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 an Nvidia GPU supporting CUDA 12.1+, CPU rendering is possible for some operations but extraordinarily slow.
For library documentation visit readthedocs.
- How to install
- Usage Examples
Help Output
usage: dgenerate [-h] [-v] [--version] [--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]
[-un UNET_URI] [-un2 UNET_URI] [-vae VAE_URI] [-vt] [-vs]
[-lra LORA_URI [LORA_URI ...]] [-ti URI [URI ...]]
[-cn CONTROL_NET_URI [CONTROL_NET_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-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] [-p PROMPT [PROMPT ...]]
[-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 ...]]
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 huggingface model repository slug, huggingface 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
--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 communicated with by another process sending it 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 will require
two newlines to submit a command due to parsing lookahead. IE: two presses
of the enter key.
--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.
--console Launch a terminal-like tkinter GUI that communicates 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.
--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 currently implement image processors and config directives.
--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 ...]
List available sub-commands, providing sub-command names will produce
their documentation. Calling a subcommand with "--sub-command name --help"
will produce argument help output for that subcommand.
-ofm, --offline-mode Whether dgenerate should try to download huggingface models that do not
exist in the disk cache, or only use what is available in the cache.
Referencing a model on huggingface 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 dgenerate configs during
batch processing from STDIN. When used as a command 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 ...]
Print a list of directives available in dgenerate configs during batch
processing from STDIN. 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 ...]
Print a list of template functions available in dgenerate configs during
batch processing from STDIN. 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, or torch-s-cascade.
(default: torch)
-rev BRANCH, --revision BRANCH
The model revision to use when loading from a huggingface repository, (The
git branch / tag, default is "main")
-var VARIANT, --variant VARIANT
If specified when loading from a huggingface repository or folder, load
weights from "variant" filename, e.g.
"pytorch_model.<variant>.safetensors". Defaults to automatic selection.
This option is ignored if using flax.
-sbf SUBFOLDER, --subfolder SUBFOLDER
Main model subfolder. If specified when loading from a huggingface
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 huggingface 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
using flax, batch size is controlled by the environmental variable
CUDA_VISIBLE_DEVICES which is a comma separated list of GPU device numbers
(as listed by nvidia-smi). Usage of this argument with --model-type flax*
will cause an error, diffusion with flax will generate an image on every
GPU that is visible to CUDA and this is currently unchangeable. 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. (Torch 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,
or when using flax with multiple GPUs visible (via the environmental
variable CUDA_VISIBLE_DEVICES). 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.
-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
huggingface repository or blob link, (The git branch / tag, default is
"main"). The "variant" argument specifies the UNet model variant, it is
only supported for torch type models it is not supported for flax. If
"variant" is specified when loading from a huggingface repository or
folder, weights will be loaded from "variant" filename, e.g.
"pytorch_model.<variant>.safetensors. "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
huggingface 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 huggingface.
-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.
-vae VAE_URI, --vae VAE_URI
Specify a VAE using a URI. When using torch models the URI syntax is:
"AutoEncoderClass;model=(huggingface 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". When using a Flax model,
there is currently only one available encoder class:
"FlaxAutoencoderKL;model=huggingface/vae". 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). Aside from the "model" argument, there
are four other optional arguments that can be specified, these include
"revision", "variant", "subfolder", "dtype". They can be specified as so
in any order, they are not positional: "AutoencoderKL;model=huggingface/va
e;revision=main;variant=fp16;subfolder=sub_folder;dtype=float16". The
"revision" argument specifies the model revision to use for the VAE when
loading from huggingface repository or blob link, (The git branch / tag,
default is "main"). The "variant" argument specifies the VAE model
variant, it is only supported for torch type models it is not supported
for flax. If "variant" is specified when loading from a huggingface
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 huggingface 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 huggingface
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 (torch Stable Diffusion only). 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 (torch Stable Diffusion models only). 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 (flax not supported). These
should be a huggingface repository slug, path to model file on disk (for
example, a .pt, .pth, .bin, .ckpt, or .safetensors file), or model folder
containing model files. huggingface blob links are not supported, see
"subfolder" and "weight-name" below instead. Optional arguments can be
provided after a LoRA model specification, these include: "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 huggingface repository, (The git branch / tag,
default is "main"). The "subfolder" argument specifies the LoRA model
subfolder, if specified when loading from a huggingface 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
huggingface 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.
