diff --git a/.github/workflows/test-invoke-pip.yml b/.github/workflows/test-invoke-pip.yml index 4985202782..a88a2fb883 100644 --- a/.github/workflows/test-invoke-pip.yml +++ b/.github/workflows/test-invoke-pip.yml @@ -8,141 +8,133 @@ on: - 'ready_for_review' - 'opened' - 'synchronize' - - 'converted_to_draft' concurrency: group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }} cancel-in-progress: true jobs: - # fail_if_pull_request_is_draft: - # if: github.event.pull_request.draft == true && github.head_ref != 'dev/diffusers' - # runs-on: ubuntu-18.04 - # steps: - # - name: Fails in order to indicate that pull request needs to be marked as ready to review and unit tests workflow needs to pass. - # run: exit 1 matrix: - if: github.event.pull_request.draft == false || github.head_ref == 'dev/diffusers' + if: github.event.pull_request.draft == false strategy: matrix: - stable-diffusion-model: - - stable-diffusion-1.5 - requirements-file: - - requirements-lin-cuda.txt - - requirements-lin-amd.txt - - requirements-mac-mps-cpu.txt - - requirements-win-colab-cuda.txt python-version: # - '3.9' - '3.10' + pytorch: + - linux-cuda-11_6 + - linux-cuda-11_7 + - linux-rocm-5_2 + - linux-cpu + - macos-default + - windows-cpu + - windows-cuda-11_6 + - windows-cuda-11_7 include: - - requirements-file: requirements-lin-cuda.txt + - pytorch: linux-cuda-11_6 + os: ubuntu-22.04 + extra-index-url: 'https://download.pytorch.org/whl/cu116' + github-env: $GITHUB_ENV + - pytorch: linux-cuda-11_7 os: ubuntu-22.04 github-env: $GITHUB_ENV - - requirements-file: requirements-lin-amd.txt + - pytorch: linux-rocm-5_2 os: ubuntu-22.04 + extra-index-url: 'https://download.pytorch.org/whl/rocm5.2' github-env: $GITHUB_ENV - - requirements-file: requirements-mac-mps-cpu.txt + - pytorch: linux-cpu + os: ubuntu-22.04 + extra-index-url: 'https://download.pytorch.org/whl/cpu' + github-env: $GITHUB_ENV + - pytorch: macos-default os: macOS-12 github-env: $GITHUB_ENV - - requirements-file: requirements-win-colab-cuda.txt + - pytorch: windows-cpu os: windows-2022 github-env: $env:GITHUB_ENV - name: ${{ matrix.requirements-file }} on ${{ matrix.python-version }} + - pytorch: windows-cuda-11_6 + os: windows-2022 + extra-index-url: 'https://download.pytorch.org/whl/cu116' + github-env: $env:GITHUB_ENV + - pytorch: windows-cuda-11_7 + os: windows-2022 + extra-index-url: 'https://download.pytorch.org/whl/cu117' + github-env: $env:GITHUB_ENV + name: ${{ matrix.pytorch }} on ${{ matrix.python-version }} runs-on: ${{ matrix.os }} - env: - INVOKE_MODEL_RECONFIGURE: '--yes' - INVOKEAI_ROOT: '${{ github.workspace }}/invokeai' - PYTHONUNBUFFERED: 1 - HAVE_SECRETS: ${{ secrets.HUGGINGFACE_TOKEN != '' }} steps: - name: Checkout sources id: checkout-sources uses: actions/checkout@v3 - - name: set INVOKEAI_ROOT Windows - if: matrix.os == 'windows-2022' - run: | - echo "INVOKEAI_ROOT=${{ github.workspace }}\invokeai" >> ${{ matrix.github-env }} - echo "INVOKEAI_OUTDIR=${{ github.workspace }}\invokeai\outputs" >> ${{ matrix.github-env }} - - - name: set INVOKEAI_ROOT others - if: matrix.os != 'windows-2022' - run: | - echo "INVOKEAI_ROOT=${{ github.workspace }}/invokeai" >> ${{ matrix.github-env }} - echo "INVOKEAI_OUTDIR=${{ github.workspace }}/invokeai/outputs" >> ${{ matrix.github-env }} - - - name: Use Cached diffusers-1.5 - id: cache-sd-model - uses: actions/cache@v3 - env: - cache-name: huggingface-${{ matrix.stable-diffusion-model }} + - name: setup python + uses: actions/setup-python@v4 with: - path: | - ${{ env.INVOKEAI_ROOT }}/models/runwayml - ${{ env.INVOKEAI_ROOT }}/models/stabilityai - ${{ env.INVOKEAI_ROOT }}/models/CompVis - key: ${{ env.cache-name }} + python-version: ${{ matrix.python-version }} + + - name: Set Cache-Directory Windows + if: runner.os == 'Windows' + id: set-cache-dir-windows + run: | + echo "CACHE_DIR=$HOME\invokeai\models" >> ${{ matrix.github-env }} + echo "PIP_NO_CACHE_DIR=1" >> ${{ matrix.github-env }} + + - name: Set Cache-Directory others + if: runner.os != 'Windows' + id: set-cache-dir-others + run: echo "CACHE_DIR=$HOME/invokeai/models" >> ${{ matrix.github-env }} - name: set test prompt to main branch validation if: ${{ github.ref == 'refs/heads/main' }} run: echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> ${{ matrix.github-env }} - - name: set test prompt to development branch validation - if: ${{ github.ref == 'refs/heads/development' }} - run: echo "TEST_PROMPTS=tests/dev_prompts.txt" >> ${{ matrix.github-env }} - - name: set test prompt to Pull Request validation - if: ${{ github.ref != 'refs/heads/main' && github.ref != 'refs/heads/development' }} + if: ${{ github.ref != 'refs/heads/main' }} run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }} - - name: create requirements.txt - run: cp 'environments-and-requirements/${{ matrix.requirements-file }}' '${{ matrix.requirements-file }}' + - name: install invokeai + run: pip3 install --use-pep517 -e . + env: + PIP_EXTRA_INDEX_URL: ${{ matrix.extra-index-url }} - - name: setup python - uses: actions/setup-python@v4 + - name: Use Cached models + id: cache-sd-model + uses: actions/cache@v3 + env: + cache-name: huggingface-models with: - python-version: ${{ matrix.python-version }} - # cache: 'pip' - # cache-dependency-path: ${{ matrix.requirements-file }} + path: ${{ env.CACHE_DIR }} + key: ${{ env.cache-name }} + enableCrossOsArchive: true - - name: install dependencies - run: pip3 install --upgrade pip setuptools wheel - - - name: install requirements - run: pip3 install -r '${{ matrix.requirements-file }}' - - - name: run configure_invokeai.py + - name: run configure_invokeai id: run-preload-models env: HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }} run: > - configure_invokeai.py + configure_invokeai --yes --default_only - --full-precision # can't use fp16 weights without a GPU + --full-precision + # can't use fp16 weights without a GPU - name: Run the tests + if: runner.os != 'Windows' id: run-tests - if: matrix.os != 'windows-2022' env: # Set offline mode to make sure configure preloaded successfully. HF_HUB_OFFLINE: 1 HF_DATASETS_OFFLINE: 1 TRANSFORMERS_OFFLINE: 1 run: > - python3 scripts/invoke.py + invoke --no-patchmatch --no-nsfw_checker - --model ${{ matrix.stable-diffusion-model }} --from_file ${{ env.TEST_PROMPTS }} - --root="${{ env.INVOKEAI_ROOT }}" - --outdir="${{ env.INVOKEAI_OUTDIR }}" - name: Archive results id: archive-results - if: matrix.os != 'windows-2022' uses: actions/upload-artifact@v3 with: - name: results_${{ matrix.requirements-file }}_${{ matrix.python-version }} + name: results_${{ matrix.pytorch }}_${{ matrix.python-version }} path: ${{ env.INVOKEAI_ROOT }}/outputs diff --git a/README.md b/README.md index 07501a4242..765bed663a 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@
-![project logo](docs/assets/invoke_ai_banner.png) +![project logo](https://github.com/mauwii/InvokeAI/raw/main/docs/assets/invoke_ai_banner.png) # InvokeAI: A Stable Diffusion Toolkit @@ -28,6 +28,7 @@ [latest commit to main link]: https://github.com/invoke-ai/InvokeAI/commits/main [latest release badge]: https://flat.badgen.net/github/release/invoke-ai/InvokeAI/development?icon=github [latest release link]: https://github.com/invoke-ai/InvokeAI/releases +
InvokeAI is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products. @@ -38,8 +39,11 @@ _Note: InvokeAI is rapidly evolving. Please use the [Issues](https://github.com/invoke-ai/InvokeAI/issues) tab to report bugs and make feature requests. Be sure to use the provided templates. They will help us diagnose issues faster._ +
-![canvas preview](docs/assets/canvas_preview.png) +![canvas preview](https://github.com/mauwii/InvokeAI/raw/main/docs/assets/canvas_preview.png) + +
# Getting Started with InvokeAI @@ -81,6 +85,7 @@ instructions, please see: InvokeAI is supported across Linux, Windows and macOS. Linux users can use either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm driver). + #### System You will need one of the following: @@ -104,19 +109,24 @@ to render 512x512 images. Feature documentation can be reviewed by navigating to [the InvokeAI Documentation page](https://invoke-ai.github.io/InvokeAI/features/) -### *Web Server & UI* +### *Web Server & UI* + InvokeAI offers a locally hosted Web Server & React Frontend, with an industry leading user experience. The Web-based UI allows for simple and intuitive workflows, and is responsive for use on mobile devices and tablets accessing the web server. ### *Unified Canvas* + The Unified Canvas is