Batch image generation and manipulation tool supporting Stable Diffusion and related techniques / algorithms, with support for video and animated image processing.
Project description
Overview
dgenerate is a command line tool and library for generating images and animation sequences using Stable Diffusion and related techniques / models. Now Featuring a Console UI and REPL shell mode for the dgenerate configuration / scripting language.
You can use dgenerate to generate multiple images or animated outputs using multiple combinations of diffusion input parameters in batch, so that the differences in generated output can be compared / curated easily.
Simple txt2img generation without image inputs is supported, as well as img2img and inpainting, and ControlNets.
Animated output can be produced by processing every frame of a Video, GIF, WebP, or APNG through various implementations of diffusion in img2img or inpainting mode, as well as with ControlNets and control guidance images, in any combination thereof. MP4 (h264) video can be written without memory constraints related to frame count. GIF, WebP, and PNG/APNG can be written WITH memory constraints, IE: all frames exist in memory at once before being written.
Video input of any runtime can be processed without memory constraints related to the video size. Many video formats are supported through the use of PyAV (ffmpeg).
Animated image input such as GIF, APNG (extension must be .apng), and WebP, can also be processed WITH memory constraints, IE: all frames exist in memory at once after an animated image is read.
PNG, JPEG, JPEG-2000, TGA (Targa), BMP, and PSD (Photoshop) are supported for static image inputs.
In addition to diffusion, dgenerate also supports the processing of any supported image, video, or animated image using any of its built in image processors, which include various edge detectors, depth detectors, segment generation, normal map generation, pose detection, non-diffusion based AI upscaling, and more.
This software requires a Nvidia GPU supporting CUDA 12.1+, or MacOS on Apple Silicon, CPU rendering is possible for some operations but extraordinarily slow.
For library documentation, and a better README reading experience which includes proper syntax highlighting for examples, and side panel navigation, please visit readthedocs.
- Usage Manual
Help Output
usage: dgenerate [-h] [-v] [--version] [--file | --shell | --no-stdin | --console]
[--plugin-modules PATH [PATH ...]] [--sub-command SUB_COMMAND]
[--sub-command-help [SUB_COMMAND ...]] [-ofm] [--templates-help [VARIABLE_NAME ...]]
[--directives-help [DIRECTIVE_NAME ...]] [--functions-help [FUNCTION_NAME ...]]
[-mt MODEL_TYPE] [-rev BRANCH] [-var VARIANT] [-sbf SUBFOLDER] [-atk TOKEN] [-bs INTEGER]
[-bgs SIZE] [-te TEXT_ENCODER_URIS [TEXT_ENCODER_URIS ...]]
[-te2 TEXT_ENCODER_URIS [TEXT_ENCODER_URIS ...]] [-un UNET_URI] [-un2 UNET_URI]
[-tf TRANSFORMER_URI] [-vae VAE_URI] [-vt] [-vs] [-lra LORA_URI [LORA_URI ...]]
[-lrfs LORA_FUSE_SCALE] [-ie IMAGE_ENCODER_URI] [-ipa IP_ADAPTER_URI [IP_ADAPTER_URI ...]]
[-ti URI [URI ...]] [-cn CONTROLNET_URI [CONTROLNET_URI ...] | -t2i T2I_ADAPTER_URI
[T2I_ADAPTER_URI ...]] [-sch SCHEDULER_URI] [-mqo | -mco] [--s-cascade-decoder MODEL_URI]
[-dqo] [-dco] [--s-cascade-decoder-prompts PROMPT [PROMPT ...]]
[--s-cascade-decoder-inference-steps INTEGER [INTEGER ...]]
[--s-cascade-decoder-guidance-scales INTEGER [INTEGER ...]]
[--s-cascade-decoder-scheduler SCHEDULER_URI] [--sdxl-refiner MODEL_URI] [-rqo] [-rco]
[--sdxl-refiner-scheduler SCHEDULER_URI] [--sdxl-refiner-edit]
[--sdxl-second-prompts PROMPT [PROMPT ...]] [--sdxl-t2i-adapter-factors FLOAT [FLOAT ...]]
[--sdxl-aesthetic-scores FLOAT [FLOAT ...]]
[--sdxl-crops-coords-top-left COORD [COORD ...]] [--sdxl-original-size SIZE [SIZE ...]]
[--sdxl-target-size SIZE [SIZE ...]] [--sdxl-negative-aesthetic-scores FLOAT [FLOAT ...]]
[--sdxl-negative-original-sizes SIZE [SIZE ...]]
[--sdxl-negative-target-sizes SIZE [SIZE ...]]
[--sdxl-negative-crops-coords-top-left COORD [COORD ...]]
[--sdxl-refiner-prompts PROMPT [PROMPT ...]]
[--sdxl-refiner-clip-skips INTEGER [INTEGER ...]]
[--sdxl-refiner-second-prompts PROMPT [PROMPT ...]]
[--sdxl-refiner-aesthetic-scores FLOAT [FLOAT ...]]
[--sdxl-refiner-crops-coords-top-left COORD [COORD ...]]
[--sdxl-refiner-original-sizes SIZE [SIZE ...]]
[--sdxl-refiner-target-sizes SIZE [SIZE ...]]
[--sdxl-refiner-negative-aesthetic-scores FLOAT [FLOAT ...]]
[--sdxl-refiner-negative-original-sizes SIZE [SIZE ...]]
[--sdxl-refiner-negative-target-sizes SIZE [SIZE ...]]
[--sdxl-refiner-negative-crops-coords-top-left COORD [COORD ...]] [-hnf FLOAT [FLOAT ...]]
[-ri INT [INT ...]] [-rg FLOAT [FLOAT ...]] [-rgr FLOAT [FLOAT ...]] [-sc] [-d DEVICE]
[-t DTYPE] [-s SIZE] [-na] [-o PATH] [-op PREFIX] [-ox] [-oc] [-om]
[-pw PROMPT_WEIGHTER_URI] [--prompt-weighter-help [PROMPT_WEIGHTER_NAMES ...]]
[-p PROMPT [PROMPT ...]] [--sd3-max-sequence-length INTEGER]
[--sd3-second-prompts PROMPT [PROMPT ...]] [--sd3-third-prompts PROMPT [PROMPT ...]]
[--flux-second-prompts PROMPT [PROMPT ...]] [--flux-max-sequence-length INTEGER]
[-cs INTEGER [INTEGER ...]] [-se SEED [SEED ...]] [-sei] [-gse COUNT] [-af FORMAT]
[-if FORMAT] [-nf] [-fs FRAME_NUMBER] [-fe FRAME_NUMBER] [-is SEED [SEED ...]]
[-sip PROCESSOR_URI [PROCESSOR_URI ...]] [-mip PROCESSOR_URI [PROCESSOR_URI ...]]
[-cip PROCESSOR_URI [PROCESSOR_URI ...]] [--image-processor-help [PROCESSOR_NAME ...]]
[-pp PROCESSOR_URI [PROCESSOR_URI ...]] [-iss FLOAT [FLOAT ...] | -uns INTEGER
[INTEGER ...]] [-gs FLOAT [FLOAT ...]] [-igs FLOAT [FLOAT ...]] [-gr FLOAT [FLOAT ...]]
[-ifs INTEGER [INTEGER ...]] [-mc EXPR [EXPR ...]] [-pmc EXPR [EXPR ...]]
[-umc EXPR [EXPR ...]] [-vmc EXPR [EXPR ...]] [-cmc EXPR [EXPR ...]] [-tmc EXPR [EXPR ...]]
[-iemc EXPR [EXPR ...]] [-amc EXPR [EXPR ...]] [-tfmc EXPR [EXPR ...]]
[-ipmc EXPR [EXPR ...]] [-ipcc EXPR [EXPR ...]]
model_path
Batch image generation and manipulation tool supporting Stable Diffusion and related techniques /
algorithms, with support for video and animated image processing.
positional arguments:
model_path Hugging Face model repository slug, Hugging Face blob link to a model file, path to
folder on disk, or path to a .pt, .pth, .bin, .ckpt, or .safetensors file.
--------------------------------------------------------------------------
options:
-h, --help show this help message and exit
-------------------------------
-v, --verbose Output information useful for debugging, such as pipeline call and model load
parameters.
-----------
--version Show dgenerate's version and exit
---------------------------------
--file Convenience argument for reading a configuration script from a file instead of using
a pipe. This is a meta argument which can not be used within a configuration script
and is only valid from the command line or during a popen invocation of dgenerate.
----------------------------------------------------------------------------------
--shell When reading configuration from STDIN (a pipe), read forever, even when
configuration errors occur. This allows dgenerate to run in the background and be
controlled by another process sending commands. Launching dgenerate with this option
and not piping it input will attach it to the terminal like a shell. Entering
configuration into this shell requires two newlines to submit a command due to
parsing lookahead. IE: two presses of the enter key. This is a meta argument which
can not be used within a configuration script and is only valid from the command
line or during a popen invocation of dgenerate.
-----------------------------------------------
--no-stdin Can be used to indicate to dgenerate that it will not receive any piped in input.
This is useful for running dgenerate via popen from Python or another application
using normal arguments, where it would otherwise try to read from STDIN and block
forever because it is not attached to a terminal. This is a meta argument which can
not be used within a configuration script and is only valid from the command line or
during a popen invocation of dgenerate.
---------------------------------------
--console Launch a terminal-like Tkinter GUI that interacts with an instance of dgenerate
running in the background. This allows you to interactively write dgenerate config
scripts as if dgenerate were a shell / REPL. This is a meta argument which can not
be used within a configuration script and is only valid from the command line or
during a popen invocation of dgenerate.
---------------------------------------
--plugin-modules PATH [PATH ...]
Specify one or more plugin module folder paths (folder containing __init__.py) or
Python .py file paths, or Python module names to load as plugins. Plugin modules can
currently implement image processors, config directives, config template functions,
prompt weighters, and sub-commands.
-----------------------------------
--sub-command SUB_COMMAND
Specify the name a sub-command to invoke. dgenerate exposes some extra image
processing functionality through the use of sub-commands. Sub commands essentially
replace the entire set of accepted arguments with those of a sub-command which
implements additional functionality. See --sub-command-help for a list of sub-
commands and help.
------------------
--sub-command-help [SUB_COMMAND ...]
Use this option alone (or with --plugin-modules) and no model specification in order
to list available sub-command names. Calling a sub-command with "--sub-command name
--help" will produce argument help output for that sub-command. When used with
--plugin-modules, sub-commands implemented by the specified plugins will also be
listed.
-------
-ofm, --offline-mode Whether dgenerate should try to download Hugging Face models that do not exist in
the disk cache, or only use what is available in the cache. Referencing a model on
Hugging Face that has not been cached because it was not previously downloaded will
result in a failure when using this option.
-------------------------------------------
--templates-help [VARIABLE_NAME ...]
Print a list of template variables available in the interpreter environment used for
dgenerate config scripts, particularly the variables set after a dgenerate
invocation occurs. When used as a command line option, their values are not
presented, just their names and types. Specifying names will print type information
for those variable names.
-------------------------
--directives-help [DIRECTIVE_NAME ...]
Use this option alone (or with --plugin-modules) and no model specification in order
to list available config directive names. Providing names will print documentation
for the specified directive names. When used with --plugin-modules, directives
implemented by the specified plugins will also be listed.
---------------------------------------------------------
--functions-help [FUNCTION_NAME ...]
Use this option alone (or with --plugin-modules) and no model specification in order
to list available config template function names. Providing names will print
documentation for the specified function names. When used with --plugin-modules,
functions implemented by the specified plugins will also be listed.
-------------------------------------------------------------------
-mt MODEL_TYPE, --model-type MODEL_TYPE
Use when loading different model types. Currently supported: torch, torch-pix2pix,
torch-sdxl, torch-sdxl-pix2pix, torch-upscaler-x2, torch-upscaler-x4, torch-if,
torch-ifs, torch-ifs-img2img, torch-s-cascade, torch-sd3, or torch-flux. (default:
torch)
------
-rev BRANCH, --revision BRANCH
The model revision to use when loading from a Hugging Face repository, (The Git
branch / tag, default is "main")
--------------------------------
-var VARIANT, --variant VARIANT
If specified when loading from a Hugging Face repository or folder, load weights
from "variant" filename, e.g. "pytorch_model.<variant>.safetensors". Defaults to
automatic selection.
--------------------
-sbf SUBFOLDER, --subfolder SUBFOLDER
Main model subfolder. If specified when loading from a Hugging Face repository or
folder, load weights from the specified subfolder.
--------------------------------------------------
-atk TOKEN, --auth-token TOKEN
Huggingface auth token. Required to download restricted repositories that have
access permissions granted to your Hugging Face account.
--------------------------------------------------------
-bs INTEGER, --batch-size INTEGER
The number of image variations to produce per set of individual diffusion parameters
in one rendering step simultaneously on a single GPU.
When generating animations with a --batch-size greater than one, a separate
animation (with the filename suffix "animation_N") will be written to for each image
in the batch.
If --batch-grid-size is specified when producing an animation then the image grid is
used for the output frames.
During animation rendering each image in the batch will still be written to the
output directory along side the produced animation as either suffixed files or image
grids depending on the options you choose. (Default: 1)
-------------------------------------------------------
-bgs SIZE, --batch-grid-size SIZE
Produce a single image containing a grid of images with the number of COLUMNSxROWS
given to this argument when --batch-size is greater than 1. If not specified with a
--batch-size greater than 1, images will be written individually with an image
number suffix (image_N) in the filename signifying which image in the batch they
are.
----
-te TEXT_ENCODER_URIS [TEXT_ENCODER_URIS ...], --text-encoders TEXT_ENCODER_URIS [TEXT_ENCODER_URIS ...]
Specify Text Encoders for the main model using URIs, main models may use one or more
text encoders depending on the --model-type value and other dgenerate arguments.
See: --text-encoders help for information about what text encoders are needed for
your invocation.
Examples: "CLIPTextModel;model=huggingface/text_encoder",
"CLIPTextModelWithProjection;model=huggingface/text_encoder;revision=main",
"T5TextModel;model=text_encoder_folder_on_disk".
For main models which require multiple text encoders, the + symbol may be used to
indicate that a default value should be used for a particular text encoder, for
example: --text-encoders + + huggingface/encoder3. Any trailing text encoders which
are not specified are given their default value.
The value "null" may be used to indicate that a specific text encoder should not be
loaded.
Blob links / single file loads are not supported for Text Encoders.
The "revision" argument specifies the model revision to use for the Text Encoder
when loading from Hugging Face repository, (The Git branch / tag, default is
"main").
The "variant" argument specifies the Text Encoder model variant. If "variant" is
specified when loading from a Hugging Face repository or folder, weights will be
loaded from "variant" filename, e.g. "pytorch_model.<variant>.safetensors". For this
argument, "variant" defaults to the value of --variant if it is not specified in the
URI.
The "subfolder" argument specifies the UNet model subfolder, if specified when
loading from a Hugging Face repository or folder, weights from the specified
subfolder.
The "dtype" argument specifies the Text Encoder model precision, it defaults to the
value of -t/--dtype and should be one of: auto, bfloat16, float16, or float32.
The "quantize" argument specifies whether or not to use optimum-quanto to quantize
the text encoder weights, and may be passed the values "qint2", "qint4", "qint8",
"qfloat8_e4m3fn", "qfloat8_e5m2", or "qfloat8" to specify the quantization datatype,
this can be utilized to run Flux models with much less GPU memory.
If you wish to load weights directly from a path on disk, you must point this
argument at the folder they exist in, which should also contain the config.json file
for the Text Encoder. For example, a downloaded repository folder from Hugging Face.
------------------------------------------------------------------------------------
-te2 TEXT_ENCODER_URIS [TEXT_ENCODER_URIS ...], --text-encoders2 TEXT_ENCODER_URIS [TEXT_ENCODER_URIS ...]
--text-encoders but for the SDXL refiner or Stable Cascade decoder model.
-------------------------------------------------------------------------
-un UNET_URI, --unet UNET_URI
Specify a UNet using a URI.
Examples: "huggingface/unet", "huggingface/unet;revision=main",
"unet_folder_on_disk".
Blob links / single file loads are not supported for UNets.
The "revision" argument specifies the model revision to use for the UNet when
loading from Hugging Face repository, (The Git branch / tag, default is "main").
The "variant" argument specifies the UNet model variant. If "variant" is specified
when loading from a Hugging Face repository or folder, weights will be loaded from
"variant" filename, e.g. "pytorch_model.<variant>.safetensors. For this argument,
"variant" defaults to the value of --variant if it is not specified in the URI.
The "subfolder" argument specifies the UNet model subfolder, if specified when
loading from a Hugging Face repository or folder, weights from the specified
subfolder.
The "dtype" argument specifies the UNet model precision, it defaults to the value of
-t/--dtype and should be one of: auto, bfloat16, float16, or float32.
If you wish to load weights directly from a path on disk, you must point this
argument at the folder they exist in, which should also contain the config.json file
for the UNet. For example, a downloaded repository folder from Hugging Face.
----------------------------------------------------------------------------
-un2 UNET_URI, --unet2 UNET_URI
Specify a second UNet, this is only valid when using SDXL or Stable Cascade model
types. This UNet will be used for the SDXL refiner, or Stable Cascade decoder model.
------------------------------------------------------------------------------------
-tf TRANSFORMER_URI, --transformer TRANSFORMER_URI
Specify a Stable Diffusion 3 or Flux Transformer model using a URI.
Examples: "huggingface/transformer", "huggingface/transformer;revision=main",
"transformer_folder_on_disk".
Blob links / single file loads are supported for SD3 Transformers.
The "revision" argument specifies the model revision to use for the Transformer when
loading from Hugging Face repository or blob link, (The Git branch / tag, default is
"main").
The "variant" argument specifies the Transformer model variant. If "variant" is
specified when loading from a Hugging Face repository or folder, weights will be
loaded from "variant" filename, e.g. "pytorch_model.<variant>.safetensors. For this
argument, "variant" defaults to the value of --variant if it is not specified in the
URI.
The "subfolder" argument specifies the Transformer model subfolder, if specified
when loading from a Hugging Face repository or folder, weights from the specified
subfolder.
The "dtype" argument specifies the Transformer model precision, it defaults to the
value of -t/--dtype and should be one of: auto, bfloat16, float16, or float32.
The "quantize" argument specifies whether or not to use optimum-quanto to quantize
the transformer weights, and may be passed the values "qint2", "qint4", "qint8",
"qfloat8_e4m3fn", "qfloat8_e5m2", or "qfloat8" to specify the quantization datatype,
this can be utilized to run Flux models with much less GPU memory.
If you wish to load a weights file directly from disk, the simplest way is:
--transformer "transformer.safetensors", or with a dtype
"transformer.safetensors;dtype=float16". All loading arguments except "dtype" and
"quantize" are unused in this case and may produce an error message if used.
If you wish to load a specific weight file from a Hugging Face repository, use the
blob link loading syntax: --transformer
"AutoencoderKL;https://huggingface.co/UserName/repository-
name/blob/main/transformer.safetensors", the "revision" argument may be used with
this syntax.
------------
-vae VAE_URI, --vae VAE_URI
Specify a VAE using a URI, the URI syntax is: "AutoEncoderClass;model=(Hugging Face
repository slug/blob link or file/folder path)".
Examples: "AutoencoderKL;model=vae.pt",
"AsymmetricAutoencoderKL;model=huggingface/vae",
"AutoencoderTiny;model=huggingface/vae",
"ConsistencyDecoderVAE;model=huggingface/vae".
The AutoencoderKL encoder class accepts Hugging Face repository slugs/blob links,
.pt, .pth, .bin, .ckpt, and .safetensors files.
Other encoders can only accept Hugging Face repository slugs/blob links, or a path
to a folder on disk with the model configuration and model file(s).
If an AutoencoderKL VAE model file exists at a URL which serves the file as a raw
download, you may provide an http/https link to it and it will be downloaded to
dgenerates web cache.
Aside from the "model" argument, there are four other optional arguments that can be
specified, these are: "revision", "variant", "subfolder", "dtype".
They can be specified as so in any order, they are not positional: "AutoencoderKL;mo
del=huggingface/vae;revision=main;variant=fp16;subfolder=sub_folder;dtype=float16".
The "revision" argument specifies the model revision to use for the VAE when loading
from Hugging Face repository or blob link, (The Git branch / tag, default is
"main").
The "variant" argument specifies the VAE model variant. If "variant" is specified
when loading from a Hugging Face repository or folder, weights will be loaded from
"variant" filename, e.g. "pytorch_model.<variant>.safetensors. "variant" in the case
of --vae does not default to the value of --variant to prevent failures during
common use cases.
The "subfolder" argument specifies the VAE model subfolder, if specified when
loading from a Hugging Face repository or folder, weights from the specified
subfolder.
The "dtype" argument specifies the VAE model precision, it defaults to the value of
-t/--dtype and should be one of: auto, bfloat16, float16, or float32.
If you wish to load a weights file directly from disk, the simplest way is: --vae
"AutoencoderKL;my_vae.safetensors", or with a dtype
"AutoencoderKL;my_vae.safetensors;dtype=float16". All loading arguments except
"dtype" are unused in this case and may produce an error message if used.
If you wish to load a specific weight file from a Hugging Face repository, use the
blob link loading syntax: --vae
"AutoencoderKL;https://huggingface.co/UserName/repository-
name/blob/main/vae_model.safetensors", the "revision" argument may be used with this
syntax.
-------
-vt, --vae-tiling Enable VAE tiling. Assists in the generation of large images with lower memory
overhead. The VAE will split the input tensor into tiles to compute decoding and
encoding in several steps. This is useful for saving a large amount of memory and to
allow processing larger images. Note that if you are using --control-nets you may
still run into memory issues generating large images, or with --batch-size greater
than 1.
-------
-vs, --vae-slicing Enable VAE slicing. Assists in the generation of large images with lower memory
overhead. The VAE will split the input tensor in slices to compute decoding in
several steps. This is useful to save some memory, especially when --batch-size is
greater than 1. Note that if you are using --control-nets you may still run into
memory issues generating large images.
--------------------------------------
-lra LORA_URI [LORA_URI ...], --loras LORA_URI [LORA_URI ...]
Specify one or more LoRA models using URIs. These should be a Hugging Face
repository slug, path to model file on disk (for example, a .pt, .pth, .bin, .ckpt,
or .safetensors file), or model folder containing model files.
If a LoRA model file exists at a URL which serves the file as a raw download, you
may provide an http/https link to it and it will be downloaded to dgenerates web
cache.
Hugging Face blob links are not supported, see "subfolder" and "weight-name" below
instead.
Optional arguments can be provided after a LoRA model specification, these are:
"scale", "revision", "subfolder", and "weight-name".
They can be specified as so in any order, they are not positional:
"huggingface/lora;scale=1.0;revision=main;subfolder=repo_subfolder;weight-
name=lora.safetensors".
The "scale" argument indicates the scale factor of the LoRA.
The "revision" argument specifies the model revision to use for the LoRA when
loading from Hugging Face repository, (The Git branch / tag, default is "main").
The "subfolder" argument specifies the LoRA model subfolder, if specified when
loading from a Hugging Face repository or folder, weights from the specified
subfolder.
The "weight-name" argument indicates the name of the weights file to be loaded when
loading from a Hugging Face repository or folder on disk.
If you wish to load a weights file directly from disk, the simplest way is: --loras
"my_lora.safetensors", or with a scale "my_lora.safetensors;scale=1.0", all other
loading arguments are unused in this case and may produce an error message if used.
-----------------------------------------------------------------------------------
-lrfs LORA_FUSE_SCALE, --lora-fuse-scale LORA_FUSE_SCALE
LoRA weights are merged into the main model at this scale. When specifying multiple
LoRA models, they are fused together into one set of weights using their individual
scale values, after which they are fused into the main model at this scale value.
(default: 1.0).
---------------
-ie IMAGE_ENCODER_URI, --image-encoder IMAGE_ENCODER_URI
Specify an Image Encoder using a URI.
Image Encoders are used with --ip-adapters models, and must be specified if none of
the loaded --ip-adapters contain one. An error will be produced in this situation,
which requires you to use this argument.
An image encoder can also be manually specified for Stable Cascade models.
Examples: "huggingface/image_encoder", "huggingface/image_encoder;revision=main",
"image_encoder_folder_on_disk".
Blob links / single file loads are not supported for Image Encoders.
The "revision" argument specifies the model revision to use for the Image Encoder
when loading from Hugging Face repository or blob link, (The Git branch / tag,
default is "main").
The "variant" argument specifies the Image Encoder model variant. If "variant" is
specified when loading from a Hugging Face repository or folder, weights will be
loaded from "variant" filename, e.g. "pytorch_model.<variant>.safetensors.
Similar to --vae, "variant" does not default to the value of --variant in order to
prevent errors with common use cases. If you specify multiple IP Adapters, they must
all have the same "variant" value or you will receive a usage error.
The "subfolder" argument specifies the Image Encoder model subfolder, if specified
when loading from a Hugging Face repository or folder, weights from the specified
subfolder.
The "dtype" argument specifies the Image Encoder model precision, it defaults to the
value of -t/--dtype and should be one of: auto, bfloat16, float16, or float32.
If you wish to load weights directly from a path on disk, you must point this
argument at the folder they exist in, which should also contain the config.json file
for the Image Encoder. For example, a downloaded repository folder from Hugging
Face.
-----
-ipa IP_ADAPTER_URI [IP_ADAPTER_URI ...], --ip-adapters IP_ADAPTER_URI [IP_ADAPTER_URI ...]
Specify one or more IP Adapter models using URIs. These should be a Hugging Face
repository slug, path to model file on disk (for example, a .pt, .pth, .bin, .ckpt,
or .safetensors file), or model folder containing model files.
If an IP Adapter model file exists at a URL which serves the file as a raw download,
you may provide an http/https link to it and it will be downloaded to dgenerates web
cache.
Hugging Face blob links are not supported, see "subfolder" and "weight-name" below
instead.
Optional arguments can be provided after an IP Adapter model specification, these
are: "scale", "revision", "subfolder", and "weight-name".
They can be specified as so in any order, they are not positional: "huggingface/ip-
adapter;scale=1.0;revision=main;subfolder=repo_subfolder;weight-
name=ip_adapter.safetensors".
The "scale" argument indicates the scale factor of the IP Adapter.
The "revision" argument specifies the model revision to use for the IP Adapter when
loading from Hugging Face repository, (The Git branch / tag, default is "main").
The "subfolder" argument specifies the IP Adapter model subfolder, if specified when
loading from a Hugging Face repository or folder, weights from the specified
subfolder.
The "weight-name" argument indicates the name of the weights file to be loaded when
loading from a Hugging Face repository or folder on disk.
If you wish to load a weights file directly from disk, the simplest way is: --ip-
adapters "ip_adapter.safetensors", or with a scale
"ip_adapter.safetensors;scale=1.0", all other loading arguments are unused in this
case and may produce an error message if used.
----------------------------------------------
-ti URI [URI ...], --textual-inversions URI [URI ...]
Specify one or more Textual Inversion models using URIs. These should be a Hugging
Face repository slug, path to model file on disk (for example, a .pt, .pth, .bin,
.ckpt, or .safetensors file), or model folder containing model files.
If a Textual Inversion model file exists at a URL which serves the file as a raw
download, you may provide an http/https link to it and it will be downloaded to
dgenerates web cache.
Hugging Face blob links are not supported, see "subfolder" and "weight-name" below
instead.
Optional arguments can be provided after the Textual Inversion model specification,
these are: "token", "revision", "subfolder", and "weight-name".
They can be specified as so in any order, they are not positional:
"huggingface/ti_model;revision=main;subfolder=repo_subfolder;weight-
name=ti_model.safetensors".
The "token" argument can be used to override the prompt token used for the textual
inversion prompt embedding. For normal Stable Diffusion the default token value is
provided by the model itself, but for Stable Diffusion XL the default token value is
equal to the model file name with no extension and all spaces replaced by
underscores.
The "revision" argument specifies the model revision to use for the Textual
Inversion model when loading from Hugging Face repository, (The Git branch / tag,
default is "main").
