From c68bd15bb845c16c71fa2987602f32c4979fb24c Mon Sep 17 00:00:00 2001 From: Thijn Date: Wed, 28 Sep 2022 21:11:28 +0200 Subject: [PATCH] init --- .gitignore | 8 + LICENSE | 21 + README.md | 124 ++ assets/a-container-0038.jpg | Bin 0 -> 377653 bytes assets/a-dog-on-top-of-sks-container-0023.jpg | Bin 0 -> 373860 bytes assets/a-red-sks-container-0021.jpg | Bin 0 -> 305001 bytes assets/photo-of-a-sks-container-0018.jpg | Bin 0 -> 235456 bytes ...o-of-a-sks-container-on-the-beach-0017.jpg | Bin 0 -> 309835 bytes ...to-of-a-sks-container-on-the-moon-0016.jpg | Bin 0 -> 220917 bytes .../autoencoder/autoencoder_kl_16x16x16.yaml | 54 + .../autoencoder/autoencoder_kl_32x32x4.yaml | 53 + .../autoencoder/autoencoder_kl_64x64x3.yaml | 54 + .../autoencoder/autoencoder_kl_8x8x64.yaml | 53 + .../latent-diffusion/celebahq-ldm-vq-4.yaml | 86 + configs/latent-diffusion/cin-ldm-vq-f8.yaml | 98 + configs/latent-diffusion/cin256-v2.yaml | 68 + configs/latent-diffusion/ffhq-ldm-vq-4.yaml | 85 + 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ldm/modules/x_transformer.py | 650 +++++++ ldm/util.py | 205 +++ main.py | 855 +++++++++ merge_embeddings.py | 111 ++ models/first_stage_models/kl-f16/config.yaml | 44 + models/first_stage_models/kl-f32/config.yaml | 46 + models/first_stage_models/kl-f4/config.yaml | 41 + models/first_stage_models/kl-f8/config.yaml | 42 + models/first_stage_models/vq-f16/config.yaml | 49 + .../vq-f4-noattn/config.yaml | 46 + models/first_stage_models/vq-f4/config.yaml | 45 + .../first_stage_models/vq-f8-n256/config.yaml | 48 + models/first_stage_models/vq-f8/config.yaml | 48 + models/ldm/bsr_sr/config.yaml | 80 + models/ldm/celeba256/config.yaml | 70 + models/ldm/cin256/config.yaml | 80 + models/ldm/ffhq256/config.yaml | 70 + models/ldm/inpainting_big/config.yaml | 67 + .../ldm/layout2img-openimages256/config.yaml | 81 + models/ldm/lsun_beds256/config.yaml | 70 + models/ldm/lsun_churches256/config.yaml | 92 + models/ldm/semantic_synthesis256/config.yaml | 59 + models/ldm/semantic_synthesis512/config.yaml | 78 + models/ldm/text2img256/config.yaml | 77 + scripts/download_first_stages.sh | 41 + scripts/download_models.sh | 49 + scripts/evaluate_model.py | 89 + scripts/inpaint.py | 98 + scripts/latent_imagenet_diffusion.ipynb | 429 +++++ scripts/prune-ckpt.py | 58 + scripts/sample_diffusion.py | 313 ++++ scripts/stable_txt2img.py | 292 +++ scripts/txt2img.py | 184 ++ setup.py | 13 + 98 files changed, 16302 insertions(+) create mode 100644 .gitignore create mode 100644 LICENSE create mode 100644 README.md create mode 100644 assets/a-container-0038.jpg create mode 100644 assets/a-dog-on-top-of-sks-container-0023.jpg create mode 100644 assets/a-red-sks-container-0021.jpg create mode 100644 assets/photo-of-a-sks-container-0018.jpg create mode 100644 assets/photo-of-a-sks-container-on-the-beach-0017.jpg create mode 100644 assets/photo-of-a-sks-container-on-the-moon-0016.jpg create mode 100644 configs/autoencoder/autoencoder_kl_16x16x16.yaml create 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ldm/modules/losses/vqperceptual.py create mode 100644 ldm/modules/x_transformer.py create mode 100644 ldm/util.py create mode 100644 main.py create mode 100644 merge_embeddings.py create mode 100644 models/first_stage_models/kl-f16/config.yaml create mode 100644 models/first_stage_models/kl-f32/config.yaml create mode 100644 models/first_stage_models/kl-f4/config.yaml create mode 100644 models/first_stage_models/kl-f8/config.yaml create mode 100644 models/first_stage_models/vq-f16/config.yaml create mode 100644 models/first_stage_models/vq-f4-noattn/config.yaml create mode 100644 models/first_stage_models/vq-f4/config.yaml create mode 100644 models/first_stage_models/vq-f8-n256/config.yaml create