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@ -1,17 +1,39 @@
# Stable Diffusion Dream Script
This is a fork of CompVis/stable-diffusion, the wonderful open source
text-to-image generator. The original has been improved in several ways:
text-to-image generator. This fork supports:
1. An interactive command-line interface that accepts the same prompt
and switches as the Discord bot.
2. Support for img2img in which you provide a seed image to build on
top of.
3. A basic Web interface that allows you to run a local web server for
generating images in your browser.
4. Upscaling and face fixing using the optional ESRGAN and GFPGAN
packages.
5. Weighted subprompts for prompt tuning.
6. Textual inversion for customization of the prompt language and images.
7. ...and more!
This fork is rapidly evolving, so use the Issues panel to report bugs
and make feature requests, and check back periodically for
improvements and bug fixes.
## Interactive command-line interface similar to the Discord bot
The *dream.py* script, located in scripts/dream.py,
The _dream.py_ script, located in scripts/dream.py,
provides an interactive interface to image generation similar to
the "dream mothership" bot that Stable AI provided on its Discord
server. Unlike the txt2img.py and img2img.py scripts provided in the
original CompViz/stable-diffusion source code repository, the
time-consuming initialization of the AI model
initialization only happens once. After that image generation
initialization only happens once. After that image generation
from the command-line interface is very fast.
The script uses the readline library to allow for in-line editing,
@ -25,7 +47,7 @@ The script is confirmed to work on Linux and Windows systems. It should
work on MacOSX as well, but this is not confirmed. Note that this script
runs from the command-line (CMD or Terminal window), and does not have a GUI.
~~~~
```
(ldm) ~/stable-diffusion$ python3 ./scripts/dream.py
* Initializing, be patient...
Loading model from models/ldm/text2img-large/model.ckpt
@ -47,13 +69,13 @@ dream> q
00009.png: "ashley judd riding a camel" -s150 -S 416354203
00010.png: "ashley judd riding a camel" -s150 -S 1362479620
00011.png: "there's a fly in my soup" -n6 -g -S 2685670268
~~~~
```
The dream> prompt's arguments are pretty much identical to those used
in the Discord bot, except you don't need to type "!dream" (it doesn't
hurt if you do). A significant change is that creation of individual
images is now the default unless --grid (-g) is given. For backward
compatibility, the -i switch is recognized. For command-line help
compatibility, the -i switch is recognized. For command-line help
type -h (or --help) at the dream> prompt.
The script itself also recognizes a series of command-line switches
@ -65,12 +87,12 @@ image outputs and the location of the model weight files.
This script also provides an img2img feature that lets you seed your
creations with a drawing or photo. This is a really cool feature that tells
stable diffusion to build the prompt on top of the image you provide, preserving
the original's basic shape and layout. To use it, provide the --init_img
the original's basic shape and layout. To use it, provide the --init_img
option as shown here:
~~~~
```
dream> "waterfall and rainbow" --init_img=./init-images/crude_drawing.png --strength=0.5 -s100 -n4
~~~~
```
The --init_img (-I) option gives the path to the seed picture. --strength (-f) controls how much
the original will be modified, ranging from 0.0 (keep the original intact), to 1.0 (ignore the original
@ -80,92 +102,131 @@ You may also pass a -v<count> option to generate count variants on the original
passing the first generated image back into img2img the requested number of times. It generates interesting
variants.
## GFPGAN Support
## GFPGAN and Real-ESRGAN Support
This script also provides the ability to invoke GFPGAN after image
generation. Doing so will enhance faces and optionally upscale the
image to a higher resolution.
The script also provides the ability to do face restoration and
upscaling with the help of GFPGAN and Real-ESRGAN respectively.
To use the ability, clone the [GFPGAN
repository](https://github.com/TencentARC/GFPGAN) and follow their
To use the ability, clone the **[GFPGAN
repository](https://github.com/TencentARC/GFPGAN)** and follow their
installation instructions. By default, we expect GFPGAN to be
installed in a 'GFPGAN' sibling directory. Be sure that the "ldm"
installed in a 'GFPGAN' sibling directory. Be sure that the `"ldm"`
conda environment is active as you install GFPGAN.
You may also want to install Real-ESRGAN, if you want to enhance
non-face regions in the image, by installing the pip Real-ESRGAN
package.
You can use the `--gfpgan_dir` argument with `dream.py` to set a
custom path to your GFPGAN directory. _There are other GFPGAN related
boot arguments if you wish to customize further._
You can install **Real-ESRGAN** by typing the following command.
```
pip install realesrgan
```
**Preloading Models**
Users whose GPU machines are isolated from the Internet (e.g. on a
University cluster) should be aware that the first time you run
dream.py with GFPGAN turned on, it will try to download model files
from the Internet. To rectify this, you may run `python3
scripts/preload_models.pl` after you have installed GFPGAN and all its
dependencies.
dream.py with GFPGAN and Real-ESRGAN turned on, it will try to
download model files from the Internet. To rectify this, you may run
`python3 scripts/preload_models.py` after you have installed GFPGAN
and all its dependencies.
Now, you can run this script by adding the **--gfpgan** option. Any
issues with GFPGAN will be reported on initialization.
**Usage**
~~~~
(ldm) ~/stable-diffusion$ python3 ./scripts/dream.py --gfpgan
* Initializing, be patient...
(...more initialization messages...)
* --gfpgan was specified, loading gfpgan...
(...even more initialization messages...)
* Initialization done! Awaiting your command...
~~~~
You will now have access to two new prompt arguments.
When generating prompts, add a -G or --gfpgan_strenth option to
control the strength of the GFPGAN enhancement. 0.0 is no
enhancement, 1.0 is maximum enhancement.
**Upscaling**
So for instance, to apply the maximum strength:
~~~~
dream> a man wearing a pineapple hat -G 1
~~~~
`-U : <upscaling_factor> <upscaling_strength>`
The upscaling prompt argument takes two values. The first value is a
scaling factor and should be set to either `2` or `4` only. This will
either scale the image 2x or 4x respectively using different models.
You can set the scaling stength between `0` and `1.0` to control
intensity of the of the scaling. This is handy because AI upscalers
generally tend to smooth out texture details. If you wish to retain
some of those for natural looking results, we recommend using values
between `0.5 to 0.8`.
If you do not explicitly specify an upscaling_strength, it will
default to 0.75.
**Face Restoration**
`-G : <gfpgan_strength>`
This prompt argument controls the strength of the face restoration
that is being applied. Similar to upscaling, values between `0.5 to
0.8` are recommended.
You can use either one or both without any conflicts. In cases where
you use both, the image will be first upscaled and then the face
restoration process will be executed to ensure you get the highest
quality facial features.
`--save_orig`
When you use either `-U` or `-G`, the final result you get is upscaled
or face modified. If you want to save the original Stable Diffusion
generation, you can use the `-save_orig` prompt argument to save the
original unaffected version too.
**Example Usage**
```
dream > superman dancing with a panda bear -U 2 0.6 -G 0.4
```
This also works with img2img:
~~~
dream> a man wearing a pineapple hat -I path/to/your/file.png -G 1
~~~
That's it!
```
dream> a man wearing a pineapple hat -I path/to/your/file.png -U 2 0.5 -G 0.6
```
There's also a bunch of options to control GFPGAN settings when
starting the script for different configs that you can read about in
the help text. This will let you control where GFPGAN is installed, if
upsampling is enabled, the upsampler to use and the model path.
**Note**
By default, images will be upscaled by 2-fold, meaning that the old
Stable Diffusion default size of 512x512 will now be a glorious
detailed 1024x1024. The extent of upscaling is set when you run the
script, and can't be changed while it's running. However, at any time
you may specify **-G0** to turn off upscaling and facial enhancement
for that image or set of images.
GFPGAN and Real-ESRGAN are both memory intensive. In order to avoid
crashes and memory overloads during the Stable Diffusion process,
these effects are applied after Stable Diffusion has completed its
work.
Note that loading GFPGAN consumes additional GPU memory, and will add
a few seconds to image generation. However, if can afford a 3090s with
24Gi, the results are well worth it.
In single image generations, you will see the output right away but
when you are using multiple iterations, the images will first be
generated and then upscaled and face restored after that process is
complete. While the image generation is taking place, you will still
be able to preview the base images.
If you wish to stop during the image generation but want to upscale or
face restore a particular generated image, pass it again with the same
prompt and generated seed along with the `-U` and `-G` prompt
arguments to perform those actions.
## Google Colab
Stable Diffusion AI Notebook: <a href="https://colab.research.google.com/github/lstein/stable-diffusion/blob/main/Stable_Diffusion_AI_Notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> <br>
Open and follow instructions to use an isolated environment running Dream.<br>
Output example:
![Colab Notebook](static/colab_notebook.png)
## Barebones Web Server
As of version 1.10, this distribution comes with a bare bones web
server (see screenshot). To use it, run the command:
server (see screenshot). To use it, run the *dream.py* script by
adding the **--web** option.
~~~~
(ldm) ~/stable-diffusion$ python3 scripts/dream_web.py
~~~~
```
(ldm) ~/stable-diffusion$ python3 scripts/dream_web.py --web
```
You can then connect to the server by pointing your web browser at
http://localhost:9090, or to the network name or IP address of the server.
Kudos to [Tesseract Cat](https://github.com/TesseractCat) for
contributing this code.
contributing this code, and to [dagf2101](https://github.com/dagf2101)
for refining it.
![Dream Web Server](static/dream_web_server.png)
@ -176,17 +237,23 @@ you want to run, one line per prompt. The text file must be composed
with a text editor (e.g. Notepad) and not a word processor. Each line
should look like what you would type at the dream> prompt:
~~~~
```
a beautiful sunny day in the park, children playing -n4 -C10
stormy weather on a mountain top, goats grazing -s100
innovative packaging for a squid's dinner -S137038382
~~~~
```
Then pass this file's name to dream.py when you invoke it:
~~~~
(ldm) ~/stable-diffusion$ python3 scripts/dream.py --from_file="path/to/prompts.txt"
~~~~
```
(ldm) ~/stable-diffusion$ python3 scripts/dream.py --from_file "path/to/prompts.txt"
```
You may read a series of prompts from standard input by providing a filename of "-":
```
(ldm) ~/stable-diffusion$ echo "a beautiful day" | python3 scripts/dream.py --from_file -
```
## Shortcut for reusing seeds from the previous command
@ -202,7 +269,7 @@ Here's an example of using this to do a quick refinement. It also
illustrates using the new **-G** switch to turn on upscaling and
face enhancement (see previous section):
~~~~
```
dream> a cute child playing hopscotch -G0.5
[...]
outputs/img-samples/000039.3498014304.png: "a cute child playing hopscotch" -s50 -b1 -W512 -H512 -C7.5 -mk_lms -S3498014304
@ -212,9 +279,7 @@ dream> a cute child playing hopscotch -G1.0 -s100 -S -1
reusing previous seed 3498014304
[...]
outputs/img-samples/000040.3498014304.png: "a cute child playing hopscotch" -G1.0 -s100 -b1 -W512 -H512 -C7.5 -mk_lms -S3498014304
~~~~
```
## Weighted Prompts
@ -222,9 +287,9 @@ You may weight different sections of the prompt to tell the sampler to attach di
priority to them, by adding :(number) to the end of the section you wish to up- or downweight.
For example consider this prompt:
~~~~
```
tabby cat:0.25 white duck:0.75 hybrid
~~~~
```
This will tell the sampler to invest 25% of its effort on the tabby
cat aspect of the image and 75% on the white duck aspect
@ -239,7 +304,7 @@ and introducing a new vocabulary to the fixed model.
To train, prepare a folder that contains images sized at 512x512 and execute the following:
~~~~
```
# As the default backend is not available on Windows, if you're using that platform, execute SET PL_TORCH_DISTRIBUTED_BACKEND=gloo
(ldm) ~/stable-diffusion$ python3 ./main.py --base ./configs/stable-diffusion/v1-finetune.yaml \
-t \
@ -248,14 +313,14 @@ To train, prepare a folder that contains images sized at 512x512 and execute the
--gpus 0, \
--data_root D:/textual-inversion/my_cat \
--init_word 'cat'
~~~~
```
During the training process, files will be created in /logs/[project][time][project]/
where you can see the process.
conditioning* contains the training prompts
conditioning\* contains the training prompts
inputs, reconstruction the input images for the training epoch
samples, samples scaled for a sample of the prompt and one with the init word provided
samples, samples scaled for a sample of the prompt and one with the init word provided
On a RTX3090, the process for SD will take ~1h @1.6 iterations/sec.
@ -269,28 +334,29 @@ heat death of the universe, when you find a low loss epoch or around
Once the model is trained, specify the trained .pt file when starting
dream using
~~~~
```
(ldm) ~/stable-diffusion$ python3 ./scripts/dream.py --embedding_path /path/to/embedding.pt --full_precision
~~~~
```
Then, to utilize your subject at the dream prompt
~~~
```
dream> "a photo of *"
~~~
```
this also works with image2image
~~~~
```
dream> "waterfall and rainbow in the style of *" --init_img=./init-images/crude_drawing.png --strength=0.5 -s100 -n4
~~~~
```
It's also possible to train multiple tokens (modify the placeholder string in configs/stable-diffusion/v1-finetune.yaml) and combine LDM checkpoints using:
~~~~
```
(ldm) ~/stable-diffusion$ python3 ./scripts/merge_embeddings.py \
--manager_ckpts /path/to/first/embedding.pt /path/to/second/embedding.pt [...] \
--output_path /path/to/output/embedding.pt
~~~~
```
Credit goes to @rinongal and the repository located at
https://github.com/rinongal/textual_inversion Please see the
@ -298,80 +364,104 @@ repository and associated paper for details and limitations.
## Changes
* v1.11 (26 August 2022)
* NEW FEATURE: Support upscaling and face enhancement using the GFPGAN module. (kudos to [Oceanswave](https://github.com/Oceanswave)
* You now can specify a seed of -1 to use the previous image's seed, -2 to use the seed for the image generated before that, etc.
Seed memory only extends back to the previous command, but will work on all images generated with the -n# switch.
* Variant generation support temporarily disabled pending more general solution.
* Created a feature branch named **yunsaki-morphing-dream** which adds experimental support for
iteratively modifying the prompt and its parameters. Please see[ Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86)
for a synopsis of how this works. Note that when this feature is eventually added to the main branch, it will may be modified
significantly.
* v1.10 (25 August 2022)
* A barebones but fully functional interactive web server for online generation of txt2img and img2img.
* v1.09 (24 August 2022)
* A new -v option allows you to generate multiple variants of an initial image
in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave). [
See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
* Added ability to personalize text to image generation (kudos to [Oceanswave](https://github.com/Oceanswave) and [nicolai256](https://github.com/nicolai256))
* Enabled all of the samplers from k_diffusion
* v1.08 (24 August 2022)
* Escape single quotes on the dream> command before trying to parse. This avoids
parse errors.
* Removed instruction to get Python3.8 as first step in Windows install.
Anaconda3 does it for you.
* Added bounds checks for numeric arguments that could cause crashes.
* Cleaned up the copyright and license agreement files.
- v1.13 (in process)
- Supports a Google Colab notebook for a standalone server running on Google hardware [Arturo Mendivil](https://github.com/artmen1516)
- WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling [Kevin Gibbons](https://github.com/bakkot)
- WebUI supports incremental display of in-progress images during generation [Kevin Gibbons](https://github.com/bakkot)
- Output directory can be specified on the dream> command line.
- The grid was displaying duplicated images when not enough images to fill the final row [Muhammad Usama](https://github.com/SMUsamaShah)
- Can specify --grid on dream.py command line as the default.
- Miscellaneous internal bug and stability fixes.
* v1.07 (23 August 2022)
* Image filenames will now never fill gaps in the sequence, but will be assigned the
next higher name in the chosen directory. This ensures that the alphabetic and chronological
sort orders are the same.
- v1.12 (28 August 2022)
* v1.06 (23 August 2022)
* Added weighted prompt support contributed by [xraxra](https://github.com/xraxra)
* Example of using weighted prompts to tweak a demonic figure contributed by [bmaltais](https://github.com/bmaltais)
- Improved file handling, including ability to read prompts from standard input.
(kudos to [Yunsaki](https://github.com/yunsaki)
- The web server is now integrated with the dream.py script. Invoke by adding --web to
the dream.py command arguments.
- Face restoration and upscaling via GFPGAN and Real-ESGAN are now automatically
enabled if the GFPGAN directory is located as a sibling to Stable Diffusion.
VRAM requirements are modestly reduced. Thanks to both [Blessedcoolant](https://github.com/blessedcoolant) and
[Oceanswave](https://github.com/oceanswave) for their work on this.
- You can now swap samplers on the dream> command line. [Blessedcoolant](https://github.com/blessedcoolant)
* v1.05 (22 August 2022 - after the drop)
* Filenames now use the following formats:
000010.95183149.png -- Two files produced by the same command (e.g. -n2),
000010.26742632.png -- distinguished by a different seed.
- v1.11 (26 August 2022)
- NEW FEATURE: Support upscaling and face enhancement using the GFPGAN module. (kudos to [Oceanswave](https://github.com/Oceanswave)
- You now can specify a seed of -1 to use the previous image's seed, -2 to use the seed for the image generated before that, etc.
Seed memory only extends back to the previous command, but will work on all images generated with the -n# switch.
- Variant generation support temporarily disabled pending more general solution.
- Created a feature branch named **yunsaki-morphing-dream** which adds experimental support for
iteratively modifying the prompt and its parameters. Please see[ Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86)
for a synopsis of how this works. Note that when this feature is eventually added to the main branch, it will may be modified
significantly.
- v1.10 (25 August 2022)
- A barebones but fully functional interactive web server for online generation of txt2img and img2img.
- v1.09 (24 August 2022)
- A new -v option allows you to generate multiple variants of an initial image
in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave). [
See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
- Added ability to personalize text to image generation (kudos to [Oceanswave](https://github.com/Oceanswave) and [nicolai256](https://github.com/nicolai256))
- Enabled all of the samplers from k_diffusion
- v1.08 (24 August 2022)
000011.455191342.01.png -- Two files produced by the same command using
000011.455191342.02.png -- a batch size>1 (e.g. -b2). They have the same seed.
- Escape single quotes on the dream> command before trying to parse. This avoids
parse errors.
- Removed instruction to get Python3.8 as first step in Windows install.
Anaconda3 does it for you.
- Added bounds checks for numeric arguments that could cause crashes.
- Cleaned up the copyright and license agreement files.
000011.4160627868.grid#1-4.png -- a grid of four images (-g); the whole grid can
be regenerated with the indicated key
- v1.07 (23 August 2022)
* It should no longer be possible for one image to overwrite another
* You can use the "cd" and "pwd" commands at the dream> prompt to set and retrieve
the path of the output directory.
* v1.04 (22 August 2022 - after the drop)
* Updated README to reflect installation of the released weights.
* Suppressed very noisy and inconsequential warning when loading the frozen CLIP
tokenizer.
- Image filenames will now never fill gaps in the sequence, but will be assigned the
next higher name in the chosen directory. This ensures that the alphabetic and chronological
sort orders are the same.
* v1.03 (22 August 2022)
* The original txt2img and img2img scripts from the CompViz repository have been moved into
a subfolder named "orig_scripts", to reduce confusion.
* v1.02 (21 August 2022)
* A copy of the prompt and all of its switches and options is now stored in the corresponding
- v1.06 (23 August 2022)
- Added weighted prompt support contributed by [xraxra](https://github.com/xraxra)
- Example of using weighted prompts to tweak a demonic figure contributed by [bmaltais](https://github.com/bmaltais)
- v1.05 (22 August 2022 - after the drop)
- Filenames now use the following formats:
000010.95183149.png -- Two files produced by the same command (e.g. -n2),
000010.26742632.png -- distinguished by a different seed.
000011.455191342.01.png -- Two files produced by the same command using
000011.455191342.02.png -- a batch size>1 (e.g. -b2). They have the same seed.
000011.4160627868.grid#1-4.png -- a grid of four images (-g); the whole grid can
be regenerated with the indicated key
- It should no longer be possible for one image to overwrite another
- You can use the "cd" and "pwd" commands at the dream> prompt to set and retrieve
the path of the output directory.
- v1.04 (22 August 2022 - after the drop)
- Updated README to reflect installation of the released weights.
- Suppressed very noisy and inconsequential warning when loading the frozen CLIP
tokenizer.
- v1.03 (22 August 2022)
- The original txt2img and img2img scripts from the CompViz repository have been moved into
a subfolder named "orig_scripts", to reduce confusion.
- v1.02 (21 August 2022)
- A copy of the prompt and all of its switches and options is now stored in the corresponding
image in a tEXt metadata field named "Dream". You can read the prompt using scripts/images2prompt.py,
or an image editor that allows you to explore the full metadata.
**Please run "conda env update -f environment.yaml" to load the k_lms dependencies!!**
**Please run "conda env update -f environment.yaml" to load the k_lms dependencies!!**
* v1.01 (21 August 2022)
* added k_lms sampling.
**Please run "conda env update -f environment.yaml" to load the k_lms dependencies!!**
* use half precision arithmetic by default, resulting in faster execution and lower memory requirements
- v1.01 (21 August 2022)
- added k_lms sampling.
**Please run "conda env update -f environment.yaml" to load the k_lms dependencies!!**
- use half precision arithmetic by default, resulting in faster execution and lower memory requirements
Pass argument --full_precision to dream.py to get slower but more accurate image generation
## Installation
There are separate installation walkthroughs for [Linux/Mac](#linuxmac) and [Windows](#windows).
@ -379,38 +469,48 @@ There are separate installation walkthroughs for [Linux/Mac](#linuxmac) and [Win
### Linux/Mac
1. You will need to install the following prerequisites if they are not already available. Use your
operating system's preferred installer
* Python (version 3.8.5 recommended; higher may work)
* git
operating system's preferred installer
- Python (version 3.8.5 recommended; higher may work)
- git
2. Install the Python Anaconda environment manager using pip3.
```
~$ pip3 install anaconda
```
After installing anaconda, you should log out of your system and log back in. If the installation
worked, your command prompt will be prefixed by the name of the current anaconda environment, "(base)".
3. Copy the stable-diffusion source code from GitHub:
```
(base) ~$ git clone https://github.com/lstein/stable-diffusion.git
```
This will create stable-diffusion folder where you will follow the rest of the steps.
4. Enter the newly-created stable-diffusion folder. From this step forward make sure that you are working in the stable-diffusion directory!
```
(base) ~$ cd stable-diffusion
(base) ~/stable-diffusion$
```
5. Use anaconda to copy necessary python packages, create a new python environment named "ldm",
and activate the environment.
and activate the environment.
```
(base) ~/stable-diffusion$ conda env create -f environment.yaml
(base) ~/stable-diffusion$ conda activate ldm
(ldm) ~/stable-diffusion$
```
After these steps, your command prompt will be prefixed by "(ldm)" as shown above.
6. Load a couple of small machine-learning models required by stable diffusion:
```
(ldm) ~/stable-diffusion$ python3 scripts/preload_models.py
```
@ -430,13 +530,14 @@ to a page that prompts you to click the "download" link. Save the file somewhere
Now run the following commands from within the stable-diffusion directory. This will create a symbolic
link from the stable-diffusion model.ckpt file, to the true location of the sd-v1-4.ckpt file.
```
(ldm) ~/stable-diffusion$ mkdir -p models/ldm/stable-diffusion-v1
(ldm) ~/stable-diffusion$ ln -sf /path/to/sd-v1-4.ckpt models/ldm/stable-diffusion-v1/model.ckpt
```
8. Start generating images!
```
# for the pre-release weights use the -l or --liaon400m switch
(ldm) ~/stable-diffusion$ python3 scripts/dream.py -l
@ -447,15 +548,18 @@ link from the stable-diffusion model.ckpt file, to the true location of the sd-v
# for additional configuration switches and arguments, use -h or --help
(ldm) ~/stable-diffusion$ python3 scripts/dream.py -h
```
9. Subsequently, to relaunch the script, be sure to run "conda activate ldm" (step 5, second command), enter the "stable-diffusion"
directory, and then launch the dream script (step 8). If you forget to activate the ldm environment, the script will fail with multiple ModuleNotFound errors.
9. Subsequently, to relaunch the script, be sure to run "conda activate ldm" (step 5, second command), enter the "stable-diffusion"
directory, and then launch the dream script (step 8). If you forget to activate the ldm environment, the script will fail with multiple ModuleNotFound errors.
#### Updating to newer versions of the script
This distribution is changing rapidly. If you used the "git clone" method (step 5) to download the stable-diffusion directory, then to update to the latest and greatest version, launch the Anaconda window, enter "stable-diffusion", and type:
```
(ldm) ~/stable-diffusion$ git pull
```
This will bring your local copy into sync with the remote one.
### Windows
@ -467,24 +571,30 @@ This will bring your local copy into sync with the remote one.
3. Launch Anaconda from the Windows Start menu. This will bring up a command window. Type all the remaining commands in this window.
4. Run the command:
```
git clone https://github.com/lstein/stable-diffusion.git
```
This will create stable-diffusion folder where you will follow the rest of the steps.
5. Enter the newly-created stable-diffusion folder. From this step forward make sure that you are working in the stable-diffusion directory!
```
cd stable-diffusion
```
6. Run the following two commands:
```
conda env create -f environment.yaml (step 6a)
conda activate ldm (step 6b)
```
This will install all python requirements and activate the "ldm" environment which sets PATH and other environment variables properly.
7. Run the command:
```
python scripts\preload_models.py
```
@ -497,30 +607,32 @@ downloaded just-in-time)
8. Now you need to install the weights for the big stable diffusion model.
For running with the released weights, you will first need to set up
an acount with Hugging Face (https://huggingface.co). Use your
an acount with Hugging Face (https://huggingface.co). Use your
credentials to log in, and then point your browser at
https://huggingface.co/CompVis/stable-diffusion-v-1-4-original. You
https://huggingface.co/CompVis/stable-diffusion-v-1-4-original. You
may be asked to sign a license agreement at this point.
Click on "Files and versions" near the top of the page, and then click
on the file named "sd-v1-4.ckpt". You'll be taken to a page that
prompts you to click the "download" link. Now save the file somewhere
safe on your local machine. The weight file is >4 GB in size, so
safe on your local machine. The weight file is >4 GB in size, so
downloading may take a while.
Now run the following commands from **within the stable-diffusion
directory** to copy the weights file to the right place:
```
mkdir -p models\ldm\stable-diffusion-v1
copy C:\path\to\sd-v1-4.ckpt models\ldm\stable-diffusion-v1\model.ckpt
```
Please replace "C:\path\to\sd-v1.4.ckpt" with the correct path to wherever
you stashed this file. If you prefer not to copy or move the .ckpt file,
you stashed this file. If you prefer not to copy or move the .ckpt file,
you may instead create a shortcut to it from within
"models\ldm\stable-diffusion-v1\".
9. Start generating images!
```
# for the pre-release weights
python scripts\dream.py -l
@ -528,14 +640,17 @@ python scripts\dream.py -l
# for the post-release weights
python scripts\dream.py
```
10. Subsequently, to relaunch the script, first activate the Anaconda command window (step 3), enter the stable-diffusion directory (step 5, "cd \path\to\stable-diffusion"), run "conda activate ldm" (step 6b), and then launch the dream script (step 9).
#### Updating to newer versions of the script
This distribution is changing rapidly. If you used the "git clone" method (step 5) to download the stable-diffusion directory, then to update to the latest and greatest version, launch the Anaconda window, enter "stable-diffusion", and type:
```
git pull
```
This will bring your local copy into sync with the remote one.
## Simplified API for text to image generation
@ -544,22 +659,21 @@ For programmers who wish to incorporate stable-diffusion into other
products, this repository includes a simplified API for text to image generation, which
lets you create images from a prompt in just three lines of code:
~~~~
```
from ldm.simplet2i import T2I
model = T2I()
outputs = model.txt2img("a unicorn in manhattan")
~~~~
```
Outputs is a list of lists in the format [[filename1,seed1],[filename2,seed2]...]
Please see ldm/simplet2i.py for more information.
## Workaround for machines with limited internet connectivity
My development machine is a GPU node in a high-performance compute
cluster which has no connection to the internet. During model
initialization, stable-diffusion tries to download the Bert tokenizer
and a file needed by the kornia library. This obviously didn't work
and a file needed by the kornia library. This obviously didn't work
for me.
To work around this, I have modified ldm/modules/encoders/modules.py
@ -570,7 +684,7 @@ prior to running the code on an isolated one. This assumes that both
machines share a common network-mounted filesystem with a common
.cache directory.
~~~~
```
(ldm) ~/stable-diffusion$ python3 ./scripts/preload_models.py
preloading bert tokenizer...
Downloading: 100%|██████████████████████████████████| 28.0/28.0 [00:00<00:00, 49.3kB/s]
@ -582,7 +696,7 @@ preloading kornia requirements...
Downloading: "https://github.com/DagnyT/hardnet/raw/master/pretrained/train_liberty_with_aug/checkpoint_liberty_with_aug.pth" to /u/lstein/.cache/torch/hub/checkpoints/checkpoint_liberty_with_aug.pth
100%|███████████████████████████████████████████████| 5.10M/5.10M [00:00<00:00, 101MB/s]
...success
~~~~
```
If you don't need this change and want to download the files just in
time, copy over the file ldm/modules/encoders/modules.py from the
@ -595,16 +709,15 @@ For support,
please use this repository's GitHub Issues tracking service. Feel free
to send me an email if you use and like the script.
*Original Author:* Lincoln D. Stein <lincoln.stein@gmail.com>
_Original Author:_ Lincoln D. Stein <lincoln.stein@gmail.com>
*Contributions by:*
_Contributions by:_
[Peter Kowalczyk](https://github.com/slix), [Henry Harrison](https://github.com/hwharrison),
[xraxra](https://github.com/xraxra), [bmaltais](https://github.com/bmaltais), [Sean McLellan](https://github.com/Oceanswave),
[nicolai256](https://github.com/nicolai256), [Benjamin Warner](https://github.com/warner-benjamin),
[tildebyte](https://github.com/tildebyte),
[tildebyte](https://github.com/tildebyte),[yunsaki](https://github.com/yunsaki)
and [Tesseract Cat](https://github.com/TesseractCat)
Original portions of the software are Copyright (c) 2020 Lincoln D. Stein (https://github.com/lstein)
#Further Reading

