dgenerate is a command line tool and library for generating images and animation sequences using Stable Diffusion and related techniques / models.
Project description
Overview
dgenerate is a command line tool and library for generating images and animation sequences using Stable Diffusion and related techniques / models.
dgenerate can 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 and WebP 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.
This software requires an Nvidia GPU supporting CUDA 11.8+, CPU rendering is possible but extraordinarily slow.
For library documentation visit readthedocs.
- How to install
- Usage Examples
Help Output
usage: dgenerate [-h] [-v] [--version] [-pm PATH [PATH ...]] [-ofm] [-th] [-mt MODEL_TYPE] [-rev BRANCH]
[-var VARIANT] [-sbf SUBFOLDER] [-atk TOKEN] [-bs INTEGER] [-bgs SIZE] [-vae VAE_URI]
[-vt] [-vs] [-lra LORA_URI [LORA_URI ...]] [-ti URI [URI ...]]
[-cn CONTROL_NET_URI [CONTROL_NET_URI ...]] [-sch SCHEDULER_NAME]
[--sdxl-refiner MODEL_URI] [--sdxl-refiner-scheduler SCHEDULER_NAME]
[--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] [-fs FRAME_NUMBER]
[-fe FRAME_NUMBER] [-is SEED [SEED ...]] [-sip PREPROCESSOR [PREPROCESSOR ...]]
[-mip PREPROCESSOR [PREPROCESSOR ...]] [-cip PREPROCESSOR [PREPROCESSOR ...]]
[-iph [PREPROCESSOR ...]] [-iss FLOAT [FLOAT ...] | -uns INTEGER [INTEGER ...]]
[-gs FLOAT [FLOAT ...]] [-igs FLOAT [FLOAT ...]] [-gr FLOAT [FLOAT ...]]
[-ifs INTEGER [INTEGER ...]] [-mc EXPR [EXPR ...]] [-pmc EXPR [EXPR ...]]
[-vmc EXPR [EXPR ...]] [-cmc EXPR [EXPR ...]]
model_path
Stable diffusion batch image generation tool with support for video / gif / webp animation transcoding.
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
-pm PATH [PATH ...], --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 only
implement image preprocessors.
-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.
-th, --templates-help
Print a list of template variables available after a dgenerate invocation during
batch processing from STDIN. When used as a command option, their values are not
presented, just their names and types.
-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, or torch-ifs-img2img. (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 seperated 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.
-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". 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/vae;revision=main;variant=fp16;subfo
lder=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, 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 --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, 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 other loading arguments 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 models 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* 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 ...], --lora 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 VAE
when loading from huggingface repository, (The git branch / tag, default is
"main"). The "subfolder" argument specifies the VAE 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: --lora
"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.
Currently all ControlNet models will receive the same guidance image, in the
future this will probably change. 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/controlnet;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,
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_NAME, --scheduler SCHEDULER_NAME
Specify a scheduler (sampler) by name. Passing "help" to this argument will print
the compatible schedulers for a model without generating any images. Torch
schedulers: (DDIMScheduler, DDPMScheduler, PNDMScheduler, LMSDiscreteScheduler,
EulerDiscreteScheduler, HeunDiscreteScheduler, EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler,
KDPM2DiscreteScheduler, KDPM2AncestralDiscreteScheduler, DEISMultistepScheduler,
UniPCMultistepScheduler, DPMSolverSDEScheduler).
--sdxl-refiner MODEL_URI
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/r
efiner_model_xl;revision=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, 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.
--sdxl-refiner-scheduler SCHEDULER_NAME
Specify a scheduler (sampler) by name for the SDXL refiner pass. Operates the
exactsame way as --scheduler including the "help" option. Defaults to the value
of --scheduler.
--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-strength 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, 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-directory 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, gif, or webp. (default: mp4)
-fs FRAME_NUMBER, --frame-start FRAME_NUMBER
Starting frame slice point for animated files, the specified frame will be
included.
-fe FRAME_NUMBER, --frame-end FRAME_NUMBER
Ending frame slice point for animated files, 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 PREPROCESSOR [PREPROCESSOR ...], --seed-image-preprocessors PREPROCESSOR [PREPROCESSOR ...]
Specify one or more image preprocessor actions to preform on the primary image
specified by --image-seeds. For example: --seed-image-preprocessors "flip"
"mirror" "grayscale". To obtain more information about what image preprocessors
are available and how to use them, see: --image-preprocessor-help.
-mip PREPROCESSOR [PREPROCESSOR ...], --mask-image-preprocessors PREPROCESSOR [PREPROCESSOR ...]