mode 100644 models/first_stage_models/vq-f8/config.yaml create mode 100644 models/ldm/bsr_sr/config.yaml create mode 100644 models/ldm/celeba256/config.yaml create mode 100644 models/ldm/cin256/config.yaml create mode 100644 models/ldm/ffhq256/config.yaml create mode 100644 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+gen_ref.bat +train.bat +__pycache__ +*/**/__pycache__ +logs \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..0dd4b65 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik and contributors + +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. diff --git a/README.md b/README.md new file mode 100644 index 0000000..724cb0e --- /dev/null +++ b/README.md @@ -0,0 +1,124 @@ +# "Dreambooth" on Stable Diffusion + +## Notes by Joe Penna +### **WARNINGS!** +- Unfreezing the model takes a lot of juice. + - ~~You're gonna need an A6000 / A40 / A100 (or similar top-of-the-line thousands-of-dollars GPU).~~ + - You can now run this on a GPU with 24GB of VRAM (e.g. 3090). Training will be slower, and you'll need to be sure this is the *only* program running. + - If, like myself, you don't happen to own one of those, I'm including a Jupyter notebook here to help you run it on a rented cloud computing platform. + - It's currently tailored to [runpod.io](https://runpod.io?ref=n8yfwyum), but can work on vast.ai / etc. + +- This implementation does not fully implement Google's ideas on how to preserve the latent space. + + - Most images that are similar to what you're training will be shifted towards that. + - e.g. If you're training a person, all people will look like you. If you're trianing an object, anything in that class will look like your object. + +- There doesn't seem to be an easy way to train two subjects consecutively. You will end up with an 11-12GB. + - I'm currently testing ways of compressing that down to ~2GB. + +- You might have better luck training with `sd-v1-4-full-ema.ckpt` + - However, it's huge and it's annoying. + +# RunPod Instructions +- Sign up for RunPod. Feel free to use my [referral link here](https://runpod.io?ref=n8yfwyum), so that I don't have to pay for it (but you do). +- Click **Deploy** on either `SECURE CLOUD` or `COMMUNITY CLOUD` +- Click `Select` on a GPU with at least 35 GB of VRAM (e.g. A100, A40, A6000, etc) +- Select a template > `Runpod / Stable Diffusion` +- Click `Connect` and choose `Jupyter Lab` +- Make a new notebook (it's just like Google Colab) and run the code below +```python +!git clone https://github.com/JoePenna/Dreambooth-Stable-Diffusion/ +``` +- With the file navigator on the left, `/workspace/Dreambooth-Stable-Diffusion/dreambooth_runpod_joepenna.ipynb` -- follow the instructions in there. + +# Original Readme from XavierXiao + +This is an implementtaion of Google's [Dreambooth](https://arxiv.org/abs/2208.12242) with [Stable Diffusion](https://github.com/CompVis/stable-diffusion). The original Dreambooth is based on [Imagen](https://imagen.research.google/) text-to-image model. However, neither the model nor the pre-trained weights of Imagen is available. To enable people to fine-tune a text-to-image model with a few examples, I implemented the idea of Dreambooth on Stable diffusion. + +This code repository is based on that of [Textual Inversion](https://github.com/rinongal/textual_inversion). Note that Textual Inversion only optimizes word ebedding, while dreambooth fine-tunes the whole diffusion model. + +The implementation makes minimum changes over the official codebase of Textual Inversion. In fact, due to lazyness, some components in Textual Inversion, such as the embedding manager, are not deleted, although they will never be used here. +## Update +**9/20/2022**: I just found a way to reduce the GPU