256
Stable_Diffusion_AI_Notebook.ipynb Normal file
View File

@ -0,0 +1,256 @@
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Stable_Diffusion_AI_Notebook.ipynb",
"provenance": [],
"collapsed_sections": [],
"private_outputs": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU",
"gpuClass": "standard"
},
"cells": [
{
"cell_type": "markdown",
"source": [
"# Stable Diffusion AI Notebook\n",
"\n",
"<img src=\"https://user-images.githubusercontent.com/60411196/186547976-d9de378a-9de8-4201-9c25-c057a9c59bad.jpeg\" alt=\"stable-diffusion-ai\" width=\"170px\"/> <br>\n",
"#### Instructions:\n",
"1. Execute each cell in order to mount a Dream bot and create images from text. <br>\n",
"2. Once cells 1-8 were run correctly you'll be executing a terminal in cell #9, you'll to enter `pipenv run scripts/dream.py` command to run Dream bot.<br> \n",
"3. After launching dream bot, you'll see: <br> `Dream > ` in terminal. <br> Insert a command, eg. `Dream > Astronaut floating in a distant galaxy`, or type `-h` for help.\n",
"3. After completion you'll see your generated images in path `stable-diffusion/outputs/img-samples/`, you can also display images in cell #10.\n",
"4. To quit Dream bot use `q` command. <br> \n",
"---\n",
"<font color=\"red\">Note:</font> It takes some time to load, but after installing all dependencies you can use the bot all time you want while colab instance is up. <br>\n",
"<font color=\"red\">Requirements:</font> For this notebook to work you need to have [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) stored in your Google Drive, it will be needed in cell #6\n",
"##### For more details visit Github repository: [lstein/stable-diffusion](https://github.com/lstein/stable-diffusion)\n",
"---\n"
],
"metadata": {
"id": "ycYWcsEKc6w7"
}
},
{
"cell_type": "code",
"source": [
"#@title 1. Check current GPU assigned\n",
"!nvidia-smi -L\n",
"!nvidia-smi"
],
"metadata": {
"cellView": "form",
"id": "a2Z5Qu_o8VtQ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "vbI9ZsQHzjqF"
},
"outputs": [],
"source": [
"#@title 2. Download stable-diffusion Repository\n",
"from os.path import exists\n",
"\n",
"if exists(\"/content/stable-diffusion/\")==True:\n",
" print(\"Already downloaded repo\")\n",
"else:\n",
" !git clone --quiet https://github.com/lstein/stable-diffusion.git # Original repo\n",
" %cd stable-diffusion/\n",
" !git checkout --quiet tags/release-1.09\n",
" "
]
},
{
"cell_type": "code",
"source": [
"#@title 3. Install Python 3.8 \n",
"%%capture --no-stderr\n",
"import gc\n",
"!apt-get -qq install python3.8\n",
"gc.collect()"
],
"metadata": {
"id": "daHlozvwKesj",
"cellView": "form"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@title 4. Install dependencies from file in a VirtualEnv\n",
"#@markdown Be patient, it takes ~ 5 - 7min <br>\n",
"%%capture --no-stderr\n",
"#Virtual environment\n",
"!pip install pipenv -q\n",
"!pip install colab-xterm\n",
"%load_ext colabxterm\n",
"!pipenv --python 3.8\n",
"!pipenv install -r requirements.txt --skip-lock\n",
"gc.collect()\n"
],
"metadata": {
"cellView": "form",
"id": "QbXcGXYEFSNB"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@title 5. Mount google Drive\n",
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"cellView": "form",
"id": "YEWPV-sF1RDM"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@title 6. Drive Path to model\n",
"#@markdown Path should start with /content/drive/path-to-your-file <br>\n",
"#@markdown <font color=\"red\">Note:</font> Model should be downloaded from https://huggingface.co <br>\n",
"#@markdown Lastest release: [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)\n",
"from os.path import exists\n",
"\n",
"model_path = \"\" #@param {type:\"string\"}\n",
"if exists(model_path)==True:\n",
" print(\"✅ Valid directory\")\n",
"else: \n",
" print(\"❌ File doesn't exist\")"
],
"metadata": {
"cellView": "form",
"id": "zRTJeZ461WGu"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@title 7. Symlink to model\n",
"\n",
"from os.path import exists\n",
"import os \n",
"\n",
"# Folder creation if it doesn't exist\n",
"if exists(\"/content/stable-diffusion/models/ldm/stable-diffusion-v1\")==True:\n",
" print(\"❗ Dir stable-diffusion-v1 already exists\")\n",
"else:\n",
" %mkdir /content/stable-diffusion/models/ldm/stable-diffusion-v1\n",
" print(\"✅ Dir stable-diffusion-v1 created\")\n",
"\n",
"# Symbolic link if it doesn't exist\n",
"if exists(\"/content/stable-diffusion/models/ldm/stable-diffusion-v1/model.ckpt\")==True:\n",
" print(\"❗ Symlink already created\")\n",
"else: \n",
" src = model_path\n",
" dst = '/content/stable-diffusion/models/ldm/stable-diffusion-v1/model.ckpt'\n",
" os.symlink(src, dst) \n",
" print(\"✅ Symbolic link created successfully\")"
],
"metadata": {
"id": "UY-NNz4I8_aG",
"cellView": "form"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@title 8. Load small ML models required\n",
"%%capture --no-stderr\n",
"!pipenv run scripts/preload_models.py\n",
"gc.collect()"
],
"metadata": {
"cellView": "form",
"id": "ChIDWxLVHGGJ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@title 9. Run Terminal and Execute Dream bot\n",
"#@markdown <font color=\"blue\">Steps:</font> <br>\n",
"#@markdown 1. Execute command `pipenv run scripts/dream.py` to run dream bot.<br>\n",
"#@markdown 2. After initialized you'll see `Dream>` line.<br>\n",
"#@markdown 3. Example text: `Astronaut floating in a distant galaxy` <br>\n",
"#@markdown 4. To quit Dream bot use: `q` command.<br>\n",
"\n",
"#Run from virtual env\n",
"\n",
"%xterm\n",
"gc.collect()"
],
"metadata": {
"id": "ir4hCrMIuUpl",
"cellView": "form"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@title 10. Show generated images\n",
"\n",
"import glob\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.image as mpimg\n",
"%matplotlib inline\n",
"\n",
"images = []\n",
"for img_path in glob.glob('/content/stable-diffusion/outputs/img-samples/*.png'):\n",
" images.append(mpimg.imread(img_path))\n",
"\n",
"# Remove ticks and labels on x-axis and y-axis both\n",
"\n",
"plt.figure(figsize=(20,10))\n",
"\n",
"columns = 5\n",
"for i, image in enumerate(images):\n",
" ax = plt.subplot(len(images) / columns + 1, columns, i + 1)\n",
" ax.axes.xaxis.set_visible(False)\n",
" ax.axes.yaxis.set_visible(False)\n",
" ax.axis('off')\n",
" plt.imshow(image)\n",
" gc.collect()\n",
"\n"
],
"metadata": {
"cellView": "form",
"id": "qnLohSHmKoGk"
},
"execution_count": null,
"outputs": []
}
]
}

