{"id":28412930,"url":"https://github.com/teriks/dgenerate","last_synced_at":"2025-06-24T18:31:39.775Z","repository":{"id":175233932,"uuid":"653408376","full_name":"Teriks/dgenerate","owner":"Teriks","description":"dgenerate is a scriptable command line tool (and library) for generating images and animation sequences using stable diffusion and related techniques, with an accompanying GUI scripting 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_homebrew_1: https://brew.sh/\n\n.. _optimum-quanto_library_1: https://github.com/huggingface/optimum-quanto\n.. _vermeer_canny_edged.png_1: https://raw.githubusercontent.com/Teriks/dgenerate/v4.5.1/examples/media/vermeer_canny_edged.png\n\n.. _spandrel_1: https://github.com/chaiNNer-org/spandrel\n.. _ncnn_1: https://github.com/Tencent/ncnn\n\n.. _Stable_Diffusion_Web_UI_1: https://github.com/AUTOMATIC1111/stable-diffusion-webui\n.. _CivitAI_1: https://civitai.com/\n.. _chaiNNer_1: https://github.com/chaiNNer-org/chaiNNer\n\n.. |Documentation| image:: https://readthedocs.org/projects/dgenerate/badge/?version=v4.5.1\n :target: http://dgenerate.readthedocs.io/en/v4.5.1/\n\n.. |Latest Release| image:: https://img.shields.io/github/v/release/Teriks/dgenerate\n :target: https://github.com/Teriks/dgenerate/releases/latest\n :alt: GitHub Latest Release\n\n.. |Support Dgenerate| image:: https://img.shields.io/badge/Ko–fi-support%20dgenerate%20-hotpink?logo=kofi\u0026logoColor=white\n :target: https://ko-fi.com/teriks\n :alt: ko-fi\n\nOverview\n========\n\n**See here for v5.0.0 dev branch:** https://github.com/Teriks/dgenerate/tree/version_5.0.0\n\n**See here for v5.0.0 nightlys:** https://github.com/Teriks/dgenerate/releases/tag/pre-release\n\n----\n\n|Documentation| |Latest Release| |Support Dgenerate|\n\n``dgenerate`` is a cross-platform command line tool and library for generating images\nand animation sequences using Stable Diffusion and related models.\n\nAlongside the command line tool, this project features a syntax-highlighting\nREPL `Console UI`_ for the dgenerate configuration / scripting language, which is built on\nTkinter to be lightweight and portable. This GUI serves as an interface to dgenerate running\nin the background via the ``--shell`` option.\n\nYou can use dgenerate to generate multiple images or animated outputs using multiple\ncombinations of diffusion input parameters in batch, so that the differences in\ngenerated output can be compared / curated easily. This can be accomplished via a single command,\nor through more advanced scripting with the built-in interpreted shell-like language if needed.\n\nAnimated output can be produced by processing every frame of a Video, GIF, WebP, or APNG through\nvarious implementations of diffusion in img2img or inpainting mode, as well as with ControlNets and\ncontrol guidance images, in any combination thereof. MP4 (h264) video can be written without memory\nconstraints related to frame count. GIF, WebP, and PNG/APNG can be written WITH memory constraints,\nIE: all frames exist in memory at once before being written.\n\nVideo input of any runtime can be processed without memory constraints related to the video size.\nMany video formats are supported through the use of PyAV (ffmpeg).\n\nAnimated image input such as GIF, APNG (extension must be .apng), and WebP, can also be processed\nWITH memory constraints, IE: all frames exist in memory at once after an animated image is read.\n\nPNG, JPEG, JPEG-2000, TGA (Targa), BMP, and PSD (Photoshop) are supported for static image inputs.\n\nIn addition to diffusion, dgenerate also supports the processing of any supported image, video, or\nanimated image using any of its built-in image processors, which include various edge detectors,\ndepth detectors, segment generation, normal map generation, pose detection, non-diffusion based\nAI upscaling, and more. dgenerate's image processors may be used to pre-process image / video\ninput to diffusion, post-process diffusion output, or to process images and video directly.\n\ndgenerate brings many major features of the HuggingFace ``diffusers`` library directly to the\ncommand line in a very flexible way with a near one-to-one mapping, akin to ffmpeg, allowing\nfor creative uses as powerful as direct implementation in python with less effort and\nenvironmental setup.\n\ndgenerate is compatible with HuggingFace as well as typical CivitAI-hosted models,\nprompt weighting and many other useful generation features are supported.\n\ndgenerate can be easily installed on Windows via a Windows Installer MSI containing a\nfrozen python environment, making setup for Windows users easy, and likely to \"just work\"\nwithout any dependency issues. This installer can be found in the release artifact under each\nrelease located on the `github releases page \u003chttps://github.com/Teriks/dgenerate/releases\u003e`_.\n\nThis software requires a Nvidia GPU supporting CUDA 12.1+, AMD GPU supporting ROCm (Linux Only),\nor MacOS on Apple Silicon, and supports ``python\u003e=3.10,\u003c3.13``. CPU rendering is possible for\nsome operations but extraordinarily slow.\n\nFor library documentation, and a better README reading experience which\nincludes proper syntax highlighting for examples, and side panel navigation,\nplease visit `readthedocs \u003chttp://dgenerate.readthedocs.io/en/v4.5.1/\u003e`_.\n\n----\n\n* `Help Output`_\n* `Diffusion Feature Table \u003chttps://github.com/Teriks/dgenerate/blob/v4.5.1/FEATURE_TABLE.rst\u003e`_\n\n* How to install\n * `Windows Install`_\n * `Linux or WSL Install`_\n * `Linux with ROCm (AMD Cards)`_\n * `MacOS Install (Apple Silicon Only)`_\n * `Google Colab Install`_\n\n* Usage Manual\n * `Basic Usage`_\n * `Negative Prompt`_\n * `Multiple Prompts`_\n * `Image Seeds`_\n * `Inpainting`_\n * `Per Image Seed Resizing`_\n * `Animated Output`_\n * `Animation Slicing`_\n * `Inpainting Animations`_\n * `Deterministic Output`_\n * `Specifying a specific GPU for CUDA`_\n * `Specifying a Scheduler (sampler)`_\n * `Specifying a VAE`_\n * `VAE Tiling and Slicing`_\n * `Specifying a UNet`_\n * `Specifying a Transformer (SD3 and Flux)`_\n * `Specifying an SDXL Refiner`_\n * `Specifying a Stable Cascade Decoder`_\n * `Specifying LoRAs`_\n * `Specifying IP Adapters`_\n * `basic --image-seeds specification`_\n * `img2img --image-seeds specification`_\n * `inpainting --image-seeds specification`_\n * `quoting IP Adapter image URLs with plus symbols`_\n * `animated inputs \u0026 combinatorics`_\n * `Specifying Textual Inversions (embeddings)`_\n * `Specifying Control Nets`_\n * `Flux Control Net Union Mode`_\n * `Specifying T2I Adapters`_\n * `Specifying Text Encoders`_\n * `Prompt Weighting and Enhancement`_\n * `The compel prompt weighter`_\n * `The sd-embed prompt weighter`_\n * `Utilizing CivitAI links and Other Hosted Models`_\n * `Specifying Generation Batch Size`_\n * `Batching Input Images and Inpaint Masks`_\n * `Image Processors`_\n * `Image processor arguments`_\n * `Multiple control net images, and input image batching`_\n * `Sub Commands`_\n * `Sub Command: image-process`_\n * `Sub Command: civitai-links`_\n * `Upscaling`_\n * `Upscaling with Diffusion Upscaler Models`_\n * `Upscaling with chaiNNer Compatible Torch Upscaler Models`_\n * `Upscaling with NCNN Upscaler Models`_\n * `Writing and Running Configs`_\n * `Basic config syntax`_\n * `Built in template variables and functions`_\n * `Directives, and applying templating`_\n * `Setting template variables, in depth`_\n * `Setting environmental variables, in depth`_\n * `Globbing and path manipulation`_\n * `The \\\\print and \\\\echo directive`_\n * `The \\\\image_process directive`_\n * `The \\\\exec directive`_\n * `The \\\\download directive`_\n * `The download() template function`_\n * `The \\\\exit directive`_\n * `Running configs from the command line`_\n * `Config argument injection`_\n * `Writing Plugins`_\n * `Image processor plugins`_\n * `Config directive and template function plugins`_\n * `Sub-command plugins`_\n * `Prompt weighter plugins`_\n * `Console UI`_\n * `File Cache Control`_\n\nHelp Output\n===========\n\n.. code-block:: text\n\n usage: dgenerate [-h] [-v] [--version] [--file | --shell | --no-stdin | --console]\n [--plugin-modules PATH [PATH ...]] [--sub-command SUB_COMMAND]\n [--sub-command-help [SUB_COMMAND ...]] [-ofm] [--templates-help [VARIABLE_NAME ...]]\n [--directives-help [DIRECTIVE_NAME ...]] [--functions-help [FUNCTION_NAME ...]]\n [-mt MODEL_TYPE] [-rev BRANCH] [-var VARIANT] [-sbf SUBFOLDER] [-atk TOKEN] [-bs INTEGER]\n [-bgs SIZE] [-te TEXT_ENCODER_URIS [TEXT_ENCODER_URIS ...]]\n [-te2 TEXT_ENCODER_URIS [TEXT_ENCODER_URIS ...]] [-un UNET_URI] [-un2 UNET_URI]\n [-tf TRANSFORMER_URI] [-vae VAE_URI] [-vt] [-vs] [-lra LORA_URI [LORA_URI ...]]\n [-lrfs LORA_FUSE_SCALE] [-ie IMAGE_ENCODER_URI] [-ipa IP_ADAPTER_URI [IP_ADAPTER_URI ...]]\n [-ti URI [URI ...]] [-cn CONTROLNET_URI [CONTROLNET_URI ...] | -t2i T2I_ADAPTER_URI\n [T2I_ADAPTER_URI ...]] [-sch SCHEDULER_URI [SCHEDULER_URI ...]] [-pag]\n [-pags FLOAT [FLOAT ...]] [-pagas FLOAT [FLOAT ...]] [-rpag] [-rpags FLOAT [FLOAT ...]]\n [-rpagas FLOAT [FLOAT ...]] [-mqo | -mco] [--s-cascade-decoder MODEL_URI] [-dqo] [-dco]\n [--s-cascade-decoder-prompts PROMPT [PROMPT ...]]\n [--s-cascade-decoder-inference-steps INTEGER [INTEGER ...]]\n [--s-cascade-decoder-guidance-scales INTEGER [INTEGER ...]]\n [--s-cascade-decoder-scheduler SCHEDULER_URI [SCHEDULER_URI ...]]\n [--sdxl-refiner MODEL_URI] [-rqo] [-rco]\n [--sdxl-refiner-scheduler SCHEDULER_URI [SCHEDULER_URI ...]] [--sdxl-refiner-edit]\n [--sdxl-second-prompts PROMPT [PROMPT ...]] [--sdxl-t2i-adapter-factors FLOAT [FLOAT ...]]