a fully integrated canvas implementation with support for all core generation capabilities, in/outpainting, brush tools, and more. This creative tool unlocks the capability for artists to create with AI as a creative collaborator, and can be used to augment AI-generated imagery, sketches, photography, renders, and more. ### *Advanced Prompt Syntax* + InvokeAI's advanced prompt syntax allows for token weighting, cross-attention control, and prompt blending, allowing for fine-tuned tweaking of your invocations and exploration of the latent space. ### *Command Line Interface* + For users utilizing a terminal-based environment, or who want to take advantage of CLI features, InvokeAI offers an extensive and actively supported command-line interface that provides the full suite of generation functionality available in the tool. ### Other features + - *Support for both ckpt and diffusers models* - *SD 2.0, 2.1 support* - *Noise Control & Tresholding* @@ -126,6 +136,7 @@ For users utilizing a terminal-based environment, or who want to take advantage - *Model Manager & Support* ### Coming Soon + - *Node-Based Architecture & UI* - And more... @@ -149,7 +160,7 @@ To join, just raise your hand on the InvokeAI Discord server (#dev-chat) or the If you are unfamiliar with how to contribute to GitHub projects, here is a -[Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github). A full set of contribution guidelines, along with templates, are in progress. You can **make your pull request against the "main" branch**. +[Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github). A full set of contribution guidelines, along with templates, are in progress. You can **make your pull request against the "main" branch**. We hope you enjoy using our software as much as we enjoy creating it, and we hope that some of those of you who are reading this will elect diff --git a/configs/INITIAL_MODELS.yaml b/configs/INITIAL_MODELS.yaml index 94502855da..ea43280340 100644 --- a/configs/INITIAL_MODELS.yaml +++ b/configs/INITIAL_MODELS.yaml @@ -93,6 +93,7 @@ voxel_art-1.0: format: ckpt vae: repo_id: stabilityai/sd-vae-ft-mse + file: vae-ft-mse-840000-ema-pruned.ckpt recommended: False width: 512 height: 512 @@ -102,7 +103,7 @@ ft-mse-improved-autoencoder-840000: format: ckpt config: VAE/default file: vae-ft-mse-840000-ema-pruned.ckpt - recommended: False + recommended: True width: 512 height: 512 trinart_vae: diff --git a/docs/assets/textual-inversion/ti-frontend.png b/docs/assets/textual-inversion/ti-frontend.png new file mode 100644 index 0000000000..0500e9b132 Binary files /dev/null and b/docs/assets/textual-inversion/ti-frontend.png differ diff --git a/docs/features/MODEL_MERGING.md b/docs/features/MODEL_MERGING.md new file mode 100644 index 0000000000..b94e1e4314 --- /dev/null +++ b/docs/features/MODEL_MERGING.md @@ -0,0 +1,77 @@ +--- +title: Model Merging +--- + +# :material-image-off: Model Merging + +## How to Merge Models + +As of version 2.3, InvokeAI comes with a script that allows you to +merge two or three diffusers-type models into a new merged model. The +resulting model will combine characteristics of the original, and can +be used to teach an old model new tricks. + +You may run the merge script by starting the invoke launcher +(`invoke.sh` or `invoke.bat`) and choosing the option for _merge +models_. This will launch a text-based interactive user interface that +prompts you to select the models to merge, how to merge them, and the +merged model name. + +Alternatively you may activate InvokeAI's virtual environment from the +command line, and call the script via `merge_models_fe.py` (the "fe" +stands for "front end"). There is also a version that accepts +command-line arguments, which you can run with the command +`merge_models.py`. + +The user interface for the text-based interactive script is +straightforward. It shows you a series of setting fields. Use control-N (^N) +to move to the next field, and control-P (^P) to move to the previous +one. You can also use TAB and shift-TAB to move forward and +backward. Once you are in a multiple choice field, use the up and down +cursor arrows to move to your desired selection, and press or + to select it. Change text fields by typing in them, and adjust +scrollbars using the left and right arrow keys. + +Once you are happy with your settings, press the OK button. Note that +there may be two pages of settings, depending on the height of your +screen, and the OK button may be on the second page. Advance past the +last field of the first page to get to the second page, and reverse +this to get back. + +If the merge runs successfully, it will create a new diffusers model +under the selected name and register it with InvokeAI. + +## The Settings + +* Model Selection -- there are three multiple choice fields that + display all the diffusers-style models that InvokeAI knows about. + If you do not see the model you are looking for, then it is probably + a legacy checkpoint model and needs to be converted using the + `invoke.py` command-line client and its `!optimize` command. You + must select at least two models to merge. The third can be left at + "None" if you desire. + +* Alpha -- This is the ratio to use when combining models. It ranges + from 0 to 1. The higher the value, the more weight is given to the + 2d and (optionally) 3d models. So if you have two models named "A" + and "B", an alpha value of 0.25 will give you a merged model that is + 25% A and 75% B. + +* Interpolation Method -- This is the method used to combine + weights. The options are "weighted_sum" (the default), "sigmoid", + "inv_sigmoid" and "add_difference". Each produces slightly different + results. When three models are in use, only "add_difference" is + available. (TODO: cite a reference that describes what these + interpolation methods actually do and how to decide among them). + +* Force -- Not all models are compatible with each other. The merge + script will check for compatibility and refuse to merge ones that + are incompatible. Set this checkbox to try merging anyway. + +* Name for merged model - This is the name for the new model. Please + use InvokeAI conventions - only alphanumeric letters and the + characters ".+-". + +## Caveats + +This is a new script and may contain bugs. diff --git a/docs/features/TEXTUAL_INVERSION.md b/docs/features/TEXTUAL_INVERSION.md index 7ce0f41c5a..7d54ea971c 100644 --- a/docs/features/TEXTUAL_INVERSION.md +++ b/docs/features/TEXTUAL_INVERSION.md @@ -10,83 +10,263 @@ You may personalize the generated images to provide your own styles or objects by training a new LDM checkpoint and introducing a new vocabulary to the fixed model as a (.pt) embeddings file. Alternatively, you may use or train HuggingFace Concepts embeddings files (.bin) from - and its associated notebooks. + and its associated +notebooks. -## **Training** +## **Hardware and Software Requirements** -To train, prepare a folder that contains images sized at 512x512 and execute the -following: +You will need a GPU to perform training in a reasonable length of +time, and at least 12 GB of VRAM. We recommend using the [`xformers` +library](../installation/070_INSTALL_XFORMERS) to accelerate the +training process further. During training, about ~8 GB is temporarily +needed in order to store intermediate models, checkpoints and logs. -### WINDOWS +## **Preparing for Training** -As the default backend is not available on Windows, if you're using that -platform, set the environment variable `PL_TORCH_DISTRIBUTED_BACKEND` to `gloo` +To train, prepare a folder that contains 3-5 images that illustrate +the object or concept. It is good to provide a variety of examples or +poses to avoid overtraining the system. Format these images as PNG +(preferred) or JPG. You do not need to resize or crop the images in +advance, but for more control you may wish to do so. -```bash -python3 ./main.py -t \ - --base ./configs/stable-diffusion/v1-finetune.yaml \ - --actual_resume ./models/ldm/stable-diffusion-v1/model.ckpt \ - -n my_cat \ - --gpus 0 \ - --data_root D:/textual-inversion/my_cat \ - --init_word 'cat' +Place the training images in a directory on the machine InvokeAI runs +on. We recommend placing them in a subdirectory of the +`text-inversion-training-data` folder located in the InvokeAI root +directory, ordinarily `~/invokeai` (Linux/Mac), or +`C:\Users\your_name\invokeai` (Windows). For example, to create an +embedding for the "psychedelic" style, you'd place the training images +into