The "subfolder" argument specifies the Textual Inversion model subfolder, if
specified when loading from a Hugging Face repository or folder, weights from the
specified subfolder.
The "weight-name" argument indicates the name of the weights file to be loaded when
loading from a Hugging Face repository or folder on disk.
If you wish to load a weights file directly from disk, the simplest way is:
--textual-inversions "my_ti_model.safetensors", all other loading arguments are
unused in this case and may produce an error message if used.
-------------------------------------------------------------
-cn CONTROLNET_URI [CONTROLNET_URI ...], --control-nets CONTROLNET_URI [CONTROLNET_URI ...]
Specify one or more ControlNet models using URIs. This should be a Hugging Face
repository slug / blob link, path to model file on disk (for example, a .pt, .pth,
.bin, .ckpt, or .safetensors file), or model folder containing model files.
If a ControlNet model file exists at a URL which serves the file as a raw download,
you may provide an http/https link to it and it will be downloaded to dgenerates web
cache.
Optional arguments can be provided after the ControlNet model specification, these
are: "scale", "start", "end", "revision", "variant", "subfolder", and "dtype".
They can be specified as so in any order, they are not positional: "huggingface/cont
rolnet;scale=1.0;start=0.0;end=1.0;revision=main;variant=fp16;subfolder=repo_subfold
er;dtype=float16".
The "scale" argument specifies the scaling factor applied to the ControlNet model,
the default value is 1.0.
The "start" argument specifies at what fraction of the total inference steps to
begin applying the ControlNet, defaults to 0.0, IE: the very beginning.
The "end" argument specifies at what fraction of the total inference steps to stop
applying the ControlNet, defaults to 1.0, IE: the very end.
The "revision" argument specifies the model revision to use for the ControlNet model
when loading from Hugging Face repository, (The Git branch / tag, default is
"main").
The "variant" argument specifies the ControlNet model variant, if "variant" is
specified when loading from a Hugging Face repository or folder, weights will be
loaded from "variant" filename, e.g. "pytorch_model.<variant>.safetensors. "variant"
defaults to automatic selection. "variant" in the case of --control-nets does not
default to the value of --variant to prevent failures during common use cases.
The "subfolder" argument specifies the ControlNet model subfolder, if specified when
loading from a Hugging Face repository or folder, weights from the specified
subfolder.
The "dtype" argument specifies the ControlNet model precision, it defaults to the
value of -t/--dtype and should be one of: auto, bfloat16, float16, or float32.
If you wish to load a weights file directly from disk, the simplest way is:
--control-nets "my_controlnet.safetensors" or --control-nets
"my_controlnet.safetensors;scale=1.0;dtype=float16", all other loading arguments
aside from "scale", "start", "end", and "dtype" are unused in this case and may
produce an error message if used.
If you wish to load a specific weight file from a Hugging Face repository, use the
blob link loading syntax: --control-nets
"https://huggingface.co/UserName/repository-name/blob/main/controlnet.safetensors",
the "revision" argument may be used with this syntax.
-----------------------------------------------------
-t2i T2I_ADAPTER_URI [T2I_ADAPTER_URI ...], --t2i-adapters T2I_ADAPTER_URI [T2I_ADAPTER_URI ...]
Specify one or more T2IAdapter models using URIs. This should be a Hugging Face
repository slug / blob link, path to model file on disk (for example, a .pt, .pth,
.bin, .ckpt, or .safetensors file), or model folder containing model files.
If a T2IAdapter model file exists at a URL which serves the file as a raw download,
you may provide an http/https link to it and it will be downloaded to dgenerates web
cache.
Optional arguments can be provided after the T2IAdapter model specification, these
are: "scale", "revision", "variant", "subfolder", and "dtype".
They can be specified as so in any order, they are not positional: "huggingface/t2ia
dapter;scale=1.0;revision=main;variant=fp16;subfolder=repo_subfolder;dtype=float16".
The "scale" argument specifies the scaling factor applied to the T2IAdapter model,
the default value is 1.0.
The "revision" argument specifies the model revision to use for the T2IAdapter model
when loading from Hugging Face repository, (The Git branch / tag, default is
"main").
The "variant" argument specifies the T2IAdapter model variant, if "variant" is
specified when loading from a Hugging Face repository or folder, weights will be
loaded from "variant" filename, e.g. "pytorch_model.<variant>.safetensors. "variant"
defaults to automatic selection. "variant" in the case of --t2i-adapters does not
default to the value of --variant to prevent failures during common use cases.
The "subfolder" argument specifies the ControlNet model subfolder, if specified when
loading from a Hugging Face repository or folder, weights from the specified
subfolder.
The "dtype" argument specifies the T2IAdapter model precision, it defaults to the
value of -t/--dtype and should be one of: auto, bfloat16, float16, or float32.
If you wish to load a weights file directly from disk, the simplest way is:
--t2i-adapters "my_t2i_adapter.safetensors" or --t2i-adapters
"my_t2i_adapter.safetensors;scale=1.0;dtype=float16", all other loading arguments
aside from "scale" and "dtype" are unused in this case and may produce an error
message if used.
If you wish to load a specific weight file from a Hugging Face repository, use the
blob link loading syntax: --t2i-adapters
"https://huggingface.co/UserName/repository-name/blob/main/t2i_adapter.safetensors",
the "revision" argument may be used with this syntax.
-----------------------------------------------------
-sch SCHEDULER_URI, --scheduler SCHEDULER_URI
Specify a scheduler (sampler) by URI. Passing "help" to this argument will print the
compatible schedulers for a model without generating any images. Passing "helpargs"
will yield a help message with a list of overridable arguments for each scheduler
and their typical defaults. Arguments listed by "helpargs" can be overridden using
the URI syntax typical to other dgenerate URI arguments.
--------------------------------------------------------
-mqo, --model-sequential-offload
Force sequential model offloading for the main pipeline, this may drastically reduce
memory consumption and allow large models to run when they would otherwise not fit
in your GPUs VRAM. Inference will be much slower. Mutually exclusive with --model-
cpu-offload
-----------
-mco, --model-cpu-offload
Force model cpu offloading for the main pipeline, this may reduce memory consumption
and allow large models to run when they would otherwise not fit in your GPUs VRAM.
Inference will be slower. Mutually exclusive with --model-sequential-offload
----------------------------------------------------------------------------
--s-cascade-decoder MODEL_URI
Specify a Stable Cascade (torch-s-cascade) decoder model path using a URI. This
should be a Hugging Face repository slug / blob link, path to model file on disk
(for example, a .pt, .pth, .bin, .ckpt, or .safetensors file), or model folder
containing model files.
Optional arguments can be provided after the decoder model specification, these are:
"revision", "variant", "subfolder", and "dtype".
They can be specified as so in any order, they are not positional: "huggingface/deco
der_model;revision=main;variant=fp16;subfolder=repo_subfolder;dtype=float16".
The "revision" argument specifies the model revision to use for the decoder model
when loading from Hugging Face repository, (The Git branch / tag, default is
"main").
The "variant" argument specifies the decoder model variant and defaults to the value
of --variant. When "variant" is specified when loading from a Hugging Face
repository or folder, weights will be loaded from "variant" filename, e.g.
"pytorch_model.<variant>.safetensors.
The "subfolder" argument specifies the decoder model subfolder, if specified when
loading from a Hugging Face repository or folder, weights from the specified
subfolder.
The "dtype" argument specifies the Stable Cascade decoder model precision, it
defaults to the value of -t/--dtype and should be one of: auto, bfloat16, float16,
or float32.
If you wish to load a weights file directly from disk, the simplest way is: --sdxl-
refiner "my_decoder.safetensors" or --sdxl-refiner
"my_decoder.safetensors;dtype=float16", all other loading arguments aside from
"dtype" are unused in this case and may produce an error message if used.
If you wish to load a specific weight file from a Hugging Face repository, use the
blob link loading syntax: --s-cascade-decoder
"https://huggingface.co/UserName/repository-name/blob/main/decoder.safetensors", the
"revision" argument may be used with this syntax.
-------------------------------------------------
-dqo, --s-cascade-decoder-sequential-offload
Force sequential model offloading for the Stable Cascade decoder pipeline, this may
drastically reduce memory consumption and allow large models to run when they would
otherwise not fit in your GPUs VRAM. Inference will be much slower. Mutually
exclusive with --s-cascade-decoder-cpu-offload
----------------------------------------------
-dco, --s-cascade-decoder-cpu-offload
Force model cpu offloading for the Stable Cascade decoder pipeline, this may reduce
memory consumption and allow large models to run when they would otherwise not fit
in your GPUs VRAM. Inference will be slower. Mutually exclusive with --s-cascade-
decoder-sequential-offload
--------------------------
--s-cascade-decoder-prompts PROMPT [PROMPT ...]
One or more prompts to try with the Stable Cascade decoder model, by default the
decoder model gets the primary prompt, this argument overrides that with a prompt of
your choosing. The negative prompt component can be specified with the same syntax
as --prompts
------------
--s-cascade-decoder-inference-steps INTEGER [INTEGER ...]
One or more inference steps values to try with the Stable Cascade decoder. (default:
[10])
-----
--s-cascade-decoder-guidance-scales INTEGER [INTEGER ...]
One or more guidance scale values to try with the Stable Cascade decoder. (default:
[0])
----
--s-cascade-decoder-scheduler SCHEDULER_URI
Specify a scheduler (sampler) by URI for the Stable Cascade decoder pass. Operates
the exact same way as --scheduler including the "help" option. Passing 'helpargs'
will yield a help message with a list of overridable arguments for each scheduler
and their typical defaults. Defaults to the value of --scheduler.
-----------------------------------------------------------------
--sdxl-refiner MODEL_URI
Specify a Stable Diffusion XL (torch-sdxl) refiner model path using a URI. This
should be a Hugging Face repository slug / blob link, path to model file on disk
(for example, a .pt, .pth, .bin, .ckpt, or .safetensors file), or model folder
containing model files.
Optional arguments can be provided after the SDXL refiner model specification, these
are: "revision", "variant", "subfolder", and "dtype".
They can be specified as so in any order, they are not positional: "huggingface/refi
ner_model_xl;revision=main;variant=fp16;subfolder=repo_subfolder;dtype=float16".
The "revision" argument specifies the model revision to use for the refiner model
when loading from Hugging Face repository, (The Git branch / tag, default is
"main").
The "variant" argument specifies the SDXL refiner model variant and defaults to the
value of --variant. When "variant" is specified when loading from a Hugging Face
repository or folder, weights will be loaded from "variant" filename, e.g.
"pytorch_model.<variant>.safetensors.
The "subfolder" argument specifies the SDXL refiner model subfolder, if specified
when loading from a Hugging Face repository or folder, weights from the specified
subfolder.
The "dtype" argument specifies the SDXL refiner model precision, it defaults to the
value of -t/--dtype and should be one of: auto, bfloat16, float16, or float32.
If you wish to load a weights file directly from disk, the simplest way is: --sdxl-
refiner "my_sdxl_refiner.safetensors" or --sdxl-refiner
"my_sdxl_refiner.safetensors;dtype=float16", all other loading arguments aside from
"dtype" are unused in this case and may produce an error message if used.
If you wish to load a specific weight file from a Hugging Face repository, use the
blob link loading syntax: --sdxl-refiner
"https://huggingface.co/UserName/repository-
name/blob/main/refiner_model.safetensors", the "revision" argument may be used with
this syntax.
------------
-rqo, --sdxl-refiner-sequential-offload
Force sequential model offloading for the SDXL refiner pipeline, this may
drastically reduce memory consumption and allow large models to run when they would
otherwise not fit in your GPUs VRAM. Inference will be much slower. Mutually
exclusive with --refiner-cpu-offload
------------------------------------
-rco, --sdxl-refiner-cpu-offload
Force model cpu offloading for the SDXL refiner pipeline, this may reduce memory
consumption and allow large models to run when they would otherwise not fit in your
GPUs VRAM. Inference will be slower. Mutually exclusive with --refiner-sequential-
offload
-------
--sdxl-refiner-scheduler SCHEDULER_URI
Specify a scheduler (sampler) by URI for the SDXL refiner pass. Operates the exact
same way as --scheduler including the "help" option. Passing 'helpargs' will yield a
help message with a list of overridable arguments for each scheduler and their
typical defaults. Defaults to the value of --scheduler.
-------------------------------------------------------
--sdxl-refiner-edit Force the SDXL refiner to operate in edit mode instead of cooperative denoising mode
as it would normally do for inpainting and ControlNet usage. The main model will
perform the full amount of inference steps requested by --inference-steps. The
output of the main model will be passed to the refiner model and processed with an
image seed strength in img2img mode determined by (1.0 - high-noise-fraction)
-----------------------------------------------------------------------------
--sdxl-second-prompts PROMPT [PROMPT ...]
One or more secondary prompts to try using SDXL's secondary text encoder. By default
the model is passed the primary prompt for this value, this option allows you to
choose a different prompt. The negative prompt component can be specified with the
same syntax as --prompts
------------------------
--sdxl-t2i-adapter-factors FLOAT [FLOAT ...]
One or more SDXL specific T2I adapter factors to try, this controls the amount of
time-steps for which a T2I adapter applies guidance to an image, this is a value
between 0.0 and 1.0. A value of 0.5 for example indicates that the T2I adapter is
only active for half the amount of time-steps it takes to completely render an
image.
------
--sdxl-aesthetic-scores FLOAT [FLOAT ...]
One or more Stable Diffusion XL (torch-sdxl) "aesthetic-score" micro-conditioning
parameters. Used to simulate an aesthetic score of the generated image by
influencing the positive text condition. Part of SDXL's micro-conditioning as
explained in section 2.2 of [https://huggingface.co/papers/2307.01952].
-----------------------------------------------------------------------
--sdxl-crops-coords-top-left COORD [COORD ...]
One or more Stable Diffusion XL (torch-sdxl) "negative-crops-coords-top-left" micro-
conditioning parameters in the format "0,0". --sdxl-crops-coords-top-left can be
used to generate an image that appears to be "cropped" from the position --sdxl-
crops-coords-top-left downwards. Favorable, well-centered images are usually
achieved by setting --sdxl-crops-coords-top-left to "0,0". Part of SDXL's micro-
conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952].
-------------------------------------------
--sdxl-original-size SIZE [SIZE ...], --sdxl-original-sizes SIZE [SIZE ...]
One or more Stable Diffusion XL (torch-sdxl) "original-size" micro-conditioning
parameters in the format (WIDTH)x(HEIGHT). If not the same as --sdxl-target-size the
image will appear to be down or up-sampled. --sdxl-original-size defaults to
--output-size or the size of any input images if not specified. Part of SDXL's
micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952]
------------------------------------------
--sdxl-target-size SIZE [SIZE ...], --sdxl-target-sizes SIZE [SIZE ...]
One or more Stable Diffusion XL (torch-sdxl) "target-size" micro-conditioning
parameters in the format (WIDTH)x(HEIGHT). For most cases, --sdxl-target-size should
be set to the desired height and width of the generated image. If not specified it
will default to --output-size or the size of any input images. Part of SDXL's micro-
conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952]
------------------------------------------
--sdxl-negative-aesthetic-scores FLOAT [FLOAT ...]
One or more Stable Diffusion XL (torch-sdxl) "negative-aesthetic-score" micro-
conditioning parameters. Part of SDXL's micro-conditioning as explained in section
2.2 of [https://huggingface.co/papers/2307.01952]. Can be used to simulate an
aesthetic score of the generated image by influencing the negative text condition.
----------------------------------------------------------------------------------
--sdxl-negative-original-sizes SIZE [SIZE ...]
One or more Stable Diffusion XL (torch-sdxl) "negative-original-sizes" micro-
conditioning parameters. Negatively condition the generation process based on a
specific image resolution. Part of SDXL's micro-conditioning as explained in section
2.2 of [https://huggingface.co/papers/2307.01952]. For more information, refer to
this issue thread: https://github.com/huggingface/diffusers/issues/4208
-----------------------------------------------------------------------
--sdxl-negative-target-sizes SIZE [SIZE ...]
One or more Stable Diffusion XL (torch-sdxl) "negative-original-sizes" micro-
conditioning parameters. To negatively condition the generation process based on a
target image resolution. It should be as same as the "--sdxl-target-size" for most
cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952]. For more information, refer to this
issue thread: https://github.com/huggingface/diffusers/issues/4208.
-------------------------------------------------------------------
--sdxl-negative-crops-coords-top-left COORD [COORD ...]
One or more Stable Diffusion XL (torch-sdxl) "negative-crops-coords-top-left" micro-
conditioning parameters in the format "0,0". Negatively condition the generation
process based on a specific crop coordinates. Part of SDXL's micro-conditioning as
explained in section 2.2 of [https://huggingface.co/papers/2307.01952]. For more
information, refer to this issue thread:
https://github.com/huggingface/diffusers/issues/4208.
-----------------------------------------------------
--sdxl-refiner-prompts PROMPT [PROMPT ...]
One or more prompts to try with the SDXL refiner model, by default the refiner model
gets the primary prompt, this argument overrides that with a prompt of your
choosing. The negative prompt component can be specified with the same syntax as
--prompts
---------
--sdxl-refiner-clip-skips INTEGER [INTEGER ...]
One or more clip skip override values to try for the SDXL refiner, which normally
uses the clip skip value for the main model when it is defined by --clip-skips.
-------------------------------------------------------------------------------
--sdxl-refiner-second-prompts PROMPT [PROMPT ...]
One or more prompts to try with the SDXL refiner models secondary text encoder, by
default the refiner model gets the primary prompt passed to its second text encoder,
this argument overrides that with a prompt of your choosing. The negative prompt
component can be specified with the same syntax as --prompts
------------------------------------------------------------
--sdxl-refiner-aesthetic-scores FLOAT [FLOAT ...]
See: --sdxl-aesthetic-scores, applied to SDXL refiner pass.
-----------------------------------------------------------
--sdxl-refiner-crops-coords-top-left COORD [COORD ...]
See: --sdxl-crops-coords-top-left, applied to SDXL refiner pass.
----------------------------------------------------------------
--sdxl-refiner-original-sizes SIZE [SIZE ...]
See: --sdxl-refiner-original-sizes, applied to SDXL refiner pass.
-----------------------------------------------------------------
--sdxl-refiner-target-sizes SIZE [SIZE ...]
See: --sdxl-refiner-target-sizes, applied to SDXL refiner pass.
---------------------------------------------------------------
--sdxl-refiner-negative-aesthetic-scores FLOAT [FLOAT ...]
See: --sdxl-negative-aesthetic-scores, applied to SDXL refiner pass.
--------------------------------------------------------------------
--sdxl-refiner-negative-original-sizes SIZE [SIZE ...]
See: --sdxl-negative-original-sizes, applied to SDXL refiner pass.
------------------------------------------------------------------
--sdxl-refiner-negative-target-sizes SIZE [SIZE ...]
See: --sdxl-negative-target-sizes, applied to SDXL refiner pass.
----------------------------------------------------------------
--sdxl-refiner-negative-crops-coords-top-left COORD [COORD ...]
See: --sdxl-negative-crops-coords-top-left, applied to SDXL refiner pass.
-------------------------------------------------------------------------
-hnf FLOAT [FLOAT ...], --sdxl-high-noise-fractions FLOAT [FLOAT ...]
One or more high-noise-fraction values for Stable Diffusion XL (torch-sdxl), this
fraction of inference steps will be processed by the base model, while the rest will
be processed by the refiner model. Multiple values to this argument will result in
additional generation steps for each value. In certain situations when the mixture
of denoisers algorithm is not supported, such as when using --control-nets and
inpainting with SDXL, the inverse proportion of this value IE: (1.0 - high-noise-
fraction) becomes the --image-seed-strengths input to the SDXL refiner. (default:
[0.8])
------
-ri INT [INT ...], --sdxl-refiner-inference-steps INT [INT ...]
One or more inference steps values for the SDXL refiner when in use. Override the
number of inference steps used by the SDXL refiner, which defaults to the value
taken from --inference-steps.
-----------------------------
-rg FLOAT [FLOAT ...], --sdxl-refiner-guidance-scales FLOAT [FLOAT ...]
One or more guidance scale values for the SDXL refiner when in use. Override the
guidance scale value used by the SDXL refiner, which defaults to the value taken
from --guidance-scales.
-----------------------
-rgr FLOAT [FLOAT ...], --sdxl-refiner-guidance-rescales FLOAT [FLOAT ...]
One or more guidance rescale values for the SDXL refiner when in use. Override the
guidance rescale value used by the SDXL refiner, which defaults to the value taken
from --guidance-rescales.
-------------------------
-sc, --safety-checker
Enable safety checker loading, this is off by default. When turned on images with
NSFW content detected may result in solid black output. Some pretrained models have
no safety checker model present, in that case this option has no effect.
------------------------------------------------------------------------
-d DEVICE, --device DEVICE
cuda / cpu, or other device supported by torch, for example mps on MacOS. (default:
cuda, mps on MacOS). Use: cuda:0, cuda:1, cuda:2, etc. to specify a specific cuda
supporting GPU.
---------------
-t DTYPE, --dtype DTYPE
Model precision: auto, bfloat16, float16, or float32. (default: auto)
---------------------------------------------------------------------
-s SIZE, --output-size SIZE
Image output size, for txt2img generation this is the exact output size. The
dimensions specified for this value must be aligned by 8 or you will receive an
error message. If an --image-seeds URI is used its Seed, Mask, and/or Control
component image sources will be resized to this dimension with aspect ratio
maintained before being used for generation by default, except in the case of Stable
Cascade where the images are used as a style prompt (not a noised seed), and can be
of varying dimensions.
If --no-aspect is not specified, width will be fixed and a new height (aligned by 8)
will be calculated for the input images. In most cases resizing the image inputs
will result in an image output of an equal size to the inputs, except for upscalers
and Deep Floyd --model-type values (torch-if*).
If only one integer value is provided, that is the value for both dimensions. X/Y
dimension values should be separated by "x".
This value defaults to 512x512 for Stable Diffusion when no --image-seeds are
specified (IE txt2img mode), 1024x1024 for Stable Cascade and Stable Diffusion 3/XL
or Flux model types, and 64x64 for --model-type torch-if (Deep Floyd stage 1).
Deep Floyd stage 1 images passed to superscaler models (--model-type torch-ifs*)
that are specified with the 'floyd' keyword argument in an --image-seeds definition
are never resized or processed in any way.
------------------------------------------
-na, --no-aspect This option disables aspect correct resizing of images provided to --image-seeds
globally. Seed, Mask, and Control guidance images will be resized to the closest
dimension specified by --output-size that is aligned by 8 pixels with no
consideration of the source aspect ratio. This can be overriden at the --image-seeds
level with the image seed keyword argument 'aspect=true/false'.
---------------------------------------------------------------
-o PATH, --output-path PATH
Output path for generated images and files. This directory will be created if it
does not exist. (default: ./output)
-----------------------------------
-op PREFIX, --output-prefix PREFIX
Name prefix for generated images and files. This prefix will be added to the
beginning of every generated file, followed by an underscore.
-------------------------------------------------------------
-ox, --output-overwrite
Enable
overwrites of files in the output directory that already exists. The default
behavior is not to do this, and instead append a filename suffix:
"_duplicate_(number)" when it is detected that the generated file name already
exists.
-------
-oc, --output-configs
Write a configuration text file for every output image or animation. The text file
can be used reproduce that particular output image or animation by piping it to
dgenerate STDIN or by using the --file option, for example "dgenerate < config.dgen"
or "dgenerate --file config.dgen". These files will be written to --output-path and
are affected by --output-prefix and --output-overwrite as well. The files will be
named after their corresponding image or animation file. Configuration files
produced for animation frame images will utilize --frame-start and --frame-end to
specify the frame number.
-------------------------
-om, --output-metadata
Write the information produced by --output-configs to the PNG metadata of each
image. Metadata will not be written to animated files (yet). The data is written to
a PNG metadata property named DgenerateConfig and can be read using ImageMagick like
so: "magick identify -format "%[Property:DgenerateConfig] generated_file.png".
------------------------------------------------------------------------------
-pw PROMPT_WEIGHTER_URI, --prompt-weighter PROMPT_WEIGHTER_URI
Specify a prompt weighter implementation by URI, for example: --prompt-weighter
compel, or --prompt-weighter sd-embed. By default, no prompt weighting syntax is
enabled, meaning that you cannot adjust token weights as you may be able to do in
software such as ComfyUI, Automatic1111, CivitAI etc. And in some cases the length
of your prompt is limited. Prompt weighters support these special token weighting
syntaxes and long prompts, currently there are two implementations "compel" and "sd-
embed". See: --prompt-weighter-help for a list of implementation names. You may also
use --prompt-weighter-help "name" to see comprehensive documentation for a specific
prompt weighter implementation.
-------------------------------
--prompt-weighter-help [PROMPT_WEIGHTER_NAMES ...]
Use this option alone (or with --plugin-modules) and no model specification in order
to list available prompt weighter names. Specifying one or more prompt weighter
names after this option will cause usage documentation for the specified prompt
weighters to be printed. When used with --plugin-modules, prompt weighters
implemented by the specified plugins will also be listed.
---------------------------------------------------------
-p PROMPT [PROMPT ...], --prompts PROMPT [PROMPT ...]
One or more prompts to try, an image group is generated for each prompt, prompt data
is split by ; (semi-colon). The first value is the positive text influence, things
you want to see. The Second value is negative influence IE. things you don't want to
see. Example: --prompts "photo of a horse in a field; artwork, painting, rain".
(default: [(empty string)])
---------------------------
--sd3-max-sequence-length INTEGER
The maximum amount of prompt tokens that the T5EncoderModel (third text encoder) of
Stable Diffusion 3 can handle. This should be an integer value between 1 and 512
inclusive. The higher the value the more resources and time are required for
processing. (default: 256)
--------------------------
--sd3-second-prompts PROMPT [PROMPT ...]
One or more secondary prompts to try using the torch-sd3 (Stable Diffusion 3)
secondary text encoder. By default the model is passed the primary prompt for this
value, this option allows you to choose a different prompt. The negative prompt
component can be specified with the same syntax as --prompts
------------------------------------------------------------
--sd3-third-prompts PROMPT [PROMPT ...]
One or more tertiary prompts to try using the torch-sd3 (Stable Diffusion 3)
tertiary (T5) text encoder. By default the model is passed the primary prompt for
this value, this option allows you to choose a different prompt. The negative prompt
component can be specified with the same syntax as --prompts
------------------------------------------------------------
--flux-second-prompts PROMPT [PROMPT ...]
One or more secondary prompts to try using the torch-flux (Flux) secondary (T5) text
encoder. By default the model is passed the primary prompt for this value, this
option allows you to choose a different prompt.
-----------------------------------------------
--flux-max-sequence-length INTEGER
The maximum amount of prompt tokens that the T5EncoderModel (second text encoder) of
Flux can handle. This should be an integer value between 1 and 512 inclusive. The
higher the value the more resources and time are required for processing. (default:
512)
----
-cs INTEGER [INTEGER ...], --clip-skips INTEGER [INTEGER ...]
One or more clip skip values to try. Clip skip is the number of layers to be skipped
from CLIP while computing the prompt embeddings, it must be a value greater than or
equal to zero. A value of 1 means that the output of the pre-final layer will be
used for computing the prompt embeddings. This is only supported for --model-type
values "torch", "torch-sdxl", and "torch-sd3".
----------------------------------------------
-se SEED [SEED ...], --seeds SEED [SEED ...]
One or more seeds to try, define fixed seeds to achieve deterministic output. This
argument may not be used when --gse/--gen-seeds is used. (default: [randint(0,
99999999999999)])
-----------------
-sei, --seeds-to-images
When this option is enabled, each provided --seeds value or value generated by
--gen-seeds is used for the corresponding image input given by --image-seeds. If the
amount of --seeds given is not identical to that of the amount of --image-seeds
given, the seed is determined as: seed = seeds[image_seed_index % len(seeds)], IE:
it wraps around.
----------------
-gse COUNT, --gen-seeds COUNT
Auto generate N random seeds to try. This argument may not be used when -se/--seeds
is used.
