shape[:-1] rays_o = rays_o.contiguous().view(-1, 3) rays_d = rays_d.contiguous().view(-1, 3) N = rays_o.shape[0] # B * N, in fact device = rays_o.device # pre-calculate near far nears, fars = raymarching.near_far_from_aabb(rays_o, rays_d, self.aabb_train if self.training else self.aabb_infer) # random sample light_d if not provided if light_d is None: # gaussian noise around the ray origin, so the light always face the view dir (avoid dark face) light_d = safe_normalize(rays_o + torch.randn(3, device=rays_o.device)) # [N, 3] results = {} if self.training: xyzs, dirs, ts, rays = raymarching.march_rays_train(rays_o, rays_d, self.bound, self.density_bitfield, self.cascade, self.grid_size, nears, fars, perturb, self.opt.dt_gamma, self.opt.max_steps) dirs = safe_normalize(dirs) if light_d.shape[0] > 1: flatten_rays = raymarching.flatten_rays(rays, xyzs.shape[0]).long() light_d = light_d[flatten_rays] sigmas, rgbs, normals = self(xyzs, dirs, light_d, ratio=ambient_ratio, shading=shading) weights, weights_sum, depth, image = raymarching.composite_rays_train(sigmas, rgbs, ts, rays, T_thresh, binarize) # normals related regularizations if self.opt.lambda_orient > 0 and normals is not None: # orientation loss loss_orient = weights.detach() * (normals * dirs).sum(-1).clamp(min=0) ** 2 results['loss_orient'] = loss_orient.mean() if self.opt.lambda_3d_normal_smooth > 0 and normals is not None: normals_perturb = self.normal(xyzs + torch.randn_like(xyzs) * 1e-2) results['loss_normal_perturb'] = (normals - normals_perturb).abs().mean() if (self.opt.lambda_2d_normal_smooth > 0 or self.opt.lambda_normal > 0) and normals is not None: _, _, _, normal_image = raymarching.composite_rays_train(sigmas.detach(), (normals + 1) / 2, ts, rays, T_thresh, binarize) results['normal_image'] = normal_image # weights normalization results['weights'] = weights else: # allocate outputs dtype = torch.float32 weights_sum = torch.zeros(N, dtype=dtype, device=device) depth = torch.zeros(N, dtype=dtype, device=device) image = torch.zeros(N, 3, dtype=dtype, device=device) n_alive = N rays_alive = torch.arange(n_alive, dtype=torch.int32, device=device) # [N] rays_t = nears.clone() # [N] step = 0 while step < self.opt.max_steps: # hard coded max step # count alive rays n_alive = rays_alive.shape[0] # exit loop if n_alive <= 0: break # decide compact_steps n_step = max(min(N // n_alive, 8), 1) xyzs, dirs, ts = raymarching.march_rays(n_alive, n_step, rays_alive, rays_t, rays_o, rays_d, self.bound, self.density_bitfield, self.cascade, self.grid_size, nears, fars, perturb if step == 0 else False, self.opt.dt_gamma, self.opt.max_steps) dirs = safe_normalize(dirs) sigmas, rgbs, normals = self(xyzs, dirs, light_d, ratio=ambient_ratio, shading=shading) raymarching.composite_rays(n_alive, n_step, rays_alive, rays_t, sigmas, rgbs, ts, weights_sum, depth, image, T_thresh, binarize) rays_alive = rays_alive[rays_alive >= 0] #print(f'step = {step}, n_step = {n_step}, n_alive = {n_alive}, xyzs: {xyzs.shape}') step += n_step # mix background color if bg_color is None: if self.opt.bg_radius > 0: # use the bg model to calculate bg_color bg_color = self.background(rays_d) # [N, 3] else: bg_color = 1 image = image + (1 - weights_sum).unsqueeze(-1) * bg_color image = image.view(*prefix, 3) depth = depth.view(*prefix) weights_sum = weights_sum.reshape(*prefix) results['image'] = image results['depth'] = depth results['weights_sum'] = weights_sum return results @torch.no_grad() def init_tet(self, mesh=None): if mesh is not None: # normalize mesh scale = 0.8 / np.array(mesh.bounds[1] - mesh.bounds[0]).max() center = np.array(mesh.bounds[1] + mesh.bounds[0]) / 2 mesh.vertices = (mesh.vertices - center) * scale # init scale # self.tet_scale = torch.from_numpy(np.abs(mesh.vertices).max(axis=0) + 1e-1).to(self.verts.dtype).cuda() self.tet_scale = torch.from_numpy(np.array([np.abs(mesh.vertices).max()]) + 1e-1).to(self.verts.dtype).cuda() self.verts = self.verts * self.tet_scale # init sdf import cubvh BVH = cubvh.cuBVH(mesh.vertices, mesh.faces) sdf, _, _ = BVH.signed_distance(self.verts, return_uvw=False, mode='watertight') sdf *= -10 # INNER is POSITIVE, also make it stronger self.sdf.data += sdf.to(self.sdf.data.dtype).clamp(-1, 1) else: if self.cuda_ray: density_thresh = min(self.mean_density, self.density_thresh) else: density_thresh = self.density_thresh if self.opt.density_activation == 'softplus': density_thresh = density_thresh * 25 # init scale sigma = self.density(self.verts)['sigma'] # verts covers [-1, 1] now mask = sigma > density_thresh valid_verts = self.verts[mask] self.tet_scale = valid_verts.abs().amax(dim=0) + 1e-1 self.verts = self.verts * self.tet_scale # init sigma sigma = self.density(self.verts)['sigma'] # new verts self.sdf.data += (sigma - density_thresh).clamp(-1, 1) print(f'[INFO] init dmtet: scale = {self.tet_scale}') def run_dmtet(self, rays_o, rays_d, mvp, h, w, light_d=None, ambient_ratio=1.0, shading='albedo', bg_color=None, **kwargs): # mvp: [B, 4, 4] device = mvp.device campos = rays_o[:, 0, :] # only need one ray per batch # random sample light_d if not provided if light_d is None: # gaussian noise around the ray origin, so the light always face the view dir (avoid dark face) light_d = safe_normalize(campos + torch.randn_like(campos)).view(-1, 1, 1, 3) # [B, 1, 1, 3] results = {} # get mesh sdf = self.sdf deform = torch.tanh(self.deform) / self.opt.tet_grid_size verts, faces = self.dmtet(self.verts + deform, sdf, self.indices) # get normals i0, i1, i2 = faces[:, 0], faces[:, 1], faces[:, 2] v0, v1, v2 = verts[i0, :], verts[i1, :], verts[i2, :] faces = faces.int() face_normals = torch.cross(v1 - v0, v2 - v0) face_normals = safe_normalize(face_normals) vn = torch.zeros_like(verts) vn.scatter_add_(0, i0[:, None].repeat(1,3), face_normals) vn.scatter_add_(0, i1[:, None].repeat(1,3), face_normals) vn.scatter_add_(0, i2[:, None].repeat(1,3), face_normals) vn = torch.where(torch.sum(vn * vn, -1, keepdim=True) > 1e-20, vn, torch.tensor([0.0, 0.0, 1.0], dtype=torch.float32, device=vn.device)) # rasterization verts_clip = torch.bmm(F.pad(verts, pad=(0, 1), mode='constant', value=1.0).unsqueeze(0).repeat(mvp.shape[0], 1, 1), mvp.permute(0,2,1)).float() # [B, N, 4] rast, rast_db = dr.rasterize(self.glctx, verts_clip, faces, (h, w)) alpha = (rast[..., 3:] > 0).float() xyzs, _ = dr.interpolate(verts.unsqueeze(0), rast, faces) # [B, H, W, 3] normal, _ = dr.interpolate(vn.unsqueeze(0).contiguous(), rast, faces) normal = safe_normalize(normal) xyzs = xyzs.view(-1, 3) mask = (rast[..., 3:] > 0).view(-1).detach() # do the lighting here since we have normal from mesh now. albedo = torch.zeros_like(xyzs, dtype=torch.float32) if mask.any(): masked_albedo = self.density(xyzs[mask])['albedo'] albedo[mask] = masked_albedo.float() albedo = albedo.view(-1, h, w, 3) # these two modes lead to no parameters to optimize if using --lock_geo. if self.opt.lock_geo and shading in ['textureless', 'normal']: shading = 'lambertian' if shading == 'albedo': color = albedo elif shading == 'textureless': lambertian = ambient_ratio + (1 - ambient_ratio) * (normal * light_d).sum(-1).float().clamp(min=0) color = lambertian.unsqueeze(-1).repeat(1, 1, 1, 3) elif shading == 'normal': color = (normal + 1) / 2 else: # 'lambertian' lambertian = ambient_ratio + (1 - ambient_ratio) * (normal * light_d).sum(-1).float().clamp(min=0) color = albedo * lambertian.unsqueeze(-1) color = dr.antialias(color, rast, verts_clip, faces).clamp(0, 1) # [B, H, W, 3] alpha = dr.antialias(alpha, rast, verts_clip, faces).clamp(0, 1) # [B, H, W, 1] # mix background color if bg_color is None: if self.opt.bg_radius > 0: # use the bg model to calculate bg_color bg_color = self.background(rays_d) # [N, 3] else: bg_color = 1 if torch.is_tensor(bg_color) and len(bg_color.shape) > 1: bg_color = bg_color.view(-1, h, w, 3) depth = rast[:, :, :, [2]] # [B, H, W] color = color + (1 - alpha) * bg_color results['depth'] = depth results['image'] = color results['weights_sum'] = alpha.squeeze(-1) if self.opt.lambda_2d_normal_smooth > 0 or self.opt.lambda_normal > 0: normal_image = dr.antialias((normal + 1) / 2, rast, verts_clip, faces).clamp(0, 1) # [B, H, W, 3] results['normal_image'] = normal_image # regularizations if self.training: if self.opt.lambda_mesh_normal > 0: results['normal_loss'] = normal_consistency(face_normals, faces) if self.opt.lambda_mesh_laplacian > 0: results['lap_loss'] = laplacian_smooth_loss(verts, faces) return results def run_taichi(self, rays_o, rays_d, light_d=None, ambient_ratio=1.0, shading='albedo', bg_color=None, perturb=False, T_thresh=1e-4, **kwargs): # rays_o, rays_d: [B, N, 3], assumes B == 1 # return: image: [B, N, 3], depth: [B, N] prefix = rays_o.shape[:-1] rays_o = rays_o.contiguous().view(-1, 3) rays_d = rays_d.contiguous().view(-1, 3) N = rays_o.shape[0] # N = B * N, in fact device = rays_o.device # pre-calculate near far exp_step_factor = kwargs.get('exp_step_factor', 0.) MAX_SAMPLES = 1024 NEAR_DISTANCE = 0.01 center = torch.zeros(1, 3) half_size = torch.ones(1, 3) _, hits_t, _ = self.ray_aabb_intersector.apply(rays_o, rays_d, center, half_size, 1) hits_t[(hits_t[:, 0, 0] >= 0) & (hits_t[:, 0, 0] < NEAR_DISTANCE), 0, 0] = NEAR_DISTANCE # TODO: should sample different light_d for each batch... but taichi end doesn't have a flatten_ray implemented currently... # random sample light_d if not provided if light_d is None: # gaussian noise around the ray origin, so the light always face the view dir (avoid dark face) light_d = (rays_o[0] + torch.randn(3, device=device, dtype=torch.float)) light_d = safe_normalize(light_d) results = {} if self.training: rays_a, xyzs, dirs, deltas, ts, _ = self.ray_marching(rays_o, rays_d, hits_t[:, 0], self.density_bitfield, self.cascade, self.bound, exp_step_factor, self.grid_size, MAX_SAMPLES) dirs = safe_normalize(dirs) # plot_pointcloud(xyzs.reshape(-1, 3).detach().cpu().numpy()) sigmas, rgbs, normals = self(xyzs, dirs, light_d, ratio=ambient_ratio, shading=shading) _, weights_sum, depth, image, weights = self.volume_render(sigmas, rgbs, deltas, ts, rays_a, kwargs.get('T_threshold', 1e-4)) # normals related regularizations if self.opt.lambda_orient > 0 and normals is not None: # orientation loss loss_orient = weights.detach() * (normals * dirs).sum(-1).clamp(min=0) ** 2 results['loss_orient'] = loss_orient.mean() if self.opt.lambda_3d_normal_smooth > 0 and normals is not None: normals_perturb = self.normal(xyzs + torch.randn_like(xyzs) * 1e-2) results['loss_normal_perturb'] = (normals - normals_perturb).abs().mean() if (self.opt.lambda_2d_normal_smooth > 0 or self.opt.lambda_normal > 0) and normals is not None: _, _, _, normal_image, _ = self.volume_render(sigmas.detach(), (normals + 1) / 2, deltas, ts, rays_a, kwargs.get('T_threshold', 1e-4)) results['normal_image'] = normal_image # weights normalization results['weights'] = weights else: # allocate outputs dtype = torch.float32 weights_sum = torch.zeros(N, dtype=dtype, device=device) depth = torch.zeros(N, dtype=dtype, device=device) image = torch.zeros(N, 3, dtype=dtype, device=device) n_alive = N rays_alive = torch.arange(n_alive, dtype=torch.int32, device=device) # [N] rays_t = hits_t[:, 0, 0] step = 0 min_samples = 1 if exp_step_factor == 0 else 4 while step < self.opt.max_steps: # hard coded max step # count alive rays n_alive = rays_alive.shape[0] # exit loop if n_alive <= 0: break # decide compact_steps # n_step = max(min(N // n_alive, 8), 1) n_step = max(min(N // n_alive, 64), min_samples) xyzs, dirs, deltas, ts, N_eff_samples = \ self.raymarching_test_taichi(rays_o, rays_d, hits_t[:, 0], rays_alive, self.density_bitfield, self.cascade, self.bound, exp_step_factor, self.grid_size, MAX_SAMPLES, n_step) xyzs = self.rearrange(xyzs, 'n1 n2 c -> (n1 n2) c') dirs = self.rearrange(dirs, 'n1 n2 c -> (n1 n2) c') dirs = safe_normalize(dirs) valid_mask = ~torch.all(dirs == 0, dim=1) if valid_mask.sum() == 0: break sigmas = torch.zeros(len(xyzs), device=device) rgbs = torch.zeros(len(xyzs), 3, device=device) normals = torch.zeros(len(xyzs), 3, device=device) sigmas[valid_mask], _rgbs, normals = self(xyzs[valid_mask], dirs[valid_mask], light_d, ratio=ambient_ratio, shading=shading) rgbs[valid_mask] = _rgbs.float() sigmas = self.rearrange(sigmas, '(n1 n2) -> n1 n2', n2=n_step) rgbs = self.rearrange(rgbs, '(n1 n2) c -> n1 n2 c', n2=n_step) if normals is not None: normals = self.rearrange(normals, '(n1 n2) c -> n1 n2 c', n2=n_step) self.composite_test_fw(sigmas, rgbs, deltas, ts, hits_t[:,0], rays_alive, kwargs.get('T_threshold', 1e-4), N_eff_samples, weights_sum, depth, image) rays_alive = rays_alive[rays_alive >= 0] step += n_step # mix background color if bg_color is None: if self.opt.bg_radius > 0: # use the bg model to calculate bg_color bg_color = self.background(rays_d) # [N, 3] else: bg_color = 1 image = image + self.rearrange(1 - weights_sum, 'n -> n 1') * bg_color image = image.view(*prefix, 3) depth = depth.view(*prefix) weights_sum = weights_sum.reshape(*prefix) results['image'] = image results['depth'] = depth results['weights_sum'] = weights_sum return results @torch.no_grad() def update_extra_state(self, decay=0.95, S=128): # call before each epoch to update extra states. if not (self.cuda_ray or self.taichi_ray): return ### update density grid tmp_grid = - torch.ones_like(self.density_grid) X = torch.arange(self.grid_size, dtype=torch.int32, device=self.aabb_train.device).split(S) Y = torch.arange(self.grid_size, dtype=torch.int32, device=self.aabb_train.device).split(S) Z = torch.arange(self.grid_size, dtype=torch.int32, device=self.aabb_train.device).split(S) for xs in X: for ys in Y: for zs in Z: # construct points xx, yy, zz = custom_meshgrid(xs, ys, zs) coords = torch.cat([xx.reshape(-1, 1), yy.reshape(-1, 1), zz.reshape(-1, 1)], dim=-1) # [N, 3], in [0, 128) indices = raymarching.morton3D(coords).long() # [N] xyzs = 2 * coords.float() / (self.grid_size - 1) - 1 # [N, 3] in [-1, 1] # cascading for cas in range(self.cascade): bound = min(2 ** cas, self.bound) half_grid_size = bound / self.grid_size # scale to current cascade's resolution cas_xyzs = xyzs * (bound - half_grid_size) # add noise in [-hgs, hgs] cas_xyzs += (torch.rand_like(cas_xyzs) * 2 - 1) * half_grid_size # query density sigmas = self.density(cas_xyzs)['sigma'].reshape(-1).detach() # assign tmp_grid[cas, indices] = sigmas # ema update valid_mask = self.density_grid >= 0 self.density_grid[valid_mask] = torch.maximum(self.density_grid[valid_mask] * decay, tmp_grid[valid_mask]) self.mean_density = torch.mean(self.density_grid[valid_mask]).item() self.iter_density += 1 # convert to bitfield density_thresh = min(self.mean_density, self.density_thresh) if self.cuda_ray: self.density_bitfield = raymarching.packbits(self.density_grid, density_thresh, self.density_bitfield) elif self.taichi_ray: self.packbits_taichi(self.density_grid.reshape(-1).contiguous(), density_thresh, self.density_bitfield) # print(f'[density grid] min={self.density_grid.min().item():.4f}, max={self.density_grid.max().item():.4f}, mean={self.mean_density:.4f}, occ_rate={(self.density_grid > density_thresh).sum() / (128**3 * self.cascade):.3f}') def render(self, rays_o, rays_d, mvp, h, w, staged=False, max_ray_batch=4096, **kwargs): # rays_o, rays_d: [B, N, 3] # return: pred_rgb: [B, N, 3] B, N = rays_o.shape[:2] device = rays_o.device if self.dmtet: results = self.run_dmtet(rays_o, rays_d, mvp, h, w, **kwargs) elif self.cuda_ray: results = self.run_cuda(rays_o, rays_d, **kwargs) elif self.taichi_ray: results = self.run_taichi(rays_o, rays_d, **kwargs) else: if staged: depth = torch.empty((B, N), device=device) image = torch.empty((B, N, 3), device=device) weights_sum = torch.empty((B, N), device=device) for b in range(B): head = 0 while head < N: tail = min(head + max_ray_batch, N) results_ = self.run(rays_o[b:b+1, head:tail], rays_d[b:b+1, head:tail], **kwargs) depth[b:b+1, head:tail] = results_['depth'] weights_sum[b:b+1, head:tail] = results_['weights_sum'] image[b:b+1, head:tail] = results_['image'] head += max_ray_batch results = {} results['depth'] = depth results['image'] = image results['weights_sum'] = weights_sum else: results = self.run(rays_o, rays_d, **kwargs) return results ================================================ FILE: stable-dreamfusion/nerf/utils.py ================================================ import os import gc import glob import tqdm import math import imageio import psutil from pathlib import Path import random import shutil import warnings import tensorboardX import numpy as np import time import cv2 import matplotlib.pyplot as plt import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import torch.distributed as dist import torchvision.transforms.functional as TF from torchmetrics import PearsonCorrCoef from rich.console import Console from torch_ema import ExponentialMovingAverage from packaging import version as pver def adjust_text_embeddings(embeddings, azimuth, opt): text_z_list = [] weights_list = [] K = 0 for b in range(azimuth.shape[0]): text_z_, weights_ = get_pos_neg_text_embeddings(embeddings, azimuth[b], opt) K = max(K, weights_.shape[0]) text_z_list.append(text_z_) weights_list.append(weights_) # Interleave text_embeddings from different dirs to form a batch text_embeddings = [] for i in range(K): for text_z in text_z_list: # if uneven length, pad with the first embedding text_embeddings.append(text_z[i] if i < len(text_z) else text_z[0]) text_embeddings = torch.stack(text_embeddings, dim=0) # [B * K, 77, 768] # Interleave weights from different dirs to form a batch weights = [] for i in range(K): for weights_ in weights_list: weights.append(weights_[i] if i < len(weights_) else torch.zeros_like(weights_[0])) weights = torch.stack(weights, dim=0) # [B * K] return text_embeddings, weights def get_pos_neg_text_embeddings(embeddings, azimuth_val, opt): if azimuth_val >= -90 and azimuth_val < 90: if azimuth_val >= 0: r = 1 - azimuth_val / 90 else: r = 1 + azimuth_val / 90 start_z = embeddings['front'] end_z = embeddings['side'] # if random.random() < 0.3: # r = r + random.gauss(0, 0.08) pos_z = r * start_z + (1 - r) * end_z text_z = torch.cat([pos_z, embeddings['front'], embeddings['side']], dim=0) if r > 0.8: front_neg_w = 0.0 else: front_neg_w = math.exp(-r * opt.front_decay_factor) * opt.negative_w if r < 0.2: side_neg_w = 0.0 else: side_neg_w = math.exp(-(1-r) * opt.side_decay_factor) * opt.negative_w weights = torch.tensor([1.0, front_neg_w, side_neg_w]) else: if azimuth_val >= 0: r = 1 - (azimuth_val - 90) / 90 else: r = 1 + (azimuth_val + 90) / 90 start_z = embeddings['side'] end_z = embeddings['back'] # if random.random() < 0.3: # r = r + random.gauss(0, 0.08) pos_z = r * start_z + (1 - r) * end_z text_z = torch.cat([pos_z, embeddings['side'], embeddings['front']], dim=0) front_neg_w = opt.negative_w if r > 0.8: side_neg_w = 0.0 else: side_neg_w = math.exp(-r * opt.side_decay_factor) * opt.negative_w / 2 weights = torch.tensor([1.0, side_neg_w, front_neg_w]) return text_z, weights.to(text_z.device) def custom_meshgrid(*args): # ref: https://pytorch.org/docs/stable/generated/torch.meshgrid.html?highlight=meshgrid#torch.meshgrid if pver.parse(torch.__version__) < pver.parse('1.10'): return torch.meshgrid(*args) else: return torch.meshgrid(*args, indexing='ij') def safe_normalize(x, eps=1e-20): return x / torch.sqrt(torch.clamp(torch.sum(x * x, -1, keepdim=True), min=eps)) @torch.cuda.amp.autocast(enabled=False) def get_rays(poses, intrinsics, H, W, N=-1, error_map=None): ''' get rays Args: poses: [B, 4, 4], cam2world intrinsics: [4] H, W, N: int error_map: [B, 128 * 128], sample probability based on training error Returns: rays_o, rays_d: [B, N, 3] inds: [B, N] ''' device = poses.device B = poses.shape[0] fx, fy, cx, cy = intrinsics i, j = custom_meshgrid(torch.linspace(0, W-1, W, device=device), torch.linspace(0, H-1, H, device=device)) i = i.t().reshape([1, H*W]).expand([B, H*W]) + 0.5 j = j.t().reshape([1, H*W]).expand([B, H*W]) + 0.5 results = {} if N > 0: N = min(N, H*W) if error_map is None: inds = torch.randint(0, H*W, size=[N], device=device) # may duplicate inds = inds.expand([B, N]) else: # weighted sample on a low-reso grid inds_coarse = torch.multinomial(error_map.to(device), N, replacement=False) # [B, N], but in [0, 128*128) # map to the original resolution with random perturb. inds_x, inds_y = inds_coarse // 128, inds_coarse % 128 # `//` will throw a warning in torch 1.10... anyway. sx, sy = H / 128, W / 128 inds_x = (inds_x * sx + torch.rand(B, N, device=device) * sx).long().clamp(max=H - 1) inds_y = (inds_y * sy + torch.rand(B, N, device=device) * sy).long().clamp(max=W - 1) inds = inds_x * W + inds_y results['inds_coarse'] = inds_coarse # need this when updating error_map i = torch.gather(i, -1, inds) j = torch.gather(j, -1, inds) results['inds'] = inds else: inds = torch.arange(H*W, device=device).expand([B, H*W]) zs = - torch.ones_like(i) xs = - (i - cx) / fx * zs ys = (j - cy) / fy * zs directions = torch.stack((xs, ys, zs), dim=-1) # directions = safe_normalize(directions) rays_d = directions @ poses[:, :3, :3].transpose(-1, -2) # (B, N, 3) rays_o = poses[..., :3, 3] # [B, 3] rays_o = rays_o[..., None, :].expand_as(rays_d) # [B, N, 3] results['rays_o'] = rays_o results['rays_d'] = rays_d return results def seed_everything(seed): random.seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) #torch.backends.cudnn.deterministic = True #torch.backends.cudnn.benchmark = True @torch.jit.script def linear_to_srgb(x): return torch.where(x < 0.0031308, 12.92 * x, 1.055 * x ** 0.41666 - 0.055) @torch.jit.script def srgb_to_linear(x): return torch.where(x < 0.04045, x / 12.92, ((x + 0.055) / 1.055) ** 2.4) class Trainer(object): def __init__(self, argv, # command line args name, # name of this experiment opt, # extra conf model, # network guidance, # guidance network criterion=None, # loss function, if None, assume inline implementation in train_step optimizer=None, # optimizer ema_decay=None, # if use EMA, set the decay lr_scheduler=None, # scheduler metrics=[], # metrics for evaluation, if None, use val_loss to measure performance, else use the first metric. local_rank=0, # which GPU am I world_size=1, # total num of GPUs device=None, # device to use, usually setting to None is OK. (auto choose device) mute=False, # whether to mute all print fp16=False, # amp optimize level max_keep_ckpt=2, # max num of saved ckpts in disk workspace='workspace', # workspace to save logs & ckpts best_mode='min', # the smaller/larger result, the better use_loss_as_metric=True, # use loss as the first metric report_metric_at_train=False, # also report metrics at training use_checkpoint="latest", # which ckpt to use at init time use_tensorboardX=True, # whether to use tensorboard for logging scheduler_update_every_step=False, # whether to call scheduler.step() after every train step ): self.argv = argv self.name = name self.opt = opt self.mute = mute self.metrics = metrics self.local_rank = local_rank self.world_size = world_size self.workspace = workspace self.ema_decay = ema_decay self.fp16 = fp16 self.best_mode = best_mode self.use_loss_as_metric = use_loss_as_metric self.report_metric_at_train = report_metric_at_train self.max_keep_ckpt = max_keep_ckpt self.use_checkpoint = use_checkpoint self.use_tensorboardX = use_tensorboardX self.time_stamp = time.strftime("%Y-%m-%d_%H-%M-%S") self.scheduler_update_every_step = scheduler_update_every_step self.device = device if device is not None else torch.device(f'cuda:{local_rank}' if torch.cuda.is_available() else 'cpu') self.console = Console() model.to(self.device) if self.world_size > 1: model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model) model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank]) self.model = model # guide model self.guidance = guidance self.embeddings = {} # text prompt / images if self.guidance is not None: for key in self.guidance: for p in self.guidance[key].parameters(): p.requires_grad = False self.embeddings[key] = {} self.prepare_embeddings() if isinstance(criterion, nn.Module): criterion.to(self.device) self.criterion = criterion if self.opt.images is not None: self.pearson = PearsonCorrCoef().to(self.device) if optimizer is None: self.optimizer = optim.Adam(self.model.parameters(), lr=0.001, weight_decay=5e-4) # naive adam else: self.optimizer = optimizer(self.model) if lr_scheduler is None: self.lr_scheduler = optim.lr_scheduler.LambdaLR(self.optimizer, lr_lambda=lambda epoch: 1) # fake scheduler else: self.lr_scheduler = lr_scheduler(self.optimizer) if ema_decay is not None: self.ema = ExponentialMovingAverage(self.model.parameters(), decay=ema_decay) else: self.ema = None self.scaler = torch.cuda.amp.GradScaler(enabled=self.fp16) # variable init self.total_train_t = 0 self.epoch = 0 self.global_step = 0 self.local_step = 0 self.stats = { "loss": [], "valid_loss": [], "results": [], # metrics[0], or valid_loss "checkpoints": [], # record path of saved ckpt, to automatically remove old ckpt "best_result": None, } # auto fix if len(metrics) == 0 or self.use_loss_as_metric: self.best_mode = 'min' # workspace prepare self.log_ptr = None if self.workspace is not None: os.makedirs(self.workspace, exist_ok=True) self.log_path = os.path.join(workspace, f"log_{self.name}.txt") self.log_ptr = open(self.log_path, "a+") self.ckpt_path = os.path.join(self.workspace, 'checkpoints') self.best_path = f"{self.ckpt_path}/{self.name}.pth" os.makedirs(self.ckpt_path, exist_ok=True) # Save a copy of image_config in the experiment workspace if opt.image_config is not None: shutil.copyfile(opt.image_config, os.path.join(self.workspace, os.path.basename(opt.image_config))) # Save a copy of images in the experiment workspace if opt.images is not None: for image_file in opt.images: shutil.copyfile(image_file, os.path.join(self.workspace, os.path.basename(image_file))) self.log(f'[INFO] Cmdline: {self.argv}') self.log(f'[INFO] opt: {self.opt}') self.log(f'[INFO] Trainer: {self.name} | {self.time_stamp} | {self.device} | {"fp16" if self.fp16 else "fp32"} | {self.workspace}') self.log(f'[INFO] #parameters: {sum([p.numel() for p in model.parameters() if p.requires_grad])}') if self.workspace is not None: if self.use_checkpoint == "scratch": self.log("[INFO] Training from scratch ...") elif self.use_checkpoint == "latest": self.log("[INFO] Loading latest checkpoint ...") self.load_checkpoint() elif self.use_checkpoint == "latest_model": self.log("[INFO] Loading latest checkpoint (model only)...") self.load_checkpoint(model_only=True) elif self.use_checkpoint == "best": if os.path.exists(self.best_path): self.log("[INFO] Loading best checkpoint ...") self.load_checkpoint(self.best_path) else: self.log(f"[INFO] {self.best_path} not found, loading latest ...") self.load_checkpoint() else: # path to ckpt self.log(f"[INFO] Loading {self.use_checkpoint} ...") self.load_checkpoint(self.use_checkpoint) # calculate the text embs. @torch.no_grad() def prepare_embeddings(self): # text embeddings (stable-diffusion) if self.opt.text is not None: if 'SD' in self.guidance: self.embeddings['SD']['default'] = self.guidance['SD'].get_text_embeds([self.opt.text]) self.embeddings['SD']['uncond'] = self.guidance['SD'].get_text_embeds([self.opt.negative]) for d in ['front', 'side', 'back']: self.embeddings['SD'][d] = self.guidance['SD'].get_text_embeds([f"{self.opt.text}, {d} view"]) if 'IF' in self.guidance: self.embeddings['IF']['default'] = self.guidance['IF'].get_text_embeds([self.opt.text]) self.embeddings['IF']['uncond'] = self.guidance['IF'].get_text_embeds([self.opt.negative]) for d in ['front', 'side', 'back']: self.embeddings['IF'][d] = self.guidance['IF'].get_text_embeds([f"{self.opt.text}, {d} view"]) if 'clip' in self.guidance: self.embeddings['clip']['text'] = self.guidance['clip'].get_text_embeds(self.opt.text) if self.opt.images is not None: h = int(self.opt.known_view_scale * self.opt.h) w = int(self.opt.known_view_scale * self.opt.w) # load processed image for image in self.opt.images: assert image.endswith('_rgba.png') # the rest of this code assumes that the _rgba image has been passed. rgbas = [cv2.cvtColor(cv2.imread(image, cv2.IMREAD_UNCHANGED), cv2.COLOR_BGRA2RGBA) for image in self.opt.images] rgba_hw = np.stack([cv2.resize(rgba, (w, h), interpolation=cv2.INTER_AREA).astype(np.float32) / 255 for rgba in rgbas]) rgb_hw = rgba_hw[..., :3] * rgba_hw[..., 3:] + (1 - rgba_hw[..., 3:]) self.rgb = torch.from_numpy(rgb_hw).permute(0,3,1,2).contiguous().to(self.device) self.mask = torch.from_numpy(rgba_hw[..., 3] > 0.5).to(self.device) print(f'[INFO] dataset: load image prompt {self.opt.images} {self.rgb.shape}') # load depth depth_paths = [image.replace('_rgba.png', '_depth.png') for image in self.opt.images] depths = [cv2.imread(depth_path, cv2.IMREAD_UNCHANGED) for depth_path in depth_paths] depth = np.stack([cv2.resize(depth, (w, h), interpolation=cv2.INTER_AREA) for depth in depths]) self.depth = torch.from_numpy(depth.astype(np.float32) / 255).to(self.device) # TODO: this should be mapped to FP16 print(f'[INFO] dataset: load depth prompt {depth_paths} {self.depth.shape}') # load normal # TODO: don't load if normal loss is 0 normal_paths = [image.replace('_rgba.png', '_normal.png') for image in self.opt.images] normals = [cv2.imread(normal_path, cv2.IMREAD_UNCHANGED) for normal_path in normal_paths] normal = np.stack([cv2.resize(normal, (w, h), interpolation=cv2.INTER_AREA) for normal in normals]) self.normal = torch.from_numpy(normal.astype(np.float32) / 255).to(self.device) print(f'[INFO] dataset: load normal prompt {normal_paths} {self.normal.shape}') # encode embeddings for zero123 if 'zero123' in self.guidance: rgba_256 = np.stack([cv2.resize(rgba, (256, 256), interpolation=cv2.INTER_AREA).astype(np.float32) / 255 for rgba in rgbas]) rgbs_256 = rgba_256[..., :3] * rgba_256[..., 3:] + (1 - rgba_256[..., 3:]) rgb_256 = torch.from_numpy(rgbs_256).permute(0,3,1,2).contiguous().to(self.device) guidance_embeds = self.guidance['zero123'].get_img_embeds(rgb_256) self.embeddings['zero123']['default'] = { 'zero123_ws' : self.opt.zero123_ws, 'c_crossattn' : guidance_embeds[0], 'c_concat' : guidance_embeds[1], 'ref_polars' : self.opt.ref_polars, 'ref_azimuths' : self.opt.ref_azimuths, 'ref_radii' : self.opt.ref_radii, } if 'clip' in self.guidance: self.embeddings['clip']['image'] = self.guidance['clip'].get_img_embeds(self.rgb) def __del__(self): if self.log_ptr: self.log_ptr.close() def log(self, *args, **kwargs): if self.local_rank == 0: if not self.mute: #print(*args) self.console.print(*args, **kwargs) if self.log_ptr: print(*args, file=self.log_ptr) self.log_ptr.flush() # write immediately to file ### ------------------------------ def train_step(self, data, save_guidance_path:Path=None): """ Args: save_guidance_path: an image that combines the NeRF render, the added latent noise, the denoised result and optionally the fully-denoised image. """ # perform RGBD loss instead of SDS if is image-conditioned do_rgbd_loss = self.opt.images is not None and \ (self.global_step % self.opt.known_view_interval == 0) # override random camera with fixed known camera if do_rgbd_loss: data = self.default_view_data # experiment iterations ratio # i.e. what proportion of this experiment have we completed (in terms of iterations) so far? exp_iter_ratio = (self.global_step - self.opt.exp_start_iter) / (self.opt.exp_end_iter - self.opt.exp_start_iter) # progressively relaxing view range if self.opt.progressive_view: r = min(1.0, self.opt.progressive_view_init_ratio + 2.0*exp_iter_ratio) self.opt.phi_range = [self.opt.default_azimuth * (1 - r) + self.opt.full_phi_range[0] * r, self.opt.default_azimuth * (1 - r) + self.opt.full_phi_range[1] * r] self.opt.theta_range = [self.opt.default_polar * (1 - r) + self.opt.full_theta_range[0] * r, self.opt.default_polar * (1 - r) + self.opt.full_theta_range[1] * r] self.opt.radius_range = [self.opt.default_radius * (1 - r) + self.opt.full_radius_range[0] * r, self.opt.default_radius * (1 - r) + self.opt.full_radius_range[1] * r] self.opt.fovy_range = [self.opt.default_fovy * (1 - r) + self.opt.full_fovy_range[0] * r, self.opt.default_fovy * (1 - r) + self.opt.full_fovy_range[1] * r] # progressively increase max_level if self.opt.progressive_level: self.model.max_level = min(1.0, 0.25 + 2.0*exp_iter_ratio) rays_o = data['rays_o'] # [B, N, 3] rays_d = data['rays_d'] # [B, N, 3] mvp = data['mvp'] # [B, 4, 4] B, N = rays_o.shape[:2] H, W = data['H'], data['W'] # When ref_data has B images > opt.batch_size if B > self.opt.batch_size: # choose batch_size images out of those B images choice = torch.randperm(B)[:self.opt.batch_size] B = self.opt.batch_size rays_o = rays_o[choice] rays_d = rays_d[choice] mvp = mvp[choice] if do_rgbd_loss: ambient_ratio = 1.0 shading = 'lambertian' # use lambertian instead of albedo to get normal as_latent = False binarize = False bg_color = torch.rand((B * N, 3), device=rays_o.device) # add camera noise to avoid grid-like artifact if self.opt.known_view_noise_scale > 0: noise_scale = self.opt.known_view_noise_scale #* (1 - self.global_step / self.opt.iters) rays_o = rays_o + torch.randn(3, device=self.device) * noise_scale rays_d = rays_d + torch.randn(3, device=self.device) * noise_scale elif exp_iter_ratio <= self.opt.latent_iter_ratio: ambient_ratio = 1.0 shading = 'normal' as_latent = True binarize = False bg_color = None else: if exp_iter_ratio <= self.opt.albedo_iter_ratio: ambient_ratio = 1.0 shading = 'albedo' else: # random shading ambient_ratio = self.opt.min_ambient_ratio + (1.0-self.opt.min_ambient_ratio) * random.random() rand = random.random() if rand >= (1.0 - self.opt.textureless_ratio): shading = 'textureless' else: shading = 'lambertian' as_latent = False # random weights binarization (like mobile-nerf) [NOT WORKING NOW] # binarize_thresh = min(0.5, -0.5 + self.global_step / self.opt.iters) # binarize = random.random() < binarize_thresh binarize = False # random background rand = random.random() if self.opt.bg_radius > 0 and rand > 0.5: bg_color = None # use bg_net else: bg_color = torch.rand(3).to(self.device) # single color random bg outputs = self.model.render(rays_o, rays_d, mvp, H, W, staged=False, perturb=True, bg_color=bg_color, ambient_ratio=ambient_ratio, shading=shading, binarize=binarize) pred_depth = outputs['depth'].reshape(B, 1, H, W) pred_mask = outputs['weights_sum'].reshape(B, 1, H, W) if 'normal_image' in outputs: pred_normal = outputs['normal_image'].reshape(B, H, W, 3) if as_latent: # abuse normal & mask as latent code for faster geometry initialization (ref: fantasia3D) pred_rgb = torch.cat([outputs['image'], outputs['weights_sum'].unsqueeze(-1)], dim=-1).reshape(B, H, W, 4).permute(0, 3, 1, 2).contiguous() # [B, 4, H, W] else: pred_rgb = outputs['image'].reshape(B, H, W, 3).permute(0, 3, 1, 2).contiguous() # [B, 3, H, W] # known view loss if do_rgbd_loss: gt_mask = self.mask # [B, H, W] gt_rgb = self.rgb # [B, 3, H, W] gt_normal = self.normal # [B, H, W, 3] gt_depth = self.depth # [B, H, W] if len(gt_rgb) > self.opt.batch_size: gt_mask = gt_mask[choice] gt_rgb = gt_rgb[choice] gt_normal = gt_normal[choice] gt_depth = gt_depth[choice] # color loss gt_rgb = gt_rgb * gt_mask[:, None].float() + bg_color.reshape(B, H, W, 3).permute(0,3,1,2).contiguous() * (1 - gt_mask[:, None].float()) loss = self.opt.lambda_rgb * F.mse_loss(pred_rgb, gt_rgb) # mask loss loss = loss + self.opt.lambda_mask * F.mse_loss(pred_mask[:, 0], gt_mask.float()) # normal loss if self.opt.lambda_normal > 0 and 'normal_image' in outputs: valid_gt_normal = 1 - 2 * gt_normal[gt_mask] # [B, 3] valid_pred_normal = 2 * pred_normal[gt_mask] - 1 # [B, 3] lambda_normal = self.opt.lambda_normal * min(1, self.global_step / self.opt.iters) loss = loss + lambda_normal * (1 - F.cosine_similarity(valid_pred_normal, valid_gt_normal).mean()) # relative depth loss if self.opt.lambda_depth > 0: valid_gt_depth = gt_depth[gt_mask] # [B,] valid_pred_depth = pred_depth[:, 0][gt_mask] # [B,] lambda_depth = self.opt.lambda_depth * min(1, self.global_step / self.opt.iters) loss = loss + lambda_depth * (1 - self.pearson(valid_pred_depth, valid_gt_depth)) # # scale-invariant # with torch.no_grad(): # A = torch.cat([valid_gt_depth, torch.ones_like(valid_gt_depth)], dim=-1) # [B, 2] # X = torch.linalg.lstsq(A, valid_pred_depth).solution # [2, 1] # valid_gt_depth = A @ X # [B, 1] # lambda_depth = self.opt.lambda_depth #* min(1, self.global_step / self.opt.iters) # loss = loss + lambda_depth * F.mse_loss(valid_pred_depth, valid_gt_depth) # novel view loss else: loss = 0 if 'SD' in self.guidance: # interpolate text_z azimuth = data['azimuth'] # [-180, 180] # ENHANCE: remove loop to handle batch size > 1 text_z = [self.embeddings['SD']['uncond']] * azimuth.shape[0] if self.opt.perpneg: text_z_comp, weights = adjust_text_embeddings(self.embeddings['SD'], azimuth, self.opt) text_z.append(text_z_comp) else: for b in range(azimuth.shape[0]): if azimuth[b] >= -90 and azimuth[b] < 90: if azimuth[b] >= 0: r = 1 - azimuth[b] / 90 else: r = 1 + azimuth[b] / 90 start_z = self.embeddings['SD']['front'] end_z = self.embeddings['SD']['side'] else: if azimuth[b] >= 0: r = 1 - (azimuth[b] - 90) / 90 else: r = 1 + (azimuth[b] + 90) / 90 start_z = self.embeddings['SD']['side'] end_z = self.embeddings['SD']['back'] text_z.append(r * start_z + (1 - r) * end_z) text_z = torch.cat(text_z, dim=0) if self.opt.perpneg: loss = loss + self.guidance['SD'].train_step_perpneg(text_z, weights, pred_rgb, as_latent=as_latent, guidance_scale=self.opt.guidance_scale, grad_scale=self.opt.lambda_guidance, save_guidance_path=save_guidance_path) else: loss = loss + self.guidance['SD'].train_step(text_z, pred_rgb, as_latent=as_latent, guidance_scale=self.opt.guidance_scale, grad_scale=self.opt.lambda_guidance, save_guidance_path=save_guidance_path) if 'IF' in self.guidance: # interpolate text_z azimuth = data['azimuth'] # [-180, 180] # ENHANCE: remove loop to handle batch size > 1 text_z = [self.embeddings['IF']['uncond']] * azimuth.shape[0] if self.opt.perpneg: text_z_comp, weights = adjust_text_embeddings(self.embeddings['IF'], azimuth, self.opt) text_z.append(text_z_comp) else: for b in range(azimuth.shape[0]): if azimuth[b] >= -90 and azimuth[b] < 90: if azimuth[b] >= 0: r = 1 - azimuth[b] / 90 else: r = 1 + azimuth[b] / 90 start_z = self.embeddings['IF']['front'] end_z = self.embeddings['IF']['side'] else: if azimuth[b] >= 0: r = 1 - (azimuth[b] - 90) / 90 else: r = 1 + (azimuth[b] + 90) / 90 start_z = self.embeddings['IF']['side'] end_z = self.embeddings['IF']['back'] text_z.append(r * start_z + (1 - r) * end_z) text_z = torch.cat(text_z, dim=0) if self.opt.perpneg: loss = loss + self.guidance['IF'].train_step_perpneg(text_z, weights, pred_rgb, guidance_scale=self.opt.guidance_scale, grad_scale=self.opt.lambda_guidance) else: loss = loss + self.guidance['IF'].train_step(text_z, pred_rgb, guidance_scale=self.opt.guidance_scale, grad_scale=self.opt.lambda_guidance) if 'zero123' in self.guidance: polar = data['polar'] azimuth = data['azimuth'] radius = data['radius'] loss = loss + self.guidance['zero123'].train_step(self.embeddings['zero123']['default'], pred_rgb, polar, azimuth, radius, guidance_scale=self.opt.guidance_scale, as_latent=as_latent, grad_scale=self.opt.lambda_guidance, save_guidance_path=save_guidance_path) if 'clip' in self.guidance: # empirical, far view should apply smaller CLIP loss lambda_guidance = 10 * (1 - abs(azimuth) / 180) * self.opt.lambda_guidance loss = loss + self.guidance['clip'].train_step(self.embeddings['clip'], pred_rgb, grad_scale=lambda_guidance) # regularizations if not self.opt.dmtet: if self.opt.lambda_opacity > 0: loss_opacity = (outputs['weights_sum'] ** 2).mean() loss = loss + self.opt.lambda_opacity * loss_opacity if self.opt.lambda_entropy > 0: alphas = outputs['weights'].clamp(1e-5, 1 - 1e-5) # alphas = alphas ** 2 # skewed entropy, favors 0 over 1 loss_entropy = (- alphas * torch.log2(alphas) - (1 - alphas) * torch.log2(1 - alphas)).mean() lambda_entropy = self.opt.lambda_entropy * min(1, 2 * self.global_step / self.opt.iters) loss = loss + lambda_entropy * loss_entropy if self.opt.lambda_2d_normal_smooth > 0 and 'normal_image' in outputs: # pred_vals = outputs['normal_image'].reshape(B, H, W, 3).permute(0, 3, 1, 2).contiguous() # smoothed_vals = TF.gaussian_blur(pred_vals.detach(), kernel_size=9) # loss_smooth = F.mse_loss(pred_vals, smoothed_vals) # total-variation loss_smooth = (pred_normal[:, 1:, :, :] - pred_normal[:, :-1, :, :]).square().mean() + \ (pred_normal[:, :, 1:, :] - pred_normal[:, :, :-1, :]).square().mean() loss = loss + self.opt.lambda_2d_normal_smooth * loss_smooth if self.opt.lambda_orient > 0 and 'loss_orient' in outputs: loss_orient = outputs['loss_orient'] loss = loss + self.opt.lambda_orient * loss_orient if self.opt.lambda_3d_normal_smooth > 0 and 'loss_normal_perturb' in outputs: loss_normal_perturb = outputs['loss_normal_perturb'] loss = loss + self.opt.lambda_3d_normal_smooth * loss_normal_perturb else: if self.opt.lambda_mesh_normal > 0: loss = loss + self.opt.lambda_mesh_normal * outputs['normal_loss'] if self.opt.lambda_mesh_laplacian > 0: loss = loss + self.opt.lambda_mesh_laplacian * outputs['lap_loss'] return pred_rgb, pred_depth, loss def post_train_step(self): # unscale grad before modifying it! # ref: https://pytorch.org/docs/stable/notes/amp_examples.html#gradient-clipping self.scaler.unscale_(self.optimizer) # clip grad if self.opt.grad_clip >= 0: torch.nn.utils.clip_grad_value_(self.model.parameters(), self.opt.grad_clip) if not self.opt.dmtet and self.opt.backbone == 'grid': if self.opt.lambda_tv > 0: lambda_tv = min(1.0, self.global_step / (0.5 * self.opt.iters)) * self.opt.lambda_tv self.model.encoder.grad_total_variation(lambda_tv, None, self.model.bound) if self.opt.lambda_wd > 0: self.model.encoder.grad_weight_decay(self.opt.lambda_wd) def eval_step(self, data): rays_o = data['rays_o'] # [B, N, 3] rays_d = data['rays_d'] # [B, N, 3] mvp = data['mvp'] B, N = rays_o.shape[:2] H, W = data['H'], data['W'] shading = data['shading'] if 'shading' in data else 'albedo' ambient_ratio = data['ambient_ratio'] if 'ambient_ratio' in data else 1.0 light_d = data['light_d'] if 'light_d' in data else None outputs = self.model.render(rays_o, rays_d, mvp, H, W, staged=True, perturb=False, bg_color=None, light_d=light_d, ambient_ratio=ambient_ratio, shading=shading) pred_rgb = outputs['image'].reshape(B, H, W, 3) pred_depth = outputs['depth'].reshape(B, H, W) # dummy loss = torch.zeros([1], device=pred_rgb.device, dtype=pred_rgb.dtype) return pred_rgb, pred_depth, loss def test_step(self, data, bg_color=None, perturb=False): rays_o = data['rays_o'] # [B, N, 3] rays_d = data['rays_d'] # [B, N, 3] mvp = data['mvp'] B, N = rays_o.shape[:2] H, W = data['H'], data['W'] if bg_color is not None: bg_color = bg_color.to(rays_o.device) shading = data['shading'] if 'shading' in data else 'albedo' ambient_ratio = data['ambient_ratio'] if 'ambient_ratio' in data else 1.0 light_d = data['light_d'] if 'light_d' in data else None outputs = self.model.render(rays_o, rays_d, mvp, H, W, staged=True, perturb=perturb, light_d=light_d, ambient_ratio=ambient_ratio, shading=shading, bg_color=bg_color) pred_rgb = outputs['image'].reshape(B, H, W, 3) pred_depth = outputs['depth'].reshape(B, H, W) return pred_rgb, pred_depth, None def save_mesh(self, loader=None, save_path=None): if save_path is None: save_path = os.path.join(self.workspace, 'mesh') self.log(f"==> Saving mesh to {save_path}") os.makedirs(save_path, exist_ok=True) self.model.export_mesh(save_path, resolution=self.opt.mcubes_resolution, decimate_target=self.opt.decimate_target) self.log(f"==> Finished saving mesh.") ### ------------------------------ def train(self, train_loader, valid_loader, test_loader, max_epochs): if self.use_tensorboardX and self.local_rank == 0: self.writer = tensorboardX.SummaryWriter(os.path.join(self.workspace, "run", self.name)) start_t = time.time() for epoch in range(self.epoch + 1, max_epochs + 1): self.epoch = epoch self.train_one_epoch(train_loader, max_epochs) if self.workspace is not None and self.local_rank == 0: self.save_checkpoint(full=True, best=False) if self.epoch % self.opt.eval_interval == 0: self.evaluate_one_epoch(valid_loader) self.save_checkpoint(full=False, best=True) if self.epoch % self.opt.test_interval == 0 or self.epoch == max_epochs: self.test(test_loader) end_t = time.time() self.total_train_t = end_t - start_t + self.total_train_t self.log(f"[INFO] training takes {(self.total_train_t)/ 60:.4f} minutes.") if self.use_tensorboardX and self.local_rank == 0: self.writer.close() def evaluate(self, loader, name=None): self.use_tensorboardX, use_tensorboardX = False, self.use_tensorboardX self.evaluate_one_epoch(loader, name) self.use_tensorboardX = use_tensorboardX def test(self, loader, save_path=None, name=None, write_video=True): if save_path is None: save_path = os.path.join(self.workspace, 'results') if name is None: name = f'{self.name}_ep{self.epoch:04d}' os.makedirs(save_path, exist_ok=True) self.log(f"==> Start Test, save results to {save_path}") pbar = tqdm.tqdm(total=len(loader) * loader.batch_size, bar_format='{percentage:3.0f}% {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]') self.model.eval() if write_video: all_preds = [] all_preds_depth = [] with torch.no_grad(): for i, data in enumerate(loader): with torch.cuda.amp.autocast(enabled=self.fp16): preds, preds_depth, _ = self.test_step(data) pred = preds[0].detach().cpu().numpy() pred = (pred * 255).astype(np.uint8) pred_depth = preds_depth[0].detach().cpu().numpy() pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() - pred_depth.min() + 1e-6) pred_depth = (pred_depth * 255).astype(np.uint8) if write_video: all_preds.append(pred) all_preds_depth.append(pred_depth) else: cv2.imwrite(os.path.join(save_path, f'{name}_{i:04d}_rgb.png'), cv2.cvtColor(pred, cv2.COLOR_RGB2BGR)) cv2.imwrite(os.path.join(save_path, f'{name}_{i:04d}_depth.png'), pred_depth) pbar.update(loader.batch_size) if write_video: all_preds = np.stack(all_preds, axis=0) all_preds_depth = np.stack(all_preds_depth, axis=0) imageio.mimwrite(os.path.join(save_path, f'{name}_rgb.mp4'), all_preds, fps=25, quality=8, macro_block_size=1) imageio.mimwrite(os.path.join(save_path, f'{name}_depth.mp4'), all_preds_depth, fps=25, quality=8, macro_block_size=1) self.log(f"==> Finished Test.") # [GUI] train text step. def train_gui(self, train_loader, step=16): self.model.train() total_loss = torch.tensor([0], dtype=torch.float32, device=self.device) loader = iter(train_loader) for _ in range(step): # mimic an infinite loop dataloader (in case the total dataset is smaller than step) try: data = next(loader) except StopIteration: loader = iter(train_loader) data = next(loader) # update grid every 16 steps if self.model.cuda_ray and self.global_step % self.opt.update_extra_interval == 0: with torch.cuda.amp.autocast(enabled=self.fp16): self.model.update_extra_state() self.global_step += 1 self.optimizer.zero_grad() with torch.cuda.amp.autocast(enabled=self.fp16): pred_rgbs, pred_depths, loss = self.train_step(data) self.scaler.scale(loss).backward() self.post_train_step() self.scaler.step(self.optimizer) self.scaler.update() if self.scheduler_update_every_step: self.lr_scheduler.step() total_loss += loss.detach() if self.ema is not None: self.ema.update() average_loss = total_loss.item() / step if not self.scheduler_update_every_step: if isinstance(self.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): self.lr_scheduler.step(average_loss) else: self.lr_scheduler.step() outputs = { 'loss': average_loss, 'lr': self.optimizer.param_groups[0]['lr'], } return outputs # [GUI] test on a single image def test_gui(self, pose, intrinsics, mvp, W, H, bg_color=None, spp=1, downscale=1, light_d=None, ambient_ratio=1.0, shading='albedo'): # render resolution (may need downscale to for better frame rate) rH = int(H * downscale) rW = int(W * downscale) intrinsics = intrinsics * downscale pose = torch.from_numpy(pose).unsqueeze(0).to(self.device) mvp = torch.from_numpy(mvp).unsqueeze(0).to(self.device) rays = get_rays(pose, intrinsics, rH, rW, -1) # from degree theta/phi to 3D normalized vec light_d = np.deg2rad(light_d) light_d = np.array([ np.sin(light_d[0]) * np.sin(light_d[1]), np.cos(light_d[0]), np.sin(light_d[0]) * np.cos(light_d[1]), ], dtype=np.float32) light_d = torch.from_numpy(light_d).to(self.device) data = { 'rays_o': rays['rays_o'], 'rays_d': rays['rays_d'], 'mvp': mvp, 'H': rH, 'W': rW, 'light_d': light_d, 'ambient_ratio': ambient_ratio, 'shading': shading, } self.model.eval() if self.ema is not None: self.ema.store() self.ema.copy_to() with torch.no_grad(): with torch.cuda.amp.autocast(enabled=self.fp16): # here spp is used as perturb random seed! preds, preds_depth, _ = self.test_step(data, bg_color=bg_color, perturb=False if spp == 1 else spp) if self.ema is not None: self.ema.restore() # interpolation to the original resolution if downscale != 1: # have to permute twice with torch... preds = F.interpolate(preds.permute(0, 3, 1, 2), size=(H, W), mode='nearest').permute(0, 2, 3, 1).contiguous() preds_depth = F.interpolate(preds_depth.unsqueeze(1), size=(H, W), mode='nearest').squeeze(1) outputs = { 'image': preds[0].detach().cpu().numpy(), 'depth': preds_depth[0].detach().cpu().numpy(), } return outputs def train_one_epoch(self, loader, max_epochs): self.log(f"==> [{time.strftime('%Y-%m-%d_%H-%M-%S')}] Start Training {self.workspace} Epoch {self.epoch}/{max_epochs}, lr={self.optimizer.param_groups[0]['lr']:.6f} ...") total_loss = 0 if self.local_rank == 0 and self.report_metric_at_train: for metric in self.metrics: metric.clear() self.model.train() # distributedSampler: must call set_epoch() to shuffle indices across multiple epochs # ref: https://pytorch.org/docs/stable/data.html if self.world_size > 1: loader.sampler.set_epoch(self.epoch) if self.local_rank == 0: pbar = tqdm.tqdm(total=len(loader) * loader.batch_size, bar_format='{desc}: {percentage:3.0f}% {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]') self.local_step = 0 if self.opt.save_guidance: save_guidance_folder = Path(self.workspace) / 'guidance' save_guidance_folder.mkdir(parents=True, exist_ok=True) for data in loader: # update grid every 16 steps if (self.model.cuda_ray or self.model.taichi_ray) and self.global_step % self.opt.update_extra_interval == 0: with torch.cuda.amp.autocast(enabled=self.fp16): self.model.update_extra_state() self.local_step += 1 self.global_step += 1 self.optimizer.zero_grad() with torch.cuda.amp.autocast(enabled=self.fp16): if self.opt.save_guidance and (self.global_step % self.opt.save_guidance_interval == 0): save_guidance_path = save_guidance_folder / f'step_{self.global_step:07d}.png' else: save_guidance_path = None pred_rgbs, pred_depths, loss = self.train_step(data, save_guidance_path=save_guidance_path) # hooked grad clipping for RGB space if self.opt.grad_clip_rgb >= 0: def _hook(grad): if self.opt.fp16: # correctly handle the scale grad_scale = self.scaler._get_scale_async() return grad.clamp(grad_scale * -self.opt.grad_clip_rgb, grad_scale * self.opt.grad_clip_rgb) else: return grad.clamp(-self.opt.grad_clip_rgb, self.opt.grad_clip_rgb) pred_rgbs.register_hook(_hook) # pred_rgbs.retain_grad() self.scaler.scale(loss).backward() self.post_train_step() self.scaler.step(self.optimizer) self.scaler.update() if self.scheduler_update_every_step: self.lr_scheduler.step() loss_val = loss.item() total_loss += loss_val if self.local_rank == 0: # if self.report_metric_at_train: # for metric in self.metrics: # metric.update(preds, truths) if self.use_tensorboardX: self.writer.add_scalar("train/loss", loss_val, self.global_step) self.writer.add_scalar("train/lr", self.optimizer.param_groups[0]['lr'], self.global_step) if self.scheduler_update_every_step: pbar.set_description(f"loss={loss_val:.4f} ({total_loss/self.local_step:.4f}), lr={self.optimizer.param_groups[0]['lr']:.6f}") else: pbar.set_description(f"loss={loss_val:.4f} ({total_loss/self.local_step:.4f})") pbar.update(loader.batch_size) if self.ema is not None: self.ema.update() average_loss = total_loss / self.local_step self.stats["loss"].append(average_loss) if self.local_rank == 0: pbar.close() if self.report_metric_at_train: for metric in self.metrics: self.log(metric.report(), style="red") if self.use_tensorboardX: metric.write(self.writer, self.epoch, prefix="train") metric.clear() if not self.scheduler_update_every_step: if isinstance(self.