the directory +`~invokeai/text-inversion-training-data/psychedelic`. + +## **Launching Training Using the Console Front End** + +InvokeAI 2.3 and higher comes with a text console-based training front +end. From within the `invoke.sh`/`invoke.bat` Invoke launcher script, +start the front end by selecting choice (3): + +```sh +Do you want to generate images using the +1. command-line +2. browser-based UI +3. textual inversion training +4. open the developer console +Please enter 1, 2, 3, or 4: [1] 3 ``` -During the training process, files will be created in -`/logs/[project][time][project]/` where you can see the process. +From the command line, with the InvokeAI virtual environment active, +you can launch the front end with the command +`textual_inversion_fe`. -Conditioning contains the training prompts inputs, reconstruction the input -images for the training epoch samples, samples scaled for a sample of the prompt -and one with the init word provided. +This will launch a text-based front end that will look like this: -On a RTX3090, the process for SD will take ~1h @1.6 iterations/sec. +
+![ti-frontend](../assets/textual-inversion/ti-frontend.png) +
-!!! note +The interface is keyboard-based. Move from field to field using +control-N (^N) to move to the next field and control-P (^P) to the +previous one. and work as well. Once a field is +active, use the cursor keys. In a checkbox group, use the up and down +cursor keys to move from choice to choice, and to select a +choice. In a scrollbar, use the left and right cursor keys to increase +and decrease the value of the scroll. In textfields, type the desired +values. - According to the associated paper, the optimal number of - images is 3-5. Your model may not converge if you use more images than - that. +The number of parameters may look intimidating, but in most cases the +predefined defaults work fine. The red circled fields in the above +illustration are the ones you will adjust most frequently. -Training will run indefinitely, but you may wish to stop it (with ctrl-c) before -the heat death of the universe, when you find a low loss epoch or around ~5000 -iterations. Note that you can set a fixed limit on the number of training steps -by decreasing the "max_steps" option in -configs/stable_diffusion/v1-finetune.yaml (currently set to 4000000) +### Model Name -## **Run the Model** +This will list all the diffusers models that are currently +installed. Select the one you wish to use as the basis for your +embedding. Be aware that if you use a SD-1.X-based model for your +training, you will only be able to use this embedding with other +SD-1.X-based models. Similarly, if you train on SD-2.X, you will only +be able to use the embeddings with models based on SD-2.X. -Once the model is trained, specify the trained .pt or .bin file when starting -invoke using +### Trigger Term -```bash -python3 ./scripts/invoke.py \ - --embedding_path /path/to/embedding.pt +This is the prompt term you will use to trigger the embedding. Type a +single word or phrase you wish to use as the trigger, example +"psychedelic" (without angle brackets). Within InvokeAI, you will then +be able to activate the trigger using the syntax ``. + +### Initializer + +This is a single character that is used internally during the training +process as a placeholder for the trigger term. It defaults to "*" and +can usually be left alone. + +### Resume from last saved checkpoint + +As training proceeds, textual inversion will write a series of +intermediate files that can be used to resume training from where it +was left off in the case of an interruption. This checkbox will be +automatically selected if you provide a previously used trigger term +and at least one checkpoint file is found on disk. + +Note that as of 20 January 2023, resume does not seem to be working +properly due to an issue with the upstream code. + +### Data Training Directory + +This is the location of the images to be used for training. When you +select a trigger term like "my-trigger", the frontend will prepopulate +this field with `~/invokeai/text-inversion-training-data/my-trigger`, +but you can change the path to wherever you want. + +### Output Destination Directory + +This is the location of the logs, checkpoint