37
ldm/dream/pngwriter.py
View File

@ -14,6 +14,8 @@ from math import sqrt, floor, ceil
from PIL import Image, PngImagePlugin
# -------------------image generation utils-----
class PngWriter:
def __init__(self, outdir, prompt=None, batch_size=1):
self.outdir = outdir
@ -23,18 +25,19 @@ class PngWriter:
self.files_written = []
os.makedirs(outdir, exist_ok=True)
def write_image(self, image, seed):
def write_image(self, image, seed, upscaled=False):
self.filepath = self.unique_filename(
seed, self.filepath
seed, upscaled, self.filepath
) # will increment name in some sensible way
try:
prompt = f'{self.prompt} -S{seed}'
self.save_image_and_prompt_to_png(image, prompt, self.filepath)
except IOError as e:
print(e)
self.files_written.append([self.filepath, seed])
if not upscaled:
self.files_written.append([self.filepath, seed])
def unique_filename(self, seed, previouspath=None):
def unique_filename(self, seed, upscaled=False, previouspath=None):
revision = 1
if previouspath is None:
@ -57,7 +60,7 @@ class PngWriter:
basename = os.path.basename(previouspath)
x = re.match('^(\d+)\..*\.png', basename)
if not x:
return self.unique_filename(seed, previouspath)
return self.unique_filename(seed, upscaled, previouspath)
basecount = int(x.groups()[0])
series = 0
@ -65,13 +68,12 @@ class PngWriter:
while not finished:
series += 1
filename = f'{basecount:06}.{seed}.png'
if self.batch_size > 1 or os.path.exists(
os.path.join(self.outdir, filename)
):
path = os.path.join(self.outdir, filename)
if self.batch_size > 1 or os.path.exists(path):
if upscaled:
break
filename = f'{basecount:06}.{seed}.{series:02}.png'
finished = not os.path.exists(
os.path.join(self.outdir, filename)
)
finished = not os.path.exists(path)
return os.path.join(self.outdir, filename)
def save_image_and_prompt_to_png(self, image, prompt, path):
@ -84,14 +86,17 @@ class PngWriter:
if None in (rows, cols):
rows = floor(sqrt(image_cnt)) # try to make it square
cols = ceil(image_cnt / rows)
width = image_list[0].width
width = image_list[0].width
height = image_list[0].height
grid_img = Image.new('RGB', (width * cols, height * rows))
i = 0
for r in range(0, rows):
for c in range(0, cols):
i = r * rows + c
if i>=len(image_list):
break
grid_img.paste(image_list[i], (c * width, r * height))
i = i + 1
return grid_img
@ -101,6 +106,8 @@ class PromptFormatter:
self.t2i = t2i
self.opt = opt
# note: the t2i object should provide all these values.
# there should be no need to or against opt values
def normalize_prompt(self):
"""Normalize the prompt and switches"""
t2i = self.t2i
@ -113,13 +120,15 @@ class PromptFormatter:
switches.append(f'-W{opt.width or t2i.width}')
switches.append(f'-H{opt.height or t2i.height}')
switches.append(f'-C{opt.cfg_scale or t2i.cfg_scale}')
switches.append(f'-m{t2i.sampler_name}')
switches.append(f'-A{opt.sampler_name or t2i.sampler_name}')
if opt.init_img:
switches.append(f'-I{opt.init_img}')
if opt.strength and opt.init_img is not None:
switches.append(f'-f{opt.strength or t2i.strength}')
if opt.gfpgan_strength:
switches.append(f'-G{opt.gfpgan_strength}')
if opt.upscale:
switches.append(f'-U {" ".join([str(u) for u in opt.upscale])}')
if t2i.full_precision:
switches.append('-F')
return ' '.join(switches)

149
ldm/dream/server.py Normal file
View File

@ -0,0 +1,149 @@
import json
import base64
import mimetypes
import os
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
from ldm.dream.pngwriter import PngWriter
class DreamServer(BaseHTTPRequestHandler):
model = None
def do_GET(self):
if self.path == "/":
self.send_response(200)
self.send_header("Content-type", "text/html")
self.end_headers()
with open("./static/dream_web/index.html", "rb") as content:
self.wfile.write(content.read())
else:
path = "." + self.path
cwd = os.getcwd()
is_in_cwd = os.path.commonprefix((os.path.realpath(path), cwd)) == cwd
if not (is_in_cwd and os.path.exists(path)):
self.send_response(404)
return
mime_type = mimetypes.guess_type(path)[0]
if mime_type is not None:
self.send_response(200)
self.send_header("Content-type", mime_type)
self.end_headers()
with open("." + self.path, "rb") as content:
self.wfile.write(content.read())
else:
self.send_response(404)
def do_POST(self):
self.send_response(200)
self.send_header("Content-type", "application/json")
self.end_headers()
content_length = int(self.headers['Content-Length'])
post_data = json.loads(self.rfile.read(content_length))
prompt = post_data['prompt']
initimg = post_data['initimg']
iterations = int(post_data['iterations'])
steps = int(post_data['steps'])
width = int(post_data['width'])
height = int(post_data['height'])
cfgscale = float(post_data['cfgscale'])
gfpgan_strength = float(post_data['gfpgan_strength'])
upscale_level = post_data['upscale_level']
upscale_strength = post_data['upscale_strength']
upscale = [int(upscale_level),float(upscale_strength)] if upscale_level != '' else None
seed = None if int(post_data['seed']) == -1 else int(post_data['seed'])
print(f"Request to generate with prompt: {prompt}")
# In order to handle upscaled images, the PngWriter needs to maintain state
# across images generated by each call to prompt2img(), so we define it in
# the outer scope of image_done()
config = post_data.copy() # Shallow copy
config['initimg'] = ''
images_generated = 0 # helps keep track of when upscaling is started
images_upscaled = 0 # helps keep track of when upscaling is completed
pngwriter = PngWriter(
"./outputs/img-samples/", config['prompt'], 1
)
# if upscaling is requested, then this will be called twice, once when
# the images are first generated, and then again when after upscaling
# is complete. The upscaling replaces the original file, so the second
# entry should not be inserted into the image list.
def image_done(image, seed, upscaled=False):
pngwriter.write_image(image, seed, upscaled)
# Append post_data to log, but only once!
if not upscaled:
current_image = pngwriter.files_written[-1]
with open("./outputs/img-samples/dream_web_log.txt", "a") as log:
log.write(f"{current_image[0]}: {json.dumps(config)}\n")
self.wfile.write(bytes(json.dumps(
{'event':'result', 'files':current_image, 'config':config}
) + '\n',"utf-8"))
# control state of the "postprocessing..." message
upscaling_requested = upscale or gfpgan_strength>0
nonlocal images_generated # NB: Is this bad python style? It is typical usage in a perl closure.
nonlocal images_upscaled # NB: Is this bad python style? It is typical usage in a perl closure.
if upscaled:
images_upscaled += 1
else:
images_generated +=1
if upscaling_requested:
action = None
if images_generated >= iterations:
if images_upscaled < iterations:
action = 'upscaling-started'
else:
action = 'upscaling-done'
if action:
x = images_upscaled+1
self.wfile.write(bytes(json.dumps(
{'event':action,'processed_file_cnt':f'{x}/{iterations}'}
) + '\n',"utf-8"))
def image_progress(image, step):
self.wfile.write(bytes(json.dumps(
{'event':'step', 'step':step}
) + '\n',"utf-8"))
if initimg is None:
# Run txt2img
self.model.prompt2image(prompt,
iterations=iterations,
cfg_scale = cfgscale,
width = width,
height = height,
seed = seed,
steps = steps,
gfpgan_strength = gfpgan_strength,
upscale = upscale,
step_callback=image_progress,
image_callback=image_done)
else:
# Decode initimg as base64 to temp file
with open("./img2img-tmp.png", "wb") as f:
initimg = initimg.split(",")[1] # Ignore mime type
f.write(base64.b64decode(initimg))
# Run img2img
self.model.prompt2image(prompt,
init_img = "./img2img-tmp.png",
iterations = iterations,
cfg_scale = cfgscale,
seed = seed,
steps = steps,
gfpgan_strength=gfpgan_strength,
upscale = upscale,
step_callback=image_progress,
image_callback=image_done)
# Remove the temp file
os.remove("./img2img-tmp.png")
print(f"Prompt generated!")
class ThreadingDreamServer(ThreadingHTTPServer):
def __init__(self, server_address):
super(ThreadingDreamServer, self).__init__(server_address, DreamServer)

165
ldm/gfpgan/gfpgan_tools.py Normal file
View File

@ -0,0 +1,165 @@
import torch
import warnings
import os
import sys
import numpy as np
from PIL import Image
from scripts.dream import create_argv_parser
arg_parser = create_argv_parser()
opt = arg_parser.parse_args()
def _run_gfpgan(image, strength, prompt, seed, upsampler_scale=4):
print(f'\n* GFPGAN - Restoring Faces: {prompt} : seed:{seed}')
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
warnings.filterwarnings('ignore', category=UserWarning)
try:
model_path = os.path.join(opt.gfpgan_dir, opt.gfpgan_model_path)
if not os.path.isfile(model_path):
raise Exception('GFPGAN model not found at path ' + model_path)
sys.path.append(os.path.abspath(opt.gfpgan_dir))
from gfpgan import GFPGANer
bg_upsampler = _load_gfpgan_bg_upsampler(
opt.gfpgan_bg_upsampler, upsampler_scale, opt.gfpgan_bg_tile
)
gfpgan = GFPGANer(
model_path=model_path,
upscale=upsampler_scale,
arch='clean',
channel_multiplier=2,
bg_upsampler=bg_upsampler,
)
except Exception:
import traceback
print('Error loading GFPGAN:', file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
if gfpgan is None:
print(
f'GFPGAN not initialized, it must be loaded via the --gfpgan argument'
)
return image
image = image.convert('RGB')
cropped_faces, restored_faces, restored_img = gfpgan.enhance(
np.array(image, dtype=np.uint8),
has_aligned=False,
only_center_face=False,
paste_back=True,
)
res = Image.fromarray(restored_img)
if strength < 1.0:
# Resize the image to the new image if the sizes have changed
if restored_img.size != image.size:
image = image.resize(res.size)
res = Image.blend(image, res, strength)
if torch.cuda.is_available():
torch.cuda.empty_cache()
gfpgan = None
return res
def _load_gfpgan_bg_upsampler(bg_upsampler, upsampler_scale, bg_tile=400):
if bg_upsampler == 'realesrgan':
if not torch.cuda.is_available(): # CPU
warnings.warn(
'The unoptimized RealESRGAN is slow on CPU. We do not use it. '
'If you really want to use it, please modify the corresponding codes.'
)
bg_upsampler = None
else:
model_path = {
2: 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
4: 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth',
}
if upsampler_scale not in model_path:
return None
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
if upsampler_scale == 4:
model = RRDBNet(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=23,
num_grow_ch=32,
scale=4,
)
if upsampler_scale == 2:
model = RRDBNet(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=23,
num_grow_ch=32,
scale=2,
)
bg_upsampler = RealESRGANer(
scale=upsampler_scale,
model_path=model_path[upsampler_scale],
model=model,
tile=bg_tile,
tile_pad=10,
pre_pad=0,
half=True,
) # need to set False in CPU mode
else:
bg_upsampler = None
return bg_upsampler
def real_esrgan_upscale(image, strength, upsampler_scale, prompt, seed):
print(
f'\n* Real-ESRGAN Upscaling: {prompt} : seed:{seed} : scale:{upsampler_scale}x'
)
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
warnings.filterwarnings('ignore', category=UserWarning)
try:
upsampler = _load_gfpgan_bg_upsampler(
opt.gfpgan_bg_upsampler, upsampler_scale, opt.gfpgan_bg_tile
)
except Exception:
import traceback
print('Error loading Real-ESRGAN:', file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
output, img_mode = upsampler.enhance(
np.array(image, dtype=np.uint8),
outscale=upsampler_scale,
alpha_upsampler=opt.gfpgan_bg_upsampler,
)
res = Image.fromarray(output)
if strength < 1.0:
# Resize the image to the new image if the sizes have changed
if output.size != image.size:
image = image.resize(res.size)
res = Image.blend(image, res, strength)
if torch.cuda.is_available():
torch.cuda.empty_cache()
upsampler = None
return res