-ti URI [URI ...], --textual-inversions URI [URI ...]
Specify one or more Textual Inversion models using URIs (flax and SDXL not
supported). These should be a huggingface repository slug, path to model
file on disk (for example, a .pt, .pth, .bin, .ckpt, or .safetensors
file), or model folder containing model files. huggingface blob links are
not supported, see "subfolder" and "weight-name" below instead. Optional
arguments can be provided after the Textual Inversion model specification,
these include: "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=lora.safetensors". The "revision" argument specifies the model
revision to use for the Textual Inversion model when loading from
huggingface repository, (The git branch / tag, default is "main"). The
"subfolder" argument specifies the Textual Inversion model subfolder, if
specified when loading from a huggingface 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 huggingface
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 CONTROL_NET_URI [CONTROL_NET_URI ...], --control-nets CONTROL_NET_URI [CONTROL_NET_URI ...]
Specify one or more ControlNet models using URIs. This should be a
huggingface 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
ControlNet model specification, for torch these include: "scale", "start",
"end", "revision", "variant", "subfolder", and "dtype". For flax: "scale",
"revision", "subfolder", "dtype", "from_torch" (bool) They can be
specified as so in any order, they are not positional: "huggingface/contro
lnet;scale=1.0;start=0.0;end=1.0;revision=main;variant=fp16;subfolder=repo
_subfolder;dtype=float16". The "scale" argument specifies the scaling
factor applied to the ControlNet model, the default value is 1.0. The
"start" (only for --model-type "torch*") 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" (only for --model-type
"torch*") 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 huggingface repository, (The git branch / tag,
default is "main"). The "variant" (only for --model-type "torch*")
argument specifies the ControlNet model variant, if "variant" is specified
when loading from a huggingface repository or folder, weights will be
loaded from "variant" filename, e.g. "pytorch_model.<variant>.safetensors.
"variant" defaults to automatic selection and is ignored if using flax.
"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 huggingface 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. The "from_torch" (only for --model-
type flax) this argument specifies that the ControlNet is to be loaded and
converted from a huggingface repository or file that is designed for
pytorch. (Defaults to false) 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" and "dtype" are unused in this
case and may produce an error message if used ("from_torch" is available
when using flax). If you wish to load a specific weight file from a
huggingface 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.
-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. Torch schedulers: (DDIMScheduler, DDPMScheduler,
PNDMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler,
HeunDiscreteScheduler, EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler,
KDPM2DiscreteScheduler, KDPM2AncestralDiscreteScheduler,
DEISMultistepScheduler, UniPCMultistepScheduler, DPMSolverSDEScheduler,
EDMEulerScheduler).
-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 huggingface 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 include: "revision",
"variant", "subfolder", and "dtype". They can be specified as so in any
order, they are not positional: "huggingface/decoder_model;revision=main;v
ariant=fp16;subfolder=repo_subfolder;dtype=float16". The "revision"
argument specifies the model revision to use for the Textual Inversion
model when loading from huggingface 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 huggingface 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 huggingface
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 huggingface
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 huggingface 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 include:
"revision", "variant", "subfolder", and "dtype". They can be specified as
so in any order, they are not positional: "huggingface/refiner_model_xl;re
vision=main;variant=fp16;subfolder=repo_subfolder;dtype=float16". The
"revision" argument specifies the model revision to use for the Textual
Inversion model when loading from huggingface 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 huggingface 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
huggingface 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 huggingface 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 preform 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-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. (default: cuda). Use: cuda:0, cuda:1, cuda:2, etc. to specify
a specific GPU. This argument is ignored when using flax, for flax use the
environmental variable CUDA_VISIBLE_DEVICES to specify which GPUs are
visible to cuda, flax will use every visible 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.
Unless --no-aspect is 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 in the case of 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 Diffusion XL
(SDXL) 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, for example "dgenerate < config.txt".
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".
-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 "shrek
flying a tesla over detroit; clouds, rain, missiles". (default: [(empty
string)])
-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" and "torch-sdxl",
including with --control-nets.
-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 preform on the primary
image 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.
-mip PROCESSOR_URI [PROCESSOR_URI ...], --mask-image-processors PROCESSOR_URI [PROCESSOR_URI ...]