--------
-af FORMAT, --animation-format FORMAT
Output format when generating an animation from an input video / gif / webp etc.
Value must be one of: mp4, png, apng, gif, or webp. You may also specify "frames" to
indicate that only frames should be output and no coalesced animation file should be
rendered. (default: mp4)
------------------------
-if FORMAT, --image-format FORMAT
Output format when writing static images. Any selection other than "png" is not
compatible with --output-metadata. Value must be one of: png, apng, blp, bmp, dib,
bufr, pcx, dds, ps, eps, gif, grib, h5, hdf, jp2, j2k, jpc, jpf, jpx, j2c, icns,
ico, im, jfif, jpe, jpg, jpeg, tif, tiff, mpo, msp, palm, pdf, pbm, pgm, ppm, pnm,
pfm, bw, rgb, rgba, sgi, tga, icb, vda, vst, webp, wmf, emf, or xbm. (default: png)
-----------------------------------------------------------------------------------
-nf, --no-frames Do not write frame images individually when rendering an animation, only write the
animation file. This option is incompatible with --animation-format frames.
---------------------------------------------------------------------------
-fs FRAME_NUMBER, --frame-start FRAME_NUMBER
Starting frame slice point for animated files (zero-indexed), the specified frame
will be included. (default: 0)
------------------------------
-fe FRAME_NUMBER, --frame-end FRAME_NUMBER
Ending frame slice point for animated files (zero-indexed), the specified frame will
be included.
------------
-is SEED [SEED ...], --image-seeds SEED [SEED ...]
One or more image seed URIs to process, these may consist of URLs or file paths.
Videos / GIFs / WEBP files will result in frames being rendered as well as an
animated output file being generated if more than one frame is available in the
input file. Inpainting for static images can be achieved by specifying a black and
white mask image in each image seed string using a semicolon as the separating
character, like so: "my-seed-image.png;my-image-mask.png", white areas of the mask
indicate where generated content is to be placed in your seed image.
Output dimensions specific to the image seed can be specified by placing the
dimension at the end of the string following a semicolon like so: "my-seed-
image.png;512x512" or "my-seed-image.png;my-image-mask.png;512x512". When using
--control-nets, a singular image specification is interpreted as the control
guidance image, and you can specify multiple control image sources by separating
them with commas in the case where multiple ControlNets are specified, IE: (--image-
seeds "control-image1.png, control-image2.png") OR (--image-seeds
"seed.png;control=control-image1.png, control-image2.png").
Using --control-nets with img2img or inpainting can be accomplished with the syntax:
"my-seed-image.png;mask=my-image-mask.png;control=my-control-
image.png;resize=512x512". The "mask" and "resize" arguments are optional when using
--control-nets. Videos, GIFs, and WEBP are also supported as inputs when using
--control-nets, even for the "control" argument.
--image-seeds is capable of reading from multiple animated files at once or any
combination of animated files and images, the animated file with the least amount of
frames dictates how many frames are generated and static images are duplicated over
the total amount of frames. The keyword argument "aspect" can be used to determine
resizing behavior when the global argument --output-size or the local keyword
argument "resize" is specified, it is a boolean argument indicating whether aspect
ratio of the input image should be respected or ignored.
The keyword argument "floyd" can be used to specify images from a previous deep
floyd stage when using --model-type torch-ifs*. When keyword arguments are present,
all applicable images such as "mask", "control", etc. must also be defined with
keyword arguments instead of with the short syntax.
---------------------------------------------------
-sip PROCESSOR_URI [PROCESSOR_URI ...], --seed-image-processors PROCESSOR_URI [PROCESSOR_URI ...]
Specify one or more image processor actions to perform on the primary image(s)
specified by --image-seeds.
For example: --seed-image-processors "flip" "mirror" "grayscale".
To obtain more information about what image processors are available and how to use
them, see: --image-processor-help.
If you have multiple images specified for batching, for example (--image-seeds
"images: img2img-1.png, img2img-2.png"), you may use the delimiter "+" to separate
image processor chains, so that a certain chain affects a certain seed image, the
plus symbol may also be used to represent a null processor.
For example: (--seed-image-processors affect-img-1 + affect-img-2), or (--seed-
image-processors + affect-img-2), or (--seed-image-processors affect-img-1 +).
The amount of processors / processor chains must not exceed the amount of input
images, or you will receive a syntax error message. To obtain more information about
what image processors are available and how to use them, see: --image-processor-
help.
-----
-mip PROCESSOR_URI [PROCESSOR_URI ...], --mask-image-processors PROCESSOR_URI [PROCESSOR_URI ...]
Specify one or more image processor actions to perform on the inpaint mask image(s)
specified by --image-seeds.
For example: --mask-image-processors "invert".
To obtain more information about what image processors are available and how to use
them, see: --image-processor-help.
If you have multiple masks specified for batching, for example --image-seeds
("images: img2img-1.png, img2img-2.png; mask-1.png, mask-2.png"), you may use the
delimiter "+" to separate image processor chains, so that a certain chain affects a
certain mask image, the plus symbol may also be used to represent a null processor.
For example: (--mask-image-processors affect-mask-1 + affect-mask-2), or (--mask-
image-processors + affect-mask-2), or (--mask-image-processors affect-mask-1 +).
The amount of processors / processor chains must not exceed the amount of input mask
images, or you will receive a syntax error message. To obtain more information about
what image processors are available and how to use them, see: --image-processor-
help.
-----
-cip PROCESSOR_URI [PROCESSOR_URI ...], --control-image-processors PROCESSOR_URI [PROCESSOR_URI ...]
Specify one or more image processor actions to perform on the control image
specified by --image-seeds, this option is meant to be used with --control-nets.
Example: --control-image-processors "canny;lower=50;upper=100".
The delimiter "+" can be used to specify a different processor group for each image
when using multiple control images with --control-nets.
For example if you have --image-seeds "img1.png, img2.png" or --image-seeds
"...;control=img1.png, img2.png" specified and multiple ControlNet models specified
with --control-nets, you can specify processors for those control images with the
syntax: (--control-image-processors "processes-img1" + "processes-img2").
This syntax also supports chaining of processors, for example: (--control-image-
processors "first-process-img1" "second-process-img1" + "process-img2").
The amount of specified processors must not exceed the amount of specified control
images, or you will receive a syntax error message.
Images which do not have a processor defined for them will not be processed, and the
plus character can be used to indicate an image is not to be processed and instead
skipped over when that image is a leading element, for example (--control-image-
processors + "process-second") would indicate that the first control guidance image
is not to be processed, only the second.
To obtain more information about what image processors are available and how to use
them, see: --image-processor-help.
----------------------------------
--image-processor-help [PROCESSOR_NAME ...]
Use this option alone (or with --plugin-modules) and no model specification in order
to list available image processor names. Specifying one or more image processor
names after this option will cause usage documentation for the specified image
processors to be printed. When used with --plugin-modules, image processors
implemented by the specified plugins will also be listed.
---------------------------------------------------------
-pp PROCESSOR_URI [PROCESSOR_URI ...], --post-processors PROCESSOR_URI [PROCESSOR_URI ...]
Specify one or more image processor actions to perform on generated output before it
is saved. For example: --post-processors "upcaler;model=4x_ESRGAN.pth". To obtain
more information about what processors are available and how to use them, see:
--image-processor-help.
-----------------------
-iss FLOAT [FLOAT ...], --image-seed-strengths FLOAT [FLOAT ...]
One or more image strength values to try when using --image-seeds for img2img or
inpaint mode. Closer to 0 means high usage of the seed image (less noise
convolution), 1 effectively means no usage (high noise convolution). Low values will
produce something closer or more relevant to the input image, high values will give
the AI
more creative freedom. This value must be greater than 0 and less than or equal to
1. (default: [0.8])
-------------------
-uns INTEGER [INTEGER ...], --upscaler-noise-levels INTEGER [INTEGER ...]
One or more upscaler noise level values to try when using the super resolution
upscaler --model-type torch-upscaler-x4 or torch-ifs. Specifying this option for
--model-type torch-upscaler-x2 will produce an error message. The higher this value
the more noise is added to the image before upscaling (similar to --image-seed-
strengths). (default: [20 for x4, 250 for torch-ifs/torch-ifs-img2img, 0 for torch-
ifs inpainting mode])
---------------------
-gs FLOAT [FLOAT ...], --guidance-scales FLOAT [FLOAT ...]
One or more guidance scale values to try. Guidance scale effects how much your text
prompt is considered. Low values draw more data from images unrelated to text
prompt. (default: [5])
----------------------
-igs FLOAT [FLOAT ...], --image-guidance-scales FLOAT [FLOAT ...]
One or more image guidance scale values to try. This can push the generated image
towards the initial image when using --model-type *-pix2pix models, it is
unsupported for other model types. Use in conjunction with --image-seeds, inpainting
(masks) and --control-nets are not supported. Image guidance scale is enabled by
setting image-guidance-scale > 1. Higher image guidance scale encourages generated
images that are closely linked to the source image, usually at the expense of lower
image quality. Requires a value of at least 1. (default: [1.5])
---------------------------------------------------------------
-gr FLOAT [FLOAT ...], --guidance-rescales FLOAT [FLOAT ...]
One or more guidance rescale factors to try. Proposed by [Common Diffusion Noise
Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf)
"guidance_scale" is defined as "φ" in equation 16. of [Common Diffusion Noise
Schedules and Sample Steps are Flawed] (https://arxiv.org/pdf/2305.08891.pdf).
Guidance rescale factor should fix overexposure when using zero terminal SNR. This
is supported for basic text to image generation when using --model-type "torch" but
not inpainting, img2img, or --control-nets. When using --model-type "torch-sdxl" it
is supported for basic generation, inpainting, and img2img, unless --control-nets is
specified in which case only inpainting is supported. It is supported for --model-
type "torch-sdxl-pix2pix" but not --model-type "torch-pix2pix". (default: [0.0])
--------------------------------------------------------------------------------
-ifs INTEGER [INTEGER ...], --inference-steps INTEGER [INTEGER ...]
One or more inference steps values to try. The amount of inference (de-noising)
steps effects image clarity to a degree, higher values bring the image closer to
what the AI is targeting for the content of the image. Values between 30-40 produce
good results, higher values may improve image quality and or change image content.
(default: [30])
---------------
-mc EXPR [EXPR ...], --cache-memory-constraints EXPR [EXPR ...]
Cache constraint expressions describing when to clear all model caches automatically
(DiffusionPipeline, UNet, VAE, ControlNet, and Text Encoder) considering current
memory usage. If any of these constraint expressions are met all models cached in
memory will be cleared. Example, and default value: "used_percent > 70" For Syntax
See: [https://dgenerate.readthedocs.io/en/v4.2.2/dgenerate_submodules.html#dgenerate
.pipelinewrapper.CACHE_MEMORY_CONSTRAINTS]
------------------------------------------
-pmc EXPR [EXPR ...], --pipeline-cache-memory-constraints EXPR [EXPR ...]
Cache constraint expressions describing when to automatically clear the in memory
DiffusionPipeline cache considering current memory usage, and estimated memory usage
of new models that are about to enter memory. If any of these constraint expressions
are met all DiffusionPipeline objects cached in memory will be cleared. Example, and
default value: "pipeline_size > (available * 0.75)" For Syntax See: [https://dgenera
te.readthedocs.io/en/v4.2.2/dgenerate_submodules.html#dgenerate.pipelinewrapper.PIPE
LINE_CACHE_MEMORY_CONSTRAINTS]
------------------------------
-umc EXPR [EXPR ...], --unet-cache-memory-constraints EXPR [EXPR ...]
Cache constraint expressions describing when to automatically clear the in memory
UNet cache considering current memory usage, and estimated memory usage of new UNet
models that are about to enter memory. If any of these constraint expressions are
met all UNet models cached in memory will be cleared. Example, and default value:
"unet_size > (available * 0.75)" For Syntax See: [https://dgenerate.readthedocs.io/e
n/v4.2.2/dgenerate_submodules.html#dgenerate.pipelinewrapper.UNET_CACHE_MEMORY_CONST
RAINTS]
-------
-vmc EXPR [EXPR ...], --vae-cache-memory-constraints EXPR [EXPR ...]
Cache constraint expressions describing when to automatically clear the in memory
VAE cache considering current memory usage, and estimated memory usage of new VAE
models that are about to enter memory. If any of these constraint expressions are
met all VAE models cached in memory will be cleared. Example, and default value:
"vae_size > (available * 0.75)" For Syntax See: [https://dgenerate.readthedocs.io/en
/v4.2.2/dgenerate_submodules.html#dgenerate.pipelinewrapper.VAE_CACHE_MEMORY_CONSTRA
INTS]
-----
-cmc EXPR [EXPR ...], --control-net-cache-memory-constraints EXPR [EXPR ...]
Cache constraint expressions describing when to automatically clear the in memory
ControlNet cache considering current memory usage, and estimated memory usage of new
ControlNet models that are about to enter memory. If any of these constraint
expressions are met all ControlNet models cached in memory will be cleared. Example,
and default value: "controlnet_size > (available * 0.75)" For Syntax See: [https://d
generate.readthedocs.io/en/v4.2.2/dgenerate_submodules.html#dgenerate.pipelinewrappe
r.CONTROLNET_CACHE_MEMORY_CONSTRAINTS]
--------------------------------------
-tmc EXPR [EXPR ...], --text-encoder-cache-memory-constraints EXPR [EXPR ...]
Cache constraint expressions describing when to automatically clear the in memory
Text Encoder cache considering current memory usage, and estimated memory usage of
new Text Encoder models that are about to enter memory. If any of these constraint
expressions are met all Text Encoder models cached in memory will be cleared.
Example, and default value: "text_encoder_size > (available * 0.75)" For Syntax See:
[https://dgenerate.readthedocs.io/en/v4.2.2/dgenerate_submodules.html#dgenerate.pipe
linewrapper.TEXT_ENCODER_CACHE_MEMORY_CONSTRAINTS]
--------------------------------------------------
-iemc EXPR [EXPR ...], --image-encoder-cache-memory-constraints EXPR [EXPR ...]
Cache constraint expressions describing when to automatically clear the in memory
Image Encoder cache considering current memory usage, and estimated memory usage of
new Image Encoder models that are about to enter memory. If any of these constraint
expressions are met all Image Encoder models cached in memory will be cleared.
Example, and default value: "image_encoder_size > (available * 0.75)" For Syntax
See: [https://dgenerate.readthedocs.io/en/v4.2.2/dgenerate_submodules.html#dgenerate
.pipelinewrapper.IMAGE_ENCODER_CACHE_MEMORY_CONSTRAINTS]
--------------------------------------------------------
-amc EXPR [EXPR ...], --adapter-cache-memory-constraints EXPR [EXPR ...]
Cache constraint expressions describing when to automatically clear the in memory
T2I Adapter cache considering current memory usage, and estimated memory usage of
new T2I Adapter models that are about to enter memory. If any of these constraint
expressions are met all T2I Adapter models cached in memory will be cleared.
Example, and default value: "adapter_size > (available * 0.75)" For Syntax See: [htt
ps://dgenerate.readthedocs.io/en/v4.2.2/dgenerate_submodules.html#dgenerate.pipeline
wrapper.ADAPTER_CACHE_MEMORY_CONSTRAINTS]
-----------------------------------------
-tfmc EXPR [EXPR ...], --transformer-cache-memory-constraints EXPR [EXPR ...]
Cache constraint expressions describing when to automatically clear the in memory
Transformer cache considering current memory usage, and estimated memory usage of
new Transformer models that are about to enter memory. If any of these constraint
expressions are met all Transformer models cached in memory will be cleared.
Example, and default value: "transformer_size > (available * 0.75)" For Syntax See:
[https://dgenerate.readthedocs.io/en/v4.2.2/dgenerate_submodules.html#dgenerate.pipe
linewrapper.TRANSFORMER_CACHE_MEMORY_CONSTRAINTS]
-------------------------------------------------
-ipmc EXPR [EXPR ...], --image-processor-memory-constraints EXPR [EXPR ...]
Cache constraint expressions describing when to automatically clear the entire in
memory diffusion model cache considering current memory usage, and estimated memory
usage of new image processor models that are about to enter memory. If any of these
constraint expressions are met all diffusion related models cached in memory will be
cleared. Example, and default value: "processor_size > (available * 0.70)" For
Syntax See: [https://dgenerate.readthedocs.io/en/v4.2.2/dgenerate_submodules.html#dg
enerate.imageprocessors.IMAGE_PROCESSOR_MEMORY_CONSTRAINTS]
-----------------------------------------------------------
-ipcc EXPR [EXPR ...], --image-processor-cuda-memory-constraints EXPR [EXPR ...]
Cache constraint expressions describing when to automatically clear the last active
diffusion model from VRAM considering current GPU memory usage, and estimated GPU
memory usage of new image processor models that are about to enter VRAM. If any of
these constraint expressions are met the last active diffusion model in VRAM will be
destroyed. Example, and default value: "processor_size > (available * 0.70)" For
Syntax See: [https://dgenerate.readthedocs.io/en/v4.2.2/dgenerate_submodules.html#dg
enerate.imageprocessors.IMAGE_PROCESSOR_CUDA_MEMORY_CONSTRAINTS]
----------------------------------------------------------------Windows Install
You can install using the Windows installer provided with each release on the Releases Page, or you can manually install with pipx, (or pip if you want) as described below.
Manual Install
Install Visual Studios (Community or other), make sure “Desktop development with C++” is selected, unselect anything you do not need.
https://visualstudio.microsoft.com/downloads/
Install rust compiler using rustup-init.exe (x64), use the default install options.
https://www.rust-lang.org/tools/install
Install Python:
https://www.python.org/ftp/python/3.12.3/python-3.12.3-amd64.exe
Make sure you select the option “Add to PATH” in the python installer, otherwise invoke python directly using it’s full path while installing the tool.
Install GIT for Windows:
Install dgenerate
Using Windows CMD
Install pipx:
pip install pipx
pipx ensurepath
# Log out and log back in so PATH takes effectInstall dgenerate:
pipx install dgenerate ^
--pip-args "--extra-index-url https://download.pytorch.org/whl/cu124/"
# with NCNN upscaler support
pipx install dgenerate[ncnn] ^
--pip-args "--extra-index-url https://download.pytorch.org/whl/cu124/"
# If you want a specific version
pipx install dgenerate==4.2.2 ^
--pip-args "--extra-index-url https://download.pytorch.org/whl/cu124/"
# with NCNN upscaler support and a specific version
pipx install dgenerate[ncnn]==4.2.2 ^
--pip-args "--extra-index-url https://download.pytorch.org/whl/cu124/"
# You can install without pipx into your own environment like so
pip install dgenerate==4.2.2 --extra-index-url https://download.pytorch.org/whl/cu124/
# Or with NCNN
pip install dgenerate[ncnn]==4.2.2 --extra-index-url https://download.pytorch.org/whl/cu124/It is recommended to install dgenerate with pipx if you are just intending to use it as a command line program, if you want to develop you can install it from a cloned repository like this:
# in the top of the repo make
# an environment and activate it
python -m venv venv
venv\Scripts\activate
# Install with pip into the environment
pip install --editable .[dev] --extra-index-url https://download.pytorch.org/whl/cu124/
# Install with pip into the environment, include NCNN
pip install --editable .[dev, ncnn] --extra-index-url https://download.pytorch.org/whl/cu124/Run dgenerate to generate images:
# Images are output to the "output" folder
# in the current working directory by default
dgenerate --help
dgenerate stabilityai/stable-diffusion-2-1 ^
--prompts "an astronaut riding a horse" ^
--output-path output ^
--inference-steps 40 ^
--guidance-scales 10Linux or WSL Install
First update your system and install build-essential
#!/usr/bin/env bash
sudo apt update && sudo apt upgrade
sudo apt install build-essentialInstall CUDA Toolkit 12.*: https://developer.nvidia.com/cuda-downloads
I recommend using the runfile option.
Do not attempt to install a driver from the prompts if using WSL.
Add libraries to linker path:
#!/usr/bin/env bash
# Add to ~/.bashrc
# For Linux add the following
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
# For WSL add the following
export LD_LIBRARY_PATH=/usr/lib/wsl/lib:/usr/local/cuda/lib64:$LD_LIBRARY_PATH
# Add this in both cases as well
export PATH=/usr/local/cuda/bin:$PATHWhen done editing ~/.bashrc do:
#!/usr/bin/env bash
source ~/.bashrcInstall Python 3.10+ (Debian / Ubuntu) and pipx
#!/usr/bin/env bash
sudo apt install python3 python3-pip pipx python3-venv python3-wheel
pipx ensurepath
source ~/.bashrcInstall dgenerate
#!/usr/bin/env bash
# install with just support for torch
pipx install dgenerate \
--pip-args "--extra-index-url https://download.pytorch.org/whl/cu124/"
# With NCNN upscaler support
# be aware that the ncnn python package depends on
# the non headless version of python-opencv and it may
# cause issues on headless systems without a window manager such
# as not being able to find the native library: libGL
# in addition you are going to probably have to do some work
# to get Vulkan driver support
pipx install dgenerate[ncnn] \
--pip-args "--extra-index-url https://download.pytorch.org/whl/cu124/"
# If you want a specific version
pipx install dgenerate==4.2.2 \
--pip-args "--extra-index-url https://download.pytorch.org/whl/cu124/"
# You can install without pipx into your own environment like so
pip3 install dgenerate==4.2.2 --extra-index-url https://download.pytorch.org/whl/cu124/
# Or with NCNN
pip3 install dgenerate[ncnn]==4.2.2 --extra-index-url https://download.pytorch.org/whl/cu124/It is recommended to install dgenerate with pipx if you are just intending to use it as a command line program, if you want to develop you can install it from a cloned repository like this:
#!/usr/bin/env bash
# in the top of the repo make
# an environment and activate it
python3 -m venv venv
source venv/bin/activate
# Install with pip into the environment
pip3 install --editable .[dev] --extra-index-url https://download.pytorch.org/whl/cu124/Run dgenerate to generate images:
#!/usr/bin/env bash
# Images are output to the "output" folder
# in the current working directory by default
dgenerate --help
dgenerate stabilityai/stable-diffusion-2-1 \
--prompts "an astronaut riding a horse" \
--output-path output \
--inference-steps 40 \
--guidance-scales 10Linux with ROCm (AMD Cards)
On Linux you can use the ROCm torch backend with AMD cards. This is only supported on Linux, as torch does not distribute this backend for Windows.
ROCm has been minimally verified to work with dgenerate using a rented MI300X AMD GPU instance / space, and has not been tested extensively.
When specifying any --device value use cuda, cuda:1, etc. as you would for Nvidia GPUs.
You need to first install ROCm support, follow: https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/quick-start.html
Then use: --extra-index-url https://download.pytorch.org/whl/rocm6.1/ when installing via pip or pipx.
Install Python 3.10+ (Debian / Ubuntu) and pipx
#!/usr/bin/env bash
sudo apt install python3 python3-pip pipx python3-venv python3-wheel
pipx ensurepath
source ~/.bashrcSetup Environment
You may need to export the environmental variable PYTORCH_ROCM_ARCH before attempting to use dgenerate.
This value will depend on the model of your card, you may wish to add this and any other necessary environmental variables to ~/.bashrc so that they persist in your shell environment.
For details, see: https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html
Generally, this information can be obtained by running the command: rocminfo
# example
export PYTORCH_ROCM_ARCH="gfx1030"Install dgenerate
#!/usr/bin/env bash
# install with just support for torch
pipx install dgenerate \
--pip-args "--extra-index-url https://download.pytorch.org/whl/rocm6.1/"
# With NCNN upscaler support
pipx install dgenerate[ncnn] \
--pip-args "--extra-index-url https://download.pytorch.org/whl/rocm6.1/"
# If you want a specific version
pipx install dgenerate==4.2.2 \
--pip-args "--extra-index-url https://download.pytorch.org/whl/rocm6.1/"
# You can install without pipx into your own environment like so
pip3 install dgenerate==4.2.2 --extra-index-url https://download.pytorch.org/whl/rocm6.1/
# Or with NCNN
pip3 install dgenerate[ncnn]==4.2.2 --extra-index-url https://download.pytorch.org/whl/rocm6.1/MacOS Install (Apple Silicon Only)
MacOS on Apple Silicon (arm64) is experimentally supported.
Rendering can be preformed in CPU only mode, and with hardware acceleration using --device mps (Metal Performance Shaders).
The default device on MacOS is mps unless specified otherwise.
You can install on MacOS by first installing python from the universal pkg installer located at: https://www.python.org/downloads/release/python-3126/
It is also possible to install Python using homebrew, though tkinter will not be available meaning that you cannot run the Console UI.
Once you have done so, you can install using pipx (recommended), or create a virtual environment in a directory of your choosing and install dgenerate into it.
Do not specify any --extra-index-url to pip, it is not necessary on MacOS.
When using SDXL on MacOS with --dtype float16, you might need to specify --vae AutoencoderKL;model=madebyollin/sdxl-vae-fp16-fix if your images are rendering solid black.
MacOS pipx install
Installing with pipx allows you to easily install dgenerate and have it available globally from the command line without installing global python site packages.
#!/usr/bin/env bash
# install pipx
pip3 install pipx
# install dgenerate into an isolated
# environment with pipx
pipx install dgenerate==4.2.2
pipx ensurepath
# open a new terminal or logout & login
# launch the Console UI to test the install.
# tkinter will be available when you install
# python using the dmg from pythons official
# website
dgenerate --console
# or generate images
dgenerate stabilityai/stable-diffusion-2-1 \
--prompts "an astronaut riding a horse" \
--output-path output \
--inference-steps 40 \
--guidance-scales 10If you want to upgrade dgenerate, uninstall it first and then install the new version with pipx.
pipx uninstall dgenerate
pipx install dgenerate==4.2.2MacOS venv install
You can also manually install into a virtual environment of your own creation.
#!/usr/bin/env bash
# create the environment
python3 -m venv dgenerate_venv
# you must activate this environment
# every time you want to use dgenerate
# with this install method
source dgenerate_venv/bin/activate
# install dgenerate into an isolated environment
pip3 install dgenerate==4.2.2
# launch the Console UI to test the install.
# tkinter will be available when you install
# python using the dmg from pythons official
# website
dgenerate --console
# or generate images
dgenerate stabilityai/stable-diffusion-2-1 \
--prompts "an astronaut riding a horse" \
--output-path output \
--inference-steps 40 \
--guidance-scales 10Google Colab Install
The following cell entries will get you started in a Google Collab environment.
Make sure you select a GPU runtime for your notebook, such as the T4 runtime.
1.) Install venv.
!apt install python3-venv2.) Create a virtual environment.
!python3 -m venv venv3.) Install dgenerate, you must activate the virtual environment in the same cell.
!source /content/venv/bin/activate; pip install dgenerate==4.2.2 --extra-index-url https://download.pytorch.org/whl/cu1214.) Finally you can run dgenerate, you must prefix all calls to dgenerate with an activation of the virtual environment, as the virtual environment is not preserved between cells. For brevity, and as an example, just print the help text here.
!source /content/venv/bin/activate; dgenerate --helpBasic Usage
The example below attempts to generate an astronaut riding a horse using 5 different random seeds, 3 different inference steps values, and 3 different guidance scale values.
It utilizes the stabilityai/stable-diffusion-2-1 model repo on Hugging Face.
45 uniquely named images will be generated (5 x 3 x 3)
Also Adjust output size to 512x512 and output generated images to the astronaut folder in the current working directory.
When --output-path is not specified, the default output location is the output folder in the current working directory, if the path that is specified does not exist then it will be created.
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-2-1 \
--prompts "an astronaut riding a horse" \
--gen-seeds 5 \
--output-path astronaut \
--inference-steps 30 40 50 \
--guidance-scales 5 7 10 \
--output-size 512x512Loading models from huggingface blob links is also supported:
#!/usr/bin/env bash
dgenerate https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-ema-pruned.safetensors \
--prompts "an astronaut riding a horse" \
--gen-seeds 5 \
--output-path astronaut \
--inference-steps 30 40 50 \
--guidance-scales 5 7 10 \
--output-size 512x512SDXL is supported and can be used to generate highly realistic images.