\n [--sdxl-aesthetic-scores FLOAT [FLOAT ...]]\n [--sdxl-crops-coords-top-left COORD [COORD ...]] [--sdxl-original-size SIZE [SIZE ...]]\n [--sdxl-target-size SIZE [SIZE ...]] [--sdxl-negative-aesthetic-scores FLOAT [FLOAT ...]]\n [--sdxl-negative-original-sizes SIZE [SIZE ...]]\n [--sdxl-negative-target-sizes SIZE [SIZE ...]]\n [--sdxl-negative-crops-coords-top-left COORD [COORD ...]]\n [--sdxl-refiner-prompts PROMPT [PROMPT ...]]\n [--sdxl-refiner-clip-skips INTEGER [INTEGER ...]]\n [--sdxl-refiner-second-prompts PROMPT [PROMPT ...]]\n [--sdxl-refiner-aesthetic-scores FLOAT [FLOAT ...]]\n [--sdxl-refiner-crops-coords-top-left COORD [COORD ...]]\n [--sdxl-refiner-original-sizes SIZE [SIZE ...]]\n [--sdxl-refiner-target-sizes SIZE [SIZE ...]]\n [--sdxl-refiner-negative-aesthetic-scores FLOAT [FLOAT ...]]\n [--sdxl-refiner-negative-original-sizes SIZE [SIZE ...]]\n [--sdxl-refiner-negative-target-sizes SIZE [SIZE ...]]\n [--sdxl-refiner-negative-crops-coords-top-left COORD [COORD ...]] [-hnf FLOAT [FLOAT ...]]\n [-ri INT [INT ...]] [-rg FLOAT [FLOAT ...]] [-rgr FLOAT [FLOAT ...]] [-sc] [-d DEVICE]\n [-t DTYPE] [-s SIZE] [-na] [-o PATH] [-op PREFIX] [-ox] [-oc] [-om]\n [-pw PROMPT_WEIGHTER_URI] [--prompt-weighter-help [PROMPT_WEIGHTER_NAMES ...]]\n [-p PROMPT [PROMPT ...]] [--sd3-max-sequence-length INTEGER]\n [--sd3-second-prompts PROMPT [PROMPT ...]] [--sd3-third-prompts PROMPT [PROMPT ...]]\n [--flux-second-prompts PROMPT [PROMPT ...]] [--flux-max-sequence-length INTEGER]\n [-cs INTEGER [INTEGER ...]] [-se SEED [SEED ...]] [-sei] [-gse COUNT] [-af FORMAT]\n [-if FORMAT] [-nf] [-fs FRAME_NUMBER] [-fe FRAME_NUMBER] [-is SEED [SEED ...]]\n [-sip PROCESSOR_URI [PROCESSOR_URI ...]] [-mip PROCESSOR_URI [PROCESSOR_URI ...]]\n [-cip PROCESSOR_URI [PROCESSOR_URI ...]] [--image-processor-help [PROCESSOR_NAME ...]]\n [-pp PROCESSOR_URI [PROCESSOR_URI ...]] [-iss FLOAT [FLOAT ...] | -uns INTEGER\n [INTEGER ...]] [-gs FLOAT [FLOAT ...]] [-igs FLOAT [FLOAT ...]] [-gr FLOAT [FLOAT ...]]\n [-ifs INTEGER [INTEGER ...]] [-mc EXPR [EXPR ...]] [-pmc EXPR [EXPR ...]]\n [-umc EXPR [EXPR ...]] [-vmc EXPR [EXPR ...]] [-cmc EXPR [EXPR ...]] [-tmc EXPR [EXPR ...]]\n [-iemc EXPR [EXPR ...]] [-amc EXPR [EXPR ...]] [-tfmc EXPR [EXPR ...]]\n [-ipmc EXPR [EXPR ...]] [-ipcc EXPR [EXPR ...]]\n model_path\n\n Batch image generation and manipulation tool supporting Stable Diffusion and related techniques /\n algorithms, with support for video and animated image processing.\n\n positional arguments:\n model_path Hugging Face model repository slug, Hugging Face blob link to a model file, path to\n folder on disk, or path to a .pt, .pth, .bin, .ckpt, or .safetensors file.\n --------------------------------------------------------------------------\n\n options:\n -h, --help show this help message and exit\n -------------------------------\n -v, --verbose Output information useful for debugging, such as pipeline call and model load\n parameters.\n -----------\n --version Show dgenerate's version and exit\n ---------------------------------\n --file Convenience argument for reading a configuration script from a file instead of using\n a pipe. This is a meta argument which can not be used within a configuration script\n and is only valid from the command line or during a popen invocation of dgenerate.\n ----------------------------------------------------------------------------------\n --shell When reading configuration from STDIN (a pipe), read forever, even when\n configuration errors occur. This allows dgenerate to run in the background and be\n controlled by another process sending commands. Launching dgenerate with this option\n and not piping it input will attach it to the terminal like a shell. Entering\n configuration into this shell requires two newlines to submit a command due to\n parsing lookahead. IE: two presses of the enter key. This is a meta argument which\n can not be used within a configuration script and is only valid from the command\n line or during a popen invocation of dgenerate.\n -----------------------------------------------\n --no-stdin Can be used to indicate to dgenerate that it will not receive any piped in input.\n This is useful for running dgenerate via popen from Python or another application\n using normal arguments, where it would otherwise try to read from STDIN and block\n forever because it is not attached to a terminal. This is a meta argument which can\n not be used within a configuration script and is only valid from the command line or\n during a popen invocation of dgenerate.\n ---------------------------------------\n --console Launch a terminal-like Tkinter GUI that interacts with an instance of dgenerate\n running in the background. This allows you to interactively write dgenerate config\n scripts as if dgenerate were a shell / REPL. This is a meta argument which can not\n be used within a configuration script and is only valid from the command line or\n during a popen invocation of dgenerate.\n ---------------------------------------\n --plugin-modules PATH [PATH ...]\n Specify one or more plugin module folder paths (folder containing __init__.py) or\n Python .py file paths, or Python module names to load as plugins. Plugin modules can\n currently implement image processors, config directives, config template functions,\n prompt weighters, and sub-commands.\n -----------------------------------\n --sub-command SUB_COMMAND\n Specify the name a sub-command to invoke. dgenerate exposes some extra image\n processing functionality through the use of sub-commands. Sub commands essentially\n replace the entire set of accepted arguments with those of a sub-command which\n implements additional functionality. See --sub-command-help for a list of sub-\n commands and help.\n ------------------\n --sub-command-help [SUB_COMMAND ...]\n Use this option alone (or with --plugin-modules) and no model specification in order\n to list available sub-command names. Calling a sub-command with \"--sub-command name\n --help\" will produce argument help output for that sub-command. When used with\n --plugin-modules, sub-commands implemented by the specified plugins will also be\n listed.\n -------\n -ofm, --offline-mode Whether dgenerate should try to download Hugging Face models that do not exist in\n the disk cache, or only use what is available in the cache. Referencing a model on\n Hugging Face that has not been cached because it was not previously downloaded will\n result in a failure when using this option.\n -------------------------------------------\n --templates-help [VARIABLE_NAME ...]\n Print a list of template variables available in the interpreter environment used for\n dgenerate config scripts, particularly the variables set after a dgenerate\n invocation occurs. When used as a command line option, their values are not\n presented, just their names and types. Specifying names will print type information\n for those variable names.\n -------------------------\n --directives-help [DIRECTIVE_NAME ...]\n Use this option alone (or with --plugin-modules) and no model specification in order\n to list available config directive names. Providing names will print documentation\n for the specified directive names. When used with --plugin-modules, directives\n implemented by the specified plugins will also be listed.\n ---------------------------------------------------------\n --functions-help [FUNCTION_NAME ...]\n Use this option alone (or with --plugin-modules) and no model specification in order\n to list available config template function names. Providing names will print\n documentation for the specified function names. When used with --plugin-modules,\n functions implemented by the specified plugins will also be listed.\n -------------------------------------------------------------------\n -mt MODEL_TYPE, --model-type MODEL_TYPE\n Use when loading different model types. Currently supported: torch, torch-pix2pix,\n torch-sdxl, torch-sdxl-pix2pix, torch-upscaler-x2, torch-upscaler-x4, torch-if,\n torch-ifs, torch-ifs-img2img, torch-s-cascade, torch-sd3, torch-flux, or torch-flux-\n fill. (default: torch)\n ----------------------\n -rev BRANCH, --revision BRANCH\n The model revision to use when loading from a Hugging Face repository, (The Git\n branch / tag, default is \"main\")\n --------------------------------\n -var VARIANT, --variant VARIANT\n If specified when loading from a Hugging Face repository or folder, load weights\n from \"variant\" filename, e.g. \"pytorch_model.\u003cvariant\u003e.safetensors\". Defaults to\n automatic selection.\n --------------------\n -sbf SUBFOLDER, --subfolder SUBFOLDER\n Main model subfolder. If specified when loading from a Hugging Face repository or\n folder, load weights from the specified subfolder.\n --------------------------------------------------\n -atk TOKEN, --auth-token TOKEN\n Huggingface auth token. Required to download restricted repositories that have\n access permissions granted to your Hugging Face account.\n --------------------------------------------------------\n -bs INTEGER, --batch-size INTEGER\n The number of image variations to produce per set of individual diffusion parameters\n in one rendering step simultaneously on a single GPU.\n\n When generating animations with a --batch-size greater than one, a separate\n animation (with the filename suffix \"animation_N\") will be written to for each image\n in the batch.\n\n If --batch-grid-size is specified when producing an animation then the image grid is\n used for the output frames.