memory a bit. Remember that this code is based on Textual Inversion, and TI's code base has [this line](https://github.com/rinongal/textual_inversion/blob/main/ldm/modules/diffusionmodules/util.py#L112), which disable gradient checkpointing in a hard-code way. This is because in TI, the Unet is not optimized. However, in Dreambooth we optimize the Unet, so we can turn on the gradient checkpoint pointing trick, as in the original SD repo [here](https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L112). The gradient checkpoint is default to be True in [config](https://github.com/XavierXiao/Dreambooth-Stable-Diffusion/blob/main/configs/stable-diffusion/v1-finetune_unfrozen.yaml#L47). I have updated the codes. +## Usage + +### Preparation +First set-up the ```ldm``` enviroment following the instruction from textual inversion repo, or the original Stable Diffusion repo. + +To fine-tune a stable diffusion model, you need to obtain the pre-trained stable diffusion models following their [instructions](https://github.com/CompVis/stable-diffusion#stable-diffusion-v1). Weights can be downloaded on [HuggingFace](https://huggingface.co/CompVis). You can decide which version of checkpoint to use, but I use ```sd-v1-4-full-ema.ckpt```. + +We also need to create a set of images for regularization, as the fine-tuning algorithm of Dreambooth requires that. Details of the algorithm can be found in the paper. Note that in the original paper, the regularization images seem to be generated on-the-fly. However, here I generated a set of regularization images before the training. The text prompt for generating regularization images can be ```photo of a ```, where `````` is a word that describes the class of your object, such as ```dog```. The command is + +``` +python scripts/stable_txt2img.py --ddim_eta 0.0 --n_samples 8 --n_iter 1 --scale 10.0 --ddim_steps 50 --ckpt /path/to/original/stable-diffusion/sd-v1-4-full-ema.ckpt --prompt "a photo of a " +``` + +I generate 8 images for regularization, but more regularization images may lead to stronger regularization and better editability. After that, save the generated images (separately, one image per ```.png``` file) at ```/root/to/regularization/images```. + +**Updates on 9/9** +We should definitely use more images for regularization. Please try 100 or 200, to better align with the original paper. To acomodate this, I shorten the "repeat" of reg dataset in the [config file](https://github.com/XavierXiao/Dreambooth-Stable-Diffusion/blob/main/configs/stable-diffusion/v1-finetune_unfrozen.yaml#L96). + +For some cases, if the generated regularization images are highly unrealistic (happens when you want to generate "man" or "woman"), you can find a diverse set of images (of man/woman) online, and use them as regularization images. + +### Training +Training can be done by running the following command + +``` +python main.py --base configs/stable-diffusion/v1-finetune_unfrozen.yaml + -t + --actual_resume /path/to/original/stable-diffusion/sd-v1-4-full-ema.ckpt + -n + --gpus 0, + --data_root /root/to/training/images + --reg_data_root /root/to/regularization/images + --class_word +``` + +Detailed configuration can be found in ```configs/stable-diffusion/v1-finetune_unfrozen.yaml```. In particular, the default learning rate is ```1.0e-6``` as I found the ```1.0e-5``` in the Dreambooth paper leads to poor editability. The parameter ```reg_weight``` corresponds to the weight of regularization in the Dreambooth paper, and the default is set to ```1.0```. + +Dreambooth requires a placeholder word ```[V]```, called identifier, as in the paper. This identifier needs to be a relatively rare tokens in the vocabulary. The original paper approaches this by using a rare word