Specify one or more image processor actions to preform on the inpaint mask
image 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.
-cip PROCESSOR_URI [PROCESSOR_URI ...], --control-image-processors PROCESSOR_URI [PROCESSOR_URI ...]
Specify one or more image processor actions to preform 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 module names.
Specifying one or more module names after this option will cause usage
documentation for the specified modules to be printed.
-pp PROCESSOR_URI [PROCESSOR_URI ...], --post-processors PROCESSOR_URI [PROCESSOR_URI ...]
Specify one or more image processor actions to preform 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.
(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, VAE, and ControlNet) 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/v
3.5.0/dgenerate_submodules.html#dgenerate.pipelinewrapper.CACHE_MEMORY_CON
STRAINTS]
-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://dgenerate.re
adthedocs.io/en/v3.5.0/dgenerate_submodules.html#dgenerate.pipelinewrapper
.PIPELINE_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/en/v3.5.0/dgenerate_submodul
es.html#dgenerate.pipelinewrapper.UNET_CACHE_MEMORY_CONSTRAINTS]
-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/v3.5.0/dgenerate_submodul
es.html#dgenerate.pipelinewrapper.VAE_CACHE_MEMORY_CONSTRAINTS]
-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: "control_net_size >
(available * 0.75)" For Syntax See: [https://dgenerate.readthedocs.io/en/v
3.5.0/dgenerate_submodules.html#dgenerate.pipelinewrapper.CONTROL_NET_CACH
E_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/cu121/"
# If you want a specific version
pipx install dgenerate==3.5.0 ^
--pip-args "--extra-index-url https://download.pytorch.org/whl/cu121/"
# You can install without pipx into your own environment like so
pip install dgenerate==3.5.0 --extra-index-url https://download.pytorch.org/whl/cu121/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/cu121/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
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:
# CUDA Toolkit 12.2.1 For Ubuntu / Debian / WSL
wget https://developer.download.nvidia.com/compute/cuda/12.2.1/local_installers/cuda_12.2.1_535.86.10_linux.run
sudo sh cuda_12.2.1_535.86.10_linux.runDo not attempt to install a driver from the prompts if using WSL.
# On linux, if you intend to use flax, you may or may not need to create a symlink for libnvrtc
# flax will look for libnvrtc.so, and may not be able to find it.
ln -s /usr/local/cuda/lib64/libnvrtc.so.12 /usr/local/cuda/lib64/libnvrtc.soAdd libraries to linker path:
# 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:
source ~/.bashrcInstall Python 3.10+ (Debian / Ubuntu) and pipx
sudo apt install python3.10 python3-pip pipx python3.10-venv python3-wheel
pipx ensurepath
source ~/.bashrcInstall dgenerate
pipx install dgenerate \
--pip-args "--extra-index-url https://download.pytorch.org/whl/cu121/"
# With flax/jax support
pipx install dgenerate[flax] \
--pip-args "--extra-index-url https://download.pytorch.org/whl/cu121/ \
-f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html"
# If you want a specific version
pipx install dgenerate==3.5.0 \
--pip-args "--extra-index-url https://download.pytorch.org/whl/cu121/"
# Specific version with flax/jax support
pipx install dgenerate[flax]==3.5.0 \
--pip-args "--extra-index-url https://download.pytorch.org/whl/cu121/ \
-f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html"
# You can install without pipx into your own environment like so
pip3 install dgenerate==3.5.0 --extra-index-url https://download.pytorch.org/whl/cu121/
# Or with flax
pip3 install dgenerate[flax]==3.5.0 --extra-index-url https://download.pytorch.org/whl/cu121/ \
-f https://storage.googleapis.com/jax-releases/jax_cuda_releases.htmlIt 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
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/cu121/
# With flax if you want
pip3 install --editable .[dev,flax] --extra-index-url https://download.pytorch.org/whl/cu121/ \
-f https://storage.googleapis.com/jax-releases/jax_cuda_releases.htmlRun 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 10Basic 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.
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:
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 :)
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.
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.
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 Seed
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.
# 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.
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.
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.
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.
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.
# 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 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
As well as the information about --image-seeds from dgenerates --help output.
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.