files, and embedding +files created during training. When you select a trigger term like +"my-trigger", the frontend will prepopulate this field with +`~/invokeai/text-inversion-output/my-trigger`, but you can change the +path to wherever you want. + +### Image resolution + +The images in the training directory will be automatically scaled to +the value you use here. For best results, you will want to use the +same default resolution of the underlying model (512 pixels for +SD-1.5, 768 for the larger version of SD-2.1). + +### Center crop images + +If this is selected, your images will be center cropped to make them +square before resizing them to the desired resolution. Center cropping +can indiscriminately cut off the top of subjects' heads for portrait +aspect images, so if you have images like this, you may wish to use a +photoeditor to manually crop them to a square aspect ratio. + +### Mixed precision + +Select the floating point precision for the embedding. "no" will +result in a full 32-bit precision, "fp16" will provide 16-bit +precision, and "bf16" will provide mixed precision (only available +when XFormers is used). + +### Max training steps + +How many steps the training will take before the model converges. Most +training sets will converge with 2000-3000 steps. + +### Batch size + +This adjusts how many training images are processed simultaneously in +each step. Higher values will cause the training process to run more +quickly, but use more memory. The default size will run with GPUs with +as little as 12 GB. + +### Learning rate + +The rate at which the system adjusts its internal weights during +training. Higher values risk overtraining (getting the same image each +time), and lower values will take more steps to train a good +model. The default of 0.0005 is conservative; you may wish to increase +it to 0.005 to speed up training. + +### Scale learning rate by number of GPUs, steps and batch size + +If this is selected (the default) the system will adjust the provided +learning rate to improve performance. + +### Use xformers acceleration + +This will activate XFormers memory-efficient attention. You need to +have XFormers installed for this to have an effect. + +### Learning rate scheduler + +This adjusts how the learning rate changes over the course of +training. The default "constant" means to use a constant learning rate +for the entire training session. The other values scale the learning +rate according to various formulas. + +Only "constant" is supported by the XFormers library. + +### Gradient accumulation steps + +This is a parameter that allows you to use bigger batch sizes than +your GPU's VRAM would ordinarily accommodate, at the cost of some +performance. + +### Warmup steps + +If "constant_with_warmup" is selected in the learning rate scheduler, +then this provides the number of warmup steps. Warmup steps have a +very low learning rate, and are one way of preventing early +overtraining. + +## The training run + +Start the training run by advancing to the OK button (bottom right) +and pressing . A series of progress messages will be displayed +as the training process proceeds. This may take an hour or two, +depending on settings and the speed of your system. Various log and +checkpoint files will be written into the output directory (ordinarily +`~/invokeai/text-inversion-output/my-model/`) + +At the end of successful training, the system will copy the file +`learned_embeds.bin` into the InvokeAI root directory's `embeddings` +directory, using a subdirectory named after the trigger token. For +example, if the trigger token was `psychedelic`, then look for the +embeddings file in +`~/invokeai/embeddings/psychedelic/learned_embeds.bin` + +You may now launch InvokeAI and try out a prompt that uses the trigger +term. For example `a plate of banana sushi in style`. + +## **Training with the Command-Line Script** + +InvokeAI also comes with a traditional command-line script for +launching textual inversion training. It is named +`textual_inversion`, and can be launched from within the +"developer's console", or from the command line after activating +InvokeAI's virtual environment. + +It accepts a large number of arguments, which can be summarized by +passing