6
ldm/models/diffusion/ksampler.py
View File

@ -61,6 +61,9 @@ class KSampler(object):
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
**kwargs,
):
def route_callback(k_callback_values):
if img_callback is not None:
img_callback(k_callback_values['x'], k_callback_values['i'])
sigmas = self.model.get_sigmas(S)
if x_T:
@ -78,7 +81,8 @@ class KSampler(object):
}
return (
K.sampling.__dict__[f'sample_{self.schedule}'](
model_wrap_cfg, x, sigmas, extra_args=extra_args
model_wrap_cfg, x, sigmas, extra_args=extra_args,
callback=route_callback
),
None,
)

10
ldm/modules/embedding_manager.py
View File

@ -215,11 +215,13 @@ class EmbeddingManager(nn.Module):
ckpt_path,
)
def load(self, ckpt_path):
def load(self, ckpt_path, full=True):
ckpt = torch.load(ckpt_path, map_location='cpu')
self.string_to_token_dict = ckpt['string_to_token']
self.string_to_param_dict = ckpt['string_to_param']
self.string_to_token_dict = ckpt["string_to_token"]
self.string_to_param_dict = ckpt["string_to_param"]
if not full:
for key, value in self.string_to_param_dict.items():
self.string_to_param_dict[key] = torch.nn.Parameter(value.half())
def get_embedding_norms_squared(self):
all_params = torch.cat(

207
ldm/simplet2i.py
View File

@ -7,7 +7,6 @@
import torch
import numpy as np
import random
import sys
import os
from omegaconf import OmegaConf
from PIL import Image
@ -21,7 +20,7 @@ from contextlib import contextmanager, nullcontext
import transformers
import time
import re
import traceback
import sys
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
@ -123,6 +122,7 @@ class T2I:
cfg_scale=7.5,
weights='models/ldm/stable-diffusion-v1/model.ckpt',
config='configs/stable-diffusion/v1-inference.yaml',
grid=False,
width=512,
height=512,
sampler_name='klms',
@ -133,9 +133,9 @@ class T2I:
full_precision=False,
strength=0.75, # default in scripts/img2img.py
embedding_path=None,
latent_diffusion_weights=False, # just to keep track of this parameter when regenerating prompt
# just to keep track of this parameter when regenerating prompt
latent_diffusion_weights=False,
device='cuda',
gfpgan=None,
):
self.batch_size = batch_size
self.iterations = iterations
@ -148,6 +148,7 @@ class T2I:
self.sampler_name = sampler_name
self.latent_channels = latent_channels
self.downsampling_factor = downsampling_factor
self.grid = grid
self.ddim_eta = ddim_eta
self.precision = precision
self.full_precision = full_precision
@ -157,7 +158,8 @@ class T2I:
self.sampler = None
self.latent_diffusion_weights = latent_diffusion_weights
self.device = device
self.gfpgan = gfpgan
self.session_peakmem = torch.cuda.max_memory_allocated()
if seed is None:
self.seed = self._new_seed()
else:
@ -175,16 +177,15 @@ class T2I:
outdir, prompt, kwargs.get('batch_size', self.batch_size)
)
for r in results:
metadata_str = f'prompt2png("{prompt}" {kwargs} seed={r[1]}' # gets written into the PNG
pngwriter.write_image(r[0], r[1])
return pngwriter.files_written
def txt2img(self, prompt, **kwargs):
outdir = kwargs.get('outdir', 'outputs/img-samples')
outdir = kwargs.pop('outdir', 'outputs/img-samples')
return self.prompt2png(prompt, outdir, **kwargs)
def img2img(self, prompt, **kwargs):
outdir = kwargs.get('outdir', 'outputs/img-samples')
outdir = kwargs.pop('outdir', 'outputs/img-samples')
assert (
'init_img' in kwargs
), 'call to img2img() must include the init_img argument'
@ -202,14 +203,18 @@ class T2I:
ddim_eta=None,
skip_normalize=False,
image_callback=None,
step_callback=None,
# these are specific to txt2img
width=None,
height=None,
# these are specific to img2img
init_img=None,
strength=None,
gfpgan_strength=None,
gfpgan_strength=0,
save_original=False,
upscale=None,
variants=None,
sampler_name=None,
**args,
): # eat up additional cruft
"""
@ -228,9 +233,14 @@ class T2I:
gfpgan_strength // strength for GFPGAN. 0.0 preserves image exactly, 1.0 replaces it completely
ddim_eta // image randomness (eta=0.0 means the same seed always produces the same image)
variants // if >0, the 1st generated image will be passed back to img2img to generate the requested number of variants
step_callback // a function or method that will be called each step
image_callback // a function or method that will be called each time an image is generated
To use the callback, define a function of method that receives two arguments, an Image object
To use the step callback, define a function that receives two arguments:
- Image GPU data
- The step number
To use the image callback, define a function of method that receives two arguments, an Image object
and the seed. You can then do whatever you like with the image, including converting it to
different formats and manipulating it. For example:
@ -262,14 +272,19 @@ class T2I:
h = int(height / 64) * 64
if h != height or w != width:
print(
f'Height and width must be multiples of 64. Resizing to {h}x{w}'
f'Height and width must be multiples of 64. Resizing to {h}x{w}.'
)
height = h
width = w
scope = autocast if self.precision == 'autocast' else nullcontext
if sampler_name and (sampler_name != self.sampler_name):
self.sampler_name = sampler_name
self._set_sampler()
tic = time.time()
torch.cuda.torch.cuda.reset_peak_memory_stats()
results = list()
try:
@ -285,6 +300,7 @@ class T2I:
skip_normalize=skip_normalize,
init_img=init_img,
strength=strength,
callback=step_callback,
)
else:
images_iterator = self._txt2img(
@ -297,32 +313,54 @@ class T2I:
skip_normalize=skip_normalize,
width=width,
height=height,
callback=step_callback,
)
with scope(self.device.type), self.model.ema_scope():
for n in trange(iterations, desc='Sampling'):
for n in trange(iterations, desc='Generating'):
seed_everything(seed)
iter_images = next(images_iterator)
for image in iter_images:
try:
# if gfpgan strength is none or less than or equal to 0.0 then
# don't even attempt to use GFPGAN.
# if the user specified a value of -G that satisifies the condition and
# --gfpgan wasn't specified, at startup then
# the net result is a message gets printed - nothing else happens.
if gfpgan_strength is not None and gfpgan_strength > 0.0:
image = self._run_gfpgan(
image, gfpgan_strength
)
except Exception as e:
print(
f'Error running GFPGAN - Your image was not enhanced.\n{e}'
)
results.append([image, seed])
if image_callback is not None:
image_callback(image, seed)
seed = self._new_seed()
if upscale is not None or gfpgan_strength > 0:
for result in results:
image, seed = result
try:
if upscale is not None:
from ldm.gfpgan.gfpgan_tools import (
real_esrgan_upscale,
)
if len(upscale) < 2:
upscale.append(0.75)
image = real_esrgan_upscale(
image,
upscale[1],
int(upscale[0]),
prompt,
seed,
)
if gfpgan_strength > 0:
from ldm.gfpgan.gfpgan_tools import _run_gfpgan
image = _run_gfpgan(
image, gfpgan_strength, prompt, seed, 1
)
except Exception as e:
print(
f'Error running RealESRGAN - Your image was not upscaled.\n{e}'
)
if image_callback is not None:
if save_original:
image_callback(image, seed)
else:
image_callback(image, seed, upscaled=True)
else: # no callback passed, so we simply replace old image with rescaled one
result[0] = image
except KeyboardInterrupt:
print('*interrupted*')
print(
@ -333,7 +371,21 @@ class T2I:
print('Are you sure your system has an adequate NVIDIA GPU?')
toc = time.time()
print(f'{len(results)} images generated in', '%4.2fs' % (toc - tic))
self.session_peakmem = max(
self.session_peakmem, torch.cuda.max_memory_allocated()
)
print('Usage stats:')
print(
f' {len(results)} image(s) generated in', '%4.2fs' % (toc - tic)
)
print(
f' Max VRAM used for this generation:',
'%4.2fG' % (torch.cuda.max_memory_allocated() / 1e9),
)
print(
f' Max VRAM used since script start: ',
'%4.2fG' % (self.session_peakmem / 1e9),
)
return results
@torch.no_grad()
@ -348,6 +400,7 @@ class T2I:
skip_normalize,
width,
height,
callback,
):
"""
An infinite iterator of images from the prompt.
@ -371,6 +424,7 @@ class T2I:
unconditional_guidance_scale=cfg_scale,
unconditional_conditioning=uc,
eta=ddim_eta,
img_callback=callback
)
yield self._samples_to_images(samples)
@ -386,6 +440,7 @@ class T2I:
skip_normalize,
init_img,
strength,
callback, # Currently not implemented for img2img
):
"""
An infinite iterator of images from the prompt and the initial image
@ -492,46 +547,50 @@ class T2I:
self.device = self._get_device()
model = self._load_model_from_config(config, self.weights)
if self.embedding_path is not None:
model.embedding_manager.load(self.embedding_path)
model.embedding_manager.load(
self.embedding_path, self.full_precision
)
self.model = model.to(self.device)
# model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
self.model.cond_stage_model.device = self.device
except AttributeError:
import traceback
print('Error loading model. Only the CUDA backend is supported',file=sys.stderr)
print(traceback.format_exc(),file=sys.stderr)
raise SystemExit
msg = f'setting sampler to {self.sampler_name}'
if self.sampler_name == 'plms':
self.sampler = PLMSSampler(self.model, device=self.device)
elif self.sampler_name == 'ddim':
self.sampler = DDIMSampler(self.model, device=self.device)
elif self.sampler_name == 'k_dpm_2_a':
self.sampler = KSampler(
self.model, 'dpm_2_ancestral', device=self.device
)
elif self.sampler_name == 'k_dpm_2':
self.sampler = KSampler(
self.model, 'dpm_2', device=self.device
)
elif self.sampler_name == 'k_euler_a':
self.sampler = KSampler(
self.model, 'euler_ancestral', device=self.device
)
elif self.sampler_name == 'k_euler':
self.sampler = KSampler(
self.model, 'euler', device=self.device
)
elif self.sampler_name == 'k_heun':
self.sampler = KSampler(self.model, 'heun', device=self.device)
elif self.sampler_name == 'k_lms':
self.sampler = KSampler(self.model, 'lms', device=self.device)
else:
msg = f'unsupported sampler {self.sampler_name}, defaulting to plms'
self.sampler = PLMSSampler(self.model, device=self.device)
print(msg)
self._set_sampler()
return self.model
def _set_sampler(self):
msg = f'>> Setting Sampler to {self.sampler_name}'
if self.sampler_name == 'plms':
self.sampler = PLMSSampler(self.model, device=self.device)
elif self.sampler_name == 'ddim':
self.sampler = DDIMSampler(self.model, device=self.device)
elif self.sampler_name == 'k_dpm_2_a':
self.sampler = KSampler(
self.model, 'dpm_2_ancestral', device=self.device
)
elif self.sampler_name == 'k_dpm_2':
self.sampler = KSampler(self.model, 'dpm_2', device=self.device)
elif self.sampler_name == 'k_euler_a':
self.sampler = KSampler(
self.model, 'euler_ancestral', device=self.device
)
elif self.sampler_name == 'k_euler':
self.sampler = KSampler(self.model, 'euler', device=self.device)
elif self.sampler_name == 'k_heun':
self.sampler = KSampler(self.model, 'heun', device=self.device)
elif self.sampler_name == 'k_lms':
self.sampler = KSampler(self.model, 'lms', device=self.device)
else:
msg = f'>> Unsupported Sampler: {self.sampler_name}, Defaulting to plms'
self.sampler = PLMSSampler(self.model, device=self.device)
print(msg)
def _load_model_from_config(self, config, ckpt):
print(f'Loading model from {ckpt}')
pl_sd = torch.load(ckpt, map_location='cpu')
@ -548,13 +607,16 @@ class T2I:
)
else:
print(
'Using half precision math. Call with --full_precision to use slower but more accurate full precision.'
'Using half precision math. Call with --full_precision to use more accurate but VRAM-intensive full precision.'
)
model.half()
return model
def _load_img(self, path):
image = Image.open(path).convert('RGB')
print(f'image path = {path}, cwd = {os.getcwd()}')
with Image.open(path) as img:
image = img.convert('RGB')
w, h = image.size
print(f'loaded input image of size ({w}, {h}) from {path}')
w, h = map(
@ -612,28 +674,3 @@ class T2I:
weights.append(1.0)
remaining = 0
return prompts, weights
def _run_gfpgan(self, image, strength):
if self.gfpgan is None:
print(
f'GFPGAN not initialized, it must be loaded via the --gfpgan argument'
)
return image
image = image.convert('RGB')
cropped_faces, restored_faces, restored_img = self.gfpgan.enhance(
np.array(image, dtype=np.uint8),
has_aligned=False,
only_center_face=False,
paste_back=True,
)
res = Image.fromarray(restored_img)
if strength < 1.0:
# Resize the image to the new image if the sizes have changed
if restored_img.size != image.size:
image = image.resize(res.size)
res = Image.blend(image, res, strength)
return res