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): self.lr_scheduler.step(average_loss) else: self.lr_scheduler.step() cpu_mem, gpu_mem = get_CPU_mem(), get_GPU_mem()[0] self.log(f"==> [{time.strftime('%Y-%m-%d_%H-%M-%S')}] Finished Epoch {self.epoch}/{max_epochs}. CPU={cpu_mem:.1f}GB, GPU={gpu_mem:.1f}GB.") def evaluate_one_epoch(self, loader, name=None): self.log(f"++> Evaluate {self.workspace} at epoch {self.epoch} ...") if name is None: name = f'{self.name}_ep{self.epoch:04d}' total_loss = 0 if self.local_rank == 0: for metric in self.metrics: metric.clear() self.model.eval() if self.ema is not None: self.ema.store() self.ema.copy_to() if self.local_rank == 0: pbar = tqdm.tqdm(total=len(loader) * loader.batch_size, bar_format='{desc}: {percentage:3.0f}% {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]') with torch.no_grad(): self.local_step = 0 for data in loader: self.local_step += 1 with torch.cuda.amp.autocast(enabled=self.fp16): preds, preds_depth, loss = self.eval_step(data) # all_gather/reduce the statistics (NCCL only support all_*) if self.world_size > 1: dist.all_reduce(loss, op=dist.ReduceOp.SUM) loss = loss / self.world_size preds_list = [torch.zeros_like(preds).to(self.device) for _ in range(self.world_size)] # [[B, ...], [B, ...], ...] dist.all_gather(preds_list, preds) preds = torch.cat(preds_list, dim=0) preds_depth_list = [torch.zeros_like(preds_depth).to(self.device) for _ in range(self.world_size)] # [[B, ...], [B, ...], ...] dist.all_gather(preds_depth_list, preds_depth) preds_depth = torch.cat(preds_depth_list, dim=0) loss_val = loss.item() total_loss += loss_val # only rank = 0 will perform evaluation. if self.local_rank == 0: # save image save_path = os.path.join(self.workspace, 'validation', f'{name}_{self.local_step:04d}_rgb.png') save_path_depth = os.path.join(self.workspace, 'validation', f'{name}_{self.local_step:04d}_depth.png') #self.log(f"==> Saving validation image to {save_path}") os.makedirs(os.path.dirname(save_path), exist_ok=True) pred = preds[0].detach().cpu().numpy() pred = (pred * 255).astype(np.uint8) pred_depth = preds_depth[0].detach().cpu().numpy() pred_depth = (pred_depth - pred_depth.min()) / (pred_depth.max() - pred_depth.min() + 1e-6) pred_depth = (pred_depth * 255).astype(np.uint8) cv2.imwrite(save_path, cv2.cvtColor(pred, cv2.COLOR_RGB2BGR)) cv2.imwrite(save_path_depth, pred_depth) pbar.set_description(f"loss={loss_val:.4f} ({total_loss/self.local_step:.4f})") pbar.update(loader.batch_size) average_loss = total_loss / self.local_step self.stats["valid_loss"].append(average_loss) if self.local_rank == 0: pbar.close() if not self.use_loss_as_metric and len(self.metrics) > 0: result = self.metrics[0].measure() self.stats["results"].append(result if self.best_mode == 'min' else - result) # if max mode, use -result else: self.stats["results"].append(average_loss) # if no metric, choose best by min loss for metric in self.metrics: self.log(metric.report(), style="blue") if self.use_tensorboardX: metric.write(self.writer, self.epoch, prefix="evaluate") metric.clear() if self.ema is not None: self.ema.restore() self.log(f"++> Evaluate epoch {self.epoch} Finished.") def save_checkpoint(self, name=None, full=False, best=False): if name is None: name = f'{self.name}_ep{self.epoch:04d}' state = { 'epoch': self.epoch, 'global_step': self.global_step, 'stats': self.stats, } if self.model.cuda_ray: state['mean_density'] = self.model.mean_density if self.opt.dmtet: state['tet_scale'] = self.model.tet_scale.cpu().numpy() if full: state['optimizer'] = self.optimizer.state_dict() state['lr_scheduler'] = self.lr_scheduler.state_dict() state['scaler'] = self.scaler.state_dict() if self.ema is not None: state['ema'] = self.ema.state_dict() if not best: state['model'] = self.model.state_dict() file_path = f"{name}.pth" self.stats["checkpoints"].append(file_path) if len(self.stats["checkpoints"]) > self.max_keep_ckpt: old_ckpt = os.path.join(self.ckpt_path, self.stats["checkpoints"].pop(0)) if os.path.exists(old_ckpt): os.remove(old_ckpt) torch.save(state, os.path.join(self.ckpt_path, file_path)) else: if len(self.stats["results"]) > 0: # always save best since loss cannot reflect performance. if True: # self.log(f"[INFO] New best result: {self.stats['best_result']} --> {self.stats['results'][-1]}") # self.stats["best_result"] = self.stats["results"][-1] # save ema results if self.ema is not None: self.ema.store() self.ema.copy_to() state['model'] = self.model.state_dict() if self.ema is not None: self.ema.restore() torch.save(state, self.best_path) else: self.log(f"[WARN] no evaluated results found, skip saving best checkpoint.") def load_checkpoint(self, checkpoint=None, model_only=False): if checkpoint is None: checkpoint_list = sorted(glob.glob(f'{self.ckpt_path}/*.pth')) if checkpoint_list: checkpoint = checkpoint_list[-1] self.log(f"[INFO] Latest checkpoint is {checkpoint}") else: self.log("[WARN] No checkpoint found, model randomly initialized.") return checkpoint_dict = torch.load(checkpoint, map_location=self.device) if 'model' not in checkpoint_dict: self.model.load_state_dict(checkpoint_dict) self.log("[INFO] loaded model.") return missing_keys, unexpected_keys = self.model.load_state_dict(checkpoint_dict['model'], strict=False) self.log("[INFO] loaded model.") if len(missing_keys) > 0: self.log(f"[WARN] missing keys: {missing_keys}") if len(unexpected_keys) > 0: self.log(f"[WARN] unexpected keys: {unexpected_keys}") if self.ema is not None and 'ema' in checkpoint_dict: try: self.ema.load_state_dict(checkpoint_dict['ema']) self.log("[INFO] loaded EMA.") except: self.log("[WARN] failed to loaded EMA.") if self.model.cuda_ray: if 'mean_density' in checkpoint_dict: self.model.mean_density = checkpoint_dict['mean_density'] if self.opt.dmtet: if 'tet_scale' in checkpoint_dict: new_scale = torch.from_numpy(checkpoint_dict['tet_scale']).to(self.device) self.model.verts *= new_scale / self.model.tet_scale self.model.tet_scale = new_scale if model_only: return self.stats = checkpoint_dict['stats'] self.epoch = checkpoint_dict['epoch'] self.global_step = checkpoint_dict['global_step'] self.log(f"[INFO] load at epoch {self.epoch}, global step {self.global_step}") if self.optimizer and 'optimizer' in checkpoint_dict: try: self.optimizer.load_state_dict(checkpoint_dict['optimizer']) self.log("[INFO] loaded optimizer.") except: self.log("[WARN] Failed to load optimizer.") if self.lr_scheduler and 'lr_scheduler' in checkpoint_dict: try: self.lr_scheduler.load_state_dict(checkpoint_dict['lr_scheduler']) self.log("[INFO] loaded scheduler.") except: self.log("[WARN] Failed to load scheduler.") if self.scaler and 'scaler' in checkpoint_dict: try: self.scaler.load_state_dict(checkpoint_dict['scaler']) self.log("[INFO] loaded scaler.") except: self.log("[WARN] Failed to load scaler.") def get_CPU_mem(): return psutil.Process(os.getpid()).memory_info().rss /1024**3 def get_GPU_mem(): num = torch.cuda.device_count() mem, mems = 0, [] for i in range(num): mem_free, mem_total = torch.cuda.mem_get_info(i) mems.append(int(((mem_total - mem_free)/1024**3)*1000)/1000) mem += mems[-1] return mem, mems ================================================ FILE: stable-dreamfusion/optimizer.py ================================================ # Copyright 2022 Garena Online Private Limited # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math from typing import List import torch from torch import Tensor from torch.optim.optimizer import Optimizer class Adan(Optimizer): """ Implements a pytorch variant of Adan Adan was proposed in Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models[J].arXiv preprint arXiv:2208.06677, 2022. https://arxiv.org/abs/2208.06677 Arguments: params (iterable): iterable of parameters to optimize or dicts defining parameter groups. lr (float, optional): learning rate. (default: 1e-3) betas (Tuple[float, float, flot], optional): coefficients used for first- and second-order moments. (default: (0.98, 0.92, 0.99)) eps (float, optional): term added to the denominator to improve numerical stability. (default: 1e-8) weight_decay (float, optional): decoupled weight decay (L2 penalty) (default: 0) max_grad_norm (float, optional): value used to clip global grad norm (default: 0.0 no clip) no_prox (bool): how to perform the decoupled weight decay (default: False) foreach (bool): if True would use torch._foreach implementation. It's faster but uses slightly more memory. (default: True) """ def __init__(self, params, lr=1e-3, betas=(0.98, 0.92, 0.99), eps=1e-8, weight_decay=0.0, max_grad_norm=0.0, no_prox=False, foreach: bool = True): if not 0.0 <= max_grad_norm: raise ValueError('Invalid Max grad norm: {}'.format(max_grad_norm)) if not 0.0 <= lr: raise ValueError('Invalid learning rate: {}'.format(lr)) if not 0.0 <= eps: raise ValueError('Invalid epsilon value: {}'.format(eps)) if not 0.0 <= betas[0] < 1.0: raise ValueError('Invalid beta parameter at index 0: {}'.format( betas[0])) if not 0.0 <= betas[1] < 1.0: raise ValueError('Invalid beta parameter at index 1: {}'.format( betas[1])) if not 0.0 <= betas[2] < 1.0: raise ValueError('Invalid beta parameter at index 2: {}'.format( betas[2])) defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, max_grad_norm=max_grad_norm, no_prox=no_prox, foreach=foreach) super().__init__(params, defaults) def __setstate__(self, state): super(Adan, self).__setstate__(state) for group in self.param_groups: group.setdefault('no_prox', False) @torch.no_grad() def restart_opt(self): for group in self.param_groups: group['step'] = 0 for p in group['params']: if p.requires_grad: state = self.state[p] # State initialization # Exponential moving average of gradient values state['exp_avg'] = torch.zeros_like(p) # Exponential moving average of squared gradient values state['exp_avg_sq'] = torch.zeros_like(p) # Exponential moving average of gradient difference state['exp_avg_diff'] = torch.zeros_like(p) @torch.no_grad() def step(self, closure=None): """Performs a single optimization step.""" loss = None if closure is not None: with torch.enable_grad(): loss = closure() if self.defaults['max_grad_norm'] > 0: device = self.param_groups[0]['params'][0].device global_grad_norm = torch.zeros(1, device=device) max_grad_norm = torch.tensor(self.defaults['max_grad_norm'], device=device) for group in self.param_groups: for p in group['params']: if p.grad is not None: grad = p.grad global_grad_norm.add_(grad.pow(2).sum()) global_grad_norm = torch.sqrt(global_grad_norm) clip_global_grad_norm = torch.clamp( max_grad_norm / (global_grad_norm + group['eps']), max=1.0).item() else: clip_global_grad_norm = 1.0 for group in self.param_groups: params_with_grad = [] grads = [] exp_avgs = [] exp_avg_sqs = [] exp_avg_diffs = [] neg_pre_grads = [] beta1, beta2, beta3 = group['betas'] # assume same step across group now to simplify things # per parameter step can be easily support # by making it tensor, or pass list into kernel if 'step' in group: group['step'] += 1 else: group['step'] = 1 bias_correction1 = 1.0 - beta1**group['step'] bias_correction2 = 1.0 - beta2**group['step'] bias_correction3 = 1.0 - beta3**group['step'] for p in group['params']: if p.grad is None: continue params_with_grad.append(p) grads.append(p.grad) state = self.state[p] if len(state) == 0: state['exp_avg'] = torch.zeros_like(p) state['exp_avg_sq'] = torch.zeros_like(p) state['exp_avg_diff'] = torch.zeros_like(p) if 'neg_pre_grad' not in state or group['step'] == 1: state['neg_pre_grad'] = p.grad.clone().mul_( -clip_global_grad_norm) exp_avgs.append(state['exp_avg']) exp_avg_sqs.append(state['exp_avg_sq']) exp_avg_diffs.append(state['exp_avg_diff']) neg_pre_grads.append(state['neg_pre_grad']) kwargs = dict( params=params_with_grad, grads=grads, exp_avgs=exp_avgs, exp_avg_sqs=exp_avg_sqs, exp_avg_diffs=exp_avg_diffs, neg_pre_grads=neg_pre_grads, beta1=beta1, beta2=beta2, beta3=beta3, bias_correction1=bias_correction1, bias_correction2=bias_correction2, bias_correction3_sqrt=math.sqrt(bias_correction3), lr=group['lr'], weight_decay=group['weight_decay'], eps=group['eps'], no_prox=group['no_prox'], clip_global_grad_norm=clip_global_grad_norm, ) if group['foreach']: _multi_tensor_adan(**kwargs) else: _single_tensor_adan(**kwargs) return loss def _single_tensor_adan( params: List[Tensor], grads: List[Tensor], exp_avgs: List[Tensor], exp_avg_sqs: List[Tensor], exp_avg_diffs: List[Tensor], neg_pre_grads: List[Tensor], *, beta1: float, beta2: float, beta3: float, bias_correction1: float, bias_correction2: float, bias_correction3_sqrt: float, lr: float, weight_decay: float, eps: float, no_prox: bool, clip_global_grad_norm: Tensor, ): for i, param in enumerate(params): grad = grads[i] exp_avg = exp_avgs[i] exp_avg_sq = exp_avg_sqs[i] exp_avg_diff = exp_avg_diffs[i] neg_grad_or_diff = neg_pre_grads[i] grad.mul_(clip_global_grad_norm) # for memory saving, we use `neg_grad_or_diff` # to get some temp variable in a inplace way neg_grad_or_diff.add_(grad) exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) # m_t exp_avg_diff.mul_(beta2).add_(neg_grad_or_diff, alpha=1 - beta2) # diff_t neg_grad_or_diff.mul_(beta2).add_(grad) exp_avg_sq.mul_(beta3).addcmul_(neg_grad_or_diff, neg_grad_or_diff, value=1 - beta3) # n_t denom = ((exp_avg_sq).sqrt() / bias_correction3_sqrt).add_(eps) step_size_diff = lr * beta2 / bias_correction2 step_size = lr / bias_correction1 if no_prox: param.mul_(1 - lr * weight_decay) param.addcdiv_(exp_avg, denom, value=-step_size) param.addcdiv_(exp_avg_diff, denom, value=-step_size_diff) else: param.addcdiv_(exp_avg, denom, value=-step_size) param.addcdiv_(exp_avg_diff, denom, value=-step_size_diff) param.div_(1 + lr * weight_decay) neg_grad_or_diff.zero_().add_(grad, alpha=-1.0) def _multi_tensor_adan( params: List[Tensor], grads: List[Tensor], exp_avgs: List[Tensor], exp_avg_sqs: List[Tensor], exp_avg_diffs: List[Tensor], neg_pre_grads: List[Tensor], *, beta1: float, beta2: float, beta3: float, bias_correction1: float, bias_correction2: float, bias_correction3_sqrt: float, lr: float, weight_decay: float, eps: float, no_prox: bool, clip_global_grad_norm: Tensor, ): if len(params) == 0: return torch._foreach_mul_(grads, clip_global_grad_norm) # for memory saving, we use `neg_pre_grads` # to get some temp variable in a inplace way torch._foreach_add_(neg_pre_grads, grads) torch._foreach_mul_(exp_avgs, beta1) torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1) # m_t torch._foreach_mul_(exp_avg_diffs, beta2) torch._foreach_add_(exp_avg_diffs, neg_pre_grads, alpha=1 - beta2) # diff_t torch._foreach_mul_(neg_pre_grads, beta2) torch._foreach_add_(neg_pre_grads, grads) torch._foreach_mul_(exp_avg_sqs, beta3) torch._foreach_addcmul_(exp_avg_sqs, neg_pre_grads, neg_pre_grads, value=1 - beta3) # n_t denom = torch._foreach_sqrt(exp_avg_sqs) torch._foreach_div_(denom, bias_correction3_sqrt) torch._foreach_add_(denom, eps) step_size_diff = lr * beta2 / bias_correction2 step_size = lr / bias_correction1 if no_prox: torch._foreach_mul_(params, 1 - lr * weight_decay) torch._foreach_addcdiv_(params, exp_avgs, denom, value=-step_size) torch._foreach_addcdiv_(params, exp_avg_diffs, denom, value=-step_size_diff) else: torch._foreach_addcdiv_(params, exp_avgs, denom, value=-step_size) torch._foreach_addcdiv_(params, exp_avg_diffs, denom, value=-step_size_diff) torch._foreach_div_(params, 1 + lr * weight_decay) torch._foreach_zero_(neg_pre_grads) torch._foreach_add_(neg_pre_grads, grads, alpha=-1.0) ================================================ FILE: stable-dreamfusion/preprocess_image.py ================================================ import os import sys import cv2 import argparse import numpy as np import matplotlib.pyplot as plt import torch import torch.nn as nn import torch.nn.functional as F from torchvision import transforms from PIL import Image class BackgroundRemoval(): def __init__(self, device='cuda'): from carvekit.api.high import HiInterface self.interface = HiInterface( object_type="object", # Can be "object" or "hairs-like". batch_size_seg=5, batch_size_matting=1, device=device, seg_mask_size=640, # Use 640 for Tracer B7 and 320 for U2Net matting_mask_size=2048, trimap_prob_threshold=231, trimap_dilation=30, trimap_erosion_iters=5, fp16=True, ) @torch.no_grad() def __call__(self, image): # image: [H, W, 3] array in [0, 255]. image = Image.fromarray(image) image = self.interface([image])[0] image = np.array(image) return image class BLIP2(): def __init__(self, device='cuda'): self.device = device from transformers import AutoProcessor, Blip2ForConditionalGeneration self.processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b") self.model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16).to(device) @torch.no_grad() def __call__(self, image): image = Image.fromarray(image) inputs = self.processor(image, return_tensors="pt").to(self.device, torch.float16) generated_ids = self.model.generate(**inputs, max_new_tokens=20) generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() return generated_text class DPT(): def __init__(self, task='depth', device='cuda'): self.task = task self.device = device from dpt import DPTDepthModel if task == 'depth': path = 'pretrained/omnidata/omnidata_dpt_depth_v2.ckpt' self.model = DPTDepthModel(backbone='vitb_rn50_384') self.aug = transforms.Compose([ transforms.Resize((384, 384)), transforms.ToTensor(), transforms.Normalize(mean=0.5, std=0.5) ]) else: # normal path = 'pretrained/omnidata/omnidata_dpt_normal_v2.ckpt' self.model = DPTDepthModel(backbone='vitb_rn50_384', num_channels=3) self.aug = transforms.Compose([ transforms.Resize((384, 384)), transforms.ToTensor() ]) # load model checkpoint = torch.load(path, map_location='cpu') if 'state_dict' in checkpoint: state_dict = {} for k, v in checkpoint['state_dict'].items(): state_dict[k[6:]] = v else: state_dict = checkpoint self.model.load_state_dict(state_dict) self.model.eval().to(device) @torch.no_grad() def __call__(self, image): # image: np.ndarray, uint8, [H, W, 3] H, W = image.shape[:2] image = Image.fromarray(image) image = self.aug(image).unsqueeze(0).to(self.device) if self.task == 'depth': depth = self.model(image).clamp(0, 1) depth = F.interpolate(depth.unsqueeze(1), size=(H, W), mode='bicubic', align_corners=False) depth = depth.squeeze(1).cpu().numpy() return depth else: normal = self.model(image).clamp(0, 1) normal = F.interpolate(normal, size=(H, W), mode='bicubic', align_corners=False) normal = normal.cpu().numpy() return normal if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('path', type=str, help="path to image (png, jpeg, etc.)") parser.add_argument('--size', default=256, type=int, help="output resolution") parser.add_argument('--border_ratio', default=0.2, type=float, help="output border ratio") parser.add_argument('--recenter', type=bool, default=True, help="recenter, potentially not helpful for multiview zero123") parser.add_argument('--dont_recenter', dest='recenter', action='store_false') opt = parser.parse_args() out_dir = os.path.dirname(opt.path) out_rgba = os.path.join(out_dir, os.path.basename(opt.path).split('.')[0] + '_rgba.png') out_depth = os.path.join(out_dir, os.path.basename(opt.path).split('.')[0] + '_depth.png') out_normal = os.path.join(out_dir, os.path.basename(opt.path).split('.')[0] + '_normal.png') out_caption = os.path.join(out_dir, os.path.basename(opt.path).split('.')[0] + '_caption.txt') # load image print(f'[INFO] loading image...') image = cv2.imread(opt.path, cv2.IMREAD_UNCHANGED) if image.shape[-1] == 4: image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGB) else: image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # carve background print(f'[INFO] background removal...') carved_image = BackgroundRemoval()(image) # [H, W, 4] mask = carved_image[..., -1] > 0 # predict depth print(f'[INFO] depth estimation...') dpt_depth_model = DPT(task='depth') depth = dpt_depth_model(image)[0] depth[mask] = (depth[mask] - depth[mask].min()) / (depth[mask].max() - depth[mask].min() + 1e-9) depth[~mask] = 0 depth = (depth * 255).astype(np.uint8) del dpt_depth_model # predict normal print(f'[INFO] normal estimation...') dpt_normal_model = DPT(task='normal') normal = dpt_normal_model(image)[0] normal = (normal * 255).astype(np.uint8).transpose(1, 2, 0) normal[~mask] = 0 del dpt_normal_model # recenter if opt.recenter: print(f'[INFO] recenter...') final_rgba = np.zeros((opt.size, opt.size, 4), dtype=np.uint8) final_depth = np.zeros((opt.size, opt.size), dtype=np.uint8) final_normal = np.zeros((opt.size, opt.size, 3), dtype=np.uint8) coords = np.nonzero(mask) x_min, x_max = coords[0].min(), coords[0].max() y_min, y_max = coords[1].min(), coords[1].max() h = x_max - x_min w = y_max - y_min desired_size = int(opt.size * (1 - opt.border_ratio)) scale = desired_size / max(h, w) h2 = int(h * scale) w2 = int(w * scale) x2_min = (opt.size - h2) // 2 x2_max = x2_min + h2 y2_min = (opt.size - w2) // 2 y2_max = y2_min + w2 final_rgba[x2_min:x2_max, y2_min:y2_max] = cv2.resize(carved_image[x_min:x_max, y_min:y_max], (w2, h2), interpolation=cv2.INTER_AREA) final_depth[x2_min:x2_max, y2_min:y2_max] = cv2.resize(depth[x_min:x_max, y_min:y_max], (w2, h2), interpolation=cv2.INTER_AREA) final_normal[x2_min:x2_max, y2_min:y2_max] = cv2.resize(normal[x_min:x_max, y_min:y_max], (w2, h2), interpolation=cv2.INTER_AREA) else: final_rgba = carved_image final_depth = depth final_normal = normal # write output cv2.imwrite(out_rgba, cv2.cvtColor(final_rgba, cv2.COLOR_RGBA2BGRA)) cv2.imwrite(out_depth, final_depth) cv2.imwrite(out_normal, final_normal) # predict caption (it's too slow... use your brain instead) # print(f'[INFO] captioning...') # blip2 = BLIP2() # caption = blip2(image) # with open(out_caption, 'w') as f: # f.write(caption) ================================================ FILE: stable-dreamfusion/pretrained/zero123/sd-objaverse-finetune-c_concat-256.yaml ================================================ model: base_learning_rate: 1.0e-04 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "image_target" cond_stage_key: "image_cond" image_size: 32 channels: 4 cond_stage_trainable: false # Note: different from the one we trained before conditioning_key: hybrid monitor: val/loss_simple_ema scale_factor: 0.18215 scheduler_config: # 10000 warmup steps target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 100 ] cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 # unused in_channels: 8 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: True legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPImageEmbedder # data: # target: ldm.data.simple.ObjaverseDataModuleFromConfig # params: # root_dir: 'views_whole_sphere' # batch_size: 192 # num_workers: 16 # total_view: 4 # train: # validation: False # image_transforms: # size: 256 # validation: # validation: True # image_transforms: # size: 256 # lightning: # find_unused_parameters: false # metrics_over_trainsteps_checkpoint: True # modelcheckpoint: # params: # every_n_train_steps: 5000 # callbacks: # image_logger: # target: main.ImageLogger # params: # batch_frequency: 500 # max_images: 32 # increase_log_steps: False # log_first_step: True # log_images_kwargs: # use_ema_scope: False # inpaint: False # plot_progressive_rows: False # plot_diffusion_rows: False # N: 32 # unconditional_scale: 3.0 # unconditional_label: [""] # trainer: # benchmark: True # val_check_interval: 5000000 # really sorry # num_sanity_val_steps: 0 # accumulate_grad_batches: 1 ================================================ FILE: stable-dreamfusion/raymarching/__init__.py ================================================ from .raymarching import * ================================================ FILE: stable-dreamfusion/raymarching/backend.py ================================================ import os from torch.utils.cpp_extension import load _src_path = os.path.dirname(os.path.abspath(__file__)) nvcc_flags = [ '-O3', '-std=c++14', '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__', ] if os.name == "posix": c_flags = ['-O3', '-std=c++14'] elif os.name == "nt": c_flags = ['/O2', '/std:c++17'] # find cl.exe def find_cl_path(): import glob for program_files in [r"C:\\Program Files (x86)", r"C:\\Program Files"]: for edition in ["Enterprise", "Professional", "BuildTools", "Community"]: paths = sorted(glob.glob(r"%s\\Microsoft Visual Studio\\*\\%s\\VC\\Tools\\MSVC\\*\\bin\\Hostx64\\x64" % (program_files, edition)), reverse=True) if paths: return paths[0] # If cl.exe is not on path, try to find it. if os.system("where cl.exe >nul 2>nul") != 0: cl_path = find_cl_path() if cl_path is None: raise RuntimeError("Could not locate a supported Microsoft Visual C++ installation") os.environ["PATH"] += ";" + cl_path _backend = load(name='_raymarching', extra_cflags=c_flags, extra_cuda_cflags=nvcc_flags, sources=[os.path.join(_src_path, 'src', f) for f in [ 'raymarching.cu', 'bindings.cpp', ]], ) __all__ = ['_backend'] ================================================ FILE: stable-dreamfusion/raymarching/raymarching.py ================================================ import numpy as np import time import torch import torch.nn as nn from torch.autograd import Function from torch.cuda.amp import custom_bwd, custom_fwd # lazy building: # `import raymarching` will not immediately build the extension, only if you actually call any functions. BACKEND = None def get_backend(): global BACKEND if BACKEND is None: try: import _raymarching as _backend except ImportError: from .backend import _backend BACKEND = _backend return BACKEND # ---------------------------------------- # utils # ---------------------------------------- class _near_far_from_aabb(Function): @staticmethod @custom_fwd(cast_inputs=torch.float32) def forward(ctx, rays_o, rays_d, aabb, min_near=0.2): ''' near_far_from_aabb, CUDA implementation Calculate rays' intersection time (near and far) with aabb Args: rays_o: float, [N, 3] rays_d: float, [N, 3] aabb: float, [6], (xmin, ymin, zmin, xmax, ymax, zmax) min_near: float, scalar Returns: nears: float, [N] fars: float, [N] ''' if not rays_o.is_cuda: rays_o = rays_o.cuda() if not rays_d.is_cuda: rays_d = rays_d.cuda() rays_o = rays_o.contiguous().view(-1, 3) rays_d = rays_d.contiguous().view(-1, 3) N = rays_o.shape[0] # num rays nears = torch.empty(N, dtype=rays_o.dtype, device=rays_o.device) fars = torch.empty(N, dtype=rays_o.dtype, device=rays_o.device) get_backend().near_far_from_aabb(rays_o, rays_d, aabb, N, min_near, nears, fars) return nears, fars near_far_from_aabb = _near_far_from_aabb.apply class _sph_from_ray(Function): @staticmethod @custom_fwd(cast_inputs=torch.float32) def forward(ctx, rays_o, rays_d, radius): ''' sph_from_ray, CUDA implementation get spherical coordinate on the background sphere from rays. Assume rays_o are inside the Sphere(radius). Args: rays_o: [N, 3] rays_d: [N, 3] radius: scalar, float Return: coords: [N, 2], in [-1, 1], theta and phi on a sphere. (further-surface) ''' if not rays_o.is_cuda: rays_o = rays_o.cuda() if not rays_d.is_cuda: rays_d = rays_d.cuda() rays_o = rays_o.contiguous().view(-1, 3) rays_d = rays_d.contiguous().view(-1, 3) N = rays_o.shape[0] # num rays coords = torch.empty(N, 2, dtype=rays_o.dtype, device=rays_o.device) get_backend().sph_from_ray(rays_o, rays_d, radius, N, coords) return coords sph_from_ray = _sph_from_ray.apply class _morton3D(Function): @staticmethod def forward(ctx, coords): ''' morton3D, CUDA implementation Args: coords: [N, 3], int32, in [0, 128) (for some reason there is no uint32 tensor in torch...) TODO: check if the coord range is valid! (current 128 is safe) Returns: indices: [N], int32, in [0, 128^3) ''' if not coords.is_cuda: coords = coords.cuda() N = coords.shape[0] indices = torch.empty(N, dtype=torch.int32, device=coords.device) get_backend().morton3D(coords.int(), N, indices) return indices morton3D = _morton3D.apply class _morton3D_invert(Function): @staticmethod def forward(ctx, indices): ''' morton3D_invert, CUDA implementation Args: indices: [N], int32, in [0, 128^3) Returns: coords: [N, 3], int32, in [0, 128) ''' if not indices.is_cuda: indices = indices.cuda() N = indices.shape[0] coords = torch.empty(N, 3, dtype=torch.int32, device=indices.device) get_backend().morton3D_invert(indices.int(), N, coords) return coords morton3D_invert = _morton3D_invert.apply class _packbits(Function): @staticmethod @custom_fwd(cast_inputs=torch.float32) def forward(ctx, grid, thresh, bitfield=None): ''' packbits, CUDA implementation Pack up the density grid into a bit field to accelerate ray marching. Args: grid: float, [C, H * H * H], assume H % 2 == 0 thresh: float, threshold Returns: bitfield: uint8, [C, H * H * H / 8] ''' if not grid.is_cuda: grid = grid.cuda() grid = grid.contiguous() C = grid.shape[0] H3 = grid.shape[1] N = C * H3 // 8 if bitfield is None: bitfield = torch.empty(N, dtype=torch.uint8, device=grid.device) get_backend().packbits(grid, N, thresh, bitfield) return bitfield packbits = _packbits.apply class _flatten_rays(Function): @staticmethod def forward(ctx, rays, M): ''' flatten rays Args: rays: [N, 2], all rays' (point_offset, point_count), M: scalar, int, count of points (we cannot get this info from rays unfortunately...) Returns: res: [M], flattened ray index. ''' if not rays.is_cuda: rays = rays.cuda() rays = rays.contiguous() N = rays.shape[0] res = torch.zeros(M, dtype=torch.int, device=rays.device) get_backend().flatten_rays(rays, N, M, res) return res flatten_rays = _flatten_rays.apply # ---------------------------------------- # train functions # ---------------------------------------- class _march_rays_train(Function): @staticmethod @custom_fwd(cast_inputs=torch.float32) def forward(ctx, rays_o, rays_d, bound, density_bitfield, C, H, nears, fars, perturb=False, dt_gamma=0, max_steps=1024, contract=False): ''' march rays to generate points (forward only) Args: rays_o/d: float, [N, 3] bound: float, scalar density_bitfield: uint8: [CHHH // 8] C: int H: int nears/fars: float, [N] step_counter: int32, (2), used to count the actual number of generated points. mean_count: int32, estimated mean steps to accelerate training. (but will randomly drop rays if the actual point count exceeded this threshold.) perturb: bool align: int, pad output so its size is dividable by align, set to -1 to disable. force_all_rays: bool, ignore step_counter and mean_count, always calculate all rays. Useful if rendering the whole image, instead of some rays. dt_gamma: float, called cone_angle in instant-ngp, exponentially accelerate ray marching if > 0. (very significant effect, but generally lead to worse performance) max_steps: int, max number of sampled points along each ray, also affect min_stepsize. Returns: xyzs: float, [M, 3], all generated points' coords. (all rays concated, need to use `rays` to extract points belonging to each ray) dirs: float, [M, 3], all generated points' view dirs. ts: float, [M, 2], all generated points' ts. rays: int32, [N, 2], all rays' (point_offset, point_count), e.g., xyzs[rays[i, 0]:(rays[i, 0] + rays[i, 1])] --> points belonging to rays[i, 0] ''' if not rays_o.is_cuda: rays_o = rays_o.cuda() if not rays_d.is_cuda: rays_d = rays_d.cuda() if not density_bitfield.is_cuda: density_bitfield = density_bitfield.cuda() rays_o = rays_o.float().contiguous().view(-1, 3) rays_d = rays_d.float().contiguous().view(-1, 3) density_bitfield = density_bitfield.contiguous() N = rays_o.shape[0] # num rays step_counter = torch.zeros(1, dtype=torch.int32, device=rays_o.device) # point counter, ray counter if perturb: noises = torch.rand(N, dtype=rays_o.dtype, device=rays_o.device) else: noises = torch.zeros(N, dtype=rays_o.dtype, device=rays_o.device) # first pass: write rays, get total number of points M to render rays = torch.empty(N, 2, dtype=torch.int32, device=rays_o.device) # id, offset, num_steps get_backend().march_rays_train(rays_o, rays_d, density_bitfield, bound, contract, dt_gamma, max_steps, N, C, H, nears, fars, None, None, None, rays, step_counter, noises) # allocate based on M M = step_counter.item() # print(M, N) # print(rays[:, 0].max()) xyzs = torch.zeros(M, 3, dtype=rays_o.dtype, device=rays_o.device) dirs = torch.zeros(M, 3, dtype=rays_o.dtype, device=rays_o.device) ts = torch.zeros(M, 2, dtype=rays_o.dtype, device=rays_o.device) # second pass: write outputs get_backend().march_rays_train(rays_o, rays_d, density_bitfield, bound, contract, dt_gamma, max_steps, N, C, H, nears, fars, xyzs, dirs, ts, rays, step_counter, noises) return xyzs, dirs, ts, rays march_rays_train = _march_rays_train.apply class _composite_rays_train(Function): @staticmethod @custom_fwd(cast_inputs=torch.float32) def forward(ctx, sigmas, rgbs, ts, rays, T_thresh=1e-4, binarize=False): ''' composite rays' rgbs, according to the ray marching formula. Args: rgbs: float, [M, 3] sigmas: float, [M,] ts: float, [M, 2] rays: int32, [N, 3] Returns: weights: float, [M] weights_sum: float, [N,], the alpha channel depth: float, [N, ], the Depth image: float, [N, 3], the RGB channel (after multiplying alpha!) ''' sigmas = sigmas.float().contiguous() rgbs = rgbs.float().contiguous() M = sigmas.shape[0] N = rays.shape[0] weights = torch.zeros(M, dtype=sigmas.dtype, device=sigmas.device) # may leave unmodified, so init with 0 weights_sum = torch.empty(N, dtype=sigmas.dtype, device=sigmas.device) depth = torch.empty(N, dtype=sigmas.dtype, device=sigmas.device) image = torch.empty(N, 3, dtype=sigmas.dtype, device=sigmas.device) get_backend().composite_rays_train_forward(sigmas, rgbs, ts, rays, M, N, T_thresh, binarize, weights, weights_sum, depth, image) ctx.save_for_backward(sigmas, rgbs, ts, rays, weights_sum, depth, image) ctx.dims = [M, N, T_thresh, binarize] return weights, weights_sum, depth, image @staticmethod @custom_bwd def backward(ctx, grad_weights, grad_weights_sum, grad_depth, grad_image): grad_weights = grad_weights.contiguous() grad_weights_sum = grad_weights_sum.contiguous() grad_depth = grad_depth.contiguous() grad_image = grad_image.contiguous() sigmas, rgbs, ts, rays, weights_sum, depth, image = ctx.saved_tensors M, N, T_thresh, binarize = ctx.dims grad_sigmas = torch.zeros_like(sigmas) grad_rgbs = torch.zeros_like(rgbs) get_backend().composite_rays_train_backward(grad_weights, grad_weights_sum, grad_depth, grad_image, sigmas, rgbs, ts, rays, weights_sum, depth, image, M, N, T_thresh, binarize, grad_sigmas, grad_rgbs) return grad_sigmas, grad_rgbs, None, None, None, None composite_rays_train = _composite_rays_train.apply # ---------------------------------------- # infer functions # ---------------------------------------- class _march_rays(Function): @staticmethod @custom_fwd(cast_inputs=torch.float32) def forward(ctx, n_alive, n_step, rays_alive, rays_t, rays_o, rays_d, bound, density_bitfield, C, H, near, far, perturb=False, dt_gamma=0, max_steps=1024, contract=False): ''' march rays to generate points (forward only, for inference) Args: n_alive: int, number of alive rays n_step: int, how many steps we march rays_alive: int, [N], the alive rays' IDs in N (N >= n_alive, but we only use first n_alive) rays_t: float, [N], the alive rays' time, we only use the first n_alive. rays_o/d: float, [N, 3] bound: float, scalar density_bitfield: uint8: [CHHH // 8] C: int H: int nears/fars: float, [N] align: int, pad output so its size is dividable by align, set to -1 to disable. perturb: bool/int, int > 0 is used as the random seed. dt_gamma: float, called cone_angle in instant-ngp, exponentially accelerate ray marching if > 0. (very significant effect, but generally lead to worse performance) max_steps: int, max number of sampled points along each ray, also affect min_stepsize. Returns: xyzs: float, [n_alive * n_step, 3], all generated points' coords dirs: float, [n_alive * n_step, 3], all generated points' view dirs. ts: float, [n_alive * n_step, 2], all generated points' ts ''' if not rays_o.is_cuda: rays_o = rays_o.cuda() if not rays_d.is_cuda: rays_d = rays_d.cuda() rays_o = rays_o.float().contiguous().view(-1, 3) rays_d = rays_d.float().contiguous().view(-1, 3) M = n_alive * n_step xyzs = torch.zeros(M, 3, dtype=rays_o.dtype, device=rays_o.device) dirs = torch.zeros(M, 3, dtype=rays_o.dtype, device=rays_o.device) ts = torch.zeros(M, 2, dtype=rays_o.dtype, device=rays_o.device) # 2 vals, one for rgb, one for depth if perturb: # torch.manual_seed(perturb) # test_gui uses spp index as seed noises = torch.rand(n_alive, dtype=rays_o.dtype, device=rays_o.device) else: noises = torch.zeros(n_alive, dtype=rays_o.dtype, device=rays_o.device) get_backend().march_rays(n_alive, n_step, rays_alive, rays_t, rays_o, rays_d, bound, contract, dt_gamma, max_steps, C, H, density_bitfield, near, far, xyzs, dirs, ts, noises) return xyzs, dirs, ts march_rays = _march_rays.apply class _composite_rays(Function): @staticmethod @custom_fwd(cast_inputs=torch.float32) # need to cast sigmas & rgbs to float def forward(ctx, n_alive, n_step, rays_alive, rays_t, sigmas, rgbs, ts, weights_sum, depth, image, T_thresh=1e-2, binarize=False): ''' composite rays' rgbs, according to the ray marching formula. (for inference) Args: n_alive: int, number of alive rays n_step: int, how many steps we march rays_alive: int, [n_alive], the alive rays' IDs in N (N >= n_alive) rays_t: float, [N], the alive rays' time sigmas: float, [n_alive * n_step,] rgbs: float, [n_alive * n_step, 3] ts: float, [n_alive * n_step, 2] In-place Outputs: weights_sum: float, [N,], the alpha channel depth: float, [N,], the depth value image: float, [N, 3], the RGB channel (after multiplying alpha!) ''' sigmas = sigmas.float().contiguous() rgbs = rgbs.float().contiguous() get_backend().composite_rays(n_alive, n_step, T_thresh, binarize, rays_alive, rays_t, sigmas, rgbs, ts, weights_sum, depth, image) return tuple() composite_rays = _composite_rays.apply ================================================ FILE: stable-dreamfusion/raymarching/setup.py ================================================ import os from setuptools import setup from torch.utils.cpp_extension import BuildExtension, CUDAExtension _src_path = os.path.dirname(os.path.abspath(__file__)) nvcc_flags = [ '-O3', '-std=c++14', '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__', ] if os.name == "posix": c_flags = ['-O3', '-std=c++14'] elif os.name == "nt": c_flags = ['/O2', '/std:c++17'] # find cl.exe def find_cl_path(): import glob for program_files in [r"C:\\Program Files (x86)", r"C:\\Program Files"]: for edition in ["Enterprise", "Professional", "BuildTools", "Community"]: paths = sorted(glob.glob(r"%s\\Microsoft Visual Studio\\*\\%s\\VC\\Tools\\MSVC\\*\\bin\\Hostx64\\x64" % (program_files, edition)), reverse=True) if paths: return paths[0] # If cl.exe is not on path, try to find it. if os.system("where cl.exe >nul 2>nul") != 0: cl_path = find_cl_path() if cl_path is None: raise RuntimeError("Could not locate a supported Microsoft Visual C++ installation") os.environ["PATH"] += ";" + cl_path ''' Usage: python setup.py build_ext --inplace # build extensions locally, do not install (only can be used from the parent directory) python setup.py install # build extensions and install (copy) to PATH. pip install . # ditto but better (e.g., dependency & metadata handling) python setup.py develop # build extensions and install (symbolic) to PATH. pip install -e . # ditto but better (e.g., dependency & metadata handling) ''' setup( name='raymarching', # package name, import this to use python API ext_modules=[ CUDAExtension( name='_raymarching', # extension name, import this to use CUDA API sources=[os.path.join(_src_path, 'src', f) for f in [ 'raymarching.cu', 'bindings.cpp', ]], extra_compile_args={ 'cxx': c_flags, 'nvcc': nvcc_flags, } ), ], cmdclass={ 'build_ext': BuildExtension, } ) ================================================ FILE: stable-dreamfusion/raymarching/src/bindings.cpp ================================================ #include #include "raymarching.h" PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { // utils m.def("flatten_rays", &flatten_rays, "flatten_rays (CUDA)"); m.def("packbits", &packbits, "packbits (CUDA)"); m.def("near_far_from_aabb", &near_far_from_aabb, "near_far_from_aabb (CUDA)"); m.def("sph_from_ray", &sph_from_ray, "sph_from_ray (CUDA)"); m.def("morton3D", &morton3D, "morton3D (CUDA)"); m.def("morton3D_invert", &morton3D_invert, "morton3D_invert (CUDA)"); // train m.def("march_rays_train", &march_rays_train, "march_rays_train (CUDA)"); m.def("composite_rays_train_forward", &composite_rays_train_forward, "composite_rays_train_forward (CUDA)"); m.def("composite_rays_train_backward", &composite_rays_train_backward, "composite_rays_train_backward (CUDA)"); // infer m.def("march_rays", &march_rays, "march rays (CUDA)"); m.def("composite_rays", &composite_rays, "composite rays (CUDA)"); } ================================================ FILE: stable-dreamfusion/raymarching/src/raymarching.cu ================================================ #include #include #include #include #include #include #include #include #include #define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") #define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be a contiguous tensor") #define CHECK_IS_INT(x) TORCH_CHECK(x.scalar_type() == at::ScalarType::Int, #x " must be an int tensor") #define CHECK_IS_FLOATING(x) TORCH_CHECK(x.scalar_type() == at::ScalarType::Float || x.scalar_type() == at::ScalarType::Half || x.scalar_type() == at::ScalarType::Double, #x " must be a floating tensor") inline constexpr __device__ float SQRT3() { return 1.7320508075688772f; } inline constexpr __device__ float RSQRT3() { return 0.5773502691896258f; } inline constexpr __device__ float PI() { return 3.141592653589793f; } inline constexpr __device__ float RPI() { return 0.3183098861837907f; } template inline __host__ __device__ T div_round_up(T val, T divisor) { return (val + divisor - 1) / divisor; } inline __host__ __device__ float signf(const float x) { return copysignf(1.0, x); } inline __host__ __device__ float clamp(const float x, const float min, const float max) { return fminf(max, fmaxf(min, x)); } inline __host__ __device__ void swapf(float& a, float& b) { float c = a; a = b; b = c; } inline __device__ int mip_from_pos(const float x, const float y, const float z, const float max_cascade) { const float mx = fmaxf(fabsf(x), fmaxf(fabsf(y), fabsf(z))); int exponent; frexpf(mx, &exponent); // [0, 0.5) --> -1, [0.5, 1) --> 0, [1, 2) --> 1, [2, 4) --> 2, ... return fminf(max_cascade - 1, fmaxf(0, exponent)); } inline __device__ int mip_from_dt(const float dt, const float H, const float max_cascade) { const float mx = dt * H * 0.5; int exponent; frexpf(mx, &exponent); return fminf(max_cascade - 1, fmaxf(0, exponent)); } inline __host__ __device__ uint32_t __expand_bits(uint32_t v) { v = (v * 0x00010001u) & 0xFF0000FFu; v = (v * 0x00000101u) & 0x0F00F00Fu; v = (v * 0x00000011u) & 0xC30C30C3u; v = (v * 0x00000005u) & 0x49249249u; return v; } inline __host__ __device__ uint32_t __morton3D(uint32_t x, uint32_t y, uint32_t z) { uint32_t xx = __expand_bits(x); uint32_t yy = __expand_bits(y); uint32_t zz = __expand_bits(z); return xx | (yy << 1) | (zz << 2); } inline __host__ __device__ uint32_t __morton3D_invert(uint32_t x) { x = x & 0x49249249; x = (x | (x >> 2)) & 0xc30c30c3; x = (x | (x >> 4)) & 0x0f00f00f; x = (x | (x >> 8)) & 0xff0000ff; x = (x | (x >> 16)) & 0x0000ffff; return x; } //////////////////////////////////////////////////// ///////////// utils ///////////// //////////////////////////////////////////////////// // rays_o/d: [N, 3] // nears/fars: [N] // scalar_t should always be float in use. template __global__ void kernel_near_far_from_aabb( const scalar_t * __restrict__ rays_o, const scalar_t * __restrict__ rays_d, const scalar_t * __restrict__ aabb, const uint32_t N, const float min_near, scalar_t * nears, scalar_t * fars ) { // parallel per ray const uint32_t n = threadIdx.x + blockIdx.x * blockDim.x; if (n >= N) return; // locate rays_o += n * 3; rays_d += n * 3; const float ox = rays_o[0], oy = rays_o[1], oz = rays_o[2]; const float dx = rays_d[0], dy = rays_d[1], dz = rays_d[2]; const float rdx = 1 / dx, rdy = 1 / dy, rdz = 1 / dz; // get near far (assume cube scene) float near = (aabb[0] - ox) * rdx; float far = (aabb[3] - ox) * rdx; if (near > far) swapf(near, far); float near_y = (aabb[1] - oy) * rdy; float far_y = (aabb[4] - oy) * rdy; if (near_y > far_y) swapf(near_y, far_y); if (near > far_y || near_y > far) { nears[n] = fars[n] = std::numeric_limits::max(); return; } if (near_y > near) near = near_y; if (far_y < far) far = far_y; float near_z = (aabb[2] - oz) * rdz; float far_z = (aabb[5] - oz) * rdz; if (near_z > far_z) swapf(near_z, far_z); if (near > far_z || near_z > far) { nears[n] = fars[n] = std::numeric_limits::max(); return; } if (near_z > near) near = near_z; if (far_z < far) far = far_z; if (near < min_near) near = min_near; nears[n] = near; fars[n] = far; } void near_far_from_aabb(const at::Tensor rays_o, const at::Tensor rays_d, const at::Tensor aabb, const uint32_t N, const float min_near, at::Tensor nears, at::Tensor fars) { static constexpr uint32_t N_THREAD = 128; AT_DISPATCH_FLOATING_TYPES_AND_HALF( rays_o.scalar_type(), "near_far_from_aabb", ([&] { kernel_near_far_from_aabb<<>>(rays_o.data_ptr(), rays_d.data_ptr(), aabb.data_ptr(), N, min_near, nears.data_ptr(), fars.data_ptr()); })); } // rays_o/d: [N, 3] // radius: float // coords: [N, 2] template __global__ void kernel_sph_from_ray( const scalar_t * __restrict__ rays_o, const scalar_t * __restrict__ rays_d, const float radius, const uint32_t N, scalar_t * coords ) { // parallel per ray const uint32_t n = threadIdx.x + blockIdx.x * blockDim.x; if (n >= N) return; // locate rays_o += n * 3; rays_d += n * 3; coords += n * 2; const float ox = rays_o[0], oy = rays_o[1], oz = rays_o[2]; const float dx = rays_d[0], dy = rays_d[1], dz = rays_d[2]; // const float rdx = 1 / dx, rdy = 1 / dy, rdz = 1 / dz; // solve t from || o + td || = radius const float A = dx * dx + dy * dy + dz * dz; const float B = ox * dx + oy * dy + oz * dz; // in fact B / 2 const float C = ox * ox + oy * oy + oz * oz - radius * radius; const float t = (- B + sqrtf(B * B - A * C)) / A; // always use the larger solution (positive) // solve theta, phi (assume y is the up axis) const float x = ox + t * dx, y = oy + t * dy, z = oz + t * dz; const float theta = atan2(sqrtf(x * x + z * z), y); // [0, PI) const float phi = atan2(z, x); // [-PI, PI) // normalize to [-1, 1] coords[0] = 2 * theta * RPI() - 1; coords[1] = phi * RPI(); } void sph_from_ray(const at::Tensor rays_o, const at::Tensor rays_d, const float radius, const uint32_t N, at::Tensor coords) { static constexpr uint32_t N_THREAD = 128; AT_DISPATCH_FLOATING_TYPES_AND_HALF( rays_o.scalar_type(), "sph_from_ray", ([&] { kernel_sph_from_ray<<>>(rays_o.data_ptr(), rays_d.data_ptr(), radius, N, coords.data_ptr()); })); } // coords: int32, [N, 3] // indices: int32, [N] __global__ void kernel_morton3D( const int * __restrict__ coords, const uint32_t N, int * indices ) { // parallel const uint32_t n = threadIdx.x + blockIdx.x * blockDim.x; if (n >= N) return; // locate coords += n * 3; indices[n] = __morton3D(coords[0], coords[1], coords[2]); } void morton3D(const at::Tensor coords, const uint32_t N, at::Tensor indices) { static constexpr uint32_t N_THREAD = 128; kernel_morton3D<<>>(coords.data_ptr(), N, indices.data_ptr()); } // indices: int32, [N] // coords: int32, [N, 3] __global__ void kernel_morton3D_invert( const int * __restrict__ indices, const uint32_t N, int * coords ) { // parallel const uint32_t n = threadIdx.x + blockIdx.x * blockDim.x; if (n >= N) return; // locate coords += n * 3; const int ind = indices[n]; coords[0] = __morton3D_invert(ind >> 0); coords[1] = __morton3D_invert(ind >> 1); coords[2] = __morton3D_invert(ind >> 2); } void morton3D_invert(const at::Tensor indices, const uint32_t N, at::Tensor coords) { static constexpr uint32_t N_THREAD = 128; kernel_morton3D_invert<<>>(indices.data_ptr(), N, coords.data_ptr()); } // grid: float, [C, H, H, H] // N: int, C * H * H * H / 8 // density_thresh: float // bitfield: uint8, [N] template __global__ void kernel_packbits( const scalar_t * __restrict__ grid, const uint32_t N, const float density_thresh, uint8_t * bitfield ) { // parallel per byte const uint32_t n = threadIdx.x + blockIdx.x * blockDim.x; if (n >= N) return; // locate grid += n * 8; uint8_t bits = 0; #pragma unroll for (uint8_t i = 0; i < 8; i++) { bits |= (grid[i] > density_thresh) ? ((uint8_t)1 << i) : 0; } bitfield[n] = bits; } void packbits(const at::Tensor grid, const uint32_t N, const float density_thresh, at::Tensor bitfield) { static constexpr uint32_t N_THREAD = 128; AT_DISPATCH_FLOATING_TYPES_AND_HALF( grid.scalar_type(), "packbits", ([&] { kernel_packbits<<>>(grid.data_ptr(), N, density_thresh, bitfield.data_ptr()); })); } __global__ void kernel_flatten_rays( const int * __restrict__ rays, const uint32_t N, const uint32_t M, int * res ) { // parallel per ray const uint32_t n = threadIdx.x + blockIdx.x * blockDim.x; if (n >= N) return; // locate uint32_t offset = rays[n * 2]; uint32_t num_steps = rays[n * 2 + 1]; // write to res res += offset; for (int i = 0; i < num_steps; i++) res[i] = n; } void flatten_rays(const at::Tensor rays, const uint32_t N, const uint32_t M, at::Tensor res) { static constexpr uint32_t N_THREAD = 128; kernel_flatten_rays<<>>(rays.data_ptr(), N, M, res.data_ptr()); } //////////////////////////////////////////////////// ///////////// training ///////////// //////////////////////////////////////////////////// // rays_o/d: [N, 3] // grid: [CHHH / 8] // xyzs, dirs, ts: [M, 3], [M, 3], [M, 2] // dirs: [M, 3] // rays: [N, 3], idx, offset, num_steps template __global__ void kernel_march_rays_train( const scalar_t * __restrict__ rays_o, const scalar_t * __restrict__ rays_d, const uint8_t * __restrict__ grid, const float bound, const bool contract, const float dt_gamma, const uint32_t max_steps, const uint32_t N, const uint32_t C, const uint32_t H, const scalar_t* __restrict__ nears, const scalar_t* __restrict__ fars, scalar_t * xyzs, scalar_t * dirs, scalar_t * ts, int * rays, int * counter, const scalar_t* __restrict__ noises ) { // parallel per ray const uint32_t n = threadIdx.x + blockIdx.x * blockDim.x; if (n >= N) return; // is first pass running. const bool first_pass = (xyzs == nullptr); // locate rays_o += n * 3; rays_d += n * 3; rays += n * 2; uint32_t num_steps = max_steps; if (!first_pass) { uint32_t point_index = rays[0]; num_steps = rays[1]; xyzs += point_index * 3; dirs += point_index * 3; ts += point_index * 2; } // ray marching const float ox = rays_o[0], oy = rays_o[1], oz = rays_o[2]; const float dx = rays_d[0], dy = rays_d[1], dz = rays_d[2]; const float rdx = 1 / dx, rdy = 1 / dy, rdz = 1 / dz; const float rH = 1 / (float)H; const float H3 = H * H * H; const float near = nears[n]; const float far = fars[n]; const float noise = noises[n]; const float dt_min = 2 * SQRT3() / max_steps; const float dt_max = 2 * SQRT3() * bound / H; // const float dt_max = 1e10f; float t0 = near; t0 += clamp(t0 * dt_gamma, dt_min, dt_max) * noise; float t = t0; uint32_t step = 0; //if (t < far) printf("valid ray %d t=%f near=%f far=%f \n", n, t, near, far); while (t < far && step < num_steps) { // current point const float x = clamp(ox + t * dx, -bound, bound); const float y = clamp(oy + t * dy, -bound, bound); const float z = clamp(oz + t * dz, -bound, bound); float dt = clamp(t * dt_gamma, dt_min, dt_max); // get mip level const int level = max(mip_from_pos(x, y, z, C), mip_from_dt(dt, H, C)); // range in [0, C - 1] const float mip_bound = fminf(scalbnf(1.0f, level), bound); const float mip_rbound = 1 / mip_bound; // contraction float cx = x, cy = y, cz = z; const float mag = fmaxf(fabsf(x), fmaxf(fabsf(y), fabsf(z))); if (contract && mag > 1) { // L-INF norm const float Linf_scale = (2 - 1 / mag) / mag; cx *= Linf_scale; cy *= Linf_scale; cz *= Linf_scale; } // convert to nearest grid position const int nx = clamp(0.5 * (cx * mip_rbound + 1) * H, 0.0f, (float)(H - 1)); const int ny = clamp(0.5 * (cy * mip_rbound + 1) * H, 0.0f, (float)(H - 1)); const int nz = clamp(0.5 * (cz * mip_rbound + 1) * H, 0.0f, (float)(H - 1)); const uint32_t index = level * H3 + __morton3D(nx, ny, nz); const bool occ = grid[index / 8] & (1 << (index % 8)); // if occpuied, advance a small step, and write to output //if (n == 0) printf("t=%f density=%f vs thresh=%f step=%d\n", t, density, density_thresh, step); if (occ) { step++; t += dt; if (!first_pass) { xyzs[0] = cx; // write contracted coordinates! xyzs[1] = cy; xyzs[2] = cz; dirs[0] = dx; dirs[1] = dy; dirs[2] = dz; ts[0] = t; ts[1] = dt; xyzs += 3; dirs += 3; ts += 2; } // contraction case: cannot apply voxel skipping. } else if (contract && mag > 1) { t += dt; // else, skip a large step (basically skip a voxel grid) } else { // calc distance to next voxel const float tx = (((nx + 0.5f + 0.5f * signf(dx)) * rH * 2 - 1) * mip_bound - cx) * rdx; const float ty = (((ny + 0.5f + 0.5f * signf(dy)) * rH * 2 - 1) * mip_bound - cy) * rdy; const float tz = (((nz + 0.5f + 0.5f * signf(dz)) * rH * 2 - 1) * mip_bound - cz) * rdz; const float tt = t + fmaxf(0.0f, fminf(tx, fminf(ty, tz))); // step until next voxel do { dt = clamp(t * dt_gamma, dt_min, dt_max); t += dt; } while (t < tt); } } //printf("[n=%d] step=%d, near=%f, far=%f, dt=%f, num_steps=%f\n", n, step, near, far, dt_min, (far - near) / dt_min); // write rays if (first_pass) { uint32_t point_index = atomicAdd(counter, step); rays[0] = point_index; rays[1] = step; } } void march_rays_train(const at::Tensor rays_o, const at::Tensor rays_d, const at::Tensor grid, const float bound, const bool contract, const float dt_gamma, const uint32_t max_steps, const uint32_t N, const uint32_t C, const uint32_t H, const at::Tensor nears, const at::Tensor fars, at::optional xyzs, at::optional dirs, at::optional ts, at::Tensor rays, at::Tensor counter, at::Tensor noises) { static constexpr uint32_t N_THREAD = 128; AT_DISPATCH_FLOATING_TYPES_AND_HALF( rays_o.scalar_type(), "march_rays_train", ([&] { kernel_march_rays_train<<>>(rays_o.data_ptr(), rays_d.data_ptr(), grid.data_ptr(), bound, contract, dt_gamma, max_steps, N, C, H, nears.data_ptr(), fars.data_ptr(), xyzs.has_value() ? xyzs.value().data_ptr() : nullptr, dirs.has_value() ? dirs.value().data_ptr() : nullptr, ts.has_value() ? ts.value().data_ptr() : nullptr, rays.data_ptr(), counter.data_ptr(), noises.data_ptr()); })); } // sigmas: [M] // rgbs: [M, 3] // ts: [M, 2] // rays: [N, 2], offset, num_steps // weights: [M] // weights_sum: [N], final pixel alpha // depth: [N,] // image: [N, 3] template __global__ void kernel_composite_rays_train_forward( const scalar_t * __restrict__ sigmas, const scalar_t * __restrict__ rgbs, const scalar_t * __restrict__ ts, const int * __restrict__ rays, const uint32_t M, const uint32_t N, const float T_thresh, const bool binarize, scalar_t * weights, scalar_t * weights_sum, scalar_t * depth, scalar_t * image ) { // parallel per ray const uint32_t n = threadIdx.x + blockIdx.x * blockDim.x; if (n >= N) return; // locate uint32_t offset = rays[n * 2]; uint32_t num_steps = rays[n * 2 + 1]; // empty ray, or ray that exceed max step count. if (num_steps == 0 || offset + num_steps > M) { weights_sum[n] = 0; depth[n] = 0; image[n * 3] = 0; image[n * 3 + 1] = 0; image[n * 3 + 2] = 0; return; } ts += offset * 2; weights += offset; sigmas += offset; rgbs += offset * 3; // accumulate uint32_t step = 0; float T = 1.0f; float r = 0, g = 0, b = 0, ws = 0, d = 0; while (step < num_steps) { const float real_alpha = 1.0f - __expf(- sigmas[0] * ts[1]); const float alpha = binarize ? (real_alpha > 0.5 ? 1.0 : 0.0) : real_alpha; const float weight = alpha * T; weights[0] = weight; r += weight * rgbs[0]; g += weight * rgbs[1]; b += weight * rgbs[2]; ws += weight; d += weight * ts[0]; T *= 1.0f - alpha; // minimal remained transmittence if (T < T_thresh) break; //printf("[n=%d] num_steps=%d, alpha=%f, w=%f, T=%f, sum_dt=%f, d=%f\n", n, step, alpha, weight, T, sum_delta, d); // locate weights++; sigmas++; rgbs += 3; ts += 2; step++; } //printf("[n=%d] rgb=(%f, %f, %f), d=%f\n", n, r, g, b, d); // write weights_sum[n] = ws; // weights_sum depth[n] = d; image[n * 3] = r; image[n * 3 + 1] = g; image[n * 3 + 2] = b; } void composite_rays_train_forward(const at::Tensor sigmas, const at::Tensor rgbs, const at::Tensor ts, const at::Tensor rays, const uint32_t M, const uint32_t N, const float T_thresh, const bool binarize, at::Tensor weights, at::Tensor weights_sum, at::Tensor depth, at::Tensor image) { static constexpr uint32_t N_THREAD = 128; AT_DISPATCH_FLOATING_TYPES_AND_HALF( sigmas.scalar_type(), "composite_rays_train_forward", ([&] { kernel_composite_rays_train_forward<<>>(sigmas.data_ptr(), rgbs.data_ptr(), ts.data_ptr(), rays.data_ptr(), M, N, T_thresh, binarize, weights.data_ptr(), weights_sum.data_ptr(), depth.data_ptr(), image.data_ptr()); })); } // grad_weights: [M,] // grad_weights_sum: [N,] // grad_image: [N, 3] // grad_depth: [N,] // sigmas: [M] // rgbs: [M, 3] // ts: [M, 2] // rays: [N, 2], offset, num_steps // weights_sum: [N,], weights_sum here // image: [N, 3] // grad_sigmas: [M] // grad_rgbs: [M, 3] template __global__ void kernel_composite_rays_train_backward( const scalar_t * __restrict__ grad_weights, const scalar_t * __restrict__ grad_weights_sum, const scalar_t * __restrict__ grad_depth, const scalar_t * __restrict__ grad_image, const scalar_t * __restrict__ sigmas, const scalar_t * __restrict__ rgbs, const scalar_t * __restrict__ ts, const int * __restrict__ rays, const scalar_t * __restrict__ weights_sum, const scalar_t * __restrict__ depth, const scalar_t * __restrict__ image, const uint32_t M, const uint32_t N, const float T_thresh, const bool binarize, scalar_t * grad_sigmas, scalar_t * grad_rgbs ) { // parallel per ray const uint32_t n = threadIdx.x + blockIdx.x * blockDim.x; if (n >= N) return; // locate uint32_t offset = rays[n * 2]; uint32_t num_steps = rays[n * 2 + 1]; if (num_steps == 0 || offset + num_steps > M) return; grad_weights += offset; grad_weights_sum += n; grad_depth += n; grad_image += n * 3; weights_sum += n; depth += n; image += n * 3; sigmas += offset; rgbs += offset * 3; ts += offset * 2; grad_sigmas += offset; grad_rgbs += offset * 3; // accumulate uint32_t step = 0; float T = 1.0f; const float r_final = image[0], g_final = image[1], b_final = image[2], ws_final = weights_sum[0], d_final = depth[0]; float r = 0, g = 0, b = 0, ws = 0, d = 0; while (step < num_steps) { const float real_alpha = 1.0f - __expf(- sigmas[0] * ts[1]); const float alpha = binarize ? (real_alpha > 0.5 ? 