in T5-XXL tokenizer. For simplicity, here I just use a random word ```sks``` and hard coded it.. If you want to change that, simply make a change in [this file](https://github.com/XavierXiao/Dreambooth-Stable-Diffusion/blob/main/ldm/data/personalized.py#L10). + +Training will be run for 800 steps, and two checkpoints will be saved at ```./logs//checkpoints```, one at 500 steps and one at final step. Typically the one at 500 steps works well enough. I train the model use two A6000 GPUs and it takes ~15 mins. + +### Generation +After training, personalized samples can be obtained by running the command + +``` +python scripts/stable_txt2img.py --ddim_eta 0.0 + --n_samples 8 + --n_iter 1 + --scale 10.0 + --ddim_steps 100 + --ckpt /path/to/saved/checkpoint/from/training + --prompt "photo of a sks " +``` + +In particular, ```sks``` is the identifier, which should be replaced by your choice if you happen to change the identifier, and `````` is the class word ```--class_word``` for training. + +## Results +Here I show some qualitative results. The training images are obtained from the [issue](https://github.com/rinongal/textual_inversion/issues/8) in the Textual Inversion repository, and they are 3 images of a large trash container. Regularization images are generated by prompt ```photo of a container```. Regularization images are shown here: + +![](assets/a-container-0038.jpg) + +After training, generated images with prompt ```photo of a sks container```: + +![](assets/photo-of-a-sks-container-0018.jpg) + +Generated images with prompt ```photo of a sks container on the beach```: + +![](assets/photo-of-a-sks-container-on-the-beach-0017.jpg) + +Generated images with prompt ```photo of a sks container on the moon```: + +![](assets/photo-of-a-sks-container-on-the-moon-0016.jpg) + +Some not-so-perfect but still interesting results: + +Generated images with prompt ```photo of a red sks container```: + +![](assets/a-red-sks-container-0021.jpg) + +Generated images with prompt ```a dog on top of sks container```: + +![](assets/a-dog-on-top-of-sks-container-0023.jpg) + diff --git a/assets/a-container-0038.jpg b/assets/a-container-0038.jpg new file mode 100644 index 0000000000000000000000000000000000000000..bd94aa242883c0395d7dcc5e023f3a0f8eb36d47 GIT binary patch literal 377653 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zo;n|zIv<=mADBAtpE_4go%bE2L6-n7ek<5A8VUcYVdhF#9^n!Y$4Vo`3w{1-qQ*Kkt~q^N)9)cZ~DgP0n+s&apX1gJapM6$zPIaAAuDf+`$}!H#Tb+}pPIHA*U*S|!XL7`?2b06j_)2GVnsegRxzp6S;~kD0 z+|hIHaQmYNcR0?u{q436Zhzu`k9Kai)w%7|nVmXi;SI{ePLUroC_E?6HV^V+oNTj` zp5P=~ojBS&h^J1p*_pY`=E2NX=Qf+2?YG`I*nW(2>y6H>V&|5(*fzN3F6S0EKW=b~ zZO+Y)b8dFiZG)SQoSSZQZnACj;3h{q+crBlo;o+0IyXGpxq=7,<8\"\n!pip install huggingface_hub\n!pip install ipywidgets", + "metadata": { + "id": "9e1bc458-091b-42f4-a125-c3f0df20f29d", + "scrolled": true + }, + "execution_count": null, + "outputs": [], + "id": "9e1bc458-091b-42f4-a125-c3f0df20f29d" + }, + { + "cell_type": "code", + "source": "## Move the sd-v1-4.ckpt to the root of this directory as \"model.ckpt\"\n#actual_locations_of_model_blob = !readlink -f {downloaded_model_path}\n#!cp {actual_locations_of_model_blob[-1]} model.ckpt\n!apt-get update ; apt-get install wget\n!wget 'https://prodesk.home.thijn.ovh/sd-v1-4.ckpt'\n!cp sd-v1-4.ckpt model.ckpt", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "execution_count": null, + "outputs": [], + "id": "ddf7a43d" + }, + { + "cell_type": "markdown", + "source": "## Download pre-generated regularization images\n\nWe've created the following image sets\n\n* man_euler - provided by Niko Pueringer (Corridor Digital) - euler @ 40 steps, CFG 7.5\n* man_unsplash - pictures from various photographers\n* person_ddim\n* woman_ddim - provided by David