Specify one or more image preprocessor actions to preform on the inpaint mask
image specified by --image-seeds. For example: --mask-image-preprocessors
"invert". To obtain more information about what image preprocessors are available
and how to use them, see: --image-preprocessor-help.
-cip PREPROCESSOR [PREPROCESSOR ...], --control-image-preprocessors PREPROCESSOR [PREPROCESSOR ...]
Specify one or more image preprocessor actions to preform on the control image
specified by --image-seeds, this option is meant to be used with --control-nets.
Example: --control-image-preprocessors "canny;lower=50;upper=100". The delimiter
"+" can be used to specify a different preprocessor 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 preprocessors for those control images with the syntax:
(--control-image-preprocessors "processes-img1" + "processes-img2"), this syntax
also supports chaining of preprocessors, for example: (--control-image-
preprocessors "first-process-img1" "second-process-img1" + "process-img2"). The
amount of specified preprocessors must not exceed the amount of specified control
images, or you will receive a syntax error message. Images which do not have a
preprocessor defined for them will not be preprocessed, and the plus character
can be used to indicate an image is not to be preprocessed and instead skipped
over when that image is a leading element, for example (--control-image-
preprocessors + "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 preprocessors are available and how to use them, see: --image-
preprocessor-help.
-iph [PREPROCESSOR ...], --image-preprocessor-help [PREPROCESSOR ...]
Use this option alone (or with --plugin-modules) and no model specification in
order to list available image preprocessor module names. Specifying one or more
module names after this option will cause usage documentation for the specified
modules to be printed.
-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. 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-strength).
(default: [20])
-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/v2.1.1/dgenerate_submodules.html#dgenerate.p
ipelinewrapper.CACHE_MEMORY_CONSTRAINTS]
-pmc EXPR [EXPR ...], --pipeline-cache-memory-constraints EXPR [EXPR ...]
Cache constraint expressions describing when to automatically clear the in memory
DiffusionPipeline cache considering current memory usage, and estimated memory
usage of new models that are about to enter memory. If any of these constraint
expressions are met all DiffusionPipeline objects cached in memory will be
cleared. Example, and default value: "pipeline_size > (available * 0.75)" For
Syntax See: [https://dgenerate.readthedocs.io/en/v2.1.1/dgenerate_submodules.html
#dgenerate.pipelinewrapper.PIPELINE_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/v2.1.1/dgenerate_submodules.html#dgenerate.pipelinewrapper.VAE_CACHE_MEMORY_C
ONSTRAINTS]
-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/v2.1.1/dgenerate_submodules.html#dgener
ate.pipelinewrapper.CONTROL_NET_CACHE_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/3.11.3/python-3.11.3-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/cu118/"
# If you want a specific version
pipx install dgenerate==2.1.1 ^
--pip-args "--extra-index-url https://download.pytorch.org/whl/cu118/"
# You can install without pipx into your own environment like so
pip install dgenerate==2.1.1 --extra-index-url https://download.pytorch.org/whl/cu118/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/cu118/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 and native dependencies
sudo apt update && sudo apt upgrade
sudo apt install build-essential
# Install libgl1 dependency for OpenCV.
# Needed on WSL, not sure about normal Ubuntu/Debian?
# I don't have a linux machine with a GPU :)
# You'll probably need to install this
# if your install is headless, you will
# know because a relevant exception will
# be produced when running dgenerate if you need it
sudo apt install libgl1Install 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.
Add 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/cu118/"
# With flax/jax support
pipx install dgenerate[flax] \
--pip-args "--extra-index-url https://download.pytorch.org/whl/cu118/ \
-f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html"
# If you want a specific version
pipx install dgenerate==2.1.1 \
--pip-args "--extra-index-url https://download.pytorch.org/whl/cu118/"
# Specific version with flax/jax support
pipx install dgenerate[flax]==2.1.1 \
--pip-args "--extra-index-url https://download.pytorch.org/whl/cu118/ \
-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==2.1.1 --extra-index-url https://download.pytorch.org/whl/cu118/
# Or with flax
pip3 install dgenerate[flax]==2.1.1 --extra-index-url https://download.pytorch.org/whl/cu118/ \
-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/cu118/
# With flax if you want
pip3 install --editable .[dev,flax] --extra-index-url https://download.pytorch.org/whl/cu118/ \
-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-scale 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 seperated 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-scale 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-scale 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.
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 files can be read back into dgenerate using Batch Processing From STDIN.
Specifying 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.