Prompt only generation, img2img, and inpainting is supported for SDXL.
Refiner models can be specified, fp16 model variant and a datatype of float16 is recommended to prevent out of memory conditions on the average GPU :)
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-xl-base-1.0 --model-type torch-sdxl \
--sdxl-high-noise-fractions 0.6 0.7 0.8 \
--gen-seeds 5 \
--inference-steps 50 \
--guidance-scales 12 \
--sdxl-refiner stabilityai/stable-diffusion-xl-refiner-1.0 \
--prompts "real photo of an astronaut riding a horse on the moon" \
--variant fp16 --dtype float16 \
--output-size 1024Negative Prompt
In order to specify a negative prompt, each prompt argument is split into two parts separated by ;
The prompt text occurring after ; is the negative influence prompt.
To attempt to avoid rendering of a saddle on the horse being ridden, you could for example add the negative prompt saddle or wearing a saddle or horse wearing a saddle etc.
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-2-1 \
--prompts "an astronaut riding a horse; horse wearing a saddle" \
--gen-seeds 5 \
--output-path astronaut \
--inference-steps 50 \
--guidance-scales 10 \
--output-size 512x512Multiple Prompts
Multiple prompts can be specified one after another in quotes in order to generate images using multiple prompt variations.
The following command generates 10 uniquely named images using two prompts and five random seeds (2x5)
5 of them will be from the first prompt and 5 of them from the second prompt.
All using 50 inference steps, and 10 for guidance scale value.
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-2-1 \
--prompts "an astronaut riding a horse" "an astronaut riding a donkey" \
--gen-seeds 5 \
--output-path astronaut \
--inference-steps 50 \
--guidance-scales 10 \
--output-size 512x512Image Seeds
The --image-seeds argument can be used to specify one or more image input resource groups for use in rendering, and allows for the specification of img2img source images, inpaint masks, control net guidance images, deep floyd stage images, image group resizing, and frame slicing values for animations. It possesses it’s own URI syntax for defining different image inputs used for image generation, the example described below is the simplest case for one image input (img2img).
This example uses a photo of Buzz Aldrin on the moon to generate a photo of an astronaut standing on mars using img2img, this uses an image seed downloaded from wikipedia.
Disk file paths may also be used for image seeds and generally that is the standard use case, multiple image seed definitions may be provided and images will be generated from each image seed individually.
#!/usr/bin/env bash
# Generate this image using 5 different seeds, 3 different inference-step values, 3 different
# guidance-scale values as above.
# In addition this image will be generated using 3 different image seed strengths.
# Adjust output size to 512x512 and output generated images to 'astronaut' folder, the image seed
# will be resized to that dimension with aspect ratio respected by default, the width is fixed and
# the height will be calculated, this behavior can be changed globally with the --no-aspect option
# if desired or locally by specifying "img2img-seed.png;aspect=false" as your image seed
# If you do not adjust the output size of the generated image, the size of the input image seed will be used.
# 135 uniquely named images will be generated (5x3x3x3)
dgenerate stabilityai/stable-diffusion-2-1 \
--prompts "an astronaut walking on mars" \
--image-seeds https://upload.wikimedia.org/wikipedia/commons/9/98/Aldrin_Apollo_11_original.jpg \
--image-seed-strengths 0.2 0.5 0.8 \
--gen-seeds 5 \
--output-path astronaut \
--inference-steps 30 40 50 \
--guidance-scales 5 7 10 \
--output-size 512x512--image-seeds serves as the entire mechanism for determining if img2img or inpainting is going to occur via it’s URI syntax described further in the section Inpainting.
In addition to this it can be used to provide control guidance images in the case of txt2img, img2img, or inpainting via the use of a URI syntax involving keyword arguments.
The syntax --image-seeds "my-image-seed.png;control=my-control-image.png" can be used with --control-nets to specify img2img mode with a ControlNet for example, see: Specifying Control Nets for more information.
IP Adapter images may be provided via a special adapters: ... syntax and via the adapters URI argument discussed in: Specifying IP Adapters
Batching or providing multiple image inputs for the same generation, resulting in multiple output variations possibly using different input images, or multiple image prompts, is possible using the images: ... syntax discussed in the section: Batching Input Images and Inpaint Masks.
Inpainting
Inpainting on an image can be preformed by providing a mask image with your image seed. This mask should be a black and white image of identical size to your image seed. White areas of the mask image will be used to tell the AI what areas of the seed image should be filled in with generated content.
For using inpainting on animated image seeds, jump to: Inpainting Animations
Some possible definitions for inpainting are:
--image-seeds "my-image-seed.png;my-mask-image.png"
--image-seeds "my-image-seed.png;mask=my-mask-image.png"
The format is your image seed and mask image separated by ;, optionally mask can be named argument. The alternate syntax is for disambiguation when preforming img2img or inpainting operations while Specifying Control Nets or other operations where keyword arguments might be necessary for disambiguation such as per image seed Animation Slicing, and the specification of the image from a previous Deep Floyd stage using the floyd argument.
Mask images can be downloaded from URL’s just like any other resource mentioned in an --image-seeds definition, however for this example files on disk are used for brevity.
You can download them here:
The command below generates a cat sitting on a bench with the images from the links above, the mask image masks out areas over the dog in the original image, causing the dog to be replaced with an AI generated cat.
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-2-inpainting \
--image-seeds "my-image-seed.png;my-mask-image.png" \
--prompts "Face of a yellow cat, high resolution, sitting on a park bench" \
--image-seed-strengths 0.8 \
--guidance-scales 10 \
--inference-steps 100Per Image Seed Resizing
If you want to specify multiple image seeds that will have different output sizes irrespective of their input size or a globally defined output size defined with --output-size, You can specify their output size individually at the end of each provided image seed.
This will work when using a mask image for inpainting as well, including when using animated inputs.
This also works when Specifying Control Nets and guidance images for control nets.
Resizing in this fashion will resize any img2img image, inpaint mask, or control image to the specified size, generally all of these images need to be the same size. In combination with the URI argument aspect=False this can be used to force multiple images of different sizes to the same dimension.
This does not resize IP Adapter images as they have their own special per image resizing syntax discussed in: Specifying IP Adapters
Here are some possible definitions:
--image-seeds "my-image-seed.png;512x512" (img2img)
--image-seeds "my-image-seed.png;my-mask-image.png;512x512" (inpainting)
--image-seeds "my-image-seed.png;resize=512x512" (img2img)
--image-seeds "my-image-seed.png;mask=my-mask-image.png;resize=512x512" (inpainting)
The alternate syntax with named arguments is for disambiguation when Specifying Control Nets, or preforming per image seed Animation Slicing, or specifying the previous Deep Floyd stage output with the floyd keyword argument.
When one dimension is specified, that dimension is the width, and the height.
The height of an image is calculated to be aspect correct by default for all resizing methods unless --no-aspect has been given as an argument on the command line or the aspect keyword argument is used in the --image-seeds definition.
The the aspect correct resize behavior can be controlled on a per image seed definition basis using the aspect keyword argument. Any value given to this argument overrides the presence or absense of the --no-aspect command line argument.
the aspect keyword argument can only be used when all other components of the image seed definition are defined using keyword arguments. aspect=false disables aspect correct resizing, and aspect=true enables it.
Some possible definitions:
--image-seeds "my-image-seed.png;resize=512x512;aspect=false" (img2img)
--image-seeds "my-image-seed.png;mask=my-mask-image.png;resize=512x512;aspect=false" (inpainting)
The following example preforms img2img generation, followed by inpainting generation using 2 image seed definitions. The involved images are resized using the basic syntax with no keyword arguments present in the image seeds.
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-2-1 \
--image-seeds "my-image-seed.png;1024" "my-image-seed.png;my-mask-image.png;512x512" \
--prompts "Face of a yellow cat, high resolution, sitting on a park bench" \
--image-seed-strengths 0.8 \
--guidance-scales 10 \
--inference-steps 100Animated Output
dgenerate supports many video formats through the use of PyAV (ffmpeg), as well as GIF & WebP.
See --help for information about all formats supported for the --animation-format option.
When an animated image seed is given, animated output will be produced in the format of your choosing.
In addition, every frame will be written to the output folder as a uniquely named image.
By specifying --animation-format frames you can tell dgenerate that you just need the frame images and not to produce any coalesced animation file for you. You may also specify --no-frames to indicate that you only want an animation file to be produced and no intermediate frames, though using this option with --animation-format frames is considered an error.
If the animation is not 1:1 aspect ratio, the width will be fixed to the width of the requested output size, and the height calculated to match the aspect ratio of the animation. Unless --no-aspect or the --image-seeds keyword argument aspect=false are specified, in which case the video will be resized to the requested dimension exactly.
If you do not set an output size, the size of the input animation will be used.
#!/usr/bin/env bash
# Use a GIF of a man riding a horse to create an animation of an astronaut riding a horse.
dgenerate stabilityai/stable-diffusion-2-1 \
--prompts "an astronaut riding a horse" \
--image-seeds https://upload.wikimedia.org/wikipedia/commons/7/7b/Muybridge_race_horse_~_big_transp.gif \
--image-seed-strengths 0.5 \
--output-path astronaut \
--inference-steps 50 \
--guidance-scales 10 \
--output-size 512x512 \
--animation-format mp4The above syntax is the same syntax used for generating an animation with a control image when --control-nets or --t2i-adapters is used.
Animations can also be generated using an alternate syntax for --image-seeds that allows the specification of a control image source when it is desired to use --control-nets with img2img or inpainting.
For more information about this see: Specifying Control Nets
And also: Specifying T2I Adapters
As well as the information about --image-seeds from dgenerates --help output.
IP Adapter images can also be animated inputs see: Specifying IP Adapters
In general, every image component of an --image-seeds specification may be an animated file, animated files may be mixed with static images. The animated input with the shortest length determines the number of output frames, and any static image components are duplicated over that amount of frames.
Animation Slicing
Animated inputs can be sliced by a frame range either globally using --frame-start and --frame-end or locally using the named argument syntax for --image-seeds, for example:
--image-seeds "animated.gif;frame-start=3;frame-end=10".
When using animation slicing at the --image-seed level, all image input definitions other than the main image must be specified using keyword arguments.
For example here are some possible definitions:
--image-seeds "seed.gif;frame-start=3;frame-end=10"
--image-seeds "seed.gif;mask=mask.gif;frame-start=3;frame-end=10
--image-seeds "seed.gif;control=control-guidance.gif;frame-start=3;frame-end=10
--image-seeds "seed.gif;mask=mask.gif;control=control-guidance.gif;frame-start=3;frame-end=10
--image-seeds "seed.gif;floyd=stage1.gif;frame-start=3;frame-end=10"
--image-seeds "seed.gif;mask=mask.gif;floyd=stage1.gif;frame-start=3;frame-end=10"
Specifying a frame slice locally in an image seed overrides the global frame slice setting defined by --frame-start or --frame-end, and is specific only to that image seed, other image seed definitions will not be affected.
Perhaps you only want to run diffusion on the first frame of an animated input in order to save time in finding good parameters for generating every frame. You could slice to only the first frame using --frame-start 0 --frame-end 0, which will be much faster than rendering the entire video/gif outright.
The slice range zero indexed and also inclusive, inclusive means that the starting and ending frames specified by --frame-start and --frame-end will be included in the slice. Both slice points do not have to be specified at the same time. You can exclude the tail end of a video with just --frame-end alone, or seek to a certain start frame in the video with --frame-start alone and render from there onward, this applies for keyword arguments in the --image-seeds definition as well.
If your slice only results in the processing of a single frame, an animated file format will not be generated, only a single image output will be generated for that image seed during the generation step.
#!/usr/bin/env bash
# Generate using only the first frame
dgenerate stabilityai/stable-diffusion-2-1 \
--prompts "an astronaut riding a horse" \
--image-seeds https://upload.wikimedia.org/wikipedia/commons/7/7b/Muybridge_race_horse_~_big_transp.gif \
--image-seed-strengths 0.5 \
--output-path astronaut \
--inference-steps 50 \
--guidance-scales 10 \
--output-size 512x512 \
--animation-format mp4 \
--frame-start 0 \
--frame-end 0Inpainting Animations
Image seeds can be supplied an animated or static image mask to define the areas for inpainting while generating an animated output.
Any possible combination of image/video parameters can be used. The animation with least amount of frames in the entire specification determines the frame count, and any static images present are duplicated across the entire animation. The first animation present in an image seed specification always determines the output FPS of the animation.
When an animated seed is used with an animated mask, the mask for every corresponding frame in the input is taken from the animated mask, the runtime of the animated output will be equal to the shorter of the two animated inputs. IE: If the seed animation and the mask animation have different length, the animated output is clipped to the length of the shorter of the two.
When a static image is used as a mask, that image is used as an inpaint mask for every frame of the animated seed.
When an animated mask is used with a static image seed, the animated output length is that of the animated mask. A video is created by duplicating the image seed for every frame of the animated mask, the animated output being generated by masking them together.
#!/usr/bin/env bash
# A video with a static inpaint mask over the entire video
dgenerate stabilityai/stable-diffusion-2-inpainting \
--prompts "an astronaut riding a horse" \
--image-seeds "my-animation.mp4;my-static-mask.png" \
--output-path inpaint \
--animation-format mp4
# Zip two videos together, masking the left video with corresponding frames
# from the right video. The two animated inputs do not have to be the same file format
# you can mask videos with gif/webp and vice versa
dgenerate stabilityai/stable-diffusion-2-inpainting \
--prompts "an astronaut riding a horse" \
--image-seeds "my-animation.mp4;my-animation-mask.mp4" \
--output-path inpaint \
--animation-format mp4
dgenerate stabilityai/stable-diffusion-2-inpainting \
--prompts "an astronaut riding a horse" \
--image-seeds "my-animation.mp4;my-animation-mask.gif" \
--output-path inpaint \
--animation-format mp4
dgenerate stabilityai/stable-diffusion-2-inpainting \
--prompts "an astronaut riding a horse" \
--image-seeds "my-animation.gif;my-animation-mask.gif" \
--output-path inpaint \
--animation-format mp4
dgenerate stabilityai/stable-diffusion-2-inpainting \
--prompts "an astronaut riding a horse" \
--image-seeds "my-animation.gif;my-animation-mask.webp" \
--output-path inpaint \
--animation-format mp4
dgenerate stabilityai/stable-diffusion-2-inpainting \
--prompts "an astronaut riding a horse" \
--image-seeds "my-animation.webp;my-animation-mask.gif" \
--output-path inpaint \
--animation-format mp4
dgenerate stabilityai/stable-diffusion-2-inpainting \
--prompts "an astronaut riding a horse" \
--image-seeds "my-animation.gif;my-animation-mask.mp4" \
--output-path inpaint \
--animation-format mp4
# etc...
# Use a static image seed and mask it with every frame from an
# Animated mask file
dgenerate stabilityai/stable-diffusion-2-inpainting \
--prompts "an astronaut riding a horse" \
--image-seeds "my-static-image-seed.png;my-animation-mask.mp4" \
--output-path inpaint \
--animation-format mp4
dgenerate stabilityai/stable-diffusion-2-inpainting \
--prompts "an astronaut riding a horse" \
--image-seeds "my-static-image-seed.png;my-animation-mask.gif" \
--output-path inpaint \
--animation-format mp4
dgenerate stabilityai/stable-diffusion-2-inpainting \
--prompts "an astronaut riding a horse" \
--image-seeds "my-static-image-seed.png;my-animation-mask.webp" \
--output-path inpaint \
--animation-format mp4
# etc...Deterministic Output
If you generate an image you like using a random seed, you can later reuse that seed in another generation.
Updates to the backing model may affect determinism in the generation.
Output images have a name format that starts with the seed, IE: s_(seed here)_ ...png
Reusing a seed has the effect of perfectly reproducing the image in the case that all other parameters are left alone, including the model version.
You can output a configuration file for each image / animation produced that will reproduce it exactly using the option --output-configs, that same information can be written to the metadata of generated PNG files using the option --output-metadata and can be read back with ImageMagick for example as so:
#!/usr/bin/env bash
magick identify -format "%[Property:DgenerateConfig]" generated_file.pngGenerated configuration can be read back into dgenerate via a pipe or file redirection.
#!/usr/bin/env bash
# DO NOT DO THIS IF THE IMAGE IS UNTRUSTED, SUCH AS IF IT IS SOMEONE ELSE'S IMAGE!
# VERIFY THAT THE METADATA CONTENT OF THE IMAGE IS NOT MALICIOUS FIRST,
# USING THE IDENTIFY COMMAND ALONE
magick identify -format "%[Property:DgenerateConfig]" generated_file.png | dgenerate
dgenerate < generated-config.dgenSpecifying a seed directly and changing the prompt slightly, or parameters such as image seed strength if using a seed image, guidance scale, or inference steps, will allow for generating variations close to the original image which may possess all the original qualities about the image that you liked as well as additional qualities. You can further manipulate the AI into producing results that you want with this method.
Changing output resolution will drastically affect image content when reusing a seed to the point where trying to reuse a seed with a different output size is pointless.
The following command demonstrates manually specifying two different seeds to try: 1234567890, and 9876543210
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-2-1 \
--prompts "an astronaut riding a horse" \
--seeds 1234567890 9876543210 \
--output-path astronaut \
--inference-steps 50 \
--guidance-scales 10 \
--output-size 512x512Specifying a specific GPU for CUDA
The desired GPU to use for CUDA acceleration can be selected using --device cuda:N where N is the device number of the GPU as reported by nvidia-smi.
#!/usr/bin/env bash
# Console 1, run on GPU 0
dgenerate stabilityai/stable-diffusion-2-1 \
--prompts "an astronaut riding a horse" \
--output-path astronaut_1 \
--inference-steps 50 \
--guidance-scales 10 \
--output-size 512x512 \
--device cuda:0
# Console 2, run on GPU 1 in parallel
dgenerate stabilityai/stable-diffusion-2-1 \
--prompts "an astronaut riding a cow" \
--output-path astronaut_2 \
--inference-steps 50 \
--guidance-scales 10 \
--output-size 512x512 \
--device cuda:1Specifying a Scheduler (sampler)
A scheduler (otherwise known as a sampler) for the main model can be selected via the use of --scheduler.
And in the case of SDXL the refiner’s scheduler can be selected independently with --sdxl-refiner-scheduler.
For Stable Cascade the decoder scheduler can be specified via the argument -s-cascade-decoder-scheduler however only one scheduler type is supported for Stable Cascade (DDPMWuerstchenScheduler).
Both of these default to the value of --scheduler, which in turn defaults to automatic selection.
Available schedulers for a specific combination of dgenerate arguments can be queried using --scheduler help, --sdxl-refiner-scheduler help, or --s-cascade-decoder-scheduler help though they cannot be queried simultaneously.
In order to use the query feature it is ideal that you provide all the other arguments that you plan on using while making the query, as different combinations of arguments will result in different underlying pipeline implementations being created, each of which may have different compatible scheduler names listed. The model needs to be loaded in order to gather this information.
For example there is only one compatible scheduler for this upscaler configuration:
#!/usr/bin/env bash
dgenerate stabilityai/sd-x2-latent-upscaler --variant fp16 --dtype float16 \
--model-type torch-upscaler-x2 \
--prompts "none" \
--image-seeds my-image.png \
--output-size 256 \
--scheduler help
# Outputs:
#
# Compatible schedulers for "stabilityai/sd-x2-latent-upscaler" are:
#
# "EulerDiscreteScheduler"Typically however, there will be many compatible schedulers:
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-2 \
--inference-steps 40 \
--guidance-scales 8 \
--output-size 1024 \
--gen-seeds 2 \
--prompts "none" \
--scheduler help
# Outputs:
#
# Compatible schedulers for "stabilityai/stable-diffusion-2" are:
#
# "DDIMScheduler"
# "DDPMScheduler"
# "DEISMultistepScheduler"
# "DPMSolverMultistepScheduler"
# "DPMSolverSDEScheduler"
# "DPMSolverSinglestepScheduler"
# "EDMEulerScheduler"
# "EulerAncestralDiscreteScheduler"
# "EulerDiscreteScheduler"
# "HeunDiscreteScheduler"
# "KDPM2AncestralDiscreteScheduler"
# "KDPM2DiscreteScheduler"
# "LCMScheduler"
# "LMSDiscreteScheduler"
# "PNDMScheduler"
# "UniPCMultistepScheduler"Passing helpargs to a --scheduler related option will reveal configuration arguments that can be overridden via a URI syntax, for every possible scheduler.
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-2 \
--inference-steps 40 \
--guidance-scales 8 \
--output-size 1024 \
--gen-seeds 2 \
--prompts "none" \
--scheduler helpargs
# Outputs (shortened for brevity...):
#
# Compatible schedulers for "stabilityai/stable-diffusion-2" are:
# ...
#
# PNDMScheduler:
# num-train-timesteps=1000
# beta-start=0.0001
# beta-end=0.02
# beta-schedule=linear
# trained-betas=None
# skip-prk-steps=False
# set-alpha-to-one=False
# prediction-type=epsilon
# timestep-spacing=leading
# steps-offset=0
#
# ...As an example, you may override the mentioned arguments for any scheduler in this manner:
#!/usr/bin/env bash
# Change prediction type of the scheduler to "v_prediction".
# for some models this may be necessary, not for this model
# this is just a syntax example
dgenerate stabilityai/stable-diffusion-2 \
--inference-steps 40 \
--guidance-scales 8 \
--output-size 1024 \
--gen-seeds 2 \
--prompts "none" \
--scheduler PNDMScheduler;prediction-type=v_predictionSpecifying a VAE
To specify a VAE directly use --vae.
VAEs are supported for these model types:
--model-type torch
--model-type torch-pix2pix
--model-type torch-upscaler-x2
--model-type torch-upscaler-x4
--model-type torch-sdxl
--model-type torch-sdxl-pix2pix
--model-type torch-sd3
--model-type torch-flux
The URI syntax for --vae is AutoEncoderClass;model=(huggingface repository slug/blob link or file/folder path)
Named arguments when loading a VAE are separated by the ; character and are not positional, meaning they can be defined in any order.
Loading arguments available when specifying a VAE are: model, revision, variant, subfolder, and dtype
The only named arguments compatible with loading a .safetensors or other model file directly off disk are model and dtype
The other named arguments are available when loading from a huggingface repository or folder that may or may not be a local git repository on disk.
Available encoder classes are:
AutoencoderKL
AsymmetricAutoencoderKL (Does not support --vae-slicing or --vae-tiling)
AutoencoderTiny
ConsistencyDecoderVAE
The AutoencoderKL encoder class accepts huggingface repository slugs/blob links, .pt, .pth, .bin, .ckpt, and .safetensors files. Other encoders can only accept huggingface repository slugs/blob links, or a path to a folder on disk with the model configuration and model file(s).
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-2-1 \
--vae "AutoencoderKL;model=stabilityai/sd-vae-ft-mse" \
--prompts "an astronaut riding a horse" \
--output-path astronaut \
--inference-steps 50 \
--guidance-scales 10 \
--output-size 512x512If you want to select the repository revision, such as main etc, use the named argument revision, subfolder is required in this example as well because the VAE model file exists in a subfolder of the specified huggingface repository.
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-2-1 \
--revision fp16 \
--dtype float16 \
--vae "AutoencoderKL;model=stabilityai/stable-diffusion-2-1;revision=fp16;subfolder=vae" \
--prompts "an astronaut riding a horse" \
--output-path astronaut \
--inference-steps 50 \
--guidance-scales 10 \
--output-size 512x512If you wish to specify a weights variant IE: load pytorch_model.<variant>.safetensors, from a huggingface repository that has variants of the same model, use the named argument variant. this value does NOT default to the value --variant to prevent errors during common use cases. If you wish to select a variant you must specify it in the URI.
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-2-1 \
--variant fp16 \
--vae "AutoencoderKL;model=stabilityai/stable-diffusion-2-1;subfolder=vae;variant=fp16" \
--prompts "an astronaut riding a horse" \
--output-path astronaut \
--inference-steps 50 \
--guidance-scales 10 \
--output-size 512x512If your weights file exists in a subfolder of the repository, use the named argument subfolder
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-2-1 \
--vae "AutoencoderKL;model=stabilityai/stable-diffusion-2-1;subfolder=vae" \
--prompts "an astronaut riding a horse" \
--output-path astronaut \
--inference-steps 50 \
--guidance-scales 10 \
--output-size 512x512If you want to specify the model precision, use the named argument dtype, accepted values are the same as --dtype, IE: float32, float16, auto
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-2-1 \
--revision fp16 \
--dtype float16 \
--vae "AutoencoderKL;model=stabilityai/stable-diffusion-2-1;revision=fp16;subfolder=vae;dtype=float16" \
--prompts "an astronaut riding a horse" \
--output-path astronaut \
--inference-steps 50 \
--guidance-scales 10 \
--output-size 512x512If you are loading a .safetensors or other file from a path on disk, only the model, and dtype arguments are available.
#!/usr/bin/env bash
# These are only syntax examples
dgenerate huggingface/diffusion_model \
--vae "AutoencoderKL;model=my_vae.safetensors" \
--prompts "Syntax example"
dgenerate huggingface/diffusion_model \
--vae "AutoencoderKL;model=my_vae.safetensors;dtype=float16" \
--prompts "Syntax example"VAE Tiling and Slicing
You can use --vae-tiling and --vae-slicing to enable to generation of huge images without running your GPU out of memory. Note that if you are using --control-nets you may still be memory limited by the size of the image being processed by the ControlNet, and still may run in to memory issues with large image inputs.
When --vae-tiling is used, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images.
When --vae-slicing is used, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory, especially when --batch-size is greater than 1.
#!/usr/bin/env bash
# Here is an SDXL example of high resolution image generation utilizing VAE tiling/slicing
dgenerate stabilityai/stable-diffusion-xl-base-1.0 --model-type torch-sdxl \
--variant fp16 --dtype float16 \
--vae "AutoencoderKL;model=madebyollin/sdxl-vae-fp16-fix" \
--vae-tiling \
--vae-slicing \
--sdxl-refiner stabilityai/stable-diffusion-xl-refiner-1.0 \
--sdxl-high-noise-fractions 0.8 \
--inference-steps 30 \
--guidance-scales 8 \
--output-size 2048 \
--sdxl-target-size 2048 \
--prompts "Photo of a horse standing near the open door of a red barn, high resolution; artwork"Specifying a UNet
An alternate UNet model can be specified via a URI with the --unet option, in a similar fashion to --vae and other model arguments that accept URIs.
UNets are supported for these model types:
--model-type torch
--model-type torch-if
--model-type torch-ifs
--model-type torch-ifs-img2img
--model-type torch-pix2pix
--model-type torch-upscaler-x2
--model-type torch-upscaler-x4
--model-type torch-sdxl
--model-type torch-sdxl-pix2pix
--model-type torch-s-cascade
This is useful in particular for using the latent consistency scheduler as well as the lite variants of the unet models used with Stable Cascade.
The first component of the --unet URI is the model path itself.
You can provide a path to a huggingface repo, or a folder on disk (downloaded huggingface repository).
The latent consistency UNet for SDXL can be specified with the --unet argument.
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-xl-base-1.0 --model-type torch-sdxl \
--variant fp16 --dtype float16 \
--unet latent-consistency/lcm-sdxl \
--scheduler LCMScheduler \
--inference-steps 4 \
--guidance-scales 8 \
--gen-seeds 2 \
--output-size 1024 \
--prompts "a close-up picture of an old man standing in the rain"Loading arguments available when specifying a UNet are: revision, variant, subfolder, and dtype
In the case of --unet the variant loading argument defaults to the value of --variant if you do not specify it in the URI.
The --unet2 option can be used to specify a UNet for the SDXL Refiner or Stable Cascade Decoder, and uses the same syntax as --unet.
Here is an example of using the lite variants of Stable Cascade’s UNet models which have a smaller memory footprint using --unet and --unet2.