\n\n During animation rendering each image in the batch will still be written to the\n output directory along side the produced animation as either suffixed files or image\n grids depending on the options you choose. (Default: 1)\n -------------------------------------------------------\n -bgs SIZE, --batch-grid-size SIZE\n Produce a single image containing a grid of images with the number of COLUMNSxROWS\n given to this argument when --batch-size is greater than 1. If not specified with a\n --batch-size greater than 1, images will be written individually with an image\n number suffix (image_N) in the filename signifying which image in the batch they\n are.\n ----\n -te TEXT_ENCODER_URIS [TEXT_ENCODER_URIS ...], --text-encoders TEXT_ENCODER_URIS [TEXT_ENCODER_URIS ...]\n Specify Text Encoders for the main model using URIs, main models may use one or more\n text encoders depending on the --model-type value and other dgenerate arguments.\n See: --text-encoders help for information about what text encoders are needed for\n your invocation.\n\n Examples: \"CLIPTextModel;model=huggingface/text_encoder\",\n \"CLIPTextModelWithProjection;model=huggingface/text_encoder;revision=main\",\n \"T5EncoderModel;model=text_encoder_folder_on_disk\".\n\n For main models which require multiple text encoders, the + symbol may be used to\n indicate that a default value should be used for a particular text encoder, for\n example: --text-encoders + + huggingface/encoder3. Any trailing text encoders which\n are not specified are given their default value.\n\n The value \"null\" may be used to indicate that a specific text encoder should not be\n loaded.\n\n Blob links / single file loads are not supported for Text Encoders.\n\n The \"revision\" argument specifies the model revision to use for the Text Encoder\n when loading from Hugging Face repository, (The Git branch / tag, default is\n \"main\").\n\n The \"variant\" argument specifies the Text Encoder model variant. If \"variant\" is\n specified when loading from a Hugging Face repository or folder, weights will be\n loaded from \"variant\" filename, e.g. \"pytorch_model.\u003cvariant\u003e.safetensors\". For this\n argument, \"variant\" defaults to the value of --variant if it is not specified in the\n URI.\n\n The \"subfolder\" argument specifies the UNet model subfolder, if specified when\n loading from a Hugging Face repository or folder, weights from the specified\n subfolder.\n\n The \"dtype\" argument specifies the Text Encoder model precision, it defaults to the\n value of -t/--dtype and should be one of: auto, bfloat16, float16, or float32.\n\n The \"quantize\" argument specifies whether or not to use optimum-quanto to quantize\n the text encoder weights, and may be passed the values \"qint2\", \"qint4\", \"qint8\",\n \"qfloat8_e4m3fn\", \"qfloat8_e4m3fnuz\", \"qfloat8_e5m2\", or \"qfloat8\" to specify the\n quantization datatype, this can be utilized to run Flux models with much less GPU\n memory.\n\n If you wish to load weights directly from a path on disk, you must point this\n argument at the folder they exist in, which should also contain the config.json file\n for the Text Encoder. For example, a downloaded repository folder from Hugging Face.\n ------------------------------------------------------------------------------------\n -te2 TEXT_ENCODER_URIS [TEXT_ENCODER_URIS ...], --text-encoders2 TEXT_ENCODER_URIS [TEXT_ENCODER_URIS ...]\n --text-encoders but for the SDXL refiner or Stable Cascade decoder model.\n -------------------------------------------------------------------------\n -un UNET_URI, --unet UNET_URI\n Specify a UNet using a URI.\n\n Examples: \"huggingface/unet\", \"huggingface/unet;revision=main\",\n \"unet_folder_on_disk\".\n\n Blob links / single file loads are not supported for UNets.\n\n The \"revision\" argument specifies the model revision to use for the UNet when\n loading from Hugging Face repository, (The Git branch / tag, default is \"main\").\n\n The \"variant\" argument specifies the UNet model variant. If \"variant\" is specified\n when loading from a Hugging Face repository or folder, weights will be loaded from\n \"variant\" filename, e.g. \"pytorch_model.\u003cvariant\u003e.safetensors. For this argument,\n \"variant\" defaults to the value of --variant if it is not specified in the URI.\n\n The \"subfolder\" argument specifies the UNet model subfolder, if specified when\n loading from a Hugging Face repository or folder, weights from the specified\n subfolder.\n\n The \"dtype\" argument specifies the UNet model precision, it defaults to the value of\n -t/--dtype and should be one of: auto, bfloat16, float16, or float32.\n\n If you wish to load weights directly from a path on disk, you must point this\n argument at the folder they exist in, which should also contain the config.json file\n for the UNet. For example, a downloaded repository folder from Hugging Face.\n ----------------------------------------------------------------------------\n -un2 UNET_URI, --unet2 UNET_URI\n Specify a second UNet, this is only valid when using SDXL or Stable Cascade model\n types. This UNet will be used for the SDXL refiner, or Stable Cascade decoder model.\n ------------------------------------------------------------------------------------\n -tf TRANSFORMER_URI, --transformer TRANSFORMER_URI\n Specify a Stable Diffusion 3 or Flux Transformer model using a URI.\n\n Examples: \"huggingface/transformer\", \"huggingface/transformer;revision=main\",\n \"transformer_folder_on_disk\".\n\n Blob links / single file loads are supported for SD3 Transformers.\n\n The \"revision\" argument specifies the model revision to use for the Transformer when\n loading from Hugging Face repository or blob link, (The Git branch / tag, default is\n \"main\").\n\n The \"variant\" argument specifies the Transformer model variant. If \"variant\" is\n specified when loading from a Hugging Face repository or folder, weights will be\n loaded from \"variant\" filename, e.g. \"pytorch_model.\u003cvariant\u003e.safetensors. For this\n argument, \"variant\" defaults to the value of --variant if it is not specified in the\n URI.\n\n The \"subfolder\" argument specifies the Transformer model subfolder, if specified\n when loading from a Hugging Face repository or folder, weights from the specified\n subfolder.\n\n The \"dtype\" argument specifies the Transformer model precision, it defaults to the\n value of -t/--dtype and should be one of: auto, bfloat16, float16, or float32.\n\n The \"quantize\" argument specifies whether or not to use optimum-quanto to quantize\n the transformer weights, and may be passed the values \"qint2\", \"qint4\", \"qint8\",\n \"qfloat8_e4m3fn\", \"qfloat8_e4m3fnuz\", \"qfloat8_e5m2\", or \"qfloat8\" to specify the\n quantization datatype, this can be utilized to run Flux models with much less GPU\n memory.\n\n If you wish to load a weights file directly from disk, the simplest way is:\n --transformer \"transformer.safetensors\", or with a dtype\n \"transformer.safetensors;dtype=float16\". All loading arguments except \"dtype\" and\n \"quantize\" are unused in this case and may produce an error message if used.\n\n If you wish to load a specific weight file from a Hugging Face repository, use the\n blob link loading syntax: --transformer\n \"AutoencoderKL;https://huggingface.co/UserName/repository-\n name/blob/main/transformer.safetensors\", the \"revision\" argument may be used with\n this syntax.\n ------------\n -vae VAE_URI, --vae VAE_URI\n Specify a VAE using a URI, the URI syntax is: \"AutoEncoderClass;model=(Hugging Face\n repository slug/blob link or file/folder path)\".\n\n Examples: \"AutoencoderKL;model=vae.pt\",\n \"AsymmetricAutoencoderKL;model=huggingface/vae\",\n \"AutoencoderTiny;model=huggingface/vae\",\n \"ConsistencyDecoderVAE;model=huggingface/vae\".\n\n The AutoencoderKL encoder class accepts Hugging Face repository slugs/blob links,\n .pt, .pth, .bin, .ckpt, and .safetensors files.\n\n Other encoders can only accept Hugging Face repository slugs/blob links, or a path\n to a folder on disk with the model configuration and model file(s).\n\n If an AutoencoderKL VAE model file exists at a URL which serves the file as a raw\n download, you may provide an http/https link to it and it will be downloaded to\n dgenerates web cache.\n\n Aside from the \"model\" argument, there are four other optional arguments that can be\n specified, these are: \"revision\", \"variant\", \"subfolder\", \"dtype\".\n\n They can be specified as so in any order, they are not positional: \"AutoencoderKL;mo\n del=huggingface/vae;revision=main;variant=fp16;subfolder=sub_folder;dtype=float16\".\n\n The \"revision\" argument specifies the model revision to use for the VAE when loading\n from Hugging Face repository or blob link, (The Git branch / tag, default is\n \"main\").\n\n The \"variant\" argument specifies the VAE model variant. If \"variant\" is specified\n when loading from a Hugging Face repository or folder, weights will be loaded from\n \"variant\" filename, e.g. \"pytorch_model.\u003cvariant\u003e.safetensors. \"variant\" in the case\n of --vae does not default to the value of --variant to prevent failures during\n common use cases.\n\n The \"subfolder\" argument specifies the VAE model subfolder, if specified when\n loading from a Hugging Face repository or folder, weights from the specified\n subfolder.\n\n The \"dtype\" argument specifies the VAE model precision, it defaults to the value of\n -t/--dtype and should be one of: auto, bfloat16, float16, or float32.