[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-70985":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":23,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":27,"readmeContent":28,"aiSummary":29,"trendingCount":16,"starSnapshotCount":16,"syncStatus":30,"lastSyncTime":31,"discoverSource":32},70985,"ai-toolkit","ostris\u002Fai-toolkit","ostris","The ultimate training toolkit for finetuning diffusion models","",null,"Python",10811,1346,87,70,0,42,98,287,126,44.39,"MIT License",false,"main",true,[],"2026-06-12 02:02:46","# Ostris AI Toolkit\n\nAI Toolkit is an easy to use all in one training suite for diffusion models. I try to support all the latest models on consumer grade hardware. Image and video models. It can be run as a GUI or CLI. It is designed to be easy to use but still have every feature imaginable. Free and open source.\n\n\n\n## Supported Models\n\n### Image\n- [black-forest-labs\u002FFLUX.1-dev](https:\u002F\u002Fhuggingface.co\u002Fblack-forest-labs\u002FFLUX.1-dev) (FLUX.1)\n- [black-forest-labs\u002FFLUX.2-dev](https:\u002F\u002Fhuggingface.co\u002Fblack-forest-labs\u002FFLUX.2-dev) (FLUX.2)\n- [black-forest-labs\u002FFLUX.2-klein-base-4B](https:\u002F\u002Fhuggingface.co\u002Fblack-forest-labs\u002FFLUX.2-klein-base-4B) (FLUX.2-klein-base-4B)\n- [black-forest-labs\u002FFLUX.2-klein-base-9B](https:\u002F\u002Fhuggingface.co\u002Fblack-forest-labs\u002FFLUX.2-klein-base-9B) (FLUX.2-klein-base-9B)\n- [ostris\u002FFlex.1-alpha](https:\u002F\u002Fhuggingface.co\u002Fostris\u002FFlex.1-alpha) (Flex.1)\n- [ostris\u002FFlex.2-preview](https:\u002F\u002Fhuggingface.co\u002Fostris\u002FFlex.2-preview) (Flex.2)\n- [lodestones\u002FChroma1-Base](https:\u002F\u002Fhuggingface.co\u002Flodestones\u002FChroma1-Base) (Chroma)\n- [Alpha-VLLM\u002FLumina-Image-2.0](https:\u002F\u002Fhuggingface.co\u002FAlpha-VLLM\u002FLumina-Image-2.0) (Lumina2)\n- [Qwen\u002FQwen-Image](https:\u002F\u002Fhuggingface.co\u002FQwen\u002FQwen-Image) (Qwen-Image)\n- [Qwen\u002FQwen-Image-2512](https:\u002F\u002Fhuggingface.co\u002FQwen\u002FQwen-Image-2512) (Qwen-Image-2512)\n- [HiDream-ai\u002FHiDream-I1-Full](https:\u002F\u002Fhuggingface.co\u002FHiDream-ai\u002FHiDream-I1-Full) (HiDream)\n- [OmniGen2\u002FOmniGen2](https:\u002F\u002Fhuggingface.co\u002FOmniGen2\u002FOmniGen2) (OmniGen2)\n- [Tongyi-MAI\u002FZ-Image-Turbo](https:\u002F\u002Fhuggingface.co\u002FTongyi-MAI\u002FZ-Image-Turbo) (Z-Image Turbo)\n- [Tongyi-MAI\u002FZ-Image](https:\u002F\u002Fhuggingface.co\u002FTongyi-MAI\u002FZ-Image) (Z-Image)\n- [ostris\u002FZ-Image-De-Turbo](https:\u002F\u002Fhuggingface.co\u002Fostris\u002FZ-Image-De-Turbo) (Z-Image De-Turbo)\n- [stabilityai\u002Fstable-diffusion-xl-base-1.0](https:\u002F\u002Fhuggingface.co\u002Fstabilityai\u002Fstable-diffusion-xl-base-1.0) (SDXL)\n- [stable-diffusion-v1-5\u002Fstable-diffusion-v1-5](https:\u002F\u002Fhuggingface.co\u002Fstable-diffusion-v1-5\u002Fstable-diffusion-v1-5) (SD 1.5)\n- [baidu\u002FERNIE-Image](https:\u002F\u002Fhuggingface.co\u002Fbaidu\u002FERNIE-Image) (ERNIE-Image)\n- [NucleusAI\u002FNucleus-Image](https:\u002F\u002Fhuggingface.co\u002FNucleusAI\u002FNucleus-Image) (Nucleus-Image)\n\n### Instruction \u002F