1.0 : 0.0) : real_alpha; const float weight = alpha * T; r += weight * rgbs[0]; g += weight * rgbs[1]; b += weight * rgbs[2]; ws += weight; d += weight * ts[0]; T *= 1.0f - alpha; // check https://note.kiui.moe/others/nerf_gradient/ for the gradient calculation. // write grad_rgbs grad_rgbs[0] = grad_image[0] * weight; grad_rgbs[1] = grad_image[1] * weight; grad_rgbs[2] = grad_image[2] * weight; // write grad_sigmas grad_sigmas[0] = ts[1] * ( grad_image[0] * (T * rgbs[0] - (r_final - r)) + grad_image[1] * (T * rgbs[1] - (g_final - g)) + grad_image[2] * (T * rgbs[2] - (b_final - b)) + (grad_weights_sum[0] + grad_weights[0]) * (T - (ws_final - ws)) + grad_depth[0] * (T * ts[0] - (d_final - d)) ); //printf("[n=%d] num_steps=%d, T=%f, grad_sigmas=%f, r_final=%f, r=%f\n", n, step, T, grad_sigmas[0], r_final, r); // minimal remained transmittence if (T < T_thresh) break; // locate sigmas++; rgbs += 3; ts += 2; grad_weights++; grad_sigmas++; grad_rgbs += 3; step++; } } void composite_rays_train_backward(const at::Tensor grad_weights, const at::Tensor grad_weights_sum, const at::Tensor grad_depth, const at::Tensor grad_image, const at::Tensor sigmas, const at::Tensor rgbs, const at::Tensor ts, const at::Tensor rays, const at::Tensor weights_sum, const at::Tensor depth, const at::Tensor image, const uint32_t M, const uint32_t N, const float T_thresh, const bool binarize, at::Tensor grad_sigmas, at::Tensor grad_rgbs) { static constexpr uint32_t N_THREAD = 128; AT_DISPATCH_FLOATING_TYPES_AND_HALF( grad_image.scalar_type(), "composite_rays_train_backward", ([&] { kernel_composite_rays_train_backward<<>>(grad_weights.data_ptr(), grad_weights_sum.data_ptr(), grad_depth.data_ptr(), grad_image.data_ptr(), sigmas.data_ptr(), rgbs.data_ptr(), ts.data_ptr(), rays.data_ptr(), weights_sum.data_ptr(), depth.data_ptr(), image.data_ptr(), M, N, T_thresh, binarize, grad_sigmas.data_ptr(), grad_rgbs.data_ptr()); })); } //////////////////////////////////////////////////// ///////////// infernce ///////////// //////////////////////////////////////////////////// template __global__ void kernel_march_rays( const uint32_t n_alive, const uint32_t n_step, const int* __restrict__ rays_alive, const scalar_t* __restrict__ rays_t, const scalar_t* __restrict__ rays_o, const scalar_t* __restrict__ rays_d, const float bound, const bool contract, const float dt_gamma, const uint32_t max_steps, const uint32_t C, const uint32_t H, const uint8_t * __restrict__ grid, const scalar_t* __restrict__ nears, const scalar_t* __restrict__ fars, scalar_t* xyzs, scalar_t* dirs, scalar_t* ts, const scalar_t* __restrict__ noises ) { const uint32_t n = threadIdx.x + blockIdx.x * blockDim.x; if (n >= n_alive) return; const int index = rays_alive[n]; // ray id const float noise = noises[n]; // locate rays_o += index * 3; rays_d += index * 3; xyzs += n * n_step * 3; dirs += n * n_step * 3; ts += n * n_step * 2; const float ox = rays_o[0], oy = rays_o[1], oz = rays_o[2]; const float dx = rays_d[0], dy = rays_d[1], dz = rays_d[2]; const float rdx = 1 / dx, rdy = 1 / dy, rdz = 1 / dz; const float rH = 1 / (float)H; const float H3 = H * H * H; const float near = nears[index], far = fars[index]; const float dt_min = 2 * SQRT3() / max_steps; const float dt_max = 2 * SQRT3() * bound / H; // const float dt_max = 1e10f; // march for n_step steps, record points float t = rays_t[index]; t += clamp(t * dt_gamma, dt_min, dt_max) * noise; uint32_t step = 0; while (t < far && step < n_step) { // current point const float x = clamp(ox + t * dx, -bound, bound); const float y = clamp(oy + t * dy, -bound, bound); const float z = clamp(oz + t * dz, -bound, bound); float dt = clamp(t * dt_gamma, dt_min, dt_max); // get mip level const int level = max(mip_from_pos(x, y, z, C), mip_from_dt(dt, H, C)); // range in [0, C - 1] const float mip_bound = fminf(scalbnf(1, level), bound); const float mip_rbound = 1 / mip_bound; // contraction float cx = x, cy = y, cz = z; const float mag = fmaxf(fabsf(x), fmaxf(fabsf(y), fabsf(z))); if (contract && mag > 1) { // L-INF norm const float Linf_scale = (2 - 1 / mag) / mag; cx *= Linf_scale; cy *= Linf_scale; cz *= Linf_scale; } // convert to nearest grid position const int nx = clamp(0.5 * (cx * mip_rbound + 1) * H, 0.0f, (float)(H - 1)); const int ny = clamp(0.5 * (cy * mip_rbound + 1) * H, 0.0f, (float)(H - 1)); const int nz = clamp(0.5 * (cz * mip_rbound + 1) * H, 0.0f, (float)(H - 1)); const uint32_t index = level * H3 + __morton3D(nx, ny, nz); const bool occ = grid[index / 8] & (1 << (index % 8)); // if occpuied, advance a small step, and write to output if (occ) { // write step xyzs[0] = cx; xyzs[1] = cy; xyzs[2] = cz; dirs[0] = dx; dirs[1] = dy; dirs[2] = dz; // calc dt t += dt; ts[0] = t; ts[1] = dt; // step xyzs += 3; dirs += 3; ts += 2; step++; // contraction case } else if (contract && mag > 1) { t += dt; // else, skip a large step (basically skip a voxel grid) } else { // calc distance to next voxel const float tx = (((nx + 0.5f + 0.5f * signf(dx)) * rH * 2 - 1) * mip_bound - cx) * rdx; const float ty = (((ny + 0.5f + 0.5f * signf(dy)) * rH * 2 - 1) * mip_bound - cy) * rdy; const float tz = (((nz + 0.5f + 0.5f * signf(dz)) * rH * 2 - 1) * mip_bound - cz) * rdz; const float tt = t + fmaxf(0.0f, fminf(tx, fminf(ty, tz))); // step until next voxel do { dt = clamp(t * dt_gamma, dt_min, dt_max); t += dt; } while (t < tt); } } } void march_rays(const uint32_t n_alive, const uint32_t n_step, const at::Tensor rays_alive, const at::Tensor rays_t, const at::Tensor rays_o, const at::Tensor rays_d, const float bound, const bool contract, const float dt_gamma, const uint32_t max_steps, const uint32_t C, const uint32_t H, const at::Tensor grid, const at::Tensor near, const at::Tensor far, at::Tensor xyzs, at::Tensor dirs, at::Tensor ts, at::Tensor noises) { static constexpr uint32_t N_THREAD = 128; AT_DISPATCH_FLOATING_TYPES_AND_HALF( rays_o.scalar_type(), "march_rays", ([&] { kernel_march_rays<<>>(n_alive, n_step, rays_alive.data_ptr(), rays_t.data_ptr(), rays_o.data_ptr(), rays_d.data_ptr(), bound, contract, dt_gamma, max_steps, C, H, grid.data_ptr(), near.data_ptr(), far.data_ptr(), xyzs.data_ptr(), dirs.data_ptr(), ts.data_ptr(), noises.data_ptr()); })); } template __global__ void kernel_composite_rays( const uint32_t n_alive, const uint32_t n_step, const float T_thresh, const bool binarize, int* rays_alive, scalar_t* rays_t, const scalar_t* __restrict__ sigmas, const scalar_t* __restrict__ rgbs, const scalar_t* __restrict__ ts, scalar_t* weights_sum, scalar_t* depth, scalar_t* image ) { const uint32_t n = threadIdx.x + blockIdx.x * blockDim.x; if (n >= n_alive) return; const int index = rays_alive[n]; // ray id // locate sigmas += n * n_step; rgbs += n * n_step * 3; ts += n * n_step * 2; rays_t += index; weights_sum += index; depth += index; image += index * 3; float t; float d = depth[0], r = image[0], g = image[1], b = image[2], weight_sum = weights_sum[0]; // accumulate uint32_t step = 0; while (step < n_step) { // ray is terminated if t == 0 if (ts[0] == 0) break; const float real_alpha = 1.0f - __expf(- sigmas[0] * ts[1]); const float alpha = binarize ? (real_alpha > 0.5 ? 1.0 : 0.0) : real_alpha; /* T_0 = 1; T_i = \prod_{j=0}^{i-1} (1 - alpha_j) w_i = alpha_i * T_i --> T_i = 1 - \sum_{j=0}^{i-1} w_j */ const float T = 1 - weight_sum; const float weight = alpha * T; weight_sum += weight; t = ts[0]; d += weight * t; // real depth r += weight * rgbs[0]; g += weight * rgbs[1]; b += weight * rgbs[2]; //printf("[n=%d] num_steps=%d, alpha=%f, w=%f, T=%f, sum_dt=%f, d=%f\n", n, step, alpha, weight, T, sum_delta, d); // ray is terminated if T is too small // use a larger bound to further accelerate inference if (T < T_thresh) break; // locate sigmas++; rgbs += 3; ts += 2; step++; } //printf("[n=%d] rgb=(%f, %f, %f), d=%f\n", n, r, g, b, d); // rays_alive = -1 means ray is terminated early. if (step < n_step) { rays_alive[n] = -1; } else { rays_t[0] = t; } weights_sum[0] = weight_sum; // this is the thing I needed! depth[0] = d; image[0] = r; image[1] = g; image[2] = b; } void composite_rays(const uint32_t n_alive, const uint32_t n_step, const float T_thresh, const bool binarize, at::Tensor rays_alive, at::Tensor rays_t, at::Tensor sigmas, at::Tensor rgbs, at::Tensor ts, at::Tensor weights, at::Tensor depth, at::Tensor image) { static constexpr uint32_t N_THREAD = 128; AT_DISPATCH_FLOATING_TYPES_AND_HALF( image.scalar_type(), "composite_rays", ([&] { kernel_composite_rays<<>>(n_alive, n_step, T_thresh, binarize, rays_alive.data_ptr(), rays_t.data_ptr(), sigmas.data_ptr(), rgbs.data_ptr(), ts.data_ptr(), weights.data_ptr(), depth.data_ptr(), image.data_ptr()); })); } ================================================ FILE: stable-dreamfusion/raymarching/src/raymarching.h ================================================ #pragma once #include #include void near_far_from_aabb(const at::Tensor rays_o, const at::Tensor rays_d, const at::Tensor aabb, const uint32_t N, const float min_near, at::Tensor nears, at::Tensor fars); void sph_from_ray(const at::Tensor rays_o, const at::Tensor rays_d, const float radius, const uint32_t N, at::Tensor coords); void morton3D(const at::Tensor coords, const uint32_t N, at::Tensor indices); void morton3D_invert(const at::Tensor indices, const uint32_t N, at::Tensor coords); void packbits(const at::Tensor grid, const uint32_t N, const float density_thresh, at::Tensor bitfield); void flatten_rays(const at::Tensor rays, const uint32_t N, const uint32_t M, at::Tensor res); void march_rays_train(const at::Tensor rays_o, const at::Tensor rays_d, const at::Tensor grid, const float bound, const bool contract, const float dt_gamma, const uint32_t max_steps, const uint32_t N, const uint32_t C, const uint32_t H, const at::Tensor nears, const at::Tensor fars, at::optional xyzs, at::optional dirs, at::optional ts, at::Tensor rays, at::Tensor counter, at::Tensor noises); void composite_rays_train_forward(const at::Tensor sigmas, const at::Tensor rgbs, const at::Tensor ts, const at::Tensor rays, const uint32_t M, const uint32_t N, const float T_thresh, const bool binarize, at::Tensor weights, at::Tensor weights_sum, at::Tensor depth, at::Tensor image); void composite_rays_train_backward(const at::Tensor grad_weights, const at::Tensor grad_weights_sum, const at::Tensor grad_depth, const at::Tensor grad_image, const at::Tensor sigmas, const at::Tensor rgbs, const at::Tensor ts, const at::Tensor rays, const at::Tensor weights_sum, const at::Tensor depth, const at::Tensor image, const uint32_t M, const uint32_t N, const float T_thresh, const bool binarize, at::Tensor grad_sigmas, at::Tensor grad_rgbs); void march_rays(const uint32_t n_alive, const uint32_t n_step, const at::Tensor rays_alive, const at::Tensor rays_t, const at::Tensor rays_o, const at::Tensor rays_d, const float bound, const bool contract, const float dt_gamma, const uint32_t max_steps, const uint32_t C, const uint32_t H, const at::Tensor grid, const at::Tensor nears, const at::Tensor fars, at::Tensor xyzs, at::Tensor dirs, at::Tensor ts, at::Tensor noises); void composite_rays(const uint32_t n_alive, const uint32_t n_step, const float T_thresh, const bool binarize, at::Tensor rays_alive, at::Tensor rays_t, at::Tensor sigmas, at::Tensor rgbs, at::Tensor ts, at::Tensor weights_sum, at::Tensor depth, at::Tensor image); ================================================ FILE: stable-dreamfusion/readme.md ================================================ # Stable-Dreamfusion + Perp-Neg A pytorch implementation of the text-to-3D model **Perp-Neg + Dreamfusion**, please use the scripts in scripts/run_if2_perpneg.sh to run the Stable DreamFusion + PerpNeg to avoid the Janus problem. This code is manily based on [Stable-DreamFusion](https://github.com/ashawkey/stable-dreamfusion) ================================================ FILE: stable-dreamfusion/requirements.txt ================================================ tqdm rich ninja numpy pandas scipy scikit-learn matplotlib opencv-python imageio imageio-ffmpeg torch torch-ema einops tensorboard tensorboardX # for gui dearpygui # for grid_tcnn # git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch # for stable-diffusion huggingface_hub diffusers >= 0.9.0 accelerate transformers # for dmtet and mesh export xatlas trimesh PyMCubes pymeshlab git+https://github.com/NVlabs/nvdiffrast/ # for zero123 carvekit-colab omegaconf pytorch-lightning taming-transformers-rom1504 kornia git+https://github.com/openai/CLIP.git # for omnidata gdown # for dpt timm # for remote debugging debugpy-run # for deepfloyd if sentencepiece ================================================ FILE: stable-dreamfusion/scripts/install_ext.sh ================================================ pip install ./raymarching pip install ./shencoder pip install ./freqencoder pip install ./gridencoder ================================================ FILE: stable-dreamfusion/scripts/res64.args ================================================ -O --vram_O --w 64 --h 64 ================================================ FILE: stable-dreamfusion/scripts/run.sh ================================================ #! /bin/bash CUDA_VISIBLE_DEVICES=1 python main.py -O --text "a DSLR photo of a delicious hamburger" --workspace trial_hamburger --iters 5000 CUDA_VISIBLE_DEVICES=1 python main.py -O --text "a DSLR photo of a delicious hamburger" --workspace trial2_hamburger --dmtet --iters 5000 --init_with trial_hamburger/checkpoints/df.pth CUDA_VISIBLE_DEVICES=1 python main.py -O --text "a highly detailed stone bust of Theodoros Kolokotronis" --workspace trial_stonehead --iters 5000 CUDA_VISIBLE_DEVICES=1 python main.py -O --text "a highly detailed stone bust of Theodoros Kolokotronis" --workspace trial2_stonehead --dmtet --iters 5000 --init_with trial_stonehead/checkpoints/df.pth CUDA_VISIBLE_DEVICES=1 python main.py -O --text "an astronaut, full body" --workspace trial_astronaut --iters 5000 CUDA_VISIBLE_DEVICES=1 python main.py -O --text "an astronaut, full body" --workspace trial2_astronaut --dmtet --iters 5000 --init_with trial_astronaut/checkpoints/df.pth CUDA_VISIBLE_DEVICES=1 python main.py -O --text "a DSLR photo of a squirrel-octopus hybrid" --workspace trial_squrrel_octopus --iters 5000 CUDA_VISIBLE_DEVICES=1 python main.py -O --text "a DSLR photo of a squirrel-octopus hybrid" --workspace trial2_squrrel_octopus --dmtet --iters 5000 --init_with trial_squrrel_octopus/checkpoints/df.pth CUDA_VISIBLE_DEVICES=1 python main.py -O --text "a baby bunny sitting on top of a stack of pancakes" --workspace trial_rabbit_pancake --iters 5000 CUDA_VISIBLE_DEVICES=1 python main.py -O --text "a metal bunny sitting on top of a stack of chocolate cookies" --workspace trial2_rabbit_pancake --dmtet --iters 5000 --init_with trial_rabbit_pancake/checkpoints/df.pth ================================================ FILE: stable-dreamfusion/scripts/run2.sh ================================================ #! /bin/bash CUDA_VISIBLE_DEVICES=5 python main.py -O --text "a DSLR photo of a shiba inu playing golf wearing tartan golf clothes and hat" --workspace trial_shiba --iters 10000 CUDA_VISIBLE_DEVICES=5 python main.py -O --text "a DSLR photo of a shiba inu playing golf wearing tartan golf clothes and hat" --workspace trial2_shiba --dmtet --iters 5000 --init_with trial_shiba/checkpoints/df.pth CUDA_VISIBLE_DEVICES=5 python main.py -O --text "a banana peeling itself" --workspace trial_banana --iters 10000 CUDA_VISIBLE_DEVICES=5 python main.py -O --text "a banana peeling itself" --workspace trial2_banana --dmtet --iters 5000 --init_with trial_banana/checkpoints/df.pth CUDA_VISIBLE_DEVICES=5 python main.py -O --text "a capybara wearing a top hat, low poly" --workspace trial_capybara --iters 10000 CUDA_VISIBLE_DEVICES=5 python main.py -O --text "a capybara wearing a top hat, low poly" --workspace trial2_capybara --dmtet --iters 5000 --init_with trial_capybara/checkpoints/df.pth ================================================ FILE: stable-dreamfusion/scripts/run3.sh ================================================ #! /bin/bash CUDA_VISIBLE_DEVICES=7 python main.py -O --text "ironman, full body" --workspace trial_ironman --iters 10000 CUDA_VISIBLE_DEVICES=7 python main.py -O --text "ironman, full body" --workspace trial2_ironman --dmtet --iters 5000 --init_with trial_ironman/checkpoints/df.pth CUDA_VISIBLE_DEVICES=7 python main.py -O --text "a DSLR photo of an ice cream sundae" --workspace trial_icecream --iters 10000 CUDA_VISIBLE_DEVICES=7 python main.py -O --text "a DSLR photo of an ice cream sundae" --workspace trial2_icecream --dmtet --iters 5000 --init_with trial_icecream/checkpoints/df.pth CUDA_VISIBLE_DEVICES=7 python main.py -O --text "a DSLR photo of a kingfisher bird" --workspace trial_bird --iters 10000 CUDA_VISIBLE_DEVICES=7 python main.py -O --text "a DSLR photo of a kingfisher bird" --workspace trial2_bird --dmtet --iters 5000 --init_with trial_bird/checkpoints/df.pth CUDA_VISIBLE_DEVICES=7 python main.py -O --text "a car made of sushi" --workspace trial_sushi --iters 10000 CUDA_VISIBLE_DEVICES=7 python main.py -O --text "a car made of sushi" --workspace trial2_sushi --dmtet --iters 5000 --init_with trial_sushi/checkpoints/df.pth ================================================ FILE: stable-dreamfusion/scripts/run4.sh ================================================ #! /bin/bash CUDA_VISIBLE_DEVICES=5 python main.py -O --text "a rabbit, animated movie character, high detail 3d model" --workspace trial_rabbit2 --iters 10000 CUDA_VISIBLE_DEVICES=5 python main.py -O --text "a rabbit, animated movie character, high detail 3d model" --workspace trial2_rabbit2 --dmtet --iters 5000 --init_with trial_rabbit2/checkpoints/df.pth CUDA_VISIBLE_DEVICES=5 python main.py -O --text "a corgi dog, highly detailed 3d model" --workspace trial_corgi --iters 10000 CUDA_VISIBLE_DEVICES=5 python main.py -O --text "a corgi dog, highly detailed 3d model" --workspace trial2_corgi --dmtet --iters 5000 --init_with trial_corgi/checkpoints/df.pth CUDA_VISIBLE_DEVICES=5 python main.py -O --text " a small saguaro cactus planted in a clay pot" --workspace trial_cactus --iters 10000 CUDA_VISIBLE_DEVICES=5 python main.py -O --text " a small saguaro cactus planted in a clay pot" --workspace trial2_cactus --dmtet --iters 5000 --init_with trial_cactus/checkpoints/df.pth CUDA_VISIBLE_DEVICES=5 python main.py -O --text "the leaning tower of Pisa" --workspace trial_pisa --iters 10000 CUDA_VISIBLE_DEVICES=5 python main.py -O --text "the leaning tower of Pisa" --workspace trial2_pisa --dmtet --iters 5000 --init_with trial_pisa/checkpoints/df.pth ================================================ FILE: stable-dreamfusion/scripts/run5.sh ================================================ #! /bin/bash CUDA_VISIBLE_DEVICES=2 python main.py -O --text "Perched blue jay bird" --workspace trial_jay --iters 10000 CUDA_VISIBLE_DEVICES=2 python main.py -O --text "Perched blue jay bird" --workspace trial2_jay --dmtet --iters 5000 --init_with trial_jay/checkpoints/df.pth CUDA_VISIBLE_DEVICES=2 python main.py -O --text "angel statue wings out" --workspace trial_angle --iters 10000 CUDA_VISIBLE_DEVICES=2 python main.py -O --text "angel statue wings out" --workspace trial2_angle --dmtet --iters 5000 --init_with trial_angle/checkpoints/df.pth CUDA_VISIBLE_DEVICES=2 python main.py -O --text "devil statue" --workspace trial_devil --iters 10000 CUDA_VISIBLE_DEVICES=2 python main.py -O --text "devil statue" --workspace trial2_devil --dmtet --iters 5000 --init_with trial_devil/checkpoints/df.pth CUDA_VISIBLE_DEVICES=2 python main.py -O --text "Einstein statue" --workspace trial_einstein --iters 10000 CUDA_VISIBLE_DEVICES=2 python main.py -O --text "Einstein statue" --workspace trial2_einstein --dmtet --iters 5000 --init_with trial_einstein/checkpoints/df.pth ================================================ FILE: stable-dreamfusion/scripts/run6.sh ================================================ #! /bin/bash CUDA_VISIBLE_DEVICES=4 python main.py -O --text "a baby bunny sitting on top of a stack of pancakes" --workspace trial_rabbit_pancake --iters 5000 CUDA_VISIBLE_DEVICES=4 python main.py -O --text "a metal bunny sitting on top of a stack of chocolate cookies" --workspace trial2_rabbit_pancake --dmtet --iters 5000 --init_with trial_rabbit_pancake/checkpoints/df.pth CUDA_VISIBLE_DEVICES=4 python main.py -O --text "a DSLR photo of a blue jay standing on a large basket of rainbow macarons" --workspace trial_jay --iters 5000 CUDA_VISIBLE_DEVICES=4 python main.py -O --text "a DSLR photo of a blue jay standing on a large basket of rainbow macarons" --workspace trial2_jay --dmtet --iters 5000 --init_with trial_jay/checkpoints/df.pth CUDA_VISIBLE_DEVICES=4 python main.py -O --text "a DSLR photo of a fox taking a photograph using a DSLR" --workspace trial_fox --iters 5000 CUDA_VISIBLE_DEVICES=4 python main.py -O --text "a DSLR photo of a fox taking a photograph using a DSLR" --workspace trial2_fox --dmtet --iters 5000 --init_with trial_fox/checkpoints/df.pth CUDA_VISIBLE_DEVICES=4 python main.py -O --text "a DSLR photo of a peacock on a surfboard" --workspace trial_peacock --iters 5000 CUDA_VISIBLE_DEVICES=4 python main.py -O --text "a DSLR photo of a peacock on a surfboard" --workspace trial2_peacock --dmtet --iters 5000 --init_with trial_peacock/checkpoints/df.pth CUDA_VISIBLE_DEVICES=4 python main.py -O --text "a flower made out of metal" --workspace trial_metal_flower --iters 5000 CUDA_VISIBLE_DEVICES=4 python main.py -O --text "a flower made out of metal" --workspace trial2_metal_flower --dmtet --iters 5000 --init_with trial_metal_flower/checkpoints/df.pth CUDA_VISIBLE_DEVICES=4 python main.py -O --text "a zoomed out DSLR photo of an egg cracked open with a newborn chick hatching out of it" --workspace trial_chicken --iters 5000 CUDA_VISIBLE_DEVICES=4 python main.py -O --text "a zoomed out DSLR photo of an egg cracked open with a newborn chick hatching out of it" --workspace trial2_chicken --dmtet --iters 5000 --init_with trial_chicken/checkpoints/df.pth ================================================ FILE: stable-dreamfusion/scripts/run_if.sh ================================================ #! /bin/bash CUDA_VISIBLE_DEVICES=2 python main.py -O --text "a baby bunny sitting on top of a stack of pancakes" --workspace trial_if_rabbit_pancake --iters 5000 --IF CUDA_VISIBLE_DEVICES=2 python main.py -O --text "a metal bunny sitting on top of a stack of chocolate cookies" --workspace trial_if2_rabbit_pancake --dmtet --iters 5000 --init_with trial_if_rabbit_pancake/checkpoints/df.pth CUDA_VISIBLE_DEVICES=2 python main.py -O --text "a DSLR photo of a blue jay standing on a large basket of rainbow macarons" --workspace trial_if_jay --iters 5000 --IF CUDA_VISIBLE_DEVICES=2 python main.py -O --text "a DSLR photo of a blue jay standing on a large basket of rainbow macarons" --workspace trial_if2_jay --dmtet --iters 5000 --init_with trial_if_jay/checkpoints/df.pth CUDA_VISIBLE_DEVICES=2 python main.py -O --text "a DSLR photo of a fox taking a photograph using a DSLR" --workspace trial_if_fox --iters 5000 --IF CUDA_VISIBLE_DEVICES=2 python main.py -O --text "a DSLR photo of a fox taking a photograph using a DSLR" --workspace trial_if2_fox --dmtet --iters 5000 --init_with trial_if_fox/checkpoints/df.pth CUDA_VISIBLE_DEVICES=2 python main.py -O --text "a DSLR photo of a peacock on a surfboard" --workspace trial_if_peacock --iters 5000 --IF CUDA_VISIBLE_DEVICES=2 python main.py -O --text "a DSLR photo of a peacock on a surfboard" --workspace trial_if2_peacock --dmtet --iters 5000 --init_with trial_if_peacock/checkpoints/df.pth CUDA_VISIBLE_DEVICES=2 python main.py -O --text "a flower made out of metal" --workspace trial_if_metal_flower --iters 5000 --IF CUDA_VISIBLE_DEVICES=2 python main.py -O --text "a flower made out of metal" --workspace trial_if2_metal_flower --dmtet --iters 5000 --init_with trial_if_metal_flower/checkpoints/df.pth CUDA_VISIBLE_DEVICES=2 python main.py -O --text "a zoomed out DSLR photo of an egg cracked open with a newborn chick hatching out of it" --workspace trial_if_chicken --iters 5000 --IF CUDA_VISIBLE_DEVICES=2 python main.py -O --text "a zoomed out DSLR photo of an egg cracked open with a newborn chick hatching out of it" --workspace trial_if2_chicken --dmtet --iters 5000 --init_with trial_if_chicken/checkpoints/df.pth ================================================ FILE: stable-dreamfusion/scripts/run_if2.sh ================================================ #! /bin/bash CUDA_VISIBLE_DEVICES=3 python main.py -O --text "a corgi taking a selfie" --workspace trial_if_corgi --iters 5000 --IF CUDA_VISIBLE_DEVICES=3 python main.py -O --text "a corgi taking a selfie" --workspace trial_if2_corgi --dmtet --iters 5000 --init_with trial_if_corgi/checkpoints/df.pth CUDA_VISIBLE_DEVICES=3 python main.py -O --text "a DSLR photo of a ghost eating a hamburger" --workspace trial_if_ghost --iters 5000 --IF CUDA_VISIBLE_DEVICES=3 python main.py -O --text "a DSLR photo of a ghost eating a hamburger" --workspace trial_if2_ghost --dmtet --iters 5000 --init_with trial_if_ghost/checkpoints/df.pth CUDA_VISIBLE_DEVICES=3 python main.py -O --text "a DSLR photo of an origami motorcycle" --workspace trial_if_motor --iters 5000 --IF CUDA_VISIBLE_DEVICES=3 python main.py -O --text "a DSLR photo of an origami motorcycle" --workspace trial_if2_motor --dmtet --iters 5000 --init_with trial_if_motor/checkpoints/df.pth CUDA_VISIBLE_DEVICES=3 python main.py -O --text "a DSLR photo of a Space Shuttle" --workspace trial_if_spaceshuttle --iters 5000 --IF CUDA_VISIBLE_DEVICES=3 python main.py -O --text "a DSLR photo of a Space Shuttle" --workspace trial_if2_spaceshuttle --dmtet --iters 5000 --init_with trial_if_spaceshuttle/checkpoints/df.pth CUDA_VISIBLE_DEVICES=3 python main.py -O --text "a palm tree, low poly 3d model" --workspace trial_if_palm --iters 5000 --IF CUDA_VISIBLE_DEVICES=3 python main.py -O --text "a palm tree, low poly 3d model" --workspace trial_if2_palm --dmtet --iters 5000 --init_with trial_if_palm/checkpoints/df.pth CUDA_VISIBLE_DEVICES=3 python main.py -O --text "a zoomed out DSLR photo of a marble bust of a cat, a real mouse is sitting on its head" --workspace trial_if_cat_mouse --iters 5000 --IF CUDA_VISIBLE_DEVICES=3 python main.py -O --text "a zoomed out DSLR photo of a marble bust of a cat, a real mouse is sitting on its head" --workspace trial_if2_cat_mouse --dmtet --iters 5000 --init_with trial_if_cat_mouse/checkpoints/df.pth ================================================ FILE: stable-dreamfusion/scripts/run_if2_perpneg.sh ================================================ #! /bin/bash CUDA_VISIBLE_DEVICES=3 python main.py -O --text "a lion bust" --workspace trial_perpneg_if_lion --iters 5000 --IF --batch_size 1 --perpneg CUDA_VISIBLE_DEVICES=3 python main.py -O --text "a DSLR photo of a lion" --workspace trial_perpneg_if2_lion_p --dmtet --iters 5000 --perpneg --init_with trial_perpneg_if_lion/checkpoints/df.pth CUDA_VISIBLE_DEVICES=3 python main.py -O --text "a DSLR photo of a lion" --workspace trial_perpneg_if2_lion_nop --dmtet --iters 5000 --init_with trial_perpneg_if_lion/checkpoints/df.pth CUDA_VISIBLE_DEVICES=3 python main.py -O --text "a tiger cub" --workspace trial_perpneg_if_tiger --iters 5000 --IF --batch_size 1 --perpneg CUDA_VISIBLE_DEVICES=3 python main.py -O --text "tiger" --workspace trial_perpneg_if2_tiger_p --dmtet --iters 5000 --perpneg --init_with trial_perpneg_if_tiger/checkpoints/df.pth CUDA_VISIBLE_DEVICES=3 python main.py -O --text "tiger" --workspace trial_perpneg_if2_tiger_nop --dmtet --iters 5000 --init_with trial_perpneg_if_tiger/checkpoints/df.pth # larger negative weight is used for the following command because the defult negative weight of -2 is not enough to make the diffusion model to produce the views as desired CUDA_VISIBLE_DEVICES=3 python main.py -O --text "a shiba dog wearing sunglasses" --workspace trial_perpneg_if_shiba --iters 5000 --IF --batch_size 1 --perpneg --negative_w -3.0 CUDA_VISIBLE_DEVICES=3 python main.py -O --text "shiba wearing sunglasses" --workspace trial_perpneg_if2_shiba_p --dmtet --iters 5000 --perpneg --negative_w -3.0 --init_with trial_perpneg_if_shiba/checkpoints/df.pth CUDA_VISIBLE_DEVICES=3 python main.py -O --text "shiba wearing sunglasses" --workspace trial_perpneg_if2_shiba_nop --dmtet --iters 5000 --init_with