# 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.
# 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 corrisponding 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:
magick identify -format "%[Property:DgenerateConfig] generated_file.pngGenerated configuration can be read back into dgenerate via a pipe or file redirection.
magick identify -format "%[Property:DgenerateConfig] generated_file.png | dgenerate
dgenerate < generated-config.txtSpecifying 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 of 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
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.
# 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:
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:
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:
#
# "EulerDiscreteScheduler"
# "HeunDiscreteScheduler"
# "UniPCMultistepScheduler"
# "DDPMScheduler"
# "EulerDiscreteScheduler"
# "DDIMScheduler"
# "DEISMultistepScheduler"
# "LMSDiscreteScheduler"
# "DPMSolverMultistepScheduler"
# "EulerAncestralDiscreteScheduler"
# "DPMSolverSinglestepScheduler"
# "DPMSolverSDEScheduler"
# "KDPM2DiscreteScheduler"
# "PNDMScheduler"
# "KDPM2AncestralDiscreteScheduler"
# "LCMScheduler"Passing helpargs to a --scheduler related option will reveal configuration arguments that can be overridden via a URI syntax, for every possible scheduler.
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:
# 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.
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 for torch --model-type values are: model, revision, variant, subfolder, and dtype
Loading arguments available when specifying VAE for flax --model-type values are: model, revision, subfolder, 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 for torch models are:
AutoencoderKL
AsymmetricAutoencoderKL (Does not support --vae-slicing or --vae-tiling)
AutoencoderTiny
ConsistencyDecoderVAE
Available encoder classes for flax models are:
FlaxAutoencoderKL (Does not support --vae-slicing or --vae-tiling)
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).
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.
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 usage is only valid when loading VAEs if --model-type is either torch or torch-sdxl. Attempting to use it with FlaxAutoencoderKL with produce an error message. When not specified in the URI, 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.
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
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’
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.
# 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.
# 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.
This is useful in particular for using the latent consistency scheduler.
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).
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 for torch --model-type values 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.
Loading arguments available when specifying UNet for flax --model-type values are: revision, subfolder, dtype. variant is not used for flax.
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.
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.
# 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
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.
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
# This is a non working example as I do not know of a repo with an SDXL refiner
# in a subfolder :) 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’
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:
# 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.
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
When multiple specifications are given, all mentioned models will be fused into the main model at a given scale.
The plural form of the argument is identical to the non-plural version, which only exists for backward compatibility.
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 LoRA scale 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.
# Don't expect great results with this example,
# Try models and LoRA's downloaded from CivitAI
dgenerate runwayml/stable-diffusion-v1-5 \
--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.
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
dgenerate runwayml/stable-diffusion-v1-5 \
--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
# This is a non working example as I do not know of a repo with a LoRA weight in a subfolder :)
# 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.
# This is only a syntax example
dgenerate runwayml/stable-diffusion-v1-5 \
--prompts "Syntax example" \
--loras "my_lora.safetensors;scale=1.0"Specifying Textual Inversions
One or more Textual Inversion models may be specified with --textual-inversions
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: 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.
# 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"If you want to select the repository revision, such as main etc, use the named argument revision
# 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
# This is a non working example as I do not know of a repo with a textual
# inversion weight in a subfolder :) 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:
# This is only a syntax example
dgenerate runwayml/stable-diffusion-v1-5 \
--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.
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 and from_torch argument when using flax --model-type values.
Available arguments when using torch --model-type values are: scale, start, end, revision, variant, subfolder, dtype
Available arguments when using flax --model-type values are: scale, revision, subfolder, dtype, from_torch
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, end, and from_torch. from_torch can be used with flax for loading pytorch models from .pt or other files designed for torch from a repo or file/folder on disk.