the `--help` argument: + +```sh +textual_inversion --help ``` -Then, to utilize your subject at the invoke prompt - -```bash -invoke> "a photo of *" +Typical usage is shown here: +```sh +python textual_inversion.py \ + --model=stable-diffusion-1.5 \ + --resolution=512 \ + --learnable_property=style \ + --initializer_token='*' \ + --placeholder_token='' \ + --train_data_dir=/home/lstein/invokeai/training-data/psychedelic \ + --output_dir=/home/lstein/invokeai/text-inversion-training/psychedelic \ + --scale_lr \ + --train_batch_size=8 \ + --gradient_accumulation_steps=4 \ + --max_train_steps=3000 \ + --learning_rate=0.0005 \ + --resume_from_checkpoint=latest \ + --lr_scheduler=constant \ + --mixed_precision=fp16 \ + --only_save_embeds ``` -This also works with image2image +## Reading -```bash -invoke> "waterfall and rainbow in the style of *" --init_img=./init-images/crude_drawing.png --strength=0.5 -s100 -n4 -``` +For more information on textual inversion, please see the following +resources: -For .pt files it's also possible to train multiple tokens (modify the -placeholder string in `configs/stable-diffusion/v1-finetune.yaml`) and combine -LDM checkpoints using: +* The [textual inversion repository](https://github.com/rinongal/textual_inversion) and + associated paper for details and limitations. +* [HuggingFace's textual inversion training + page](https://huggingface.co/docs/diffusers/training/text_inversion) +* [HuggingFace example script + documentation](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion) + (Note that this script is similar to, but not identical, to + `textual_inversion`, but produces embed files that are completely compatible. -```bash -python3 ./scripts/merge_embeddings.py \ - --manager_ckpts /path/to/first/embedding.pt \ - [,[...]] \ - --output_path /path/to/output/embedding.pt -``` +--- -Credit goes to rinongal and the repository - -Please see [the repository](https://github.com/rinongal/textual_inversion) and -associated paper for details and limitations. +copyright (c) 2023, Lincoln Stein and the InvokeAI Development Team \ No newline at end of file diff --git a/docs/index.md b/docs/index.md index c38f840d32..e20535d591 100644 --- a/docs/index.md +++ b/docs/index.md @@ -157,6 +157,8 @@ images in full-precision mode: - [Prompt Engineering](features/PROMPTS.md) +- [Model Merging](features/MODEL_MERGING.md) + - Miscellaneous - [NSFW Checker](features/NSFW.md) - [Embiggen upscaling](features/EMBIGGEN.md) diff --git a/docs/installation/020_INSTALL_MANUAL.md b/docs/installation/020_INSTALL_MANUAL.md index ead1eb6536..1fad83121f 100644 --- a/docs/installation/020_INSTALL_MANUAL.md +++ b/docs/installation/020_INSTALL_MANUAL.md @@ -119,10 +119,8 @@ manager, please follow these steps: 6. Run PIP - Be sure that the `invokeai` environment is active before doing this: - ```bash - pip install --prefer-binary -r requirements.txt + pip --python invokeai install --use-pep517 . ``` 7. Set up the runtime directory @@ -137,7 +135,7 @@ manager, please follow these steps: default to `invokeai` in your home directory. ```bash - configure_invokeai.py --root_dir ~/Programs/invokeai + configure_invokeai --root_dir ~/Programs/invokeai ``` The script `configure_invokeai.py` will interactively guide you through the @@ -452,7 +450,7 @@ time. Note that this method only works with the PIP method. step. 3. Run one additional step while you are in the source code repository - directory `pip install .` (note the dot at the end). + directory `pip install --use-pep517 .` (note the dot at the end). 4. That's all! Now, whenever you activate the virtual environment, `invoke.py` will know where to look for the runtime directory without diff --git a/docs/installation/060_INSTALL_PATCHMATCH.md b/docs/installation/060_INSTALL_PATCHMATCH.md index 592e1f56c3..989be24731 100644 --- a/docs/installation/060_INSTALL_PATCHMATCH.md +++ b/docs/installation/060_INSTALL_PATCHMATCH.md @@ -18,7 +18,13 @@ Windows systems with no extra intervention. ## Macintosh -PyPatchMatch is not currently supported, but the team is working on it. +You need to have opencv installed so that pypatchmatch can be built: + +```bash +brew install opencv +``` + +The next time you start `invoke`, after sucesfully installing opencv, pypatchmatch