Bielejeski - ddim @ 50 steps, CFG 10.0\n\n`person_ddim` is recommended", + "metadata": { + "id": "mxPL2O0OLvBW" + }, + "id": "mxPL2O0OLvBW" + }, + { + "cell_type": "code", + "source": "# Grab the existing regularization images\n# Choose the dataset that best represents what you are trying to do and matches what you used for your token\n# man_euler, man_unsplash, person_ddim, woman_ddim\ndataset=\"man_euler\"\n!rm -rf Stable-Diffusion-Regularization-Images-{dataset}\n!git clone https://github.com/djbielejeski/Stable-Diffusion-Regularization-Images-{dataset}.git\n\n!mkdir -p outputs/txt2img-samples/samples/{dataset}\n!mv -v Stable-Diffusion-Regularization-Images-{dataset}/{dataset}/*.* outputs/txt2img-samples/samples/{dataset}", + "metadata": { + "id": "e7EydXCjOV1v" + }, + "execution_count": null, + "outputs": [], + "id": "e7EydXCjOV1v" + }, + { + "cell_type": "markdown", + "source": "# Upload your training images\nUpload 10-20 images of someone to\n\n```\n/workspace/Dreambooth-Stable-Diffusion/training_samples\n```\n\nWARNING: Be sure to upload an *even* amount of images, otherwise the training inexplicably stops at 1500 steps.\n\n* 2-3 full body\n* 3-5 upper body \n* 5-12 close-up on face", + "metadata": { + "id": "zshrC_JuMXmM" + }, + "id": "zshrC_JuMXmM" + }, + { + "cell_type": "code", + "source": "#@markdown Add here the URLs to the images of the concept you are adding\nurls = [\n\"https://prodesk.home.thijn.ovh/gijs/IMG_20220926_121625.png\",\n\"https://prodesk.home.thijn.ovh/gijs/IMG_20220926_121630.png\",\n\"https://prodesk.home.thijn.ovh/gijs/IMG_20220926_121632.png\",\n\"https://prodesk.home.thijn.ovh/gijs/IMG_20220926_121636.png\",\n\"https://prodesk.home.thijn.ovh/gijs/IMG_20220926_121637.png\",\n\"https://prodesk.home.thijn.ovh/gijs/IMG_20220926_121640.png\",\n\"https://prodesk.home.thijn.ovh/gijs/IMG_20220926_121642.png\",\n\"https://prodesk.home.thijn.ovh/gijs/IMG_20220926_121644.png\",\n\"https://prodesk.home.thijn.ovh/gijs/IMG_20220926_121647.png\",\n\"https://prodesk.home.thijn.ovh/gijs/IMG_20220926_121649.png\",\n\"https://prodesk.home.thijn.ovh/gijs/IMG_20220926_121653.png\",\n\"https://prodesk.home.thijn.ovh/gijs/IMG_20220926_121656.png\",\n\"https://prodesk.home.thijn.ovh/gijs/IMG_20220926_121659.png\",\n\"https://prodesk.home.thijn.ovh/gijs/IMG_20220926_121702.png\"\n ## You can add additional images here\n]", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "execution_count": null, + "outputs": [], + "id": "60e37ee0" + }, + { + "cell_type": "code", + "source": "#@title Download and check the images you have just added\nimport os\nimport requests\nfrom io import BytesIO\nfrom PIL import Image\n\n\ndef image_grid(imgs, rows, cols):\n assert len(imgs) == rows*cols\n\n w, h = imgs[0].size\n grid = Image.new('RGB', size=(cols*w, rows*h))\n grid_w, grid_h = grid.size\n\n for i, img in enumerate(imgs):\n grid.paste(img, box=(i%cols*w, i//cols*h))\n return grid\n\ndef download_image(url):\n try:\n response = requests.get(url)\n except:\n return None\n return Image.open(BytesIO(response.content)).convert(\"RGB\")\n\nimages = list(filter(None,[download_image(url) for url in urls]))\nsave_path = \"./training_samples\"\nif not os.path.exists(save_path):\n os.mkdir(save_path)\n[image.save(f\"{save_path}/{i}.png\", format=\"png\") for i, image in enumerate(images)]\nimage_grid(images, 1, len(images))", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "execution_count": null, + "outputs": [], + "id": "df314e2e" + }, + { + "cell_type": "markdown", + "source": "## Training\n\nIf training a person or subject, keep an eye on your project's `logs/{folder}/images/train/samples_scaled_gs-00xxxx` generations.