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
# Torch example, use "vermeer_canny_edged.png" as a control guidance image
dgenerate runwayml/stable-diffusion-v1-5 \
--inference-steps 40 \
--guidance-scales 8 \
--prompts "Painting, Girl with a pearl earing 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 runwayml/stable-diffusion-v1-5 \
--inference-steps 40 \
--guidance-scales 8 \
--prompts "Painting, Girl with a pearl earing 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 runwayml/stable-diffusion-v1-5 \
--inference-steps 40 \
--guidance-scales 8 \
--prompts "Painting, Girl with a pearl earing 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"
# Flax example
dgenerate runwayml/stable-diffusion-v1-5 --model-type flax \
--revision bf16 \
--dtype float16 \
--inference-steps 40 \
--guidance-scales 8 \
--prompts "Painting, Girl with a pearl earing by Leonardo Da Vinci, masterpiece; low quality, low resolution, blank eyeballs" \
--control-nets "lllyasviel/sd-controlnet-canny;scale=0.5;from_torch=true" \
--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" \
--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
# This is a non working example as I do not know of a repo that utilizes revisions with
# ControlNet weights :) 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
# This is a non working example as I do not know of a repo with a textual
# inversion weight in a subfolder :) 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:
# This is only a syntax example
dgenerate runwayml/stable-diffusion-v1-5 \
--prompts "Syntax example" \
--control-nets "my_cn_model.safetensors"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.
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.
dgenerate --image-processor-help
# Output:
#
# Available image processors:
#
# "sam"
# "pidi"
# "normal-bae"
# "upscaler"
# "grayscale"
# "invert"
# "posterize"
# "mirror"
# "flip"
# "mlsd"
# "leres"
# "hed"
# "solarize"
# "midas"
# "canny"
# "lineart"
# "openpose"
# "lineart-anime"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.
All processors posses the arguments: output-file, output-overwrite, device, and model-offload
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.
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 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. This 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, 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.
# --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 unicornThe --control-image-processors has a special additional syntax that the other processor specification options do not, which is used to describe which processor group is affecting which control guidance image source 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.
Upscaling with Diffusion Upscaler Models
Stable diffusion image upscaling models can be used via the model types torch-upscaler-x2 and torch-upscaler-x4.
The image used in the example below is this low resolution cat
# 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 20Sub Commands (image-process)
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 here if desired however currently there is only one available.
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.
currently the only implemented sub-command is image-process, which you can read the help output of using dgenerate --sub-command image-process --help
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".
-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
# 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:
# 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"Upscaling with chaiNNer Compatible Upscaler Models
chaiNNer compatible 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.
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.
# print the help output of the sub command "image-process"
# the image-process subcommand 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"Writing and Running Configs
Program configuration can be read from STDIN and processed in batch with model caching, in order to increase speed when many invocations with different arguments are desired.
Loading the necessary libraries and bringing models into memory is quite slow, so using the program this way allows for multiple invocations using different arguments, without needing to load the libraries and models multiple times, only the first time, or in the case of models the first time the model is encountered.
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.
Diffusion Pipelines, user specified UNets, VAEs, and ControlNet models are cached individually.
UNets, VAEs and ControlNet model objects can be reused by diffusion pipelines in certain situations when specified explicitly and this is taken advantage of by using an in memory cache of these objects.
In effect, creation of a 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, or \clear_control_net_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.
Environmental variables will be expanded in the provided input to STDIN when using this feature, you may use Unix style notation for environmental variables even on Windows.
There is also information about the previous file output of dgenerate that is available to use via Jinja2 templating which can be passed to --image-seeds, these include:
{{ last_images }} (An iterable of un-quoted filenames)
{{ last_animations }} (An iterable of un-quoted filenames)
There are templates 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 }}
A list of template variables with their types and values that are assigned by a dgenerate invocation can be printed out using the \templates_help directive mentioned in an example further down.
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)
{{ gen_seeds(n) }} (Return a list of random integer seeds in the form of strings)
{{ cwd() }} (Return the current working directory as a string)
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.
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, if the next line starts with - it is considered part of a continuation as well even if \ had not been used previously. Comments cannot be interspersed with invocation or directive arguments without the use of \, at least on the last line before whitespace and comments start.
The following is a config file example that covers very basic syntax concepts:
#! dgenerate 3.5.0
# 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.txt
# 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 ControlNet models
\clear_control_net_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_4To 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 of 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 and \setp directive will also be mentioned in this output.