trial_perpneg_if_shiba/checkpoints/df.pth ================================================ FILE: stable-dreamfusion/scripts/run_image.sh ================================================ # zero123 backend (single object, images like 3d model rendering) CUDA_VISIBLE_DEVICES=6 python main.py -O --image data/teddy_rgba.png --workspace trial_image_teddy --iters 5000 CUDA_VISIBLE_DEVICES=6 python main.py -O --image data/teddy_rgba.png --workspace trial2_image_teddy --iters 5000 --dmtet --init_with trial_image_teddy/checkpoints/df.pth CUDA_VISIBLE_DEVICES=6 python main.py -O --image data/catstatue_rgba.png --workspace trial_image_catstatue --iters 5000 CUDA_VISIBLE_DEVICES=6 python main.py -O --image data/catstatue_rgba.png --workspace trial2_image_catstatue --iters 5000 --dmtet --init_with trial_image_catstatue/checkpoints/df.pth CUDA_VISIBLE_DEVICES=6 python main.py -O --image data/firekeeper_rgba.png --workspace trial_image_firekeeper --iters 5000 CUDA_VISIBLE_DEVICES=6 python main.py -O --image data/firekeeper_rgba.png --workspace trial2_image_firekeeper --iters 5000 --dmtet --init_with trial_image_firekeeper/checkpoints/df.pth CUDA_VISIBLE_DEVICES=6 python main.py -O --image data/hamburger_rgba.png --workspace trial_image_hamburger --iters 5000 CUDA_VISIBLE_DEVICES=6 python main.py -O --image data/hamburger_rgba.png --workspace trial2_image_hamburger --iters 5000 --dmtet --init_with trial_image_hamburger/checkpoints/df.pth CUDA_VISIBLE_DEVICES=6 python main.py -O --image data/corgi_rgba.png --workspace trial_image_corgi --iters 5000 CUDA_VISIBLE_DEVICES=6 python main.py -O --image data/corgi_rgba.png --workspace trial2_image_corgi --iters 5000 --dmtet --init_with trial_image_corgi/checkpoints/df.pth CUDA_VISIBLE_DEVICES=6 python main.py -O --image data/cactus_rgba.png --workspace trial_image_cactus --iters 5000 CUDA_VISIBLE_DEVICES=6 python main.py -O --image data/cactus_rgba.png --workspace trial2_image_cactus --iters 5000 --dmtet --init_with trial_image_cactus/checkpoints/df.pth CUDA_VISIBLE_DEVICES=6 python main.py -O --image data/cake_rgba.png --workspace trial_image_cake --iters 5000 CUDA_VISIBLE_DEVICES=6 python main.py -O --image data/cake_rgba.png --workspace trial2_image_cake --iters 5000 --dmtet --init_with trial_image_cake/checkpoints/df.pth # CUDA_VISIBLE_DEVICES=6 python main.py -O --image data/warrior_rgba.png --workspace trial_image_warrior --iters 5000 # CUDA_VISIBLE_DEVICES=6 python main.py -O --image data/warrior_rgba.png --workspace trial2_image_warrior --iters 5000 --dmtet --init_with trial_image_warrior/checkpoints/df.pth ================================================ FILE: stable-dreamfusion/scripts/run_image_anya.sh ================================================ # Phase 1 - barely fits in A100 40GB. # Conclusion: results in concave-ish face, no neck, excess hair in the back CUDA_VISIBLE_DEVICES=0 python main.py -O --image data/anya_front_rgba.png --workspace trial_anya_1_refimage \ --iters 10000 --save_guidance --save_guidance_interval 10 --ckpt scratch --batch_size 2 --test_interval 2 \ --h 128 --w 128 --zero123_grad_scale None # Phase 2 - barely fits in A100 40GB. # 20X smaller lambda_3d_normal_smooth, --known_view_interval 2, 3X LR # Much higher jitter to increase disparity (and eliminate some of the flatness)... not too high either (to avoid cropping the face) CUDA_VISIBLE_DEVICES=0 python main.py -O --image data/anya_front_rgba.png --workspace trial_anya_1_refimage_B_GPU2_reproduction1_GPU2 \ --text "A DSLR 3D photo of a cute anime schoolgirl stands proudly with her arms in the air, pink hair ( unreal engine 5 trending on Artstation Ghibli 4k )" \ --iters 12500 --ckpt trial_anya_1_refimage/checkpoints/df_ep0100.pth --save_guidance --save_guidance_interval 1 \ --h 256 --w 256 --albedo_iter_ratio 0.0 --t_range 0.2 0.6 --batch_size 4 --radius_range 2.2 2.6 --test_interval 2 \ --vram_O --guidance_scale 10 --jitter_pose --jitter_center 0.1 --jitter_target 0.1 --jitter_up 0.05 \ --known_view_noise_scale 0 --lambda_depth 0 --lr 0.003 --progressive_view --known_view_interval 2 --dont_override_stuff --lambda_3d_normal_smooth 1 \ --exp_start_iter 10000 --exp_end_iter 12500 # Phase 3 - increase resolution to 512 # Disable textureless since they can cause catastrophic divergence # Since radius range is inconsistent, increase it, and reduce the jitter to avoid excessively cropped renders. # Learning rate may be set too high, since `--batch_size 1`. CUDA_VISIBLE_DEVICES=0 python main.py -O --image data/anya_front_rgba.png --workspace trial_anya_1_refimage_B_GPU2_reproduction1_GPU2_refinedGPU2 \ --text "A DSLR 3D photo of a cute anime schoolgirl stands proudly with her arms in the air, pink hair ( unreal engine 5 trending on Artstation Ghibli 4k )" \ --iters 25000 --ckpt trial_anya_1_refimage_B_GPU2_reproduction1_GPU2/checkpoints/df_ep0125.pth --save_guidance --save_guidance_interval 1 \ --h 512 --w 512 --albedo_iter_ratio 0.0 --t_range 0.0 0.5 --batch_size 1 --radius_range 3.2 3.6 --test_interval 2 \ --vram_O --guidance_scale 10 --jitter_pose --jitter_center 0.015 --jitter_target 0.015 --jitter_up 0.05 \ --known_view_noise_scale 0 --lambda_depth 0 --lr 0.003 --known_view_interval 2 --dont_override_stuff --lambda_3d_normal_smooth 0.5 --textureless_ratio 0.0 --min_ambient_ratio 0.3 \ --exp_start_iter 12500 --exp_end_iter 25000 # Generate 6 views CUDA_VISIBLE_DEVICES=0 python main.py -O --image data/anya_front_rgba.png --ckpt trial_anya_1_refimage_B_GPU2_reproduction1_GPU2_refinedGPU2/checkpoints/df_ep0250.pth --six_views # Phase 4 - untested, need to adjust # CUDA_VISIBLE_DEVICES=0 python main.py -O --image data/anya_front_rgba.png --workspace trial_anya_1_refimage --iters 5000 --dmtet --init_with trial_anya_1_refimage/checkpoints/df.pth ================================================ FILE: stable-dreamfusion/scripts/run_image_hard_examples.sh ================================================ bash scripts/run_image_procedure.sh 0 30 90 anya_front "A DSLR 3D photo of a cute anime schoolgirl stands proudly with her arms in the air, pink hair ( unreal engine 5 trending on Artstation Ghibli 4k )" bash scripts/run_image_procedure.sh 1 30 70 baby_phoenix_on_ice "A DSLR 3D photo of an adorable baby phoenix made in Swarowski crystal highly detailed intricate concept art 8K ( unreal engine 5 trending on Artstation )" bash scripts/run_image_procedure.sh 2 30 90 bollywood_actress "A DSLR 3D photo of a beautiful bollywood indian actress, pretty eyes, full body shot composition, sunny outdoor, seen from far away ( highly detailed intricate 8K unreal engine 5 trending on Artstation )" bash scripts/run_image_procedure.sh 3 30 40 beach_house_1 "A DSLR 3D photo of a very beautiful small house on a beach ( highly detailed intricate 8K unreal engine 5 trending on Artstation )" bash scripts/run_image_procedure.sh 4 30 60 beach_house_2 "A DSLR 3D photo of a very beautiful high-tech small house with solar panels and wildflowers on a beach ( highly detailed intricate 8K unreal engine 5 trending on Artstation )" bash scripts/run_image_procedure.sh 5 30 90 mona_lisa "A DSLR 3D photo of a beautiful young woman dressed like Mona Lisa ( highly detailed intricate 8K unreal engine 5 trending on Artstation )" bash scripts/run_image_procedure.sh 6 30 80 futuristic_car "A DSLR 3D photo of a crazily futuristic electric car ( highly detailed intricate 8K unreal engine 5 trending on Artstation )" # the church ruins probably require a wider field of view... e.g. 90 degrees, maybe even more... so may not work with Zero123 etc. bash scripts/run_image_procedure.sh 7 30 90 church_ruins "A DSLR 3D photo of the remains of an isolated old church ruin covered in ivy ( highly detailed intricate 8K unreal engine 5 trending on Artstation )" # young woman dressed like mona lisa ================================================ FILE: stable-dreamfusion/scripts/run_image_procedure.sh ================================================ # Perform a 2D-to-3D reconstruction, similar to the Anya case study: https://github.com/ashawkey/stable-dreamfusion/issues/263 # Args: # bash scripts/run_image_procedure.sh GPU_ID guidance_interval image_name "prompt" # e.g.: # bash scripts/run_image_procedure 1 30 baby_phoenix_on_ice "An adorable baby phoenix made in Swarowski crystal highly detailed intricated concept art 8K" GPU_ID=$1 GUIDANCE_INTERVAL=$2 DEFAULT_POLAR=$3 PREFIX=$4 PROMPT=$5 EPOCHS1=100 EPOCHS2=200 EPOCHS3=300 IMAGE=data/$PREFIX.png IMAGE_RGBA=data/${PREFIX}_rgba.png WS_PH1=trial_$PREFIX-ph1 WS_PH2=trial_$PREFIX-ph2 WS_PH3=trial_$PREFIX-ph3 CKPT1=$WS_PH1/checkpoints/df_ep0${EPOCHS1}.pth CKPT2=$WS_PH2/checkpoints/df_ep0${EPOCHS2}.pth CKPT3=$WS_PH3/checkpoints/df_ep0${EPOCHS3}.pth # Can uncomment to clear up trial folders. Be careful - mistakes could erase important work! # rm -r $WS_PH1 $WS_PH2 $WS_PH3 # Preprocess if [ ! -f $IMAGE_RGBA ] then python preprocess_image.py $IMAGE fi if [ ! -f $CKPT1 ] then # Phase 1 - zero123-guidance # WARNING: claforte: constantly runs out of VRAM with resolution of 128x128 and batch_size 2... no longer able to reproduce Anya result because of this... # I added these to try to reduce mem usage, but this might degrade the quality... `--lambda_depth 0 --lambda_3d_normal_smooth 0` # Remove: --ckpt scratch CUDA_VISIBLE_DEVICES=$GPU_ID python main.py -O --image $IMAGE_RGBA --workspace $WS_PH1 --default_polar $DEFAULT_POLAR \ --iters ${EPOCHS1}00 --save_guidance --save_guidance_interval $GUIDANCE_INTERVAL --batch_size 1 --test_interval 2 \ --h 96 --w 96 --zero123_grad_scale None --lambda_3d_normal_smooth 0 --dont_override_stuff \ --fovy_range 20 20 --guidance_scale 5 fi GUIDANCE_INTERVAL=7 if [ ! -f $CKPT2 ] then # Phase 2 - SD-guidance at 256x256 CUDA_VISIBLE_DEVICES=$GPU_ID python main.py -O --image $IMAGE_RGBA --workspace $WS_PH2 \ --text "${PROMPT}" --default_polar $DEFAULT_POLAR \ --iters ${EPOCHS2}00 --ckpt $CKPT1 --save_guidance --save_guidance_interval 7 \ --h 128 --w 128 --albedo_iter_ratio 0.0 --t_range 0.2 0.6 --batch_size 4 --radius_range 2.2 2.6 --test_interval 2 \ --vram_O --guidance_scale 10 --jitter_pose --jitter_center 0.1 --jitter_target 0.1 --jitter_up 0.05 \ --known_view_noise_scale 0 --lambda_depth 0 --lr 0.003 --progressive_view --progressive_view_init_ratio 0.05 --known_view_interval 2 --dont_override_stuff --lambda_3d_normal_smooth 1 --textureless_ratio 0.0 --min_ambient_ratio 0.3 \ --exp_start_iter ${EPOCHS1}00 --exp_end_iter ${EPOCHS2}00 fi if [ ! -f $CKPT3 ] then # # Phase 3 - increase resolution to 512 CUDA_VISIBLE_DEVICES=$GPU_ID python main.py -O --image $IMAGE_RGBA --workspace $WS_PH3 \ --text "${PROMPT}" --default_polar $DEFAULT_POLAR \ --iters ${EPOCHS3}00 --ckpt $CKPT2 --save_guidance --save_guidance_interval 7 \ --h 512 --w 512 --albedo_iter_ratio 0.0 --t_range 0.0 0.5 --batch_size 1 --radius_range 3.2 3.6 --test_interval 2 \ --vram_O --guidance_scale 10 --jitter_pose --jitter_center 0.015 --jitter_target 0.015 --jitter_up 0.05 \ --known_view_noise_scale 0 --lambda_depth 0 --lr 0.003 --known_view_interval 2 --dont_override_stuff --lambda_3d_normal_smooth 0.5 --textureless_ratio 0.0 --min_ambient_ratio 0.3 \ --exp_start_iter ${EPOCHS2}00 --exp_end_iter ${EPOCHS3}00 fi # Generate 6 views CUDA_VISIBLE_DEVICES=$GPU_ID python main.py -O --image $IMAGE_RGBA --ckpt $CKPT3 --six_views ================================================ FILE: stable-dreamfusion/scripts/run_image_text.sh ================================================ # sd backend (realistic images) CUDA_VISIBLE_DEVICES=4 python main.py -O --image data/teddy_rgba.png --text "a brown teddy bear sitting on a ground" --workspace trial_imagetext_teddy --iters 5000 CUDA_VISIBLE_DEVICES=4 python main.py -O --image data/teddy_rgba.png --text "a brown teddy bear sitting on a ground" --workspace trial2_imagetext_teddy --iters 10000 --dmtet --init_with trial_imagetext_teddy/checkpoints/df.pth CUDA_VISIBLE_DEVICES=4 python main.py -O --image data/corgi_rgba.png --text "a corgi running" --workspace trial_imagetext_corgi --iters 5000 CUDA_VISIBLE_DEVICES=4 python main.py -O --image data/corgi_rgba.png --text "a corgi running" --workspace trial2_imagetext_corgi --iters 10000 --dmtet --init_with trial_imagetext_corgi/checkpoints/df.pth CUDA_VISIBLE_DEVICES=4 python main.py -O --image data/hamburger_rgba.png --text "a DSLR photo of a delicious hamburger" --workspace trial_imagetext_hamburger --iters 5000 CUDA_VISIBLE_DEVICES=4 python main.py -O --image data/hamburger_rgba.png --text "a DSLR photo of a delicious hamburger" --workspace trial2_imagetext_hamburger --iters 10000 --dmtet --init_with trial_imagetext_hamburger/checkpoints/df.pth CUDA_VISIBLE_DEVICES=4 python main.py -O --image data/cactus_rgba.png --text "a potted cactus plant" --workspace trial_imagetext_cactus --iters 5000 CUDA_VISIBLE_DEVICES=4 python main.py -O --image data/cactus_rgba.png --text "a potted cactus plant" --workspace trial2_imagetext_cactus --iters 10000 --dmtet --init_with trial_imagetext_cactus/checkpoints/df.pth ================================================ FILE: stable-dreamfusion/scripts/run_images.sh ================================================ # zero123 backend (single object, images like 3d model rendering) CUDA_VISIBLE_DEVICES=6 python main.py -O --image_config config/corgi.csv --workspace trial_images_corgi --iters 5000 CUDA_VISIBLE_DEVICES=6 python main.py -O --image_config config/corgi.csv --workspace trial2_images_corgi --iters 10000 --dmtet --init_with trial_images_corgi/checkpoints/df.pth CUDA_VISIBLE_DEVICES=6 python main.py -O --image_config config/car.csv --workspace trial_images_car --iters 5000 CUDA_VISIBLE_DEVICES=6 python main.py -O --image_config config/car.csv --workspace trial2_images_car --iters 10000 --dmtet --init_with trial_images_car/checkpoints/df.pth CUDA_VISIBLE_DEVICES=6 python main.py -O --image_config config/anya.csv --workspace trial_images_anya --iters 5000 CUDA_VISIBLE_DEVICES=6 python main.py -O --image_config config/anya.csv --workspace trial2_images_anya --iters 10000 --dmtet --init_with trial_images_anya/checkpoints/df.pth ================================================ FILE: stable-dreamfusion/shencoder/__init__.py ================================================ from .sphere_harmonics import SHEncoder ================================================ FILE: stable-dreamfusion/shencoder/backend.py ================================================ import os from torch.utils.cpp_extension import load _src_path = os.path.dirname(os.path.abspath(__file__)) nvcc_flags = [ '-O3', '-std=c++14', '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__', ] if os.name == "posix": c_flags = ['-O3', '-std=c++14'] elif os.name == "nt": c_flags = ['/O2', '/std:c++17'] # find cl.exe def find_cl_path(): import glob for program_files in [r"C:\\Program Files (x86)", r"C:\\Program Files"]: for edition in ["Enterprise", "Professional", "BuildTools", "Community"]: paths = sorted(glob.glob(r"%s\\Microsoft Visual Studio\\*\\%s\\VC\\Tools\\MSVC\\*\\bin\\Hostx64\\x64" % (program_files, edition)), reverse=True) if paths: return paths[0] # If cl.exe is not on path, try to find it. if os.system("where cl.exe >nul 2>nul") != 0: cl_path = find_cl_path() if cl_path is None: raise RuntimeError("Could not locate a supported Microsoft Visual C++ installation") os.environ["PATH"] += ";" + cl_path _backend = load(name='_sh_encoder', extra_cflags=c_flags, extra_cuda_cflags=nvcc_flags, sources=[os.path.join(_src_path, 'src', f) for f in [ 'shencoder.cu', 'bindings.cpp', ]], ) __all__ = ['_backend'] ================================================ FILE: stable-dreamfusion/shencoder/setup.py ================================================ import os from setuptools import setup from torch.utils.cpp_extension import BuildExtension, CUDAExtension _src_path = os.path.dirname(os.path.abspath(__file__)) nvcc_flags = [ '-O3', '-std=c++14', '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '-U__CUDA_NO_HALF2_OPERATORS__', ] if os.name == "posix": c_flags = ['-O3', '-std=c++14'] elif os.name == "nt": c_flags = ['/O2', '/std:c++17'] # find cl.exe def find_cl_path(): import glob for program_files in [r"C:\\Program Files (x86)", r"C:\\Program Files"]: for edition in ["Enterprise", "Professional", "BuildTools", "Community"]: paths = sorted(glob.glob(r"%s\\Microsoft Visual Studio\\*\\%s\\VC\\Tools\\MSVC\\*\\bin\\Hostx64\\x64" % (program_files, edition)), reverse=True) if paths: return paths[0] # If cl.exe is not on path, try to find it. if os.system("where cl.exe >nul 2>nul") != 0: cl_path = find_cl_path() if cl_path is None: raise RuntimeError("Could not locate a supported Microsoft Visual C++ installation") os.environ["PATH"] += ";" + cl_path setup( name='shencoder', # package name, import this to use python API ext_modules=[ CUDAExtension( name='_shencoder', # extension name, import this to use CUDA API sources=[os.path.join(_src_path, 'src', f) for f in [ 'shencoder.cu', 'bindings.cpp', ]], extra_compile_args={ 'cxx': c_flags, 'nvcc': nvcc_flags, } ), ], cmdclass={ 'build_ext': BuildExtension, } ) ================================================ FILE: stable-dreamfusion/shencoder/sphere_harmonics.py ================================================ import numpy as np import torch import torch.nn as nn from torch.autograd import Function from torch.autograd.function import once_differentiable from torch.cuda.amp import custom_bwd, custom_fwd try: import _shencoder as _backend except ImportError: from .backend import _backend class _sh_encoder(Function): @staticmethod @custom_fwd(cast_inputs=torch.float32) # force float32 for better precision def forward(ctx, inputs, degree, calc_grad_inputs=False): # inputs: [B, input_dim], float in [-1, 1] # RETURN: [B, F], float inputs = inputs.contiguous() B, input_dim = inputs.shape # batch size, coord dim output_dim = degree ** 2 outputs = torch.empty(B, output_dim, dtype=inputs.dtype, device=inputs.device) if calc_grad_inputs: dy_dx = torch.empty(B, input_dim * output_dim, dtype=inputs.dtype, device=inputs.device) else: dy_dx = None _backend.sh_encode_forward(inputs, outputs, B, input_dim, degree, dy_dx) ctx.save_for_backward(inputs, dy_dx) ctx.dims = [B, input_dim, degree] return outputs @staticmethod #@once_differentiable @custom_bwd def backward(ctx, grad): # grad: [B, C * C] inputs, dy_dx = ctx.saved_tensors if dy_dx is not None: grad = grad.contiguous() B, input_dim, degree = ctx.dims grad_inputs = torch.zeros_like(inputs) _backend.sh_encode_backward(grad, inputs, B, input_dim, degree, dy_dx, grad_inputs) return grad_inputs, None, None else: return None, None, None sh_encode = _sh_encoder.apply class SHEncoder(nn.Module): def __init__(self, input_dim=3, degree=4): super().__init__() self.input_dim = input_dim # coord dims, must be 3 self.degree = degree # 0 ~ 4 self.output_dim = degree ** 2 assert self.input_dim == 3, "SH encoder only support input dim == 3" assert self.degree > 0 and self.degree <= 8, "SH encoder only supports degree in [1, 8]" def __repr__(self): return f"SHEncoder: input_dim={self.input_dim} degree={self.degree}" def forward(self, inputs, size=1): # inputs: [..., input_dim], normalized real world positions in [-size, size] # return: [..., degree^2] inputs = inputs / size # [-1, 1] prefix_shape = list(inputs.shape[:-1]) inputs = inputs.reshape(-1, self.input_dim) outputs = sh_encode(inputs, self.degree, inputs.requires_grad) outputs = outputs.reshape(prefix_shape + [self.output_dim]) return outputs ================================================ FILE: stable-dreamfusion/shencoder/src/bindings.cpp ================================================ #include #include "shencoder.h" PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("sh_encode_forward", &sh_encode_forward, "SH encode forward (CUDA)"); m.def("sh_encode_backward", &sh_encode_backward, "SH encode backward (CUDA)"); } ================================================ FILE: stable-dreamfusion/shencoder/src/shencoder.cu ================================================ #include #include #include #include #include #include #include #include #include #define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") #define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be a contiguous tensor") #define CHECK_IS_INT(x) TORCH_CHECK(x.scalar_type() == at::ScalarType::Int, #x " must be an int tensor") #define CHECK_IS_FLOATING(x) TORCH_CHECK(x.scalar_type() == at::ScalarType::Float || x.scalar_type() == at::ScalarType::Half || x.scalar_type() == at::ScalarType::Double, #x " must be a floating tensor") template __host__ __device__ T div_round_up(T val, T divisor) { return (val + divisor - 1) / divisor; } template __global__ void kernel_sh( const scalar_t * __restrict__ inputs, scalar_t * outputs, uint32_t B, uint32_t D, uint32_t C, scalar_t * dy_dx ) { const uint32_t b = threadIdx.x + blockIdx.x * blockDim.x; if (b >= B) return; const uint32_t C2 = C * C; // locate inputs += b * D; outputs += b * C2; scalar_t x = inputs[0], y = inputs[1], z = inputs[2]; scalar_t xy=x*y, xz=x*z, yz=y*z, x2=x*x, y2=y*y, z2=z*z, xyz=xy*z; scalar_t x4=x2*x2, y4=y2*y2, z4=z2*z2; scalar_t x6=x4*x2, y6=y4*y2, z6=z4*z2; auto write_sh = [&]() { outputs[0] = 0.28209479177387814f ; // 1/(2*sqrt(pi)) if (C <= 1) { return; } outputs[1] = -0.48860251190291987f*y ; // -sqrt(3)*y/(2*sqrt(pi)) outputs[2] = 0.48860251190291987f*z ; // sqrt(3)*z/(2*sqrt(pi)) outputs[3] = -0.48860251190291987f*x ; // -sqrt(3)*x/(2*sqrt(pi)) if (C <= 2) { return; } outputs[4] = 1.0925484305920792f*xy ; // sqrt(15)*xy/(2*sqrt(pi)) outputs[5] = -1.0925484305920792f*yz ; // -sqrt(15)*yz/(2*sqrt(pi)) outputs[6] = 0.94617469575755997f*z2 - 0.31539156525251999f ; // sqrt(5)*(3*z2 - 1)/(4*sqrt(pi)) outputs[7] = -1.0925484305920792f*xz ; // -sqrt(15)*xz/(2*sqrt(pi)) outputs[8] = 0.54627421529603959f*x2 - 0.54627421529603959f*y2 ; // sqrt(15)*(x2 - y2)/(4*sqrt(pi)) if (C <= 3) { return; } outputs[9] = 0.59004358992664352f*y*(-3.0f*x2 + y2) ; // sqrt(70)*y*(-3*x2 + y2)/(8*sqrt(pi)) outputs[10] = 2.8906114426405538f*xy*z ; // sqrt(105)*xy*z/(2*sqrt(pi)) outputs[11] = 0.45704579946446572f*y*(1.0f - 5.0f*z2) ; // sqrt(42)*y*(1 - 5*z2)/(8*sqrt(pi)) outputs[12] = 0.3731763325901154f*z*(5.0f*z2 - 3.0f) ; // sqrt(7)*z*(5*z2 - 3)/(4*sqrt(pi)) outputs[13] = 0.45704579946446572f*x*(1.0f - 5.0f*z2) ; // sqrt(42)*x*(1 - 5*z2)/(8*sqrt(pi)) outputs[14] = 1.4453057213202769f*z*(x2 - y2) ; // sqrt(105)*z*(x2 - y2)/(4*sqrt(pi)) outputs[15] = 0.59004358992664352f*x*(-x2 + 3.0f*y2) ; // sqrt(70)*x*(-x2 + 3*y2)/(8*sqrt(pi)) if (C <= 4) { return; } outputs[16] = 2.5033429417967046f*xy*(x2 - y2) ; // 3*sqrt(35)*xy*(x2 - y2)/(4*sqrt(pi)) outputs[17] = 1.7701307697799304f*yz*(-3.0f*x2 + y2) ; // 3*sqrt(70)*yz*(-3*x2 + y2)/(8*sqrt(pi)) outputs[18] = 0.94617469575756008f*xy*(7.0f*z2 - 1.0f) ; // 3*sqrt(5)*xy*(7*z2 - 1)/(4*sqrt(pi)) outputs[19] = 0.66904654355728921f*yz*(3.0f - 7.0f*z2) ; // 3*sqrt(10)*yz*(3 - 7*z2)/(8*sqrt(pi)) outputs[20] = -3.1735664074561294f*z2 + 3.7024941420321507f*z4 + 0.31735664074561293f ; // 3*(-30*z2 + 35*z4 + 3)/(16*sqrt(pi)) outputs[21] = 0.66904654355728921f*xz*(3.0f - 7.0f*z2) ; // 3*sqrt(10)*xz*(3 - 7*z2)/(8*sqrt(pi)) outputs[22] = 0.47308734787878004f*(x2 - y2)*(7.0f*z2 - 1.0f) ; // 3*sqrt(5)*(x2 - y2)*(7*z2 - 1)/(8*sqrt(pi)) outputs[23] = 1.7701307697799304f*xz*(-x2 + 3.0f*y2) ; // 3*sqrt(70)*xz*(-x2 + 3*y2)/(8*sqrt(pi)) outputs[24] = -3.7550144126950569f*x2*y2 + 0.62583573544917614f*x4 + 0.62583573544917614f*y4 ; // 3*sqrt(35)*(-6*x2*y2 + x4 + y4)/(16*sqrt(pi)) if (C <= 5) { return; } outputs[25] = 0.65638205684017015f*y*(10.0f*x2*y2 - 5.0f*x4 - y4) ; // 3*sqrt(154)*y*(10*x2*y2 - 5*x4 - y4)/(32*sqrt(pi)) outputs[26] = 8.3026492595241645f*xy*z*(x2 - y2) ; // 3*sqrt(385)*xy*z*(x2 - y2)/(4*sqrt(pi)) outputs[27] = -0.48923829943525038f*y*(3.0f*x2 - y2)*(9.0f*z2 - 1.0f) ; // -sqrt(770)*y*(3*x2 - y2)*(9*z2 - 1)/(32*sqrt(pi)) outputs[28] = 4.7935367849733241f*xy*z*(3.0f*z2 - 1.0f) ; // sqrt(1155)*xy*z*(3*z2 - 1)/(4*sqrt(pi)) outputs[29] = 0.45294665119569694f*y*(14.0f*z2 - 21.0f*z4 - 1.0f) ; // sqrt(165)*y*(14*z2 - 21*z4 - 1)/(16*sqrt(pi)) outputs[30] = 0.1169503224534236f*z*(-70.0f*z2 + 63.0f*z4 + 15.0f) ; // sqrt(11)*z*(-70*z2 + 63*z4 + 15)/(16*sqrt(pi)) outputs[31] = 0.45294665119569694f*x*(14.0f*z2 - 21.0f*z4 - 1.0f) ; // sqrt(165)*x*(14*z2 - 21*z4 - 1)/(16*sqrt(pi)) outputs[32] = 2.3967683924866621f*z*(x2 - y2)*(3.0f*z2 - 1.0f) ; // sqrt(1155)*z*(x2 - y2)*(3*z2 - 1)/(8*sqrt(pi)) outputs[33] = -0.48923829943525038f*x*(x2 - 3.0f*y2)*(9.0f*z2 - 1.0f) ; // -sqrt(770)*x*(x2 - 3*y2)*(9*z2 - 1)/(32*sqrt(pi)) outputs[34] = 2.0756623148810411f*z*(-6.0f*x2*y2 + x4 + y4) ; // 3*sqrt(385)*z*(-6*x2*y2 + x4 + y4)/(16*sqrt(pi)) outputs[35] = 0.65638205684017015f*x*(10.0f*x2*y2 - x4 - 5.0f*y4) ; // 3*sqrt(154)*x*(10*x2*y2 - x4 - 5*y4)/(32*sqrt(pi)) if (C <= 6) { return; } outputs[36] = 1.3663682103838286f*xy*(-10.0f*x2*y2 + 3.0f*x4 + 3.0f*y4) ; // sqrt(6006)*xy*(-10*x2*y2 + 3*x4 + 3*y4)/(32*sqrt(pi)) outputs[37] = 2.3666191622317521f*yz*(10.0f*x2*y2 - 5.0f*x4 - y4) ; // 3*sqrt(2002)*yz*(10*x2*y2 - 5*x4 - y4)/(32*sqrt(pi)) outputs[38] = 2.0182596029148963f*xy*(x2 - y2)*(11.0f*z2 - 1.0f) ; // 3*sqrt(91)*xy*(x2 - y2)*(11*z2 - 1)/(8*sqrt(pi)) outputs[39] = -0.92120525951492349f*yz*(3.0f*x2 - y2)*(11.0f*z2 - 3.0f) ; // -sqrt(2730)*yz*(3*x2 - y2)*(11*z2 - 3)/(32*sqrt(pi)) outputs[40] = 0.92120525951492349f*xy*(-18.0f*z2 + 33.0f*z4 + 1.0f) ; // sqrt(2730)*xy*(-18*z2 + 33*z4 + 1)/(32*sqrt(pi)) outputs[41] = 0.58262136251873131f*yz*(30.0f*z2 - 33.0f*z4 - 5.0f) ; // sqrt(273)*yz*(30*z2 - 33*z4 - 5)/(16*sqrt(pi)) outputs[42] = 6.6747662381009842f*z2 - 20.024298714302954f*z4 + 14.684485723822165f*z6 - 0.31784601133814211f ; // sqrt(13)*(105*z2 - 315*z4 + 231*z6 - 5)/(32*sqrt(pi)) outputs[43] = 0.58262136251873131f*xz*(30.0f*z2 - 33.0f*z4 - 5.0f) ; // sqrt(273)*xz*(30*z2 - 33*z4 - 5)/(16*sqrt(pi)) outputs[44] = 0.46060262975746175f*(x2 - y2)*(11.0f*z2*(3.0f*z2 - 1.0f) - 7.0f*z2 + 1.0f) ; // sqrt(2730)*(x2 - y2)*(11*z2*(3*z2 - 1) - 7*z2 + 1)/(64*sqrt(pi)) outputs[45] = -0.92120525951492349f*xz*(x2 - 3.0f*y2)*(11.0f*z2 - 3.0f) ; // -sqrt(2730)*xz*(x2 - 3*y2)*(11*z2 - 3)/(32*sqrt(pi)) outputs[46] = 0.50456490072872406f*(11.0f*z2 - 1.0f)*(-6.0f*x2*y2 + x4 + y4) ; // 3*sqrt(91)*(11*z2 - 1)*(-6*x2*y2 + x4 + y4)/(32*sqrt(pi)) outputs[47] = 2.3666191622317521f*xz*(10.0f*x2*y2 - x4 - 5.0f*y4) ; // 3*sqrt(2002)*xz*(10*x2*y2 - x4 - 5*y4)/(32*sqrt(pi)) outputs[48] = 10.247761577878714f*x2*y4 - 10.247761577878714f*x4*y2 + 0.6831841051919143f*x6 - 0.6831841051919143f*y6 ; // sqrt(6006)*(15*x2*y4 - 15*x4*y2 + x6 - y6)/(64*sqrt(pi)) if (C <= 7) { return; } outputs[49] = 0.70716273252459627f*y*(-21.0f*x2*y4 + 35.0f*x4*y2 - 7.0f*x6 + y6) ; // 3*sqrt(715)*y*(-21*x2*y4 + 35*x4*y2 - 7*x6 + y6)/(64*sqrt(pi)) outputs[50] = 5.2919213236038001f*xy*z*(-10.0f*x2*y2 + 3.0f*x4 + 3.0f*y4) ; // 3*sqrt(10010)*xy*z*(-10*x2*y2 + 3*x4 + 3*y4)/(32*sqrt(pi)) outputs[51] = -0.51891557872026028f*y*(13.0f*z2 - 1.0f)*(-10.0f*x2*y2 + 5.0f*x4 + y4) ; // -3*sqrt(385)*y*(13*z2 - 1)*(-10*x2*y2 + 5*x4 + y4)/(64*sqrt(pi)) outputs[52] = 4.1513246297620823f*xy*z*(x2 - y2)*(13.0f*z2 - 3.0f) ; // 3*sqrt(385)*xy*z*(x2 - y2)*(13*z2 - 3)/(8*sqrt(pi)) outputs[53] = -0.15645893386229404f*y*(3.0f*x2 - y2)*(13.0f*z2*(11.0f*z2 - 3.0f) - 27.0f*z2 + 3.0f) ; // -3*sqrt(35)*y*(3*x2 - y2)*(13*z2*(11*z2 - 3) - 27*z2 + 3)/(64*sqrt(pi)) outputs[54] = 0.44253269244498261f*xy*z*(-110.0f*z2 + 143.0f*z4 + 15.0f) ; // 3*sqrt(70)*xy*z*(-110*z2 + 143*z4 + 15)/(32*sqrt(pi)) outputs[55] = 0.090331607582517306f*y*(-135.0f*z2 + 495.0f*z4 - 429.0f*z6 + 5.0f) ; // sqrt(105)*y*(-135*z2 + 495*z4 - 429*z6 + 5)/(64*sqrt(pi)) outputs[56] = 0.068284276912004949f*z*(315.0f*z2 - 693.0f*z4 + 429.0f*z6 - 35.0f) ; // sqrt(15)*z*(315*z2 - 693*z4 + 429*z6 - 35)/(32*sqrt(pi)) outputs[57] = 0.090331607582517306f*x*(-135.0f*z2 + 495.0f*z4 - 429.0f*z6 + 5.0f) ; // sqrt(105)*x*(-135*z2 + 495*z4 - 429*z6 + 5)/(64*sqrt(pi)) outputs[58] = 0.07375544874083044f*z*(x2 - y2)*(143.0f*z2*(3.0f*z2 - 1.0f) - 187.0f*z2 + 45.0f) ; // sqrt(70)*z*(x2 - y2)*(143*z2*(3*z2 - 1) - 187*z2 + 45)/(64*sqrt(pi)) outputs[59] = -0.15645893386229404f*x*(x2 - 3.0f*y2)*(13.0f*z2*(11.0f*z2 - 3.0f) - 27.0f*z2 + 3.0f) ; // -3*sqrt(35)*x*(x2 - 3*y2)*(13*z2*(11*z2 - 3) - 27*z2 + 3)/(64*sqrt(pi)) outputs[60] = 1.0378311574405206f*z*(13.0f*z2 - 3.0f)*(-6.0f*x2*y2 + x4 + y4) ; // 3*sqrt(385)*z*(13*z2 - 3)*(-6*x2*y2 + x4 + y4)/(32*sqrt(pi)) outputs[61] = -0.51891557872026028f*x*(13.0f*z2 - 1.0f)*(-10.0f*x2*y2 + x4 + 5.0f*y4) ; // -3*sqrt(385)*x*(13*z2 - 1)*(-10*x2*y2 + x4 + 5*y4)/(64*sqrt(pi)) outputs[62] = 2.6459606618019f*z*(15.0f*x2*y4 - 15.0f*x4*y2 + x6 - y6) ; // 3*sqrt(10010)*z*(15*x2*y4 - 15*x4*y2 + x6 - y6)/(64*sqrt(pi)) outputs[63] = 0.70716273252459627f*x*(-35.0f*x2*y4 + 21.0f*x4*y2 - x6 + 7.0f*y6) ; // 3*sqrt(715)*x*(-35*x2*y4 + 21*x4*y2 - x6 + 7*y6)/(64*sqrt(pi)) }; write_sh(); if (dy_dx) { scalar_t *dx = dy_dx + b * D * C2; scalar_t *dy = dx + C2; scalar_t *dz = dy + C2; auto write_sh_dx = [&]() { dx[0] = 0.0f ; // 0 if (C <= 1) { return; } dx[1] = 0.0f ; // 0 dx[2] = 0.0f ; // 0 dx[3] = -0.48860251190291992f ; // -sqrt(3)/(2*sqrt(pi)) if (C <= 2) { return; } dx[4] = 1.0925484305920792f*y ; // sqrt(15)*y/(2*sqrt(pi)) dx[5] = 0.0f ; // 0 dx[6] = 0.0f ; // 0 dx[7] = -1.0925484305920792f*z ; // -sqrt(15)*z/(2*sqrt(pi)) dx[8] = 1.0925484305920792f*x ; // sqrt(15)*x/(2*sqrt(pi)) if (C <= 3) { return; } dx[9] = -3.5402615395598609f*xy ; // -3*sqrt(70)*xy/(4*sqrt(pi)) dx[10] = 2.8906114426405538f*yz ; // sqrt(105)*yz/(2*sqrt(pi)) dx[11] = 0.0f ; // 0 dx[12] = 0.0f ; // 0 dx[13] = 0.45704579946446572f - 2.2852289973223288f*z2 ; // sqrt(42)*(1 - 5*z2)/(8*sqrt(pi)) dx[14] = 2.8906114426405538f*xz ; // sqrt(105)*xz/(2*sqrt(pi)) dx[15] = -1.7701307697799304f*x2 + 1.7701307697799304f*y2 ; // 3*sqrt(70)*(-x2 + y2)/(8*sqrt(pi)) if (C <= 4) { return; } dx[16] = 2.5033429417967046f*y*(3.0f*x2 - y2) ; // 3*sqrt(35)*y*(3*x2 - y2)/(4*sqrt(pi)) dx[17] = -10.620784618679583f*xy*z ; // -9*sqrt(70)*xy*z/(4*sqrt(pi)) dx[18] = 0.94617469575756008f*y*(7.0f*z2 - 1.0f) ; // 3*sqrt(5)*y*(7*z2 - 1)/(4*sqrt(pi)) dx[19] = 0.0f ; // 0 dx[20] = 0.0f ; // 0 dx[21] = 0.66904654355728921f*z*(3.0f - 7.0f*z2) ; // 3*sqrt(10)*z*(3 - 7*z2)/(8*sqrt(pi)) dx[22] = 0.94617469575756008f*x*(7.0f*z2 - 1.0f) ; // 3*sqrt(5)*x*(7*z2 - 1)/(4*sqrt(pi)) dx[23] = 5.3103923093397913f*z*(-x2 + y2) ; // 9*sqrt(70)*z*(-x2 + y2)/(8*sqrt(pi)) dx[24] = 2.5033429417967046f*x*(x2 - 3.0f*y2) ; // 3*sqrt(35)*x*(x2 - 3*y2)/(4*sqrt(pi)) if (C <= 5) { return; } dx[25] = 13.127641136803401f*xy*(-x2 + y2) ; // 15*sqrt(154)*xy*(-x2 + y2)/(8*sqrt(pi)) dx[26] = 8.3026492595241645f*yz*(3.0f*x2 - y2) ; // 3*sqrt(385)*yz*(3*x2 - y2)/(4*sqrt(pi)) dx[27] = 2.9354297966115022f*xy*(1.0f - 9.0f*z2) ; // 3*sqrt(770)*xy*(1 - 9*z2)/(16*sqrt(pi)) dx[28] = 4.7935367849733241f*yz*(3.0f*z2 - 1.0f) ; // sqrt(1155)*yz*(3*z2 - 1)/(4*sqrt(pi)) dx[29] = 0.0f ; // 0 dx[30] = 0.0f ; // 0 dx[31] = 6.3412531167397574f*z2 - 9.5118796751096362f*z4 - 0.45294665119569694f ; // sqrt(165)*(14*z2 - 21*z4 - 1)/(16*sqrt(pi)) dx[32] = 4.7935367849733241f*xz*(3.0f*z2 - 1.0f) ; // sqrt(1155)*xz*(3*z2 - 1)/(4*sqrt(pi)) dx[33] = -13.209434084751759f*x2*z2 + 1.4677148983057511f*x2 + 13.209434084751759f*y2*z2 - 1.4677148983057511f*y2 ; // 3*sqrt(770)*(-9*x2*z2 + x2 + 9*y2*z2 - y2)/(32*sqrt(pi)) dx[34] = 8.3026492595241645f*xz*(x2 - 3.0f*y2) ; // 3*sqrt(385)*xz*(x2 - 3*y2)/(4*sqrt(pi)) dx[35] = 19.6914617052051f*x2*y2 - 3.2819102842008503f*x4 - 3.2819102842008503f*y4 ; // 15*sqrt(154)*(6*x2*y2 - x4 - y4)/(32*sqrt(pi)) if (C <= 6) { return; } dx[36] = 4.0991046311514854f*y*(-10.0f*x2*y2 + 5.0f*x4 + y4) ; // 3*sqrt(6006)*y*(-10*x2*y2 + 5*x4 + y4)/(32*sqrt(pi)) dx[37] = 47.332383244635047f*xy*z*(-x2 + y2) ; // 15*sqrt(2002)*xy*z*(-x2 + y2)/(8*sqrt(pi)) dx[38] = 2.0182596029148963f*y*(3.0f*x2 - y2)*(11.0f*z2 - 1.0f) ; // 3*sqrt(91)*y*(3*x2 - y2)*(11*z2 - 1)/(8*sqrt(pi)) dx[39] = 5.5272315570895412f*xy*z*(3.0f - 11.0f*z2) ; // 3*sqrt(2730)*xy*z*(3 - 11*z2)/(16*sqrt(pi)) dx[40] = 0.92120525951492349f*y*(-18.0f*z2 + 33.0f*z4 + 1.0f) ; // sqrt(2730)*y*(-18*z2 + 33*z4 + 1)/(32*sqrt(pi)) dx[41] = 0.0f ; // 0 dx[42] = 0.0f ; // 0 dx[43] = 0.58262136251873131f*z*(30.0f*z2 - 33.0f*z4 - 5.0f) ; // sqrt(273)*z*(30*z2 - 33*z4 - 5)/(16*sqrt(pi)) dx[44] = 0.92120525951492349f*x*(-18.0f*z2 + 33.0f*z4 + 1.0f) ; // sqrt(2730)*x*(-18*z2 + 33*z4 + 1)/(32*sqrt(pi)) dx[45] = -2.7636157785447706f*z*(x2 - y2)*(11.0f*z2 - 3.0f) ; // -3*sqrt(2730)*z*(x2 - y2)*(11*z2 - 3)/(32*sqrt(pi)) dx[46] = 2.0182596029148963f*x*(x2 - 3.0f*y2)*(11.0f*z2 - 1.0f) ; // 3*sqrt(91)*x*(x2 - 3*y2)*(11*z2 - 1)/(8*sqrt(pi)) dx[47] = 11.833095811158762f*z*(6.0f*x2*y2 - x4 - y4) ; // 15*sqrt(2002)*z*(6*x2*y2 - x4 - y4)/(32*sqrt(pi)) dx[48] = 4.0991046311514854f*x*(-10.0f*x2*y2 + x4 + 5.0f*y4) ; // 3*sqrt(6006)*x*(-10*x2*y2 + x4 + 5*y4)/(32*sqrt(pi)) if (C <= 7) { return; } dx[49] = 9.9002782553443485f*xy*(10.0f*x2*y2 - 3.0f*x4 - 3.0f*y4) ; // 21*sqrt(715)*xy*(10*x2*y2 - 3*x4 - 3*y4)/(32*sqrt(pi)) dx[50] = 15.875763970811402f*yz*(-10.0f*x2*y2 + 5.0f*x4 + y4) ; // 9*sqrt(10010)*yz*(-10*x2*y2 + 5*x4 + y4)/(32*sqrt(pi)) dx[51] = -10.378311574405206f*xy*(x2 - y2)*(13.0f*z2 - 1.0f) ; // -15*sqrt(385)*xy*(x2 - y2)*(13*z2 - 1)/(16*sqrt(pi)) dx[52] = 4.1513246297620823f*yz*(3.0f*x2 - y2)*(13.0f*z2 - 3.0f) ; // 3*sqrt(385)*yz*(3*x2 - y2)*(13*z2 - 3)/(8*sqrt(pi)) dx[53] = 0.93875360317376422f*xy*(66.0f*z2 - 143.0f*z4 - 3.0f) ; // 9*sqrt(35)*xy*(66*z2 - 143*z4 - 3)/(32*sqrt(pi)) dx[54] = 0.44253269244498261f*yz*(-110.0f*z2 + 143.0f*z4 + 15.0f) ; // 3*sqrt(70)*yz*(-110*z2 + 143*z4 + 15)/(32*sqrt(pi)) dx[55] = 0.0f ; // 0 dx[56] = 0.0f ; // 0 dx[57] = -12.194767023639836f*z2 + 44.714145753346067f*z4 - 38.752259652899923f*z6 + 0.45165803791258652f ; // sqrt(105)*(-135*z2 + 495*z4 - 429*z6 + 5)/(64*sqrt(pi)) dx[58] = 0.44253269244498261f*xz*(-110.0f*z2 + 143.0f*z4 + 15.0f) ; // 3*sqrt(70)*xz*(-110*z2 + 143*z4 + 15)/(32*sqrt(pi)) dx[59] = 30.97886890473422f*x2*z2 - 67.120882626924143f*x2*z4 - 1.4081304047606462f*x2 - 30.97886890473422f*y2*z2 + 67.120882626924143f*y2*z4 + 1.4081304047606462f*y2 ; // 9*sqrt(35)*(66*x2*z2 - 143*x2*z4 - 3*x2 - 66*y2*z2 + 143*y2*z4 + 3*y2)/(64*sqrt(pi)) dx[60] = 4.1513246297620823f*xz*(x2 - 3.0f*y2)*(13.0f*z2 - 3.0f) ; // 3*sqrt(385)*xz*(x2 - 3*y2)*(13*z2 - 3)/(8*sqrt(pi)) dx[61] = -0.51891557872026028f*(13.0f*z2 - 1.0f)*(-10.0f*x2*y2 + 4.0f*x2*(x2 - 5.0f*y2) + x4 + 5.0f*y4) ; // -3*sqrt(385)*(13*z2 - 1)*(-10*x2*y2 + 4*x2*(x2 - 5*y2) + x4 + 5*y4)/(64*sqrt(pi)) dx[62] = 15.875763970811402f*xz*(-10.0f*x2*y2 + x4 + 5.0f*y4) ; // 9*sqrt(10010)*xz*(-10*x2*y2 + x4 + 5*y4)/(32*sqrt(pi)) dx[63] = -74.252086915082614f*x2*y4 + 74.252086915082614f*x4*y2 - 4.9501391276721742f*x6 + 4.9501391276721742f*y6 ; // 21*sqrt(715)*(-15*x2*y4 + 15*x4*y2 - x6 + y6)/(64*sqrt(pi)) }; auto write_sh_dy = [&]() { dy[0] = 0.0f ; // 0 if (C <= 1) { return; } dy[1] = -0.48860251190291992f ; // -sqrt(3)/(2*sqrt(pi)) dy[2] = 0.0f ; // 0 dy[3] = 0.0f ; // 0 if (C <= 2) { return; } dy[4] = 1.0925484305920792f*x ; // sqrt(15)*x/(2*sqrt(pi)) dy[5] = -1.0925484305920792f*z ; // -sqrt(15)*z/(2*sqrt(pi)) dy[6] = 0.0f ; // 0 dy[7] = 0.0f ; // 0 dy[8] = -1.0925484305920792f*y ; // -sqrt(15)*y/(2*sqrt(pi)) if (C <= 3) { return; } dy[9] = -1.7701307697799304f*x2 + 1.7701307697799304f*y2 ; // 3*sqrt(70)*(-x2 + y2)/(8*sqrt(pi)) dy[10] = 2.8906114426405538f*xz ; // sqrt(105)*xz/(2*sqrt(pi)) dy[11] = 0.45704579946446572f - 2.2852289973223288f*z2 ; // sqrt(42)*(1 - 5*z2)/(8*sqrt(pi)) dy[12] = 0.0f ; // 0 dy[13] = 0.0f ; // 0 dy[14] = -2.8906114426405538f*yz ; // -sqrt(105)*yz/(2*sqrt(pi)) dy[15] = 3.5402615395598609f*xy ; // 3*sqrt(70)*xy/(4*sqrt(pi)) if (C <= 4) { return; } dy[16] = 2.5033429417967046f*x*(x2 - 3.0f*y2) ; // 3*sqrt(35)*x*(x2 - 3*y2)/(4*sqrt(pi)) dy[17] = 5.3103923093397913f*z*(-x2 + y2) ; // 9*sqrt(70)*z*(-x2 + y2)/(8*sqrt(pi)) dy[18] = 0.94617469575756008f*x*(7.0f*z2 - 1.0f) ; // 3*sqrt(5)*x*(7*z2 - 1)/(4*sqrt(pi)) dy[19] = 0.66904654355728921f*z*(3.0f - 7.0f*z2) ; // 3*sqrt(10)*z*(3 - 7*z2)/(8*sqrt(pi)) dy[20] = 0.0f ; // 0 dy[21] = 0.0f ; // 0 dy[22] = 0.94617469575756008f*y*(1.0f - 7.0f*z2) ; // 3*sqrt(5)*y*(1 - 7*z2)/(4*sqrt(pi)) dy[23] = 10.620784618679583f*xy*z ; // 9*sqrt(70)*xy*z/(4*sqrt(pi)) dy[24] = 2.5033429417967046f*y*(-3.0f*x2 + y2) ; // 3*sqrt(35)*y*(-3*x2 + y2)/(4*sqrt(pi)) if (C <= 5) { return; } dy[25] = 19.6914617052051f*x2*y2 - 3.2819102842008503f*x4 - 3.2819102842008503f*y4 ; // 15*sqrt(154)*(6*x2*y2 - x4 - y4)/(32*sqrt(pi)) dy[26] = 8.3026492595241645f*xz*(x2 - 3.0f*y2) ; // 3*sqrt(385)*xz*(x2 - 3*y2)/(4*sqrt(pi)) dy[27] = -1.4677148983057511f*(x2 - y2)*(9.0f*z2 - 1.0f) ; // -3*sqrt(770)*(x2 - y2)*(9*z2 - 1)/(32*sqrt(pi)) dy[28] = 4.7935367849733241f*xz*(3.0f*z2 - 1.0f) ; // sqrt(1155)*xz*(3*z2 - 1)/(4*sqrt(pi)) dy[29] = 6.3412531167397574f*z2 - 9.5118796751096362f*z4 - 0.45294665119569694f ; // sqrt(165)*(14*z2 - 21*z4 - 1)/(16*sqrt(pi)) dy[30] = 0.0f ; // 0 dy[31] = 0.0f ; // 0 dy[32] = 4.7935367849733241f*yz*(1.0f - 3.0f*z2) ; // sqrt(1155)*yz*(1 - 3*z2)/(4*sqrt(pi)) dy[33] = 2.9354297966115022f*xy*(9.0f*z2 - 1.0f) ; // 3*sqrt(770)*xy*(9*z2 - 1)/(16*sqrt(pi)) dy[34] = 8.3026492595241645f*yz*(-3.0f*x2 + y2) ; // 3*sqrt(385)*yz*(-3*x2 + y2)/(4*sqrt(pi)) dy[35] = 13.127641136803401f*xy*(x2 - y2) ; // 15*sqrt(154)*xy*(x2 - y2)/(8*sqrt(pi)) if (C <= 6) { return; } dy[36] = 4.0991046311514854f*x*(-10.0f*x2*y2 + x4 + 5.0f*y4) ; // 3*sqrt(6006)*x*(-10*x2*y2 + x4 + 5*y4)/(32*sqrt(pi)) dy[37] = 11.833095811158762f*z*(6.0f*x2*y2 - x4 - y4) ; // 15*sqrt(2002)*z*(6*x2*y2 - x4 - y4)/(32*sqrt(pi)) dy[38] = 2.0182596029148963f*x*(x2 - 3.0f*y2)*(11.0f*z2 - 1.0f) ; // 3*sqrt(91)*x*(x2 - 3*y2)*(11*z2 - 1)/(8*sqrt(pi)) dy[39] = -2.7636157785447706f*z*(x2 - y2)*(11.0f*z2 - 3.0f) ; // -3*sqrt(2730)*z*(x2 - y2)*(11*z2 - 3)/(32*sqrt(pi)) dy[40] = 0.92120525951492349f*x*(-18.0f*z2 + 33.0f*z4 + 1.0f) ; // sqrt(2730)*x*(-18*z2 + 33*z4 + 1)/(32*sqrt(pi)) dy[41] = 0.58262136251873131f*z*(30.0f*z2 - 33.0f*z4 - 5.0f) ; // sqrt(273)*z*(30*z2 - 33*z4 - 5)/(16*sqrt(pi)) dy[42] = 0.0f ; // 0 dy[43] = 0.0f ; // 0 dy[44] = 0.92120525951492349f*y*(18.0f*z2 - 33.0f*z4 - 1.0f) ; // sqrt(2730)*y*(18*z2 - 33*z4 - 1)/(32*sqrt(pi)) dy[45] = 5.5272315570895412f*xy*z*(11.0f*z2 - 3.0f) ; // 3*sqrt(2730)*xy*z*(11*z2 - 3)/(16*sqrt(pi)) dy[46] = -2.0182596029148963f*y*(3.0f*x2 - y2)*(11.0f*z2 - 1.0f) ; // -3*sqrt(91)*y*(3*x2 - y2)*(11*z2 - 1)/(8*sqrt(pi)) dy[47] = 47.332383244635047f*xy*z*(x2 - y2) ; // 15*sqrt(2002)*xy*z*(x2 - y2)/(8*sqrt(pi)) dy[48] = 4.0991046311514854f*y*(10.0f*x2*y2 - 5.0f*x4 - y4) ; // 3*sqrt(6006)*y*(10*x2*y2 - 5*x4 - y4)/(32*sqrt(pi)) if (C <= 7) { return; } dy[49] = -74.252086915082614f*x2*y4 + 74.252086915082614f*x4*y2 - 4.9501391276721742f*x6 + 4.9501391276721742f*y6 ; // 21*sqrt(715)*(-15*x2*y4 + 15*x4*y2 - x6 + y6)/(64*sqrt(pi)) dy[50] = 15.875763970811402f*xz*(-10.0f*x2*y2 + x4 + 5.0f*y4) ; // 9*sqrt(10010)*xz*(-10*x2*y2 + x4 + 5*y4)/(32*sqrt(pi)) dy[51] = 0.51891557872026028f*(13.0f*z2 - 1.0f)*(10.0f*x2*y2 - 5.0f*x4 + 4.0f*y2*(5.0f*x2 - y2) - y4) ; // 3*sqrt(385)*(13*z2 - 1)*(10*x2*y2 - 5*x4 + 4*y2*(5*x2 - y2) - y4)/(64*sqrt(pi)) dy[52] = 4.1513246297620823f*xz*(x2 - 3.0f*y2)*(13.0f*z2 - 3.0f) ; // 3*sqrt(385)*xz*(x2 - 3*y2)*(13*z2 - 3)/(8*sqrt(pi)) dy[53] = -0.46937680158688211f*(x2 - y2)*(13.0f*z2*(11.0f*z2 - 3.0f) - 27.0f*z2 + 3.0f) ; // -9*sqrt(35)*(x2 - y2)*(13*z2*(11*z2 - 3) - 27*z2 + 3)/(64*sqrt(pi)) dy[54] = 0.44253269244498261f*xz*(-110.0f*z2 + 143.0f*z4 + 15.0f) ; // 3*sqrt(70)*xz*(-110*z2 + 143*z4 + 15)/(32*sqrt(pi)) dy[55] = -12.194767023639836f*z2 + 44.714145753346067f*z4 - 38.752259652899923f*z6 + 0.45165803791258652f ; // sqrt(105)*(-135*z2 + 495*z4 - 429*z6 + 5)/(64*sqrt(pi)) dy[56] = 0.0f ; // 0 dy[57] = 0.0f ; // 0 dy[58] = 0.44253269244498261f*yz*(110.0f*z2 - 143.0f*z4 - 15.0f) ; // 3*sqrt(70)*yz*(110*z2 - 143*z4 - 15)/(32*sqrt(pi)) dy[59] = 0.93875360317376422f*xy*(-66.0f*z2 + 143.0f*z4 + 3.0f) ; // 9*sqrt(35)*xy*(-66*z2 + 143*z4 + 3)/(32*sqrt(pi)) dy[60] = -4.1513246297620823f*yz*(3.0f*x2 - y2)*(13.0f*z2 - 3.0f) ; // -3*sqrt(385)*yz*(3*x2 - y2)*(13*z2 - 3)/(8*sqrt(pi)) dy[61] = 10.378311574405206f*xy*(x2 - y2)*(13.0f*z2 - 1.0f) ; // 15*sqrt(385)*xy*(x2 - y2)*(13*z2 - 1)/(16*sqrt(pi)) dy[62] = 15.875763970811402f*yz*(10.0f*x2*y2 - 5.0f*x4 - y4) ; // 9*sqrt(10010)*yz*(10*x2*y2 - 5*x4 - y4)/(32*sqrt(pi)) dy[63] = 9.9002782553443485f*xy*(-10.0f*x2*y2 + 3.0f*x4 + 3.0f*y4) ; // 21*sqrt(715)*xy*(-10*x2*y2 + 3*x4 + 3*y4)/(32*sqrt(pi)) }; auto write_sh_dz = [&]() { dz[0] = 0.0f ; // 0 if (C <= 1) { return; } dz[1] = 0.0f ; // 0 dz[2] = 0.48860251190291992f ; // sqrt(3)/(2*sqrt(pi)) dz[3] = 0.0f ; // 0 if (C <= 2) { return; } dz[4] = 0.0f ; // 0 dz[5] = -1.0925484305920792f*y ; // -sqrt(15)*y/(2*sqrt(pi)) dz[6] = 1.8923493915151202f*z ; // 3*sqrt(5)*z/(2*sqrt(pi)) dz[7] = -1.0925484305920792f*x ; // -sqrt(15)*x/(2*sqrt(pi)) dz[8] = 0.0f ; // 0 if (C <= 3) { return; } dz[9] = 0.0f ; // 0 dz[10] = 2.8906114426405538f*xy ; // sqrt(105)*xy/(2*sqrt(pi)) dz[11] = -4.5704579946446566f*yz ; // -5*sqrt(42)*yz/(4*sqrt(pi)) dz[12] = 5.597644988851731f*z2 - 1.1195289977703462f ; // 3*sqrt(7)*(5*z2 - 1)/(4*sqrt(pi)) dz[13] = -4.5704579946446566f*xz ; // -5*sqrt(42)*xz/(4*sqrt(pi)) dz[14] = 1.4453057213202769f*x2 - 1.4453057213202769f*y2 ; // sqrt(105)*(x2 - y2)/(4*sqrt(pi)) dz[15] = 0.0f ; // 0 if (C <= 4) { return; } dz[16] = 0.0f ; // 0 dz[17] = 1.7701307697799304f*y*(-3.0f*x2 + y2) ; // 3*sqrt(70)*y*(-3*x2 + y2)/(8*sqrt(pi)) dz[18] = 13.246445740605839f*xy*z ; // 21*sqrt(5)*xy*z/(2*sqrt(pi)) dz[19] = 2.0071396306718676f*y*(1.0f - 7.0f*z2) ; // 9*sqrt(10)*y*(1 - 7*z2)/(8*sqrt(pi)) dz[20] = 14.809976568128603f*pow(z, 3) - 6.3471328149122579f*z ; // (105*z**3 - 45*z)/(4*sqrt(pi)) dz[21] = 2.0071396306718676f*x*(1.0f - 7.0f*z2) ; // 9*sqrt(10)*x*(1 - 7*z2)/(8*sqrt(pi)) dz[22] = 6.6232228703029197f*z*(x2 - y2) ; // 21*sqrt(5)*z*(x2 - y2)/(4*sqrt(pi)) dz[23] = 1.7701307697799304f*x*(-x2 + 3.0f*y2) ; // 3*sqrt(70)*x*(-x2 + 3*y2)/(8*sqrt(pi)) dz[24] = 0.0f ; // 0 if (C <= 5) { return; } dz[25] = 0.0f ; // 0 dz[26] = 8.3026492595241645f*xy*(x2 - y2) ; // 3*sqrt(385)*xy*(x2 - y2)/(4*sqrt(pi)) dz[27] = 8.8062893898345074f*yz*(-3.0f*x2 + y2) ; // 9*sqrt(770)*yz*(-3*x2 + y2)/(16*sqrt(pi)) dz[28] = 4.7935367849733241f*xy*(9.0f*z2 - 1.0f) ; // sqrt(1155)*xy*(9*z2 - 1)/(4*sqrt(pi)) dz[29] = 12.682506233479513f*yz*(1.0f - 3.0f*z2) ; // 7*sqrt(165)*yz*(1 - 3*z2)/(4*sqrt(pi)) dz[30] = -24.559567715218954f*z2 + 36.839351572828434f*z4 + 1.754254836801354f ; // 15*sqrt(11)*(-14*z2 + 21*z4 + 1)/(16*sqrt(pi)) dz[31] = 12.682506233479513f*xz*(1.0f - 3.0f*z2) ; // 7*sqrt(165)*xz*(1 - 3*z2)/(4*sqrt(pi)) dz[32] = 2.3967683924866621f*(x2 - y2)*(9.0f*z2 - 1.0f) ; // sqrt(1155)*(x2 - y2)*(9*z2 - 1)/(8*sqrt(pi)) dz[33] = 8.8062893898345074f*xz*(-x2 + 3.0f*y2) ; // 9*sqrt(770)*xz*(-x2 + 3*y2)/(16*sqrt(pi)) dz[34] = -12.453973889286246f*x2*y2 + 2.0756623148810411f*x4 + 2.0756623148810411f*y4 ; // 3*sqrt(385)*(-6*x2*y2 + x4 + y4)/(16*sqrt(pi)) dz[35] = 0.0f ; // 0 if (C <= 6) { return; } dz[36] = 0.0f ; // 0 dz[37] = 2.3666191622317521f*y*(10.0f*x2*y2 - 5.0f*x4 - y4) ; // 3*sqrt(2002)*y*(10*x2*y2 - 5*x4 - y4)/(32*sqrt(pi)) dz[38] = 44.401711264127719f*xy*z*(x2 - y2) ; // 33*sqrt(91)*xy*z*(x2 - y2)/(4*sqrt(pi)) dz[39] = -2.7636157785447706f*y*(3.0f*x2 - y2)*(11.0f*z2 - 1.0f) ; // -3*sqrt(2730)*y*(3*x2 - y2)*(11*z2 - 1)/(32*sqrt(pi)) dz[40] = 11.054463114179082f*xy*z*(11.0f*z2 - 3.0f) ; // 3*sqrt(2730)*xy*z*(11*z2 - 3)/(8*sqrt(pi)) dz[41] = 2.9131068125936568f*y*(18.0f*z2 - 33.0f*z4 - 1.0f) ; // 5*sqrt(273)*y*(18*z2 - 33*z4 - 1)/(16*sqrt(pi)) dz[42] = 2.6699064952403937f*z*(-30.0f*z2 + 33.0f*z4 + 5.0f) ; // 21*sqrt(13)*z*(-30*z2 + 33*z4 + 5)/(16*sqrt(pi)) dz[43] = 2.9131068125936568f*x*(18.0f*z2 - 33.0f*z4 - 1.0f) ; // 5*sqrt(273)*x*(18*z2 - 33*z4 - 1)/(16*sqrt(pi)) dz[44] = 5.5272315570895412f*z*(x2 - y2)*(11.0f*z2 - 3.0f) ; // 3*sqrt(2730)*z*(x2 - y2)*(11*z2 - 3)/(16*sqrt(pi)) dz[45] = -2.7636157785447706f*x*(x2 - 3.0f*y2)*(11.0f*z2 - 1.0f) ; // -3*sqrt(2730)*x*(x2 - 3*y2)*(11*z2 - 1)/(32*sqrt(pi)) dz[46] = 11.10042781603193f*z*(-6.0f*x2*y2 + x4 + y4) ; // 33*sqrt(91)*z*(-6*x2*y2 + x4 + y4)/(16*sqrt(pi)) dz[47] = 2.3666191622317521f*x*(10.0f*x2*y2 - x4 - 5.0f*y4) ; // 3*sqrt(2002)*x*(10*x2*y2 - x4 - 5*y4)/(32*sqrt(pi)) dz[48] = 0.0f ; // 0 if (C <= 7) { return; } dz[49] = 0.0f ; // 0 dz[50] = 5.2919213236038001f*xy*(-10.0f*x2*y2 + 3.0f*x4 + 3.0f*y4) ; // 3*sqrt(10010)*xy*(-10*x2*y2 + 3*x4 + 3*y4)/(32*sqrt(pi)) dz[51] = 13.491805046726766f*yz*(10.0f*x2*y2 - 5.0f*x4 - y4) ; // 39*sqrt(385)*yz*(10*x2*y2 - 5*x4 - y4)/(32*sqrt(pi)) dz[52] = 12.453973889286248f*xy*(x2 - y2)*(13.0f*z2 - 1.0f) ; // 9*sqrt(385)*xy*(x2 - y2)*(13*z2 - 1)/(8*sqrt(pi)) dz[53] = -6.8841930899409371f*yz*(3.0f*x2 - y2)*(13.0f*z2 - 3.0f) ; // -33*sqrt(35)*yz*(3*x2 - y2)*(13*z2 - 3)/(16*sqrt(pi)) dz[54] = 2.2126634622249131f*xy*(-66.0f*z2 + 143.0f*z4 + 3.0f) ; // 15*sqrt(70)*xy*(-66*z2 + 143*z4 + 3)/(32*sqrt(pi)) dz[55] = 1.6259689364853116f*yz*(110.0f*z2 - 143.0f*z4 - 15.0f) ; // 9*sqrt(105)*yz*(110*z2 - 143*z4 - 15)/(32*sqrt(pi)) dz[56] = 64.528641681844675f*z2 - 236.60501950009714f*z4 + 205.05768356675085f*z6 - 2.3899496919201733f ; // 7*sqrt(15)*(135*z2 - 495*z4 + 429*z6 - 5)/(32*sqrt(pi)) dz[57] = 1.6259689364853116f*xz*(110.0f*z2 - 143.0f*z4 - 15.0f) ; // 9*sqrt(105)*xz*(110*z2 - 143*z4 - 15)/(32*sqrt(pi)) dz[58] = 0.07375544874083044f*(x2 - y2)*(143.0f*z2*(3.0f*z2 - 1.0f) + 132.0f*z2*(13.0f*z2 - 5.0f) - 187.0f*z2 + 45.0f) ; // sqrt(70)*(x2 - y2)*(143*z2*(3*z2 - 1) + 132*z2*(13*z2 - 5) - 187*z2 + 45)/(64*sqrt(pi)) dz[59] = -6.8841930899409371f*xz*(x2 - 3.0f*y2)*(13.0f*z2 - 3.0f) ; // -33*sqrt(35)*xz*(x2 - 3*y2)*(13*z2 - 3)/(16*sqrt(pi)) dz[60] = 3.1134934723215619f*(13.0f*z2 - 1.0f)*(-6.0f*x2*y2 + x4 + y4) ; // 9*sqrt(385)*(13*z2 - 1)*(-6*x2*y2 + x4 + y4)/(32*sqrt(pi)) dz[61] = 13.491805046726766f*xz*(10.0f*x2*y2 - x4 - 5.0f*y4) ; // 39*sqrt(385)*xz*(10*x2*y2 - x4 - 5*y4)/(32*sqrt(pi)) dz[62] = 39.6894099270285f*x2*y4 - 39.6894099270285f*x4*y2 + 2.6459606618019f*x6 - 2.6459606618019f*y6 ; // 3*sqrt(10010)*(15*x2*y4 - 15*x4*y2 + x6 - y6)/(64*sqrt(pi)) dz[63] = 0.0f ; // 0 }; write_sh_dx(); write_sh_dy(); write_sh_dz(); } } template __global__ void kernel_sh_backward( const scalar_t * __restrict__ grad, const scalar_t * __restrict__ inputs, uint32_t B, uint32_t D, uint32_t C, const scalar_t * __restrict__ dy_dx, scalar_t * grad_inputs ) { const uint32_t t = threadIdx.x + blockIdx.x * blockDim.x; const uint32_t b = t / D; if (b >= B) return; const uint32_t d = t - b * D; const uint32_t C2 = C * C; // locate grad += b * C2; dy_dx += b * D * C2 + d * C2; for (int ch = 0; ch < C2; ch++) { grad_inputs[t] += grad[ch] * dy_dx[ch]; //printf("t=%d, b=%d, d=%d, ch=%d, grad=%f (+= %f * %f)\n", t, b, d, ch, grad_inputs[t], grad[ch], dy_dx[ch]); } } // inputs: [B, D], float, in [0, 1] // outputs: [B, L * C], float template void sh_encode_forward_cuda(const scalar_t *inputs, scalar_t *outputs, const uint32_t B, const uint32_t D, const uint32_t C, scalar_t *dy_dx) { static constexpr uint32_t N_THREADS = 256; kernel_sh<<>>(inputs, outputs, B, D, C, dy_dx); } template void sh_encode_backward_cuda(const scalar_t *grad, const scalar_t *inputs, const uint32_t B, const uint32_t D, const uint32_t C, scalar_t *dy_dx, scalar_t *grad_inputs) { static constexpr uint32_t N_THREADS = 256; kernel_sh_backward<<>>(grad, inputs, B, D, C, dy_dx, grad_inputs); } void sh_encode_forward(at::Tensor inputs, at::Tensor outputs, const uint32_t B, const uint32_t D, const uint32_t C, at::optional dy_dx) { CHECK_CUDA(inputs); CHECK_CUDA(outputs); // CHECK_CUDA(dy_dx); CHECK_CONTIGUOUS(inputs); CHECK_CONTIGUOUS(outputs); // CHECK_CONTIGUOUS(dy_dx); CHECK_IS_FLOATING(inputs); CHECK_IS_FLOATING(outputs); // CHECK_IS_FLOATING(dy_dx); AT_DISPATCH_FLOATING_TYPES_AND_HALF( inputs.scalar_type(), "sh_encode_forward_cuda", ([&] { sh_encode_forward_cuda(inputs.data_ptr(), outputs.data_ptr(), B, D, C, dy_dx.has_value() ? dy_dx.value().data_ptr() : nullptr); })); } void sh_encode_backward(at::Tensor grad, at::Tensor inputs, const uint32_t B, const uint32_t D, const uint32_t C, at::Tensor dy_dx, at::Tensor grad_inputs) { CHECK_CUDA(grad); CHECK_CUDA(inputs); CHECK_CUDA(dy_dx); CHECK_CUDA(grad_inputs); CHECK_CONTIGUOUS(grad); CHECK_CONTIGUOUS(inputs); CHECK_CONTIGUOUS(dy_dx); CHECK_CONTIGUOUS(grad_inputs); CHECK_IS_FLOATING(grad); CHECK_IS_FLOATING(inputs); CHECK_IS_FLOATING(dy_dx); CHECK_IS_FLOATING(grad_inputs); AT_DISPATCH_FLOATING_TYPES_AND_HALF( grad.scalar_type(), "sh_encode_backward_cuda", ([&] { sh_encode_backward_cuda(grad.data_ptr(), inputs.data_ptr(), B, D, C, dy_dx.data_ptr(), grad_inputs.data_ptr()); })); } ================================================ FILE: stable-dreamfusion/shencoder/src/shencoder.h ================================================ # pragma once #include #include // inputs: [B, D], float, in [-1, 1] // outputs: [B, F], float void sh_encode_forward(at::Tensor inputs, at::Tensor outputs, const uint32_t B, const uint32_t D, const uint32_t C, at::optional dy_dx); void sh_encode_backward(at::Tensor grad, at::Tensor inputs, const uint32_t B, const uint32_t D, const uint32_t C, at::Tensor dy_dx, at::Tensor grad_inputs); ================================================ FILE: stable-dreamfusion/taichi_modules/__init__.py ================================================ from .ray_march import RayMarcherTaichi, raymarching_test from .volume_train import VolumeRendererTaichi from .intersection import RayAABBIntersector from .volume_render_test import composite_test from .utils import packbits ================================================ FILE: stable-dreamfusion/taichi_modules/hash_encoder.py ================================================ import numpy as np import taichi as ti import torch from taichi.math import uvec3 from torch.cuda.amp import custom_bwd, custom_fwd from .utils import (data_type, ti2torch, ti2torch_grad, ti2torch_grad_vec, ti2torch_vec, torch2ti, torch2ti_grad, torch2ti_grad_vec, torch2ti_vec, torch_type) half2 = ti.types.vector(n=2, dtype=ti.f16) @ti.kernel def random_initialize(data: ti.types.ndarray()): for I in ti.grouped(data): data[I] = (ti.random() * 2.0 - 1.0) * 1e-4 @ti.kernel def ti_copy(data1: ti.template(), data2: ti.template()): for I in ti.grouped(data1): data1[I] = data2[I] @ti.kernel def ti_copy_array(data1: ti.types.ndarray(), data2: ti.types.ndarray()): for I in ti.grouped(data1): data1[I] = data2[I] @ti.kernel def ti_copy_field_array(data1: ti.template(), data2: ti.types.ndarray()): for I in ti.grouped(data1): data1[I] = data2[I] @ti.func def fast_hash(pos_grid_local): result = ti.uint32(0) # primes = uvec3(ti.uint32(1), ti.uint32(1958374283), ti.uint32(2654435761)) primes = uvec3(ti.uint32(1), ti.uint32(2654435761), ti.uint32(805459861)) for i in ti.static(range(3)): result ^= ti.uint32(pos_grid_local[i]) * primes[i] return result @ti.func def under_hash(pos_grid_local, resolution): result = ti.uint32(0) stride = ti.uint32(1) for i in ti.static(range(3)): result += ti.uint32(pos_grid_local[i] * stride) stride *= resolution return result @ti.func def grid_pos2hash_index(indicator, pos_grid_local, resolution, map_size): hash_result = ti.uint32(0) if indicator == 1: hash_result = under_hash(pos_grid_local, resolution) else: hash_result = fast_hash(pos_grid_local) return hash_result % map_size @ti.kernel def hash_encode_kernel( xyzs: ti.template(), table: ti.template(), xyzs_embedding: ti.template(), hash_map_indicator: ti.template(), hash_map_sizes_field: ti.template(), offsets: ti.template(), B: ti.i32, per_level_scale: ti.f32): # get hash table embedding ti.loop_config(block_dim=16) for i, level in ti.ndrange(B, 16): xyz = ti.Vector([xyzs[i, 0], xyzs[i, 1], xyzs[i, 2]]) scale = 16 * ti.exp(level * ti.log(per_level_scale)) - 1.0 resolution = ti.cast(ti.ceil(scale), ti.uint32) + 1 offset = offsets[level] * 2 pos = xyz * scale + 0.5 pos_grid_uint = ti.cast(ti.floor(pos), ti.uint32) pos -= pos_grid_uint indicator = hash_map_indicator[level] map_size = hash_map_sizes_field[level] local_feature_0 = 0.0 local_feature_1 = 0.0 for idx in ti.static(range(8)): w = 1. pos_grid_local = uvec3(0) for d in ti.static(range(3)): if (idx & (1 << d)) == 0: pos_grid_local[d] = pos_grid_uint[d] w *= 1 - pos[d] else: pos_grid_local[d] = pos_grid_uint[d] + 1 w *= pos[d] index = grid_pos2hash_index(indicator, pos_grid_local, resolution, map_size) index_table = offset + index * 2 index_table_int = ti.cast(index_table, ti.int32) local_feature_0 += w * table[index_table_int] local_feature_1 += w * table[index_table_int + 1] xyzs_embedding[i, level * 2] = local_feature_0 xyzs_embedding[i, level * 2 + 1] = local_feature_1 @ti.kernel def hash_encode_kernel_half2( xyzs: ti.template(), table: ti.template(), xyzs_embedding: ti.template(), hash_map_indicator: ti.template(), hash_map_sizes_field: ti.template(), offsets: ti.template(), B: ti.i32, per_level_scale: ti.f16): # get hash table embedding ti.loop_config(block_dim=32) for i, level in ti.ndrange(B, 16): xyz = ti.Vector([xyzs[i, 0], xyzs[i, 1], xyzs[i, 2]]) scale = 16 * ti.exp(level * ti.log(per_level_scale)) - 1.0 resolution = ti.cast(ti.ceil(scale), ti.uint32) + 1 offset = offsets[level] pos = xyz * scale + 0.5 pos_grid_uint = ti.cast(ti.floor(pos), ti.uint32) pos -= pos_grid_uint indicator = hash_map_indicator[level] map_size = hash_map_sizes_field[level] local_feature = half2(0.0) for idx in ti.static(range(8)): w = ti.f32(1.0) pos_grid_local = uvec3(0) for d in ti.static(range(3)): if (idx & (1 << d)) == 0: pos_grid_local[d] = pos_grid_uint[d] w *= 1 - pos[d] else: pos_grid_local[d] = pos_grid_uint[d] + 1 w *= pos[d] index = grid_pos2hash_index(indicator, pos_grid_local, resolution, map_size) index_table = offset + index index_table_int = ti.cast(index_table, ti.int32) local_feature += w * table[index_table_int] xyzs_embedding[i, level] = local_feature class HashEncoderTaichi(torch.nn.Module): def __init__(self, b=1.3195079565048218, batch_size=8192, data_type=data_type, half2_opt=False): super(HashEncoderTaichi, self).