\n\n If you wish to load a weights file directly from disk, the simplest way is: --vae\n \"AutoencoderKL;my_vae.safetensors\", or with a dtype\n \"AutoencoderKL;my_vae.safetensors;dtype=float16\". All loading arguments except\n \"dtype\" are unused in this case and may produce an error message if used.\n\n If you wish to load a specific weight file from a Hugging Face repository, use the\n blob link loading syntax: --vae\n \"AutoencoderKL;https://huggingface.co/UserName/repository-\n name/blob/main/vae_model.safetensors\", the \"revision\" argument may be used with this\n syntax.\n -------\n -vt, --vae-tiling Enable VAE tiling. Assists in the generation of large images with lower memory\n overhead. The VAE will split the input tensor into tiles to compute decoding and\n encoding in several steps. This is useful for saving a large amount of memory and to\n allow processing larger images. Note that if you are using --control-nets you may\n still run into memory issues generating large images, or with --batch-size greater\n than 1.\n -------\n -vs, --vae-slicing Enable VAE slicing. Assists in the generation of large images with lower memory\n overhead. The VAE will split the input tensor in slices to compute decoding in\n several steps. This is useful to save some memory, especially when --batch-size is\n greater than 1. Note that if you are using --control-nets you may still run into\n memory issues generating large images.\n --------------------------------------\n -lra LORA_URI [LORA_URI ...], --loras LORA_URI [LORA_URI ...]\n Specify one or more LoRA models using URIs. These should be a Hugging Face\n repository slug, path to model file on disk (for example, a .pt, .pth, .bin, .ckpt,\n or .safetensors file), or model folder containing model files.\n\n If a LoRA model file exists at a URL which serves the file as a raw download, you\n may provide an http/https link to it and it will be downloaded to dgenerates web\n cache.\n\n Hugging Face blob links are not supported, see \"subfolder\" and \"weight-name\" below\n instead.\n\n Optional arguments can be provided after a LoRA model specification, these are:\n \"scale\", \"revision\", \"subfolder\", and \"weight-name\".\n\n They can be specified as so in any order, they are not positional:\n \"huggingface/lora;scale=1.0;revision=main;subfolder=repo_subfolder;weight-\n name=lora.safetensors\".\n\n The \"scale\" argument indicates the scale factor of the LoRA.\n\n The \"revision\" argument specifies the model revision to use for the LoRA when\n loading from Hugging Face repository, (The Git branch / tag, default is \"main\").\n\n The \"subfolder\" argument specifies the LoRA model subfolder, if specified when\n loading from a Hugging Face repository or folder, weights from the specified\n subfolder.\n\n The \"weight-name\" argument indicates the name of the weights file to be loaded when\n loading from a Hugging Face repository or folder on disk.\n\n If you wish to load a weights file directly from disk, the simplest way is: --loras\n \"my_lora.safetensors\", or with a scale \"my_lora.safetensors;scale=1.0\", all other\n loading arguments are unused in this case and may produce an error message if used.\n -----------------------------------------------------------------------------------\n -lrfs LORA_FUSE_SCALE, --lora-fuse-scale LORA_FUSE_SCALE\n LoRA weights are merged into the main model at this scale. When specifying multiple\n LoRA models, they are fused together into one set of weights using their individual\n scale values, after which they are fused into the main model at this scale value.\n (default: 1.0).\n ---------------\n -ie IMAGE_ENCODER_URI, --image-encoder IMAGE_ENCODER_URI\n Specify an Image Encoder using a URI.\n\n Image Encoders are used with --ip-adapters models, and must be specified if none of\n the loaded --ip-adapters contain one. An error will be produced in this situation,\n which requires you to use this argument.\n\n An image encoder can also be manually specified for Stable Cascade models.\n\n Examples: \"huggingface/image_encoder\", \"huggingface/image_encoder;revision=main\",\n \"image_encoder_folder_on_disk\".\n\n Blob links / single file loads are not supported for Image Encoders.\n\n The \"revision\" argument specifies the model revision to use for the Image Encoder\n when loading from Hugging Face repository or blob link, (The Git branch / tag,\n default is \"main\").\n\n The \"variant\" argument specifies the Image Encoder model variant. If \"variant\" is\n specified when loading from a Hugging Face repository or folder, weights will be\n loaded from \"variant\" filename, e.g. \"pytorch_model.\u003cvariant\u003e.safetensors.\n\n Similar to --vae, \"variant\" does not default to the value of --variant in order to\n prevent errors with common use cases. If you specify multiple IP Adapters, they must\n all have the same \"variant\" value or you will receive a usage error.\n\n The \"subfolder\" argument specifies the Image Encoder model subfolder, if specified\n when loading from a Hugging Face repository or folder, weights from the specified\n subfolder.\n\n The \"dtype\" argument specifies the Image Encoder model precision, it defaults to the\n value of -t/--dtype and should be one of: auto, bfloat16, float16, or float32.\n\n If you wish to load weights directly from a path on disk, you must point this\n argument at the folder they exist in, which should also contain the config.json file\n for the Image Encoder. For example, a downloaded repository folder from Hugging\n Face.\n -----\n -ipa IP_ADAPTER_URI [IP_ADAPTER_URI ...], --ip-adapters IP_ADAPTER_URI [IP_ADAPTER_URI ...]\n Specify one or more IP Adapter models using URIs. These should be a Hugging Face\n repository slug, path to model file on disk (for example, a .pt, .pth, .bin, .ckpt,\n or .safetensors file), or model folder containing model files.\n\n If an IP Adapter model file exists at a URL which serves the file as a raw download,\n you may provide an http/https link to it and it will be downloaded to dgenerates web\n cache.\n\n Hugging Face blob links are not supported, see \"subfolder\" and \"weight-name\" below\n instead.\n\n Optional arguments can be provided after an IP Adapter model specification, these\n are: \"scale\", \"revision\", \"subfolder\", and \"weight-name\".\n\n They can be specified as so in any order, they are not positional: \"huggingface/ip-\n adapter;scale=1.0;revision=main;subfolder=repo_subfolder;weight-\n name=ip_adapter.safetensors\".\n\n The \"scale\" argument indicates the scale factor of the IP Adapter.\n\n The \"revision\" argument specifies the model revision to use for the IP Adapter when\n loading from Hugging Face repository, (The Git branch / tag, default is \"main\").\n\n The \"subfolder\" argument specifies the IP Adapter model subfolder, if specified when\n loading from a Hugging Face repository or folder, weights from the specified\n subfolder.\n\n The \"weight-name\" argument indicates the name of the weights file to be loaded when\n loading from a Hugging Face repository or folder on disk.\n\n If you wish to load a weights file directly from disk, the simplest way is: --ip-\n adapters \"ip_adapter.safetensors\", or with a scale\n \"ip_adapter.safetensors;scale=1.0\", all other loading arguments are unused in this\n case and may produce an error message if used.\n ----------------------------------------------\n -ti URI [URI ...], --textual-inversions URI [URI ...]\n Specify one or more Textual Inversion models using URIs. These should be a Hugging\n Face repository slug, path to model file on disk (for example, a .pt, .pth, .bin,\n .ckpt, or .safetensors file), or model folder containing model files.\n\n If a Textual Inversion model file exists at a URL which serves the file as a raw\n download, you may provide an http/https link to it and it will be downloaded to\n dgenerates web cache.\n\n Hugging Face blob links are not supported, see \"subfolder\" and \"weight-name\" below\n instead.\n\n Optional arguments can be provided after the Textual Inversion model specification,\n these are: \"token\", \"revision\", \"subfolder\", and \"weight-name\".\n\n They can be specified as so in any order, they are not positional:\n \"huggingface/ti_model;revision=main;subfolder=repo_subfolder;weight-\n name=ti_model.safetensors\".\n\n The \"token\" argument can be used to override the prompt token used for the textual\n inversion prompt embedding. For normal Stable Diffusion the default token value is\n provided by the model itself, but for Stable Diffusion XL and Flux the default token\n value is equal to the model file name with no extension and all spaces replaced by\n underscores.\n\n The \"revision\" argument specifies the model revision to use for the Textual\n Inversion model when loading from Hugging Face repository, (The Git branch / tag,\n default is \"main\").\n\n The \"subfolder\" argument specifies the Textual Inversion model subfolder, if\n specified when loading from a Hugging Face repository or folder, weights from the\n specified subfolder.\n\n The \"weight-name\" argument indicates the name of the weights file to be loaded when\n loading from a Hugging Face repository or folder on disk.\n\n If you wish to load a weights file directly from disk, the simplest way is:\n --textual-inversions \"my_ti_model.safetensors\", all other loading arguments are\n unused in this case and may produce an error message if used.\n -------------------------------------------------------------\n -cn CONTROLNET_URI [CONTROLNET_URI ...], --control-nets CONTROLNET_URI [CONTROLNET_URI ...]\n Specify one or more ControlNet models using URIs. This should be a Hugging Face\n repository slug / blob link, path to model file on disk (for example, a .pt, .pth,\n .bin, .ckpt, or .safetensors file), or model folder containing model files.