#! dgenerate 3.5.0
# 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: "last_model_path"
Type: typing.Optional[str]
Value: stabilityai/stable-diffusion-2-1
Name: "last_subfolder"
Type: typing.Optional[str]
Value: None
Name: "last_sdxl_refiner_uri"
Type: typing.Optional[str]
Value: None
Name: "last_sdxl_refiner_edit"
Type: typing.Optional[bool]
Value: None
Name: "last_batch_size"
Type: typing.Optional[int]
Value: 1
Name: "last_batch_grid_size"
Type: typing.Optional[tuple[int, int]]
Value: None
Name: "last_prompts"
Type: collections.abc.Sequence[dgenerate.prompt.Prompt]
Value: ['a man walking on the moon without a space suit']
Name: "last_sdxl_second_prompts"
Type: typing.Optional[collections.abc.Sequence[dgenerate.prompt.Prompt]]
Value: []
Name: "last_sdxl_refiner_prompts"
Type: typing.Optional[collections.abc.Sequence[dgenerate.prompt.Prompt]]
Value: []
Name: "last_sdxl_refiner_second_prompts"
Type: typing.Optional[collections.abc.Sequence[dgenerate.prompt.Prompt]]
Value: []
Name: "last_seeds"
Type: collections.abc.Sequence[int]
Value: [98030306583037]
Name: "last_seeds_to_images"
Type: <class 'bool'>
Value: False
Name: "last_guidance_scales"
Type: collections.abc.Sequence[float]
Value: [5]
Name: "last_inference_steps"
Type: collections.abc.Sequence[int]
Value: [30]
Name: "last_clip_skips"
Type: typing.Optional[collections.abc.Sequence[int]]
Value: []
Name: "last_sdxl_refiner_clip_skips"
Type: typing.Optional[collections.abc.Sequence[int]]
Value: []
Name: "last_image_seeds"
Type: typing.Optional[collections.abc.Sequence[str]]
Value: []
Name: "last_parsed_image_seeds"
Type: typing.Optional[collections.abc.Sequence[dgenerate.mediainput.ImageSeedParseResult]]
Value: []
Name: "last_image_seed_strengths"
Type: typing.Optional[collections.abc.Sequence[float]]
Value: []
Name: "last_upscaler_noise_levels"
Type: typing.Optional[collections.abc.Sequence[int]]
Value: []
Name: "last_guidance_rescales"
Type: typing.Optional[collections.abc.Sequence[float]]
Value: []
Name: "last_image_guidance_scales"
Type: typing.Optional[collections.abc.Sequence[float]]
Value: []
Name: "last_s_cascade_decoder_uri"
Type: typing.Optional[str]
Value: None
Name: "last_s_cascade_decoder_prompts"
Type: typing.Optional[collections.abc.Sequence[dgenerate.prompt.Prompt]]
Value: []
Name: "last_s_cascade_decoder_inference_steps"
Type: typing.Optional[collections.abc.Sequence[int]]
Value: []
Name: "last_s_cascade_decoder_guidance_scales"
Type: typing.Optional[collections.abc.Sequence[float]]
Value: []
Name: "last_sdxl_high_noise_fractions"
Type: typing.Optional[collections.abc.Sequence[float]]
Value: []
Name: "last_sdxl_refiner_inference_steps"
Type: typing.Optional[collections.abc.Sequence[int]]
Value: []
Name: "last_sdxl_refiner_guidance_scales"
Type: typing.Optional[collections.abc.Sequence[float]]
Value: []
Name: "last_sdxl_refiner_guidance_rescales"
Type: typing.Optional[collections.abc.Sequence[float]]
Value: []
Name: "last_sdxl_aesthetic_scores"
Type: typing.Optional[collections.abc.Sequence[float]]
Value: []
Name: "last_sdxl_original_sizes"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Value: []
Name: "last_sdxl_target_sizes"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Value: []
Name: "last_sdxl_crops_coords_top_left"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Value: []
Name: "last_sdxl_negative_aesthetic_scores"
Type: typing.Optional[collections.abc.Sequence[float]]
Value: []
Name: "last_sdxl_negative_original_sizes"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Value: []
Name: "last_sdxl_negative_target_sizes"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Value: []
Name: "last_sdxl_negative_crops_coords_top_left"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Value: []
Name: "last_sdxl_refiner_aesthetic_scores"
Type: typing.Optional[collections.abc.Sequence[float]]
Value: []
Name: "last_sdxl_refiner_original_sizes"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Value: []
Name: "last_sdxl_refiner_target_sizes"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Value: []
Name: "last_sdxl_refiner_crops_coords_top_left"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Value: []