__init__() self.per_level_scale = b if batch_size < 2048: batch_size = 2048 # per_level_scale = 1.3195079565048218 print("per_level_scale: ", b) self.offsets = ti.field(ti.i32, shape=(16, )) self.hash_map_sizes_field = ti.field(ti.uint32, shape=(16, )) self.hash_map_indicator = ti.field(ti.i32, shape=(16, )) base_res = 16 max_params = 2**19 offset_ = 0 hash_map_sizes = [] for i in range(16): resolution = int( np.ceil(base_res * np.exp(i * np.log(self.per_level_scale)) - 1.0)) + 1 params_in_level = resolution**3 params_in_level = int(resolution** 3) if params_in_level % 8 == 0 else int( (params_in_level + 8 - 1) / 8) * 8 params_in_level = min(max_params, params_in_level) self.offsets[i] = offset_ hash_map_sizes.append(params_in_level) self.hash_map_indicator[ i] = 1 if resolution**3 <= params_in_level else 0 offset_ += params_in_level print("offset_: ", offset_) size = np.uint32(np.array(hash_map_sizes)) self.hash_map_sizes_field.from_numpy(size) self.total_hash_size = offset_ * 2 print("total_hash_size: ", self.total_hash_size) self.hash_table = torch.nn.Parameter(torch.zeros(self.total_hash_size, dtype=torch_type), requires_grad=True) random_initialize(self.hash_table) if half2_opt: assert self.total_hash_size % 2 == 0 self.parameter_fields = half2.field(shape=(self.total_hash_size // 2, ), needs_grad=True) self.output_fields = half2.field(shape=(batch_size * 1024, 16), needs_grad=True) self.torch2ti = torch2ti_vec self.ti2torch = ti2torch_vec self.ti2torch_grad = ti2torch_grad_vec self.torch2ti_grad = torch2ti_grad_vec self._hash_encode_kernel = hash_encode_kernel_half2 else: self.parameter_fields = ti.field(data_type, shape=(self.total_hash_size, ), needs_grad=True) self.output_fields = ti.field(dtype=data_type, shape=(batch_size * 1024, 32), needs_grad=True) self.torch2ti = torch2ti self.ti2torch = ti2torch self.ti2torch_grad = ti2torch_grad self.torch2ti_grad = torch2ti_grad self._hash_encode_kernel = hash_encode_kernel self.input_fields = ti.field(dtype=data_type, shape=(batch_size * 1024, 3), needs_grad=True) self.output_dim = 32 # the output dim: num levels (16) x level num (2) self.register_buffer( 'hash_grad', torch.zeros(self.total_hash_size, dtype=torch_type)) self.register_buffer( 'output_embedding', torch.zeros(batch_size * 1024, 32, dtype=torch_type)) class _module_function(torch.autograd.Function): @staticmethod @custom_fwd(cast_inputs=torch_type) def forward(ctx, input_pos, params): output_embedding = self.output_embedding[:input_pos. shape[0]].contiguous( ) torch2ti(self.input_fields, input_pos.contiguous()) self.torch2ti(self.parameter_fields, params.contiguous()) self._hash_encode_kernel( self.input_fields, self.parameter_fields, self.output_fields, self.hash_map_indicator, self.hash_map_sizes_field, self.offsets, input_pos.shape[0], self.per_level_scale, ) self.ti2torch(self.output_fields, output_embedding) return output_embedding @staticmethod @custom_bwd def backward(ctx, doutput): self.zero_grad() self.torch2ti_grad(self.output_fields, doutput.contiguous()) self._hash_encode_kernel.grad( self.input_fields, self.parameter_fields, self.output_fields, self.hash_map_indicator, self.hash_map_sizes_field, self.offsets, doutput.shape[0], self.per_level_scale, ) self.ti2torch_grad(self.parameter_fields, self.hash_grad.contiguous()) return None, self.hash_grad self._module_function = _module_function def zero_grad(self): self.parameter_fields.grad.fill(0.) def forward(self, positions, bound=1): positions = (positions + bound) / (2 * bound) return self._module_function.apply(positions, self.hash_table) ================================================ FILE: stable-dreamfusion/taichi_modules/intersection.py ================================================ import taichi as ti import torch from taichi.math import vec3 from torch.cuda.amp import custom_fwd from .utils import NEAR_DISTANCE @ti.kernel def simple_ray_aabb_intersec_taichi_forward( hits_t: ti.types.ndarray(ndim=2), rays_o: ti.types.ndarray(ndim=2), rays_d: ti.types.ndarray(ndim=2), centers: ti.types.ndarray(ndim=2), half_sizes: ti.types.ndarray(ndim=2)): for r in ti.ndrange(hits_t.shape[0]): ray_o = vec3([rays_o[r, 0], rays_o[r, 1], rays_o[r, 2]]) ray_d = vec3([rays_d[r, 0], rays_d[r, 1], rays_d[r, 2]]) inv_d = 1.0 / ray_d center = vec3([centers[0, 0], centers[0, 1], centers[0, 2]]) half_size = vec3( [half_sizes[0, 0], half_sizes[0, 1], half_sizes[0, 1]]) t_min = (center - half_size - ray_o) * inv_d t_max = (center + half_size - ray_o) * inv_d _t1 = ti.min(t_min, t_max) _t2 = ti.max(t_min, t_max) t1 = _t1.max() t2 = _t2.min() if t2 > 0.0: hits_t[r, 0, 0] = ti.max(t1, NEAR_DISTANCE) hits_t[r, 0, 1] = t2 class RayAABBIntersector(torch.autograd.Function): """ Computes the intersections of rays and axis-aligned voxels. Inputs: rays_o: (N_rays, 3) ray origins rays_d: (N_rays, 3) ray directions centers: (N_voxels, 3) voxel centers half_sizes: (N_voxels, 3) voxel half sizes max_hits: maximum number of intersected voxels to keep for one ray (for a cubic scene, this is at most 3*N_voxels^(1/3)-2) Outputs: hits_cnt: (N_rays) number of hits for each ray (followings are from near to far) hits_t: (N_rays, max_hits, 2) hit t's (-1 if no hit) hits_voxel_idx: (N_rays, max_hits) hit voxel indices (-1 if no hit) """ @staticmethod @custom_fwd(cast_inputs=torch.float32) def forward(ctx, rays_o, rays_d, center, half_size, max_hits): hits_t = (torch.zeros( rays_o.size(0), 1, 2, device=rays_o.device, dtype=torch.float32) - 1).contiguous() simple_ray_aabb_intersec_taichi_forward(hits_t, rays_o, rays_d, center, half_size) return None, hits_t, None ================================================ FILE: stable-dreamfusion/taichi_modules/ray_march.py ================================================ import taichi as ti import torch from taichi.math import vec3 from torch.cuda.amp import custom_fwd from .utils import __morton3D, calc_dt, mip_from_dt, mip_from_pos @ti.kernel def raymarching_train(rays_o: ti.types.ndarray(ndim=2), rays_d: ti.types.ndarray(ndim=2), hits_t: ti.types.ndarray(ndim=2), density_bitfield: ti.types.ndarray(ndim=1), noise: ti.types.ndarray(ndim=1), counter: ti.types.ndarray(ndim=1), rays_a: ti.types.ndarray(ndim=2), xyzs: ti.types.ndarray(ndim=2), dirs: ti.types.ndarray(ndim=2), deltas: ti.types.ndarray(ndim=1), ts: ti.types.ndarray(ndim=1), cascades: int, grid_size: int, scale: float, exp_step_factor: float, max_samples: float): # ti.loop_config(block_dim=256) for r in noise: ray_o = vec3(rays_o[r, 0], rays_o[r, 1], rays_o[r, 2]) ray_d = vec3(rays_d[r, 0], rays_d[r, 1], rays_d[r, 2]) d_inv = 1.0 / ray_d t1, t2 = hits_t[r, 0], hits_t[r, 1] grid_size3 = grid_size**3 grid_size_inv = 1.0 / grid_size if t1 >= 0: dt = calc_dt(t1, exp_step_factor, grid_size, scale) t1 += dt * noise[r] t = t1 N_samples = 0 while (0 <= t) & (t < t2) & (N_samples < max_samples): xyz = ray_o + t * ray_d dt = calc_dt(t, exp_step_factor, grid_size, scale) mip = ti.max(mip_from_pos(xyz, cascades), mip_from_dt(dt, grid_size, cascades)) # mip_bound = 0.5 # mip_bound = ti.min(ti.pow(2., mip - 1), scale) mip_bound = scale mip_bound_inv = 1 / mip_bound nxyz = ti.math.clamp(0.5 * (xyz * mip_bound_inv + 1) * grid_size, 0.0, grid_size - 1.0) # nxyz = ti.ceil(nxyz) idx = mip * grid_size3 + __morton3D(ti.cast(nxyz, ti.u32)) occ = density_bitfield[ti.u32(idx // 8)] & (1 << ti.u32(idx % 8)) # idx = __morton3D(ti.cast(nxyz, ti.uint32)) # occ = density_bitfield[mip, idx//8] & (1 << ti.cast(idx%8, ti.uint32)) if occ: t += dt N_samples += 1 else: # t += dt txyz = (((nxyz + 0.5 + 0.5 * ti.math.sign(ray_d)) * grid_size_inv * 2 - 1) * mip_bound - xyz) * d_inv t_target = t + ti.max(0, txyz.min()) t += calc_dt(t, exp_step_factor, grid_size, scale) while t < t_target: t += calc_dt(t, exp_step_factor, grid_size, scale) start_idx = ti.atomic_add(counter[0], N_samples) ray_count = ti.atomic_add(counter[1], 1) rays_a[ray_count, 0] = r rays_a[ray_count, 1] = start_idx rays_a[ray_count, 2] = N_samples t = t1 samples = 0 while (t < t2) & (samples < N_samples): xyz = ray_o + t * ray_d dt = calc_dt(t, exp_step_factor, grid_size, scale) mip = ti.max(mip_from_pos(xyz, cascades), mip_from_dt(dt, grid_size, cascades)) # mip_bound = 0.5 # mip_bound = ti.min(ti.pow(2., mip - 1), scale) mip_bound = scale mip_bound_inv = 1 / mip_bound nxyz = ti.math.clamp(0.5 * (xyz * mip_bound_inv + 1) * grid_size, 0.0, grid_size - 1.0) # nxyz = ti.ceil(nxyz) idx = mip * grid_size3 + __morton3D(ti.cast(nxyz, ti.u32)) occ = density_bitfield[ti.u32(idx // 8)] & (1 << ti.u32(idx % 8)) # idx = __morton3D(ti.cast(nxyz, ti.uint32)) # occ = density_bitfield[mip, idx//8] & (1 << ti.cast(idx%8, ti.uint32)) if occ: s = start_idx + samples xyzs[s, 0] = xyz[0] xyzs[s, 1] = xyz[1] xyzs[s, 2] = xyz[2] dirs[s, 0] = ray_d[0] dirs[s, 1] = ray_d[1] dirs[s, 2] = ray_d[2] ts[s] = t deltas[s] = dt t += dt samples += 1 else: # t += dt txyz = (((nxyz + 0.5 + 0.5 * ti.math.sign(ray_d)) * grid_size_inv * 2 - 1) * mip_bound - xyz) * d_inv t_target = t + ti.max(0, txyz.min()) t += calc_dt(t, exp_step_factor, grid_size, scale) while t < t_target: t += calc_dt(t, exp_step_factor, grid_size, scale) @ti.kernel def raymarching_train_backword(segments: ti.types.ndarray(ndim=2), ts: ti.types.ndarray(ndim=1), dL_drays_o: ti.types.ndarray(ndim=2), dL_drays_d: ti.types.ndarray(ndim=2), dL_dxyzs: ti.types.ndarray(ndim=2), dL_ddirs: ti.types.ndarray(ndim=2)): for s in segments: index = segments[s] dxyz = dL_dxyzs[index] ddir = dL_ddirs[index] dL_drays_o[s] = dxyz dL_drays_d[s] = dxyz * ts[index] + ddir class RayMarcherTaichi(torch.nn.Module): def __init__(self, batch_size=8192): super(RayMarcherTaichi, self).__init__() self.register_buffer('rays_a', torch.zeros(batch_size, 3, dtype=torch.int32)) self.register_buffer( 'xyzs', torch.zeros(batch_size * 1024, 3, dtype=torch.float32)) self.register_buffer( 'dirs', torch.zeros(batch_size * 1024, 3, dtype=torch.float32)) self.register_buffer( 'deltas', torch.zeros(batch_size * 1024, dtype=torch.float32)) self.register_buffer( 'ts', torch.zeros(batch_size * 1024, dtype=torch.float32)) # self.register_buffer('dL_drays_o', torch.zeros(batch_size, dtype=torch.float32)) # self.register_buffer('dL_drays_d', torch.zeros(batch_size, dtype=torch.float32)) class _module_function(torch.autograd.Function): @staticmethod @custom_fwd(cast_inputs=torch.float32) def forward(ctx, rays_o, rays_d, hits_t, density_bitfield, cascades, scale, exp_step_factor, grid_size, max_samples): # noise to perturb the first sample of each ray noise = torch.rand_like(rays_o[:, 0]) counter = torch.zeros(2, device=rays_o.device, dtype=torch.int32) raymarching_train(\ rays_o, rays_d, hits_t.contiguous(), density_bitfield, noise, counter, self.rays_a.contiguous(), self.xyzs.contiguous(), self.dirs.contiguous(), self.deltas.contiguous(), self.ts.contiguous(), cascades, grid_size, scale, exp_step_factor, max_samples) # ti.sync() total_samples = counter[0] # total samples for all rays # remove redundant output xyzs = self.xyzs[:total_samples] dirs = self.dirs[:total_samples] deltas = self.deltas[:total_samples] ts = self.ts[:total_samples] return self.rays_a, xyzs, dirs, deltas, ts, total_samples # @staticmethod # @custom_bwd # def backward(ctx, dL_drays_a, dL_dxyzs, dL_ddirs, dL_ddeltas, dL_dts, # dL_dtotal_samples): # rays_a, ts = ctx.saved_tensors # # rays_a = rays_a.contiguous() # ts = ts.contiguous() # segments = torch.cat([rays_a[:, 1], rays_a[-1:, 1] + rays_a[-1:, 2]]) # dL_drays_o = torch.zeros_like(rays_a[:, 0]) # dL_drays_d = torch.zeros_like(rays_a[:, 0]) # raymarching_train_backword(segments.contiguous(), ts, dL_drays_o, # dL_drays_d, dL_dxyzs, dL_ddirs) # # ti.sync() # # dL_drays_o = segment_csr(dL_dxyzs, segments) # # dL_drays_d = \ # # segment_csr(dL_dxyzs*rearrange(ts, 'n -> n 1')+dL_ddirs, segments) # return dL_drays_o, dL_drays_d, None, None, None, None, None, None, None self._module_function = _module_function def forward(self, rays_o, rays_d, hits_t, density_bitfield, cascades, scale, exp_step_factor, grid_size, max_samples): return self._module_function.apply(rays_o, rays_d, hits_t, density_bitfield, cascades, scale, exp_step_factor, grid_size, max_samples) @ti.kernel def raymarching_test_kernel( rays_o: ti.types.ndarray(ndim=2), rays_d: ti.types.ndarray(ndim=2), hits_t: ti.types.ndarray(ndim=2), alive_indices: ti.types.ndarray(ndim=1), density_bitfield: ti.types.ndarray(ndim=1), cascades: int, grid_size: int, scale: float, exp_step_factor: float, N_samples: int, max_samples: int, xyzs: ti.types.ndarray(ndim=2), dirs: ti.types.ndarray(ndim=2), deltas: ti.types.ndarray(ndim=1), ts: ti.types.ndarray(ndim=1), N_eff_samples: ti.types.ndarray(ndim=1), ): for n in alive_indices: r = alive_indices[n] grid_size3 = grid_size**3 grid_size_inv = 1.0 / grid_size ray_o = vec3(rays_o[r, 0], rays_o[r, 1], rays_o[r, 2]) ray_d = vec3(rays_d[r, 0], rays_d[r, 1], rays_d[r, 2]) d_inv = 1.0 / ray_d t = hits_t[r, 0] t2 = hits_t[r, 1] s = 0 while (0 <= t) & (t < t2) & (s < N_samples): xyz = ray_o + t * ray_d dt = calc_dt(t, exp_step_factor, grid_size, scale) mip = ti.max(mip_from_pos(xyz, cascades), mip_from_dt(dt, grid_size, cascades)) # mip_bound = 0.5 # mip_bound = ti.min(ti.pow(2., mip - 1), scale) mip_bound = scale mip_bound_inv = 1 / mip_bound nxyz = ti.math.clamp(0.5 * (xyz * mip_bound_inv + 1) * grid_size, 0.0, grid_size - 1.0) # nxyz = ti.ceil(nxyz) idx = mip * grid_size3 + __morton3D(ti.cast(nxyz, ti.u32)) occ = density_bitfield[ti.u32(idx // 8)] & (1 << ti.u32(idx % 8)) if occ: xyzs[n, s, 0] = xyz[0] xyzs[n, s, 1] = xyz[1] xyzs[n, s, 2] = xyz[2] dirs[n, s, 0] = ray_d[0] dirs[n, s, 1] = ray_d[1] dirs[n, s, 2] = ray_d[2] ts[n, s] = t deltas[n, s] = dt t += dt hits_t[r, 0] = t s += 1 else: txyz = (((nxyz + 0.5 + 0.5 * ti.math.sign(ray_d)) * grid_size_inv * 2 - 1) * mip_bound - xyz) * d_inv t_target = t + ti.max(0, txyz.min()) t += calc_dt(t, exp_step_factor, grid_size, scale) while t < t_target: t += calc_dt(t, exp_step_factor, grid_size, scale) N_eff_samples[n] = s def raymarching_test(rays_o, rays_d, hits_t, alive_indices, density_bitfield, cascades, scale, exp_step_factor, grid_size, max_samples, N_samples): N_rays = alive_indices.size(0) xyzs = torch.zeros(N_rays, N_samples, 3, device=rays_o.device, dtype=rays_o.dtype) dirs = torch.zeros(N_rays, N_samples, 3, device=rays_o.device, dtype=rays_o.dtype) deltas = torch.zeros(N_rays, N_samples, device=rays_o.device, dtype=rays_o.dtype) ts = torch.zeros(N_rays, N_samples, device=rays_o.device, dtype=rays_o.dtype) N_eff_samples = torch.zeros(N_rays, device=rays_o.device, dtype=torch.int32) raymarching_test_kernel(rays_o, rays_d, hits_t, alive_indices, density_bitfield, cascades, grid_size, scale, exp_step_factor, N_samples, max_samples, xyzs, dirs, deltas, ts, N_eff_samples) # ti.sync() return xyzs, dirs, deltas, ts, N_eff_samples ================================================ FILE: stable-dreamfusion/taichi_modules/utils.py ================================================ import taichi as ti import torch from taichi.math import uvec3 taichi_block_size = 128 data_type = ti.f32 torch_type = torch.float32 MAX_SAMPLES = 1024 NEAR_DISTANCE = 0.01 SQRT3 = 1.7320508075688772 SQRT3_MAX_SAMPLES = SQRT3 / 1024 SQRT3_2 = 1.7320508075688772 * 2 @ti.func def scalbn(x, exponent): return x * ti.math.pow(2, exponent) @ti.func def calc_dt(t, exp_step_factor, grid_size, scale): return ti.math.clamp(t * exp_step_factor, SQRT3_MAX_SAMPLES, SQRT3_2 * scale / grid_size) @ti.func def frexp_bit(x): exponent = 0 if x != 0.0: # frac = ti.abs(x) bits = ti.bit_cast(x, ti.u32) exponent = ti.i32((bits & ti.u32(0x7f800000)) >> 23) - 127 # exponent = (ti.i32(bits & ti.u32(0x7f800000)) >> 23) - 127 bits &= ti.u32(0x7fffff) bits |= ti.u32(0x3f800000) frac = ti.bit_cast(bits, ti.f32) if frac < 0.5: exponent -= 1 elif frac > 1.0: exponent += 1 return exponent @ti.func def mip_from_pos(xyz, cascades): mx = ti.abs(xyz).max() # _, exponent = _frexp(mx) exponent = frexp_bit(ti.f32(mx)) + 1 # frac, exponent = ti.frexp(ti.f32(mx)) return ti.min(cascades - 1, ti.max(0, exponent)) @ti.func def mip_from_dt(dt, grid_size, cascades): # _, exponent = _frexp(dt*grid_size) exponent = frexp_bit(ti.f32(dt * grid_size)) # frac, exponent = ti.frexp(ti.f32(dt*grid_size)) return ti.min(cascades - 1, ti.max(0, exponent)) @ti.func def __expand_bits(v): v = (v * ti.uint32(0x00010001)) & ti.uint32(0xFF0000FF) v = (v * ti.uint32(0x00000101)) & ti.uint32(0x0F00F00F) v = (v * ti.uint32(0x00000011)) & ti.uint32(0xC30C30C3) v = (v * ti.uint32(0x00000005)) & ti.uint32(0x49249249) return v @ti.func def __morton3D(xyz): xyz = __expand_bits(xyz) return xyz[0] | (xyz[1] << 1) | (xyz[2] << 2) @ti.func def __morton3D_invert(x): x = x & (0x49249249) x = (x | (x >> 2)) & ti.uint32(0xc30c30c3) x = (x | (x >> 4)) & ti.uint32(0x0f00f00f) x = (x | (x >> 8)) & ti.uint32(0xff0000ff) x = (x | (x >> 16)) & ti.uint32(0x0000ffff) return ti.int32(x) @ti.kernel def morton3D_invert_kernel(indices: ti.types.ndarray(ndim=1), coords: ti.types.ndarray(ndim=2)): for i in indices: ind = ti.uint32(indices[i]) coords[i, 0] = __morton3D_invert(ind >> 0) coords[i, 1] = __morton3D_invert(ind >> 1) coords[i, 2] = __morton3D_invert(ind >> 2) def morton3D_invert(indices): coords = torch.zeros(indices.size(0), 3, device=indices.device, dtype=torch.int32) morton3D_invert_kernel(indices.contiguous(), coords) ti.sync() return coords @ti.kernel def morton3D_kernel(xyzs: ti.types.ndarray(ndim=2), indices: ti.types.ndarray(ndim=1)): for s in indices: xyz = uvec3([xyzs[s, 0], xyzs[s, 1], xyzs[s, 2]]) indices[s] = ti.cast(__morton3D(xyz), ti.int32) def morton3D(coords1): indices = torch.zeros(coords1.size(0), device=coords1.device, dtype=torch.int32) morton3D_kernel(coords1.contiguous(), indices) ti.sync() return indices @ti.kernel def packbits(density_grid: ti.types.ndarray(ndim=1), density_threshold: float, density_bitfield: ti.types.ndarray(ndim=1)): for n in density_bitfield: bits = ti.uint8(0) for i in ti.static(range(8)): bits |= (ti.uint8(1) << i) if ( density_grid[8 * n + i] > density_threshold) else ti.uint8(0) density_bitfield[n] = bits @ti.kernel def torch2ti(field: ti.template(), data: ti.types.ndarray()): for I in ti.grouped(data): field[I] = data[I] @ti.kernel def ti2torch(field: ti.template(), data: ti.types.ndarray()): for I in ti.grouped(data): data[I] = field[I] @ti.kernel def ti2torch_grad(field: ti.template(), grad: ti.types.ndarray()): for I in ti.grouped(grad): grad[I] = field.grad[I] @ti.kernel def torch2ti_grad(field: ti.template(), grad: ti.types.ndarray()): for I in ti.grouped(grad): field.grad[I] = grad[I] @ti.kernel def torch2ti_vec(field: ti.template(), data: ti.types.ndarray()): for I in range(data.shape[0] // 2): field[I] = ti.Vector([data[I * 2], data[I * 2 + 1]]) @ti.kernel def ti2torch_vec(field: ti.template(), data: ti.types.ndarray()): for i, j in ti.ndrange(data.shape[0], data.shape[1] // 2): data[i, j * 2] = field[i, j][0] data[i, j * 2 + 1] = field[i, j][1] @ti.kernel def ti2torch_grad_vec(field: ti.template(), grad: ti.types.ndarray()): for I in range(grad.shape[0] // 2): grad[I * 2] = field.grad[I][0] grad[I * 2 + 1] = field.grad[I][1] @ti.kernel def torch2ti_grad_vec(field: ti.template(), grad: ti.types.ndarray()): for i, j in ti.ndrange(grad.shape[0], grad.shape[1] // 2): field.grad[i, j][0] = grad[i, j * 2] field.grad[i, j][1] = grad[i, j * 2 + 1] def extract_model_state_dict(ckpt_path, model_name='model', prefixes_to_ignore=[]): checkpoint = torch.load(ckpt_path, map_location='cpu') checkpoint_ = {} if 'state_dict' in checkpoint: # if it's a pytorch-lightning checkpoint checkpoint = checkpoint['state_dict'] for k, v in checkpoint.items(): if not k.startswith(model_name): continue k = k[len(model_name) + 1:] for prefix in prefixes_to_ignore: if k.startswith(prefix): break else: checkpoint_[k] = v return checkpoint_ def load_ckpt(model, ckpt_path, model_name='model', prefixes_to_ignore=[]): if not ckpt_path: return model_dict = model.state_dict() checkpoint_ = extract_model_state_dict(ckpt_path, model_name, prefixes_to_ignore) model_dict.update(checkpoint_) model.load_state_dict(model_dict) def depth2img(depth): depth = (depth - depth.min()) / (depth.max() - depth.min()) depth_img = cv2.applyColorMap((depth * 255).astype(np.uint8), cv2.COLORMAP_TURBO) return depth_img ================================================ FILE: stable-dreamfusion/taichi_modules/volume_render_test.py ================================================ import taichi as ti @ti.kernel def composite_test( sigmas: ti.types.ndarray(ndim=2), rgbs: ti.types.ndarray(ndim=3), deltas: ti.types.ndarray(ndim=2), ts: ti.types.ndarray(ndim=2), hits_t: ti.types.ndarray(ndim=2), alive_indices: ti.types.ndarray(ndim=1), T_threshold: float, N_eff_samples: ti.types.ndarray(ndim=1), opacity: ti.types.ndarray(ndim=1), depth: ti.types.ndarray(ndim=1), rgb: ti.types.ndarray(ndim=2)): for n in alive_indices: samples = N_eff_samples[n] if samples == 0: alive_indices[n] = -1 else: r = alive_indices[n] T = 1 - opacity[r] rgb_temp_0 = 0.0 rgb_temp_1 = 0.0 rgb_temp_2 = 0.0 depth_temp = 0.0 opacity_temp = 0.0 for s in range(samples): a = 1.0 - ti.exp(-sigmas[n, s] * deltas[n, s]) w = a * T rgb_temp_0 += w * rgbs[n, s, 0] rgb_temp_1 += w * rgbs[n, s, 1] rgb_temp_2 += w * rgbs[n, s, 2] depth[r] += w * ts[n, s] opacity[r] += w T *= 1.0 - a if T <= T_threshold: alive_indices[n] = -1 break rgb[r, 0] += rgb_temp_0 rgb[r, 1] += rgb_temp_1 rgb[r, 2] += rgb_temp_2 depth[r] += depth_temp opacity[r] += opacity_temp ================================================ FILE: stable-dreamfusion/taichi_modules/volume_train.py ================================================ import taichi as ti import torch from torch.cuda.amp import custom_bwd, custom_fwd from .utils import (data_type, ti2torch, ti2torch_grad, torch2ti, torch2ti_grad, torch_type) @ti.kernel def composite_train_fw_array( sigmas: ti.types.ndarray(), rgbs: ti.types.ndarray(), deltas: ti.types.ndarray(), ts: ti.types.ndarray(), rays_a: ti.types.ndarray(), T_threshold: float, total_samples: ti.types.ndarray(), opacity: ti.types.ndarray(), depth: ti.types.ndarray(), rgb: ti.types.ndarray(), ws: ti.types.ndarray(), ): for n in opacity: ray_idx = rays_a[n, 0] start_idx = rays_a[n, 1] N_samples = rays_a[n, 2] T = 1.0 samples = 0 while samples < N_samples: s = start_idx + samples a = 1.0 - ti.exp(-sigmas[s] * deltas[s]) w = a * T rgb[ray_idx, 0] += w * rgbs[s, 0] rgb[ray_idx, 1] += w * rgbs[s, 1] rgb[ray_idx, 2] += w * rgbs[s, 2] depth[ray_idx] += w * ts[s] opacity[ray_idx] += w ws[s] = w T *= 1.0 - a # if T T_threshold: # s = start_idx + sample_ a = 1.0 - ti.exp(-sigmas[s] * deltas[s]) w = a * T_ rgb[ray_idx, 0] += w * rgbs[s, 0] rgb[ray_idx, 1] += w * rgbs[s, 1] rgb[ray_idx, 2] += w * rgbs[s, 2] depth[ray_idx] += w * ts[s] opacity[ray_idx] += w ws[s] = w # T_ *= (1.0-a) T[s + 1] = T_ * (1.0 - a) # if T[s+1]>=T_threshold: # samples += 1 total_samples[ray_idx] += 1 else: T[s + 1] = 0.0 # total_samples[ray_idx] = N_samples @ti.kernel def check_value( fields: ti.template(), array: ti.types.ndarray(), checker: ti.types.ndarray(), ): for I in ti.grouped(array): if fields[I] == array[I]: checker[I] = 1 class VolumeRendererTaichi(torch.nn.Module): def __init__(self, batch_size=8192, data_type=data_type): super(VolumeRendererTaichi, self).__init__() # samples level self.sigmas_fields = ti.field(dtype=data_type, shape=(batch_size * 1024, ), needs_grad=True) self.rgbs_fields = ti.field(dtype=data_type, shape=(batch_size * 1024, 3), needs_grad=True) self.deltas_fields = ti.field(dtype=data_type, shape=(batch_size * 1024, ), needs_grad=True) self.ts_fields = ti.field(dtype=data_type, shape=(batch_size * 1024, ), needs_grad=True) self.ws_fields = ti.field(dtype=data_type, shape=(batch_size * 1024, ), needs_grad=True) self.T = ti.field(dtype=data_type, shape=(batch_size * 1024), needs_grad=True) # rays level self.rays_a_fields = ti.field(dtype=ti.i64, shape=(batch_size, 3)) self.total_samples_fields = ti.field(dtype=ti.i64, shape=(batch_size, )) self.opacity_fields = ti.field(dtype=data_type, shape=(batch_size, ), needs_grad=True) self.depth_fields = ti.field(dtype=data_type, shape=(batch_size, ), needs_grad=True) self.rgb_fields = ti.field(dtype=data_type, shape=(batch_size, 3), needs_grad=True) # preallocate tensor self.register_buffer('total_samples', torch.zeros(batch_size, dtype=torch.int64)) self.register_buffer('rgb', torch.zeros(batch_size, 3, dtype=torch_type)) self.register_buffer('opacity', torch.zeros(batch_size, dtype=torch_type)) self.register_buffer('depth', torch.zeros(batch_size, dtype=torch_type)) self.register_buffer('ws', torch.zeros(batch_size * 1024, dtype=torch_type)) self.register_buffer('sigma_grad', torch.zeros(batch_size * 1024, dtype=torch_type)) self.register_buffer( 'rgb_grad', torch.zeros(batch_size * 1024, 3, dtype=torch_type)) class _module_function(torch.autograd.Function): @staticmethod @custom_fwd(cast_inputs=torch_type) def forward(ctx, sigmas, rgbs, deltas, ts, rays_a, T_threshold): # If no output gradient is provided, no need to # automatically materialize it as torch.zeros. ctx.T_threshold = T_threshold ctx.samples_size = sigmas.shape[0] ws = self.ws[:sigmas.shape[0]] torch2ti(self.sigmas_fields, sigmas.contiguous()) torch2ti(self.rgbs_fields, rgbs.contiguous()) torch2ti(self.deltas_fields, deltas.contiguous()) torch2ti(self.ts_fields, ts.contiguous()) torch2ti(self.rays_a_fields, rays_a.contiguous()) composite_train_fw(self.sigmas_fields, self.rgbs_fields, self.deltas_fields, self.ts_fields, self.rays_a_fields, T_threshold, self.T, self.total_samples_fields, self.opacity_fields, self.depth_fields, self.rgb_fields, self.ws_fields) ti2torch(self.total_samples_fields, self.total_samples) ti2torch(self.opacity_fields, self.opacity) ti2torch(self.depth_fields, self.depth) ti2torch(self.rgb_fields, self.rgb) return self.total_samples.sum( ), self.opacity, self.depth, self.rgb, ws @staticmethod @custom_bwd def backward(ctx, dL_dtotal_samples, dL_dopacity, dL_ddepth, dL_drgb, dL_dws): T_threshold = ctx.T_threshold samples_size = ctx.samples_size sigma_grad = self.sigma_grad[:samples_size].contiguous() rgb_grad = self.rgb_grad[:samples_size].contiguous() self.zero_grad() torch2ti_grad(self.opacity_fields, dL_dopacity.contiguous()) torch2ti_grad(self.depth_fields, dL_ddepth.contiguous()) torch2ti_grad(self.rgb_fields, dL_drgb.contiguous()) torch2ti_grad(self.ws_fields, dL_dws.contiguous()) composite_train_fw.grad(self.sigmas_fields, self.rgbs_fields, self.deltas_fields, self.ts_fields, self.rays_a_fields, T_threshold, self.T, self.total_samples_fields, self.opacity_fields, self.depth_fields, self.rgb_fields, self.ws_fields) ti2torch_grad(self.sigmas_fields, sigma_grad) ti2torch_grad(self.rgbs_fields, rgb_grad) return sigma_grad, rgb_grad, None, None, None, None self._module_function = _module_function def zero_grad(self): self.sigmas_fields.grad.fill(0.) self.rgbs_fields.grad.fill(0.) self.T.grad.fill(0.) def forward(self, sigmas, rgbs, deltas, ts, rays_a, T_threshold): return self._module_function.apply(sigmas, rgbs, deltas, ts, rays_a, T_threshold) ================================================ FILE: stable-dreamfusion/tets/README.md ================================================ Place the tet grid files in this folder. We provide a few example grids. See the main README.md for a download link. You can also generate your own grids using https://github.com/crawforddoran/quartet Please see the `generate_tets.py` script for an example. ================================================ FILE: stable-dreamfusion/tets/generate_tets.py ================================================ # Copyright (c) 2020-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # NVIDIA CORPORATION, its affiliates and licensors retain all intellectual # property and proprietary rights in and to this material, related # documentation and any modifications thereto. Any use, reproduction, # disclosure or distribution of this material and related documentation # without an express license agreement from NVIDIA CORPORATION or # its affiliates is strictly prohibited. import os import numpy as np ''' This code segment shows how to use Quartet: https://github.com/crawforddoran/quartet, to generate a tet grid 1) Download, compile and run Quartet as described in the link above. Example usage `quartet meshes/cube.obj 0.5 cube_5.tet` 2) Run the function below to generate a file `cube_32_tet.tet` ''' def generate_tetrahedron_grid_file(res=32, root='..'): frac = 1.0 / res command = 'cd %s/quartet; ' % (root) + \ './quartet_release meshes/cube.obj %f meshes/cube_%f_tet.tet -s meshes/cube_boundary_%f.obj' % (frac, res, res) os.system(command) ''' This code segment shows how to convert from a quartet .tet file to compressed npz file ''' def convert_from_quartet_to_npz(quartetfile = 'cube_32_tet.tet', npzfile = '32_tets.npz'): file1 = open(quartetfile, 'r') header = file1.readline() numvertices = int(header.split(" ")[1]) numtets = int(header.split(" ")[2]) print(numvertices, numtets) # load vertices vertices = np.loadtxt(quartetfile, skiprows=1, max_rows=numvertices) vertices = vertices - 0.5 print(vertices.shape, vertices.min(), vertices.max()) # load indices indices = np.loadtxt(quartetfile, dtype=int, skiprows=1+numvertices, max_rows=numtets) print(indices.shape) np.savez_compressed(npzfile, vertices=vertices, indices=indices) if __name__ == '__main__': import argparse parser = argparse.ArgumentParser() parser.add_argument('--res', type=int, default=32) parser.add_argument('--root', type=str, default='..') args = parser.parse_args() generate_tetrahedron_grid_file(res=args.res, root=args.root) convert_from_quartet_to_npz(quartetfile=os.path.join(args.root, 'quartet', 'meshes', f'cube_{args.res}.000000_tet.tet'), npzfile=os.path.join('./tets', f'{args.res}_tets.npz'))