370
scripts/dream.py
View File

@ -9,8 +9,7 @@ import copy
import warnings
import ldm.dream.readline
from ldm.dream.pngwriter import PngWriter, PromptFormatter
debugging = False
from ldm.dream.server import DreamServer, ThreadingDreamServer
def main():
@ -52,7 +51,9 @@ def main():
weights=weights,
full_precision=opt.full_precision,
config=config,
latent_diffusion_weights=opt.laion400m, # this is solely for recreating the prompt
grid = opt.grid,
# this is solely for recreating the prompt
latent_diffusion_weights=opt.laion400m,
embedding_path=opt.embedding_path,
device=opt.device,
)
@ -64,80 +65,50 @@ def main():
# gets rid of annoying messages about random seed
logging.getLogger('pytorch_lightning').setLevel(logging.ERROR)
# load the infile as a list of lines
infile = None
try:
if opt.infile is not None:
infile = open(opt.infile, 'r')
except FileNotFoundError as e:
print(e)
exit(-1)
if opt.infile:
try:
if os.path.isfile(opt.infile):
infile = open(opt.infile, 'r', encoding='utf-8')
elif opt.infile == '-': # stdin
infile = sys.stdin
else:
raise FileNotFoundError(f'{opt.infile} not found.')
except (FileNotFoundError, IOError) as e:
print(f'{e}. Aborting.')
sys.exit(-1)
# preload the model
t2i.load_model()
# load GFPGAN if requested
if opt.use_gfpgan:
print('\n* --gfpgan was specified, loading gfpgan...')
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
if not infile:
print(
"\n* Initialization done! Awaiting your command (-h for help, 'q' to quit)"
)
try:
model_path = os.path.join(
opt.gfpgan_dir, opt.gfpgan_model_path
)
if not os.path.isfile(model_path):
raise Exception(
'GFPGAN model not found at path ' + model_path
)
sys.path.append(os.path.abspath(opt.gfpgan_dir))
from gfpgan import GFPGANer
bg_upsampler = load_gfpgan_bg_upsampler(
opt.gfpgan_bg_upsampler, opt.gfpgan_bg_tile
)
t2i.gfpgan = GFPGANer(
model_path=model_path,
upscale=opt.gfpgan_upscale,
arch='clean',
channel_multiplier=2,
bg_upsampler=bg_upsampler,
)
except Exception:
import traceback
print('Error loading GFPGAN:', file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print(
"\n* Initialization done! Awaiting your command (-h for help, 'q' to quit, 'cd' to change output dir, 'pwd' to print output dir)..."
)
log_path = os.path.join(opt.outdir, 'dream_log.txt')
with open(log_path, 'a') as log:
cmd_parser = create_cmd_parser()
main_loop(t2i, opt.outdir, cmd_parser, log, infile)
log.close()
if infile:
infile.close()
cmd_parser = create_cmd_parser()
if opt.web:
dream_server_loop(t2i)
else:
main_loop(t2i, opt.outdir, cmd_parser, infile)
def main_loop(t2i, outdir, parser, log, infile):
def main_loop(t2i, outdir, parser, infile):
"""prompt/read/execute loop"""
done = False
last_seeds = []
while not done:
try:
command = infile.readline() if infile else input('dream> ')
command = get_next_command(infile)
except EOFError:
done = True
break
if infile and len(command) == 0:
done = True
break
# skip empty lines
if not command.strip():
continue
if command.startswith(('#', '//')):
continue
@ -152,25 +123,10 @@ def main_loop(t2i, outdir, parser, log, infile):
print(str(e))
continue
if len(elements) == 0:
continue
if elements[0] == 'q':
done = True
break
if elements[0] == 'cd' and len(elements) > 1:
if os.path.exists(elements[1]):
print(f'setting image output directory to {elements[1]}')
outdir = elements[1]
else:
print(f'directory {elements[1]} does not exist')
continue
if elements[0] == 'pwd':
print(f'current output directory is {outdir}')
continue
if elements[0].startswith(
'!dream'
): # in case a stored prompt still contains the !dream command
@ -207,18 +163,26 @@ def main_loop(t2i, outdir, parser, log, infile):
opt.seed = None
normalized_prompt = PromptFormatter(t2i, opt).normalize_prompt()
individual_images = not opt.grid
do_grid = opt.grid or t2i.grid
individual_images = not do_grid
if opt.outdir:
if not os.path.exists(opt.outdir):
os.makedirs(opt.outdir)
current_outdir = opt.outdir
else:
current_outdir = outdir
# Here is where the images are actually generated!
try:
file_writer = PngWriter(outdir, normalized_prompt, opt.batch_size)
callback = file_writer.write_image if individual_images else None
image_list = t2i.prompt2image(image_callback=callback, **vars(opt))
file_writer = PngWriter(current_outdir, normalized_prompt, opt.batch_size)
callback = file_writer.write_image if individual_images else None
image_list = t2i.prompt2image(image_callback=callback, **vars(opt))
results = (
file_writer.files_written if individual_images else image_list
)
if opt.grid and len(results) > 0:
if do_grid and len(results) > 0:
grid_img = file_writer.make_grid([r[0] for r in results])
filename = file_writer.unique_filename(results[0][1])
seeds = [a[1] for a in results]
@ -239,94 +203,74 @@ def main_loop(t2i, outdir, parser, log, infile):
continue
print('Outputs:')
write_log_message(t2i, normalized_prompt, results, log)
log_path = os.path.join(current_outdir, 'dream_log.txt')
write_log_message(normalized_prompt, results, log_path)
print('goodbye!')
def load_gfpgan_bg_upsampler(bg_upsampler, bg_tile=400):
import torch
if bg_upsampler == 'realesrgan':
if not torch.cuda.is_available(): # CPU
import warnings
warnings.warn(
'The unoptimized RealESRGAN is slow on CPU. We do not use it. '
'If you really want to use it, please modify the corresponding codes.'
)
bg_upsampler = None
else:
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
model = RRDBNet(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=23,
num_grow_ch=32,
scale=2,
)
bg_upsampler = RealESRGANer(
scale=2,
model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
model=model,
tile=bg_tile,
tile_pad=10,
pre_pad=0,
half=True,
) # need to set False in CPU mode
def get_next_command(infile=None) -> str: #command string
if infile is None:
command = input('dream> ')
else:
bg_upsampler = None
command = infile.readline()
if not command:
raise EOFError
else:
command = command.strip()
print(f'#{command}')
return command
return bg_upsampler
def dream_server_loop(t2i):
print('\n* --web was specified, starting web server...')
# Change working directory to the stable-diffusion directory
os.chdir(
os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
)
# Start server
DreamServer.model = t2i
dream_server = ThreadingDreamServer(("0.0.0.0", 9090))
print("\nStarted Stable Diffusion dream server!")
print("Point your browser at http://localhost:9090 or use the host's DNS name or IP address.")
try:
dream_server.serve_forever()
except KeyboardInterrupt:
pass
dream_server.server_close()
# variant generation is going to be superseded by a generalized
# "prompt-morph" functionality
# def generate_variants(t2i,outdir,opt,previous_gens):
# variants = []
# print(f"Generating {opt.variants} variant(s)...")
# newopt = copy.deepcopy(opt)
# newopt.iterations = 1
# newopt.variants = None
# for r in previous_gens:
# newopt.init_img = r[0]
# prompt = PromptFormatter(t2i,newopt).normalize_prompt()
# print(f"] generating variant for {newopt.init_img}")
# for j in range(0,opt.variants):
# try:
# file_writer = PngWriter(outdir,prompt,newopt.batch_size)
# callback = file_writer.write_image
# t2i.prompt2image(image_callback=callback,**vars(newopt))
# results = file_writer.files_written
# variants.append([prompt,results])
# except AssertionError as e:
# print(e)
# continue
# print(f'{opt.variants} variants generated')
# return variants
def write_log_message(prompt, results, log_path):
"""logs the name of the output image, prompt, and prompt args to the terminal and log file"""
log_lines = [f'{r[0]}: {prompt} -S{r[1]}\n' for r in results]
print(*log_lines, sep='')
with open(log_path, 'a', encoding='utf-8') as file:
file.writelines(log_lines)
def write_log_message(t2i, prompt, results, logfile):
"""logs the name of the output image, its prompt and seed to the terminal, log file, and a Dream text chunk in the PNG metadata"""
last_seed = None
img_num = 1
seenit = {}
for r in results:
seed = r[1]
log_message = f'{r[0]}: {prompt} -S{seed}'
print(log_message)
logfile.write(log_message + '\n')
logfile.flush()
SAMPLER_CHOICES=[
'ddim',
'k_dpm_2_a',
'k_dpm_2',
'k_euler_a',
'k_euler',
'k_heun',
'k_lms',
'plms',
]
def create_argv_parser():
parser = argparse.ArgumentParser(
description="Parse script's command line args"
description="""Generate images using Stable Diffusion.
Use --web to launch the web interface.
Use --from_file to load prompts from a file path or standard input ("-").
Otherwise you will be dropped into an interactive command prompt (type -h for help.)
Other command-line arguments are defaults that can usually be overridden
prompt the command prompt.
"""
)
parser.add_argument(
'--laion400m',
@ -334,51 +278,50 @@ def create_argv_parser():
'-l',
dest='laion400m',
action='store_true',
help='fallback to the latent diffusion (laion400m) weights and config',
help='Fallback to the latent diffusion (laion400m) weights and config',
)
parser.add_argument(
'--from_file',
dest='infile',
type=str,
help='if specified, load prompts from this file',
help='If specified, load prompts from this file',
)
parser.add_argument(
'-n',
'--iterations',
type=int,
default=1,
help='number of images to generate',
help='Number of images to generate',
)
parser.add_argument(
'-F',
'--full_precision',
dest='full_precision',
action='store_true',
help='use slower full precision math for calculations',
help='Use slower full precision math for calculations',
)
parser.add_argument(
'--sampler',
'-g',
'--grid',
action='store_true',
help='Generate a grid instead of individual images',
)
parser.add_argument(
'-A',
'-m',
'--sampler',
dest='sampler_name',
choices=[
'ddim',
'k_dpm_2_a',
'k_dpm_2',
'k_euler_a',
'k_euler',
'k_heun',
'k_lms',
'plms',
],
choices=SAMPLER_CHOICES,
metavar='SAMPLER_NAME',
default='k_lms',
help='which sampler to use (k_lms) - can only be set on command line',
help=f'Set the initial sampler. Default: k_lms. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
)
parser.add_argument(
'--outdir',
'-o',
type=str,
default='outputs/img-samples',
help='directory in which to place generated images and a log of prompts and seeds (outputs/img-samples',
help='Directory to save generated images and a log of prompts and seeds. Default: outputs/img-samples',
)
parser.add_argument(
'--embedding_path',
@ -390,44 +333,39 @@ def create_argv_parser():
'-d',
type=str,
default='cuda',
help='device to run stable diffusion on. defaults to cuda `torch.cuda.current_device()` if avalible',
help='Device to run Stable Diffusion on. Defaults to cuda `torch.cuda.current_device()` if avalible',
)
# GFPGAN related args
parser.add_argument(
'--gfpgan',
dest='use_gfpgan',
action='store_true',
help='load gfpgan for use in the dreambot. Note: Enabling GFPGAN will require more GPU memory',
)
parser.add_argument(
'--gfpgan_upscale',
type=int,
default=2,
help='The final upsampling scale of the image. Default: 2. Only used if --gfpgan is specified',
)
parser.add_argument(
'--gfpgan_bg_upsampler',
type=str,
default='realesrgan',
help='Background upsampler. Default: realesrgan. Options: realesrgan, none. Only used if --gfpgan is specified',
)
parser.add_argument(
'--gfpgan_bg_tile',
type=int,
default=400,
help='Tile size for background sampler, 0 for no tile during testing. Default: 400. Only used if --gfpgan is specified',
help='Tile size for background sampler, 0 for no tile during testing. Default: 400.',
)
parser.add_argument(
'--gfpgan_model_path',
type=str,
default='experiments/pretrained_models/GFPGANv1.3.pth',
help='indicates the path to the GFPGAN model, relative to --gfpgan_dir. Only used if --gfpgan is specified',
help='Indicates the path to the GFPGAN model, relative to --gfpgan_dir.',
)
parser.add_argument(
'--gfpgan_dir',
type=str,
default='../GFPGAN',
help='indicates the directory containing the GFPGAN code. Only used if --gfpgan is specified',
help='Indicates the directory containing the GFPGAN code.',
)
parser.add_argument(
'--web',
dest='web',
action='store_true',
help='Start in web server mode.',
)
return parser
@ -437,76 +375,108 @@ def create_cmd_parser():
description='Example: dream> a fantastic alien landscape -W1024 -H960 -s100 -n12'
)
parser.add_argument('prompt')
parser.add_argument('-s', '--steps', type=int, help='number of steps')
parser.add_argument('-s', '--steps', type=int, help='Number of steps')
parser.add_argument(
'-S',
'--seed',
type=int,
help='image seed; a +ve integer, or use -1 for the previous seed, -2 for the one before that, etc',
help='Image seed; a +ve integer, or use -1 for the previous seed, -2 for the one before that, etc',
)
parser.add_argument(
'-n',
'--iterations',
type=int,
default=1,
help='number of samplings to perform (slower, but will provide seeds for individual images)',
help='Number of samplings to perform (slower, but will provide seeds for individual images)',
)
parser.add_argument(
'-b',
'--batch_size',
type=int,
default=1,
help='number of images to produce per sampling (will not provide seeds for individual images!)',
help='Number of images to produce per sampling (will not provide seeds for individual images!)',
)
parser.add_argument(
'-W', '--width', type=int, help='image width, multiple of 64'
'-W', '--width', type=int, help='Image width, multiple of 64'
)
parser.add_argument(
'-H', '--height', type=int, help='image height, multiple of 64'
'-H', '--height', type=int, help='Image height, multiple of 64'
)
parser.add_argument(
'-C',
'--cfg_scale',
default=7.5,
type=float,
help='prompt configuration scale',
help='Prompt configuration scale',
)
parser.add_argument(
'-g', '--grid', action='store_true', help='generate a grid'
)
parser.add_argument(
'--outdir',
'-o',
type=str,
default=None,
help='Directory to save generated images and a log of prompts and seeds',
)
parser.add_argument(
'-i',
'--individual',
action='store_true',
help='generate individual files (default)',
help='Generate individual files (default)',
)
parser.add_argument(
'-I',
'--init_img',
type=str,
help='path to input image for img2img mode (supersedes width and height)',
help='Path to input image for img2img mode (supersedes width and height)',
)
parser.add_argument(
'-f',
'--strength',
default=0.75,
type=float,
help='strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely',
help='Strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely',
)
parser.add_argument(
'-G',
'--gfpgan_strength',
default=None,
default=0,
type=float,
help='The strength at which to apply the GFPGAN model to the result, in order to improve faces.',
)
parser.add_argument(
'-U',
'--upscale',
nargs='+',
default=None,
type=float,
help='Scale factor (2, 4) for upscaling followed by upscaling strength (0-1.0). If strength not specified, defaults to 0.75'
)
parser.add_argument(
'-save_orig',
'--save_original',
action='store_true',
help='Save original. Use it when upscaling to save both versions.',
)
# variants is going to be superseded by a generalized "prompt-morph" function
# parser.add_argument('-v','--variants',type=int,help="in img2img mode, the first generated image will get passed back to img2img to generate the requested number of variants")
parser.add_argument(
'-x',
'--skip_normalize',
action='store_true',
help='skip subprompt weight normalization',
help='Skip subprompt weight normalization',
)
parser.add_argument(
'-A',
'-m',
'--sampler',
dest='sampler_name',
default=None,
type=str,
choices=SAMPLER_CHOICES,
metavar='SAMPLER_NAME',
help=f'Switch to a different sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
)
return parser