#!/usr/bin/env bash
dgenerate stabilityai/stable-cascade-prior \
--model-type torch-s-cascade \
--variant bf16 \
--dtype bfloat16 \
--unet "stabilityai/stable-cascade-prior;subfolder=prior_lite" \
--unet2 "stabilityai/stable-cascade;subfolder=decoder_lite" \
--model-cpu-offload \
--s-cascade-decoder-cpu-offload \
--s-cascade-decoder "stabilityai/stable-cascade;dtype=float16" \
--inference-steps 20 \
--guidance-scales 4 \
--s-cascade-decoder-inference-steps 10 \
--s-cascade-decoder-guidance-scales 0 \
--gen-seeds 2 \
--prompts "an image of a shiba inu, donning a spacesuit and helmet"Specifying a Transformer (SD3 and Flux)
Stable Diffusion 3 and Flux do not use a UNet architecture, and instead use a Transformer model in place of a UNet.
A specific transformer model can be specified using the --transformer argument.
This argument is nearly identical to --unet, however it can support single file loads from safetensors files or huggingface blob links if desired.
In addition to the arguments that --unet supports, --transformer supports the quantize URI argument to enable weights quantization via the optimum-quanto library, allowing for lower GPU memory usage. quantize may be passed the values qint2, qint4, qint8, qfloat8_e4m3fn, qfloat8_e5m2, or qfloat8, to indicate the quantization data type.
SD3 Example:
#!/usr/bin/env bash
# This just loads the default transformer out of the repo on huggingface
dgenerate stabilityai/stable-diffusion-3-medium-diffusers \
--model-type torch-sd3 \
--transformer stabilityai/stable-diffusion-3-medium-diffusers;subfolder=transformer \
--variant fp16 \
--dtype float16 \
--inference-steps 30 \
--guidance-scales 5.00 \
--clip-skips 0 \
--gen-seeds 2 \
--output-path output \
--model-sequential-offload \
--prompts "Photo of a horse standing near the open door of a red barn, high resolution; artwork"Flux Example:
#!/usr/bin/env bash
# use Flux with quantized transformer and text encoder (qfloat8)
dgenerate black-forest-labs/FLUX.1-dev \
--model-type torch-flux \
--dtype bfloat16 \
--transformer https://huggingface.co/Kijai/flux-fp8/blob/main/flux1-dev-fp8.safetensors;quantize=qfloat8 \
--text-encoders + T5EncoderModel;model=black-forest-labs/FLUX.1-dev;subfolder=text_encoder_2;quantize=qfloat8 \
--model-cpu-offload \
--inference-steps 20 \
--guidance-scales 3.5 \
--gen-seeds 1 \
--output-path output \
--output-size 512x512 \
--prompts "Photo of a horse standing near the open door of a red barn, high resolution"Specifying an SDXL Refiner
When the main model is an SDXL model and --model-type torch-sdxl is specified, you may specify a refiner model with --sdxl-refiner.
You can provide a path to a huggingface repo/blob link, folder on disk, or a model file on disk such as a .pt, .pth, .bin, .ckpt, or .safetensors file.
This argument is parsed in much the same way as the argument --vae, except the model is the first value specified.
Loading arguments available when specifying a refiner are: revision, variant, subfolder, and dtype
The only named argument compatible with loading a .safetensors or other file directly off disk is dtype
The other named arguments are available when loading from a huggingface repo/blob link, or folder that may or may not be a local git repository on disk.
#!/usr/bin/env bash
# Basic usage of SDXL with a refiner
dgenerate stabilityai/stable-diffusion-xl-base-1.0 --model-type torch-sdxl \
--variant fp16 --dtype float16 \
--sdxl-refiner stabilityai/stable-diffusion-xl-refiner-1.0 \
--sdxl-high-noise-fractions 0.8 \
--inference-steps 40 \
--guidance-scales 8 \
--output-size 1024 \
--prompts "Photo of a horse standing near the open door of a red barn, high resolution; artwork"If you want to select the repository revision, such as main etc, use the named argument revision
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-xl-base-1.0 --model-type torch-sdxl \
--variant fp16 --dtype float16 \
--sdxl-refiner "stabilityai/stable-diffusion-xl-refiner-1.0;revision=main" \
--sdxl-high-noise-fractions 0.8 \
--inference-steps 40 \
--guidance-scales 8 \
--output-size 1024 \
--prompts "Photo of a horse standing near the open door of a red barn, high resolution; artwork"If you wish to specify a weights variant IE: load pytorch_model.<variant>.safetensors, from a huggingface repository that has variants of the same model, use the named argument variant. By default this value is the same as --variant unless you override it.
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-xl-base-1.0 --model-type torch-sdxl \
--variant fp16 --dtype float16 \
--sdxl-refiner "stabilityai/stable-diffusion-xl-refiner-1.0;variant=fp16" \
--sdxl-high-noise-fractions 0.8 \
--inference-steps 40 \
--guidance-scales 8 \
--output-size 1024 \
--prompts "Photo of a horse standing near the open door of a red barn, high resolution; artwork"If your weights file exists in a subfolder of the repository, use the named argument subfolder
#!/usr/bin/env bash
# This is only a syntax example
dgenerate huggingface/sdxl_model --model-type torch-sdxl \
--variant fp16 --dtype float16 \
--sdxl-refiner "huggingface/sdxl_refiner;subfolder=repo_subfolder"If you want to select the model precision, use the named argument dtype. By default this value is the same as --dtype unless you override it. Accepted values are the same as --dtype, IE: ‘float32’, ‘float16’, ‘auto’
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-xl-base-1.0 --model-type torch-sdxl \
--variant fp16 --dtype float16 \
--sdxl-refiner "stabilityai/stable-diffusion-xl-refiner-1.0;dtype=float16" \
--sdxl-high-noise-fractions 0.8 \
--inference-steps 40 \
--guidance-scales 8 \
--output-size 1024 \
--prompts "Photo of a horse standing near the open door of a red barn, high resolution; artwork"If you are loading a .safetensors or other file from a path on disk, simply do:
#!/usr/bin/env bash
# This is only a syntax example
dgenerate huggingface/sdxl_model --model-type torch-sdxl \
--sdxl-refiner my_refinermodel.safetensorsWhen preforming inpainting or when using ControlNets, the refiner will automatically operate in edit mode instead of cooperative denoising mode. Edit mode can be forced in other situations with the option --sdxl-refiner-edit.
Edit mode means that the refiner model is accepting the fully (or mostly) denoised output of the main model generated at the full number of inference steps specified, and acting on it with an image strength (image seed strength) determined by (1.0 - high-noise-fraction).
The output latent from the main model is renoised with a certain amount of noise determined by the strength, a lower number means less noise and less modification of the latent output by the main model.
This is similar to what happens when using dgenerate in img2img with a standalone model, technically it is just img2img, however refiner models are better at enhancing details from the main model in this use case.
Specifying a Stable Cascade Decoder
When the main model is a Stable Cascade prior model and --model-type torch-s-cascade is specified, you may specify a decoder model with --s-cascade-decoder.
The syntax (and URI arguments) for specifying the decoder model is identical to specifying an SDXL refiner model as mentioned above.
#!/usr/bin/env bash
dgenerate stabilityai/stable-cascade-prior \
--model-type torch-s-cascade \
--variant bf16 \
--dtype bfloat16 \
--model-cpu-offload \
--s-cascade-decoder-cpu-offload \
--s-cascade-decoder "stabilityai/stable-cascade;dtype=float16" \
--inference-steps 20 \
--guidance-scales 4 \
--s-cascade-decoder-inference-steps 10 \
--s-cascade-decoder-guidance-scales 0 \
--gen-seeds 2 \
--prompts "an image of a shiba inu, donning a spacesuit and helmet"Specifying LoRAs
It is possible to specify one or more LoRA models using --loras
LoRAs are supported for these model types:
--model-type torch
--model-type torch-pix2pix
--model-type torch-upscaler-x4
--model-type torch-sdxl
--model-type torch-sdxl-pix2pix
--model-type torch-sd3
--model-type torch-flux
When multiple specifications are given, all mentioned models will be fused together into one set of weights at their individual scale, and then those weights will be fused into the main model at the scale value of --lora-fuse-scale, which defaults to 1.0.
You can provide a huggingface repository slug, .pt, .pth, .bin, .ckpt, or .safetensors files. Blob links are not accepted, for that use subfolder and weight-name described below.
The individual LoRA scale for each provided model can be specified after the model path by placing a ; (semicolon) and then using the named argument scale
When a scale is not specified, 1.0 is assumed.
Named arguments when loading a LoRA are separated by the ; character and are not positional, meaning they can be defined in any order.
Loading arguments available when specifying a LoRA are: scale, revision, subfolder, and weight-name
The only named argument compatible with loading a .safetensors or other file directly off disk is scale
The other named arguments are available when loading from a huggingface repository or folder that may or may not be a local git repository on disk.
This example shows loading a LoRA using a huggingface repository slug and specifying scale for it.
#!/usr/bin/env bash
# Don't expect great results with this example,
# Try models and LoRA's downloaded from CivitAI
dgenerate Lykon/dreamshaper-8 \
--loras "pcuenq/pokemon-lora;scale=0.5" \
--prompts "Gengar standing in a field at night under a full moon, highquality, masterpiece, digital art" \
--inference-steps 40 \
--guidance-scales 10 \
--gen-seeds 5 \
--output-size 800Specifying the file in a repository directly can be done with the named argument weight-name
Shown below is an SDXL compatible LoRA being used with the SDXL base model and a refiner.
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-xl-base-1.0 --model-type torch-sdxl \
--inference-steps 30 \
--sdxl-refiner stabilityai/stable-diffusion-xl-refiner-1.0 \
--prompts "sketch of a horse by Leonardo da Vinci" \
--variant fp16 --dtype float16 \
--loras "goofyai/SDXL-Lora-Collection;scale=1.0;weight-name=leonardo_illustration.safetensors" \
--output-size 1024If you want to select the repository revision, such as main etc, use the named argument revision
#!/usr/bin/env bash
dgenerate Lykon/dreamshaper-8 \
--loras "pcuenq/pokemon-lora;scale=0.5;revision=main" \
--prompts "Gengar standing in a field at night under a full moon, highquality, masterpiece, digital art" \
--inference-steps 40 \
--guidance-scales 10 \
--gen-seeds 5 \
--output-size 800If your weights file exists in a subfolder of the repository, use the named argument subfolder
#!/usr/bin/env bash
# This is only a syntax example
dgenerate huggingface/model \
--prompts "Syntax example" \
--loras "huggingface/lora_repo;scale=1.0;subfolder=repo_subfolder;weight-name=lora_weights.safetensors"If you are loading a .safetensors or other file from a path on disk, only the scale argument is available.
#!/usr/bin/env bash
# This is only a syntax example
dgenerate Lykon/dreamshaper-8 \
--prompts "Syntax example" \
--loras "my_lora.safetensors;scale=1.0"Specifying Textual Inversions (embeddings)
One or more Textual Inversion models (otherwise known as embeddings) may be specified with --textual-inversions
Textual inversions are supported for these model types:
--model-type torch
--model-type torch-pix2pix
--model-type torch-upscaler-x4
--model-type torch-sdxl
--model-type torch-sdxl-pix2pix
You can provide a huggingface repository slug, .pt, .pth, .bin, .ckpt, or .safetensors files. Blob links are not accepted, for that use subfolder and weight-name described below.
Arguments pertaining to the loading of each textual inversion model may be specified in the same way as when using --loras minus the scale argument.
Available arguments are: token, revision, subfolder, and weight-name
Named arguments are available when loading from a huggingface repository or folder that may or may not be a local git repository on disk, when loading directly from a .safetensors file or other file from a path on disk they should not be used.
The token argument may be used to override the prompt token value, which is the text token in the prompt that triggers the inversion, textual inversions for stable diffusion usually include this token value in the model itself, for instance in the example below the token for Isometric_Dreams-1000.pt is Isometric_Dreams.
The token value used for SDXL (Stable Diffusion XL) models is a bit different, a default value is not provided in the model file. If you do not provide a token value, dgenerate will assign the tokens default value to the filename of the model with any spaces converted to underscores, and with the file extension removed.
#!/usr/bin/env bash
# Load a textual inversion from a huggingface repository specifying it's name in the repository
# as an argument
dgenerate Duskfallcrew/isometric-dreams-sd-1-5 \
--textual-inversions "Duskfallcrew/IsometricDreams_TextualInversions;weight-name=Isometric_Dreams-1000.pt" \
--scheduler KDPM2DiscreteScheduler \
--inference-steps 30 \
--guidance-scales 7 \
--prompts "a bright photo of the Isometric_Dreams, a tv and a stereo in it and a book shelf, a table, a couch,a room with a bed"You can change the token value to affect the prompt token used to trigger the embedding
#!/usr/bin/env bash
# Load a textual inversion from a huggingface repository specifying it's name in the repository
# as an argument
dgenerate Duskfallcrew/isometric-dreams-sd-1-5 \
--textual-inversions "Duskfallcrew/IsometricDreams_TextualInversions;weight-name=Isometric_Dreams-1000.pt;token=<MY_TOKEN>" \
--scheduler KDPM2DiscreteScheduler \
--inference-steps 30 \
--guidance-scales 7 \
--prompts "a bright photo of the <MY_TOKEN>, a tv and a stereo in it and a book shelf, a table, a couch,a room with a bed"If you want to select the repository revision, such as main etc, use the named argument revision
#!/usr/bin/env bash
# This is a non working example as I do not know of a repo that utilizes revisions with
# textual inversion weights :) this is only a syntax example
dgenerate huggingface/model \
--prompts "Syntax example" \
--textual-inversions "huggingface/ti_repo;revision=main"If your weights file exists in a subfolder of the repository, use the named argument subfolder
#!/usr/bin/env bash
# This is only a syntax example
dgenerate huggingface/model \
--prompts "Syntax example" \
--textual-inversions "huggingface/ti_repo;subfolder=repo_subfolder;weight-name=ti_model.safetensors"If you are loading a .safetensors or other file from a path on disk, simply do:
#!/usr/bin/env bash
# This is only a syntax example
dgenerate Lykon/dreamshaper-8 \
--prompts "Syntax example" \
--textual-inversions "my_ti_model.safetensors"Specifying Control Nets
One or more ControlNet models may be specified with --control-nets, and multiple control net guidance images can be specified via --image-seeds in the case that you specify multiple control net models.
ControlNet models are supported for these model types:
--model-type torch
--model-type torch-sdxl
--model-type torch-sd3 (img2img and inpainting not supported)
You can provide a huggingface repository slug / blob link, .pt, .pth, .bin, .ckpt, or .safetensors files.
Control images for the Control Nets can be provided using --image-seeds
When using --control-nets specifying control images via --image-seeds can be accomplished in these ways:
--image-seeds "control-image.png" (txt2img)
--image-seeds "img2img-seed.png;control=control-image.png" (img2img)
--image-seeds "img2img-seed.png;mask=mask.png;control=control-image.png" (inpainting)
Multiple control image sources can be specified in these ways when using multiple control nets:
--image-seeds "control-1.png, control-2.png" (txt2img)
--image-seeds "img2img-seed.png;control=control-1.png, control-2.png" (img2img)
--image-seeds "img2img-seed.png;mask=mask.png;control=control-1.png, control-2.png" (inpainting)
It is considered a syntax error if you specify a non-equal amount of control guidance images and --control-nets URIs and you will receive an error message if you do so.
resize=WIDTHxHEIGHT can be used to select a per --image-seeds resize dimension for all image sources involved in that particular specification, as well as aspect=true/false and the frame slicing arguments frame-start and frame-end.
ControlNet guidance images may actually be animations such as MP4s, GIFs etc. Frames can be taken from multiple videos simultaneously. Any possible combination of image/video parameters can be used. The animation with least amount of frames in the entire specification determines the frame count, and any static images present are duplicated across the entire animation. The first animation present in an image seed specification always determines the output FPS of the animation.
Arguments pertaining to the loading of each ControlNet model specified with --control-nets may be declared in the same way as when using --vae with the addition of a scale argument.
Available arguments are: --model-type values are: scale, start, end, revision, variant, subfolder, dtype
Most named arguments apply to loading from a huggingface repository or folder that may or may not be a local git repository on disk, when loading directly from a .safetensors file or other file from a path on disk the available arguments are scale, start, and end.
The scale argument indicates the affect scale of the control net model.
For torch, the start argument indicates at what fraction of the total inference steps at which the control net model starts to apply guidance. If you have multiple control net models specified, they can apply guidance over different segments of the inference steps using this option, it defaults to 0.0, meaning start at the first inference step.
for torch, the end argument indicates at what fraction of the total inference steps at which the control net model stops applying guidance. It defaults to 1.0, meaning stop at the last inference step.
These examples use: vermeer_canny_edged.png
#!/usr/bin/env bash
# SD1.5 example, use "vermeer_canny_edged.png" as a control guidance image
dgenerate Lykon/dreamshaper-8 \
--inference-steps 40 \
--guidance-scales 8 \
--prompts "Painting, Girl with a pearl earring by Leonardo Da Vinci, masterpiece; low quality, low resolution, blank eyeballs" \
--control-nets "lllyasviel/sd-controlnet-canny;scale=0.5" \
--image-seeds "vermeer_canny_edged.png"
# If you have an img2img image seed, use this syntax
dgenerate Lykon/dreamshaper-8 \
--inference-steps 40 \
--guidance-scales 8 \
--prompts "Painting, Girl with a pearl earring by Leonardo Da Vinci, masterpiece; low quality, low resolution, blank eyeballs" \
--control-nets "lllyasviel/sd-controlnet-canny;scale=0.5" \
--image-seeds "my-image-seed.png;control=vermeer_canny_edged.png"
# If you have an img2img image seed and an inpainting mask, use this syntax
dgenerate Lykon/dreamshaper-8 \
--inference-steps 40 \
--guidance-scales 8 \
--prompts "Painting, Girl with a pearl earring by Leonardo Da Vinci, masterpiece; low quality, low resolution, blank eyeballs" \
--control-nets "lllyasviel/sd-controlnet-canny;scale=0.5" \
--image-seeds "my-image-seed.png;mask=my-inpaint-mask.png;control=vermeer_canny_edged.png"
# SDXL example
dgenerate stabilityai/stable-diffusion-xl-base-1.0 --model-type torch-sdxl \
--variant fp16 --dtype float16 \
--vae "AutoencoderKL;model=madebyollin/sdxl-vae-fp16-fix" \
--sdxl-refiner stabilityai/stable-diffusion-xl-refiner-1.0 \
--inference-steps 30 \
--guidance-scales 8 \
--prompts "Taylor Swift, high quality, masterpiece, high resolution; low quality, bad quality, sketches" \
--control-nets "diffusers/controlnet-canny-sdxl-1.0;scale=0.5" \
--image-seeds "vermeer_canny_edged.png" \
--output-size 1024If you want to select the repository revision, such as main etc, use the named argument revision
#!/usr/bin/env bash
# This is only a syntax example
dgenerate huggingface/model \
--prompts "Syntax example" \
--control-nets "huggingface/cn_repo;revision=main"If your weights file exists in a subfolder of the repository, use the named argument subfolder
#!/usr/bin/env bash
# This is only a syntax example
dgenerate huggingface/model \
--prompts "Syntax example" \
--control-nets "huggingface/cn_repo;subfolder=repo_subfolder"If you are loading a .safetensors or other file from a path on disk, simply do:
#!/usr/bin/env bash
# This is only a syntax example
dgenerate Lykon/dreamshaper-8 \
--prompts "Syntax example" \
--control-nets "my_cn_model.safetensors"Specifying T2I Adapters
One or more T2I Adapters models may be specified with --t2i-adapters, and multiple T2I Adapter guidance images can be specified via --image-seeds in the case that you specify multiple T2I Adapter models.
T2I Adapters are similar to Control Net models and are mutually exclusive with Control Net models, IE: they cannot be used together.
T2I Adapters are more lightweight than Control Net models, but only support txt2img generation with control images for guidance, img2img and inpainting is not supported with T2I Adapters.
T2I Adapter models are supported for these model types:
--model-type torch
--model-type torch-sdxl
You can provide a huggingface repository slug / blob link, .pt, .pth, .bin, .ckpt, or .safetensors files.
Control images for the T2I Adapters can be provided using --image-seeds
When using --t2i-adapters specifying control images via --image-seeds can be accomplished like this:
--image-seeds "control-image.png" (txt2img)
Multiple control image sources can be specified like this when using multiple T2I Adapters:
--image-seeds "control-1.png, control-2.png" (txt2img)
It is considered a syntax error if you specify a non-equal amount of control guidance images and --t2i-adapters URIs and you will receive an error message if you do so.
Available URI arguments are: scale, revision, variant, subfolder, dtype
The scale argument indicates the affect scale of the T2I Adapter model.
When using SDXL, the dgenerate argument --sdxl-t2i-adapter-factors can be used to specify multiple adapter factors to try generating images with, the adapter factor is value between 0.0 and 1.0 indicating the fraction of time-steps over which the T2I adapter guidance is applied.
For example, a --sdxl-t2i-adapter-factors value of 0.5 would mean to only apply guidance over the first half of the time-steps needed to generate the image.
When using multiple T2I Adapters, this value applies to all T2I Adapter models mentioned.
These examples use: vermeer_canny_edged.png
#!/usr/bin/env bash
# SD1.5 example, use "vermeer_canny_edged.png" as a control guidance image
dgenerate Lykon/dreamshaper-8 \
--inference-steps 40 \
--guidance-scales 8 \
--prompts "Painting, Girl with a pearl earring by Leonardo Da Vinci, masterpiece; low quality, low resolution, blank eyeballs" \
--t2i-adapters "TencentARC/t2iadapter_canny_sd15v2;scale=0.5" \
--image-seeds "vermeer_canny_edged.png"
# SDXL example
dgenerate stabilityai/stable-diffusion-xl-base-1.0 --model-type torch-sdxl \
--variant fp16 --dtype float16 \
--vae "AutoencoderKL;model=madebyollin/sdxl-vae-fp16-fix" \
--sdxl-refiner stabilityai/stable-diffusion-xl-refiner-1.0 \
--inference-steps 30 \
--guidance-scales 8 \
--prompts "Taylor Swift, high quality, masterpiece, high resolution; low quality, bad quality, sketches" \
--t2i-adapters "TencentARC/t2i-adapter-canny-sdxl-1.0;scale=0.5" \
--image-seeds "vermeer_canny_edged.png" \
--output-size 1024If you want to select the repository revision, such as main etc, use the named argument revision
#!/usr/bin/env bash
# This is only a syntax example
dgenerate huggingface/model \
--prompts "Syntax example" \
--t2i-adapters "huggingface/t2i_repo;revision=main"If your weights file exists in a subfolder of the repository, use the named argument subfolder
#!/usr/bin/env bash
# This is only a syntax example
dgenerate huggingface/model \
--prompts "Syntax example" \
--t2i-adapters "huggingface/t2i_repo;subfolder=repo_subfolder"If you are loading a .safetensors or other file from a path on disk, simply do:
#!/usr/bin/env bash
# This is only a syntax example
dgenerate Lykon/dreamshaper-8 \
--prompts "Syntax example" \
--t2i-adapters "my_t2i_model.safetensors"Specifying IP Adapters
One or more IP Adapter models can be specified with the --ip-adapters argument.
The URI syntax for this argument is identical to --loras, which is discussed in: Specifying LoRAs
IP Adapters are supported for these model types:
--model-type torch
--model-type torch-pix2pix
--model-type torch-sdxl
Here is a brief example of loading an IP Adapter in the most basic way and passing it an image via --image-seeds.
This example nearly duplicates an image created with a code snippet in the diffusers documentation page found here.
#!/usr/bin/env bash
# this uses one IP Adapter input image with the IP Adapter h94/IP-Adapter
dgenerate stabilityai/stable-diffusion-xl-base-1.0 \
--model-type torch-sdxl \
--dtype float16 \
--variant fp16 \
--sdxl-refiner stabilityai/stable-diffusion-xl-refiner-1.0 \
--inference-steps 30 \
--guidance-scales 5 \
--sdxl-high-noise-fractions 0.8 \
--seeds 0 \
--output-path basic \
--model-cpu-offload \
--image-seeds "adapter: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_diner.png" \
--ip-adapters h94/IP-Adapter;subfolder=sdxl_models;weight-name=ip-adapter_sdxl.bin \
--output-size 1024x1024 \
--prompts "a polar bear sitting in a chair drinking a milkshake; \
deformed, ugly, wrong proportion, low res, bad anatomy, worst quality, low quality"The main complexity of working with IP Adapters comes when specifying the --image-seeds URI for tasks other than the most basic usage shown above.
Each IP Adapter model can accept multiple IP Adapter input images, and they do not need to all be the same dimension or aligned in any particular way for the model to work.
In addition, IP Adapter models can be used with Control Net and T2I Adapter models introducing additional complexities in specifying image input.
If you specify multiple IP Adapters, they must all have the same variant URI argument value or you will receive a usage error.
basic –image-seeds specification
The first syntax we can use with --image-seeds is designed to allow using IP Adapter images alone or with Control Net images.
--image-seeds "adapter: adapter-image.png" (txt2img)
--image-seeds "adapter: adapter-image.png;control=control-image.png" (txt2img + Control Net or T2I Adapter)
You may specify multiple IP Adapter images with the + image syntax, and multiple control images as you normally would with control images.
--image-seeds "adapter: adapter-image1.png + adapter-image2.png"
--image-seeds "adapter: adapter-image1.png + adapter-image2.png;control=control-image1.png, control-image2.png"
If you have multiple IP Adapter models loaded via --ip-adapters, a comma delimits the images passed to each IP Adapter model.
--image-seeds "adapter: model1-adapter-image1.png + model1-adapter-image2.png, model2-adapter-image1.png + model2-adapter-image2.png"
If you specify the resize, aspect, or align arguments for resizing the --image-seeds components, these arguments do not affect the IP Adapter images. Only the control images in the cases being discussed here.
In order to resize IP adapter images from the --image-seeds URI, you must use a sub-uri syntax for each adapter image.
This is always true for all adapter image specification syntaxes.
This sub-uri syntax uses the pipe | symbol to delimit its URI arguments for the specific IP Adapter image.
--image-seeds "adapter: adapter-image.png|resize=256|align=8|aspect=True"
--image-seeds "adapter: adapter-image1.png|resize=256|align=8|aspect=True + adapter-image2.png|resize=256|align=8|aspect=True"
This sub-uri syntax allows resizing each IP Adapter input image individually.
This syntax supports the arguments resize, align, and aspect, which refer to the resize dimension, image alignment, and whether or not the image resize that occurs is aspect correct.
These arguments mirror the behavior of the top level --image-seeds arguments with the same names.
However, alignment for IP Adapter images defaults to 1, meaning that there is no forced alignment unless you force it manually.
img2img –image-seeds specification
You may use a traditional img2img input image along with IP Adapter input images.
The adapter images are then specified with the URI argument adapter.
The exact same syntax is used when specifying the IP Adapter images this way as when using the adapter: prefix mentioned in the section above.
Including the + syntax and sub-uri resizing syntax.
--image-seeds "img2img-input.png;adapter=adapter-image.png" (img2img)
--image-seeds "img2img-input.png;adapter=adapter-image.png;control=control-image.png" (img2img + Control Net or T2I Adapter)
inpainting –image-seeds specification
You may use inpainting with IP Adapter images by specifying an img2img input image and the mask argument of the --image-seeds URI.
The mask argument in this case does not refer to IP Adapter mask images, but simply inpainting mask images.
--image-seeds "img2img-input.png;mask=inpaint-mask.png;adapter=adapter-image.png" (inpaint)
--image-seeds "img2img-input.png;mask=inpaint-mask.png;adapter=adapter-image.png;control=control-image.png" (inpaint + Control Net or T2I Adapter)
quoting IP Adapter image URLs with plus symbols
If you happen to need to download an IP Adapter image from a URL containing a plus symbol, the URL can be quoted using single or double quotes depending on context.
There are quite a few different ways to quote the URI itself that will work, especially in config scripts where ; is not considered to be any kind of significant operator, and | is only used as an operator with the \exec directive.