Edit\n- [black-forest-labs\u002FFLUX.1-Kontext-dev](https:\u002F\u002Fhuggingface.co\u002Fblack-forest-labs\u002FFLUX.1-Kontext-dev) (FLUX.1-Kontext-dev)\n- [Qwen\u002FQwen-Image-Edit](https:\u002F\u002Fhuggingface.co\u002FQwen\u002FQwen-Image-Edit) (Qwen-Image-Edit)\n- [Qwen\u002FQwen-Image-Edit-2509](https:\u002F\u002Fhuggingface.co\u002FQwen\u002FQwen-Image-Edit-2509) (Qwen-Image-Edit-2509)\n- [Qwen\u002FQwen-Image-Edit-2511](https:\u002F\u002Fhuggingface.co\u002FQwen\u002FQwen-Image-Edit-2511) (Qwen-Image-Edit-2511)\n- [HiDream-ai\u002FHiDream-E1-1](https:\u002F\u002Fhuggingface.co\u002FHiDream-ai\u002FHiDream-E1-1) (HiDream E1)\n\n### Video\n- [Wan-AI\u002FWan2.1-T2V-1.3B-Diffusers](https:\u002F\u002Fhuggingface.co\u002FWan-AI\u002FWan2.1-T2V-1.3B-Diffusers) (Wan 2.1 1.3B)\n- [Wan-AI\u002FWan2.1-I2V-14B-480P-Diffusers](https:\u002F\u002Fhuggingface.co\u002FWan-AI\u002FWan2.1-I2V-14B-480P-Diffusers) (Wan 2.1 I2V 14B-480P)\n- [Wan-AI\u002FWan2.1-I2V-14B-720P-Diffusers](https:\u002F\u002Fhuggingface.co\u002FWan-AI\u002FWan2.1-I2V-14B-720P-Diffusers) (Wan 2.1 I2V 14B-720P)\n- [Wan-AI\u002FWan2.1-T2V-14B-Diffusers](https:\u002F\u002Fhuggingface.co\u002FWan-AI\u002FWan2.1-T2V-14B-Diffusers) (Wan 2.1 14B)\n- [Wan-AI\u002FWan2.2-T2V-A14B-Diffusers](https:\u002F\u002Fhuggingface.co\u002FWan-AI\u002FWan2.2-T2V-A14B-Diffusers) (Wan 2.2 14B)\n- [Wan-AI\u002FWan2.2-I2V-A14B-Diffusers](https:\u002F\u002Fhuggingface.co\u002FWan-AI\u002FWan2.2-I2V-A14B-Diffusers) (Wan 2.2 I2V 14B)\n- [Wan-AI\u002FWan2.2-TI2V-5B-Diffusers](https:\u002F\u002Fhuggingface.co\u002FWan-AI\u002FWan2.2-TI2V-5B-Diffusers) (Wan 2.2 TI2V 5B)\n- [Lightricks\u002FLTX-2](https:\u002F\u002Fhuggingface.co\u002FLightricks\u002FLTX-2) (LTX-2)\n- [Lightricks\u002FLTX-2.3](https:\u002F\u002Fhuggingface.co\u002FLightricks\u002FLTX-2.3) (LTX-2.3)\n\n### Audio\n- [ACE-Step\u002FAce-Step1.5](https:\u002F\u002Fhuggingface.co\u002FACE-Step\u002FAce-Step1.5) (Ace Step 1.5)\n- [ACE-Step\u002Facestep-v15-xl-base](https:\u002F\u002Fhuggingface.co\u002FACE-Step\u002Facestep-v15-xl-base) (Ace Step 1.5 XL)\n\n### Experimental\n- [lodestones\u002FZeta-Chroma](https:\u002F\u002Fhuggingface.co\u002Flodestones\u002FZeta-Chroma) (Zeta Chroma)\n\n## Installation\n\nRequirements:\n- python >=3.10 (3.12 recommended)\n- Nvidia GPU with enough ram to do what you need\n- python venv\n- git\n\n\nLinux:\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fostris\u002Fai-toolkit.git\ncd ai-toolkit\npython3 -m venv venv\nsource venv\u002Fbin\u002Factivate\n# install torch first\npip3 install --no-cache-dir torch==2.9.1 torchvision==0.24.1 torchaudio==2.9.1 --index-url https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Fcu128\npip3 install -r requirements.txt\n```\n\nFor devices running **DGX OS** (including DGX Spark), follow [these](dgx_instructions.md) instructions.\n\n\nWindows:\n\nIf you are having issues with Windows. I recommend using the easy install script at [https:\u002F\u002Fgithub.com\u002FTavris1\u002FAI-Toolkit-Easy-Install](https:\u002F\u002Fgithub.com\u002FTavris1\u002FAI-Toolkit-Easy-Install)\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fostris\u002Fai-toolkit.git\ncd ai-toolkit\npython -m venv venv\n.\\venv\\Scripts\\activate\npip install --no-cache-dir torch==2.9.1 torchvision==0.24.1 torchaudio==2.9.1 --index-url https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Fcu128\npip install -r requirements.txt\n```\n\nMacOS:\n\nExperimental support for Silicon Macs is available. I do not have a Mac with enough RAM to fully test this\nso please let me know if there are issues. There is a convience script to install and run on MacOS \nlocates at `.