The refiner scheduler defaults 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, or --sdxl-refiner-scheduler help, though both 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"
# "DPMSolverSinglestepScheduler"
# "DDIMScheduler"
# "KDPM2DiscreteScheduler"
# "KDPM2AncestralDiscreteScheduler"
# "HeunDiscreteScheduler"
# "DEISMultistepScheduler"
# "DPMSolverSDEScheduler"
# "DDPMScheduler"
# "PNDMScheduler"
# "UniPCMultistepScheduler"
# "EulerAncestralDiscreteScheduler"
# "DPMSolverMultistepScheduler"
# "LMSDiscreteScheduler"Specifying a VAE
To specify a VAE directly use --vae.
The syntax for --vae is AutoEncoderClass;model=(huggingface repository slug/blob link or file/folder path)
Named arguments when loading a VAE are seperated 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 is model, dtype, and revision
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
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. By default this value is the same as --variant when that option is specified for the main model.
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 LoRAs
It is possible to specify one or more LoRA models using --lora/--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 seperated 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 \
--lora "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 \
--lora "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 \
--lora "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" \
--lora "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" \
--lora "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 --lora 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 Preprocessors
Images provided through --image-seeds can be preprocessed before being used for image generation through the use of the arguments --seed-image-preprocessors, --mask-image-preprocessors, and --control-image-preprocessors.
Each of these options can receive one or more specifications for image preprocessing actions.
For example images can be preprocessed 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-preprocessors 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-preprocessors "canny;lower=50;upper=100" \
--gen-seeds 2 \
--output-size 1024 \
--output-path unicornThe --control-image-preprocessors has a special additional syntax that the other preprocessor specification options do not, which is used to describe which preprocessor 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 preprocess each guidance image with a separate preprocessor OR preprocessor chain. You can specify how each image is processed by delimiting the preprocessor specification groups with + (the plus symbol)
Like this:
--control-nets "huggingface/controlnet1" "huggingface/controlnet2"
--image-seeds "image1.png, image2.png"
--control-image-preprocessors "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 preprocessor chaining as well:
--control-nets "huggingface/controlnet1" "huggingface/controlnet2"
--image-seeds "image1.png, image2.png"
--control-image-preprocessors "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-preprocessors + "affect-image2"
The plus symbol effectively creates a Null preprocessor as the first entry in the example above.
When multiple guidance images are present, it is a syntax error to specify more preprocessor chains than control guidance images. Specifying less preprocessor chains simply means that the trailing guidance images will not be preprocessed, you can avoid preprocessing 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-preprocessors + + "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 preprocessed by any preprocessor.
Using the option --image-preprocessor-help with no arguments will yield a list of available image preprocessor names.
You can also use --plugin-modules with his argument to include plugin modules into the preprocessor module search path.
Specifying one or more specific preprocessors for example: --image-preprocessor-help canny openpose will yield documentation pertaining to those preprocessor modules. This includes accepted arguments for the preprocessor module and a description of the module.
All preprocessors posses the arguments: output-file, output-overwrite, and device
The output-file argument can be used to write the preprocessed 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 preprocessor actually modifies an input image in some way.
The output-overwrite is a boolean argument can be used to tell the preprocessor 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.
Custom image preprocessor modules can also be loaded through the --plugin-modules option as discussed in the next section.
Writing Plugins
dgenerate has the capability of loading in additional functionality through the use of the --plugin-modules option.
You simply specify one or more module directories on disk, or paths to python files, using this argument.
Currently the only supported functionality of plugin modules is to add image preprocessors.
A code example as well as a command line usage example for image preprocessor plugins can be found in the “plugins/image_preprocessor” folder of the examples folder.
The source code for the built in canny preprocessor, the openpose preprocessor, and the simple pillow image operations preprocessors can also be of reference as they are written as internal image preprocessor plugins.
Upscaling with 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 20Specifying 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-path.
You can provide paths 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_subfolderIf 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.safetensorsBatch Processing From STDIN
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), attached control nets, vae’s, lora’s and textual inversions. If another invocation of the model occurs with creation parameters that are identical, it will be loaded out of cache.
Diffusion Pipelines, VAE’s, and ControlNet models are cached individually.
VAE’s and ControlNet model objects can be reused by diffusion pipelines (Main or Refiner models) in certain situations and this is taken advantage of by using in memory caching.