\n\nIf training a style, keep an eye on your project's `logs/{folder}/images/train/samples_gs-00xxxx` generations.", + "metadata": { + "id": "ad4e50df" + }, + "id": "ad4e50df" + }, + { + "cell_type": "code", + "source": "# START THE TRAINING\nproject_name = \"gijsbert\"\nbatch_size = 1000\nclass_word = \"man\" # << match this word to the class word from regularization images above\nreg_data_root = \"/workspace/Dreambooth-Stable-Diffusion/outputs/txt2img-samples/samples/\" + dataset\n\n!rm -rf training_samples/.ipynb_checkpoints\n!python \"main.py\" \\\n --base configs/stable-diffusion/v1-finetune_unfrozen.yaml \\\n -t \\\n --actual_resume \"model.ckpt\" \\\n --reg_data_root {reg_data_root} \\\n -n {project_name} \\\n --gpus 0, \\\n --data_root \"/workspace/Dreambooth-Stable-Diffusion/training_samples\" \\\n --batch_size {batch_size} \\\n --class_word class_word", + "metadata": { + "id": "6fa5dd66-2ca0-4819-907e-802e25583ae6", + "tags": [] + }, + "execution_count": null, + "outputs": [], + "id": "6fa5dd66-2ca0-4819-907e-802e25583ae6" + }, + { + "cell_type": "markdown", + "source": "## Pruning (12GB to 2GB)\nWe are working on having this happen automatically (TODO: PR's welcome)", + "metadata": {}, + "id": "dc49d0bd" + }, + { + "cell_type": "code", + "source": "directory_paths = !ls -d logs/*", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "execution_count": null, + "outputs": [], + "id": "27cea333" + }, + { + "cell_type": "code", + "source": "# This version should automatically prune around 10GB from the ckpt file\nlast_checkpoint_file = directory_paths[-1] + \"/checkpoints/last.ckpt\"\n!python \"prune_ckpt.py\" --ckpt {last_checkpoint_file}", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "execution_count": null, + "outputs": [], + "id": "965b4654" + }, + { + "cell_type": "code", + "source": "last_checkpoint_file_pruned = directory_paths[-1] + \"/checkpoints/last-pruned.ckpt\"\ntraining_samples = !ls training_samples\ndate_string = !date +\"%Y-%m-%dT%H-%M-%S\"\nfile_name = date_string[-1] + \"_\" + project_name + \"_\" + str(len(training_samples)) + \"_training_images_\" + str(batch_size) + \"_batch_size_\" + class_word + \"_class_word.ckpt\"\n!mkdir -p trained_models\n!mv {last_checkpoint_file_pruned} trained_models/{file_name}", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "execution_count": null, + "outputs": [], + "id": "b7a8cec3" + }, + { + "cell_type": "code", + "source": "# Download your trained model file from `trained_models` and use in your favorite Stable Diffusion repo!", + "metadata": {}, + "execution_count": null, + "outputs": [], + "id": "ff1a46d9" + }, + { + "cell_type": "markdown", + "source": "## Generate Images With Your Trained Model!", + "metadata": {}, + "id": "d28d0139" + }, + { + "cell_type": "code", + "source": "!echo python scripts/stable_txt2img.py \\\n --ddim_eta 0.0 \\\n --n_samples 1 \\\n --n_iter 4 \\\n --scale 7.0 \\\n --ddim_steps 50 \\\n --ckpt \"/workspace/Dreambooth-Stable-Diffusion/trained_models/\" + {file_name} \\\n --prompt \"gijsbert person as a masterpiece portrait painting by John Singer Sargent in the style of Rembrandt\"", + "metadata": { + "trusted": true + }, + "execution_count": 2, + "outputs": [ + { + "ename": "", + "evalue": "module 'pexpect' has no attribute 'TIMEOUT'", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39msystem(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mchangeme python scripts/stable_txt2img.py --ddim_eta 0.0 --n_samples 1 --n_iter 4 --scale 7.0 --ddim_steps 50 --ckpt \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/workspace/Dreambooth-Stable-Diffusion/trained_models/\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m + \u001b[39m\u001b[38;5;132;01m{file_name}\u001b[39;00m\u001b[38;5;124m --prompt \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mjoepenna person as a masterpiece portrait painting by John Singer Sargent in the style of Rembrandt\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m/lib/python3.10/site-packages/IPython/core/interactiveshell.py:2466\u001b[0m, in \u001b[0;36mInteractiveShell.system_piped\u001b[0;34m(self, cmd)\u001b[0m\n\u001b[1;32m 2461\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBackground processes not supported.