will be built. ## Linux @@ -39,23 +45,16 @@ Prior to installing PyPatchMatch, you need to take the following steps: sudo apt install python3-opencv libopencv-dev ``` -3. Fix the naming of the `opencv` package configuration file: - - ```sh - cd /usr/lib/x86_64-linux-gnu/pkgconfig/ - ln -sf opencv4.pc opencv.pc - ``` - -4. Activate the environment you use for invokeai, either with `conda` or with a +3. Activate the environment you use for invokeai, either with `conda` or with a virtual environment. -5. Install pypatchmatch: +4. Install pypatchmatch: ```sh - pip install "git+https://github.com/invoke-ai/PyPatchMatch@0.1.3#egg=pypatchmatch" + pip install pypatchmatch ``` -6. Confirm that pypatchmatch is installed. At the command-line prompt enter +5. Confirm that pypatchmatch is installed. At the command-line prompt enter `python`, and then at the `>>>` line type `from patchmatch import patch_match`: It should look like the follwing: diff --git a/docs/installation/INSTALL_MANUAL.md b/docs/installation/INSTALL_MANUAL.md index 4d62df9415..f45463c535 100644 --- a/docs/installation/INSTALL_MANUAL.md +++ b/docs/installation/INSTALL_MANUAL.md @@ -254,65 +254,10 @@ steps: source invokeai/bin/activate ``` -4. Pick the correct `requirements*.txt` file for your hardware and operating - system. - - We have created a series of environment files suited for different operating - systems and GPU hardware. They are located in the - `environments-and-requirements` directory: - -
- - | filename | OS | - | :---------------------------------: | :-------------------------------------------------------------: | - | requirements-lin-amd.txt | Linux with an AMD (ROCm) GPU | - | requirements-lin-arm64.txt | Linux running on arm64 systems | - | requirements-lin-cuda.txt | Linux with an NVIDIA (CUDA) GPU | - | requirements-mac-mps-cpu.txt | Macintoshes with MPS acceleration | - | requirements-lin-win-colab-cuda.txt | Windows with an NVIDA (CUDA) GPU
(supports Google Colab too) | - -
- - Select the appropriate requirements file, and make a link to it from - `requirements.txt` in the top-level InvokeAI directory. The command to do - this from the top-level directory is: - - !!! example "" - - === "Macintosh and Linux" - - !!! info "Replace `xxx` and `yyy` with the appropriate OS and GPU codes." - - ```bash - ln -sf environments-and-requirements/requirements-xxx-yyy.txt requirements.txt - ``` - - === "Windows" - - !!! info "on Windows, admin privileges are required to make links, so we use the copy command instead" - - ```cmd - copy environments-and-requirements\requirements-lin-win-colab-cuda.txt requirements.txt - ``` - - !!! warning - - Please do not link or copy `environments-and-requirements/requirements-base.txt`. - This is a base requirements file that does not have the platform-specific - libraries. Also, be sure to link or copy the platform-specific file to - a top-level file named `requirements.txt` as shown here. Running pip on - a requirements file in a subdirectory will not work as expected. - - When this is done, confirm that a file named `requirements.txt` has been - created in the InvokeAI root directory and that it points to the correct - file in `environments-and-requirements`. - -5. Run PIP - - Be sure that the `invokeai` environment is active before doing this: +4. Run PIP ```bash - pip install --prefer-binary -r requirements.txt + pip --python invokeai install --use-pep517 . ``` --- diff --git a/frontend/dist/assets/index-legacy-474a75fe.js b/frontend/dist/assets/index-legacy-6edbec57.js similarity index 59% rename from frontend/dist/assets/index-legacy-474a75fe.js rename to frontend/dist/assets/index-legacy-6edbec57.js index b48c03f8f8..df48761d1e 100644 --- a/frontend/dist/assets/index-legacy-474a75fe.js +++ b/frontend/dist/assets/index-legacy-6edbec57.js @@ -1,4 +1,4 @@ -!function(){function e(e,t,n){return(t=function(e){var t=function(e,t){if("object"!=typeof e||null===e)return e;var n=e[Symbol.toPrimitive];if(void 0!==n){var r=n.call(e,t||"default");if("object"!=typeof r)return r;throw new TypeError("@@toPrimitive must return a primitive value.")}return("string"===t?String:Number)(e)}(e,"string");return"symbol"==typeof t?t:String(t)}(t))in 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