\u002Frun_mac.zsh` that will install the dependencies locally and run the UI. To run this, \ndo the following:\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fostris\u002Fai-toolkit.git\ncd ai-toolkit\nchmod +x run_mac.zsh\n.\u002Frun_mac.zsh\n```\n\n\n# AI Toolkit UI\n\n\u003Cimg src=\"https:\u002F\u002Fostris.com\u002Fwp-content\u002Fuploads\u002F2025\u002F02\u002Ftoolkit-ui.jpg\" alt=\"AI Toolkit UI\" width=\"100%\">\n\nThe AI Toolkit UI is a web interface for the AI Toolkit. It allows you to easily start, stop, and monitor jobs. It also allows you to easily train models with a few clicks. It also allows you to set a token for the UI to prevent unauthorized access so it is mostly safe to run on an exposed server.\n\n## Running the UI\n\nRequirements:\n- Node.js > 20\n\nThe UI does not need to be kept running for the jobs to run. It is only needed to start\u002Fstop\u002Fmonitor jobs. The commands below\nwill install \u002F update the UI and it's dependencies and start the UI. \n\n```bash\ncd ui\nnpm run build_and_start\n```\n\nYou can now access the UI at `http:\u002F\u002Flocalhost:8675` or `http:\u002F\u002F\u003Cyour-ip>:8675` if you are running it on a server.\n\n## Securing the UI\n\nIf you are hosting the UI on a cloud provider or any network that is not secure, I highly recommend securing it with an auth token. \nYou can do this by setting the environment variable `AI_TOOLKIT_AUTH` to super secure password. This token will be required to access\nthe UI. You can set this when starting the UI like so:\n\n```bash\n# Linux\nAI_TOOLKIT_AUTH=super_secure_password npm run build_and_start\n\n# Windows\nset AI_TOOLKIT_AUTH=super_secure_password && npm run build_and_start\n\n# Windows Powershell\n$env:AI_TOOLKIT_AUTH=\"super_secure_password\"; npm run build_and_start\n```\n\n### Training\n1. Copy the example config file located at `config\u002Fexamples\u002Ftrain_lora_flux_24gb.yaml` (`config\u002Fexamples\u002Ftrain_lora_flux_schnell_24gb.yaml` for schnell) to the `config` folder and rename it to `whatever_you_want.yml`\n2. Edit the file following the comments in the file\n3. Run the file like so `python run.py config\u002Fwhatever_you_want.yml`\n\nA folder with the name and the training folder from the config file will be created when you start. It will have all \ncheckpoints and images in it. You can stop the training at any time using ctrl+c and when you resume, it will pick back up\nfrom the last checkpoint.\n\nIMPORTANT. If you press crtl+c while it is saving, it will likely corrupt that checkpoint. So wait until it is done saving\n\n### Need help?\n\nPlease do not open a bug report unless it is a bug in the code. You are welcome to [Join my Discord](https:\u002F\u002Fdiscord.gg\u002FVXmU2f5WEU)\nand ask for help there. However, please refrain from PMing me directly with general question or support. Ask in the discord\nand I will answer when I can.\n\n## Gradio UI\n\nTo get started training locally with a with a custom UI, once you followed the steps above and `ai-toolkit` is installed:\n\n```bash\ncd ai-toolkit #in case you are not yet in the ai-toolkit folder\nhuggingface-cli login #provide a `write` token to publish your LoRA at the end\npython flux_train_ui.py\n```\n\nYou will instantiate a UI that will let you upload your images, caption them, train and publish your LoRA\n![image](assets\u002Flora_ease_ui.png)\n\n\n## Training in RunPod\nIf you would like to use Runpod, but have not signed up yet, please consider using [my Runpod affiliate link](https:\u002F\u002Frunpod.io?ref=h0y9jyr2) to help support this project.