\n\n If a ControlNet model file exists at a URL which serves the file as a raw download,\n you may provide an http/https link to it and it will be downloaded to dgenerates web\n cache.\n\n Optional arguments can be provided after the ControlNet model specification, these\n are: \"scale\", \"start\", \"end\", \"revision\", \"variant\", \"subfolder\", and \"dtype\".\n\n They can be specified as so in any order, they are not positional: \"huggingface/cont\n rolnet;scale=1.0;start=0.0;end=1.0;revision=main;variant=fp16;subfolder=repo_subfold\n er;dtype=float16\".\n\n The \"scale\" argument specifies the scaling factor applied to the ControlNet model,\n the default value is 1.0.\n\n The \"start\" argument specifies at what fraction of the total inference steps to\n begin applying the ControlNet, defaults to 0.0, IE: the very beginning.\n\n The \"end\" argument specifies at what fraction of the total inference steps to stop\n applying the ControlNet, defaults to 1.0, IE: the very end.\n\n The \"mode\" argument can be used when using --model-type torch-flux and ControlNet\n Union to specify the ControlNet mode. Acceptable values are: \"canny\", \"tile\",\n \"depth\", \"blur\", \"pose\", \"gray\", \"lq\". This value may also be an integer between 0\n and 6, inclusive.\n\n The \"revision\" argument specifies the model revision to use for the ControlNet model\n when loading from Hugging Face repository, (The Git branch / tag, default is\n \"main\").\n\n The \"variant\" argument specifies the ControlNet model variant, if \"variant\" is\n specified when loading from a Hugging Face repository or folder, weights will be\n loaded from \"variant\" filename, e.g. \"pytorch_model.\u003cvariant\u003e.safetensors. \"variant\"\n defaults to automatic selection. \"variant\" in the case of --control-nets does not\n default to the value of --variant to prevent failures during common use cases.\n\n The \"subfolder\" argument specifies the ControlNet model subfolder, if specified when\n loading from a Hugging Face repository or folder, weights from the specified\n subfolder.\n\n The \"dtype\" argument specifies the ControlNet model precision, it defaults to the\n value of -t/--dtype and should be one of: auto, bfloat16, float16, or float32.\n\n If you wish to load a weights file directly from disk, the simplest way is:\n --control-nets \"my_controlnet.safetensors\" or --control-nets\n \"my_controlnet.safetensors;scale=1.0;dtype=float16\", all other loading arguments\n aside from \"scale\", \"start\", \"end\", and \"dtype\" are unused in this case and may\n produce an error message if used.\n\n If you wish to load a specific weight file from a Hugging Face repository, use the\n blob link loading syntax: --control-nets\n \"https://huggingface.co/UserName/repository-name/blob/main/controlnet.safetensors\",\n the \"revision\" argument may be used with this syntax.\n -----------------------------------------------------\n -t2i T2I_ADAPTER_URI [T2I_ADAPTER_URI ...], --t2i-adapters T2I_ADAPTER_URI [T2I_ADAPTER_URI ...]\n Specify one or more T2IAdapter models using URIs. This should be a Hugging Face\n repository slug / blob link, path to model file on disk (for example, a .pt, .pth,\n .bin, .ckpt, or .safetensors file), or model folder containing model files.\n\n If a T2IAdapter model file exists at a URL which serves the file as a raw download,\n you may provide an http/https link to it and it will be downloaded to dgenerates web\n cache.\n\n Optional arguments can be provided after the T2IAdapter model specification, these\n are: \"scale\", \"revision\", \"variant\", \"subfolder\", and \"dtype\".\n\n They can be specified as so in any order, they are not positional: \"huggingface/t2ia\n dapter;scale=1.0;revision=main;variant=fp16;subfolder=repo_subfolder;dtype=float16\".\n\n The \"scale\" argument specifies the scaling factor applied to the T2IAdapter model,\n the default value is 1.0.\n\n The \"revision\" argument specifies the model revision to use for the T2IAdapter model\n when loading from Hugging Face repository, (The Git branch / tag, default is\n \"main\").\n\n The \"variant\" argument specifies the T2IAdapter model variant, if \"variant\" is\n specified when loading from a Hugging Face repository or folder, weights will be\n loaded from \"variant\" filename, e.g. \"pytorch_model.\u003cvariant\u003e.safetensors. \"variant\"\n defaults to automatic selection. \"variant\" in the case of --t2i-adapters does not\n default to the value of --variant to prevent failures during common use cases.\n\n The \"subfolder\" argument specifies the ControlNet model subfolder, if specified when\n loading from a Hugging Face repository or folder, weights from the specified\n subfolder.\n\n The \"dtype\" argument specifies the T2IAdapter model precision, it defaults to the\n value of -t/--dtype and should be one of: auto, bfloat16, float16, or float32.\n\n If you wish to load a weights file directly from disk, the simplest way is:\n --t2i-adapters \"my_t2i_adapter.safetensors\" or --t2i-adapters\n \"my_t2i_adapter.safetensors;scale=1.0;dtype=float16\", all other loading arguments\n aside from \"scale\" and \"dtype\" are unused in this case and may produce an error\n message if used.\n\n If you wish to load a specific weight file from a Hugging Face repository, use the\n blob link loading syntax: --t2i-adapters\n \"https://huggingface.co/UserName/repository-name/blob/main/t2i_adapter.safetensors\",\n the \"revision\" argument may be used with this syntax.\n -----------------------------------------------------\n -sch SCHEDULER_URI [SCHEDULER_URI ...], --scheduler SCHEDULER_URI [SCHEDULER_URI ...], --schedulers SCHEDULER_URI [SCHEDULER_URI ...]\n Specify a scheduler (sampler) by URI. Passing \"help\" to this argument will print the\n compatible schedulers for a model without generating any images. Passing \"helpargs\"\n will yield a help message with a list of overridable arguments for each scheduler\n and their typical defaults. Arguments listed by \"helpargs\" can be overridden using\n the URI syntax typical to other dgenerate URI arguments.\n\n You may pass multiple scheduler URIs to this argument, each URI will be tried in\n turn.\n -----\n -pag, --pag Use perturbed attention guidance? This is supported for --model-type torch, torch-\n sdxl, and torch-sd3 for most use cases. This enables PAG for the main model using\n default scale values.\n ---------------------\n -pags FLOAT [FLOAT ...], --pag-scales FLOAT [FLOAT ...]\n One or more perturbed attention guidance scales to try. Specifying values enables\n PAG for the main model. (default: [3.0])\n ----------------------------------------\n -pagas FLOAT [FLOAT ...], --pag-adaptive-scales FLOAT [FLOAT ...]\n One or more adaptive perturbed attention guidance scales to try. Specifying values\n enables PAG for the main model. (default: [0.0])\n ------------------------------------------------\n -rpag, --sdxl-refiner-pag\n Use perturbed attention guidance in the SDXL refiner? This is supported for --model-\n type torch-sdxl for most use cases. This enables PAG for the SDXL refiner model\n using default scale values.\n ---------------------------\n -rpags FLOAT [FLOAT ...], --sdxl-refiner-pag-scales FLOAT [FLOAT ...]\n One or more perturbed attention guidance scales to try with the SDXL refiner pass.\n Specifying values enables PAG for the refiner. (default: [3.0])\n ---------------------------------------------------------------\n -rpagas FLOAT [FLOAT ...], --sdxl-refiner-pag-adaptive-scales FLOAT [FLOAT ...]\n One or more adaptive perturbed attention guidance scales to try with the SDXL\n refiner pass. Specifying values enables PAG for the refiner. (default: [0.0])\n -----------------------------------------------------------------------------\n -mqo, --model-sequential-offload\n Force sequential model offloading for the main pipeline, this may drastically reduce\n memory consumption and allow large models to run when they would otherwise not fit\n in your GPUs VRAM. Inference will be much slower. Mutually exclusive with --model-\n cpu-offload\n -----------\n -mco, --model-cpu-offload\n Force model cpu offloading for the main pipeline, this may reduce memory consumption\n and allow large models to run when they would otherwise not fit in your GPUs VRAM.\n Inference will be slower. Mutually exclusive with --model-sequential-offload\n ----------------------------------------------------------------------------\n --s-cascade-decoder MODEL_URI\n Specify a Stable Cascade (torch-s-cascade) decoder model path using a URI. This\n should be a Hugging Face repository slug / blob link, path to model file on disk\n (for example, a .pt, .pth, .bin, .ckpt, or .safetensors file), or model folder\n containing model files.\n\n Optional arguments can be provided after the decoder model specification, these are:\n \"revision\", \"variant\", \"subfolder\", and \"dtype\".\n\n They can be specified as so in any order, they are not positional: \"huggingface/deco\n der_model;revision=main;variant=fp16;subfolder=repo_subfolder;dtype=float16\".\n\n The \"revision\" argument specifies the model revision to use for the decoder model\n when loading from Hugging Face repository, (The Git branch / tag, default is\n \"main\").\n\n The \"variant\" argument specifies the decoder model variant and defaults to the value\n of --variant. When \"variant\" is specified when loading from a Hugging Face\n repository or folder, weights will be loaded from \"variant\" filename, e.g.\n \"pytorch_model.