114
scripts/dream_web.py
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@ -1,114 +0,0 @@
import json
import base64
import mimetypes
import os
from pytorch_lightning import logging
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
print("Loading model...")
from ldm.simplet2i import T2I
model = T2I(sampler_name='k_lms')
# to get rid of annoying warning messages from pytorch
import transformers
transformers.logging.set_verbosity_error()
logging.getLogger("pytorch_lightning").setLevel(logging.ERROR)
print("Initializing model, be patient...")
model.load_model()
class DreamServer(BaseHTTPRequestHandler):
def do_GET(self):
if self.path == "/":
self.send_response(200)
self.send_header("Content-type", "text/html")
self.end_headers()
with open("./static/dream_web/index.html", "rb") as content:
self.wfile.write(content.read())
elif os.path.exists("." + self.path):
mime_type = mimetypes.guess_type(self.path)[0]
if mime_type is not None:
self.send_response(200)
self.send_header("Content-type", mime_type)
self.end_headers()
with open("." + self.path, "rb") as content:
self.wfile.write(content.read())
else:
self.send_response(404)
else:
self.send_response(404)
def do_POST(self):
self.send_response(200)
self.send_header("Content-type", "application/json")
self.end_headers()
content_length = int(self.headers['Content-Length'])
post_data = json.loads(self.rfile.read(content_length))
prompt = post_data['prompt']
initimg = post_data['initimg']
iterations = int(post_data['iterations'])
steps = int(post_data['steps'])
width = int(post_data['width'])
height = int(post_data['height'])
cfgscale = float(post_data['cfgscale'])
seed = None if int(post_data['seed']) == -1 else int(post_data['seed'])
print(f"Request to generate with prompt: {prompt}")
outputs = []
if initimg is None:
# Run txt2img
outputs = model.txt2img(prompt,
iterations=iterations,
cfg_scale = cfgscale,
width = width,
height = height,
seed = seed,
steps = steps)
else:
# Decode initimg as base64 to temp file
with open("./img2img-tmp.png", "wb") as f:
initimg = initimg.split(",")[1] # Ignore mime type
f.write(base64.b64decode(initimg))
# Run img2img
outputs = model.img2img(prompt,
init_img = "./img2img-tmp.png",
iterations = iterations,
cfg_scale = cfgscale,
seed = seed,
steps = steps)
# Remove the temp file
os.remove("./img2img-tmp.png")
print(f"Prompt generated with output: {outputs}")
post_data['initimg'] = '' # Don't send init image back
# Append post_data to log
with open("./outputs/img-samples/dream_web_log.txt", "a") as log:
for output in outputs:
log.write(f"{output[0]}: {json.dumps(post_data)}\n")
outputs = [x + [post_data] for x in outputs] # Append config to each output
result = {'outputs': outputs}
self.wfile.write(bytes(json.dumps(result), "utf-8"))
if __name__ == "__main__":
# Change working directory to the stable-diffusion directory
os.chdir(
os.path.abspath(os.path.join(os.path.dirname( __file__ ), '..'))
)
# Start server
dream_server = ThreadingHTTPServer(("0.0.0.0", 9090), DreamServer)
print("\n\n* Started Stable Diffusion dream server! Point your browser at http://localhost:9090 or use the host's DNS name or IP address. *")
try:
dream_server.serve_forever()
except KeyboardInterrupt:
pass
dream_server.server_close()

20
scripts/preload_models.py
View File

@ -3,6 +3,9 @@
# Before running stable-diffusion on an internet-isolated machine,
# run this script from one with internet connectivity. The
# two machines must share a common .cache directory.
from transformers import CLIPTokenizer, CLIPTextModel
import clip
from transformers import BertTokenizerFast
import sys
import transformers
import os
@ -12,7 +15,6 @@ transformers.logging.set_verbosity_error()
# this will preload the Bert tokenizer fles
print('preloading bert tokenizer...')
from transformers import BertTokenizerFast
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
print('...success')
@ -28,8 +30,6 @@ version = 'openai/clip-vit-large-patch14'
print('preloading CLIP model (Ignore the deprecation warnings)...')
sys.stdout.flush()
import clip
from transformers import CLIPTokenizer, CLIPTextModel
tokenizer = CLIPTokenizer.from_pretrained(version)
transformer = CLIPTextModel.from_pretrained(version)
@ -63,6 +63,20 @@ if gfpgan:
scale=2,
),
)
RealESRGANer(
scale=4,
model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth',
model=RRDBNet(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=23,
num_grow_ch=32,
scale=4,
),
)
FaceRestoreHelper(1, det_model='retinaface_resnet50')
print('...success')
except Exception:

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130
static/dream_web/index.css
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@ -1,61 +1,69 @@
* {
font-family: 'Arial';
}
#header {
text-decoration: dotted underline;
}
#search {
margin-top: 20vh;
margin-left: auto;
margin-right: auto;
max-width: 800px;
text-align: center;
}
fieldset {
border: none;
}
#fieldset-search {
display: flex;
}
#prompt {
flex-grow: 1;
border-radius: 20px 0px 0px 20px;
padding: 5px 10px 5px 10px;
border: 1px solid black;
border-right: none;
outline: none;
}
#submit {
border-radius: 0px 20px 20px 0px;
padding: 5px 10px 5px 10px;
border: 1px solid black;
}
#results {
text-align: center;
max-width: 1000px;
margin: auto;
padding-top: 10px;
}
img {
cursor: pointer;
height: 30vh;
border-radius: 5px;
margin: 10px;
}
#fieldset-config {
line-height:2em;
}
input[type="number"] {
width: 60px;
}
#seed {
width: 150px;
}
hr {
width: 200px;
}
label {
white-space: nowrap;
}
* {
font-family: 'Arial';
}
#header {
text-decoration: dotted underline;
}
#search {
margin-top: 20vh;
margin-left: auto;
margin-right: auto;
max-width: 800px;
text-align: center;
}
fieldset {
border: none;
}
#fieldset-search {
display: flex;
}
#scaling-inprocess-message{
font-weight: bold;
font-style: italic;
display: none;
}
#prompt {
flex-grow: 1;
border-radius: 20px 0px 0px 20px;
padding: 5px 10px 5px 10px;
border: 1px solid black;
border-right: none;
outline: none;
}
#submit {
border-radius: 0px 20px 20px 0px;
padding: 5px 10px 5px 10px;
border: 1px solid black;
}
#reset-all {
background-color: pink;
}
#results {
text-align: center;
max-width: 1000px;
margin: auto;
padding-top: 10px;
}
img {
cursor: pointer;
height: 30vh;
border-radius: 5px;
margin: 10px;
}
#fieldset-config {
line-height:2em;
}
input[type="number"] {
width: 60px;
}
#seed {
width: 150px;
}
hr {
width: 200px;
}
label {
white-space: nowrap;
}