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 2463\u001b[0m \u001b[38;5;66;03m# we explicitly do NOT return the subprocess status code, because\u001b[39;00m\n\u001b[1;32m 2464\u001b[0m \u001b[38;5;66;03m# a non-None value would trigger :func:`sys.displayhook` calls.\u001b[39;00m\n\u001b[1;32m 2465\u001b[0m \u001b[38;5;66;03m# Instead, we store the exit_code in user_ns.\u001b[39;00m\n\u001b[0;32m-> 2466\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muser_ns[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_exit_code\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43msystem\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvar_expand\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcmd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdepth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/lib/python3.10/site-packages/IPython/utils/_process_posix.py:129\u001b[0m, in \u001b[0;36mProcessHandler.system\u001b[0;34m(self, cmd)\u001b[0m\n\u001b[1;32m 125\u001b[0m enc \u001b[38;5;241m=\u001b[39m DEFAULT_ENCODING\n\u001b[1;32m 127\u001b[0m \u001b[38;5;66;03m# Patterns to match on the output, for pexpect. We read input and\u001b[39;00m\n\u001b[1;32m 128\u001b[0m \u001b[38;5;66;03m# allow either a short timeout or EOF\u001b[39;00m\n\u001b[0;32m--> 129\u001b[0m patterns \u001b[38;5;241m=\u001b[39m [\u001b[43mpexpect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTIMEOUT\u001b[49m, pexpect\u001b[38;5;241m.\u001b[39mEOF]\n\u001b[1;32m 130\u001b[0m \u001b[38;5;66;03m# the index of the EOF pattern in the list.\u001b[39;00m\n\u001b[1;32m 131\u001b[0m \u001b[38;5;66;03m# even though we know it's 1, this call means we don't have to worry if\u001b[39;00m\n\u001b[1;32m 132\u001b[0m \u001b[38;5;66;03m# we change the above list, and forget to change this value:\u001b[39;00m\n\u001b[1;32m 133\u001b[0m EOF_index \u001b[38;5;241m=\u001b[39m patterns\u001b[38;5;241m.\u001b[39mindex(pexpect\u001b[38;5;241m.\u001b[39mEOF)\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'pexpect' has no attribute 'TIMEOUT'" + ], + "output_type": "error" + } + ], + "id": "80ddb03b" + }, + { + "cell_type": "code", + "source": "", + "metadata": {}, + "execution_count": null, + "outputs": [], + "id": "0e3c10d9-2c40-4f50-9cf4-97e88a57288c" + } + ] +} \ No newline at end of file diff --git a/environment.yaml b/environment.yaml new file mode 100644 index 0000000..5eb97b3 --- /dev/null +++ b/environment.yaml @@ -0,0 +1,31 @@ +name: ldm +channels: + - pytorch + - defaults +dependencies: + - python=3.8.10 + - pip=20.3 + - cudatoolkit=11.3 + - pytorch=1.10.2 + - torchvision=0.11.3 + - numpy=1.22.3 + - pip: + - albumentations==1.1.0 + - opencv-python==4.2.0.34 + - pudb==2019.2 + - imageio==2.14.1 + - imageio-ffmpeg==0.4.7 + - pytorch-lightning==1.5.9 + - omegaconf==2.1.1 + - test-tube>=0.7.5 + - streamlit>=0.73.1 + - setuptools==59.5.0 + - pillow==9.0.1 + - einops==0.4.1 + - torch-fidelity==0.3.0 + - transformers==4.18.0 + - torchmetrics==0.6.0 + - kornia==0.6 + - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers + - -e git+https://github.com/openai/CLIP.git@main#egg=clip + - -e . diff --git a/evaluation/clip_eval.py b/evaluation/clip_eval.py new file mode 100644 index 0000000..62e7f8d --- /dev/null +++ b/evaluation/clip_eval.py @@ -0,0 +1,113 @@ +import clip +import torch +from