\u003cvariant\u003e.safetensors.\n\n The \"subfolder\" argument specifies the decoder model subfolder, if specified when\n loading from a Hugging Face repository or folder, weights from the specified\n subfolder.\n\n The \"dtype\" argument specifies the Stable Cascade decoder model precision, it\n defaults to the value of -t/--dtype and should be one of: auto, bfloat16, float16,\n or float32.\n\n If you wish to load a weights file directly from disk, the simplest way is: --sdxl-\n refiner \"my_decoder.safetensors\" or --sdxl-refiner\n \"my_decoder.safetensors;dtype=float16\", all other loading arguments aside from\n \"dtype\" are unused in this case and may produce an error message if used.\n\n If you wish to load a specific weight file from a Hugging Face repository, use the\n blob link loading syntax: --s-cascade-decoder\n \"https://huggingface.co/UserName/repository-name/blob/main/decoder.safetensors\", the\n \"revision\" argument may be used with this syntax.\n -------------------------------------------------\n -dqo, --s-cascade-decoder-sequential-offload\n Force sequential model offloading for the Stable Cascade decoder pipeline, this may\n drastically reduce memory consumption and allow large models to run when they would\n otherwise not fit in your GPUs VRAM. Inference will be much slower. Mutually\n exclusive with --s-cascade-decoder-cpu-offload\n ----------------------------------------------\n -dco, --s-cascade-decoder-cpu-offload\n Force model cpu offloading for the Stable Cascade decoder pipeline, this may reduce\n memory consumption and allow large models to run when they would otherwise not fit\n in your GPUs VRAM. Inference will be slower. Mutually exclusive with --s-cascade-\n decoder-sequential-offload\n --------------------------\n --s-cascade-decoder-prompts PROMPT [PROMPT ...]\n One or more prompts to try with the Stable Cascade decoder model, by default the\n decoder model gets the primary prompt, this argument overrides that with a prompt of\n your choosing. The negative prompt component can be specified with the same syntax\n as --prompts\n ------------\n --s-cascade-decoder-inference-steps INTEGER [INTEGER ...]\n One or more inference steps values to try with the Stable Cascade decoder. (default:\n [10])\n -----\n --s-cascade-decoder-guidance-scales INTEGER [INTEGER ...]\n One or more guidance scale values to try with the Stable Cascade decoder. (default:\n [0])\n ----\n --s-cascade-decoder-scheduler SCHEDULER_URI [SCHEDULER_URI ...], --s-cascade-decoder-schedulers SCHEDULER_URI [SCHEDULER_URI ...]\n Specify a scheduler (sampler) by URI for the Stable Cascade decoder pass. Operates\n the exact same way as --scheduler including the \"help\" option. Passing 'helpargs'\n will yield a help message with a list of overridable arguments for each scheduler\n and their typical defaults. Defaults to the value of --scheduler.\n\n You may pass multiple scheduler URIs to this argument, each URI will be tried in\n turn.\n -----\n --sdxl-refiner MODEL_URI\n Specify a Stable Diffusion XL (torch-sdxl) refiner model path using a URI. This\n should be a Hugging Face repository slug / blob link, path to model file on disk\n (for example, a .pt, .pth, .bin, .ckpt, or .safetensors file), or model folder\n containing model files.\n\n Optional arguments can be provided after the SDXL refiner model specification, these\n are: \"revision\", \"variant\", \"subfolder\", and \"dtype\".\n\n They can be specified as so in any order, they are not positional: \"huggingface/refi\n ner_model_xl;revision=main;variant=fp16;subfolder=repo_subfolder;dtype=float16\".\n\n The \"revision\" argument specifies the model revision to use for the refiner model\n when loading from Hugging Face repository, (The Git branch / tag, default is\n \"main\").\n\n The \"variant\" argument specifies the SDXL refiner model variant and defaults to the\n value of --variant. When \"variant\" is specified when loading from a Hugging Face\n repository or folder, weights will be loaded from \"variant\" filename, e.g.\n \"pytorch_model.\u003cvariant\u003e.safetensors.\n\n The \"subfolder\" argument specifies the SDXL refiner model subfolder, if specified\n when loading from a Hugging Face repository or folder, weights from the specified\n subfolder.\n\n The \"dtype\" argument specifies the SDXL refiner model precision, it defaults to the\n value of -t/--dtype and should be one of: auto, bfloat16, float16, or float32.\n\n If you wish to load a weights file directly from disk, the simplest way is: --sdxl-\n refiner \"my_sdxl_refiner.safetensors\" or --sdxl-refiner\n \"my_sdxl_refiner.safetensors;dtype=float16\", all other loading arguments aside from\n \"dtype\" are unused in this case and may produce an error message if used.\n\n If you wish to load a specific weight file from a Hugging Face repository, use the\n blob link loading syntax: --sdxl-refiner\n \"https://huggingface.co/UserName/repository-\n name/blob/main/refiner_model.safetensors\", the \"revision\" argument may be used with\n this syntax.\n ------------\n -rqo, --sdxl-refiner-sequential-offload\n Force sequential model offloading for the SDXL refiner pipeline, this may\n drastically reduce memory consumption and allow large models to run when they would\n otherwise not fit in your GPUs VRAM. Inference will be much slower. Mutually\n exclusive with --refiner-cpu-offload\n ------------------------------------\n -rco, --sdxl-refiner-cpu-offload\n Force model cpu offloading for the SDXL refiner pipeline, this may reduce memory\n consumption and allow large models to run when they would otherwise not fit in your\n GPUs VRAM. Inference will be slower. Mutually exclusive with --refiner-sequential-\n offload\n -------\n --sdxl-refiner-scheduler SCHEDULER_URI [SCHEDULER_URI ...], --sdxl-refiner-schedulers SCHEDULER_URI [SCHEDULER_URI ...]\n Specify a scheduler (sampler) by URI for the SDXL refiner pass. Operates the exact\n same way as --scheduler including the \"help\" option. Passing 'helpargs' will yield a\n help message with a list of overridable arguments for each scheduler and their\n typical defaults. Defaults to the value of --scheduler.\n\n You may pass multiple scheduler URIs to this argument, each URI will be tried in\n turn.\n -----\n --sdxl-refiner-edit Force the SDXL refiner to operate in edit mode instead of cooperative denoising mode\n as it would normally do for inpainting and ControlNet usage. The main model will\n perform the full amount of inference steps requested by --inference-steps. The\n output of the main model will be passed to the refiner model and processed with an\n image seed strength in img2img mode determined by (1.0 - high-noise-fraction)\n -----------------------------------------------------------------------------\n --sdxl-second-prompts PROMPT [PROMPT ...]\n One or more secondary prompts to try using SDXL's secondary text encoder. By default\n the model is passed the primary prompt for this value, this option allows you to\n choose a different prompt. The negative prompt component can be specified with the\n same syntax as --prompts\n ------------------------\n --sdxl-t2i-adapter-factors FLOAT [FLOAT ...]\n One or more SDXL specific T2I adapter factors to try, this controls the amount of\n time-steps for which a T2I adapter applies guidance to an image, this is a value\n between 0.0 and 1.0. A value of 0.5 for example indicates that the T2I adapter is\n only active for half the amount of time-steps it takes to completely render an\n image.\n ------\n --sdxl-aesthetic-scores FLOAT [FLOAT ...]\n One or more Stable Diffusion XL (torch-sdxl) \"aesthetic-score\" micro-conditioning\n parameters. Used to simulate an aesthetic score of the generated image by\n influencing the positive text condition. Part of SDXL's micro-conditioning as\n explained in section 2.2 of [https://huggingface.co/papers/2307.01952].\n -----------------------------------------------------------------------\n --sdxl-crops-coords-top-left COORD [COORD ...]\n One or more Stable Diffusion XL (torch-sdxl) \"negative-crops-coords-top-left\" micro-\n conditioning parameters in the format \"0,0\". --sdxl-crops-coords-top-left can be\n used to generate an image that appears to be \"cropped\" from the position --sdxl-\n crops-coords-top-left downwards. Favorable, well-centered images are usually\n achieved by setting --sdxl-crops-coords-top-left to \"0,0\". Part of SDXL's micro-\n conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952].\n -------------------------------------------\n --sdxl-original-size SIZE [SIZE ...], --sdxl-original-sizes SIZE [SIZE ...]\n One or more Stable Diffusion XL (torch-sdxl) \"original-size\" micro-conditioning\n parameters in the format (WIDTH)x(HEIGHT). If not the same as --sdxl-target-size the\n image will appear to be down or up-sampled. --sdxl-original-size defaults to\n --output-size or the size of any input images if not specified. Part of SDXL's\n micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952]\n ------------------------------------------\n --sdxl-target-size SIZE [SIZE ...], --sdxl-target-sizes SIZE [SIZE ...]\n One or more Stable Diffusion XL (torch-sdxl) \"target-size\" micro-conditioning\n parameters in the format (WIDTH)x(HEIGHT). For most cases, --sdxl-target-size should\n be set to the desired height and width of the generated image. If not specified it\n will default to --output-size or the size of any input images. Part of SDXL's micro-\n conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952]\n ------------------------------------------\n --sdxl-negative-aesthetic-scores FLOAT [FLOAT ...]