A number of things affect cache hit or miss upon model 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 or individually using one of: \clear_pipeline_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:
{{ 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 a prompt object with its delimiter, works on lists)
The above can be used as either a function or filter IE: {{ "quote_me" | quote }}
Empty lines and comments starting with # will 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 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 2.1.1
# 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
# 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
--inference-steps 30 \
# There can be comments or newlines within the continuation
# but you must provide \ 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 -
# 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 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.
#! dgenerate 2.1.1
# 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:
Available post invocation template variables are:
=================================================
Name: "last_model_path"
Type: typing.Optional[str]
Value: stabilityai/stable-diffusion-2-1
Name: "last_model_subfolder"
Type: typing.Optional[str]
Value: None
Name: "last_sdxl_refiner_uri"
Type: typing.Optional[str]
Value: None
Name: "last_batch_size"
Type: typing.Optional[int]
Value: 1
Name: "last_batch_grid_size"
Type: typing.Optional[typing.Tuple[int, int]]
Value: None
Name: "last_prompts"
Type: typing.List[dgenerate.prompt.Prompt]
Value: ['a man walking on the moon without a space suit']
Name: "last_sdxl_second_prompts"
Type: typing.Optional[typing.List[dgenerate.prompt.Prompt]]
Value: []
Name: "last_sdxl_refiner_prompts"
Type: typing.Optional[typing.List[dgenerate.prompt.Prompt]]
Value: []
Name: "last_sdxl_refiner_second_prompts"
Type: typing.Optional[typing.List[dgenerate.prompt.Prompt]]
Value: []
Name: "last_seeds"
Type: typing.List[int]
Value: [78670947807228]
Name: "last_guidance_scales"
Type: typing.List[float]
Value: [5]
Name: "last_inference_steps"
Type: typing.List[int]
Value: [30]
Name: "last_image_seeds"
Type: typing.Optional[typing.List[str]]
Value: []
Name: "last_image_seed_strengths"
Type: typing.Optional[typing.List[float]]
Value: []
Name: "last_upscaler_noise_levels"
Type: typing.Optional[typing.List[int]]
Value: []
Name: "last_guidance_rescales"
Type: typing.Optional[typing.List[float]]
Value: []
Name: "last_image_guidance_scales"
Type: typing.Optional[typing.List[float]]
Value: []
Name: "last_sdxl_high_noise_fractions"
Type: typing.Optional[typing.List[float]]
Value: []
Name: "last_sdxl_refiner_inference_steps"
Type: typing.Optional[typing.List[int]]
Value: []
Name: "last_sdxl_refiner_guidance_scales"
Type: typing.Optional[typing.List[float]]
Value: []
Name: "last_sdxl_refiner_guidance_rescales"
Type: typing.Optional[typing.List[float]]
Value: []
Name: "last_sdxl_aesthetic_scores"
Type: typing.Optional[typing.List[float]]
Value: []
Name: "last_sdxl_original_sizes"
Type: typing.Optional[typing.List[typing.Tuple[int, int]]]
Value: []
Name: "last_sdxl_target_sizes"
Type: typing.Optional[typing.List[typing.Tuple[int, int]]]
Value: []
Name: "last_sdxl_crops_coords_top_left"
Type: typing.Optional[typing.List[typing.Tuple[int, int]]]
Value: []
Name: "last_sdxl_negative_aesthetic_scores"
Type: typing.Optional[typing.List[float]]
Value: []
Name: "last_sdxl_negative_original_sizes"
Type: typing.Optional[typing.List[typing.Tuple[int, int]]]
Value: []
Name: "last_sdxl_negative_target_sizes"
Type: typing.Optional[typing.List[typing.Tuple[int, int]]]
Value: []
Name: "last_sdxl_negative_crops_coords_top_left"
Type: typing.Optional[typing.List[typing.Tuple[int, int]]]
Value: []
Name: "last_sdxl_refiner_aesthetic_scores"
Type: typing.Optional[typing.List[float]]
Value: []
Name: "last_sdxl_refiner_original_sizes"
Type: typing.Optional[typing.List[typing.Tuple[int, int]]]
Value: []
Name: "last_sdxl_refiner_target_sizes"
Type: typing.Optional[typing.List[typing.Tuple[int, int]]]
Value: []
Name: "last_sdxl_refiner_crops_coords_top_left"
Type: typing.Optional[typing.List[typing.Tuple[int, int]]]
Value: []
Name: "last_sdxl_refiner_negative_aesthetic_scores"
Type: typing.Optional[typing.List[float]]
Value: []
Name: "last_sdxl_refiner_negative_original_sizes"
Type: typing.Optional[typing.List[typing.Tuple[int, int]]]
Value: []
Name: "last_sdxl_refiner_negative_target_sizes"
Type: typing.Optional[typing.List[typing.Tuple[int, int]]]
Value: []
Name: "last_sdxl_refiner_negative_crops_coords_top_left"
Type: typing.Optional[typing.List[typing.Tuple[int, int]]]
Value: []
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[typing.List[str]]
Value: []
Name: "last_textual_inversion_uris"
Type: typing.Optional[typing.List[str]]
Value: []
Name: "last_control_net_uris"
Type: typing.Optional[typing.List[str]]
Value: []
Name: "last_scheduler"
Type: typing.Optional[str]
Value: None
Name: "last_sdxl_refiner_scheduler"