\n\n\nI maintain an official Runpod Pod template here which can be accessed [here](https:\u002F\u002Fconsole.runpod.io\u002Fdeploy?template=0fqzfjy6f3&ref=h0y9jyr2).\n\nI have also created a short video showing how to get started using AI Toolkit with Runpod [here](https:\u002F\u002Fyoutu.be\u002FHBNeS-F6Zz8).\n\n## Training in Modal\n\n### 1. Setup\n#### ai-toolkit:\n```\ngit clone https:\u002F\u002Fgithub.com\u002Fostris\u002Fai-toolkit.git\ncd ai-toolkit\ngit submodule update --init --recursive\npython -m venv venv\nsource venv\u002Fbin\u002Factivate\npip install torch\npip install -r requirements.txt\npip install --upgrade accelerate transformers diffusers huggingface_hub #Optional, run it if you run into issues\n```\n#### Modal:\n- Run `pip install modal` to install the modal Python package.\n- Run `modal setup` to authenticate (if this doesn’t work, try `python -m modal setup`).\n\n#### Hugging Face:\n- Get a READ token from [here](https:\u002F\u002Fhuggingface.co\u002Fsettings\u002Ftokens) and request access to Flux.1-dev model from [here](https:\u002F\u002Fhuggingface.co\u002Fblack-forest-labs\u002FFLUX.1-dev).\n- Run `huggingface-cli login` and paste your token.\n\n### 2. Upload your dataset\n- Drag and drop your dataset folder containing the .jpg, .jpeg, or .png images and .txt files in `ai-toolkit`.\n\n### 3. Configs\n- Copy an example config file located at ```config\u002Fexamples\u002Fmodal``` to the `config` folder and rename it to ```whatever_you_want.yml```.\n- Edit the config following the comments in the file, **\u003Cins>be careful and follow the example `\u002Froot\u002Fai-toolkit` paths\u003C\u002Fins>**.\n\n### 4. Edit run_modal.py\n- Set your entire local `ai-toolkit` path at `code_mount = modal.Mount.from_local_dir` like:\n  \n   ```\n   code_mount = modal.Mount.from_local_dir(\"\u002FUsers\u002Fusername\u002Fai-toolkit\", remote_path=\"\u002Froot\u002Fai-toolkit\")\n   ```\n- Choose a `GPU` and `Timeout` in `@app.function` _(default is A100 40GB and 2 hour timeout)_.\n\n### 5. Training\n- Run the config file in your terminal: `modal run run_modal.py --config-file-list-str=\u002Froot\u002Fai-toolkit\u002Fconfig\u002Fwhatever_you_want.yml`.\n- You can monitor your training in your local terminal, or on [modal.com](https:\u002F\u002Fmodal.com\u002F).\n- Models, samples and optimizer will be stored in `Storage > flux-lora-models`.\n\n### 6. Saving the model\n- Check contents of the volume by running `modal volume ls flux-lora-models`. \n- Download the content by running `modal volume get flux-lora-models your-model-name`.\n- Example: `modal volume get flux-lora-models my_first_flux_lora_v1`.\n\n### Screenshot from Modal\n\n\u003Cimg width=\"1728\" alt=\"Modal Traning Screenshot\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F7497eb38-0090-49d6-8ad9-9c8ea7b5388b\">\n\n---\n\n## Dataset Preparation\n\nDatasets generally need to be a folder containing images and associated text files. Currently, the only supported\nformats are jpg, jpeg, and png. Webp currently has issues. The text files should be named the same as the images\nbut with a `.txt` extension. For example `image2.jpg` and `image2.txt`. The text file should contain only the caption.