Name: "last_sdxl_refiner_negative_aesthetic_scores"
Type: typing.Optional[collections.abc.Sequence[float]]
Value: []
Name: "last_sdxl_refiner_negative_original_sizes"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Value: []
Name: "last_sdxl_refiner_negative_target_sizes"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Value: []
Name: "last_sdxl_refiner_negative_crops_coords_top_left"
Type: typing.Optional[collections.abc.Sequence[tuple[int, int]]]
Value: []
Name: "last_unet_uri"
Type: typing.Optional[str]
Value: None
Name: "last_second_unet_uri"
Type: typing.Optional[str]
Value: None
Name: "last_vae_uri"
Type: typing.Optional[str]
Value: None
Name: "last_vae_tiling"
Type: <class 'bool'>
Value: False
Name: "last_vae_slicing"
Type: <class 'bool'>
Value: False
Name: "last_lora_uris"
Type: typing.Optional[collections.abc.Sequence[str]]
Value: []
Name: "last_textual_inversion_uris"
Type: typing.Optional[collections.abc.Sequence[str]]
Value: []
Name: "last_control_net_uris"
Type: typing.Optional[collections.abc.Sequence[str]]
Value: []
Name: "last_scheduler"
Type: typing.Optional[str]
Value: None
Name: "last_sdxl_refiner_scheduler"
Type: typing.Optional[str]
Value: None
Name: "last_s_cascade_decoder_scheduler"
Type: typing.Optional[str]
Value: None
Name: "last_safety_checker"
Type: <class 'bool'>
Value: False
Name: "last_model_type"
Type: <enum 'ModelType'>
Value: ModelType.TORCH
Name: "last_device"
Type: <class 'str'>
Value: cuda
Name: "last_dtype"
Type: <enum 'DataType'>
Value: DataType.AUTO
Name: "last_revision"
Type: <class 'str'>
Value: main
Name: "last_variant"
Type: typing.Optional[str]
Value: None
Name: "last_output_size"
Type: typing.Optional[tuple[int, int]]
Value: (512, 512)
Name: "last_no_aspect"
Type: <class 'bool'>
Value: False
Name: "last_output_path"
Type: <class 'str'>
Value: output
Name: "last_output_prefix"
Type: typing.Optional[str]
Value: None
Name: "last_output_overwrite"
Type: <class 'bool'>
Value: False
Name: "last_output_configs"
Type: <class 'bool'>
Value: False
Name: "last_output_metadata"
Type: <class 'bool'>
Value: False
Name: "last_animation_format"
Type: <class 'str'>
Value: mp4
Name: "last_image_format"
Type: <class 'str'>
Value: png
Name: "last_no_frames"
Type: <class 'bool'>
Value: False
Name: "last_frame_start"
Type: <class 'int'>
Value: 0
Name: "last_frame_end"
Type: typing.Optional[int]
Value: None
Name: "last_auth_token"
Type: typing.Optional[str]
Value: None
Name: "last_seed_image_processors"
Type: typing.Optional[collections.abc.Sequence[str]]
Value: []
Name: "last_mask_image_processors"
Type: typing.Optional[collections.abc.Sequence[str]]
Value: []
Name: "last_control_image_processors"
Type: typing.Optional[collections.abc.Sequence[str]]
Value: []
Name: "last_post_processors"
Type: typing.Optional[collections.abc.Sequence[str]]
Value: []
Name: "last_offline_mode"
Type: <class 'bool'>
Value: False
Name: "last_model_cpu_offload"
Type: <class 'bool'>
Value: False
Name: "last_model_sequential_offload"
Type: <class 'bool'>
Value: False
Name: "last_sdxl_refiner_cpu_offload"
Type: typing.Optional[bool]
Value: None
Name: "last_sdxl_refiner_sequential_offload"
Type: typing.Optional[bool]
Value: None
Name: "last_s_cascade_decoder_cpu_offload"
Type: typing.Optional[bool]
Value: None
Name: "last_s_cascade_decoder_sequential_offload"
Type: typing.Optional[bool]
Value: None
Name: "last_plugin_module_paths"
Type: collections.abc.Sequence[str]
Value: []
Name: "last_verbose"
Type: <class 'bool'>
Value: False
Name: "last_cache_memory_constraints"
Type: typing.Optional[collections.abc.Sequence[str]]
Value: []
Name: "last_pipeline_cache_memory_constraints"
Type: typing.Optional[collections.abc.Sequence[str]]
Value: []
Name: "last_unet_cache_memory_constraints"
Type: typing.Optional[collections.abc.Sequence[str]]
Value: []
Name: "last_vae_cache_memory_constraints"
Type: typing.Optional[collections.abc.Sequence[str]]
Value: []
Name: "last_control_net_cache_memory_constraints"
Type: typing.Optional[collections.abc.Sequence[str]]
Value: []
Name: "last_images"
Type: collections.abc.Iterable[str]
Value: <dgenerate.renderloop.RenderLoop.written_images.<locals>.Iterable object at ...>