160
static/dream_web/index.html
View File

@ -1,66 +1,94 @@
<html>
<head>
<title>Stable Diffusion Dream Server</title>
<link rel="icon" href="data:,">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<link rel="stylesheet" href="static/dream_web/index.css">
<script src="static/dream_web/index.js"></script>
</head>
<body>
<div id="search">
<h2 id="header">Stable Diffusion Dream Server</h2>
<form id="generate-form" method="post" action="#">
<fieldset id="fieldset-search">
<input type="text" id="prompt" name="prompt">
<input type="submit" id="submit" value="Generate">
</fieldset>
<fieldset id="fieldset-config">
<label for="iterations">Images to generate:</label>
<input value="1" type="number" id="iterations" name="iterations">
<label for="steps">Steps:</label>
<input value="50" type="number" id="steps" name="steps">
<label for="cfgscale">Cfg Scale:</label>
<input value="7.5" type="number" id="cfgscale" name="cfgscale" step="any">
<span>&bull;</span>
<label title="Set to multiple of 64" for="width">Width:</label>
<select id="width" name="width" value="512">
<option value="64">64</option> <option value="128">128</option>
<option value="192">192</option> <option value="256">256</option>
<option value="320">320</option> <option value="384">384</option>
<option value="448">448</option> <option value="512">512</option>
<option value="576">576</option> <option value="640">640</option>
<option value="704">704</option> <option value="768">768</option>
<option value="832">832</option> <option value="896">896</option>
<option value="960">960</option> <option value="1024">1024</option>
</select>
<label title="Set to multiple of 64" for="height">Height:</label>
<select id="height" name="height" value="512">
<option value="64">64</option> <option value="128">128</option>
<option value="192">192</option> <option value="256">256</option>
<option value="320">320</option> <option value="384">384</option>
<option value="448">448</option> <option value="512">512</option>
<option value="576">576</option> <option value="640">640</option>
<option value="704">704</option> <option value="768">768</option>
<option value="832">832</option> <option value="896">896</option>
<option value="960">960</option> <option value="1024">1024</option>
</select>
<br>
<label title="Upload an image to use img2img" for="initimg">Img2Img Init:</label>
<input type="file" id="initimg" name="initimg" accept=".jpg, .jpeg, .png">
<label title="Set to -1 for random seed" for="seed">Seed:</label>
<input value="-1" type="number" id="seed" name="seed">
<button type="button" id="reset">&olarr;</button>
</fieldset>
</form>
<div id="about">For news and support for this web service, visit our <a href="http://github.com/lstein/stable-diffusion">GitHub site</a></div>
</div>
<hr>
<div id="results">
<div id="no-results-message">
<i><p>No results...</p></i>
</div>
</div>
</body>
</html>
<html>
<head>
<title>Stable Diffusion Dream Server</title>
<link rel="icon" href="data:,">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<link rel="stylesheet" href="static/dream_web/index.css">
<script src="static/dream_web/index.js"></script>
</head>
<body>
<div id="search">
<h2 id="header">Stable Diffusion Dream Server</h2>
<form id="generate-form" method="post" action="#">
<fieldset id="fieldset-search">
<input type="text" id="prompt" name="prompt">
<input type="submit" id="submit" value="Generate">
</fieldset>
<fieldset id="fieldset-config">
<label for="iterations">Images to generate:</label>
<input value="1" type="number" id="iterations" name="iterations" size="4">
<label for="steps">Steps:</label>
<input value="50" type="number" id="steps" name="steps">
<label for="cfgscale">Cfg Scale:</label>
<input value="7.5" type="number" id="cfgscale" name="cfgscale" step="any">
<label for="sampler">Sampler:</label>
<select id="sampler" name="sampler" value="k_lms">
<option value="ddim">DDIM</option>
<option value="plms">PLMS</option>
<option value="k_lms" selected>KLMS</option>
<option value="k_dpm_2">KDPM_2</option>
<option value="k_dpm_2_a">KDPM_2A</option>
<option value="k_euler">KEULER</option>
<option value="k_heun">KHEUN</option>
</select>
<br>
<label title="Set to multiple of 64" for="width">Width:</label>
<select id="width" name="width" value="512">
<option value="64">64</option> <option value="128">128</option>
<option value="192">192</option> <option value="256">256</option>
<option value="320">320</option> <option value="384">384</option>
<option value="448">448</option> <option value="512" selected>512</option>
<option value="576">576</option> <option value="640">640</option>
<option value="704">704</option> <option value="768">768</option>
<option value="832">832</option> <option value="896">896</option>
<option value="960">960</option> <option value="1024">1024</option>
</select>
<label title="Set to multiple of 64" for="height">Height:</label>
<select id="height" name="height" value="512">
<option value="64">64</option> <option value="128">128</option>
<option value="192">192</option> <option value="256">256</option>
<option value="320">320</option> <option value="384">384</option>
<option value="448">448</option> <option value="512" selected>512</option>
<option value="576">576</option> <option value="640">640</option>
<option value="704">704</option> <option value="768">768</option>
<option value="832">832</option> <option value="896">896</option>
<option value="960">960</option> <option value="1024">1024</option>
</select>
<br>
<label title="Upload an image to use img2img" for="initimg">Img2Img Init:</label>
<input type="file" id="initimg" name="initimg" accept=".jpg, .jpeg, .png">
<label title="Set to -1 for random seed" for="seed">Seed:</label>
<input value="-1" type="number" id="seed" name="seed">
<button type="button" id="reset-seed">&olarr;</button>
<span>&bull;</span>
<button type="button" id="reset-all">Reset to Defaults</button>
<br>
<p><em>The options below require the GFPGAN and ESRGAN packages to be installed</em></p>
<label title="Strength of the gfpgan (face fixing) algorithm." for="gfpgan_strength">GPFGAN Strength:</label>
<input value="0.8" min="0" max="1" type="number" id="gfpgan_strength" name="gfpgan_strength" step="0.05">
<label title="Upscaling to perform using ESRGAN." for="upscale_level">Upscaling Level</label>
<select id="upscale_level" name="upscale_level" value="">
<option value="" selected></option>
<option value="2">2x</option>
<option value="4">4x</option>
</select>
<label title="Strength of the esrgan (upscaling) algorithm." for="upscale_strength">Upscale Strength:</label>
<input value="0.75" min="0" max="1" type="number" id="upscale_strength" name="upscale_strength" step="0.05">
</fieldset>
</form>
<div id="about">For news and support for this web service, visit our <a href="http://github.com/lstein/stable-diffusion">GitHub site</a></div>
<br>
<progress id="progress" value="0" max="1"></progress>
<div id="scaling-inprocess-message">
<i><span>Postprocessing...</span><span id="processing_cnt">1/3</span></i>
</div>
</div>
<div id="results">
<div id="no-results-message">
<i><p>No results...</p></i>
</div>
</div>
</body>
</html>

230
static/dream_web/index.js
View File

@ -1,101 +1,129 @@
function toBase64(file) {
return new Promise((resolve, reject) => {
const r = new FileReader();
r.readAsDataURL(file);
r.onload = () => resolve(r.result);
r.onerror = (error) => reject(error);
});
}
function appendOutput(output) {
let outputNode = document.createElement("img");
outputNode.src = output[0];
let outputConfig = output[2];
let altText = output[1].toString() + " | " + outputConfig.prompt;
outputNode.alt = altText;
outputNode.title = altText;
// Reload image config
outputNode.addEventListener('click', () => {
let form = document.querySelector("#generate-form");
for (const [k, v] of new FormData(form)) {
form.querySelector(`*[name=${k}]`).value = outputConfig[k];
}
document.querySelector("#seed").value = output[1];
saveFields(document.querySelector("#generate-form"));
});
document.querySelector("#results").prepend(outputNode);
}
function appendOutputs(outputs) {
for (const output of outputs) {
appendOutput(output);
}
}
function saveFields(form) {
for (const [k, v] of new FormData(form)) {
if (typeof v !== 'object') { // Don't save 'file' type
localStorage.setItem(k, v);
}
}
}
function loadFields(form) {
for (const [k, v] of new FormData(form)) {
const item = localStorage.getItem(k);
if (item != null) {
form.querySelector(`*[name=${k}]`).value = item;
}
}
}
async function generateSubmit(form) {
const prompt = document.querySelector("#prompt").value;
// Convert file data to base64
let formData = Object.fromEntries(new FormData(form));
formData.initimg = formData.initimg.name !== '' ? await toBase64(formData.initimg) : null;
// Post as JSON
fetch(form.action, {
method: form.method,
body: JSON.stringify(formData),
}).then(async (result) => {
let data = await result.json();
// Re-enable form, remove no-results-message
form.querySelector('fieldset').removeAttribute('disabled');
document.querySelector("#prompt").value = prompt;
if (data.outputs.length != 0) {
document.querySelector("#no-results-message")?.remove();
appendOutputs(data.outputs);
} else {
alert("Error occurred while generating.");
}
});
// Disable form while generating
form.querySelector('fieldset').setAttribute('disabled','');
document.querySelector("#prompt").value = `Generating: "${prompt}"`;
}
window.onload = () => {
document.querySelector("#generate-form").addEventListener('submit', (e) => {
e.preventDefault();
const form = e.target;
generateSubmit(form);
});
document.querySelector("#generate-form").addEventListener('change', (e) => {
saveFields(e.target.form);
});
document.querySelector("#reset").addEventListener('click', (e) => {
document.querySelector("#seed").value = -1;
saveFields(e.target.form);
});
loadFields(document.querySelector("#generate-form"));
};
function toBase64(file) {
return new Promise((resolve, reject) => {
const r = new FileReader();
r.readAsDataURL(file);
r.onload = () => resolve(r.result);
r.onerror = (error) => reject(error);
});
}
function appendOutput(src, seed, config) {
let outputNode = document.createElement("img");
outputNode.src = src;
let altText = seed.toString() + " | " + config.prompt;
outputNode.alt = altText;
outputNode.title = altText;
// Reload image config
outputNode.addEventListener('click', () => {
let form = document.querySelector("#generate-form");
for (const [k, v] of new FormData(form)) {
form.querySelector(`*[name=${k}]`).value = config[k];
}
document.querySelector("#seed").value = seed;
saveFields(document.querySelector("#generate-form"));
});
document.querySelector("#results").prepend(outputNode);
}
function saveFields(form) {
for (const [k, v] of new FormData(form)) {
if (typeof v !== 'object') { // Don't save 'file' type
localStorage.setItem(k, v);
}
}
}
function loadFields(form) {
for (const [k, v] of new FormData(form)) {
const item = localStorage.getItem(k);
if (item != null) {
form.querySelector(`*[name=${k}]`).value = item;
}
}
}
function clearFields(form) {
localStorage.clear();
let prompt = form.prompt.value;
form.reset();
form.prompt.value = prompt;
}
async function generateSubmit(form) {
const prompt = document.querySelector("#prompt").value;
// Convert file data to base64
let formData = Object.fromEntries(new FormData(form));
formData.initimg = formData.initimg.name !== '' ? await toBase64(formData.initimg) : null;
document.querySelector('progress').setAttribute('max', formData.steps);
// Post as JSON, using Fetch streaming to get results
fetch(form.action, {
method: form.method,
body: JSON.stringify(formData),
}).then(async (response) => {
const reader = response.body.getReader();
let noOutputs = true;
while (true) {
let {value, done} = await reader.read();
value = new TextDecoder().decode(value);
if (done) break;
for (let event of value.split('\n').filter(e => e !== '')) {
const data = JSON.parse(event);
if (data.event == 'result') {
noOutputs = false;
document.querySelector("#no-results-message")?.remove();
appendOutput(data.files[0],data.files[1],data.config)
} else if (data.event == 'upscaling-started') {
document.getElementById("processing_cnt").textContent=data.processed_file_cnt;
document.getElementById("scaling-inprocess-message").style.display = "block";
} else if (data.event == 'upscaling-done') {
document.getElementById("scaling-inprocess-message").style.display = "none";
} else if (data.event == 'step') {
document.querySelector('progress').setAttribute('value', data.step.toString());
}
}
}
// Re-enable form, remove no-results-message
form.querySelector('fieldset').removeAttribute('disabled');
document.querySelector("#prompt").value = prompt;
document.querySelector('progress').setAttribute('value', '0');
if (noOutputs) {
alert("Error occurred while generating.");
}
});
// Disable form while generating
form.querySelector('fieldset').setAttribute('disabled','');
document.querySelector("#prompt").value = `Generating: "${prompt}"`;
}
window.onload = () => {
document.querySelector("#generate-form").addEventListener('submit', (e) => {
e.preventDefault();
const form = e.target;
generateSubmit(form);
});
document.querySelector("#generate-form").addEventListener('change', (e) => {
saveFields(e.target.form);
});
document.querySelector("#reset-seed").addEventListener('click', (e) => {
document.querySelector("#seed").value = -1;
saveFields(e.target.form);
});
document.querySelector("#reset-all").addEventListener('click', (e) => {
clearFields(e.target.form);
});
loadFields(document.querySelector("#generate-form"));
};