--image-seeds "adapter: 'https://url.com?arg=hello+world' + image2.png"
--image-seeds 'adapter:"https://url.com?arg=hello+world" + image2.png'
--image-seeds "img2img.png;adapter='https://url.com?arg=hello+world' + image2.png"
--image-seeds 'img2img.png;adapter="https://url.com?arg=hello+world" + image2.png'
animated inputs & combinatorics
Animated inputs work for IP Adapter images, when you specify an image seed with animated components such as videos or gifs, the shortest animation dictates the amount of frames which will be processed in total, and any static images specified in the image seed are duplicated across those frames.
The IP Adapter syntax introduces a lot of possible combinations for --image-seeds input images, and not all possible combinations are covered in this documentation as it would be hard to do so.
If you find a combination that behaves strangely or incorrectly, or that should work but doesn’t, please submit an issue :)
Specifying Text Encoders
Diffusion pipelines supported by dgenerate may use a varying number of text encoder sub models, currently up to 3. --model-type torch-sd3 for instance uses 3 text encoder sub models, all of which can be individually specified from the command line if desired.
To specify a Text Encoder models directly use --text-encoders for the primary model and --text-encoders2 for the SDXL Refiner or Stable Cascade decoder.
Text Encoder URIs do not support loading from blob links or a single file, text encoders must be loaded from a huggingface slug or a folder on disk containing the models and configuration.
The syntax for specifying text encoders is similar to that of --vae
The URI syntax for --text-encoders is TextEncoderClass;model=(huggingface repository slug or folder path)
Loading arguments available when specifying a Text Encoder are: model, revision, variant, subfolder, dtype, and quantize
The variant argument defaults to the value of --variant
The dtype argument defaults to the value of --dtype
The quantize URI argument enables weights quantization via the optimum-quanto library, allowing for lower GPU memory usage. This is useful when generating with Flux models. quantize may be passed the values qint2, qint4, qint8, qfloat8_e4m3fn, qfloat8_e5m2, or qfloat8, to indicate the quantization data type.
The other named arguments are available when loading from a huggingface repository or folder that may or may not be a local git repository on disk.
Available encoder classes are:
CLIPTextModel
CLIPTextModelWithProjection
T5EncoderModel
You can query the text encoder types and position for a model by passing help as an argument to --text-encoders or --text-encoders2. This feature may not be used for both arguments simultaneously, and also may not be used when passing help or helpargs to any --scheduler type argument.
#!/usr/bin/env bash
# ask for text encoder help on the main model that is mentioned
dgenerate https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/sd3_medium_incl_clips.safetensors \
--model-type torch-sd3 \
--variant fp16 \
--dtype float16 \
--text-encoders help
# outputs:
# Text encoder type help:
#
# 0 = CLIPTextModelWithProjection
# 1 = CLIPTextModelWithProjection
# 2 = T5EncoderModel
# this means that there are 3 text encoders that we
# could potentially specify manually in the order
# displayed for this modelWhen specifying multiple text encoders, a special syntax is allowed to indicate that a text encoder should be loaded from defaults, this syntax involves the plus symbol. When a plus symbol is encountered it is regarded as “use default”.
For instance in the example below, only the last of the three text encoders involved in the Stable Diffusion 3 pipeline is specified, as it is the only one not included with the main model file.
This text encoder is loaded from a subfolder of the Stable Diffusion 3 repository on huggingface.
#!/usr/bin/env bash
# This is an example of individually specifying text encoders
# specifically for stable diffusion 3, this model from the blob
# link includes the clip encoders, so we only need to specify
# the T5 encoder, which is encoder number 3, the + symbols indicate
# the first 2 encoders are assigned their default value, they are
# loaded from the checkpoint file for the main model
dgenerate https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/sd3_medium_incl_clips.safetensors \
--model-type torch-sd3 \
--variant fp16 \
--dtype float16 \
--inference-steps 30 \
--guidance-scales 5.00 \
--text-encoders + + \
T5EncoderModel;model=stabilityai/stable-diffusion-3-medium-diffusers;subfolder=text_encoder_3 \
--clip-skips 0 \
--gen-seeds 2 \
--output-path output \
--model-sequential-offload \
--prompts "a horse outside a barn"You may also use the URI value null, to indicate that you do not want to ever load a specific text encoder at all.
For instance, you can prevent Stable Diffusion 3 from loading and using the T5 encoder all together.
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-3-medium-diffusers \
--model-type torch-sd3 \
--variant fp16 \
--dtype float16 \
--inference-steps 30 \
--guidance-scales 5.00 \
--text-encoders + + null \
--clip-skips 0 \
--gen-seeds 2 \
--output-path output \
--model-sequential-offload \
--prompts "a horse outside a barn"Any text encoder shared via the \use_modules directive in a config files is considered a default value for the text encoder in the next pipeline that runs, using + will maintain this value and using null will override it.
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
# this model will load all three text encoders,
# they are not cached individually as we did not explicitly
# specify any of them, they are cached with the pipeline
# as a whole
stabilityai/stable-diffusion-3-medium-diffusers
--model-type torch-sd3
--variant fp16
--dtype float16
--inference-steps 30
--guidance-scales 5.00
--clip-skips 0
--gen-seeds 2
--output-path output
--model-sequential-offload
--prompts "a horse outside a barn"
# store all the text encoders from the last pipeline
# into the variable "encoders"
\save_modules encoders text_encoder text_encoder_2 text_encoder_3
# share them with the next pipeline
\use_modules encoders
# use all the encoders except the T5 encoder (third encoder)
# sharing modules this way saves a significant amount
# of memory
stabilityai/stable-diffusion-3-medium-diffusers
--model-type torch-sd3
--variant fp16
--dtype float16
--inference-steps 30
--guidance-scales 5.00
--clip-skips 0
--text-encoders + + null
--gen-seeds 2
--output-path output
--model-sequential-offload
--prompts "a horse outside a barn"Prompt Weighting and Enhancement
By default, the prompt token weighting syntax that you may be familiar with from other software such as ComfyUI, Stable Diffusion Web UI, and CivitAI etc. is not enabled, and prompts over 77 tokens in length are not supported.
However! dgenerate implements prompt weighting and prompt enhancements through internal plugins called prompt weighters, which can be selectively enabled to process your prompts. They support special token weighting syntaxes, and overcome limitations on prompt length.
The names of all prompt weighter implementations can be seen by using the argument --prompt-weighter-help, and specific documentation for a prompt weighter can be printed py passing its name to this argument.
You may also use the config directive \prompt_weighter_help inside of a config, or more likely when you are working inside the Console UI shell.
There are currently two prompt weighter implementations, the compel prompt weighter, and the sd-embed prompt weighter.
The compel prompt weighter
The compel prompt weighter uses the compel library to support InvokeAI style prompt token weighting syntax for Stable Diffusion 1/2, and Stable Diffusion XL.
You can read about InvokeAI prompt syntax here: Invoke AI prompting documentation
It is a bit different than Stable Diffusion Web UI syntax, which is a syntax used by the majority of other image generation software. It possesses some neat features not mentioned in this documentation, that are worth reading about in the links provided above.
#!/usr/bin/env bash
# print out the documentation for the compel prompt weighter
dgenerate --prompt-weighter-help compelcompel:
arguments:
syntax: str = "compel"
Implements prompt weighting syntax for Stable Diffusion 1/2 and Stable Diffusion XL using
compel. The default syntax is "compel" which is analogous to the syntax used by InvokeAI.
Specifying the syntax "sdwui" will translate your prompt from Stable Diffusion Web UI syntax
into compel / InvokeAI syntax before generating the prompt embeddings.
If you wish to use prompt syntax for weighting tokens that is similar to ComfyUI, Automatic1111,
or CivitAI for example, use: 'compel;syntax=sdwui'
The underlying weighting behavior for tokens is not exactly the same as other software that uses
the more common "sdwui" syntax, so your prompt may need adjusting if you are reusing a prompt
from those other pieces of software.
You can read about compel here: https://github.com/damian0815/compel
And InvokeAI here: https://github.com/invoke-ai/InvokeAI
This prompt weighter supports the model types:
--model-type torch
--model-type torch-pix2pix
--model-type torch-upscaler-x4
--model-type torch-sdxl
--model-type torch-sdxl-pix2pix
--model-type torch-s-cascade
The secondary prompt option for SDXL --sdxl-second-prompts is supported by this prompt weighter
implementation. However, --sdxl-refiner-second-prompts is not supported and will be ignored
with a warning message.
====================================================================================================You can enable the compel prompt weighter by specifying it with the --prompt-weighter argument.
#!/usr/bin/env bash
# Some very simple examples
# Increase the weight of (picking apricots)
dgenerate stabilityai/stable-diffusion-2-1 \
--inference-steps 30 \
--guidance-scales 5.00 \
--clip-skips 0 \
--gen-seeds 1 \
--output-path output \
--output-size 1024 \
--prompt-weighter compel \
--prompts "a tall man (picking apricots)++"
# Specify a weight
dgenerate stabilityai/stable-diffusion-2-1 \
--inference-steps 30 \
--guidance-scales 5.00 \
--clip-skips 0 \
--gen-seeds 1 \
--output-path output \
--output-size 1024 \
--prompt-weighter compel \
--prompts "a tall man (picking apricots)1.3"If you prefer the prompt weighting syntax used by Stable Diffusion Web UI, you can specify the plugin argument syntax=sdwui which will translate your prompt from that syntax into compel / InvokeAI syntax for you.
#!/usr/bin/env bash
# Some very simple examples
# Increase the weight of (picking apricots)
dgenerate stabilityai/stable-diffusion-2-1 \
--inference-steps 30 \
--guidance-scales 5.00 \
--clip-skips 0 \
--gen-seeds 1 \
--output-path output \
--output-size 1024 \
--prompt-weighter "compel;syntax=sdwui" \
--prompts "a tall man ((picking apricots))"
# Specify a weight
dgenerate stabilityai/stable-diffusion-2-1 \
--inference-steps 30 \
--guidance-scales 5.00 \
--clip-skips 0 \
--gen-seeds 1 \
--output-path output \
--output-size 1024 \
--prompt-weighter "compel;syntax=sdwui" \
--prompts "a tall man (picking apricots:1.3)"The weighting algorithm is not entirely identical to other pieces of software, so if you are migrating prompts they will likely require some adjustment.
The sd-embed prompt weighter
The sd-embed prompt weighter uses the sd_embed library to support Stable Diffusion Web UI style prompt token weighting syntax for Stable Diffusion 1/2, Stable Diffusion XL, and Stable Diffusion 3.
The syntax that sd-embed uses is the more wide spread prompt syntax used by software such as Stable Diffusion Web UI and CivitAI
Quite notably, the sd-embed prompt weighter supports Stable Diffusion 3 and Flux, where as the compel prompt weighter currently does not.
#!/usr/bin/env bash
# print out the documentation for the sd-embed prompt weighter
dgenerate --prompt-weighter-help sd-embedsd-embed:
Implements prompt weighting syntax for Stable Diffusion 1/2, Stable Diffusion XL, and Stable
Diffusion 3, and Flux using sd_embed.
sd_embed uses a Stable Diffusion Web UI compatible prompt syntax.
See: https://github.com/xhinker/sd_embed
@misc{sd_embed_2024,
author = {Shudong Zhu(Andrew Zhu)},
title = {Long Prompt Weighted Stable Diffusion Embedding},
howpublished = {\url{https://github.com/xhinker/sd_embed}},
year = {2024},
}
--model-type torch
--model-type torch-pix2pix
--model-type torch-upscaler-x4
--model-type torch-sdxl
--model-type torch-sdxl-pix2pix
--model-type torch-s-cascade
--model-type torch-sd3
--model-type torch-flux
The secondary prompt option for SDXL --sdxl-second-prompts is supported by this prompt weighter
implementation. However, --sdxl-refiner-second-prompts is not supported and will be ignored with
a warning message.
The secondary prompt option for SD3 --sd3-second-prompts is not supported by this prompt
weighter implementation. Neither is --sd3-third-prompts. The prompts from these arguments will
be ignored.
The secondary prompt option for Flux --flux-second-prompts is supported by this prompt weighter.
Flux does not support negative prompting in either prompt.
====================================================================================================You can enable the sd-embed prompt weighter by specifying it with the --prompt-weighter argument.
#!/usr/bin/env bash
# You need a huggingface API token to run this example
dgenerate stabilityai/stable-diffusion-3-medium-diffusers \
--model-type torch-sd3 \
--variant fp16 \
--dtype float16 \
--inference-steps 30 \
--guidance-scales 5.00 \
--clip-skips 0 \
--gen-seeds 1 \
--output-path output \
--output-size 1024x1024 \
--model-sequential-offload \
--prompt-weighter sd-embed \
--auth-token $HF_TOKEN \
--prompts "a (man:1.2) standing on the (beach:1.2) looking out in to the water during a (sunset)"Utilizing CivitAI links and Other Hosted Models
Any model accepted by dgenerate that can be specified as a single file inside of a URI or otherwise can be specified by a URL link to a model file itself. dgenerate will attempt to download the file from the link, store it in the web cache, and then use it.
You may also use the \download config directive to assist in pre downloading other resources from the internet. The directive has the ability to specify arbitrary storage locations. See: The \download directive
You can also use the download() template function for similar purposes. See: The download() template function
In the case of CivitAI you can use this to bake models into your script that will be automatically downloaded for you, you just need a CivitAI account and API token to download models.
Your API token can be created on this page: https://civitai.com/user/account
Near the bottom of the page in the section: API Keys
You can use the civitai-links sub-command to fetch the necessary model links from a CivitAI model page. You may also use this sub-command in the form of the config directive \civitai_links from a config file or the Console UI.
If you plan to download many large models to the web cache in this manner you may wish to adjust the global cache expiry time so that they exist in the cache longer than the default of 12 hours.
You can see how to change the cache expiry time in this section File Cache Control
#!/usr/bin/env bash
# Download the main model from civitai using an api token
# https://civitai.com/models/122822?modelVersionId=133832
TOKEN=your_api_token_here
MODEL="https://civitai.com/api/download/models/133832?type=Model&format=SafeTensor&size=full&fp=fp16&token=$TOKEN"
dgenerate $MODEL \
--model-type torch-sdxl \
--variant fp16 --dtype float16 \
--sdxl-refiner stabilityai/stable-diffusion-xl-refiner-1.0 \
--sdxl-high-noise-fractions 0.8 \
--guidance-scales 8 \
--inference-steps 40 \
--prompts "a fluffy cat playing in the grass"This method can be used for VAEs, LoRAs, ControlNets, and Textual Inversions as well, whenever single file loads are supported by the argument.
Specifying Generation Batch Size
Multiple image variations from the same seed can be produce on a GPU simultaneously using the --batch-size option of dgenerate. This can be used in combination with --batch-grid-size to output image grids if desired.
When not writing to image grids the files in the batch will be written to disk with the suffix _image_N where N is index of the image in the batch of images that were generated.
When producing an animation, you can either write N animation output files with the filename suffixes _animation_N where N is the index of the image in the batch which makes up the frames. Or you can use --batch-grid-size to write frames to a single animated output where the frames are all image grids produced from the images in the batch.
With larger --batch-size values, the use of --vae-slicing can make the difference between an out of memory condition and success, so it is recommended that you try this option if you experience an out of memory condition due to the use of --batch-size.
Batching Input Images and Inpaint Masks
For most model types excluding Stable Cascade, you can process multiple input images for img2img and inpaint mode on the GPU simultaneously.
This is done using the images: ... syntax of --image-seeds
Here is an example of img2img usage:
#! /usr/bin/env bash
# Standard img2img, this results in two outputs
# each of the images are resized to 1024 so they match
# in dimension, which is a requirement for batching
dgenerate stabilityai/stable-diffusion-2 \
--inference-steps 30 \
--guidance-scales 8 \
--image-seeds "images: examples/media/earth.jpg, examples/media/mountain.png;1024" \
--image-seed-strengths 0.9 \
--vae-tiling \
--vae-slicing \
--seeds 70466855166895 \
--output-path batching \
--prompts "A detailed view of the planet mars"
# The --batch-size must be divisible by the number of provided images
# this results in 4 images being produced, 2 variations of each input image
dgenerate stabilityai/stable-diffusion-2 \
--inference-steps 30 \
--guidance-scales 8 \
--image-seeds "images: examples/media/earth.jpg, examples/media/mountain.png;1024" \
--batch-size 4
--image-seed-strengths 0.9 \
--vae-tiling \
--vae-slicing \
--seeds 70466855166895 \
--output-path batching \
--prompts "A detailed view of the planet mars"And an inpainting example:
#! /usr/bin/env bash
# With inpainting, we can either provide just one mask
# for every input image, or a separate mask for each input image
# if we wish to provide separate masks we could simply separate
# them with commas as we do with the images in the images:
# specification
# These images have different aspect ratios and dimensions
# so we are using the extended syntax of --image-seeds to
# force them to all be the same shape
# The same logic for --batch-size still applies as mentioned
# in the img2img example
dgenerate stabilityai/stable-diffusion-2-inpainting \
--inference-steps 30 \
--guidance-scales 8 \
--image-seeds "images: ../../media/dog-on-bench.png, ../../media/beach.jpg;mask=../../media/dog-on-bench-mask.png;resize=1024;aspect=False" \
--image-seed-strengths 1 \
--vae-tiling \
--vae-slicing \
--seeds 39877139643371 \
--output-path batching \
--prompts "A fluffy orange cat, realistic, high quality; deformed, scary"In the case of Stable Cascade, this syntax results in multiple images being passed to Stable Cascade as an image/style prompt, and does not result in multiple outputs or batching behavior.
This Stable Cascade functionality is demonstrated in the example config: examples/stablecascade/img2img/multiple-inputs-config.dgen
Image Processors
Images provided through --image-seeds can be processed before being used for image generation through the use of the arguments --seed-image-processors, --mask-image-processors, and --control-image-processors. In addition, dgenerates output can be post processed with the used of the --post-processors argument, which is useful for using the upscaler processor. An important note about --post-processors is that post processing occurs before any image grid rendering is preformed when --batch-grid-size is specified with a --batch-size greater than one, meaning that the output images are processed with your processor before being put into a grid.
Each of these options can receive one or more specifications for image processing actions, multiple processing actions will be chained together one after another.
Using the option --image-processor-help with no arguments will yield a list of available image processor names.
#!/usr/bin/env bash
dgenerate --image-processor-helpOutput:
Available image processors:
"anyline"
"canny"
"flip"
"grayscale"
"hed"
"invert"
"leres"
"letterbox"
"lineart"
"lineart-anime"
"lineart-standard"
"midas"
"mirror"
"mlsd"
"normal-bae"
"openpose"
"pidi"
"posterize"
"resize"
"sam"
"solarize"
"teed"
"upscaler"
"zoe"Specifying one or more specific processors for example: --image-processor-help canny openpose will yield documentation pertaining to those processor modules. This includes accepted arguments and their types for the processor module and a description of what the module does.
Custom image processor modules can also be loaded through the --plugin-modules option as discussed in the Writing Plugins section.
Image processor arguments
All processors posses the arguments: output-file and output-overwrite.
The output-file argument can be used to write the processed image to a specific file, if multiple processing steps occur such as when rendering an animation or multiple generation steps, a numbered suffix will be appended to this filename. Note that an output file will only be produced in the case that the processor actually modifies an input image in some way. This can be useful for debugging an image that is being fed into diffusion or a ControlNet.
The output-overwrite is a boolean argument can be used to tell the processor that you do not want numbered suffixes to be generated for output-file and to simply overwrite it.
Some processors inherit the arguments: device, and model-offload.
The device argument can be used to override what device any hardware accelerated image processing occurs on if any. It defaults to the value of --device and has the same syntax for specifying device ordinals, for instance if you have multiple GPUs you may specify device=cuda:1 to run image processing on your second GPU, etc. Not all image processors respect this argument as some image processing is only ever CPU based.
The model-offload argument is a boolean argument that can be used to force any torch modules / tensors associated with an image processor to immediately evacuate the GPU or other non CPU processing device as soon as the processor finishes processing an image. Usually, any modules / tensors will be brought on to the desired device right before processing an image, and left on the device until the image processor object leaves scope and is garbage collected.
model-offload can be useful for achieving certain GPU or processing device memory constraints, however it is slower when processing multiple images in a row, as the modules / tensors must be brought on to the desired device repeatedly for each image. In the context of dgenerate invocations where processors can be used as preprocessors or postprocessors, the image processor object is garbage collected when the invocation completes, this is also true for the \image_process directive. Using this argument with a preprocess specification, such as --control-image-processors may yield a noticeable memory overhead reduction when using a single GPU, as any models from the image processor will be moved to the CPU immediately when it is done with an image, clearing up VRAM space before the diffusion models enter GPU VRAM.
For an example, images can be processed with the canny edge detection algorithm or OpenPose (rigging generation) before being used for generation with a model + a ControlNet.
This image of a horse is used in the example below with a ControlNet that is trained to generate images from canny edge detected input.
#!/usr/bin/env bash
# --control-image-processors is only used for control images
# in this case the single image seed is considered a control image
# because --control-nets is being used
dgenerate stabilityai/stable-diffusion-xl-base-1.0 --model-type torch-sdxl \
--variant fp16 --dtype float16 \
--vae "AutoencoderKL;model=madebyollin/sdxl-vae-fp16-fix" \
--sdxl-refiner stabilityai/stable-diffusion-xl-refiner-1.0 \
--inference-steps 30 \
--guidance-scales 8 \
--prompts "Majestic unicorn, high quality, masterpiece, high resolution; low quality, bad quality, sketches" \
--control-nets "diffusers/controlnet-canny-sdxl-1.0;scale=0.5" \
--image-seeds "horse.jpeg" \
--control-image-processors "canny;lower=50;upper=100" \
--gen-seeds 2 \
--output-size 1024 \
--output-path unicornMultiple control net images, and input image batching
Each --*-image-processors option has a special additional syntax, which is used to describe which processor or processor chain is affecting which input image in an --image-seeds specification.
For instance if you have multiple control guidance images, and multiple control nets which are going to use those images, or frames etc. and you want to process each guidance image with a separate processor OR processor chain. You can specify how each image is processed by delimiting the processor specification groups with + (the plus symbol)
Like this:
--control-nets "huggingface/controlnet1" "huggingface/controlnet2"
--image-seeds "image1.png, image2.png"
--control-image-processors "affect-image1" + "affect-image2"
Specifying a non-equal amount of control guidance images and --control-nets URIs is considered a syntax error and you will receive an error message if you do so.
You can use processor chaining as well:
--control-nets "huggingface/controlnet1" "huggingface/controlnet2"
--image-seeds "image1.png, image2.png"
--control-image-processors "affect-image1" "affect-image1-again" + "affect-image2"
In the case that you would only like the second image affected:
--control-nets "huggingface/controlnet1" "huggingface/controlnet2"
--image-seeds "image1.png, image2.png"
--control-image-processors + "affect-image2"
The plus symbol effectively creates a NULL processor as the first entry in the example above.
When multiple guidance images are present, it is a syntax error to specify more processor chains than control guidance images. Specifying less processor chains simply means that the trailing guidance images will not be processed, you can avoid processing leading guidance images with the mechanism described above.
This can be used with an arbitrary amount of control image sources and control nets, take for example the specification:
--control-nets "huggingface/controlnet1" "huggingface/controlnet2" "huggingface/controlnet3"
--image-seeds "image1.png, image2.png, image3.png"
--control-image-processors + + "affect-image3"
The two + (plus symbol) arguments indicate that the first two images mentioned in the control image specification in --image-seeds are not to be processed by any processor.
This same syntax applies to img2img and mask images when using the images: ... batching syntax described in: Batching Input Images and Inpaint Masks
#! /usr/bin/env bash
# process these two images as img2img inputs in one go on the GPU
# mirror the second image horizontally, the + indicates that
# we are skipping processing the first image
dgenerate stabilityai/stable-diffusion-2 \
--inference-steps 30 \
--guidance-scales 8 \
--image-seeds "images: examples/media/horse2.jpeg, examples/media/horse2.jpeg" \
--seed-image-processors + mirror \
--image-seed-strengths 0.9 \
--vae-tiling \
--vae-slicing \
--output-path unicorn \
--prompts "A fancy unicorn"
# Now with inpainting
dgenerate stabilityai/stable-diffusion-2 \
--inference-steps 30 \
--guidance-scales 8 \
--image-seeds "images: examples/media/horse1.jpg, examples/media/horse1.jpg;mask=examples/media/horse1-mask.jpg, examples/media/horse1-mask.jpg" \
--seed-image-processors + mirror \
--mask-image-processors + mirror \
--image-seed-strengths 0.9 \
--vae-tiling \
--vae-slicing \
--output-path mars_horse \
--prompts "A photo of a horse standing on mars"Sub Commands
dgenerate implements additional functionality through the option --sub-command.
For a list of available sub-commands use --sub-command-help, which by default will list available sub-command names.
For additional information on a specific sub-command use --sub-command-help NAME
Multiple sub-command names can be specified to --sub-command-help if desired.
All sub-commands respect the --plugin-modules and --verbose arguments even if their help output does not specify them, these arguments are handled by dgenerate and not the sub-command.
Sub Command: image-process
The image-process sub-command can be used to run image processors implemented by dgenerate on any file of your choosing including animated images and videos.
It has a similar but slightly different design/usage to the main dgenerate command itself.
It can be used to run canny edge detection, openpose, etc. on any image or video/animated file that you want.
The help output of image-process is as follows:
usage: image-process [-h] [-p PROCESSORS [PROCESSORS ...]] [--plugin-modules PATH [PATH ...]]
[-o OUTPUT [OUTPUT ...]] [-ff FRAME_FORMAT] [-ox] [-r RESIZE] [-na] [-al ALIGN]
[-d DEVICE] [-fs FRAME_NUMBER] [-fe FRAME_NUMBER] [-nf | -naf]
input [input ...]
This command allows you to use dgenerate image processors directly on files of your choosing.
positional arguments:
input Input file paths, may be a static images or animated files supported by dgenerate.
URLs will be downloaded.
------------------------
options:
-h, --help show this help message and exit
-------------------------------
-p PROCESSORS [PROCESSORS ...], --processors PROCESSORS [PROCESSORS ...]
One or more image processor URIs, specifying multiple will chain them together. See:
dgenerate --image-processor-help
--------------------------------
--plugin-modules PATH [PATH ...]
Specify one or more plugin module folder paths (folder containing __init__.py) or
python .py file paths to load as plugins. Plugin modules can implement image
processors.
-----------
-o OUTPUT [OUTPUT ...], --output OUTPUT [OUTPUT ...]
Output files, parent directories mentioned in output paths will be created for you
if they do not exist. If you do not specify output files, the output file will be
placed next to the input file with the added suffix '_processed_N' unless --output-
overwrite is specified, in that case it will be overwritten. If you specify multiple
input files and output files, you must specify an output file for every input file,
or a directory (indicated with a trailing directory seperator character, for example
"my_dir/" or "my_dir\" if the directory does not exist yet). Failure to specify an
output file with a URL as an input is considered an error. Supported file extensions
for image output are equal to those listed under --frame-format.
----------------------------------------------------------------
-ff FRAME_FORMAT, --frame-format FRAME_FORMAT
Image format for animation frames. Must be one of: png, apng, blp, bmp, dib, bufr,
pcx, dds, ps, eps, gif, grib, h5, hdf, jp2, j2k, jpc, jpf, jpx, j2c, icns, ico, im,
jfif, jpe, jpg, jpeg, tif, tiff, mpo, msp, palm, pdf, pbm, pgm, ppm, pnm, pfm, bw,
rgb, rgba, sgi, tga, icb, vda, vst, webp, wmf, emf, or xbm.
-----------------------------------------------------------
-ox, --output-overwrite
Indicate that it is okay to overwrite files, instead of appending a duplicate
suffix.
-------
-r RESIZE, --resize RESIZE
Preform naive image resizing (LANCZOS).
---------------------------------------
-na, --no-aspect Make --resize ignore aspect ratio.
----------------------------------
-al ALIGN, --align ALIGN
Align images / videos to this value in pixels, default is 8. Specifying 1 will
disable resolution alignment.