\n One or more Stable Diffusion XL (torch-sdxl) \"negative-aesthetic-score\" micro-\n conditioning parameters. Part of SDXL's micro-conditioning as explained in section\n 2.2 of [https://huggingface.co/papers/2307.01952]. Can be used to simulate an\n aesthetic score of the generated image by influencing the negative text condition.\n ----------------------------------------------------------------------------------\n --sdxl-negative-original-sizes SIZE [SIZE ...]\n One or more Stable Diffusion XL (torch-sdxl) \"negative-original-sizes\" micro-\n conditioning parameters. Negatively condition the generation process based on a\n specific image resolution. Part of SDXL's micro-conditioning as explained in section\n 2.2 of [https://huggingface.co/papers/2307.01952]. For more information, refer to\n this issue thread: https://github.com/huggingface/diffusers/issues/4208\n -----------------------------------------------------------------------\n --sdxl-negative-target-sizes SIZE [SIZE ...]\n One or more Stable Diffusion XL (torch-sdxl) \"negative-original-sizes\" micro-\n conditioning parameters. To negatively condition the generation process based on a\n target image resolution. It should be as same as the \"--sdxl-target-size\" for most\n cases. Part of SDXL's micro-conditioning as explained in section 2.2 of\n [https://huggingface.co/papers/2307.01952]. For more information, refer to this\n issue thread: https://github.com/huggingface/diffusers/issues/4208.\n -------------------------------------------------------------------\n --sdxl-negative-crops-coords-top-left COORD [COORD ...]\n One or more Stable Diffusion XL (torch-sdxl) \"negative-crops-coords-top-left\" micro-\n conditioning parameters in the format \"0,0\". Negatively condition the generation\n process based on a specific crop coordinates. Part of SDXL's micro-conditioning as\n explained in section 2.2 of [https://huggingface.co/papers/2307.01952]. For more\n information, refer to this issue thread:\n https://github.com/huggingface/diffusers/issues/4208.\n -----------------------------------------------------\n --sdxl-refiner-prompts PROMPT [PROMPT ...]\n One or more prompts to try with the SDXL refiner model, by default the refiner model\n gets the primary prompt, this argument overrides that with a prompt of your\n choosing. The negative prompt component can be specified with the same syntax as\n --prompts\n ---------\n --sdxl-refiner-clip-skips INTEGER [INTEGER ...]\n One or more clip skip override values to try for the SDXL refiner, which normally\n uses the clip skip value for the main model when it is defined by --clip-skips.\n -------------------------------------------------------------------------------\n --sdxl-refiner-second-prompts PROMPT [PROMPT ...]\n One or more prompts to try with the SDXL refiner models secondary text encoder, by\n default the refiner model gets the primary prompt passed to its second text encoder,\n this argument overrides that with a prompt of your choosing. The negative prompt\n component can be specified with the same syntax as --prompts\n ------------------------------------------------------------\n --sdxl-refiner-aesthetic-scores FLOAT [FLOAT ...]\n See: --sdxl-aesthetic-scores, applied to SDXL refiner pass.\n -----------------------------------------------------------\n --sdxl-refiner-crops-coords-top-left COORD [COORD ...]\n See: --sdxl-crops-coords-top-left, applied to SDXL refiner pass.\n ----------------------------------------------------------------\n --sdxl-refiner-original-sizes SIZE [SIZE ...]\n See: --sdxl-refiner-original-sizes, applied to SDXL refiner pass.\n -----------------------------------------------------------------\n --sdxl-refiner-target-sizes SIZE [SIZE ...]\n See: --sdxl-refiner-target-sizes, applied to SDXL refiner pass.\n ---------------------------------------------------------------\n --sdxl-refiner-negative-aesthetic-scores FLOAT [FLOAT ...]\n See: --sdxl-negative-aesthetic-scores, applied to SDXL refiner pass.\n --------------------------------------------------------------------\n --sdxl-refiner-negative-original-sizes SIZE [SIZE ...]\n See: --sdxl-negative-original-sizes, applied to SDXL refiner pass.\n ------------------------------------------------------------------\n --sdxl-refiner-negative-target-sizes SIZE [SIZE ...]\n See: --sdxl-negative-target-sizes, applied to SDXL refiner pass.\n ----------------------------------------------------------------\n --sdxl-refiner-negative-crops-coords-top-left COORD [COORD ...]\n See: --sdxl-negative-crops-coords-top-left, applied to SDXL refiner pass.\n -------------------------------------------------------------------------\n -hnf FLOAT [FLOAT ...], --sdxl-high-noise-fractions FLOAT [FLOAT ...]\n One or more high-noise-fraction values for Stable Diffusion XL (torch-sdxl), this\n fraction of inference steps will be processed by the base model, while the rest will\n be processed by the refiner model. Multiple values to this argument will result in\n additional generation steps for each value. In certain situations when the mixture\n of denoisers algorithm is not supported, such as when using --control-nets and\n inpainting with SDXL, the inverse proportion of this value IE: (1.0 - high-noise-\n fraction) becomes the --image-seed-strengths input to the SDXL refiner. (default:\n [0.8])\n ------\n -ri INT [INT ...], --sdxl-refiner-inference-steps INT [INT ...]\n One or more inference steps values for the SDXL refiner when in use. Override the\n number of inference steps used by the SDXL refiner, which defaults to the value\n taken from --inference-steps.\n -----------------------------\n -rg FLOAT [FLOAT ...], --sdxl-refiner-guidance-scales FLOAT [FLOAT ...]\n One or more guidance scale values for the SDXL refiner when in use. Override the\n guidance scale value used by the SDXL refiner, which defaults to the value taken\n from --guidance-scales.\n -----------------------\n -rgr FLOAT [FLOAT ...], --sdxl-refiner-guidance-rescales FLOAT [FLOAT ...]\n One or more guidance rescale values for the SDXL refiner when in use. Override the\n guidance rescale value used by the SDXL refiner, which defaults to the value taken\n from --guidance-rescales.\n -------------------------\n -sc, --safety-checker\n Enable safety checker loading, this is off by default. When turned on images with\n NSFW content detected may result in solid black output. Some pretrained models have\n no safety checker model present, in that case this option has no effect.\n ------------------------------------------------------------------------\n -d DEVICE, --device DEVICE\n cuda / cpu, or other device supported by torch, for example mps on MacOS. (default:\n cuda, mps on MacOS). Use: cuda:0, cuda:1, cuda:2, etc. to specify a specific cuda\n supporting GPU.\n ---------------\n -t DTYPE, --dtype DTYPE\n Model precision: auto, bfloat16, float16, or float32. (default: auto)\n ---------------------------------------------------------------------\n -s SIZE, --output-size SIZE\n Image output size, for txt2img generation this is the exact output size. The\n dimensions specified for this value must be aligned by 8 or you will receive an\n error message. If an --image-seeds URI is used its Seed, Mask, and/or Control\n component image sources will be resized to this dimension with aspect ratio\n maintained before being used for generation by default, except in the case of Stable\n Cascade where the images are used as a style prompt (not a noised seed), and can be\n of varying dimensions.\n\n If --no-aspect is not specified, width will be fixed and a new height (aligned by 8)\n will be calculated for the input images. In most cases resizing the image inputs\n will result in an image output of an equal size to the inputs, except for upscalers\n and Deep Floyd --model-type values (torch-if*).\n\n If only one integer value is provided, that is the value for both dimensions. X/Y\n dimension values should be separated by \"x\".\n\n This value defaults to 512x512 for Stable Diffusion when no --image-seeds are\n specified (IE txt2img mode), 1024x1024 for Stable Cascade and Stable Diffusion 3/XL\n or Flux model types, and 64x64 for --model-type torch-if (Deep Floyd stage 1).\n\n Deep Floyd stage 1 images passed to superscaler models (--model-type torch-ifs*)\n that are specified with the 'floyd' keyword argument in an --image-seeds definition\n are never resized or processed in any way.\n ------------------------------------------\n -na, --no-aspect This option disables aspect correct resizing of images provided to --image-seeds\n globally. Seed, Mask, and Control guidance images will be resized to the closest\n dimension specified by --output-size that is aligned by 8 pixels with no\n consideration of the source aspect ratio. This can be overriden at the --image-seeds\n level with the image seed keyword argument 'aspect=true/false'.\n ---------------------------------------------------------------\n -o PATH, --output-path PATH\n Output path for generated images and files. This directory will be created if it\n does not exist. (default: ./output)\n -----------------------------------\n -op PREFIX, --output-prefix PREFIX\n Name prefix for generated images and files. This prefix will be added to the\n beginning of every generated file, followed by an underscore.\n -------------------------------------------------------------\n -ox, --output-overwrite\n Enable overwrites of files in the output directory that already exists. The default\n behavior is not to do this, and instead append a filename suffix:\n \"_duplicate_(number)\" when it is detected that the generated file name already\n exists.