torchvision import transforms + +from ldm.models.diffusion.ddim import DDIMSampler + +class CLIPEvaluator(object): + def __init__(self, device, clip_model='ViT-B/32') -> None: + self.device = device + self.model, clip_preprocess = clip.load(clip_model, device=self.device) + + self.clip_preprocess = clip_preprocess + + self.preprocess = transforms.Compose([transforms.Normalize(mean=[-1.0, -1.0, -1.0], std=[2.0, 2.0, 2.0])] + # Un-normalize from [-1.0, 1.0] (generator output) to [0, 1]. + clip_preprocess.transforms[:2] + # to match CLIP input scale assumptions + clip_preprocess.transforms[4:]) # + skip convert PIL to tensor + + def tokenize(self, strings: list): + return clip.tokenize(strings).to(self.device) + + @torch.no_grad() + def encode_text(self, tokens: list) -> torch.Tensor: + return self.model.encode_text(tokens) + + @torch.no_grad() + def encode_images(self, images: torch.Tensor) -> torch.Tensor: + images = self.preprocess(images).to(self.device) + return self.model.encode_image(images) + + def get_text_features(self, text: str, norm: bool = True) -> torch.Tensor: + + tokens = clip.tokenize(text).to(self.device) + + text_features = self.encode_text(tokens).detach() + + if norm: + text_features /= text_features.norm(dim=-1, keepdim=True) + + return text_features + + def get_image_features(self, img: torch.Tensor, norm: bool = True) -> torch.Tensor: + image_features = self.encode_images(img) + + if norm: + image_features /= image_features.clone().norm(dim=-1, keepdim=True) + + return image_features + + def img_to_img_similarity(self, src_images, generated_images): + src_img_features = self.get_image_features(src_images) + gen_img_features = self.get_image_features(generated_images) + + return (src_img_features @ gen_img_features.T).mean() + + def txt_to_img_similarity(self, text, generated_images): + text_features = self.get_text_features(text) + gen_img_features = self.get_image_features(generated_images) + + return (text_features @ gen_img_features.T).mean() + + +class LDMCLIPEvaluator(CLIPEvaluator): + def __init__(self, device, clip_model='ViT-B/32') -> None: + super().__init__(device, clip_model) + + def evaluate(self, ldm_model, src_images, target_text, n_samples=64, n_steps=50): + + sampler = DDIMSampler(ldm_model) + + samples_per_batch = 8 + n_batches = n_samples // samples_per_batch + + # generate samples + all_samples=list() + with torch.no_grad(): + with ldm_model.ema_scope(): + uc = ldm_model.get_learned_conditioning(samples_per_batch * [""]) + + for batch in range(n_batches): + c = ldm_model.get_learned_conditioning(samples_per_batch * [target_text]) + shape = [4, 256//8, 256//8] + samples_ddim, _ = sampler.sample(S=n_steps, + conditioning=c, + batch_size=samples_per_batch, + shape=shape, + verbose=False, + unconditional_guidance_scale=5.0, + unconditional_conditioning=uc, + eta=0.0) + + x_samples_ddim = ldm_model.decode_first_stage(samples_ddim) + x_samples_ddim = torch.clamp(x_samples_ddim, min=-1.0, max=1.0) + + all_samples.append(x_samples_ddim) + + all_samples = torch.cat(all_samples, axis=0) + + sim_samples_to_img = self.img_to_img_similarity(src_images, all_samples) + sim_samples_to_text = self.txt_to_img_similarity(target_text.replace("*", ""), all_samples) + + return sim_samples_to_img, sim_samples_to_text + + +class ImageDirEvaluator(CLIPEvaluator): + def __init__(self, device, clip_model='ViT-B/32') -> None: + super().__init__(device, clip_model) + + def evaluate(self, gen_samples, src_images, target_text): + + sim_samples_to_img = self.img_to_img_similarity(src_images, gen_samples) + sim_samples_to_text = self.txt_to_img_similarity(target_text.replace("*", ""), gen_samples) + + return sim_samples_to_img, 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