\nYou can add the word `[trigger]` in the caption file and if you have `trigger_word` in your config, it will be automatically\nreplaced. \n\nImages are never upscaled but they are downscaled and placed in buckets for batching. **You do not need to crop\u002Fresize your images**.\nThe loader will automatically resize them and can handle varying aspect ratios. \n\n\n## Training Specific Layers\n\nTo train specific layers with LoRA, you can use the `only_if_contains` network kwargs. For instance, if you want to train only the 2 layers\nused by The Last Ben, [mentioned in this post](https:\u002F\u002Fx.com\u002F__TheBen\u002Fstatus\u002F1829554120270987740), you can adjust your\nnetwork kwargs like so:\n\n```yaml\n      network:\n        type: \"lora\"\n        linear: 128\n        linear_alpha: 128\n        network_kwargs:\n          only_if_contains:\n            - \"transformer.single_transformer_blocks.7.proj_out\"\n            - \"transformer.single_transformer_blocks.20.proj_out\"\n```\n\nThe naming conventions of the layers are in diffusers format, so checking the state dict of a model will reveal \nthe suffix of the name of the layers you want to train. You can also use this method to only train specific groups of weights.\nFor instance to only train the `single_transformer` for FLUX.1, you can use the following:\n\n```yaml\n      network:\n        type: \"lora\"\n        linear: 128\n        linear_alpha: 128\n        network_kwargs:\n          only_if_contains:\n            - \"transformer.single_transformer_blocks.\"\n```\n\nYou can also exclude layers by their names by using `ignore_if_contains` network kwarg. So to exclude all the single transformer blocks,\n\n\n```yaml\n      network:\n        type: \"lora\"\n        linear: 128\n        linear_alpha: 128\n        network_kwargs:\n          ignore_if_contains:\n            - \"transformer.single_transformer_blocks.\"\n```\n\n`ignore_if_contains` takes priority over `only_if_contains`. So if a weight is covered by both,\nif will be ignored.\n\n## LoKr Training\n\nTo learn more about LoKr, read more about it at [KohakuBlueleaf\u002FLyCORIS](https:\u002F\u002Fgithub.com\u002FKohakuBlueleaf\u002FLyCORIS\u002Fblob\u002Fmain\u002Fdocs\u002FGuidelines.md). To train a LoKr model, you can adjust the network type in the config file like so:\n\n```yaml\n      network:\n        type: \"lokr\"\n        lokr_full_rank: true\n        lokr_factor: 8\n```\n\nEverything else should work the same including layer targeting.\n\n\n## Support My Work\n\nIf you enjoy my projects or use them commercially, please consider sponsoring me. Every bit helps! 💖\n\n\u003Ca href=\"https:\u002F\u002Fostris.com\u002Fsponsors\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Fostris.com\u002Fwp-content\u002Fuploads\u002F2025\u002F05\u002Fsupport-banner2.png\" alt=\"Support my work\" style=\"max-width:100%;height:auto;\">\u003C\u002Fa>\n\n### Current Sponsors\n\nAll of these people \u002F organizations are the ones who selflessly make this project possible. 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inline-block;\">\n\u003C\u002Fp>\n\n---\n\n","Ostris AI Toolkit 是一个用于微调扩散模型的全面训练工具包。它支持最新的图像和视频模型，并且可以在消费级硬件上运行，提供了图形界面（GUI）和命令行界面（CLI）两种使用方式。该工具包采用Python编写，旨在提供易用性的同时不牺牲功能多样性，涵盖了从基础到高级的各种需求。适用于需要对生成模型进行个性化调整的研究人员、开发者以及AI爱好者，特别是那些希望在有限资源下也能实现高质量图像与视频生成效果的用户。",2,"2026-06-11 03:35:18","high_star"]