-----------------------------
-d DEVICE, --device DEVICE
Processing device, for example "cuda", "cuda:1". Or "mps" on MacOS. (default: cuda,
mps on MacOS)
-------------
-fs FRAME_NUMBER, --frame-start FRAME_NUMBER
Starting frame slice point for animated files (zero-indexed), the specified frame
will be included. (default: 0)
------------------------------
-fe FRAME_NUMBER, --frame-end FRAME_NUMBER
Ending frame slice point for animated files (zero-indexed), the specified frame will
be included.
------------
-nf, --no-frames Do not write frames, only an animation file. Cannot be used with --no-animation-
file.
-----
-naf, --no-animation-file
Do not write an animation file, only frames. Cannot be used with --no-frames.
-----------------------------------------------------------------------------Overview of specifying image-process inputs and outputs
#!/usr/bin/env bash
# Overview of specifying outputs, image-process can do simple operations
# like resizing images and forcing image alignment with --align, without the
# need to specify any other processing operations with --processors. Running
# image-process on an image with no other arguments simply aligns it to 8 pixels,
# given the defaults for its command line arguments
# More file formats than .png are supported for static image output, all
# extensions mentioned in the image-process --help documentation for --frame-format
# are supported, the supported formats are identical to that mentioned in the --image-format
# option help section of dgenerates --help output
# my_file.png -> my_file_processed_1.png
dgenerate --sub-command image-process my_file.png --resize 512x512
# my_file.png -> my_file.png (overwrite)
dgenerate --sub-command image-process my_file.png --resize 512x512 --output-overwrite
# my_file.png -> my_file.png (overwrite)
dgenerate --sub-command image-process my_file.png -o my_file.png --resize 512x512 --output-overwrite
# my_file.png -> my_dir/my_file_processed_1.png
dgenerate --sub-command image-process my_file.png -o my_dir/ --resize 512x512 --no-aspect
# my_file_1.png -> my_dir/my_file_1_processed_1.png
# my_file_2.png -> my_dir/my_file_2_processed_2.png
dgenerate --sub-command image-process my_file_1.png my_file_2.png -o my_dir/ --resize 512x512
# my_file_1.png -> my_dir_1/my_file_1_processed_1.png
# my_file_2.png -> my_dir_2/my_file_2_processed_2.png
dgenerate --sub-command image-process my_file_1.png my_file_2.png \
-o my_dir_1/ my_dir_2/ --resize 512x512
# my_file_1.png -> my_dir_1/renamed.png
# my_file_2.png -> my_dir_2/my_file_2_processed_2.png
dgenerate --sub-command image-process my_file_1.png my_file_2.png \
-o my_dir_1/renamed.png my_dir_2/ --resize 512x512A few usage examples with processors:
#!/usr/bin/env bash
# image-process can support any input format that dgenerate itself supports
# including videos and animated files. It also supports all output formats
# supported by dgenerate for writing videos/animated files, and images.
# create a video rigged with OpenPose, frames will be rendered to the directory "output" as well.
dgenerate --sub-command image-process my-video.mp4 \
-o output/rigged-video.mp4 --processors "openpose;include-hand=true;include-face=true"
# Canny edge detected video, also using processor chaining to mirror the frames
# before they are edge detected
dgenerate --sub-command image-process my-video.mp4 \
-o output/canny-video.mp4 --processors mirror "canny;blur=true;threshold-algo=otsu"Sub Command: civitai-links
The civitai-links sub-command can be used to list the hard links for models available on a CivitAI model page.
These links can be used directly with dgenerate, it will automatically download the model for you.
You only need to select which models you wish to use from the links listed by this command.
See: Utilizing CivitAI links and Other Hosted Models for more information about how to use these links.
To get direct links to CivitAI models you can use the civitai-links sub-command or the \civitai_links directive inside of a config to list all available models on a CivitAI model page.
For example:
#!/usr/bin/env bash
# get links for the Crystal Clear XL model on CivitAI
dgenerate --sub-command civitai-links "https://civitai.com/models/122822?modelVersionId=133832"
# you can also automatically append your API token to the end of the URLs with --token
# some models will require that you authenticate to download, this will add your token
# to the URL for you
dgenerate --sub-command civitai-links "https://civitai.com/models/122822?modelVersionId=133832" --token $MY_API_TOKENThis will list every model link on the page, with title, there may be many model links depending on what the page has available for download.
Output from the above example:
Models at: https://civitai.com/models/122822?modelVersionId=133832
==================================================================
CCXL (Model): https://civitai.com/api/download/models/133832?format=SafeTensor&size=full&fp=fp16Upscaling
dgenerate implements four different methods of upscaling images, animated images, or video.
Upscaling with the Stable Diffusion based x2 and x4 upscalers from the diffusers library.
With the upscale image processor, which is compatible with torch models implemented in the spandrel library.
And with the upscaler-ncnn image processor, which implements upscaling with generic NCNN upscaling models using the ncnn library.
The spandrel library supports the use of most torch models on: https://openmodeldb.info/
The ncnn library supports models compatible with upscayl as well as chaiNNer.
ONNX upscaler models can be converted to NCNN format for use with the upscaler-ncnn image processor.
Upscaling with Diffusion Upscaler Models
Stable diffusion image upscaling models can be used via the model types:
--model-type torch-upscaler-x2
--model-type torch-upscaler-x4
The image used in the example below is this low resolution cat
#!/usr/bin/env bash
# The image produced with this model will be
# two times the --output-size dimension IE: 512x512 in this case
# The image is being resized to 256x256, and then upscaled by 2x
dgenerate stabilityai/sd-x2-latent-upscaler --variant fp16 --dtype float16 \
--model-type torch-upscaler-x2 \
--prompts "a picture of a white cat" \
--image-seeds low_res_cat.png \
--output-size 256
# The image produced with this model will be
# four times the --output-size dimension IE: 1024x1024 in this case
# The image is being resized to 256x256, and then upscaled by 4x
dgenerate stabilityai/stable-diffusion-x4-upscaler --variant fp16 --dtype float16 \
--model-type torch-upscaler-x4 \
--prompts "a picture of a white cat" \
--image-seeds low_res_cat.png \
--output-size 256 \
--upscaler-noise-levels 20Upscaling with chaiNNer Compatible Torch Upscaler Models
chaiNNer compatible torch upscaler models from https://openmodeldb.info/ and elsewhere can be utilized for tiled upscaling using dgenerates upscaler image processor and the --post-processors option. The upscaler image processor can also be used for processing input images via the other options mentioned in Image Processors such as --seed-image-processors
The upscaler image processor can make use of URLs or files on disk.
In this example we reference a link to the SwinIR x4 upscaler from the creators github release.
This uses the upscaler to upscale the output image by x4 producing an image that is 4096x4096
The upscaler image processor respects the --device option of dgenerate, and is CUDA accelerated by default.
#!/usr/bin/env bash
dgenerate stabilityai/stable-diffusion-xl-base-1.0 --model-type torch-sdxl \
--variant fp16 --dtype float16 \
--sdxl-refiner stabilityai/stable-diffusion-xl-refiner-1.0 \
--sdxl-high-noise-fractions 0.8 \
--inference-steps 40 \
--guidance-scales 8 \
--output-size 1024 \
--prompts "Photo of a horse standing near the open door of a red barn, high resolution; artwork" \
--post-processors "upscaler;model=https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/001_classicalSR_DIV2K_s48w8_SwinIR-M_x4.pth"In addition to this the \image_process config directive, or --sub-command image-process can be used to upscale any file that you want including animated images and videos. It is worth noting that the sub-command and directive will work with any named image processor implemented by dgenerate.
#!/usr/bin/env bash
# print the help output of the sub command "image-process"
# the image-process sub-command can process multiple files and do
# and several other things, it is worth reading :)
dgenerate --sub-command image-process --help
# any directory mentioned in the output spec is created automatically
dgenerate --sub-command image-process my-file.png \
--output output/my-file-upscaled.png \
--processors "upscaler;model=https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/001_classicalSR_DIV2K_s48w8_SwinIR-M_x4.pth"For more information see: dgenerate --image-processor-help upscaler
Control over tiling parameters and specifics are discussed in the image processor help documentation from the above command.
Upscaling with NCNN Upscaler Models
The upscaler-ncnn image processor will be available if you have manually installed dgenerate with the [ncnn] extra, or if you are using dgenerate from the packaged windows installer or portable windows install zip from the releases page.
NCNN can use Vulkan for hardware accelerated inference and is also heavily optimized for CPU use if needed.
When using the upscaler-ncnn processor, you must specify both the model and param arguments, these refer to the model.bin and model.param file associated with the model.
These arguments may be a path to a file on disk or a hard link to a downloadable model in raw form.
This upscaler utilizes the same tiling algorithm as the upscaler image processor and features the same tile and overlap arguments, albeit with slightly different defaults and constraints. The tile argument may not exceed 400 pixels and defaults to the max value of 400. Tiling can be disabled for input images under 400 pixels by passing tile=0.
By default the upscaler-ncnn processor does not run on the GPU, you must enable this with the use-gpu argument.
When using this processor as a pre-processor or post-processor for diffusion, GPU memory will be fenced, any cached models related to diffusion on the GPU will be evacuated entirely before this processor runs if they exist on the same GPU as the processor, this is to prevent catastrophic interaction between the Vulkan and Torch cuda allocators.
Once a Vulkan allocator exists on a specific GPU it cannot be destroyed except via the process exiting due to issues with the ncnn python binding. If you create this processor on a GPU you intend to perform diffusion on, you are going to run into memory errors after the first image generation and there on out until the process exits.
When the process exits it is very likely to exit with a non-zero return code after using this processor even if the upscale operations were successful, this is due to problems with the ncnn python binding creating a segfault at exit. If you are using dgenerate interactively in shell mode or from the Console UI, this will occur without consequence when the interpreter process exits.
Note that if any other process runs diffusion / inference via torch on the same GPU as this image processor while ncnn is preforming inference, you will likely encounter a segfault in either of the processes and a very hard crash.
You can safely run this processor in parallel with diffusion, or other torch based image processors with GPU acceleration, by placing it on a separate gpu using the gpu-index argument.
Since the ncnn upscaler can run on GPUs other than Nvidia GPUs, figuring out what index you need to use is platform specific, but for Nvidia users just use the nvidia-smi command from a terminal to get this value.
If you do not specify a gpu-index, index 0 is used, which is most likely your main GPU.
The --device argument to dgenerate and the image-process sub-command / \image_process directive is ignored by this image processor.
#! /usr/bin/env bash # this auto downloads x2 upscaler models from the upscayl repository into # dgenerates web cache, and then use them MODEL=https://github.com/upscayl/upscayl/raw/main/models/realesr-animevideov3-x2.bin PARAM=https://github.com/upscayl/upscayl/raw/main/models/realesr-animevideov3-x2.param dgenerate --sub-command image-process my-file.png \ --output output/my-file-upscaled.png \ --processors "upscaler-ncnn;model=${MODEL};param=${PARAM};use-gpu=true"
If you are upscaling using the CPU, you can specify a thread count using the threads argument.
This argument can be an integer quantity of threads, the keyword auto (max logical processors, max threads) or the keyword half (half your logical processors).
#! /usr/bin/env bash # this auto downloads x2 upscaler models from the upscayl repository into # dgenerates web cache, and then use them MODEL=https://github.com/upscayl/upscayl/raw/main/models/realesr-animevideov3-x2.bin PARAM=https://github.com/upscayl/upscayl/raw/main/models/realesr-animevideov3-x2.param dgenerate --sub-command image-process my-file.png \ --output output/my-file-upscaled.png \ --processors "upscaler-ncnn;model=${MODEL};param=${PARAM};threads=half"
The argument winograd=true can be used to enable the winograd convolution when running on CPU, similarly the sgemm=true argument can be used to enable the sgemm convolution optimization.
In addition, you can control OpenMP blocktime using the blocktime argument, which should be an integer value between 0 and 400 inclusive, representing milliseconds.
These arguments can only be used when running on the CPU and will throw an argument error otherwise.
When they are not specified, optimal defaults from ncnn for your platform are used.
For more information see: dgenerate --image-processor-help upscaler-ncnn
Writing and Running Configs
Config scripts can be read from stdin using a shell pipe or file redirection, or by using the --file argument to specify a file to interpret.
Config scripts are processed with model caching and other optimizations, in order to increase speed when many dgenerate invocations with different arguments are desired.
Loading the necessary libraries and bringing models into memory is quite slow, so using dgenerate this way allows for multiple invocations using different arguments, without needing to load the machine learning libraries and models multiple times in a row.
When a model is loaded dgenerate caches it in memory with it’s creation parameters, which includes among other things the pipeline mode (basic, img2img, inpaint), user specified UNets, VAEs, LoRAs, Textual Inversions, and ControlNets.
If another invocation of the model occurs with creation parameters that are identical, it will be loaded out of an in memory cache, which greatly increases the speed of the invocation.
Diffusion Pipelines, user specified UNets, VAEs, Text Encoders, Image Encoders, ControlNet, and IP Adapter models are cached individually.
All user specifiable model objects can be reused by diffusion pipelines in certain situations and this is taken advantage of by using an in memory cache of these objects.
In effect, the creation of a diffusion pipeline is memoized, as well as the creation of any pipeline subcomponents when you have specified them explicitly with a URI.
A number of things effect cache hit or miss upon a dgenerate invocation, extensive information regarding runtime caching behavior of a pipelines and other models can be observed using -v/--verbose
When loading multiple different models be aware that they will all be retained in memory for the duration of program execution, unless all models are flushed using the \clear_model_cache directive or individually using one of:
\clear_pipeline_cache
\clear_unet_cache
\clear_vae_cache
\clear_text_encoder_cache
\clear_image_encoder_cache
\clear_controlnet_cache
\clear_adapter_cache
\clear_transformer_cache
dgenerate uses heuristics to clear the in memory cache automatically when needed, including a size estimation of models before they enter system memory, however by default it will use system memory very aggressively and it is not entirely impossible to run your system out of memory if you are not careful.
Basic config syntax
The basic idea of the dgenerate config syntax is that it is a pseudo Unix shell mixed with Jinja2 templating.
The config language provides many niceties for batch processing large amounts of images and image output in a Unix shell like environment with Jinja2 control constructs.
Shell builtins, known as directives, are prefixed with \, for example: \print
Environmental variables will be expanded in config scripts using both Unix and Windows CMD syntax
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
# these all expand from your system environment
# if the variable is not set, they expand to nothing
\print $VARIABLE
\print ${VARIABLE}
\print %VARIABLE%Empty lines and comments starting with # will be ignored, comments that occur at the end of lines will also be ignored.
You can create a multiline continuation using \ to indicate that a line continues similar to bash.
Unlike bash, if the next line starts with - it is considered part of a continuation as well even if \ had not been used previously. This allows you to list out many Posix style shell options starting with - without having to end every line with \.
Comments can be interspersed with invocation or directive arguments on their own line with the use of \ on the last line before comments and whitespace begin. This can be used to add documentation above individual arguments instead of at the tail end of them.
The following is a config file example that covers the most basic syntax concepts.
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
# If a hash-bang version is provided in the format above
# a warning will be produced if the version you are running
# is not compatible (SemVer), this can be used anywhere in the
# config file, a line number will be mentioned in the warning when the
# version check fails
# Comments in the file will be ignored
# Each dgenerate invocation in the config begins with the path to a model,
# IE. the first argument when using dgenerate from the command line, the
# rest of the options that follow are the options to dgenerate that you
# would use on the command line
# Guarantee unique file names are generated under the output directory by specifying unique seeds
stabilityai/stable-diffusion-2-1 --prompts "an astronaut riding a horse" --seeds 41509644783027 --output-path output --inference-steps 30 --guidance-scales 10
stabilityai/stable-diffusion-2-1 --prompts "a cowboy riding a horse" --seeds 78553317097366 --output-path output --inference-steps 30 --guidance-scales 10
stabilityai/stable-diffusion-2-1 --prompts "a martian riding a horse" --seeds 22797399276707 --output-path output --inference-steps 30 --guidance-scales 10
# Guarantee that no file name collisions happen by specifying different output paths for each invocation
stabilityai/stable-diffusion-2-1 --prompts "an astronaut riding a horse" --output-path unique_output_1 --inference-steps 30 --guidance-scales 10
stabilityai/stable-diffusion-2-1 --prompts "a cowboy riding a horse" --output-path unique_output_2 --inference-steps 30 --guidance-scales 10
# Multiline continuations are possible implicitly for argument
# switches IE lines starting with '-'
stabilityai/stable-diffusion-2-1 --prompts "a martian riding a horse"
--output-path unique_output_3 # there can be comments at the end of lines
--inference-steps 30 \ # this comment is also ignored
# There can be comments or newlines within the continuation
# but you must provide \ on the previous line to indicate that
# it is going to happen
--guidance-scales 10
# The continuation ends (on the next line) when the last line does
# not end in \ or start with -
# the ability to use tail comments means that escaping of the # is sometimes
# necessary when you want it to appear literally, see: examples/config_syntax/tail-comments-config.dgen
# for examples.
# Configuration directives provide extra functionality in a config, a directive
# invocation always starts with a backslash
# A clear model cache directive can be used inbetween invocations if cached models that
# are no longer needed in your generation pipeline start causing out of memory issues
\clear_model_cache
# Additionally these other directives exist to clear user loaded models
# out of dgenerates in memory cache individually
# Clear specifically diffusion pipelines
\clear_pipeline_cache
# Clear specifically user specified UNet models
\clear_unet_cache
# Clear specifically user specified VAE models
\clear_vae_cache
# Clear specifically user specified Text Encoder models
\clear_text_encoder_cache
# Clear specifically ControlNet models
\clear_controlnet_cache
# This model was used before but will have to be fully instantiated from scratch again
# after a cache flush which may take some time
stabilityai/stable-diffusion-2-1 --prompts "a martian riding a horse"
--output-path unique_output_4Built in template variables and functions
There is valuable information about the previous invocation of dgenerate that is set in the environment and available to use via Jinja2 templating or in the \setp directive, some of these include:
{{ last_images }} (An iterable of un-quoted filenames which were generated)
{{ last_animations }} (An iterable of un-quoted filenames which were generated)
There are template variables for prompts, containing the previous prompt values:
{{ last_prompts }} (List of prompt objects with the un-quoted attributes ‘positive’ and ‘negative’)
{{ last_sdxl_second_prompts }}
{{ last_sdxl_refiner_prompts }}
{{ last_sdxl_refiner_second_prompts }}
Some available custom jinja2 functions/filters are:
{{ first(list_of_items) }} (First element in a list)
{{ last(list_of_items) }} (Last element in a list)
{{ unquote('"unescape-me"') }} (shell unquote / split, works on strings and lists)
{{ quote('escape-me') }} (shell quote, works on strings and lists)
{{ format_prompt(prompt_object) }} (Format and quote one or more prompt objects with their delimiter, works on single prompts and lists)
{{ format_size(size_tuple) }} (Format a size tuple / iterable, join with “x” character)
{{ align_size('700x700', 8) }} (Align a size string or tuple to a specific alignment, return a formatted string by default)
{{ pow2_size('700x700', 8) }} (Round a size string or tuple to the nearest power of 2, return a formatted string by default)
{{ size_is_aligned('700x700', 8) }} (Check if a size string or tuple is aligned to a specific alignment, return True or False)
{{ size_is_pow2('700x700') }} (Check if a size string or tuple is a power of 2 dimension, return True or False)
{{ format_model_type(last_model_type) }} (Format a ModelType enum to a value to --model-type)
{{ format_dtype(last_dtype) }} (Format a DataType enum to a value to --dtype)
{{ gen_seeds(n) }} (Return a list of random integer seeds in the form of strings)
{{ cwd() }} (Return the current working directory as a string)
{{ download(url) }} (Download from a url to the web cache and return the file path)
{{ have_feature(feature_name) }} (Check for feature and return bool, value examples: ncnn)
{{ platform() }} (Return platform.system())
The above functions which possess arguments can be used as either a function or filter IE: {{ "quote_me" | quote }}
The option --functions-help and the directive \functions_help can be used to print documentation for template functions. When the option or directive is used alone all built in functions will be printed with their signature, specifying function names as arguments will print documentation for those specific functions.
To receive information about Jinja2 template variables that are set after a dgenerate invocation. You can use the \templates_help directive which is similar to the --templates-help option except it will print out all the template variables assigned values instead of just their names and types. This is useful for figuring out the values of template variables set after a dgenerate invocation in a config file for debugging purposes. You can specify one or more template variable names as arguments to \templates_help to receive help for only the mentioned variable names.
Template variables set with the \set, \setp, and \sete directive will also be mentioned in this output.
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
# Invocation will proceed as normal
stabilityai/stable-diffusion-2-1 --prompts "a man walking on the moon without a space suit"
# Print all set template variables
\templates_helpThe \templates_help output from the above example is:
Config template variables are:
Name: "glob"
Type: <class 'module'>
Name: "injected_args"
Type: collections.abc.Sequence[str]
Name: "injected_device"
Type: typing.Optional[str]
Name: "injected_plugin_modules"
Type: typing.Optional[collections.abc.Sequence[str]]
Name: "injected_verbose"
Type: typing.Optional[bool]
Name: "last_animation_format"
Type: <class 'str'>
Name: "last_animations"
Type: collections.abc.Iterable[str]
Name: "last_auth_token"
Type: typing.Optional[str]
Name: "last_batch_grid_size"
Type: typing.Optional[tuple[int, int]]
Name: "last_batch_size"
Type: typing.Optional[int]
Name: "last_clip_skips"
Type: typing.Optional[collections.abc.Sequence[int]]
Name: "last_control_image_processors"
Type: typing.Optional[collections.abc.Sequence[str]]
Name: "last_controlnet_uris"
Type: typing.Optional[collections.abc.Sequence[str]]
Name: "last_device"
Type: <class 'str'>
Name: "last_dtype"
Type: <enum 'DataType'>
Name: "last_frame_end"
Type: typing.Optional[int]
Name: "last_frame_start"
Type: <class 'int'>
Name: "last_guidance_rescales"
Type: typing.Optional[collections.abc.Sequence[float]]
Name: "last_guidance_scales"
Type: collections.abc.Sequence[float]
Name: "last_image_format"
Type: <class 'str'>
Name: "last_image_guidance_scales"
Type: typing.Optional[collections.abc.Sequence[float]]
Name: "last_image_seed_strengths"
Type: typing.Optional[collections.abc.Sequence[float]]
Name: "last_image_seeds"
Type: typing.Optional[collections.abc.Sequence[str]]
Name: "last_images"
Type: collections.abc.Iterable[str]
Name: "last_inference_steps"
Type: collections.abc.Sequence[int]
Name: "last_lora_uris"
Type: typing.Optional[collections.abc.Sequence[str]]
Name: "last_mask_image_processors"
Type: typing.Optional[collections.abc.Sequence[str]]
Name: "last_model_cpu_offload"
Type: <class 'bool'>
Name: "last_model_path"
Type: typing.Optional[str]
Name: "last_model_sequential_offload"
Type: <class 'bool'>
Name: "last_model_type"
Type: <enum 'ModelType'>
Name: "last_no_aspect"
Type: <class 'bool'>
Name: "last_no_frames"
Type: <class 'bool'>
Name: "last_offline_mode"
Type: <class 'bool'>
Name: "last_output_configs"
Type: <class 'bool'>
Name: "last_output_metadata"
Type: <class 'bool'>
Name: "last_output_overwrite"
Type: <class 'bool'>
Name: "last_output_path"
Type: <class 'str'>
Name: "last_output_prefix"
Type: typing.Optional[str]
Name: "last_output_size"
Type: typing.Optional[tuple[int, int]]
Name: "last_parsed_image_seeds"
Type: typing.Optional[collections.abc.Sequence[dgenerate.mediainput.ImageSeedParseResult]]
Name: "last_post_processors"
Type: typing.Optional[collections.abc.Sequence[str]]
Name: "last_prompt_weighter_uri"
Type: typing.Optional[str]
Name: "last_prompts"
Type: collections.abc.Sequence[dgenerate.prompt.Prompt]
Name: "last_revision"
Type: <class 'str'>
Name: "last_s_cascade_decoder_cpu_offload"
Type: typing.Optional[bool]
Name: "last_s_cascade_decoder_guidance_scales"
Type: typing.Optional[collections.abc.Sequence[float]]
Name: "last_s_cascade_decoder_inference_steps"
Type: typing.Optional[collections.abc.Sequence[int]]
Name: "last_s_cascade_decoder_prompts"
Type: typing.Optional[collections.abc.Sequence[dgenerate.prompt.Prompt]]
Name: "last_s_cascade_decoder_scheduler"
Type: typing.Optional[str]
Name: "last_s_cascade_decoder_sequential_offload"
Type: typing.Optional[bool]
Name: "last_s_cascade_decoder_uri"
Type: typing.Optional[str]
Name: "last_safety_checker"
Type: <class 'bool'>
Name: "last_scheduler"
Type: typing.Optional[str]
Name: "last_sd3_max_sequence_length"
Type: typing.Optional[int]
Name: "last_sd3_second_prompts"
Type: typing.Optional[collections.abc.Sequence[dgenerate.prompt.Prompt]]
Name: "last_sd3_third_prompts"
Type: typing.Optional[collections.abc.Sequence[dgenerate.prompt.Prompt]]
Name: "last_sdxl_aesthetic_scores"
Type: typing.Optional[collections.abc.Sequence[float]]
Name: "last_sdxl_crops_coords_top_left"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Name: "last_sdxl_high_noise_fractions"
Type: typing.Optional[collections.abc.Sequence[float]]
Name: "last_sdxl_negative_aesthetic_scores"
Type: typing.Optional[collections.abc.Sequence[float]]
Name: "last_sdxl_negative_crops_coords_top_left"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Name: "last_sdxl_negative_original_sizes"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Name: "last_sdxl_negative_target_sizes"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Name: "last_sdxl_original_sizes"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Name: "last_sdxl_refiner_aesthetic_scores"
Type: typing.Optional[collections.abc.Sequence[float]]
Name: "last_sdxl_refiner_clip_skips"
Type: typing.Optional[collections.abc.Sequence[int]]
Name: "last_sdxl_refiner_cpu_offload"
Type: typing.Optional[bool]
Name: "last_sdxl_refiner_crops_coords_top_left"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Name: "last_sdxl_refiner_edit"
Type: typing.Optional[bool]
Name: "last_sdxl_refiner_guidance_rescales"
Type: typing.Optional[collections.abc.Sequence[float]]
Name: "last_sdxl_refiner_guidance_scales"
Type: typing.Optional[collections.abc.Sequence[float]]
Name: "last_sdxl_refiner_inference_steps"
Type: typing.Optional[collections.abc.Sequence[int]]
Name: "last_sdxl_refiner_negative_aesthetic_scores"
Type: typing.Optional[collections.abc.Sequence[float]]
Name: "last_sdxl_refiner_negative_crops_coords_top_left"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Name: "last_sdxl_refiner_negative_original_sizes"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Name: "last_sdxl_refiner_negative_target_sizes"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Name: "last_sdxl_refiner_original_sizes"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Name: "last_sdxl_refiner_prompts"
Type: typing.Optional[collections.abc.Sequence[dgenerate.prompt.Prompt]]
Name: "last_sdxl_refiner_scheduler"
Type: typing.Optional[str]
Name: "last_sdxl_refiner_second_prompts"
Type: typing.Optional[collections.abc.Sequence[dgenerate.prompt.Prompt]]
Name: "last_sdxl_refiner_sequential_offload"
Type: typing.Optional[bool]
Name: "last_sdxl_refiner_target_sizes"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Name: "last_sdxl_refiner_uri"
Type: typing.Optional[str]
Name: "last_sdxl_second_prompts"
Type: typing.Optional[collections.abc.Sequence[dgenerate.prompt.Prompt]]
Name: "last_sdxl_target_sizes"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Name: "last_second_text_encoder_uris"
Type: typing.Optional[collections.abc.Sequence[str]]
Name: "last_second_unet_uri"
Type: typing.Optional[str]
Name: "last_seed_image_processors"
Type: typing.Optional[collections.abc.Sequence[str]]
Name: "last_seeds"
Type: collections.abc.Sequence[int]
Name: "last_seeds_to_images"
Type: <class 'bool'>
Name: "last_subfolder"
Type: typing.Optional[str]
Name: "last_text_encoder_uris"
Type: typing.Optional[collections.abc.Sequence[str]]
Name: "last_textual_inversion_uris"
Type: typing.Optional[collections.abc.Sequence[str]]
Name: "last_unet_uri"
Type: typing.Optional[str]
Name: "last_upscaler_noise_levels"
Type: typing.Optional[collections.abc.Sequence[int]]
Name: "last_vae_slicing"
Type: <class 'bool'>
Name: "last_vae_tiling"
Type: <class 'bool'>
Name: "last_vae_uri"
Type: typing.Optional[str]
Name: "last_variant"
Type: typing.Optional[str]
Name: "path"
Type: <class 'module'>
Name: "saved_modules"
Type: dict[str, dict[str, typing.Any]]The following is output from \functions_help showing every implemented template function signature.