Type: typing.Optional[str]
Value: None
Name: "last_safety_checker"
Type: <class 'bool'>
Value: False
Name: "last_model_type"
Type: <enum 'ModelTypes'>
Value: ModelTypes.TORCH
Name: "last_device"
Type: <class 'str'>
Value: cuda
Name: "last_dtype"
Type: <enum 'DataTypes'>
Value: DataTypes.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[typing.Tuple[int, int]]
Value: (512, 512)
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_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_preprocessors"
Type: typing.Optional[typing.List[str]]
Value: []
Name: "last_mask_image_preprocessors"
Type: typing.Optional[typing.List[str]]
Value: []
Name: "last_control_image_preprocessors"
Type: typing.Optional[typing.List[str]]
Value: []
Name: "last_offline_mode"
Type: <class 'bool'>
Value: False
Name: "last_plugin_module_paths"
Type: typing.List[str]
Value: []
Name: "last_verbose"
Type: <class 'bool'>
Value: False
Name: "last_images"
Type: typing.Iterator[str]
Name: "last_animations"
Type: typing.Iterator[str]
Name: "saved_modules"
Type: typing.Dict[str, typing.Dict[str, typing.Any]]
Value: {}
Name: "glob"
Type: <class 'module'>
Value: <module 'glob'>
==============================================================================Here are examples of other available directives such as \set 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 2.1.1
# You can define your own template variables with the \set directive
\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 \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 seperated by spaces if there is multiple, this could
# also be passed to --image-seeds
\print {{ quote(last_images) | join(' ') }}
# For loops are possible
\print {% for image in last_images %}{{ quote(image) }} {% endfor %}
# For loops are possible with continuation
# however continuations 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
stabilityai/stable-diffusion-2-1 --prompts "{{ 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 a newline within
# the config file generated by the template and is required
# when the template generates multiple lines of configuration.
# You really should not need to use this feature.
{% for image in last_images %} \
stabilityai/stable-diffusion-2-1 --image-seeds {{ quote(image) }} --prompt {{ my_prompt }} !END \
{% endfor %}
# Multiple lines with continuations inside the config template.
# Probably try to avoid this :)
{% for image in last_images %} \
stabilityai/stable-diffusion-2-1 !END
--image-seeds {{ quote(image) }} !END
--prompt {{ my_prompt }} !END \
{% endfor %}
# The above are both basically equivalent to this
stabilityai/stable-diffusion-2-1 --image-seeds {{ quote(last_images) | join(' ') }} --prompt {{ 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
stabilityai/stable-diffusion-2-1
--variant fp16
--dtype float16
--prompt "an astronaut walking on the moon"
--safety-checker
--output-size 512
\save_modules stage_1_modules feature_extractor
# that saves the feature_extractor module object in the pipeline above,
# you can specify multiple module names to save if desired
# Possible Module Names:
# vae
# text_encoder
# text_encoder_2
# tokenizer
# tokenizer_2
# safety_checker
# feature_extractor
# controlnet
# scheduler
# unet
# 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
\clear_modules stage_1_modulesThe entirety of pythons builtin glob module is also accessible during templating, you can glob directories using functions from the glob module like so:
# The most basic usage is full expansion of every file
\set myfiles {{ quote(glob.glob('my_images/*.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('my_images/*.png') %} \
\print {{ quote(file) }} !END \
{% endfor %}
# Simple inline usage
stabilityai/stable-diffusion-2-1
--variant fp16
--dtype float16
--prompt "In the style of picaso"
--image-seeds {{ quote(glob.glob('my_images/*.png')) }}To 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-arguments.txtOn Windows Powershell:
Get-Content my-arguments.txt | dgenerateBatch Processing Argument Injection
You can inject arguments into every generation call 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 10File Cache Control
dgenerate will cache --image-seeds files downloaded from the web while it is running in the directory ~/.cache/dgenerate/web, on Windows this equates to %HOME%\.cache\dgenerate\web
You can control where image seed files are cached with the environmental variable DGENERATE_WEB_CACHE.
This directory is automatically cleared when all instances of dgenerate have finished running.
If you start multiple dgenerate processes simultaneously they will share the cache while running in a manner that is multiprocess safe, the last running instance of dgenerate will clean out the cache.
Files downloaded from huggingface by the diffusers/huggingface_hub library will be cached under ~/.cache/huggingface/, on Windows this equates to %HOME%\.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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