Name: "last_animations"
Type: collections.abc.Iterable[str]
Value: <dgenerate.renderloop.RenderLoop.written_animations.<locals>.Iterable object at ...>
Name: "injected_args"
Type: collections.abc.Sequence[str]
Value: []
Name: "injected_device"
Type: typing.Optional[str]
Value: None
Name: "injected_verbose"
Type: typing.Optional[bool]
Value: False
Name: "injected_plugin_modules"
Type: typing.Optional[collections.abc.Sequence[str]]
Value: []
Name: "saved_modules"
Type: dict[str, dict[str, typing.Any]]
Value: {}
Name: "glob"
Type: <class 'module'>
Value: <module 'glob'>
Name: "path"
Type: <class 'module'>
Value: <module 'ntpath' (frozen)>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:
"\help"
"\templates_help"
"\directives_help"
"\functions_help"
"\image_processor_help"
"\clear_model_cache"
"\clear_pipeline_cache"
"\clear_unet_cache"
"\clear_vae_cache"
"\clear_control_net_cache"
"\save_modules"
"\use_modules"
"\clear_modules"
"\gen_seeds"
"\pwd"
"\ls"
"\cd"
"\pushd"
"\popd"
"\exec"
"\mv"
"\cp"
"\mkdir"
"\rmdir"
"\rm"
"\exit"
"\image_process"
"\import_plugins"
"\set"
"\sete"
"\setp"
"\unset"
"\print"
"\echo"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.
#! dgenerate 3.5.0
# 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
# text_encoder
# text_encoder_2
# tokenizer
# tokenizer_2
# safety_checker
# feature_extractor
# 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_modulesThe 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.
#! dgenerate 3.5.0
# 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 preform 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')) }}The dgenerate sub-command image-process has a config directive implementation.
#! dgenerate 3.5.0
# 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 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.
#! dgenerate 3.5.0
# run dgenerate as a subprocess, read a config
# and send stdout and stderr to a file
\exec dgenerate < my_config.txt &> log.txt
# chaining processes together with pipes is supported
# this example emulates 'cat' on Windows using cmd
\exec cmd /c "type my_config.txt" | dgenerate &> log.txt
# on a Unix platform you could simply use cat
\exec cat my_config.txt | dgenerate &> log.txtYou can exit a config early if need be using the \exit directive
#! dgenerate 3.5.0
# exit the process with return code 1, which indicates an error
\print "some error occurred"
\exit 1To utilize configuration files on Linux, pipe them into the command or use redirection:
# Pipe
cat my-config.txt | dgenerate
# Redirection
dgenerate < my-config.txtOn Windows CMD:
dgenerate < my-config.txtOn Windows Powershell:
Get-Content my-config.txt | 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.
# Pipe
cat my-animations-config.txt | dgenerate --frame-start 0 --frame-end 10
# Redirection
dgenerate --frame-start 0 --frame-end 10 < my-animations-config.txtOn Windows CMD:
dgenerate --frame-start 0 --frame-end 10 < my-animations-config.txtOn Windows Powershell:
Get-Content my-animations-config.txt | 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.
# 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 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 a blinking red cursor, 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.
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, or paths to python files, using this argument.
dgenerate supports implementing image processors and config directives through 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.
An example for writing config directives can be found in the “writing_plugins/config_directive” folder of the examples 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 this directive is terse as most of \image_process is implemented as reusable code as mentioned below.
The behavior of \image_process which is also used for --sub-command image-process is is implemented here.
File Cache Control
dgenerate will cache --image-seeds files and files used by image processors that are downloaded from the web while it is running 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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