Available config template functions:
abs(args, kwargs)
align_size(size: str | tuple, align: int, format_size: bool = True) -> str | tuple
all(args, kwargs)
any(args, kwargs)
ascii(args, kwargs)
bin(args, kwargs)
bool(args, kwargs)
bytearray(args, kwargs)
bytes(args, kwargs)
callable(args, kwargs)
chr(args, kwargs)
complex(args, kwargs)
cwd() -> str
dict(args, kwargs)
divmod(args, kwargs)
download(url: str, output: str | None = None, overwrite: bool = False, text: bool = False) -> str
enumerate(args, kwargs)
filter(args, kwargs)
first(iterable: collections.abc.Iterable[typing.Any]) -> typing.Any
float(args, kwargs)
format(args, kwargs)
format_dtype(dtype: <enum 'DataType'>) -> str
format_model_type(model_type: <enum 'ModelType'>) -> str
format_prompt(prompts: dgenerate.prompt.Prompt | collections.abc.Iterable[dgenerate.prompt.Prompt]) -> str
format_size(size: collections.abc.Iterable[int]) -> str
frange(start, stop = None, step = 0.1)
frozenset(args, kwargs)
gen_seeds(n: int) -> list[str]
getattr(args, kwargs)
hasattr(args, kwargs)
hash(args, kwargs)
have_feature(feature_name: str) -> bool
hex(args, kwargs)
int(args, kwargs)
iter(args, kwargs)
last(iterable: list | collections.abc.Iterable[typing.Any]) -> typing.Any
len(args, kwargs)
list(args, kwargs)
map(args, kwargs)
max(args, kwargs)
min(args, kwargs)
next(args, kwargs)
object(args, kwargs)
oct(args, kwargs)
ord(args, kwargs)
platform() -> str
pow(args, kwargs)
pow2_size(size: str | tuple, format_size: bool = True) -> str | tuple
quote(strings: str | collections.abc.Iterable[typing.Any]) -> str
range(args, kwargs)
repr(args, kwargs)
reversed(args, kwargs)
round(args, kwargs)
set(args, kwargs)
size_is_aligned(size: str | tuple, align: int) -> bool
size_is_pow2(size: str | tuple) -> bool
slice(args, kwargs)
sorted(args, kwargs)
str(args, kwargs)
sum(args, kwargs)
tuple(args, kwargs)
type(args, kwargs)
unquote(strings: str | collections.abc.Iterable[typing.Any], expand: bool = False) -> list
zip(args, kwargs)Directives, and applying templating
You can see all available config directives with the command dgenerate --directives-help, providing this option with a name, or multiple names such as: dgenerate --directives-help save_modules use_modules will print the documentation for the specified directives. The backslash may be omitted. This option is also available as the config directive \directives_help.
Example output:
Available config directives:
"\cd"
"\civitai_links"
"\clear_controlnet_cache"
"\clear_model_cache"
"\clear_modules"
"\clear_pipeline_cache"
"\clear_text_encoder_cache"
"\clear_unet_cache"
"\clear_vae_cache"
"\cp"
"\directives_help"
"\download"
"\echo"
"\env"
"\exec"
"\exit"
"\functions_help"
"\gen_seeds"
"\help"
"\image_process"
"\image_processor_help"
"\import_plugins"
"\ls"
"\mkdir"
"\mv"
"\popd"
"\print"
"\prompt_weighter_help"
"\pushd"
"\pwd"
"\rm"
"\rmdir"
"\save_modules"
"\set"
"\sete"
"\setp"
"\templates_help"
"\unset"
"\unset_env"
"\use_modules"Here are examples of other available directives such as \set, \setp, and \print as well as some basic Jinja2 templating usage. This example also covers the usage and purpose of \save_modules for saving and reusing pipeline modules such as VAEs etc. outside of relying on the caching system.
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
# You can define your own template variables with the \set directive
# the \set directive does not do any shell args parsing on its value
# operand, meaning the quotes will be in the string that is assigned
# to the variable my_prompt
\set my_prompt "an astronaut riding a horse; bad quality"
# If your variable is long you can use continuation, note that
# continuation replaces newlines and surrounding whitespace
# with a single space
\set my_prompt "my very very very very very very very \
very very very very very very very very \
long long long long long prompt"
# You can print to the console with templating using the \print directive
# for debugging purposes
\print {{ my_prompt }}
# The \setp directive can be used to define python literal template variables
\setp my_list [1, 2, 3, 4]
\print {{ my_list | join(' ') }}
# Literals defined by \setp can reference other template variables by name.
# the following creates a nested list
\setp my_list [1, 2, my_list, 4]
\print {{ my_list }}
# \setp can evaluate template functions
\setp directory_content glob.glob('*')
\setp current_directory cwd()
# the \gen_seeds directive can be used to store a list of
# random seed integers into a template variable.
# (they are strings for convenience)
\gen_seeds my_seeds 10
\print {{ my_seeds | join(' ') }}
# An invocation sets various template variables related to its
# execution once it is finished running
stabilityai/stable-diffusion-2-1 --prompts {{ my_prompt }} --gen-seeds 5
# Print a quoted filename of the last image produced by the last invocation
# This could potentially be passed to --image-seeds of the next invocation
# If you wanted to run another pass over the last image that was produced
\print {{ quote(last(last_images)) }}
# you can also get the first image easily with the function "first"
\print {{ quote(first(last_images)) }}
# if you want to append a mask image file name
\print "{{ last(last_images) }};my-mask.png"
# Print a list of properly quoted filenames produced by the last
# invocation separated by spaces if there is multiple, this could
# also be passed to --image-seeds
# in the case that you have generated an animated output with frame
# output enabled, this will contain paths to the frames
\print {{ quote(last_images) }}
# For loops are possible
\print {% for image in last_images %}{{ quote(image) }} {% endfor %}
# For loops are possible with normal continuation
# when not using a heredoc template continuation (mentioned below),
# such as when the loop occurs in the body of a directive or a
# dgenerate invocation, however this sort of continuation usage will
# replace newlines and whitespace with a single space.
# IE this template will be: "{% for image in last_images %} {{ quote(image) }} {% endfor %}"
\print {% for image in last_images %} \
{{ quote(image) }} \
{% endfor %}
# Access to the last prompt is available in a parsed representation
# via "last_prompt", which can be formatted properly for reuse
# by using the function "format_prompt"
stabilityai/stable-diffusion-2-1 --prompts {{ format_prompt(last(last_prompts)) }}
# You can get only the positive or negative part if you want via the "positive"
# and "negative" properties on a prompt object, these attributes are not
# quoted so you need to quote them one way or another, preferably using the
# dgenerate template function "quote" which will shell quote any special
# characters that the argument parser is not going to understand
stabilityai/stable-diffusion-2-1 --prompts {{ quote(last(last_prompts).positive) }}
# "last_prompts" returns all the prompts used in the last invocation as a list
# the "format_prompt" function can also work on a list
stabilityai/stable-diffusion-2-1 --prompts "prompt 1" "prompt 2" "prompt 3"
stabilityai/stable-diffusion-2-1 --prompts {{ format_prompt(last_prompts) }}
# Execute additional config with full templating.
# The sequence !END is interpreted as the end of a
# template continuation, a template continuation is
# started when a line begins with the character {
# and is effectively a heredoc, in that all whitespace
# within is preserved including newlines
{% for image in last_images %}
stabilityai/stable-diffusion-2-1 --image-seeds {{ quote(image) }} --prompts {{ my_prompt }}
{% endfor %} !END
# Multiple lines can be used with a template continuation
# the inside of the template will be expanded to raw config
# and then be ran, so make sure to use line continuations within
# where they are necessary as you would do in the top level of
# a config file. The whole of the template continuation is
# processed by Jinja, from { to !END, so only one !END is
# ever necessary after the external template
# when dealing with nested templates
{% for image in last_images %}
stabilityai/stable-diffusion-2-1
--image-seeds {{ quote(image) }}
--prompts {{ my_prompt }}
{% endfor %} !END
# The above are both basically equivalent to this
stabilityai/stable-diffusion-2-1 --image-seeds {{ quote(last_images) }} --prompts {{ my_prompt }}
# You can save modules from the main pipeline used in the last invocation
# for later reuse using the \save_modules directive, the first argument
# is a variable name and the rest of the arguments are diffusers pipeline
# module names to save to the variable name, this is an advanced usage
# and requires some understanding of the diffusers library to utilize correctly
stabilityai/stable-diffusion-2-1
--variant fp16
--dtype float16
--prompts "an astronaut walking on the moon"
--safety-checker
--output-size 512
\save_modules stage_1_modules feature_extractor safety_checker
# that saves the feature_extractor module object in the pipeline above,
# you can specify multiple module names to save if desired
# Possible Module Names:
# unet
# vae
# transformer
# text_encoder
# text_encoder_2
# text_encoder_3
# tokenizer
# tokenizer_2
# tokenizer_3
# safety_checker
# feature_extractor
# image_encoder
# adapter
# controlnet
# scheduler
# To use the saved modules in the next invocation use \use_modules
\use_modules stage_1_modules
# now the next invocation will use those modules instead of loading them from internal
# in memory cache, disk, or huggingface
stabilityai/stable-diffusion-x4-upscaler
--variant fp16
--dtype float16
--model-type torch-upscaler-x4
--prompts {{ format_prompt(last_prompts) }}
--image-seeds {{ quote(last_images) }}
--vae-tiling
# you should clear out the saved modules if you no longer need them
# and your config file is going to continue, or if the dgenerate
# process is going to be kept alive for some reason such as in
# some library usage scenarios, or perhaps if you are using it
# like a server that reads from stdin :)
\clear_modules stage_1_modulesSetting template variables, in depth
The directives \set, \sete, and \setp can be used to set the value of template variables within a configuration. The directive \unset can be used to undefine template variables.
All three of the assignment directives have unique behavior.
The \set directive sets a value with templating and environmental variable expansion applied to it, and nothing else aside from the value being striped of leading and trailing whitespace. The value that is set to the template variables is essentially the text that you supply as the value, as is. Or the text that the templates or environment variables in the value expand to, unmodified or parsed in any way.
This is for assigning literal text values to a template variable.
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
\set my_variable "I am an incomplete string and this is completely fine because I am a raw value
# prints exactly what is above, including the quote at the beginning
\print {{ my_variable }}
# add a quote to the end of the string using templates
\set my_variable {{ my_variable }}"
# prints a fully quoted string
\print {{ my_variable }}
# indirect expansion is allowed
\set var_name template_variable
\env ENV_VAR_NAMED=env_var_named
\set {{ var_name }} Hello!
\set $ENV_VAR_NAMED Hi!
# prints Hello!, Hi!
\print {{ template_variable }}
\print {{ env_var_named }}The \sete directive can be used to assign the result of shell parsing and expansion to a template variable, the value provided will be shell parsed into tokens as if it were a line of dgenerate config. This is useful because you can use the config languages built in shell globbing feature to assign template variables.
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
# lets pretend the directory "my_files" is full of files
\sete my_variable --argument my_files/*
# prints the python array ['--argument', 'my_files/file1', 'my_files/file2', ...]
\print {{ my_variable }}
# Templates and environmental variable references
# are also parsed in the \sete directive, just as they are with \set
\set directory my_files
\sete my_variable --argument {{ directory }}/*
# indirect expansion is allowed
\set var_name template_variable
\env ENV_VAR_NAMED=env_var_named
\sete {{ var_name }} my_files/*
\sete $ENV_VAR_NAMED my_files/*
# both print ['my_files/file1', 'my_files/file2', ...]
\print {{ template_variable }}
\print {{ env_var_named }}The \setp directive can be used to assign the result of evaluating a limited subset of python expressions to a template variable. This can be used to set a template variable to the result of a mathematical expression, python literal value such as a list, dictionary, set, etc… python comprehension, or python ternary statement. In addition, all template functions implemented by dgenerate are available for use in the evaluated expressions.
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
\setp my_variable 10*10
# prints 100
\print {{ my_variable }}
# you can reference variables defined in the environment
\setp my_variable [my_variable, my_variable*2]
# prints [100, 200]
\print {{ my_variable }}
# all forms of python comprehensions are supported
# such as list, dict, and set comprehensions
\setp my_variable [i for i in range(0,5)]
# prints [0, 1, 2, 3, 4]
\print {{ my_variable }}
# declare a literal string value
\setp my_variable "my string value"
# prints the string without quotes included, the string was parsed
\print {{ my_variable }}
# templates and environmental variable references
# are also expanded in \setp values
\setp my_variable [my_variable, "{{ my_variable }}"]
# prints ["my string value", "my string value"]
\print {{ my_variable }}
# my_variable is a literal list so it can be
# looped over with a jinja template continuation
{% for value in my_variable %}
\print {{ value }}
{% endfor %} !END
# indirect expansion is allowed
\set var_name template_variable
\env ENV_VAR_NAMED=env_var_named
\setp {{ var_name }} "Hello!"
\setp $ENV_VAR_NAMED [template_variable]
# prints "Hello!", ["Hello!"]
\print {{ template_variable }}
\print {{ env_var_named }}Setting environmental variables, in depth
The directives \env and \unset_env can be used to manipulate multiple environmental variables at once.
The directive \env can also be used without arguments to print out the values of all environment variables that exist in your environment for debugging purposes.
Indirect expansion is allowed just like with \set, \sete, and \setp.
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
\env MY_ENV_VAR=1 MY_ENV_VAR2=2
# prints 1 2
\print $MY_ENV_VAR $MY_ENV_VAR2
# indirect expansion is allowed
\set name env_var_name
\set value Hello!
\set name_holder {{ name }}
\env {{ name_holder }}={{ value }}
# this treats the expansion of {{ name }} as an environmental variable name
\set output ${{ name }}
# prints Hello!
\print {{ output }}
# unset an environmental variable, the names
# undergo expansion, and are undefined in order
\env NAME_HOLDER=MY_ENV_VAR2
\unset_env MY_ENV_VAR $NAME_HOLDER {{ name }} NAME_HOLDER
# prints every defined environmental variable
# we have undefined everything that we defined
# above so the names from this script will not
# be present
\envGlobbing and path manipulation
The entirety of pythons builtin glob and os.path module are also accessible during templating, you can glob directories using functions from the glob module, you can also glob directory’s using shell globbing.
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
# globbing can be preformed via shell expansion or using
# the glob module inside jinja templates
# note that shell globbing and home directory expansion
# does not occur inside quoted strings
# \echo can be use to show the results of globbing that
# occurs during shell expansion. \print does not perform shell
# expansion nor does \set or \setp, all other directives do, as well
# as dgenerate invocations
# shell globs which produce 0 files are considered an error
\echo ../media/*.png
\echo ~
# \sete can be used to set a template variable to the result
# of one or more shell globs
\sete myfiles ../media/*.png
# with Jinja2:
# The most basic usage is full expansion of every file
\set myfiles {{ quote(glob.glob('../media/*.png')) }}
\print {{ myfiles }}
# If you have a LOT of files, you may want to
# process them using an iterator like so
{% for file in glob.iglob('../media/*.png') %}
\print {{ quote(file) }}
{% endfor %} !END
# usage of os.path via path
\print {{ path.abspath('.') }}
# Simple inline usage
stabilityai/stable-diffusion-2-1
--variant fp16
--dtype float16
--prompts "In the style of picaso"
--image-seeds {{ quote(glob.glob('../media/*.png')) }}
--output-path {{ quote(path.join(path.abspath('.'), 'output')) }}
# equivalent
stabilityai/stable-diffusion-2-1
--variant fp16
--dtype float16
--prompts "In the style of picaso"
--image-seeds ../media/*.png
--output-path ./outputThe \print and \echo directive
The \print and \echo directive can both be used to output text to the console.
The difference between the two directives is that \print only ever prints the raw value with templating and environmental variable expansion applied, similar to the behavior of \set
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
# the text after \print(space) will be printed verbatim
\print I am a raw value, I have no ability to * glob
# Print the PATH environmental variable
\set header Path Elements:
\print {{ header }} $PATH
\print {{ header }} ${PATH}
\print {{ header }} %PATH%The \echo directive preforms shell expansion into tokens before printing, like \sete, This can be useful for debugging / displaying the results of a shell expansion.
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
# lets pretend "directory" is full of files
# this prints: directory/file1 directory/file2 ...
\echo directory/*
# Templates and environmental variables are expanded
# this prints: Files: directory/file1 directory/file2 ...
\set header Files:
\echo {{ header }} directory/*The \image_process directive
The dgenerate sub-command image-process has a config directive implementation.
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
# print the help message of --sub-command image-process, this does
# not cause the config to exit
\image_process --help
\set myfiles {{ quote(glob.glob('my_images/*.png')) }}
# this will create the directory "upscaled"
# the files will be named "upscaled/FILENAME_processed_1.png" "upscaled/FILENAME_processed_2.png" ...
\image_process {{ myfiles }} \
--output upscaled/
--processors upscaler;model=https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth
# the last_images template variable will be set, last_animations is also usable if
# animations were written. In the case that you have generated an animated output with frame
# output enabled, this will contain paths to the frames
\print {{ quote(last_images) }}The \exec directive
The \exec directive can be used to run native system commands and supports bash pipe and file redirection syntax in a platform independent manner. All file redirection operators supported by bash are supported. This can be useful for running other image processing utilities as subprocesses from within a config script.
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
# run dgenerate as a subprocess, read a config
# and send stdout and stderr to a file
\exec dgenerate < my_config.dgen &> log.txt
# chaining processes together with pipes is supported
# this example emulates 'cat' on Windows using cmd
\exec cmd /c "type my_config.dgen" | dgenerate &> log.txt
# on a Unix platform you could simply use cat
\exec cat my_config.dgen | dgenerate &> log.txtThe \download directive
Arbitrary files can be downloaded via the \download directive.
This directive can be used to download a file and assign its downloaded path to a template variable.
Files can either be inserted into dgenerates web cache or downloaded to a specific directory or absolute path.
This directive is designed with using cached files in mind, so it will reuse existing files by default when downloading to an explicit path.
See the directives help output for more details: \download --help
If you plan to download many large models to the web cache in this manner you may wish to adjust the global cache expiry time so that they exist in the cache longer than the default of 12 hours.
You can see how to do this in the section File Cache Control
This directive is primarily intended to download models and or other binary file formats such as images and will raise an error if it encounters a text mimetype. This behavior can be overridden with the -t/--text argument.
Be weary that if you have a long-running loop in your config using a top level jinja template, which refers to your template variable, cache expiry may invalidate the file stored in your variable.
You can rectify this by putting the download directive inside of your processing loop so that the file is simply re-downloaded if it expires in the cache.
Or you may be better off using the download template function which provides this functionality as a template function. See: The download() template function
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
# download a model into the web cache,
# assign its path to the variable "path"
\download path https://modelhost.com/somemodel.safetensors
# download to the models folder in the current directory
# the models folder will be created if it does not exist
# if somemodel.safetensors already exists it will be reused
# instead of being downloaded again
\download path https://modelhost.com/somemodel.safetensors -o models/somemodel.safetensors
# download into the folder without specifying a name
# the name will be derived from the URL or content disposition
# header from the http request, if you are not careful you may
# end up with a file named in a way you were not expecting.
# only use this if you know how the service you are downloading
# from behaves in this regard
\download path https://modelhost.com/somemodel.safetensors -o models/
# download a model into the web cache an overwrite any cached model using -x
\download path https://modelhost.com/somemodel.safetensors -x
# Download to an explicit path without any cached file reuse
# using the -x/--overwrite argument. In effect, always freshly
# download the file
\download path https://modelhost.com/somemodel.safetensors -o models/somemodel.safetensors -x
\download path https://modelhost.com/somemodel.safetensors -o models/ -xThe download() template function
The template function download is analogous to the \download directive
And can be used to download a file with the same behaviour and return its path as a string, this may be easier to use inside of certain jinja flow control constructs.
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
\set my_variable {{ download('https://modelhost.com/model.safetensors') }}
\set my_variable {{ download('https://modelhost.com/model.safetensors', output='model.safetensors') }}
\set my_variable {{ download('https://modelhost.com/model.safetensors', output='directory/') }}
# you can also use any template function with \setp (python expression evaluation)
\setp my_variable download('https://modelhost.com/model.safetensors')The signature for this template function is: download(url: str, output: str | None = None, overwrite: bool = False, text: bool = False) -> str
The \exit directive
You can exit a config early if need be using the \exit directive
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
# exit the process with return code 0, which indicates success
\print "success"
\exitAn explicit return code can be provided as well
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
# exit the process with return code 1, which indicates an error
\print "some error occurred"
\exit 1Running configs from the command line
To utilize configuration files use the --file option, or pipe them into the command, or use file redirection:
Use the --file option
#!/usr/bin/env bash
dgenerate --file my-config.dgenPiping or redirection in Bash:
#!/usr/bin/env bash
# Pipe
cat my-config.dgen | dgenerate
# Redirection
dgenerate < my-config.dgenRedirection in Windows CMD:
dgenerate < my-config.dgenPiping Windows Powershell:
Get-Content my-config.dgen | dgenerateConfig argument injection
You can inject arguments into every dgenerate invocation of a batch processing configuration by simply specifying them. The arguments will added to the end of the argument specification of every call.
#!/usr/bin/env bash
# Pipe
cat my-animations-config.dgen | dgenerate --frame-start 0 --frame-end 10
# Redirection
dgenerate --frame-start 0 --frame-end 10 < my-animations-config.dgenOn Windows CMD:
dgenerate --frame-start 0 --frame-end 10 < my-animations-config.dgenOn Windows Powershell:
Get-Content my-animations-config.dgen | dgenerate --frame-start 0 --frame-end 10If you need arguments injected from the command line within the config for some other purpose such as for using with the \image_process directive which does not automatically recieve injected arguments, use the injected_args and related injected_* template variables.
#! /usr/bin/env dgenerate --file
#! dgenerate 4.2.2
# all injected args
\print {{ quote(injected_args) }}
# just the injected device
\print {{ '--device '+injected_device if injected_device else '' }}
# was -v/--verbose injected?
\print {{ '-v' if injected_verbose else '' }}
# plugin module paths injected with --plugin-modules
\print {{ quote(injected_plugin_modules) if injected_plugin_modules else '' }}Console UI
You can launch a cross platform Tkinter GUI for interacting with a live dgenerate process using dgenerate --console or via the optionally installed desktop shortcut on Windows.
This provides a basic REPL for the dgenerate config language utilizing a dgenerate --shell subprocess to act as the live interpreter, it also features full context aware syntax highlighting for the dgenerate config language.
It can be used to work with dgenerate without encountering the startup overhead of loading large python modules for every command line invocation.
The GUI console supports command history via the up and down arrow keys as a normal terminal would, optional multiline input for sending multiline commands / configuration to the shell. And various editing niceties such as GUI file / directory path insertion, the ability to insert templated command recipes for quickly getting started and getting results, and a selection menu for inserting karras schedulers by name.
Also supported is the ability to view the latest image as it is produced by dgenerate or \image_process via an image pane or standalone window.
The console UI always starts in single line entry mode (terminal mode), multiline input mode is activated via the insert key and indicated by the presence of line numbers, you must deactivate this mode to submit commands via the enter key, however you can use the run button from the run menu (or Ctrl+Space) to run code in this mode. You cannot page through command history in this mode, and code will remain in the console input pane upon running it making the UI function more like a code editor than a terminal.
The console can be opened with a file loaded in multiline input mode by using the command: dgenerate --console filename.dgen
Ctrl+Q can be used in input pane for killing and then restarting the background interpreter process.
Ctrl+F (find) and Ctrl+R (find/replace) is supported for both the input and output panes.
All common text editing features that you would expect to find in a basic text editor are present, as well as python regex support for find / replace, with group substitution supporting the syntax \n or \{n} where n is the match group number.
Scroll back history in the output window is currently limited to 10000 lines however the console app itself echos all stdout and stderr of the interpreter, so you can save all output to a log file via file redirection if desired when launching the console from the terminal.
This can be configured by setting the environmental variable DGENERATE_CONSOLE_MAX_SCROLLBACK=10000
Command history is currently limited to 500 commands, multiline commands are also saved to command history. The command history file is stored at -/.dgenerate_console_history, on Windows this equates to %USERPROFILE%\.dgenerate_console_history
This can be configured by setting the environmental variable DGENERATE_CONSOLE_MAX_HISTORY=500
Any UI settings that persist on startup are stored in -/.dgenerate_console_settings or on Windows %USERPROFILE%\.dgenerate_console_settings
Writing Plugins
dgenerate has the capability of loading in additional functionality through the use of the --plugin-modules option and \import_plugins config directive.
You simply specify one or more module directories on disk, paths to python files, or references to modules installed in the python environment using the argument or import directive.
dgenerate supports implementing image processors, config directives, config template functions, prompt weighters, and sub-commands through plugins.
Image processor plugins
A code example as well as a usage example for image processor plugins can be found in the writing_plugins/image_processor folder of the examples folder.
The source code for the built in canny processor, the openpose processor, and the simple pillow image operations processors can also be of reference as they are written as internal image processor plugins.
Config directive and template function plugins
An example for writing config directives can be found in the writing_plugins/config_directive example folder.
Config template functions can also be implemented by plugins, see: writing_plugins/template_function
Currently the only internal directive that is implemented as a plugin is the \image_process directive, who’s source file can be located here.
The source file for the \image_process directive is terse as most of it is implemented as reusable code.
The behavior of \image_process which is also used for --sub-command image-process is is implemented here.
Sub-command plugins
Reference for writing sub-commands can be found in the image-process sub-command implementation, and a plugin skeleton file for sub-commands can be found in the writing_plugins/sub_command example folder.
Prompt weighter plugins
Reference for writing prompt weighters can be found in the CompelPromptWeighter and SdEmbedPromptWeighter internal prompt weighter implementations.
A plugin skeleton file for prompt weighters can be found in the writing_plugins/prompt_weighter example folder.
File Cache Control
dgenerate will cache downloaded non hugging face models, downloaded --image-seeds files, files downloaded by the \download directive, download template function, and downloaded files used by image processors in the directory ~/.cache/dgenerate/web
On Windows this equates to: %USERPROFILE%\.cache\dgenerate\web
You can control where these files are cached with the environmental variable DGENERATE_WEB_CACHE.
Files are cleared from the web cache automatically after an expiry time upon running dgenerate or when downloading additional files, the default value is after 12 hours.
This can be controlled with the environmental variable DGENERATE_WEB_CACHE_EXPIRY_DELTA.
The value of DGENERATE_WEB_CACHE_EXPIRY_DELTA is that of the named arguments of pythons datetime.timedelta class seperated by semicolons.
For example: DGENERATE_WEB_CACHE_EXPIRY_DELTA="days=5;hours=6"
Specifying "forever" or an empty string will disable cache expiration for every downloaded file.
Files downloaded from huggingface by the diffusers/huggingface_hub library will be cached under ~/.cache/huggingface/, on Windows this equates to %USERPROFILE%\.cache\huggingface\.
This is controlled by the environmental variable HF_HOME
In order to specify that all large model files be stored in another location, for example on another disk, simply set HF_HOME to a new path in your environment.
You can read more about environmental variables that affect huggingface libraries on this huggingface documentation page.
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