\n -------\n -oc, --output-configs\n Write a configuration text file for every output image or animation. The text file\n can be used reproduce that particular output image or animation by piping it to\n dgenerate STDIN or by using the --file option, for example \"dgenerate \u003c config.dgen\"\n or \"dgenerate --file config.dgen\". These files will be written to --output-path and\n are affected by --output-prefix and --output-overwrite as well. The files will be\n named after their corresponding image or animation file. Configuration files\n produced for animation frame images will utilize --frame-start and --frame-end to\n specify the frame number.\n -------------------------\n -om, --output-metadata\n Write the information produced by --output-configs to the PNG metadata of each\n image. Metadata will not be written to animated files (yet). The data is written to\n a PNG metadata property named DgenerateConfig and can be read using ImageMagick like\n so: \"magick identify -format \"%[Property:DgenerateConfig] generated_file.png\".\n ------------------------------------------------------------------------------\n -pw PROMPT_WEIGHTER_URI, --prompt-weighter PROMPT_WEIGHTER_URI\n Specify a prompt weighter implementation by URI, for example: --prompt-weighter\n compel, or --prompt-weighter sd-embed. By default, no prompt weighting syntax is\n enabled, meaning that you cannot adjust token weights as you may be able to do in\n software such as ComfyUI, Automatic1111, CivitAI etc. And in some cases the length\n of your prompt is limited. Prompt weighters support these special token weighting\n syntaxes and long prompts, currently there are two implementations \"compel\" and \"sd-\n embed\". See: --prompt-weighter-help for a list of implementation names. You may also\n use --prompt-weighter-help \"name\" to see comprehensive documentation for a specific\n prompt weighter implementation.\n -------------------------------\n --prompt-weighter-help [PROMPT_WEIGHTER_NAMES ...]\n Use this option alone (or with --plugin-modules) and no model specification in order\n to list available prompt weighter names. Specifying one or more prompt weighter\n names after this option will cause usage documentation for the specified prompt\n weighters to be printed. When used with --plugin-modules, prompt weighters\n implemented by the specified plugins will also be listed.\n ---------------------------------------------------------\n -p PROMPT [PROMPT ...], --prompts PROMPT [PROMPT ...]\n One or more prompts to try, an image group is generated for each prompt, prompt data\n is split by ; (semi-colon). The first value is the positive text influence, things\n you want to see. The Second value is negative influence IE. things you don't want to\n see. Example: --prompts \"photo of a horse in a field; artwork, painting, rain\".\n (default: [(empty string)])\n ---------------------------\n --sd3-max-sequence-length INTEGER\n The maximum amount of prompt tokens that the T5EncoderModel (third text encoder) of\n Stable Diffusion 3 can handle. This should be an integer value between 1 and 512\n inclusive. The higher the value the more resources and time are required for\n processing. (default: 256)\n --------------------------\n --sd3-second-prompts PROMPT [PROMPT ...]\n One or more secondary prompts to try using the torch-sd3 (Stable Diffusion 3)\n secondary text encoder. By default the model is passed the primary prompt for this\n value, this option allows you to choose a different prompt. The negative prompt\n component can be specified with the same syntax as --prompts\n ------------------------------------------------------------\n --sd3-third-prompts PROMPT [PROMPT ...]\n One or more tertiary prompts to try using the torch-sd3 (Stable Diffusion 3)\n tertiary (T5) text encoder. By default the model is passed the primary prompt for\n this value, this option allows you to choose a different prompt. The negative prompt\n component can be specified with the same syntax as --prompts\n ------------------------------------------------------------\n --flux-second-prompts PROMPT [PROMPT ...]\n One or more secondary prompts to try using the torch-flux (Flux) secondary (T5) text\n encoder. By default the model is passed the primary prompt for this value, this\n option allows you to choose a different prompt.\n -----------------------------------------------\n --flux-max-sequence-length INTEGER\n The maximum amount of prompt tokens that the T5EncoderModel (second text encoder) of\n Flux can handle. This should be an integer value between 1 and 512 inclusive. The\n higher the value the more resources and time are required for processing. (default:\n 512)\n ----\n -cs INTEGER [INTEGER ...], --clip-skips INTEGER [INTEGER ...]\n One or more clip skip values to try. Clip skip is the number of layers to be skipped\n from CLIP while computing the prompt embeddings, it must be a value greater than or\n equal to zero. A value of 1 means that the output of the pre-final layer will be\n used for computing the prompt embeddings. This is only supported for --model-type\n values \"torch\", \"torch-sdxl\", and \"torch-sd3\".\n ----------------------------------------------\n -se SEED [SEED ...], --seeds SEED [SEED ...]\n One or more seeds to try, define fixed seeds to achieve deterministic output. This\n argument may not be used when --gse/--gen-seeds is used. (default: [randint(0,\n 99999999999999)])\n -----------------\n -sei, --seeds-to-images\n When this option is enabled, each provided --seeds value or value generated by\n --gen-seeds is used for the corresponding image input given by --image-seeds. If the\n amount of --seeds given is not identical to that of the amount of --image-seeds\n given, the seed is determined as: seed = seeds[image_seed_index % len(seeds)], IE:\n it wraps around.\n ----------------\n -gse COUNT, --gen-seeds COUNT\n Auto generate N random seeds to try. This argument may not be used when -se/--seeds\n is used.\n --------\n -af FORMAT, --animation-format FORMAT\n Output format when generating an animation from an input video / gif / webp etc.\n Value must be one of: mp4, png, apng, gif, or webp. You may also specify \"frames\" to\n indicate that only frames should be output and no coalesced animation file should be\n rendered. (default: mp4)\n ------------------------\n -if FORMAT, --image-format FORMAT\n Output format when writing static images. Any selection other than \"png\" is not\n compatible with --output-metadata. Value must be one of: png, apng, blp, bmp, dib,\n bufr, pcx, dds, ps, eps, gif, grib, h5, hdf, jp2, j2k, jpc, jpf, jpx, j2c, icns,\n ico, im, jfif, jpe, jpg, jpeg, tif, tiff, mpo, msp, palm, pdf, pbm, pgm, ppm, pnm,\n pfm, bw, rgb, rgba, sgi, tga, icb, vda, vst, webp, wmf, emf, or xbm. (default: png)\n -----------------------------------------------------------------------------------\n -nf, --no-frames Do not write frame images individually when rendering an animation, only write the\n animation file. This option is incompatible with --animation-format frames.\n ---------------------------------------------------------------------------\n -fs FRAME_NUMBER, --frame-start FRAME_NUMBER\n Starting frame slice point for animated files (zero-indexed), the specified frame\n will be included. (default: 0)\n ------------------------------\n -fe FRAME_NUMBER, --frame-end FRAME_NUMBER\n Ending frame slice point for animated files (zero-indexed), the specified frame will\n be included.\n ------------\n -is SEED [SEED ...], --image-seeds SEED [SEED ...]\n One or more image seed URIs to process, these may consist of URLs or file paths.\n Videos / GIFs / WEBP files will result in frames being rendered as well as an\n animated output file being generated if more than one frame is available in the\n input file. Inpainting for static images can be achieved by specifying a black and\n white mask image in each image seed string using a semicolon as the separating\n character, like so: \"my-seed-image.png;my-image-mask.png\", white areas of the mask\n indicate where generated content is to be placed in your seed image.\n\n Output dimensions specific to the image seed can be specified by placing the\n dimension at the end of the string following a semicolon like so: \"my-seed-\n image.png;512x512\" or \"my-seed-image.png;my-image-mask.png;512x512\". When using\n --control-nets, a singular image specification is interpreted as the control\n guidance image, and you can specify multiple control image sources by separating\n them with commas in the case where multiple ControlNets are specified, IE: (--image-\n seeds \"control-image1.png, control-image2.png\") OR (--image-seeds\n \"seed.png;control=control-image1.png, control-image2.png\").\n\n Using --control-nets with img2img or inpainting can be accomplished with the syntax:\n \"my-seed-image.png;mask=my-image-mask.png;control=my-control-\n image.png;resize=512x512\". The \"mask\" and \"resize\" arguments are optional when using\n --control-nets. Videos, GIFs, and WEBP are also supported as inputs when using\n --control-nets, even for the \"control\" argument.\n\n --image-seeds is capable of reading from multiple animated files at once or any\n combination of animated files and images, the animated file with the least amount of\n frames dictates how many frames are generated and static images are duplicated over\n th","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fteriks%2Fdgenerate","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fteriks%2Fdgenerate","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fteriks%2Fdgenerate/lists"}